Academic literature on the topic 'Metabolomic analyses'
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Journal articles on the topic "Metabolomic analyses"
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Szczerbinski, Lukasz, Gladys Wojciechowska, Adam Olichwier, Mark A. Taylor, Urszula Puchta, Paulina Konopka, Adam Paszko, et al. "Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity." Nutrients 14, no. 1 (January 4, 2022): 214. http://dx.doi.org/10.3390/nu14010214.
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Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.
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Kim, Hyun Woo. "Metabolomic Approaches to Investigate the Effect of Metformin: An Overview." International Journal of Molecular Sciences 22, no. 19 (September 24, 2021): 10275. http://dx.doi.org/10.3390/ijms221910275.
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Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.
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Qi, Jinwei, Kang Li, Yunxia Shi, Yufei Li, Long Dong, Ling Liu, Mingyang Li, et al. "Cross-Species Comparison of Metabolomics to Decipher the Metabolic Diversity in Ten Fruits." Metabolites 11, no. 3 (March 12, 2021): 164. http://dx.doi.org/10.3390/metabo11030164.
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Fruits provide humans with multiple kinds of nutrients and protect humans against worldwide nutritional deficiency. Therefore, it is essential to understand the nutrient composition of various fruits in depth. In this study, we performed LC-MS-based non-targeted metabolomic analyses with ten kinds of fruit, including passion fruit, mango, starfruit, mangosteen, guava, mandarin orange, grape, apple, blueberry, and strawberry. In total, we detected over 2500 compounds and identified more than 300 nutrients. Although the ten fruits shared 909 common-detected compounds, each species accumulated a variety of species-specific metabolites. Additionally, metabolic profiling analyses revealed a constant variation in each metabolite’s content across the ten fruits. Moreover, we constructed a neighbor-joining tree using metabolomic data, which resembles the single-copy protein-based phylogenetic tree. This indicates that metabolome data could reflect the genetic relationship between different species. In conclusion, our work enriches knowledge on the metabolomics of fruits, and provides metabolic evidence for the genetic relationships among these fruits.
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Patterson, Jeffrey, Xiaojian Shi, William Bresette, Ryan Eghlimi, Sarah Atlas, Kristin Farr, Sonia Vega-López, and Haiwei Gu. "A Metabolomic Analysis of the Sex-Dependent Hispanic Paradox." Metabolites 11, no. 8 (August 20, 2021): 552. http://dx.doi.org/10.3390/metabo11080552.
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In Mexican Americans, metabolic conditions, such as obesity and type 2 diabetes (T2DM), are not necessarily associated with an increase in mortality; this is the so-called Hispanic paradox. In this cross-sectional analysis, we used a metabolomic analysis to look at the mechanisms behind the Hispanic paradox. To do this, we examined dietary intake and body mass index (BMI; kg/m2) in men and women and their effects on serum metabolomic fingerprints in 70 Mexican Americans (26 men, 44 women). Although having different BMI values, the participants had many similar anthropometric and biochemical parameters, such as systolic and diastolic blood pressure, total cholesterol, and LDL cholesterol, which supported the paradox in these subjects. Plasma metabolomic phenotypes were measured using liquid chromatography tandem mass spectrometry (LC-MS/MS). A two-way ANOVA assessing sex, BMI, and the metabolome revealed 23 significant metabolites, such as 2-pyrrolidinone (p = 0.007), TMAO (p = 0.014), 2-aminoadipic acid (p = 0.019), and kynurenine (p = 0.032). Pathway and enrichment analyses discovered several significant metabolic pathways between men and women, including lysine degradation, tyrosine metabolism, and branch-chained amino acid (BCAA) degradation and biosynthesis. A log-transformed OPLS-DA model was employed and demonstrated a difference due to BMI in the metabolomes of both sexes. When stratified for caloric intake (<2200 kcal/d vs. >2200 kcal/d), a separate OPLS-DA model showed clear separation in men, while females remained relatively unchanged. After accounting for caloric intake and BMI status, the female metabolome showed substantial resistance to alteration. Therefore, we provide a better understanding of the Mexican-American metabolome, which may help demonstrate how this population—particularly women—possesses a longer life expectancy despite several comorbidities, and reveal the underlying mechanisms of the Hispanic paradox.
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Delporte, Cédric, Nausicaa Noret, Cécile Vanhaverbeke, Olivier J. Hardy, Jean-François Martin, Marie Tremblay-Franco, David Touboul, et al. "Does the Phytochemical Diversity of Wild Plants Like the Erythrophleum genus Correlate with Geographical Origin?" Molecules 26, no. 6 (March 17, 2021): 1668. http://dx.doi.org/10.3390/molecules26061668.
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Secondary metabolites are essential for plant survival and reproduction. Wild undomesticated and tropical plants are expected to harbor highly diverse metabolomes. We investigated the metabolomic diversity of two morphologically similar trees of tropical Africa, Erythrophleum suaveolens and E. ivorense, known for particular secondary metabolites named the cassaine-type diterpenoids. To assess how the metabolome varies between and within species, we sampled leaves from individuals of different geographic origins but grown from seeds in a common garden in Cameroon. Metabolites were analyzed using reversed phase LC-HRMS(/MS). Data were interpreted by untargeted metabolomics and molecular networks based on MS/MS data. Multivariate analyses enabled us to cluster samples based on species but also on geographic origins. We identified the structures of 28 cassaine-type diterpenoids among which 19 were new, 10 were largely specific to E. ivorense and five to E. suaveolens. Our results showed that the metabolome allows an unequivocal distinction of morphologically-close species, suggesting the potential of metabolite fingerprinting for these species. Plant geographic origin had a significant influence on relative concentrations of metabolites with variations up to eight (suaveolens) and 30 times (ivorense) between origins of the same species. This shows that the metabolome is strongly influenced by the geographical origin of plants (i.e., genetic factors).
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Cheng, Leo L., Adam S. Feldman, Lindsey A. Vandergrift, Isabella H. Muti, Florian Rumpf, Andrew Gusev, Yannick Berker, et al. "Abstract 2222: Detecting clinically significant prostate cancers: Tissue metabolomics refines multiparametric MRI-ultrasound fusion prostate biopsy." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2222. http://dx.doi.org/10.1158/1538-7445.am2022-2222.
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Abstract The advent of prostate specific antigen (PSA) testing led to increased early prostate cancer (PCa) detection and has decreased PCa-related death. However, PSA is not cancer-specific, and the challenge persists of differentiating those PCa patients with indolent tumors from those requiring definitive therapy. Metabolomic profiles have the potential to capture molecular dynamics of disease and to reflect disease status before cellular manifestations become observable by histopathology. With clinical, multiparametric magnetic resonance imaging (mpMRI)-positive, fusion biopsy-targeted tissue cores and mpMRI-negative controls in a training-testing cohort design, we studied the potential of magnetic resonance spectroscopy (MRS) to yield cancer metabolomic profiles that could help discriminate likely indolent from clinically significant disease. Using MRS-based PCa metabolomic analyses, performed prior to histology, our approach is able to: determine metabolomic relevations identified in fusion biopsy targets, estimate the scale of PCa metabolomic fields, and detect clinically significant disease in tissues deemed benign or low-risk PCa by pathology and imaging. Our intact tissue MRS metabolomics evaluations indicated significant differences in individual prostate tissue metabolites based on Target-Contralateral (Contral) paired comparisons for both Training and Testing cohorts. We identified metabolomic differences among Target prostate biopsy cores obtained from mpMRI lesions of different PI-RADS scores, and between Target and non-target Contral cores. As a retrospective study, we also analyzed data collected at the time of the initial prostate biopsy alongside patient status across follow up. By introducing metabolomics, as compared with using PSAd or PI-RADS alone, the sensitivity predictions increased by 80.0% and 25.0%, respectively; NPV increased by 18.1% and 8.0%; and accuracy for PSAd increased by 13.0%. PI-RADS accuracy stayed the same Our results show that tissue metabolomic profiles could augment current MR-based imaging findings and histopathological evaluations of fusion biopsies for certain patient populations by more accurately characterizing them into clinically significant or insignificant subgroups. In our analyses, tissue metabolomics alone, or its combination with other clinical parameters, improved sensitivity and negative predictive values, as well as overall accuracy, for our testing cohort. This method, which relies on performing tissue MRS of needle biopsy cores prior to histopathologic analysis, causes no interruption to patient care. Findings from our study demonstrate the utility and translational potential of cancer metabolomics in personalized treatment for PCa and encourages the development of in vivo PCa metabolomic imaging to enhance the diagnostic utility of mpMRI. Citation Format: Leo L. Cheng, Adam S. Feldman, Lindsey A. Vandergrift, Isabella H. Muti, Florian Rumpf, Andrew Gusev, Yannick Berker, Marcella R. Cardoso, Taylor L. Fuss, Emily D. Negroponte, Shulin Wu, Felix Ehret, Christopher A. Dietz, Sarah S. Dinges, Thitinan Chulroek, Edouard Nicaise, Piet Habbel, Martin Ayree, Johannes Nowak, Douglas M. Dahl, Chin-Lee Wu, Mukesh Harisinghani. Detecting clinically significant prostate cancers: Tissue metabolomics refines multiparametric MRI-ultrasound fusion prostate biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2222.
