Back to Previous Chapter: Databases
Next chapter: Contributors
First Category: Disease Research
Go to first page: Nutrigenomics
Go back to: Animal Metabolomes

Introduction to Plant Metabolomics edit

Plant Metabolomics is the study of metabolic pathways and processes through the use of analytical methods in model species. The information gained from this research is used to understand how plants grow and carry out functions, as well as improve the quality of food or medicines. This page provides an overview of five articles and one website that relates to the understanding of plant metabolomes.

The first article summarized is, “Plant metabolomics: from holistic hope, to hype, to hot topic”. In this article, the author discusses some of the fundamental issues regarding the area of plant metabolomics, and provides a survey of some of the analytical methods used in research. The article concludes with some applications for the field and providing some examples of why plant systems are a valuable resource for understanding biochemicals and metabolic pathways. The second article, “Potential of metabolomics as a functional genomics tool”, discusses how to improve coverage of the plant metabolome, comparing results between different laboratories and enhancing metabolomic data with functional genomic information. “Using Metabolomics to estimate unintended effects in transgenic crop plants: Problems, Promises and Opportunities” is the third article. The focus of this article is transgenic crops, and it discusses analytical methods in plants as well as the impacts of trying to improve fruit quality, color and aroma among other things. The fourth article is, “A Liquid Chromatography-Mass Spectrometry-Based Metabolome Database for Tomato”. This article discusses analytical methods such as Reverse-phase liquid chromotography, quadropole time-of-flight mass spectrometry, and photodiode array for tomato fruits in particular. Finally, the fifth article is, “Metabolite and light regulation of metabolism in plants: lessons from the study of a single biochemical pathway”. This article focuses on research that showed how light affects the reciprocal regulation of glutamine synthetase and asparagine synthetase. The researchers studied the gene expression of these two enzymes with relation to other factors, such as the presence of sucrose.

One website is also reviewed on this page. A link and summary are provided for the “National Centre for Plant and Microbial Metabolomics” based in the UK. This website provides information about some of the research activities and programs currently underway to improve understanding of metabolites in model plant species such as Arabidopsis thaliana. The purpose of their work is to gather and catalog new information, and share it with others such as through the Genomic Arabidopsis Resource Network (GARNet).

Articles edit

Plant metabolomics: from holistic hope, to hype, to hot topic edit

Robert D. Hall

New Phytologist 169:453-468 (2006)

http://www3.interscience.wiley.com/cgi-bin/fulltext/118627110/HTMLSTART

General Overview edit

Lemna gibba commonly known Duckweed
Medicago truncatula a small genome model legume	Populus tremula a model in forest genetics and woody plant studies

Metabolomics focuses on the biochemical compliments of cells and tissues. In molecular biology in recent years, there has been a switch to a more holistic view of metabolomes. Instead of looking at individual pathways and metabolites scientists are looking at interactions, broad pictures, and regulations. This holistic view is something that needs to be applied to plant metabolomes. Plants have very complex multi-cellular structures to respond and adapt to changing environments. Organs from different parts of plants have different metabolomes. The complexity of plant metabolomes would make it impossible, given our current technology, to get complete coverage of metabolites in plant cells. This complexity has also created large gaps in our understanding of plant metabolomes. There are numerous unidentified compounds and undefined pathways. Already, over 100,000 secondary metabolites have been found in plants. The qualities of plants, or everything that makes a plant valuable, can be defined by their metabolic profile, which is one of the many reasons why understanding plant metabolomes may be important. There are several ways data for plant metabolomes is acquired and other than NMR spectroscopy, many of these methods only show general snapshots of metabolomes at specific times and locations. These methods donít take into account the oscillations of homeostasis that is common in plants. Methods for gathering data include GC-MS, liquid chromatography, Fourier transform ICR, Capillary Electrophoresis, flow or direct infusion-MS, and NMR spectroscopy, all of which are defined below. One of the biggest obstacles with all these methods of collecting data is the metabolic heterogeneity of plants. Each of these methods tends to have biases towards certain metabolite groups, so often one method is not enough and it is a combination of data gathering techniques that works best. Data interpreting and data storage are two more limitations that have arose in the past decade or two as large amounts of data have been gathered. Manual handling of data is no longer feasible. The volume of data and the number of data errors has to be minimalized and in order to accomplish this; better reporting standards have to be put into practice. Methods of visualizing the data will also be important in understanding its complexity. As out capacity to produce data increases, researchers must remember that the goal is to advance knowledge, not just to generate data. To advance knowledge, there will be larger amounts of multi-disciplinary studies and there needs to more communication between plant metabolomics communities.

