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Functional Genomics: High-Throughput mRNA, Protein, and Metabolite Analyses
Abstract
A tremendous amount of DNA sequence information is now available to scientists and engineers. These DNA sequences provide the foundation for studying how the genome of an organism is functioning and they are particularly useful for metabolic engineers interested in manipulating plants for the production of chemicals and enzymes. Functional genomics relies on high-throughput techniques for measuring the mRNA (the transcriptome), protein (the proteome), and metabolite (the metabolome) components of plants as well as their organs and tissues. Microarray technologies, recent advances in protein mass spectrometry, and high-throughput metabolite analyses are beginning to provide detailed information on the total mRNA, protein, and metabolite components of plants. This knowledge will allow scientists to monitor changes in proteins and metabolites in plants. Ultimately, it may allow them to discover new metabolic pathways and to model metabolic and regulatory networks in plants.
... The development of metabolomics has depended on advances in a diverse range of instrumental techniques, such as liquid chromatography (LC), electro-spray ionisation mass spectrometry (ESI-MS), capillary electrophoresis (CE) and microchip arrays among others. 3,[6][7][8] Each of these methods provides unique capabilities for the separation of different chemical classes of metabolites. 7 At the same time, developments in mathematics have provided algorithms capable of unravelling the complexity of data sets generated. ...
... 3,[6][7][8] Each of these methods provides unique capabilities for the separation of different chemical classes of metabolites. 7 At the same time, developments in mathematics have provided algorithms capable of unravelling the complexity of data sets generated. Over the last few years, both microbial and plant analyses have shifted from specifi c, targeted assays toward methods offering both high accuracy and sensitivity in highly complex mixtures of compounds. ...
... Large-scale metabolome analysis is traditionally based on the use of gas chromatography coupled with mass spectroscopy (GC/MS), liquid chromatography/MS (LC/MS), high performance liquid chromatography (HPLC), ESI-MS, midinfrared (MIR) and high resolution mass spectrometry. [3][4][5][6][7][8][9] It has been generally accepted that a single analytical technique will not provide suffi cient visualisation of the metabolome, therefore holistic techniques are needed for comprehensive analysis. 3 Near infrared (NIR) spectroscopy has gained wide acceptance within the food and agriculture industries as a rapid analytical tool and it is mainly used in the wine industry to measure the alcohol content of wines. ...
... Another interesting approach for metabolomics data analysis is SOP, which is based on neural network technology, however, such analysis is time consuming and resultant data are difficult to analyse (Hirai et al. 2004;Fukusaki and Kobayashi 2005). Computational schemes have been designed and developed to analyse huge metabolomic datasets, which allows metabolic modelling (Oliver et al. 2002). ...
... Furthermore, the studies pertaining to metabolomics explained the overall arrangement of abiotic stress responses and tolerance in plants. Thus, the combination of metabolomics and other functional genomics aspects allow the development of a network system to comprehend nearly all possible characteristics of abiotic stress responses in plants (Oliver et al. 2002;Bino et al. 2004). With several new developments in metabolomics techniques, several hundred of metabolite could be detected in a tissue extract at the same time and the differences in metabolite composition can be compared more reliably among the stressed and unstressed plants in both targeted and global (untargeted) manner. ...
The post-genomic era has witnessed several new possibilities to understand diverse functional aspects of plants quite precisely. From genomics to metabolomics and now phenomics, the complex interplay of these biological networks has been successfully elucidated. Abiotic stresses, such as drought, flooding, exposure to heavy metals and metalloids, and high or low temperature are foremost constraints in agriculture, and remains as the major reason for poor crop productivity and low yield globally. The primary aim of metabolomics is to identify final gene products, the metabolites, which serve as prospective markers (or traits) to comprehend abiotic stress adaptation and tolerance in plants. This review provides an overview on the application of metabolomics as a comprehensive tool for “Systems Biology Approach” to unravel the complex interaction of networks and components in plants towards abiotic stress adaptation and tolerance.
... Advancements in mass spectrometry have enabled the analysis of cellular proteins and metabolites on a large scale, which was previously not possible [8][9][10][11][12][13][14][15][16][17][18]. The cumulative application of these technologies in various fields has led to advancement in the research of functional genomics and systems biology [19][20][21][22][23]. The foundations of both functional genomics and systems biology rely on comprehensive genome-scale molecular analysis [16,18]. ...