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Byerley, Lauri O., Karyn M. Gallivan, Courtney J. Christopher, Christopher M. Taylor, Meng Luo, Scot E. Dowd, Gregory M. Davis, Hector F. Castro, Shawn R. Campagna, and Kristin S. Ondrak. "Gut Microbiome and Metabolome Variations in Self-Identified Muscle Builders Who Report Using Protein Supplements." Nutrients 14, no. 3 (January 26, 2022): 533. http://dx.doi.org/10.3390/nu14030533.
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Muscle builders frequently consume protein supplements, but little is known about their effect on the gut microbiota. This study compared the gut microbiome and metabolome of self-identified muscle builders who did or did not report consuming a protein supplement. Twenty-two participants (14 males and 8 females) consumed a protein supplement (PS), and seventeen participants (12 males and 5 females) did not (No PS). Participants provided a fecal sample and completed a 24-h food recall (ASA24). The PS group consumed significantly more protein (118 ± 12 g No PS vs. 169 ± 18 g PS, p = 0.02). Fecal metabolome and microbiome were analyzed by using untargeted metabolomics and 16S rRNA gene sequencing, respectively. Metabolomic analysis identified distinct metabolic profiles driven by allantoin (VIP score = 2.85, PS 2.3-fold higher), a catabolic product of uric acid. High-protein diets contain large quantities of purines, which gut microbes degrade to uric acid and then allantoin. The bacteria order Lactobacillales was higher in the PS group (22.6 ± 49 No PS vs. 136.5 ± 38.1, PS (p = 0.007)), and this bacteria family facilitates purine absorption and uric acid decomposition. Bacterial genes associated with nucleotide metabolism pathways (p < 0.001) were more highly expressed in the No PS group. Both fecal metagenomic and metabolomic analyses revealed that the PS group’s higher protein intake impacted nitrogen metabolism, specifically altering nucleotide degradation.
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Loras, Alba, M. Carmen Martínez-Bisbal, Guillermo Quintás, Salvador Gil, Ramón Martínez-Máñez, and José Luis Ruiz-Cerdá. "Urinary Metabolic Signatures Detect Recurrences in Non-Muscle Invasive Bladder Cancer." Cancers 11, no. 7 (June 29, 2019): 914. http://dx.doi.org/10.3390/cancers11070914.
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Patients with non-muscle invasive bladder cancer (NMIBC) undergo lifelong monitoring based on repeated cystoscopy and urinary cytology due to the high recurrence rate of this tumor. Nevertheless, these techniques have some drawbacks, namely, low accuracy in detection of low-grade tumors, omission of pre-neoplastic lesions and carcinomas in situ (CIS), invasiveness, and high costs. This work aims to identify a urinary metabolomic signature of recurrence by proton Nuclear Magnetic Resonance (1H NMR) spectroscopy for the follow-up of NMIBC patients. To do this, changes in the urinary metabolome before and after transurethral resection (TUR) of tumors are analyzed and a Partial Least Square Discriminant Analysis (PLS-DA) model is developed. The usefulness of this discriminant model for the detection of tumor recurrences is assessed using a cohort of patients undergoing monitoring. The trajectories of the metabolomic profile in the follow-up period provide a negative predictive value of 92.7% in the sample classification. Pathway analyses show taurine, alanine, aspartate, glutamate, and phenylalanine perturbed metabolism associated with NMIBC. These results highlight the potential of 1H NMR metabolomics to detect bladder cancer (BC) recurrences through a non-invasive approach.
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Mok, Jeong-Hun, Minjoong Joo, Van-An Duong, Seonghyeon Cho, Jong-Moon Park, Young-Sic Eom, Tae-Hwa Song, Hee-Joung Lim, and Hookeun Lee. "Proteomic and Metabolomic Analyses of Maggots in Porcine Corpses for Post-Mortem Interval Estimation." Applied Sciences 11, no. 17 (August 26, 2021): 7885. http://dx.doi.org/10.3390/app11177885.
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Post-mortem interval (PMI) estimation is a critical task in forensic science. In this study, we used maggots collected from pig carcasses and applied an integrated proteomics and metabolomics approach to determine potential candidate substances for the estimation of PMI. After methanol precipitation, the supernatant containing metabolites and the protein pellet were separated and subjected to metabolomic and proteomic analyses using liquid chromatography-tandem mass spectrometry (LC-MS/MS). MS/MS data were analyzed for identification and quantification using Proteome Discoverer and Compound Discoverer software. A total of 573 metabolites and more than 800 porcine proteins were identified in maggots. This is the first dataset of proteins and metabolites in maggots collected from porcine carcasses. In this study, guanosine monophosphate, xanthine, inosine, adenosine, and guanine were detected with a similar tendency to increase during early days of maggot development and then decreased gradually. We broadly profiled various biomolecules through analysis in the spot of incident. Especially, we confirmed that proteome and metabolome profiling could be performed directly and indirectly.
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Fitzpatrick, Garrett, Maryam Rahman, Timothy Garrett, and Jesse Kresak. "MNGI-11. HIGH-GRADE AND LOW-GRADE MENINGIOMAS HARBOR DIFFERING METABOLOMIC PROFILES." Neuro-Oncology 21, Supplement_6 (November 2019): vi141—vi142. http://dx.doi.org/10.1093/neuonc/noz175.593.
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Abstract BACKGROUND Meningiomas are the most common primary brain tumor in adults. While the majority of meningiomas are low-grade and effectively treated by resection alone, there is a subset of tumors that have a high incidence of recurrence, metastatic potential, and morbidity. Radiation has been employed with variable success for high-grade meningiomas. No chemotherapeutic approaches have proven effective against these tumors to date. There is a need for a better understanding of this tumor type in order to provide our patients with better treatment options. OBJECTIVE The purpose of this study is to investigate the metabolomic profile of meningiomas with a focus on comparing low- and high-grade tumors and identifying biologically significant metabolites which could correlate with overall and disease-free survival. METHODS Ten tumor samples of each meningioma grade (WHO grades I-III) were collected from the Florida Center for Brain Tumor Research. Global metabolomic profiling by liquid chromatography mass spectrometry was performed on the frozen tumor samples. Statistical analyses were performed using the Southeast Center for Integrated Metabolomics Galaxy interface. Select metabolites which significantly differed between low-grade (WHO Grade I) and high-grade (WHO grade II-III) were identified using the Human Metabolome Database. RESULTS Differing metabolomic profiles between low-grade and high-grade meningiomas were confirmed by multivariate analysis and demonstrated by unsupervised hierarchical clustering. Notably, lysophospholipid and sphingolipid metabolism was increased in the high-grade tumors, while FAPy-adenine, an oxidized nucleoside which may serve as a tumor marker, was decreased. Guanine was found to be consistently decreased in patients with negative outcomes. CONCLUSIONS High-grade and low-grade meningiomas harbor different metabolomic profiles. The significance of these specific differences requires further investigation.
Dissertations / Theses on the topic "Metabolomic analyses"
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Robinson, Andrew Raymond. "Metabolomic analyses of wood attributes in tree species." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/7697.