New Terms edit

Metabolic fingerprinting: a forerunner to metabolic profiling, high throughput screening of the metabolic composition of an organism or tissue
Metabolic profiling: Identifying and quantifying the metabolites present in an organism or tissue
Targeted analysis: detailed analysis of more specific parts of metabolomes based on previous, more broad-scale knowledge
Secondary metabolites: organic compounds not directly involved in growth or development of organisms
GC-MS (Gas chromatography - mass spectrometry): combination of gas-liquid chromatography and mass spectrometry to analyze elements of a sample
Fourier transform-ICR-MS (FTMS): type of mass analyzer that determines mass to charge ratio of ions based on frequency - high sensitivity and mass accuracy
Capillary Electrophoresis-MS: separation of particles based on charge and frictional forces
Flow or direct infusion/injection (FI/DI)-MS: mass spectrometry without separation, good fast qualitative screening tool

Potential of metabolomics as a functional genomics tool edit

Raoul J. Bino, Robert D. Hall, Oliver Fiehn, Joachim Kopka, Kazuki Saito, John Draper, Basil J. Nikolau, Pedro Mendes, Ute Roessner-Tunali, Michael H. Beale, Richard N. Trethewey, B. Markus Lange, Eve Syrkin Wurtele and Lloyd W. Sumner

TRENDS in Plant Science Vol.9 No.9 September 2004

http://metnet.vrac.iastate.edu/publications/TIPS04.pdf

General Overview edit

Arabidopsis thaliana is one of the many plant model organism used for studying plant genomics and metabolomics.

Before it can be relied upon as a genomic tool, metabolomics must be standardized for use and reference across research communities. This paper analyzes the unique challenges presented by linking metabolomics to plant genotype and phenotype.

The first step is having a large database of encountered and identified metabolites. Other functional genomic techniques such as proteomics or transcriptomics already have such databases established. Approximately 50% of Arabidopsis genes have been recorded and annotated in databases. Currently, however, it is estimated that only 10% of the metabolites within an Arabidopsis leaf have been characterized. This is largely a shortcoming of technology.

Another key factor towards advancing the field of metabolomics is establishing complimentary information to conduct experiments. Much like transcriptomics, the conditions of the experiment are incredibly important to the presence of metabolites. For this reason, the authors suggest the establishment of a protocol like the MIAME which allows for reproducible experimentation.

The most challenging aspect of using metabolomics as a tool is the amount of redundancy. Metabolites are often linked to several enzymes or the appearance of several phenotypes. This puts metabolomics at a disadvantage to other techniques, as they can generally verify each other. Genetic sequences correspond to protein sequences, which can be linked to mRNA sequences, and so forth. The multiple appearances of metabolites during different conditions and in different locations makes profiling a plant's genome particularly hard.

New Terms edit

NIST (http://www.nist.gov/)
Wiley (http://www.wileyregistry.com/)
Sigma–Adrich (http://www.sigmaaldrich.com/catalog/ProductDetail.do?N4=Z541389%7CALDRICH&N5=SEARCH_CONCAT_PNO%7CBRAND_KEY&F=SPEC): All of the above are spectral libraries of metabolites. Currently, metabolites are identified by comparing them to entries in these databases.
MIAME (Minimal Information About a Microarray Experiment): Example here http://www.mged.org/Workgroups/MIAME/miame.html. This describes the minimum background necessary to conduct and interpret reproducible microarray experiments. This paper suggests such methods are necessary if metabolomics is to progress.

Using Metabolomics To Estimate Unintended Effects in Transgenic Crop Plants: Problems, Promises, and Opportunities edit

Owen A. Hoekenga

Journal of Biomolecular Techniques 19:159-166 (2008)

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2563928&tool=pmcentrez.

General Overview edit

Transgenic crops have become a major food source in many countries. However, the use of transgenic crops is often still a topic under debate. No matter what method is used to produce transgenic plants there will always be unintended effects. Some effects are predictable effects, which can be explained by our current understanding of biology, but some are unpredictable effects, which fall outside our ability to explain. These unintended effects are often an argument for those who oppose the genetic modification of plants. Supporters of transforming plants argue for substantial equivalence, or the similarity between modified organisms and their conventional parents or siblings. Opponents often respond that substantial equivalence has no statistical basis or standard. Systems biology is being used to further understand the unintended effects of transforming plants and will hopefully be able to supply more information to customers, regulators, businesses, etc to gradually get rid of fear and uncertainty that rests with transgenic plants. Both targeted metabolomics and non-targeted metabolomics are being used. Targeted metabolomics is often times more common as investigators are looking to improve or understand specific traits. However, non-targeted metabolomics holds promise for determining unintended effects as it looks beyond the actions of single genes. Large amounts of metabolomic data have been collected on several plants. One of the plants with the most data is Tomato, for which there has been a large amount of research in fruit quality, color, and aroma. Nuclear magnetic resonance imaging and mass spectrometry, which have been used in research with tomatoes, could be used to make assessment of food quality and content of different plants. The technology for analyzing plant metabolomes is present, but it is our experimental designs and analytical methods that are our limiting factors. The large amount of data that is being gathered needs a way of being sorted and interpreted. Standards like the MIAME and MIAPE that are in place for microarray and proteomic experiments are standards that are needed for data collecting in plant metabolomics.