... A. Ghatak et al. A single analytical technique is not sufficient for detection and quantification of the metabolome and, therefore, multiple technologies are needed for a comprehensive view [16,22,29,44]. Analytical technologies used in metabolomics include thin layer chromatography (TLC), HPLC with ultraviolet and photodiode array detection (LC/UV/PDA), gas chromatography-mass spectrometry (GC-MS), capillary electrophoresis-mass spectrometry (CE-MS), liquid chromatography-mass spectrometry (LC-MS), liquid chromatography-electrochemistry-mass spectrometry (LC-EC-MS), NMR, LC-NMR, direct infusion mass spectrometry (DIMS), and Fourier-transform infrared (FT-IR), etc. [16,25,43,45,46]. ...
Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies.
... No presente, a humanidade vive uma revolução cultural caracterizada pelos avanços tecnológicos e pelo intenso fluxo de informações, comparável em impacto ao surgimento da agricultura há aproximadamente 10 mil anos e à Revolução Industrial iniciada no século XVIII 1 . Algumas inovações tecnológicas ocorridas nas três últimas décadas levaram a uma nova forma de pensar os sistemas biológicos e, principalmente, de pesquisá-los 2,3 . Na área biológica, o marco inicial desta revolução foi denominado de era genômica, sendo caracterizado pelo desenvolvimento, padronização e otimização das técnicas de engenharia genética 4 . ...
... Alterações induzidas geneticamente, epigeneticamente ou por influência do ambiente são manifestadas, em última instância, através de alterações na composição e concentração de metabólitos. Assim, comparando-se os perfis metabólicos gerados em tecidos que diferem geneticamente ou no seu estado epigenético diferenças genômicas funcionais podem ser inferidas 2,4,10,13 . ...
... These methods are able to identify underlying latent factors that carry information (e.g. biochemistry, chemistry, process) about the sample (Wishart 2008;Khakimov et al. 2014Khakimov et al. , 2015Skov et al. 2014;Allen et al. 2003;Dunn and Ellis 2005;Nielsen and Oliver 2005;Oliver et al. 1998Oliver et al. , 2002Sumner et al. 2003;Sweetlove et al. 2004;Cozzolino 2011;Unger 2009;Wolfender 2009;Roullier-Gall et al. 2015;Hong 2011;Gromski et al. 2015). The most important and effective way to show the best of the success in how different teams tackle complexity is by the use of the 'holistic' or 'systems' approach. ...
... Development of metabolomics has depended on advances in a diverse range of instrumental techniques such as liquid chromatography (LC), electrospray ionisation mass spectrometry (ESI-MS), capillary electrophoresis (CE), gas chromatography (GC), NMR spectroscopy, high-performance liquid chromatography (HPLC), mass spectrometry (MS) and vibrational spectroscopy (e.g. NIR, MIR, Raman) among other techniques (Wishart 2008;Khakimov et al. 2014Khakimov et al. , 2015Skov et al. 2014;Allen et al. 2003;Dunn and Ellis 2005;Nielsen and Oliver 2005;Oliver et al. 1998Oliver et al. , 2002Sumner et al. 2003;Sweetlove et al. 2004;Cozzolino 2011;Unger 2009;Wolfender 2009;Roullier-Gall et al. 2015;Hong 2011;Gromski et al. 2015). Recently, the increase in resolution on mass measurements using direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) and ultra-high-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) allowed an improvement on the separation ability of isomeric and isobaric substances, increasing the scope of detectable unknown metabolites in wines (Gromski et al. 2015;Khakimov et al. 2014). ...
One of the main challenges that face the modern wine sciences is how to optimise grape and wine production in order to have a minimum environmental footprint, lower production costs, as well as how to improve or maintain the quality of the wine produced. It has been generally accepted that a single analytical technique will not provide sufficient information about the wine metabolome and therefore a holistic-omics approach is suggested for a more comprehensive analysis. Metabolomics is an emerging field in grape and wine research enabling chemical and biochemical profiling of samples (e.g. grapes, wine) in order to obtain insight into its biological characteristics and properties. By means of a literature review of the most recent published reports on the use of the metabolomics approach, the aim of this paper is to provide with an overview on the use of this approach in grape and wine research. Most of the studies presented in this review have highlighted the importance of metabolomics in wine science, as well as emphasised on the need of a multidisciplinary team approach where the participation of scientists from different disciplines such as biology, biochemistry, chemistry and chemometrics (mathematics and statistics) being equally important to deliver successful and reliable data, in order to improve our knowledge about wine. The combination of different techniques provides both the research and industry with powerful and complementary tools that differ from the conventional routine methods currently in use by the grape and wine industry.