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Metabolomics is an emerging field in functional plant biology that attempts to relate patterns in the molecular intermediates and products of metabolic pathways with genetic, gene expression, environmental and phenotypic traits - at the whole-tissue and/or whole-organism level. There is enormous potential for metabolomics tools to be applied in the study of tree species, and the demand for widespread application is promoting an ongoing evolution and refinement of newly-developed techniques. This body of research addresses the application of broad-scale, non-targeted metabolomics to questions of wood formation and quality in tree systems. Overall, it was shown that variation in metabolite profiles from developing xylem tissue was indeed correlated with the strength of specific phenotypic traits. Frequently, the strength of these relationships was such that phenotypic severity could be predicted accurately on the basis of metabolite profile data alone. The specific correlative patterns and metabolite/trait pairings observed in each study provided insight into the biological mechanisms by which these traits arise. Studies of secondary xylem development were conducted on breeding populations of Douglas-fir and radiata pine, as well as genetically modified hybrid poplar. In the Douglas-fir families studied, environment-induced variation in growth rate, fibre morphology and wood chemistry were correlated with metabolite profiles from developing xylem; metabolites involved in carbohydrate and lignin biosynthesis were primarily implicated in these relationships. Similarly, in juvenile trees from a series of radiata pine families, correlations were observed between metabolite profiles of developing xylem and the internal checking wood defect, a known heritable trait. In a different approach, two poplar hybrids, each modified separately with two exogenous gene constructs related to lignin biosynthesis, provided controlled model systems in which to investigate the interaction between genotype, metabolite profiles of developing xylem, and physico-chemical wood traits. Wood traits and metabolite profiles alike were altered by the genetic modifications, and it was found that the metabolic impact of the transgenes was not confined to pathways that were directly coupled to lignin biosynthesis. In fact, the scarcity of lignin-related metabolites in profiles from either the wild-type or modified genotypes suggested that metabolite channelling phenomena operate in the lignin biosynthetic pathway. Moreover, the analyses demonstrated that transgene-induced gradients in phenotypic traits could be associated with similar gradients within broad-scale metabolite profiles, and also that the wood-forming metabolisms of different poplar hybrids can respond similarly to the influences of genetic manipulation, at a global level. To conclude, the demonstrated associations between genotype, the metabolism of wood formation, and wood phenotype, as revealed by metabolite profiles, confirm the value of non-targeted metabolomics as a systems biology approach to understanding and modeling growth and secondary cell wall biosynthesis in trees.
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Hobani, Yahya Hasan. "Metabolomic analyses of Drosophila models for human renal disease." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3222/.
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Inborn errors of metabolism (IEMs) constitute a major class of genetic disorder. Most of IEMs are transmitted recessively, so consanguinity has a huge impact on disease prevalence, particularly in societies like Saudi Arabia, where consanguineous marriage is common. Understanding and treatment are very important in genetic diseases, and simple models would be helpful. Thus, the feasibility of applying the fruit fly, Drosophila melanogaster, as a model for a human renal genetic disease - xanthinuria - was investigated. Xanthinuria is a rare human genetic disease, caused by mutations in xanthine oxidase or molybdenum cofactor sulphurase; in Drosophila, the homologous genes are rosy (ry) and maroon-like (mal), respectively. The new Orbitrap technology of mass spectrometry has the potential to determine levels of many metabolites simultaneously by exact mass, and a major part of this thesis was to investigate the utility of Orbitrap technology in metabolomics of both wild-type and Drosophila mutant. Repeatable significant differences were identified between ry and wild-type flies, which recapitulated painstaking analytical biochemical determinations of the 1950s, but with greater precision. Additionally, completely novel impacts of the ry mutation (on pyrimidine metabolism, the urea cycle and osmolyte biosynthesis) were identified. As expected mal mutants showed more similar changes as ry, but with widespread metabolic perturbations. The online resource, FlyAtlas.org, provides detailed microarray-based expression data for multiple tissues and life-stages of Drosophila. Downstream genes, such as urate oxidase, are utterly tubule-specific. Accordingly, the utility of Orbitrap technology in elucidating tissue-specific metabolomes was also investigated. Additionally, genetic interventions using designed RNAi constructs were also made and validated by QPCR and metabolomics. As urate is a potent antioxidant, survival of urate oxidase knockdowns was tested in vivo, and a significant impact on survival identified. An Affymetrix microarray was performed, comparing ry506 mutant flies against wild-type and differences were identified in a second experiment, the anti-gout drug allopurinol was used to phenocopy the effects of ry. Overall, the thesis showed that Orbitrap technology was highly suitable for metabolomic analysis of both wild-type and mutant Drosophila, and had potential in the analysis of metabolomes of single tissues. The possibility of using Orbitrap-based metabolomicsin human diagnosis is discussed.
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Wibom, Carl. "Multivariate analyses of proteomic and metabolomic patterns in brain tumors." Doctoral thesis, Umeå universitet, Onkologi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-25670.
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Glioblastoma multiforme (GBM) is the most common primary brain tumor. Given the current standard of care, the prognosis for patients diagnosed with this disease is still poor. There consequently exists a need to improve current treatments, as well as to develop new ones. Many obstacles however need to be overcome to facilitate this effort and one of these involves the development of improved methods to monitor treatment effects. At present, the effects of treatment are typically assessed by radiological means several months after its initiation, which is unsatisfactory for a fast growing tumor like GBM. It is however likely that treatment effects can be detected on a molecular level long before radiological response, especially considering many of the targeted therapies that are currently being developed. Biomarkers for treatment efficacy may be of great importance in the future individualization of brain tumor treatment. The work presented herein was primarily focused on detecting early effects of GBM treatment. To this end, we designed experiments in the BT4C rat glioma model in which we studied effects of both conventional radiotherapy and an experimental angiogenesis inhibitor, vandetanib. Brain tissue samples were analyzed using a high throughput mass spectrometry (MS) based screening, known as Surface Enhanced Laser Desorption/Ionization - Time of Flight - Mass Spectrometry (SELDI-TOF-MS). The vast amounts of data generated were subsequently analyzed by established multivariate statistical methods, such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and Orthogonal Partial Least Squares (OPLS), developed for analysis of large and complex datasets. In the radiotherapy study we detected a protein spectrum pattern clearly related to tumor progression. We notably observed how this progression pattern was hampered by radiotherapy. The vandetanib study also revealed significant alterations of protein expression following treatment of different durations, both in tumor tissue and in normal brain contralateral to the tumor. In an effort to further elucidate the pathophysiology of GBM, particularly in relation to treatment, we collected extracellular fluid (ECF) samples from 11 patients diagnosed with inoperable GBM. The samples were collected by means of stereotactic microdialysis, both from within the contrast enhancing tumor and the brain adjacent to tumor (BAT). Samples were collected longitudinally from each patient in a time span of up to two weeks, during which the patient received the first five fractions of radiotherapy. The ECF samples were then analyzed by Gas Chromatography Mass Spectrometry (GC-MS) to screen them with respect to concentrations of low molecular weight compounds (metabolites). Suitable multivariate analysis strategies enabled us to extract patterns of varying metabolite concentrations distinguishing between samples collected at different locations in the brain as well as between samples collected at different time points in relation to treatment. In a separate study, we also applied SELDI-TOF-MS and multivariate statistical methods to unravel possible differences in protein spectra between invasive and non-invasive WHO grade I meningiomas. This type of tumor can usually be cured by surgical resection however sometimes it grows invasively into the bone, ultimately causing clinical problems. This study revealed the possibility to differentiate between invasive and non-invasive benign meningioma based on the expression pattern of a few proteins. Our approach, which includes sample analysis and data handling, is applicable to a wide range of screening studies. In this work we demonstrated that the combination of MS screening and multivariate analyses is a powerful tool in the search for patterns related to treatment effects and diagnostics in brain tumors.
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Taraboletti, Alexandra Anna. "Chemical and Metabolomic Analyses of Cuprizone-Induced Demyelination and Remyelination." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1498535047689141.
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Orland, Annika [Verfasser]. "Metabolomic and Transcriptomic Analyses in the Characterization of Herbal Substances and their Preparations = Metabolom- und Transkriptom-Analysen zur Charakterisierung von pflanzlichen Substanzen und daraus hergestellten Zubereitungen / Annika Orland." Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1077290357/34.
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Vincent, Isabel May. "Using metabolomic analyses to study mode of action of and resistance to Eflornithine in Trypanosoma brucei." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/3125/.