New Terms edit

predicatable effects: explainable based on an understanding of biology
unpredictable effects: fall outside obvious explanation
substanstial equivalence: the similarity between transgenic and wild, addition of only one or a few genes
nucleotide polymorphism: variation between genomes of a certain organism
near isogenic lines(NILs): lines of plants constructed by crossing back into one parental variety for several generations so there is little difference between daughter and parent
acceptable phenotypic variation: an ok amount of difference from normal
molecular fingerprinting: characterizations of effects of levels of macro and small molecules; good for first draft of metabolic between conventional and transgenic varieties
data mining: extracting patterns/conclusions from large amounts of data

A Liquid Chromatography-Mass Spectrometry-Based Metabolome Database for Tomato edit

Sofia Moco, Raoul J. Bino, Oscar Vorst, Harrie A. Verhoeven, Joost de Groot, Teris A. van Beek, Jacques Vervoort and C.H. Ric de Vos

Plant Physiology 141:1205-1218 (2006)

http://www.plantphysiol.org/cgi/content/full/141/4/1205

General Overview edit

Solanum lycopersicum

Community is key for the development of plant metabolomics. Data must be compiled, compared, and standardized so that everyone can benefit from the information gained in individual research efforts. This paper presents a suggested method for analyzing metabolites that is both high-resolution and reproducible.

Reverse-phase liquid chromotography, quadropole time-of-flight mass spectrometry, and photodiode array are all used to profile (mostly semipolar) metabolites from tomato fruit (Solanum lycopersicum). This method was tested against highly and widely reported values for metabolites in literature. Also, metabolites were located in their expected tissues of expression. The data collected was used to assemble the MoTo DB, or Metabolome Tomato Database.

The MoTo DB is searchable by observed mass and allows for a range of deviation, allowing data to be compared from low to high accuracy analyses. This allows researchers to first gain an idea about the identity of a metabolite and then analyze further with more scrutiny.

Overall, if metabolomics is to be a useful approach, researchers must be able to identify and agree upon the metabolites present. Through these techniques, a database of tomato metabolites will help researchers to determine when and to what extent certain metabolites are present, leading to a better understanding of their role in tomato metabolism.

New Terms edit

Quadrupole time-of-flight MS: A highly specific form of Mass Spectrometry. Quadrupole mass analyzers act as a selective mass filter by using electrical fields to stabilize or destabilize ions. Time-of-flight systems similarly use an electric field, but to accelerate ions through the same potential in order to determine their charge. Together, these two systems will profile a molecules mass and charge by exactly when they reach the detector.
Photodiode array detection: Photodiode array detectors can be used to measure and detect samples over the entire UV to visible spectrum. The signals emitted by analyzed samples are converted to electrical signals, which can then be recorded and interpreted to identify particular compounds.
MoTo DB: Metabolome Tomato Database: http://appliedbioinformatics.wur.nl/moto/
Phenylpropanoids: A class of plant-derived organic compounds synthesized from phenylalanine. In tomatos, they function as pigments and offer defense from UV radiation.
Flavanoids: A phenylpropanoid plant secondary metabolite that has anti-oxidant activity and is thought to convey health benefits against cancer, viruses, and allergies.

Metabolite and light regulation of metabolism in plants: lessons from the study of a single biochemical pathway edit

I.C. Oliveira, E. Brenner, J. Chiu, M.-H. Hsieh, A. Kouranov, H.-M. Lam, M.J. Shin and G. Coruzzi

Brazilian Journal of Medical and Biological Research 34:567-575 (2001)

http://www.scielo.br/scielo.php?script=sci_abstract&pid=S0100879X2001000500003&tlng=en&lng=en&nrm=iso

Main Focus edit

The main focus of this article is the Arabidopsis thaliana species and the relation of light and other metabolites on the regulation of glutamine synthetase and asparagine synthetase expression. This article describes some experiments related to finding out the relationships between these elements of the system.

General Overview edit

The specific interest in this work was to study how inorganic nitrogen is assimilated into the amino acids glutamine, glutamate, aspartate and asparagine. The model for the pathway in the experiments was the plant Arabidopsis thaliana. In the xylem and phloem of plants, asparagine and glutamine are the most abundant free amino acids. This is due to the fact that they are key nitrogen-transporting compounds. Enzymes responsible for the synthesis of asparagine and glutamine are asparagine synthetase (AS) and glutamine synthetase (GS) respectively. Two different forms of these synthetase molecules that were of interest in these studies were GLN2 (a chloroplastic form) and ASN1. The researchers found that light and metabolites cause a reciprocal regulation of glutamine synthetase (GLN2) and asparagine synthetase (ASN1). That is, when GLN2 expression is increased, the corresponding ASN1 gene expression is repressed. They also found that repression of ASN1 involves a calcium/cGMP-dependent signaling pathway that is downstream from the phytochrome. In addition, the light regulation of the two genes could be explained by transcriptional mechanisms since light regulatory elements are required for phytochrome-mediated regulation and these have already been identified in both AS and GS promoters.

