Integrated transcriptomics and metabolomics reveal the role of soluble sugars and GABA in rice leaf response to Pb stress
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Metabolomics is the study of metabolism at the global level. This rapidly developing new discipline has important potential implications for pharmacologic science. The concept that metabolic state is representative of the overall physiologic status of the organism lies at the heart of metabolomics. Metabolomic studies capture global biochemical events by assaying thousands of small molecules in cells, tissues, organs, or biological fluids-followed by the application of informatic techniques to define metabolomic signatures. Metabolomic studies can lead to enhanced understanding of disease mechanisms and to new diagnostic markers as well as enhanced understanding of mechanisms for drug or xenobiotic effect and increased ability to predict individual variation in drug response phenotypes (pharmacometabolomics). This review outlines the conceptual basis for metabolomics as well as analytical and informatic techniques used to study the metabolome and to define metabolomic signatures. It also highlights potential metabolomic applications to pharmacology and clinical pharmacology.
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The field of metabolomics for cancer diagnosis and characterization is relatively new, and especially the application of metabolomics for in-vivo cancer imaging is still in its infancy. Cancer metabolomics involves the study of global variations of metabolites, with which malignancy conditions can be evaluated by profiling the entire measurable metabolome, instead of focusing only on certain metabolites or isolated metabolic pathways. At present, the study of cancer metabolomics is mainly accomplished utilizing magnetic resonance spectroscopy (MRS) and mass spectrometry (MS). These studies aim to uncover disease-specific metabolomic profiles in order to better understand variations in the involved pathways of cancer metabolism, and to utilize these profiles to establish metabolomic criteria for cancer detection, characterization, and patient prognostication and monitoring. The ultimate goal for cancer metabolomics is its implementation in the clinic to improve clinical abilities for cancer diagnosis and characterization, and through the development of in-vivo metabolomic imaging these clinical goals can be achieved noninvasively.
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cancer;
metabolomics;
metabolomic imaging;
magnetic resonance spectroscopy;
mass spectrometry
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Abstract Age is well-known to be a significant factor in both disease pathology and response to treatment, yet the molecular changes that occur with age in humans remain ill-defined. Here, using transcriptome profiling of healthy human male skin, we demonstrate that there is a period of significantly elevated, transcriptome-wide expression changes occurring predominantly in middle age. Both pre and post this period, the transcriptome appears to undergo much smaller, linear changes with increasing age. Functional analysis of the transient changes in middle age suggest a period of heightened metabolic activity and cellular damage associated with NF-kappa-B and TNF signaling pathways. Through meta-analysis we also show the presence of global, tissue independent linear transcriptome changes with age which appear to be regulated by NF-kappa-B. These results suggest that aging in human skin is associated with a critical mid-life period with widespread transcriptome changes, both preceded and proceeded by a relatively steady rate of linear change in the transcriptome. The data provides insight into molecular changes associated with normal aging and will help to better understand the increasingly important pathological changes associated with aging.
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The major aim of metabolomics is to identify and quantify all endogenous and exogenous small molecule metabolites in a biological system in a high-throughput manner. Metabolomics has the potential to deliver diagnostic biomarkers for the detection and prognosis of diseases, and the prediction of the efficacy and safety of pharmaceutical treatment. Metabolomics can also provide insights into the biochemical mechanisms of diseases and their modulation by drugs. The analytical platforms include NMR spectroscopy, masspectrometry combined with gas or liquid chromatography. Due to the complexity of the metabolome, no single analytical platform can be applied to detect all metabolites in a biological sample.Different conceptual approaches are used in metabolomics. Targeted (specific) metabolomics is a quantitative approach where a set of known metabolites are quantitated. The identities of metabolites were initially established based on the available databases and using standard compounds; the identified metabolite peaks are then quantified based on internal or external reference compounds. Targeted metabolomics can provide greater insights into the dynamics and fluxes of metabolites. Non-targeted (global) metabolomics aims for a quick and reliable identification of small molecule biomarkers characteristic for a particular physiological state in response to internal or external stimuli. This approach is often used in pharmacokinetic studies of drug metabolism and when looking at the effect of therapeutics or genetic modifications on a specific enzyme.The development of personalised metabolomics in the future, will give the opportunity to track the trends of the metabolome for personalised drugs and improved treatment strategies.
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Drug Development
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Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach.
Functional Genomics
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Each cell contains many different metabolites and chemical molecules which are generated during cellular process. All the metabolites present in a cell at a particular time is called metabolome. The study of all the metabolites and their modification in a particular condition is called metabolomics. Metabolome is closely linked with genotype, physiology and environment. So,in a nutshell, metabolomics is the study of substrates and products of metabolism which are influenced by the genetic and environmental factors. In plants, metabolomics has now been frequently developed and studied in biotic and abiotic stress resistance. High throughput metabolomics includes time efficient and effective metabolite profiling techniques. These techniques are chromatography based and chromatography free methods. Chromatographic methods are NMR, GC-MS and LC-MS . Chromatographv free techniques include DI-MS,FI-MS,MALDI and Ambient MS . This paper will give an idea about how metabolomics work in elucidating plants phenotype, how sample is prepared for metabolite profiling, different techniques of metabolite profiling and various metabolomic databases.
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Metabolomics is a field of systems biology that draws on the scientific methods of other groups to qualitatively or quantitatively characterize small molecule metabolites in organisms, revealing their interconnections with the state of the organism at an overall relative macroscopic level. Diabetic kidney disease (DKD) is well known as a chronic metabolic disease, and metabolomics provides an excellent platform for its clinical study. A growing number of metabolomic analyses have revealed that individuals with DKD have metabolic disturbances of multiple substances in their bodies. With the continuous development and improvement of metabolomic analysis technology, the application of metabolomics in the clinical research of DKD is also expanding. This review discusses the recent progress of metabolomics in the early diagnosis, disease prognosis, and pathogenesis of DKD at the level of small molecule metabolites in vivo.
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Metabolomics is the study which detects the changes of metabolites level. Metabolomics is a terminal view of the biological system. The end products of the metabolism, metabolites, reflect the responses to external environment. Therefore metabolomics gives the additional information about understanding the metabolic pathways. These metabolites can be used as biomarkers that indicate the disease or external stresses such as exposure to toxicant. Many kinds of biological samples are used in metabolomics, for example, cell, tissue, and bio fluids. NMR spectroscopy is one of the tools of metabolomics. NMR data are analyzed by multivariate statistical analysis and target profiling technique. Recently, NMR-based metabolomics is a growing field in various studies such as disease diagnosis, forensic science, and toxicity assessment.
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Abstract Hyperuricemia (HUA) seriously harms human health but the exact etiology and pathogenesis of HUA are not fully understood. Therefore, it is still of great significance to find effective biomarkers and explore the pathogenesis of HUA. Metabolomics reflects the influence of internal and external factors on system metabolism, explains the changes in metabolite levels during the development of diseases, and reveals the molecular mechanism of pathogenesis. Metabolomics is divided into untargeted metabolomics and targeted metabolomics according to different research modes. Each other's advantages can be fully utilized by combining the two so that the results of metabolomics research can be consummated. 20 HUA patients and 20 healthy individuals participated in the experiment, and untargeted metabolomics was employed to find 50 differential metabolites in HUA serum samples. Twelve candidate biomarkers were screened based on literature research and ROC Curve analysis for subsequent verification. Based on the UPLC-TQ-MS analysis platform, the targeted metabolomics detection methods were established and the content of 12 candidate biomarkers was precisely quantified. Compare with the results of untargeted metabolomics, the targeted metabolomics results were considered more reliable.
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