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Human African trypanosomiasis (HAT) is a disease that is in desperate need of new pharmacological agents active against the causative parasite, the flagellated protozoan Trypanosoma brucei. In this thesis, new metabolomics techniques have been developed to study pathways in response to drug action with the aim of defining the mode of action of current and future drugs. Eflornithine, a polyamine pathway inhibitor, was used as a proof of principle, revealing both expected changes that correlate well with the literature and unexpected changes that lead to pathways and metabolites not previously described in bloodstream form trypanosomes. One metabolite not previously described in trypanosomes is acetylornithine, whose levels correlate well with ornithine and whose production comes directly from ornithine transported from the medium. Nifurtimox and the nifurtimox- eflornithine combination therapy were assayed for changes to their metabolomes revealing changes in nifurtimox treatment that included alterations to sugar and purine levels. The combination therapy had reduced changes to some metabolites compared to each drug in isolation suggesting reasons for the combination‟s lack of synergy. Isotopically labelled metabolites were also of use in determining flux through the pathways identified as being affected by drug perturbation. These techniques, along with other biochemical techniques, were used to show arginase activity is absent in bloodstream form trypanosomes and that ornithine is not made from arginine when ornithine is present in the medium. Arginine can, however, be used to produce ornithine through an arginase-independent mechanism when exogenous ornithine is lacking. Evidence is also provided that parts of the pentose phosphate pathway, not thought to be active in bloodstream form trypanosomes, may still be active in in vitro grown cells. A mechanism of resistance to eflornithine involving the deletion of an amino acid transporter that is able to transport eflornithine is also described. It is hoped that simple PCR-based tests for this resistance mechanism will be of use in resistant foci in prescribing appropriate drugs to HAT patients.
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Boudah, Samia. "Développement et application de méthodes de chromatographie liquide couplées à la spectrométrie de masse à haute résolution pour les analyses métabolomiques et lipidomiques de larges cohortes." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066281/document.
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Le profilage métabolomique global de matrices biologiques dans de larges séries d'échantillon est un enjeu majeur. Dans ce contexte, notre travail vise à développer des approches LC-HRMS et outils bioinformatiques pour les analyses métabolomique et lipidomique de larges cohortes. Dans un premier temps, nous avons développé puis évalué la pertinence de 4 méthodes LC-HRMS dans l'annotation du métabolome/lipidome sérique humain. Ainsi, une base de données spectrales a été implémentée à l'aide de spectres MS, MS/MS et les temps de rétention de composés de référence afin d'assurer l'annotation de jeux de données. La combinaison de méthodes RP, HILIC et PFPP-HRMS a permis l'identification de 266 métabolites et 706 espèces lipidiques sériques répartis sur 20 et 24 classes chimiques respectivement dont 27% d'espèces isomères. Ces outils ont été appliqués, dans un second temps, à la stratification de 78 patients diabétiques. Outre le syndrome métabolique marqué (perturbation du métabolisme énergétique), nos analyses ont montré l'impact délétère de facteurs physiologiques confondants -âge et IMC-. Nous en avons évalué l'influence sur une cohorte de 227 salariés du CEA. Les empreintes lipidomiques sont robustes, néanmoins l'impact de l'IMC est marqué pour les lipides neutres. L'effet du genre démontre un catabolisme masculin important. L'effet de l'âge se manifeste par des activités enzymatiques altérées. Ces études combinent une analyse globale métabolomique et lipidomique des mêmes échantillons humains. Elles visent à construire une base de données relationnelle incluant données spectrales et biologiques servant à la caractérisation de biomarqueurs dans le cas d'études cliniquesGlobal metabolomic profiling of biological media in large sample sets is a major challenge. In this context, our work aims to develop LC-HRMS approaches and data mining tools for metabolomics and lipidomics analysis of large cohorts. We have first developed and evaluated the reliability of four LC-HRMS methods in the annotation of human serum metabolome and lipidome. Thus, spectral database was implemented using MS spectra, MS/MS and retention times of reference compounds to further ensure datasets annotation. The combination of RP, PFPP and HILIC-HRMS methods allowed identification of 266 metabolites and 706 lipid species in human serum over 20 to 24 chemical classes respectively including 27% of isomeric species. These analytical tools were then applied for the stratification of 78 diabetic patients. Unsurprisingly, we highlighted a metabolic syndrome (energy metabolism disruption), moreover our analyses have shown the deleterious impact of confounding physiological factors on diabetes biomarker discovery –age and BMI-. We finally evaluated their influence on a cohort of 227 CEA employees. Lipidomic fingerprints are robust, however BMI impact is marked for neutral lipids. Gender effect shows significant male catabolism and age altered enzyme activities. These studies combine an overall metabolomics and lipidomics analyses of the same human samples. They aim to build up a relational database including spectral and biological data for biomarker characterization in clinical studies
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Díaz, San Pedro Ramón. "Potential of LC-(Q)TOF MS in target and non-target analyses: wide scope screening of organic contaminants and metabolomic applications." Doctoral thesis, Universitat Jaume I, 2016. http://hdl.handle.net/10803/669026.
Full textAbstract:
En la presente tesis se ha investigado el potencial de la cromatografía líquida acoplada a la espectrometría de masas de alta resolución en aproximaciones analíticas tanto de tipo target (análisis de compuestos diana) como non-target (análisis no dirigido). Las ventajas que este tipo de instrumentación ofrece, derivadas de la adquisición del espectro completo con alta resolución y medidas de masa exacta, la hacen idónea para el screening de contaminantes orgánicos así como para estudios metabolómicos tanto de denominación de origen de alimentos como de carácter biomédico.
El trabajo presentado se ha estructurado en tres grandes bloques. En el primero de ellos se aborda el desarrollo y optimización de un método de screening “universal” de contaminantes orgánicos usando un acoplamiento instrumental LC-QTOF MS. Con este fin, se ha desarrollado una base de datos de compuestos, se ha optimizado la metodología analítica y, finalmente, se ha validado dicha metodología en matrices medioambientales. El segundo bloque muestra el potencial de la técnica para la investigación y diagnosis en exhalados pulmonares, los cuales presentan un bajísima concentración de metabolitos. En el tercer y último bloque, se lleva a cabo la búsqueda de compuestos que puedan utilizarse como marcadores de D.O. en muestras de naranja y vino, centrándose especialmente en aquellas pertenecientes a la Comunidad Valenciana.
El primer bloque se inicia con la optimización de los parámetros instrumentales que puedan tener un efecto notorio en la creación de la base de datos y/o en el análisis de las muestras. Para ello, se han investigado los factores que afectan a la sensibilidad instrumental y a la exactitud de masa. Además, se estudia la fragmentación de los compuestos incluidos en la base de datos con el fin de facilitar de forma automática y simultánea la confirmación de la identidad de los compuestos detectados en las muestras. Finalmente se ha evaluado la influencia de la resolución de masa y cromatográfica en la exactitud de masa en el caso de matrices complejas. Posteriormente, se han creado dos bases de datos de contaminantes orgánicos: una teórica, la cual contiene alrededor de 1000 contaminantes orgánicos reportados en la literatura como analizables por LC-MS. Esta base de datos incluye tiempo de retención y fórmula empírica de fragmentos y aductos de aquellos compuestos de los que se dispone de patrones de referencia y que han sido inyectados de acuerdo con los parámetros previamente optimizados. La segunda base de datos se trata de una librería de espectros experimental que contiene los espectros a alta y baja energía de colisión (estos últimos proporcionando información sobre la fragmentación), así como tiempos de retención, de los compuestos con patrón disponible. Finalmente, se ha evaluado la eficacia de la librería en el análisis de muestras reales.
