Difference between revisions of "Metabolomics"
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==What is Metabolomics ?== | ==What is Metabolomics ?== | ||
| − | * Metabolomics is the scientific study of chemical processes involving metabolites. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles.<ref name="ref1"/> The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes.[2] mRNA gene expression data and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to provide a better understanding of cellular biology. | + | * Metabolomics is the scientific study of chemical processes involving metabolites. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles.<ref name="ref1"/> The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes.[2] mRNA gene expression data and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to provide a better understanding of cellular biology.<br><br> |
| − | [[File: IC4R-Metabolomics-overview-1.png|center|thumb|970px|'''Figure 1. Integrated functional genomics. The effects of gene perturbations are evaluated at multiple levels including the transcriptome, proteome, and metabolome. Changes in the metabolome occur as a consequence of those changes in the transcriptome that result in changes in the levels or catalytic activities of enzymes. Therefore, metabolome analysis is a valuable tool for inferring gene function.''']] | + | [[File: IC4R-Metabolomics-overview-1.png|center|thumb|970px|'''Figure 1. Integrated functional genomics. The effects of gene perturbations are evaluated at multiple levels including the transcriptome, proteome, and metabolome. Changes in the metabolome occur as a consequence of those changes in the transcriptome that result in changes in the levels or catalytic activities of enzymes. Therefore, metabolome analysis is a valuable tool for inferring gene function.''']]<br> |
* Advances in mass spectrometry have enabled the analysis of cellular proteins and metabolites (proteome and metabolome respectively) on a scale previously unimaginable. The cumulative utilization of these technologies has advanced the fields of functional genomics (Holtorf et al., 2002; Oliver et al., 2002; Somerville and Somerville, 1999) and systems biology (Ideker et al., 2001; Kitano, 2000). Both fields comprise traditional molecular biology, enzymology and bio- chemistry; however, the predominant difference from previous approaches is the significantly larger scale upon which they are conducted. | * Advances in mass spectrometry have enabled the analysis of cellular proteins and metabolites (proteome and metabolome respectively) on a scale previously unimaginable. The cumulative utilization of these technologies has advanced the fields of functional genomics (Holtorf et al., 2002; Oliver et al., 2002; Somerville and Somerville, 1999) and systems biology (Ideker et al., 2001; Kitano, 2000). Both fields comprise traditional molecular biology, enzymology and bio- chemistry; however, the predominant difference from previous approaches is the significantly larger scale upon which they are conducted. | ||
| + | |||
==Limitations of metabolomics == | ==Limitations of metabolomics == | ||
* The major limitation of metabolomics is its current inability to comprehensively profile all of the metabolome. This inability is directly related to the chemical complexity of the metabolome, the biological variance inherent in most living organisms, and the dynamic range limitations of most instrumental approaches. In many ways, this is similar to the situation of the Human Genome Project in 1990, when the technological means to sequence genomes were not yet available. | * The major limitation of metabolomics is its current inability to comprehensively profile all of the metabolome. This inability is directly related to the chemical complexity of the metabolome, the biological variance inherent in most living organisms, and the dynamic range limitations of most instrumental approaches. In many ways, this is similar to the situation of the Human Genome Project in 1990, when the technological means to sequence genomes were not yet available. | ||
| Line 21: | Line 22: | ||
|2013 | |2013 | ||
|Proceedings of the National Academy of Sciences | |Proceedings of the National Academy of Sciences | ||
| − | |[[IC4R001-Metabolomics-2013-24259710]] | + | |'''[[IC4R001-Metabolomics-2013-24259710]]''' |
|- | |- | ||
|'''Folate fortification of rice by metabolic engineering''' | |'''Folate fortification of rice by metabolic engineering''' | ||
| Line 27: | Line 28: | ||
|2007 | |2007 | ||
|Nature Biotechnology | |Nature Biotechnology | ||
| − | |[[IC4R002-Metabolomics-2007-17934451]] | + | |'''[[IC4R002-Metabolomics-2007-17934451]]''' |
|- | |- | ||
|'''Characterization of Volatile Aroma Compounds in Cooked Black Rice''' | |'''Characterization of Volatile Aroma Compounds in Cooked Black Rice''' | ||