It is interesting that light has this ability to modify the plant’s gene expression, because this generally occurs through carbon metabolites. For example, the researchers also identified the fact that sucrose can partially mimic the effects of light by having the same effect on the plants in the dark. Sucrose was found to cause induction of GLN2 and repress the expression of ASN1 mRNA. This is the same effect as what the light does for the metabolic pathway, except the experiment was carried out in the dark. Sucrose can also be antagonized, when in the dark, by adding a treatment of the amino acids glutamine, glutamate, aspartate and asparagine. When adding these amino acids to the sucrose acting without the influence of light, the researchers found that the expression of GLN2 and ASN2 were affected to differing extents.

New Terms edit

Reciprocal Regulation: One stimulus increasing the effect of a particular enzyme, but decreasing the effect of another – for example, the use of light increases the expression of GLN2 and represses expression of ASN1 genes.
Nitrogen Assimilatory Pathway: A biological pathway in plants and algae incapable of nitrogen fixation.(http://en.wikipedia.org/wiki/Nitrogen_assimilation)
Phytochrome: A photoreceptor that plants use to detect light. (http://en.wikipedia.org/wiki/Phytochrome)
Operon: A functioning unit of nucleotide sequences of DNA, which is controlled as a unit to produce mRNA in transcription. (http://en.wikipedia.org/wiki/Operon)
Arabidopsis thaliana: A small flowering plant that is used as a model organism in plant studies because of a relatively small genome of about 157 million base pairs and 5 chromosomes. (http://en.wikipedia.org/wiki/Arabidopsis_thaliana)
Gene expression: A process by which information from a gene is used in the synthesis of a functional gene product such as protein or functional RNA. (http://en.wikipedia.org/wiki/Gene_expression)

Course Relevance edit

This article deals with a process of regulation in the Arabidopsis thaliana plant species, and regulation is one of the topics covered in the metabolism course. In the course we discussed molecules that activate and inhibit enzymatic activity, and this article also discusses how light and metabolites affect the expression of GLN2 and ASN1 genes.

Nitrogen and metabolic regulation of the expression of plastidic glutamine synthetase in alfalfa (Medicago sativa) edit

Marcela Zozaya-Hinchliffe, Carol Potenza, Jose Luis Ortega and Champa Sengupta-Gopalan

Plant Science 168.4:1041-1052 (April 2005)

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TBH-4F31MB3-2&_user=47004&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000005018&_version=1&_urlVersion=0&_userid=47004&md5=023f46390af237de72e8d15d54f7d9a1

Main Focus edit

The main focus of this article was the regulation of expression of plastidic glutamine synthetase and effect of nitrogen and 2-oxogluterate in alfalfa. The article also describes some experiments related to finding in this direction.

General Overview edit

Medicago sativa

The study was done to understand how carbon:nitrogen status modulates the expression of glutamine synthetase (GS) at transcriptional and post-transcriptional levels. Glutamine synthetase (GS) is an enzyme that plays an essential role in the metabolism of nitrogen by catalyzing the condensation of glutamate and ammonia to form glutamine. They identified a single putative plastidic glutamine synthetase (GS2), isolated from Medicago sativa (alfalfa) leaf. Researchers have analyzed organ/tissue specific expression of the GS2 gene in this study and reached the conclusion that it is expressed in all green tissues. They showed that the alfalfa GS2 gene is also expressed in nitrogen fixing root nodules where its expression is not regulated by fixed nitrogen. Treatment with nitrate (NO3−) resulted in the induction of GS2 in the roots and leaves of alfalfa, but the signaling mechanism in the two organs is different. In the roots NO3− appeared to act as a direct signal for the induction of GS2 whereas in the leaves secondary metabolites of NO3− probably acted as the signal. They also demonstrated that 2-oxoglutarate (2-OG), in combination with NO3−, significantly induced GS2 expression, pointing to 2-OG as a potential primary metabolic inducer of alfalfa GS2. Treatment with glutamine or sucrose, in combination with NO3−, also appears to induce GS2 in the roots, but there is a lag in the induction when compared to the 2-OG/NO3− combination. It offers an insight on the regulation and expression of plastidic glutamine synthetase. These results enhance our understanding of the plant metabolism and see how different cycles and pathways are interconnected. It shows how two molecules working together can increase the induction of GS2 and also how the same molecules can have different effects or have effect at a different stage in a process when they act in different tissues.

New Terms edit

2-Oxoglutarate: It is one of two ketone derivatives of glutaric acid.
Glutamine synthetase: It is an enzyme that plays an essential role in the metabolism of nitrogen by catalyzing the condensation of glutamate and ammonia to form glutamine.( http://en.wikipedia.org/wiki/Glutamine_synthetase)
Medicago sativa: Alfalfa (Medicago sativa) is a flowering plant in the pea family Fabaceae cultivated as an important forage crop.( http://en.wikipedia.org/wiki/Alfalfa)
Plastid: A double membrane bound organelle involved in the synthesis and storage of food, and is commonly found within the cells of photosynthetic organisms, like plants.( http://www.biology-online.org/dictionary/Plastid)

Course Relevance edit

The article was related to glutamine synthetase regulation and effect of nitrogen on it. In our course we studied how change in different factors affect functioning of different molecules and activate or inhibit them. Sometimes introduction of a molecule or removal of it can slow down or even stop a pathway. It’s interesting to see the effect of nitrogen on glutamine synthetase.