En un segundo trabajo se evalúa el potencial de los dos principales procedimientos para la investigación de contaminantes orgánicos mediante métodos de screening: non-target y post-target. Para ello, se lleva a cabo un screening de muestras ambientales, alimentos y muestras de interés toxicológico mediante las dos metodologías y se comparan los resultados obtenidos. La primera aproximación (non-target), basada en la deconvolución del cromatograma para la búsqueda de componentes en la muestra, ha demostrado una notable dependencia de la intensidad del pico cromatográfico debido a la baja eficiencia del algoritmo de deconvolución. En el caso de la aproximación non-target, tras la búsqueda de los componentes, los espectros correspondientes son automáticamente comparados con la librería de espectros experimental creada anteriormente, así como con una librería teórica generada a partir de los compuestos incluidos en la base de datos de contaminantes. En cuanto a la segunda aproximación, tipo post-target, esta se basa en la búsqueda, después de la inyección de las muestras, de compuestos seleccionados (target) incluidos en la base de datos. Como se ha indicado anteriormente, ésta incluye información de fragmentación y tiempo de retención de aquellos compuestos con patrón de referencia disponible, es decir aquellos que están también presentes en la librería experimental de espectros. A la vista de los resultados, se concluye que la aproximación post-target resulta la más ventajosa para abordar un screening “universal” de un elevado número de compuestos. Además, los procesos de revisión de datos y los tiempos de procesamiento se reducen considerablemente. Sin embargo, la metodología non-target presenta una excelente capacidad de confirmación de la identidad de los contaminantes encontrados ya que facilita la comparación de los espectros de fragmentación de patrones con los obtenidos en la muestra. Mediante la aproximación post-target, se encontró un importante número de contaminantes en muestras ambientales y alimentarias, así como drogas de abuso y fármacos en las muestras de orina de voluntarios en tratamientos de desintoxicación.
El primer bloque de la tesis finaliza con una validación cualitativa de la metodología desarrollada en muestras de agua subterránea, superficial y el efluente de una planta de tratamiento de aguas residuales. Se han evaluado dos tipos de relleno en la extracción en fase sólida aplicada a las muestras: Oasis HLB y MCX. El primero ha resultado más genérico, perdiéndose únicamente el fármaco Gabapentina en dicho proceso de preconcentración. Para la validación cualitativa, se fortifican 3 muestras independientes de cada tipo de agua analizada a dos niveles de concentración (0.1 y 1 µg/L) y se comprueba la capacidad del método para detectar (típicamente, usando la molécula protonada) e identificar (mediante al menos dos iones: molécula protonada y un fragmento) los contaminantes seleccionados como modelo: 146 compuestos entre los que se incluyen 52 pesticidas, 52 medicamentos (21 antibióticos), 13 drogas de abuso, 11 hormonas, 11 micotoxinas y 7 agentes de protección UV. El método desarrollado permite la detección de la gran mayoría de los compuestos ensayados y la identificación de un buen número de ellos. Posteriormente, se ha aplicado dicha metodología al análisis de muestras reales, identificando varios de los contaminantes seleccionados, incluso a niveles de concentración inferiores al más bajo validado. También ha sido posible la detección e identificación tentativa de varios contaminantes no incluidos en la validación del método, incluso sin patrón de referencia disponible, gracias a la valiosa información suministrada por el analizador QTOF-MS.
En el segundo bloque se aprovechan las ventajas que ofrece el acoplamiento LC-MS, y en especial HRMS, para aplicaciones metabolómicas que requieren de una elevada sensibilidad instrumental. Este es el caso de los condensados de exhalados pulmonares, cuyas concentraciones de metabolitos son extremadamente bajas y, por tanto, requieren de técnicas más sensibles como son las LC-HRMS frente al típico análisis mediante NMR. En el trabajo realizado, en colaboración con el Instituto de Estudios Biofuncionales de la Universidad Complutense de Madrid (UCM, Madrid, Spain) y llevado a cabo en el Department of Biomolecular Medicine en Imperial College London (London, UK), se realiza un estudio preliminar sobre las capacidades de LC-HRMS y NMR para abordar la diferenciación de pacientes sanos de aquellos con enfermedades respiratorias, concretamente con obstrucción pulmonar crónica, mediante el análisis no invasivo de condensados de exhalados pulmonares. En base a los resultados obtenidos, se propone el uso de LC-HRMS como aproximación metabolómica estándar para este tipo de análisis.
En el tercer y último bloque se investigan los marcadores que permiten la diferenciación de alimentos según su origen, especialmente en productos de interés para la Comunidad Valenciana y cuya calidad está directamente relacionada con su D.O. El primero de los trabajos incluidos en este capítulo se centra en el análisis de las diferencias a nivel químico presentes en naranjas de diferentes orígenes. Para ello, se han seleccionado muestras de la Comunidad Valenciana y de países del hemisferio sur (Sudáfrica y Argentina) de variedades de maduración tardía. Las muestras completas (piel y pulpa) se trituran, homogenizan y extraen con una mezcla agua:metanol, se diluyen con agua y finalmente se analizan mediante UHPLC-HRMS. Posteriormente, se procesan los datos mediante análisis multivariante para encontrar los que permitirían la distinción en función del origen. Finalmente, se ha ampliado el muestreo a la siguiente campaña, incluyendo también muestras de Brasil, con la finalidad de comprobar la validez de los marcadores encontrados y su posible aplicación a otros destinos. Se ha encontrado un marcador idóneo en ambos casos (primer y segundo muestreo) que corresponde a un compuesto de tiempo de retención 4.83 minutos, que finalmente y gracias a la información espectral en masa exacta ofrecida por LC-HRMS se ha identificado como Citrusin D.
Finalmente, en el último trabajo presentado se ha realizado un estudio similar al de las naranjas. El vino es un producto muy preciado cuyo valor está extremadamente ligado a su origen, entre otros. En este artículo científico se realiza un comparación interlaboratorio para el estudio metabolómico de muestras de vino procedentes de tres importantes denominaciones de origen en España (Ribera del Duero, Rioja y Penedés). Los resultados de ambas plataformas ofrecen una clasificación óptima de las muestras en base a marcadores definidos en las rutas metabólicas de la vitis vinífera. Además, se discute sobre las ventajas y desventajas de ambas plataformas, no solo en el aspecto instrumental (TOF vs Orbitrap) sino también en el procesamiento de los datos y la selección de los marcadores. Como cabía esperar por ser geográficamente la más diversa, la denomiancion de origen Penedés era separada de las otras dos mediante un simple análisis no-supervisado PCA. Para separar completamente las 3 D.O. se analizaron mediante PLS-DA obteniéndose una clasificación correcta para todas las muestras. Se encontraron diversos marcadores de la familia de los polifenoles como la Catequina, Epicatequina y Galactocatequina, entre otros. Finalmente, los marcadores de cada plataforma fueron cuantificados en un modo target en la otra plataforma. Se demostró que, para ambas plataformas, los diferentes marcadores eran significativos y que por tanto, el tratamiento de datos había filtrado estos marcadores. El modelo estadístico aplicado haciendo uso exclusivamente de los marcadores fue capaz de separar perfectamente las diferentes denominaciones de origen mediante PCA.
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Conan, Cécile. "Metabolomics investigations of seaweed extracts used as plant growth biostimulants and transcriptomic studies of their physiological effects on A. thaliana." Electronic Thesis or Diss., Paris 6, 2016. http://www.theses.fr/2016PA066760.