... The measurement of all small molecules (metabolites) synthesized by microorganisms, which represent the interaction of the genome, transcriptome and proteome with the environment is called metabolomics (Dunn & Ellis, 2005;Nielsen & Oliver, 2005;Sumner, Mendes, & Dixon, 2003). The development of metabolomics has depended on advances in a diverse range of instrumental techniques such as liquid chromatography (LC), electro spray ionization mass spectrometry (ESI-MS), capillary electrophoresis (CE) and microchip arrays among others (Allen et al., 2003;Nielsen & Oliver, 2005;Oliver, Nikolau, & Wurtele, 2002;Oliver, Winson, Kell, & Baganz, 1998;Sumner et al., 2003;Sweetlove et al., 2004). Each of these methods provides unique capabilities to separate different chemical classes of metabolites (Oliver et al., 2002). ...
... The development of metabolomics has depended on advances in a diverse range of instrumental techniques such as liquid chromatography (LC), electro spray ionization mass spectrometry (ESI-MS), capillary electrophoresis (CE) and microchip arrays among others (Allen et al., 2003;Nielsen & Oliver, 2005;Oliver, Nikolau, & Wurtele, 2002;Oliver, Winson, Kell, & Baganz, 1998;Sumner et al., 2003;Sweetlove et al., 2004). Each of these methods provides unique capabilities to separate different chemical classes of metabolites (Oliver et al., 2002). ...
The wine industry requires rapid, comprehensive methods and techniques to answer the new challenges driven by the market demands. Recent advances in spectroscopy technologies have brought about a revolution in the manner in which biological systems are visualised and analysed. The measurement of numerous small molecules (metabolites) metabolised by microorganisms during growth in wine, will benefit from techniques that require minimal sample preparation, permit the automatic analysis of many samples with negligible reagent costs, allow their rapid characterization against a stable database, and are easy to use. With recent developments in analytical instrumentation, these requirements are being fulfilled by vibrational spectroscopic methods, often referred to as “whole-organism fingerprinting” and more recently “metabolic fingerprinting”. The objective of this study was to evaluate and compare the use of near (NIR) and mid infrared (MIR) spectroscopy as rapid methods to distinguish red and white wines obtained by using different strains of Oenococcus oeni following malolactic fermentation. Using NIR and MIR wines produced with different O. oeni strains could be distinguished both in red and white wines yielding correct classification rates between 67 and 100% depending on the strain.
... Many genes have been discovered, and their functions have been clarified. The establishment of a network system to understand nearly all potential characteristics of stress responses in plants is thus made possible by the merging of metabolomics and other functional genomics components [60,61]. With a number of recent advancements in metabolomics techniques, hundreds of metabolites could be found in a tissue extract at once, and the differences in metabolite composition between stressed and unstressed plants could be more accurately compared in both a targeted and a global (untargeted) manner. ...
... Quantitative information of known and unknown metabolites in the sample to be tested can help comprehensively understand the biological phenomena [30][31][32]. Currently, metabolomics and transcriptomics were combined and analyzed in many biological studies [33][34][35]. The metabolic pathways and information involved in the growth and quality of water dropwort under nutrient solution culture was still limited. ...
Water dropwort (Oenanthe javanica (Blume) DC.) is an important vegetable crop. Nutrient liquid culture has become an important cultivation method in the production of water dropwort. However, the effects of different nutrient solution cultivation methods on the growth and quality of water dropwort remains unclear. In this study, to screen the most suitable nutrient solution formula for the cultivation of water dropwort, the effects of different nutrient solution formulas (Hoagland, Cooper, Dutch greenhouse, Garden-style, Yamasaki and SCAU) on plant physiological and quality characteristics are investigated. The plant height, root length, water content (%), distribution rate of dry matter (%), chlorophyll, VC, flavonoid, total phenolic, DPPH and dietary fiber of water dropwort under different nutrient solutions were determined. According to the analytic hierarchy process (AHP) of the growth index and quality index of water dropwort under different nutrient solutions, the Yamazaki nutrient solution was considered to be the most suitable nutrient solution formula for water dropwort. To further confirm the differences of water dropwort under nutrient solution culture and soil culture, the broadly targeted metabolomics were performed. A total of 485 metabolites were detected in water dropwort under optimal nutrient solution and soil cultivation. Metabolomics analysis showed that flavonoids were the most abundant differential accumulated metabolites, and most flavonoids were up-regulated. A qRT-PCR assay indicated that the structural genes of the flavonoid biosynthesis pathway (PAL, C4H, CHS, CHI, F3H, DFR, UFGT) were significantly higher under the Yamasaki nutrient solution treatment. The current study provided a theoretical basis and technical guidance for the nutrient solution cultivation of water dropwort. Meanwhile, this study provides new insights into the study of flavonoids in water dropwort.