Co-ordinated expression of amino acid metabolism in response to N and S deficiency during wheat grain filling edit

Jonathan R. Howarth, Saroj Parmar, Janina Jones, Caroline E. Shepherd, Delia-Irina Corol, Aimee M. Galster, Nathan D. Hawkins, Sonia J. Miller, John M. Baker, Paul J. Verrier, Jane L. Ward, Michael H. Beale, Peter B. Barraclough, and Malcolm J. Hawkesford.

Journal of Experiment Botany 59.13:3675–3689 (2008 October)

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2561146&tool=pmcentrez

Main Focus edit

The main focus of this article is to study the coordinated expression of amino metabolism in response to N and S deficiency during wheat grain filling. The article shows the effect different fertilizers can have on winter wheat and shows what fertilizers might be better.

General Overview edit

An ear of winter wheat

There is an ever-increasing demand for wheat and other grains with the increase in population. There is also a decrease in the total area of agricultural land and in this scenario it becomes increasingly important to increase the efficiency of fertilizers. The sustainability of agricultural systems will depend on improving fertilizer use efficiency and making them custom designed for specific functions. They need to be environmentally safe and unlike most fertilizers today that are not taken up completely by crops, the new generation of fertilizers needs to be designed so that it is utilized completely reducing the amount of fertilizers going to waste and hence decreasing pollution. N fertilizer application is directly linked to wheat grain yield and quality (protein content). Sulphur (S) nutrition is more specifically associated with levels of glutenin in the endosperm and the ratio of glutenin to other grain storage proteins, which is responsible for dough elasticity and loaf quality. During growth of the wheat crop, N and S are accumulated in the vegetative tissues and are then redistributed to the developing seed during the concurrent processes of vegetative tissue senescence and grain development. Amino acids are the major form in which N is remobilized from the leaf to the grain during grain filling. The leaves of wheat plants grown under high N accumulate free amino acids from recently reduced nitrate which are subsequently loaded into the phloem. In the leaves, S is either stored as sulphate or reduced and incorporated into an organic form by the reductive sulphate assimilation pathway. This series of reactions takes place in the plastids and produces the amino acid cysteine (Cys) which is used to synthesize a wide range of S-containing organic molecules such as methionine (Met) and glutathione (GSH). Whilst the physical processes of N and S remobilization have been studied in detail, the genetic control of these processes and their contribution to agronomic productivity are less well understood. Studies at the metabolic and genetic level using genomic-era analytical techniques will aid in providing novel insights into the regulation of the many contributing traits involved in N and S remobilization during wheat grain filling and may suggest targets for the enhancement of these processes in arable crops. In this report a metabolomic and transcriptomic assessment of the leaf and grain following anthesis in field-grown winter wheat plants with varying N and S fertilizer applications is presented. Material was harvested from the Broadbalk winter wheat experiment at Rothamsted Research (Harpenden, UK), which is the longest continually running scientific experiment in the world and has been providing agronomic data on wheat crop nutrition for 164 years. With the advent of modern analytical technologies the experiment is a valuable resource for genetic and metabolomic studies.

New Terms edit

Metabolomics: Metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind" - specifically, the study of their small-molecule metabolite profiles. The metabolome represents the collection of all metabolites in a biological organism, which are the end products of its gene expression.(http://en.wikipedia.org/wiki/Metabolomics)
Transcriptomics: Transcriptomics is the study of the transcriptome, the complete set of RNA transcripts produced by the genome at any one time.
Anthesis: Anthesis is the period during which a flower is fully open and functional.(http://en.wikipedia.org/wiki/Anthesis)
Nuclear magnetic resonance (NMR): Nuclear magnetic resonance (NMR) is the name given to a physical resonance phenomenon involving the observation of specific quantum mechanical magnetic properties of an atomic nucleus in the presence of an applied, external magnetic field.(http://en.wikipedia.org/wiki/Nuclear_magnetic_resonance)

Course Relevance edit

In Biochemistry: Metabolism class we specifically study the pathways and the reactions involved in the metabolic processes in cells and see how they affect the animal/plant physiologically. This article shows how N and S are accumulated and transported in various forms in wheat. This helps us understand the physiological significance of the pathways involved in the transport and accumulation of these two elements in the plant and also be able to design fertilizers for them that are readily taken in by the plant to increase the efficiency of fertilizers.

Websites edit

The National Centre for Plant and Microbial Metabolomics edit

http://www.metabolomics.bbsrc.ac.uk/

Main Focus edit

The main focus of this website is to serve as an information portal for a set of research laboratories in the UK. It gives an overview of projects, equipment, publications and resources that are available to the public to increase knowledge of plant metabolomics as well as the applications this information can have for a series of related fields.