Full textAbstract:
Développer une agriculture durable et respectueuse de l’environnement, implique l’utilisation de biostimulants tels que les extraits de macro-algues marines dans le but d’améliorer la croissance des plantes ainsi que leur tolérance aux stress biotiques et abiotiques. Ces extraits commerciaux d’algues sont utilisés en agriculture afin de favoriser la nutrition des plantes, d’améliorer leur qualité nutritionnelle et d’accroitre leur rendement. Dans ce domaine quelques modes d’action ont été élucidés par le centre R&D des Laboratoires Goëmar-Arysta. Cependant, jusqu’à présent, les matières actives n’ont pas été identifiées via une approche classique de fractionnement bio-guidé. De ce fait, leurs mécanismes d’action restent non élucidés. L’objectif premier de ce projet de thèse était d’identifier ces molécules biostimulantes via une approche de fractionnement assistée par la métabolomique, réalisée sur des extraits d’algues commerciaux. Les analyses RMN et LC-MS réalisées sur ces extraits se sont révélées infructueuses dans l’identification de molécules candidates. Ainsi, un classique fractionnement bio-guidé a conduit à la purification d’une fraction favorisant la croissance des plantes. Les analyses U-HPLC-HR-MS réalisées sur cette fraction et ses sous-fractions ont permis d’identifier deux molécules candidates. Un procédé de fractionnement utilisé au cours de ce travail fait l’objet d’une procédure de dépôt de brevet, afin d’apporter une valeur ajoutée à ces extraits biostimulants et de valoriser de nouveaux produits. Le deuxième objectif de ce projet, était d’étudier les réponses physiologiques de la plante modèle Arabidopsis thaliana à l’aide d’analyse transcriptomique. Ceci afin d’élucider les voies métaboliques régulées suite à l’application d’un extrait d’algue produit par Goëmar et d’une fraction stimulante de croissance purifiée au cours de ce projet. L’analyse du transcriptome d’Arabidopsis thaliana révèle la régulation de voies métaboliques complétement différentes par l’extrait d’algues en comparaison de celles régulées par sa fraction purifiée. De plus, les gènes dérégulés par la fraction purifiée constituent des biomarqueurs potentiels de croissance chez les plantes qui pourront être utilisés pour assister l’isolement bio-guidé de molécules candidates. Finalement, ces deux approches combinant fractionnement bio-guidé et analyses métabolomiques sur l’extrait d’Ascophyllum nodosum ainsi que les analyses transcriptomiques réalisées apportent de nouvelles connaissances sur les structures et les modes d’action de molécules candidatesTo further develop a sustainable agriculture, new bio-solutions include the use of biostimulants such as seaweed aqueous extracts to improve plant growth or/and alleviate the effect of biotic and abiotic stress. These commercial products aim to improve plant nutrition, in order to impact yield and quality parameters. In this domain, some modes of action have been proposed by the Goëmar-Arysta R&D center. However, the bioactive ingredients have not been identified so far, using classical methods of bioassay-guided fractionation. Therefore, their mechanisms of action remain also elusive. The aim of this thesis project was first to identify, using a strategy of metabolomic profiling of seaweed extracts, the bioactive compounds responsible for plant growth stimulation. The 1H-NMR-based profiling and LC-MS metabolomic analyses of commercial seaweed extracts were not suitable to identify candidate molecules that promote plant growth. A classical bioassay-guided fractionation achieved on a Goëmar extract provided a growth promoting purified fraction and further bioactive sub-fractions. The U-HPLC-HR-MS analyses of these sub-fractions highlighted two candidate molecules. A fractionation process used in this work should be patented in order to improve added-value of growth-promoting filtrate and valorize new by-products. In parallel, the physiological effects of these seaweed extracts were studied in the model plant Arabidopsis thaliana through transcriptomic approaches in order to decipher patterns of gene regulation in response to a crude commercial extract and its purified fraction. The transcriptome in response to the application of seaweed extract was completely different of those obtained using its purified fraction. Genes dysregulated by this purified fraction provided potential biomarkers of plant growth that could be used. to assist the bioactive molecule isolation. Finally these two approaches combining, metabolomics-guided and bioassay-guided fractionation of extracts from the brown seaweed Ascophyllum nodosum, and global transcriptomics in Arabidopsis provided several new insights into the nature and structure of different molecules that trigger different physiological responses in plants
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Karimpour, Masoumeh. "Multi-platform metabolomics assays to study the responsiveness of the human plasma and lung lavage metabolome." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-120591.
Full textAbstract:
Metabolomics as a field has been used to track changes and perturbations in the human body by investigating metabolite profiles indicating the change of metabolite levels over time and in response to different challenges. In this thesis work, the main focus was on applying multiplatform-metabolomics to study the human metabolome following exposure to perturbations, such as diet (in the form of a challenge meal) and exhaust emissions (air pollution exposure in a controlled setting). The cutting-edge analytical platforms used for this purpose were nuclear magnetic resonance (NMR), as well as gas chromatography (GC) and liquid chromatography (LC) coupled to mass spectrometry (MS). Each platform offered unique characterization features, allowing detection and identification of a specific range of metabolites. The use of multiplatform-metabolomics was found to enhance the metabolome coverage and to provide complementary findings that enabled a better understanding of the biochemical processes reflected by the metabolite profiles. Using non-targeted analysis, a wide range of unknown metabolites in plasma were identified during the postprandial stage after a well-defined challenge meal (in Paper I). In addition, a considerable number of metabolites were detected and identified in lung lavage fluid after biodiesel exhaust exposure compared to filtered air exposure (in Paper II). In parallel, using targeted analysis, both lung lavage and plasma fatty acid metabolites were detected and quantified in response to filtered air and biodiesel exhaust exposure (in Paper III and IV). Data processing of raw data followed by data analysis, using both univariate and multivariate methods, enabled changes occurring in metabolites levels to be screened and investigated. For the initial pilot postprandial study, the aim was to investigate the plasma metabolome response after a well-defined meal during the postprandial stage for two types of diet. It was found that independent of the background diet type, levels of metabolites returned to their baseline levels after three hours. This finding was taken into consideration for the biodiesel exhaust exposures studies, designed to limit the impact of dietary effects. Both targeted and non-targeted approaches resulted in important findings. For instance, different metabolite profiles were detected in bronchial wash (BW) compared to bronchoalveolar lavage (BAL) fluid with mainly NMR and LC-MS. Furthermore, biodiesel exhaust exposure resulted in different metabolite profiles as observed by GC-MS, especially in BAL. In addition, fatty acid metabolites in BW, BAL, and plasma were shown to be responsive to biodiesel exhaust exposure, as measured by a targeted LC-MS/MS protocol. In summary, the new analytical methods developed to investigate the responsiveness of the human plasma and lung lavage metabolome proved to be useful in an analytical perspective, and provided important biological findings. However, further studies are needed to validate these results.Metabolomik har använts för att spåra förändringar och störningar i kroppens funktioner genom undersökning av metabolit-profiler. I detta avhandlingasarbete har huvudfokus varit på tillämpning av flera olika analytiska plattformar för metabolomikstudier av det mänskliga metabolomet efter exponering för olika kost och avgasutsläpp från biodieselbränsle. De sofistikerade analytiska plattformarna som användes för detta ändamål var kärnmagnetisk resonans (NMR), samt gaskromatografi (GC) och vätskekromatografi (LC) kopplat till masspektrometri (MS). Varje plattform erbjöd unika karakteriseringsmöjligheter med detektion och identifiering av specifika grupper av metaboliter. Användningen av multipattformmetabolomik förbättrade täckningen av metabolomet och genererade kompletterande resultat som möjliggjorde en bättre förståelse av de biokemiska processer som reflekteras av metabolitprofilerna. Med hjälp av breda analyser har ett stort antal okända metaboliter i plasma identifierats under den postprandial fasen efter en väldefinerad måltid (i Paper I). Dessutom har ett stort antal metaboliter påvisats och identifierats i lungsköljvätska efter exponering av biodieselavgaser jämfört med kontollexponering med filtrerad luft (i Paper II). Parallellt med dessa breda analyser har också riktade analyser genomförts av både lungsköljvätska och plasma. Därigenom har bioaktiva lipider detekterats och kvantifieras efter avgasexponering och resultaten har jämförts med filtrerad luft som kontrollexponering (Paper III och IV). Processning av rådata följt av dataanalys, med både univariata och multivariata metoder möjliggjorde screening och fördjupad undersökning av förändringen i metabolitnivåer. I den första pilotstudien av postprandiala nivåer var syftet att undersöka responsen i plasmametabolomet efter en väldefinierad måltid under den postprandiala fasen vid två olika typer av kost. Resultaten visade att oberoende av kosten, så återvände metabolitnivåerna till sina baslinjenivåer tre timmar efter måltiden. Detta togs i beaktande vid exponeringsstudierna för biodieselavgaser, som designades så att dietens inverkan minimerades. Både breda och riktade analyser resulterade i viktiga resultat. Exempelvis så detekterades olika metabolitprofiler i bronkiell sköljvätska (BW) jämfört med bronkoalveolär sköljvätska (BAL), speciellt med NMR och LC-MS. Dessutom resulterade avgasexponering i förändrade metabolitprofiler, observerade med GC-MS, särskilt i BAL. Dessutom uppvisade fettsyrametaboliter i BW, BAL och plasma förändrade halter efter avgasexponering, uppmätt genom en riktad LC-MS/MS-analys. Sammanfattningsvis så visade sig de nya metoderna som utvecklats för att undersöka förändringar i metabolithalterna i plasma och lungsköljvätska fungera väl ur ett analytiskt perspektiv och resulterade i viktiga biologiska fynd. Fördjupade studier behövs dock för att validera resultaten.