| Line 33: | Line 34: | ||
|2007 | |2007 | ||
|Journal of Agricultural and Food Chemistry | |Journal of Agricultural and Food Chemistry | ||
| − | |[[IC4R003-Metabolomics-2007-18081248]] | + | |'''[[IC4R003-Metabolomics-2007-18081248]]''' |
|- | |- | ||
|'''A targeted metabolomics approach toward understanding metabolic variations in rice under pesticide stress''' | |'''A targeted metabolomics approach toward understanding metabolic variations in rice under pesticide stress''' | ||
| Line 39: | Line 40: | ||
|2015 | |2015 | ||
|Analytical Biochemistry | |Analytical Biochemistry | ||
| − | |[[IC4R004-Metabolomics-2015-25766578]] | + | |'''[[IC4R004-Metabolomics-2015-25766578]]''' |
|- | |- | ||
|'''Metabolomic screening applied to rice FOX Arabidopsis lines leads to the identification of a gene-changing nitrogen metabolism.''' | |'''Metabolomic screening applied to rice FOX Arabidopsis lines leads to the identification of a gene-changing nitrogen metabolism.''' | ||
| Line 45: | Line 46: | ||
|2010 | |2010 | ||
|Molecular Plant | |Molecular Plant | ||
| − | |[[IC4R005-Metabolomics-2010-20085895]] | + | |'''[[IC4R005-Metabolomics-2010-20085895]]''' |
|- | |- | ||
|'''Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice''' | |'''Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice''' | ||
| Line 51: | Line 52: | ||
|2007 | |2007 | ||
|Journal of Chromatography B | |Journal of Chromatography B | ||
| − | |[[IC4R006-Metabolomics-2007-17556050]] | + | |'''[[IC4R006-Metabolomics-2007-17556050]]''' |
|- | |- | ||
|'''Metabolic profiling of transgenic rice with cryIAc and sck genes: An evaluation of unintended effects at metabolic level by using GC-FID and GC–MS''' | |'''Metabolic profiling of transgenic rice with cryIAc and sck genes: An evaluation of unintended effects at metabolic level by using GC-FID and GC–MS''' | ||
| Line 57: | Line 58: | ||
|2009 | |2009 | ||
|Journal of Chromatography B | |Journal of Chromatography B | ||
| − | |[[IC4R007-Metabolomics-2009-19233746]] | + | |'''[[IC4R007-Metabolomics-2009-19233746]]''' |
| + | |- | ||
| + | |'''Toward better annotation in plant metabolomics: isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses''' | ||
| + | |''Oryza sativa'' | ||
| + | |2014 | ||
| + | |Metabolomics | ||
| + | |'''[[IC4R008-Metabolomics-2014-25057267]]''' | ||
| + | |- | ||
| + | |'''Comparative metabolic profiling of pigmented rice (Oryza sativa L.) cultivars reveals primary metabolites are correlated with secondary metabolites''' | ||
| + | |''Oryza sativa L.'' | ||
| + | |2013 | ||
| + | |Journal of Cereal Science | ||
| + | |'''[[IC4R009-Metabolomics-2013-25056584]]''' | ||
| + | |- | ||
| + | |'''Using metabolomic approaches to explore chemical diversity in rice''' | ||
| + | |''Oryza sativa'' | ||
| + | |2015 | ||
| + | |Plant Molecular Biology Reporter | ||
| + | |'''[[IC4R010-Metabolomics-2015-25578272]]''' | ||
| + | |- | ||
| + | |'''Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism''' | ||
| + | |''Oryza sativa'' | ||
| + | |2015 | ||
| + | |Plant Journal | ||
| + | |'''[[IC4R011-Metabolomics-2015-25267402]]''' | ||
| + | |- | ||
| + | |'''Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis''' | ||
| + | |''Oryza sativa'' | ||
| + | |2012 | ||
| + | |Plant Journal | ||
| + | |'''[[IC4R012-Metabolomics-2012-22229385]]''' | ||
|} | |} | ||
| + | |||
==References== | ==References== | ||
<references> | <references> | ||
Latest revision as of 02:02, 28 June 2016
Contents
What is Metabolomics ?
- Metabolomics is the scientific study of chemical processes involving metabolites. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles.[1] The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes.[2] mRNA gene expression data and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to provide a better understanding of cellular biology.
Figure 1. Integrated functional genomics. The effects of gene perturbations are evaluated at multiple levels including the transcriptome, proteome, and metabolome. Changes in the metabolome occur as a consequence of those changes in the transcriptome that result in changes in the levels or catalytic activities of enzymes. Therefore, metabolome analysis is a valuable tool for inferring gene function.
- Advances in mass spectrometry have enabled the analysis of cellular proteins and metabolites (proteome and metabolome respectively) on a scale previously unimaginable. The cumulative utilization of these technologies has advanced the fields of functional genomics (Holtorf et al., 2002; Oliver et al., 2002; Somerville and Somerville, 1999) and systems biology (Ideker et al., 2001; Kitano, 2000). Both fields comprise traditional molecular biology, enzymology and bio- chemistry; however, the predominant difference from previous approaches is the significantly larger scale upon which they are conducted.