General Overview edit

Nuclear Magnetic Resonance Spectroscopy is one of the commonly used analytical methods in Plant Metabolomics

This website describes the activities of the National Centre for Plant and Microbial Metabolomics, which is a collaboration of Rothamsted Research and the Biological Research Council in the UK. The Center is primarily focused on research in plant and microbial metabolomics. The main site provides links to Background on Metabolomics, as well as descriptions of Current Projects, Publications, and a summary of the Equipment available at the Center. The site describes the fact that metabolomics is a multi-disciplinary science, and it requires the collaboration of chemists, biologists and “informaticians”. Since no spectroscopy method is currently available that allows the detection of every class of metabolite, a variety of methods are used to determine metabolic information. These methods include nuclear magnetic resonance, gas chromatography, high performance liquid chromatography and direct injection mass spectrometry.

Some of the projects described on the website include establishing a research center for high-throughput plant and microbial metabolomics (MeT-RO), developing hierarchical methods for Arabidopsis thaliana gene function analysis (HiMet) and exploring the genomics of Arabidopsis thaliana (GARNet). The MeT-RO project uses plants, microbes and mutant collections to provide information for medicinal, food quality and safety research. It involves the acquisition of analytical data from biological material grown in controlled experimental conditions. It also aims to create a user-accessible web resource that contains phenotypic data and libraries of plant and microbial metabolite spectra. The HiMet project involves high-throughput metabolite fingerprinting and targeted analytical methods applied to Arabidopsis thaliana mutants. After this, the researchers intend to apply pattern recognition and machine learning methods to develop a biochemical phenotype classification. The purpose of this fingerprinting is to assign gene functions. Finally, GARNet is intended to be an international platform for Arabidopsis thaliana research, where methods and research reports can be shared. The National Centre site includes a link to the GARNet site where the reports and downloads are available (http://garnet.arabidopsis.org.uk/).

In summary, this website provides a good overview of some of the research efforts in identifying plant metabolites, and genomic information for a variety of agricultural and biochemical applications. In addition, there is a good deal of information on experimentation, particularly with respect to the Arabidopsis thaliana plant species that is occurring in the UK.

New Terms edit

Target compound analysis: The quantification of specific metabolites.
Metabolic profiling: The quantitative or qualitative determination of a group of related compounds or of members of specific metabolic pathways.
Chemometrics: The application of multivariable statistical, pattern recognition and informatics methods to chemical data.
Proteomics: The large scale study of proteins, particularly structures and functions. (http://en.wikipedia.org/wiki/Proteomics)
Transcriptomics: The quantitative measurement of gene expression in a cell or tissue, generally involving the measurement of mRNA levels through methods such as gene chips.
Carotenoids: Organic pigments naturally occurring in the chromoplasts of plants and other photosynthetic organisms like algae, some types of fungus and some bacteria. (http://en.wikipedia.org/wiki/Carotenoid)
Phenylpropanoids: A class of plant-derived organic compounds that are synthesized from phenylalanine. (http://en.wikipedia.org/wiki/Phenylpropanoid)

Course Relevance edit

The relevance of this site to the course is that it discusses a variety of experimental techniques for analyzing plant metabolomics, and details the specifics of the analytical tools that are required to uncover metabolic pathways and regulatory molecules. One of the subjects in the course that is being covered currently is the process of photosynthesis in plants, and how it relates to oxidative phosphorylation. This site is relevant because it focuses on mechanisms such as photosynthesis that occur in plants and describes related metabolites.

Articles for future review as Metabolism class assignments edit

Nitrogen and metabolic regulation of the expression of plastidic glutamine synthetase in alfalfa (Medicago sativa) edit

Main Focus edit

New Terms edit

New Term 1: Definition. (source: http://)
New Term 2: Definition. (source: http://)
New Term 3: Definition. (source: http://)
New Term 4: Definition. (source: http://)
New Term 5: Definition. (source: http://)
New Term 6: Definition. (source: http://)
New Term 7: Definition. (source: http://)
New Term 8: Definition. (source: http://)
New Term 9: Definition. (source: http://)
New Term 10: Definition. (source: http://)

Summary edit

Relevance to a Traditional Metabolism Course edit

A plant resource and experiment management system based on the Golm Plant Database as a basic tool for omics research edit

Main Focus edit

New Terms edit

New Term 1: Definition. (source: http://)
New Term 2: Definition. (source: http://)
New Term 3: Definition. (source: http://)
New Term 4: Definition. (source: http://)
New Term 5: Definition. (source: http://)
New Term 6: Definition. (source: http://)
New Term 7: Definition. (source: http://)
New Term 8: Definition. (source: http://)
New Term 9: Definition. (source: http://)
New Term 10: Definition. (source: http://)

Summary edit

Relevance to a Traditional Metabolism Course edit

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants edit

Main Focus edit

New Terms edit

New Term 1: Definition. (source: http://)
New Term 2: Definition. (source: http://)
New Term 3: Definition. (source: http://)
New Term 4: Definition. (source: http://)
New Term 5: Definition. (source: http://)
New Term 6: Definition. (source: http://)
New Term 7: Definition. (source: http://)
New Term 8: Definition. (source: http://)
New Term 9: Definition. (source: http://)
New Term 10: Definition. (source: http://)

Summary edit

Relevance to a Traditional Metabolism Course edit

Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period edit

Reviewer Mohamad A.M.