Books on the topic "Metabolomic analyses"
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Xia, Yinglin, and Jun Sun. Microbiome and Metabolomics: Statistical Data Analyses. Washington, DC, USA: American Chemical Society, 2022. http://dx.doi.org/10.1021/acsinfocus.7e5003.
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Vaidyanathan, Seetharaman, George G. Harrigan, and Royston Goodacre, eds. Metabolome Analyses: Strategies for Systems Biology. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/b106967.
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Li, Shuzhao, ed. Computational Methods and Data Analysis for Metabolomics. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3.
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Xia, Yinglin, Jun Sun, and Xiaotao Shen, Ph.D., Stanford University School of Medicine. Statistical Data Analysis of Microbiomes and Metabolomics. Washington, DC, USA: American Chemical Society, 2022. http://dx.doi.org/10.1021/acsinfocus.7e5035.
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Datta, Susmita, and Bart J. A. Mertens, eds. Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45809-0.
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Roessner, Ute, and Daniel Anthony Dias. Metabolomics tools for natural product discovery: Methods and protocols. New York: Humana Press, 2013.
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Bagchi, Debasis. Genomics, proteomics, and metabolomics in nutraceuticals and functional foods. Ames, Iowa: Wiley-Blackwell, 2010.
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Bagchi, Debasis, Anand Swaroop, and Manashi Bagchi. Genomics, proteomics and metabolomics in nutraceuticals and functional foods. Chichester, West Sussex: John Wiley & Sons, Inc., 2015.
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Bjerrum, Jacob T. Metabonomics: Methods and protocols. New York: Humana Press, 2015.
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Armitage, Emily G. Correlation-based network analysis of cancer metabolism: A new systems biology approach in metabolomics. New York: Springer, 2014.
Find full textBook chapters on the topic "Metabolomic analyses"
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Buszewska-Forajta, Magdalena, Joanna Raczak-Gutknecht, Anna Rajska, and Michał J. Markuszewski. "Metabolomic Analyses of Natural Medicinal Products." In Handbook of Bioanalytics, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63957-0_21-1.
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Kanani, H., B. Dutta, J. Quackenbush, and M. I. Klapa. "Time-Series Integrated Metabolomic and Transcriptional Profiling Analyses." In Concepts in Plant Metabolomics, 93–110. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5608-6_7.
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Richard, Vincent R., Simon D. Bourque, and Vladimir I. Titorenko. "Metabolomic and Lipidomic Analyses of Chronologically Aging Yeast." In Methods in Molecular Biology, 359–73. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1363-3_21.
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Hayashi, Yohei, and Yasuhisa Matsui. "Metabolomic and Proteomic Analyses of Mouse Primordial Germ Cells." In Stem Cells and Aging, 259–69. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/7651_2018_164.
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Marrocco, Cristina, Angelo D’Alessandro, Sara Rinalducci, Cristiana Mirasole, and Lello Zolla. "Untargeted metabolomic analyses open new scenarios in post mortem pig muscles: Casertana and Large White." In Farm animal proteomics 2013, 270–73. Wageningen: Wageningen Academic Publishers, 2013. http://dx.doi.org/10.3920/978-90-8686-776-9_68.
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Scholz, Matthias, and Joachim Selbig. "Visualization and Analysis of Molecular Data." In Metabolomics, 87–104. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_6.
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Wishart, David S. "Metabolomic Data Exploration and Analysis with the Human Metabolome Database." In Computational Methods and Data Analysis for Metabolomics, 165–84. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3_10.
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Schuster, Stefan, Axel Kamp, and Mikhail Pachkov. "Understanding the Roadmap of Metabolism by Pathway Analysis." In Metabolomics, 199–226. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_12.
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Dailey, Allyson L. "Metabolomic Bioinformatic Analysis." In Methods in Molecular Biology, 341–52. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6990-6_22.
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Steuer, Ralf, Katja Morgenthal, Wolfram Weckwerth, and Joachim Selbig. "A Gentle Guide to the Analysis of Metabolomic Data." In Metabolomics, 105–26. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_7.
Full textConference papers on the topic "Metabolomic analyses"
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Yang, P., X. Hu, E. Iffrig, G. S. Martin, D. P. Jones, and A. M. Esper. "Serial Metabolomic Analyses in Sepsis-Induced Acute Respiratory Distress Syndrome." 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.a5139.
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Ha, Soo Jung, Gordon Showalter, Jenna Rickus, Shanbao Cai, Haiyan Wang, Wei Michael Liu, Jann N. Sarkaria, et al. "Abstract B37: Proteomic and metabolomic analyses of glioblastoma using mass spectrometry." In Abstracts: AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics--Oct 19-23, 2013; Boston, MA. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1535-7163.targ-13-b37.
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Chu, Ching-Yu, Szu-Yuan Chen, Fu-Yu Chueh, Mei-Ling Cheng, and Chao-Lan Yu. "Abstract 2564: Integrated transcriptomic, proteomic, and metabolomic analyses of human and mouse T cell leukemia." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-2564.
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"PREDICTED RELATIVE METABOLOMIC TURNOVER - Predicting Changes in the Environmental Metabolome from the Metagenome." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003314803370345.
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Chang, Chun, Ying Liang, Juan Wang, Yongchang Sun, and Wanzhen Yao. "Metabolomic profiling differences among asthma, COPD and healthy controls: a LC-MS-based metabolomics analysis." In ERS International Congress 2019 abstracts. European Respiratory Society, 2019. http://dx.doi.org/10.1183/13993003.congress-2019.pa1701.
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Ngo, D., B. Peterson, R. Montanez, T. Sofer, J. Morningstar, X. Shi, D. J. Gottlieb, et al. "Metabolomic Analysis of Obstructive Sleep Apnea Traits." In American Thoracic Society 2019 International Conference, May 17-22, 2019 - Dallas, TX. American Thoracic Society, 2019. http://dx.doi.org/10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a7339.
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Cardoso, Sara, Miguel Rocha, Telma Afonso, and Marcelo Maraschin. "WebSpecmine: a website for metabolomics data analysis and mining." In 3rd International Electronic Conference on Metabolomics. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05842.
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Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.
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Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictions and cancer development outcomes. The proposed model integrates patient-specific multi-omics data (i.e., genomic, metabolomic and clinical data) into a metabolic model of cancer to produce a list of metabolic reactions affecting cancer progression. Quantitative and predictive analysis, through survival analysis and machine learning techniques, is then performed on the list of selected reactions. Since our model performs an analysis of patient-specific data, the outcome of our pipeline provides a personalised prediction of survival outcome and cancer development based on a subset of identified multi-omics features (genomic, metabolomic and clinical data). In particular, our work aims to develop a computational pipeline for clinicians that relates the omic profile of each patient to their survival probability, based on a combination of machine learning and metabolic modelling techniques. The model provides patient-specific predictions on cancer development and survival outcomes towards the development of personalised medicine.
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González-Domínguez, Raúl, Ana Sayago, and Ángeles Fernández-Recamales. "Comparison of complementary statistical analysis approaches in metabolomic food traceability." In 3rd International Electronic Conference on Metabolomics. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05839.
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Laatikainen, Reino, Pekka Laatikainen, and Elias Hakalehto. "QUANTITATIVE QUANTUM MECHANICAL NMR ANALYSIS: THE SUPERIOR TOOL FOR ANALYSIS OF BIOFLUIDS." In The 1st International Electronic Conference on Metabolomics. Basel, Switzerland: MDPI, 2016. http://dx.doi.org/10.3390/iecm-1-c005.
Full textReports on the topic "Metabolomic analyses"
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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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Arp, Daniel, and Luis Sayavedra-Soto. Metabolomic Functional Analysis of Bacterial Genomes: Final Report. Office of Scientific and Technical Information (OSTI), January 2008. http://dx.doi.org/10.2172/951563.
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Rabinowitz, Joshua D. Final Technical Report--Quantitative analysis of metabolic regulation by integration of metabolomics, proteomics, and fluxomics. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1487155.
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Lidstrom, Mary E., Ludmila Chistoserdova, Marina G. Kalyuzhnaya, Victoria J. Orphan, and David A. Beck. Systems level insights into alternate methane cycling modes in a freshwater lake via community transcriptomics, metabolomics and nano-SIMS analysis. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1149958.