... Therefore, exploring novel biomarkers will open a new window to the clinical diagnosis of childhood TB. As the ultimate downstream pool of genome transcription, the metabolites can reflect changes in the biochemistry of living cells or organisms more directly, when compared with genetics and proteomics [28]. Therefore, the metabolites underlying the dysregulated metabolic pathways can be defined as biomarkers to diagnose the disease and reflect the disease progression. ...
Metabolic profiling using nonsputum samples has demonstrated excellent performance in diagnosing infectious diseases. But little is known about the lipid metabolism alternation in children with tuberculosis (TB). Therefore, the study was performed to explore lipid metabolic changes caused by Mycobacterium tuberculosis infection and identify specific lipids as diagnostic biomarkers in children with TB using UHPLC-MS/MS. Plasma samples obtained from 70 active TB children, 21 non-TB infectious disease children, and 21 healthy controls were analyzed by a partial least-squares discriminant analysis model in the training set, and 12 metabolites were identified that can separate children with TB from non-TB controls. In the independent testing cohort with 49 subjects, three of the markers, PC (15:0/17:1), PC (17:1/18:2), and PE (18:1/20:3), presented with high diagnostic values. The areas under the curve of the three metabolites were 0.904, 0.833, and 0.895, respectively. The levels of the altered lipid metabolites were found to be associated with the severity of the TB disease. Taken together, plasma lipid metabolites are potentially useful for diagnosis of active TB in children and would provide insights into the pathogenesis of the disease.
... Due to the immense economic, medicinal, and horticultural importance of this crop, it has been widely studied by various crop researchers, especially in the last two decades (Chhapekar et al., 2020;Dubey et al., 2019;Heiser & Smith, 1953;Ramchiary et al., 2014;Sarpras et al., 2019). There is considerable progress in the development of functional genomics tools that make it easy to investigate the individual plant cells, tissues, organs, and whole plants at the genome level, particularly in response to the pathogenic attack (Noman et al., 2020;Oliver et al., 2002). Physiological, biochemical, and molecular aspects of stress tolerance, once identified, can assist in taking measures against the stresses in plants (Vij & Tyagi, 2007). ...
The Capsicum L. belonging to the family Solanaceae is an economically important genus of flowering plants. This genus comprises more than 30 species with its unique characteristic feature of pungency in its fruits, which are used as spices, in raw salads and pickles. People also use the species of Capsicum for ethnomedicinal and ethnoveterinary purposes. The fruits of Capsicum contain several metabolites including capsaicinoids (alkaloids responsible for the unique pungent taste), vitamins, flavonoids, carotenoids, mineral elements, and other health beneficial compounds, which have medicinal potential. Pharmacological researches have also demonstrated the role of Capsicum in the treatment of many diseases. However, the cultivated species of Capsicum face biotic and abiotic stresses that affect the normal growth and development of the plant and fruits. These stresses negatively affect yields and incur high economic losses to the farmers and the exchequer. Therefore breeding varieties resistant to these stresses is a desirable strategy to avoid/reduce the loss. The development of improved stress-resistant cultivars of Capsicum requires the knowledge of the genetic, genomic, proteomic, and metabolomic basis of stress responses active inside the plants. The recent advancements in high-throughput genomics, proteomics, and metabolomics technologies enable the understanding, quantification, and analysis of genes, proteomes, and metabolomes in various tissues and their response to different stresses at substantial speed and precision. These tools, en masse, known as omics tools allow comparative investigation of wild and cultivated species of plants; thereby in many of the cases could lead to the identification of the genes or proteins and the elucidation of their functions and assisting in the development of multiple stress-tolerant crop designs. In this chapter an attempt has been made to give an overview of the studies currently available toward understating genomic, proteomic, and metabolomic responses to biotic and abiotic stresses in Capsicum species. It further provides insights into designing resistant cultivars using genome editing and other biotechnological approaches.