Main Focus edit

New Terms edit

New Term 1: Definition. (source: http://)
New Term 2: Definition. (source: http://)
New Term 3: Definition. (source: http://)
New Term 4: Definition. (source: http://)
New Term 5: Definition. (source: http://)
New Term 6: Definition. (source: http://)
New Term 7: Definition. (source: http://)
New Term 8: Definition. (source: http://)
New Term 9: Definition. (source: http://)
New Term 10: Definition. (source: http://)

Summary edit

Relevance to a Traditional Metabolism Course edit

Co-ordinated expression of amino acid metabolism in response to N and S deficiency during wheat grain filling edit

Main Focus edit

New Terms edit

New Term 1: Definition. (source: http://)
New Term 2: Definition. (source: http://)
New Term 3: Definition. (source: http://)
New Term 4: Definition. (source: http://)
New Term 5: Definition. (source: http://)
New Term 6: Definition. (source: http://)
New Term 7: Definition. (source: http://)
New Term 8: Definition. (source: http://)
New Term 9: Definition. (source: http://)
New Term 10: Definition. (source: http://)

Summary edit

Relevance to a Traditional Metabolism Course edit

Metabolome analysis of biosynthetic mutants reveals a diversity of metabolic changes and allows identification of a large number of new compounds in Arabidopsis. edit

Reviewer: Ahmad S.K.

Main Focus edit

New Terms edit

Features: a unique m/z at a unique time point. (http://www.plantphysiol.org/cgi/content/full/147/4/2107)
Matrix Effects: In chemical analysis, matrix refers to the components of a sample other than the analyte.[1] The matrix can have a considerable effect on the way the analysis is conducted and the quality of the results obtained. (http://en.wikipedia.org/wiki/Matrix_(chemical_analysis))
Isotope Abundance: The ratio of the number of atoms of a particular isotope in a sample of an element to the number of atoms of a specified isotope, or to the total number of atoms of the element. (http://www.answers.com/topic/isotope-abundance)
Null Mutant: A mutant that completely lacks a copy of a gene. This can be the result of the complete absence of the gene product. (http://en.wikipedia.org/wiki/Null_allele)

Summary edit

The lack of analytical approaches to study metabolomics in higher eukaryotes has been identified as one of the major hurdles in study of plant metabolomics. This article addresses the potential of using the LC/ESI-QTOF-MS technique to study metabolomics in model organisms such as Arabidopsis thaliana. The researcher in the article studied the phenylpropanoid and flavonoid biosynthesis pathways in Arabidopsis thaliana in order to identify the metabolites involved in these two pathways. The article also discussed the connections between the metabolites of the wild-type and two mutants (tt4 and tt5). Figure 1 shows the phenylpropanoids pathways. tt4 lacks chalcone syntase and tt5 lacks chalcone isomerase. Liquid chromatography to electrospray ionization quadrupole time-of-flight mass spectrometry (LC/ESI-QTOF-MS) is employed to detect the mass signals emitted by the metabolites. Comparison between metabolic profiles in Ler (wild-type) and tt4 mutant revealed 18 different putative metabolites expressed highly in Ler. 12 of these metabolites involved in the flavonoid biosynthesis pathway. 2 of the metabolites were determined to be hydroxy-cannamoyl spermidine conjugates. In tt4 mutant, there was a 4-29 fold increase in phenolic choline esters expression and a 4-29 fold increase and 10-25 fold increase in dimerizations of sinapoylcholine expression when compared to Ler. In tt5, 14 metabolites were identified and involved in the metabolism of naringenin chalcone. Overall 660 features were identified in both wild-type and Arabidopsis mutants. About 220 compounds were detected from these features, 40 have not been identified in Arabidopsis before. This study demonstrated the potential of (LC/ESI-QTOF-MS) technique in cataloging the metabolomes of Arabidopsis.

Relevance to a Traditional Metabolism Course edit

Metabolomes analysis has a huge potential to become a tool for functional genomics and systems biology. The study of plant metabolomes is expected to contribute economically because of its impact on improvements of food products and the discovery of bioactive compounds. The article studies Arabidopsis mutants that lack in chalcone syntase (tt4) and chalcone isomerase (tt5) in order to understand the connection between the metabolites. These enzymes are involved in the phenylpropanoids and flavonoids biosynthesis pathways. The article is able to demonstrate that liquid chromatography to electrospray ionization quadrupole time-of-flight mass is a feasible method in cataloging the metabolomes in Arabidopsis.