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Cytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598174.bard.
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Control of agro-associated pathogens is becoming increasingly difficult due to increased resistance and mounting restrictions on chemical pesticides and antibiotics. Likewise, in veterinary and human environments, there is increasing resistance of pathogens to currently available antibiotics requiring discovery of novel antibiotic compounds. These drawbacks necessitate discovery and application of microorganisms that can be used as biocontrol agents (BCAs) and the isolation of novel biologically-active compounds. This highly-synergistic one year project implemented an innovative pipeline aimed at detecting BCAs and associated biologically-active compounds, which included: (A) isolation of multidrug-resistant desert soil bacteria and root-associated bacteria from medicinal plants; (B) invitro screening of bacterial isolates against known plant, animal and human pathogens; (C) nextgeneration sequencing of isolates that displayed antagonistic activity against at least one of the model pathogens and (D) in-planta screening of promising BCAs in a model bean-Sclerotiumrolfsii system. The BCA genome data were examined for presence of: i) secondary metabolite encoding genes potentially linked to the anti-pathogenic activity of the isolates; and ii) rhizosphere competence-associated genes, associated with the capacity of microorganisms to successfully inhabit plant roots, and a prerequisite for the success of a soil amended BCA. Altogether, 56 phylogenetically-diverse isolates with bioactivity against bacterial, oomycete and fungal plant pathogens were identified. These strains were sent to Auburn University where bioassays against a panel of animal and human pathogens (including multi-drug resistant pathogenic strains such as A. baumannii 3806) were conducted. Nineteen isolates that showed substantial antagonistic activity against at least one of the screened pathogens were sequenced, assembled and subjected to bioinformatics analyses aimed at identifying secondary metabolite-encoding and rhizosphere competence-associated genes. The genome size of the bacteria ranged from 3.77 to 9.85 Mbp. All of the genomes were characterized by a plethora of secondary metabolite encoding genes including non-ribosomal peptide synthase, polyketidesynthases, lantipeptides, bacteriocins, terpenes and siderophores. While some of these genes were highly similar to documented genes, many were unique and therefore may encode for novel antagonistic compounds. Comparative genomic analysis of root-associated isolates with similar strains not isolated from root environments revealed genes encoding for several rhizospherecompetence- associated traits including urea utilization, chitin degradation, plant cell polymerdegradation, biofilm formation, mechanisms for iron, phosphorus and sulfur acquisition and antibiotic resistance. Our labs are currently writing a continuation of this feasibility study that proposes a unique pipeline for the detection of BCAs and biopesticides that can be used against phytopathogens. It will combine i) metabolomic screening of strains from our collection that contain unique secondary metabolite-encoding genes, in order to isolate novel antimicrobial compounds; ii) model plant-based experiments to assess the antagonistic capacities of selected BCAs toward selected phytopathogens; and iii) an innovative next-generation-sequencing based method to monitor the relative abundance and distribution of selected BCAs in field experiments in order to assess their persistence in natural agro-environments. We believe that this integrated approach will enable development of novel strains and compounds that can be used in large-scale operations.
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Mizrahi, Itzhak, and Bryan A. White. Exploring the role of the rumen microbiota in determining the feed efficiency of dairy cows. United States Department of Agriculture, October 2011. http://dx.doi.org/10.32747/2011.7594403.bard.
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Expanding world hunger calls for increasing available food resources. Ruminants have the remarkable ability to convert human-indigestible plant biomass into human-digestible food products, due to a complex microbiome residing in the rumen compartment of their upper digestive tract. One way to tackle the problem of diminishing food resources is to increase the animals' energetic efficiency, i.e., the efficiency with which they convert energy from feed, thereby increasing food availability while lowering the environmental burden, as these animals would produce more and eat less. We hypothesize that the cow's feed efficiency is dependent on the taxonomic composition, coding capacity and activity of its reticulorumenmicrobiota. To test this hypothesis, three aims are defined: (1) Evaluation of the feed efficiency of 146 dairy cows and defining two groups representing the highest and lowest 25% using the Israeli group's unique facility; (2) Comparing these two groups for microbiota diversity, identity and coding capacity using next-generation sequencing and metagenomic approaches; (3) Comparing the reticulorumenmicrobiota metabolic activity parameters. We measured feed efficiency in 146 milking cows and analyzed the taxonomic composition, gene content, microbial activity and metabolomic composition of rumen microbiomes from the 78 most extreme animals. Lower richness of microbiome gene content and taxa was tightly linked to higher feed efficiency. Microbiome genes and species accurately predicted the animals' feed-efficiency phenotype. Specific enrichment of microbes and metabolic pathways in each of these microbiome groups resulted in increasing valuable metabolites and decreasing unusable ones such as methane in efficient animals. This ecological and mechanistic understanding of the rumen microbiome could lead to an increase in available food resources and environmentally friendly livestock agriculture.
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Jander, Georg, and Daniel Chamovitz. Investigation of growth regulation by maize benzoxazinoid breakdown products. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600031.bard.
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Introduction Previous research had suggested that benzoxazinoids, a class of defensive metabolites found in maize, wheat, rye, and wild barley, are not only direct insect deterrents, but also influence other areas of plant metabolism. In particular, the benzoxazinoid 2,4-dihydroxy-7-methoxy-2H-1,4-benzoxa- zin-3(4H)- one (DIMBOA) was implicated in: (i) altering plant growth by interfering with auxin signaling, and (ii) leading to the induction of gene expression changes and secondary plant defense responses. The overall goal of this proposal was to identify mechanisms by which benzoxazinoids influence other aspects of plant growth and defense. Specifically, the following hypotheses were proposed to be tested as part of an approved BARD proposal: Benzoxazinoid breakdown products directly interfere with auxin perception Global changes in maize and barley gene expression are induced by benzoxazinoid activation. There is natural variation in the maize photomorphogenic response to benzoxazinoids. Although the initial proposal included experiments with both maize and barley, there were some technical difficulties with the proposed transgenic barley experiments and most of the experimental results were generated with maize. Summary of major findings Previous research by other labs, involving both maize and other plant species, had suggested that DIMBOA alters plant growth by interfering with auxin signaling. However, experiments conducted in both the Chamovitz and the Jander labs using Arabidopsis and maize, respectively, were unable to confirm previously published reports of exogenously added DIMBOA effects on auxin signaling. Nevertheless, analysis of bx1 and bx2 maize mutant lines, which have almost no detectable benzoxazinoids, showed altered responses to blue light signaling. Transcriptomic analysis of maize mutant lines, variation in inbred lines, and responses to exogenously added DIMBOA showed alteration in the transcription of a blue light receptor, which is required for plant growth responses. This finding provides a novel mechanistic explanation of the trade-off between growth and defense that is often observed in plants. Experiments by the Jander lab and others had demonstrated that DIMBOA not only has direct toxicity against insect pests and microbial pathogens, but also induces the formation of callose in both maize and wheat. In the current project, non-targeted metabolomic assays of wildtype maize and mutants with defects in benzoxazinoid biosynthesis were used to identify unrelated metabolites that are regulated in a benzoxazinoid-dependent manner. Further investigation identified a subset of these DIMBOA-responsive compounds as catechol, as well as its glycosylated and acetylated derivatives. Analysis of co-expression data identified indole-3-glycerol phosphate synthase (IGPS) as a possible regulator of benzoxazinoid biosynthesis in maize. In the current project, enzymatic activity of three predicted maize IGPS genes was confirmed by heterologous expression. Transposon knockout mutations confirmed the function of the maize genes in benzoxazinoid biosynthesis. Sub-cellular localization studies showed that the three maize IGPS proteins are co-localized in the plastids, together with BX1 and BX2, two previously known enzymes of the benzoxazinoid biosynthesis pathway. Implications Benzoxazinoids are among the most abundant and effective defensive metabolites in maize, wheat, and rye. Although there is considerable with-in species variation in benzoxazinoid content, very little is known about the regulation of this variation and the specific effects on plant growth and defense. The results of this research provide further insight into the complex functions of maize benzoxazinoids, which are not only toxic to pests and pathogens, but also regulate plant growth and other defense responses. Knowledge gained through the current project will make it possible to engineer benzoxazinoid biosynthesis in a more targeted manner to produce pest-tolerant crops without negative effects on growth and yield.