... In the 1990s, studies on herbicide mechanisms or modes of action initiated such profiling approaches in plants (Sauter et al. 1991). In the mid-1990s, the association of metabolomics with functional genomics emerged and extended parallel to enhancing capacities of these technologies in mass spectrometry, chromatography, and imaging techniques (Oliver et al. 2002;Ward et al. 2007). ...
... In the 1990s, studies on herbicide mechanisms or modes of action initiated such profiling approaches in plants (Sauter et al. 1991). In the mid-1990s, the association of metabolomics with functional genomics emerged and extended parallel to enhancing capacities of these technologies in mass spectrometry, chromatography, and imaging techniques (Oliver et al. 2002;Ward et al. 2007). ...
... In the 1990s, studies on herbicide mechanisms or modes of action initiated such profiling approaches in plants (Sauter et al. 1991). In the mid-1990s, the association of metabolomics with functional genomics emerged and extended parallel to enhancing capacities of these technologies in mass spectrometry, chromatography, and imaging techniques (Oliver et al. 2002;Ward et al. 2007). ...
... Developments in the so-called omic technologies have depended on advances in a diverse range of instrumental techniques [1][2][3] as well as in computer power, hardware and software. Together, they have determined improvements in analytical methods such as liquid chromatography (LC), high-performance liquid chromatography (HPLC), electrospray ionization mass spectrometry (ESI-MS), mass spectrometry (MS), capillary electrophoresis (CE), gas chromatography (GC), microchip arrays, infrared (IR), UV-vis (UV-vis) and Raman spectroscopy, currently used in the analysis of beer and wine samples [3-14,15 ,16-18]. ...
Spectroscopy has become a method of choice by research and industry for the routine analysis of food. The inherent advantages of easy to use in routine analysis, the availability of a wide range of instrumentation and sampling handling options allowed for a quick uptake of these methods by several research groups and by the beer and wine industries worldwide. However, some issues related to the poor understanding of the technology, the high dependence on mathematics, are still some of the few issues that are facing the implementation of these techniques. This article highlights advantages and disadvantages on the use of spectroscopy methods (NIR, MIR, Raman and UV-VIS) for the rapid analysis of beer and wine as well as presenting some novel and recent applications related with routine analysis of composition, authentication and traceability.
... Finally, the metabolic balance in plants is adjusted by controlling the metabolite synthesis. 17 Metabolites are the nal products of this genetic transcription and protein modication, and belong to specic metabolic pathways. 18 Thus, the plant metabolism may be perturbed under such stress. ...
A hypothetic model for the adaptation of maize to Y 2 O 3 NPs stress during seed germination.
... 6 Recent advancements in high throughput technologies like DNA microarrays, next generation sequencing had produced massive data which are analysed with modern approach to gain better insights and also to identify novel targets. 7,8 In the present study, various bioinformatics analysis has been carried out in order to understand mechanism and interaction of oncogenes and tumor suppressor genes in breast cancer. With available breast cancer microarray data, differentially expressed genes (DEG) were identified and classified into five gene sets. ...
Breast cancer is the leading cause for mortality among women worldwide. Dysregulation of oncogenes and tumor suppressor genes is the major reason for the cause of cancer. Understanding these genes will provide clues and insights about their regulatory mechanism and their interplay in cancer. In the present study, an attempt is made to compare the functional characteristics and interactions of oncogenes and tumor suppressor genes to understand their biological role. 431 breast cancer samples from seven publicly available microarray datasets were collected and analysed using GEO2R tool. The identified 416 differentially expressed genes were classified into five gene sets as oncogenes (OG), tumor suppressor genes (TSG), druggable genes, essential genes and other genes. The gene sets were subjected to various analysis such as enrichment analysis (viz., GO, Pathways, Diseases and Drugs), network analysis, calculation of mutation frequencies and Guanine-Cytosine (GC) content. From the results, it was observed that the OG were having high GC content as well as high interactions than TSG. Moreover, the OG are found to have frequent mutations than TSG. The enrichment analysis results suggest that the oncogenes are involved in positive regulation of cellular protein metabolic process, macromolecule biosynthetic process and majorly in cell cycle and focal adhesion pathway in cancer. It was also found that these oncogenes are involved in other diseases such as skin diseases and viral infections. Collagenase, paclitaxel and docetaxel are some of the drugs found to be enriched for oncogenes.
... For understanding the mechanisms underlying the biosynthesis of secondary metabolites on elicitation, powerful integrated platform such as Functional genomics (that includes transcriptomics, proteomics, and metabolomics) is required (Oliver et al. 2002). Functional genomics can aid in novel gene discovery, assigning functions to gene and detection of novel pathways. ...