Using metabolomics to estimate unintended effects in transgenic crop plants: problems, promises, and opportunities. edit

Don't do this one edit

Transgenic crops are widespread in some countries and sectors of the agro-economy, but are also very argumentative. Proponents of transgenic crop improvement have often sited the “substantial equivalence” of transgenic crops to their nontransgenic parents and sibling varieties. Opponents of transgenic crop improvement often dismiss the substantial equivalence standard and claim that it is without statistical basis. Opponents also emphasize possible, unintended impacts on food quality and composition form genetic transformations. A systems biology approach is expected to assist consumers, regulators, and other stakeholders in making educated decisions regarding transgenic crop improvement by characterizing the composition of conventional and transgenically improved crop species and products. Specifically, metabolomics profiling assisted by mass spectrometry (MS) and nuclear magnetic resonance (NMR) is expected to make efficient and substantial assessments of food content and quality. This will also enable the observed transgenic metabolomes to be assessed relative to consumer and regulator accepted phenotypic ranges that are observed among conventional varieties. Targeted (closed architecture) and nontargeted (open architecture) metabolomics with respect to the transgenic crop debate have already been discussed.

MIAME and MIAPE standards are currently requirements for publishing microarray and proteomic experiments in many journals. It is anticipated that similar standards will eventually be applied to metabolomics data, as well. As more datasets or deposited into publicly accessible databases, meta-analyses that integrate multiple levels of information are expected to raise a myriad of systems biology questions. Incorporating controlled vocabularies for gene, trait, and phenotypic ontologies is expected to further assist these meta-analyses. Identifying genetically informative populations has proven to be effective for addressing important biomedical and agronomic questions that include identifying cancer risk factors and genes that are crucial for carotenoid biofortification in staple crops. A substantial and efficient resource can be developed from genetically informative populations that are studied using metabolomics, genomic, and proteomic methods. The range and identity of unintended effects to composition and quality of transgenic foods can help inform consumers, regulators, and other stakeholders.

Widely targeted metabolomics based on large-scale MS/MS data for elucidating metabolite accumulation patterns in plants. edit

Don't do this one edit

Metabolomics is an ‘omics’ approach that aims to comprehensively analyze all metabolites in a biological sample. Detailed metabolic profiling of thousands of plant samples has proven to have enormous potential for thoroughly mapping plant metabolic processes. However, a comprehensive analysis and high throughput are difficult to achieve, simultaneously. This is a result of the expansive diversity of plant metabolites. Multiple reaction monitoring (MRM) with tandem quadrupole mass spectrometry (TQMS) has been established as a pragmatic and efficient method for the quantification of hundreds of targeted metabolites, in a high-throughput manner. In this technique, TQMS monitors both the specific precursor ions and product ions of each metabolite. MRM using TQMS is a fundamental technique used in targeted metabolomics. This method provides high sensitivity, reproducibility and a broad dynamic range. In this study, MRM conditions were optimized for specific compounds via automated flow injection analyses with TQMS. These conditions were applied to high-throughput automated analysis of biological samples using TQMS coupled with ultra performance liquid chromatography (UPLC). Hierarchical cluster analysis that was based on metabolite accumulation patterns showed differences among plant families, which enabled the prediction of family-specific metabolites while using batch-learning self-organizing map analysis. As a result, an automated widely targeted metabolomics approach is believed to contribute to the future advancement of large-scale metabolite profiling and comparative metabolomics.

Websites for future review as Metabolism class assignments edit

Metabolomics/Applications/Nutrition/Plant Metabolomes

Introduction to Plant Metabolomics edit

Articles edit

Plant metabolomics: from holistic hope, to hype, to hot topic edit

General Overview edit

New Terms edit

Potential of metabolomics as a functional genomics tool edit

General Overview edit

New Terms edit

Using Metabolomics To Estimate Unintended Effects in Transgenic Crop Plants: Problems, Promises, and Opportunities edit

General Overview edit

New Terms edit

A Liquid Chromatography-Mass Spectrometry-Based Metabolome Database for Tomato edit

General Overview edit

New Terms edit

Metabolite and light regulation of metabolism in plants: lessons from the study of a single biochemical pathway edit

Main Focus edit

General Overview edit

New Terms edit

Course Relevance edit

Nitrogen and metabolic regulation of the expression of plastidic glutamine synthetase in alfalfa (Medicago sativa) edit

Main Focus edit

General Overview edit

New Terms edit

Course Relevance edit

Co-ordinated expression of amino acid metabolism in response to N and S deficiency during wheat grain filling edit

Main Focus edit

General Overview edit

New Terms edit

Course Relevance edit

Websites edit

The National Centre for Plant and Microbial Metabolomics edit

Main Focus edit

General Overview edit

New Terms edit

Course Relevance edit

Articles for future review as Metabolism class assignments edit

Main Focus edit

New Terms edit

Summary edit

Relevance to a Traditional Metabolism Course edit

Main Focus edit

New Terms edit

Summary edit

Relevance to a Traditional Metabolism Course edit

Main Focus edit

New Terms edit

Summary edit

Relevance to a Traditional Metabolism Course edit

Main Focus edit

New Terms edit

Summary edit

Relevance to a Traditional Metabolism Course edit

Main Focus edit

New Terms edit

Summary edit

Relevance to a Traditional Metabolism Course edit

Main Focus edit

New Terms edit

Summary edit

Relevance to a Traditional Metabolism Course edit

Don't do this one edit

Don't do this one edit

Websites for future review as Metabolism class assignments edit