Limitations of metabolomics
- The major limitation of metabolomics is its current inability to comprehensively profile all of the metabolome. This inability is directly related to the chemical complexity of the metabolome, the biological variance inherent in most living organisms, and the dynamic range limitations of most instrumental approaches. In many ways, this is similar to the situation of the Human Genome Project in 1990, when the technological means to sequence genomes were not yet available.
Metabolome technologies
- It is generally accepted that a single analytical technique will not provide sufficient visualization of the metabolome and, therefore, multiple technologies are needed for a comprehensive view (Hall et al., 2002; Sumner et al., 2002). Accordingly, many analytical technologies have been enlisted to profile the metabolome. Methods based on infrared spectroscopy (IR) (Oliver et al., 1998), nuclear magnetic resonance (NMR(Bligny and Douce, 2001; Ratcliffe and Shachar-Hill,2001; Roberts, 2000), thin layer chromatography (TLC) (Tweeddale et al., 1998), HPLC with ultraviolet and photodiode array detection (LC/UV/PDA) (Fraser et al., 2000), capillary electrophoresis coupled to ultravio- let absorbance detection (CE/UV) (Baggett et al., 2002), capillary electrophoresis coupled to laser induced fluorescence detection (CE/LIF) (Arlt et al., 2001), capillary electrophoresis coupled to mass spectrometry (CE/MS) (Soga et al., 2002), gas chromatography-mass spectrometry (GC/MS), liquid chromatography-mass spectro- metry (LC/MS) (Huhman and Sumner, 2002), liquid chromatography tandem mass spectrometry (LC/MS/ MS) (Huhman and Sumner, 2002), Fourier transform ion cyclotron mass spectrometry (FTMS) (Aharoni et al., 2002), HPLC coupled with both mass spectrometry and nuclear magnetic resonance detection (LC/NMR/ MS) (Bailey et al., 2000a), and LC/NMR/MS/MS (Bai- ley et al., 2000b) have all been used.
Projects List
| Project Title | Species | Published years | Academic Journal | RiceWiki Project ID |
|---|---|---|---|---|
| Genetic analysis of the metabolome exemplified using a rice population | Oryza sativa | 2013 | Proceedings of the National Academy of Sciences | IC4R001-Metabolomics-2013-24259710 |
| Folate fortification of rice by metabolic engineering | Oryza sativa L. ssp. Japnoica | 2007 | Nature Biotechnology | IC4R002-Metabolomics-2007-17934451 |
| Characterization of Volatile Aroma Compounds in Cooked Black Rice | Oryza sativa | 2007 | Journal of Agricultural and Food Chemistry | IC4R003-Metabolomics-2007-18081248 |
| A targeted metabolomics approach toward understanding metabolic variations in rice under pesticide stress | Oryza sativa | 2015 | Analytical Biochemistry | IC4R004-Metabolomics-2015-25766578 |
| Metabolomic screening applied to rice FOX Arabidopsis lines leads to the identification of a gene-changing nitrogen metabolism. | Oryza sativa L. ssp. japonica | 2010 | Molecular Plant | IC4R005-Metabolomics-2010-20085895 |
| Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice | Oryza sativa L. | 2007 | Journal of Chromatography B | IC4R006-Metabolomics-2007-17556050 |
| Metabolic profiling of transgenic rice with cryIAc and sck genes: An evaluation of unintended effects at metabolic level by using GC-FID and GC–MS | Oryza sativa L. | 2009 | Journal of Chromatography B | IC4R007-Metabolomics-2009-19233746 |
| Toward better annotation in plant metabolomics: isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses | Oryza sativa | 2014 | Metabolomics | IC4R008-Metabolomics-2014-25057267 |
| Comparative metabolic profiling of pigmented rice (Oryza sativa L.) cultivars reveals primary metabolites are correlated with secondary metabolites | Oryza sativa L. | 2013 | Journal of Cereal Science | IC4R009-Metabolomics-2013-25056584 |
| Using metabolomic approaches to explore chemical diversity in rice | Oryza sativa | 2015 | Plant Molecular Biology Reporter | IC4R010-Metabolomics-2015-25578272 |
| Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism | Oryza sativa | 2015 | Plant Journal | IC4R011-Metabolomics-2015-25267402 |
| Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis | Oryza sativa | 2012 | Plant Journal | IC4R012-Metabolomics-2012-22229385 |