Higher plants undergo a variety of stresses and to combat those stresses they acclimatize themselves by producing diverse secondary metabolites. These secondary metabolites also have a wide range of industrial applications and hence they serve as candidates for commercialization. Owing to the constraints faced by natural plant extraction, plant cell/tissue culture has emerged as an alternative platform for the in vitro production of value added bioactive secondary metabolites. Implementation of several productivity enhancement strategies, including elicitation, can overcome the limitations faced by plant cell technology that hampers its extensive commercialization. Elicitation is a technique that involves exogenous addition of elicitors (abiotic or biotic) in the growth medium which consequently triggers stress response with concomitant enhancement in secondary metabolite production. Elicitor induced stress results in the activation of several defense-related genes or inactivation of non-defense-related genes, transient phosphorylation/dephosphorylation of proteins, expression of enzymes whose information can be used to ascertain the biosynthetic pathways of many secondary metabolites. Furthermore, integration of transcriptomics, proteomics and metabolomics with system biology can aid in discovery of novel genes, transcriptional factors and several biosynthetic pathways which in turn can serve as a valuable tool for metabolic engineering and gene manipulation for enhancing the yield and productivity of secondary metabolites.
... Metabolomics, as a systemic biology approach, has demonstrated great potential in many fields (Shockcor and Holmes, 2002), such as, toxicological evaluation (Arakaki et al., 2008), disease process (Oliver et al., 2002), and drug discovery (Lindon et al., 2003). Metabolomics provides an important method to trace the metabolic global changes in the biological processes in biofluids (e.g., blood and urine) or tissues (e.g., liver and kidney) of an organism (Nicholson et al., 2002;Xuan et al., 2011). ...
“RenqingMangjue” pill (RMP), as an effective prescription of Traditional Tibetan Medicine (TTM), has been widely used in treating digestive diseases and ulcerative colitis for over a thousand years. In certain classical Tibetan Medicine, heavy metal may add as an active ingredient, but it may cause contamination unintentionally in some cases. Therefore, the toxicity and adverse effects of TTM became to draw public attention. In this study, 48 male Wistar rats were orally administrated with different dosages of RMP once a day for 15 consecutive days, then half of the rats were euthanized on the 15th day and the remaining were euthanized on the 30th day. Plasma, kidney and liver samples were acquired to ¹H NMR metabolomics analysis. Histopathology and ICP-MS were applied to support the metabolomics findings. The metabolic signature of plasma from RMP-administrated rats exhibited increasing levels of glucose, betaine, and creatine, together with decreasing levels of lipids, 3-hydroxybutate, pyruvate, citrate, valine, leucine, isoleucine, glutamate, and glutamine. The metabolomics analysis results of liver showed that after RMP administration, the concentrations of valine, leucine, proline, tyrosine, and tryptophan elevated, while glucose, sarcosine and 3-hydroxybutyrate decreased. The levels of metabolites in kidney, such as, leucine, valine, isoleucine and tyrosine, were increased, while taurine, glutamate, and glutamine decreased. The study provides several potential biomarkers for the toxicity mechanism research of RMP and shows that RMP may cause injury in kidney and liver and disturbance of several pathways, such as energy metabolism, oxidative stress, glucose and amino acids metabolism.
... Integrated functional genomics attempts to utilize the vast wealth of data produced by modern large scale genomic and post-genomic projects to understand the functions of cells and organisms [1]. The rapidly increasing amount of high throughput sequencing data makes it essential to develop new analytical tools that can systematically process and integrate those datasets. ...
Background
Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimental and theoretical methodologies. Less work has focused on using these models to examine how TF networks respond to changes in the cellular environment.
Methods
In this paper, we have developed a simple, pragmatic methodology, TIGERi (Transcription-factor-activity Illustrator for Global Explanation of Regulatory interaction), to model the response of an inferred TF network to changes in cellular environment. The methodology was tested using publicly available data comparing gene expression profiles of a mouse p38α (Mapk14) knock-out line to the original wild-type.
Results
Using the model, we have examined changes in the TF network resulting from the presence or absence of p38α. A part of this network was confirmed by experimental work in the original paper. Additional relationships were identified by our analysis, for example between p38α and HNF3, and between p38α and SOX9, and these are strongly supported by published evidence. FXR and MYC were also discovered in our analysis as two novel links of p38α. To provide a computational methodology to the biomedical communities that has more user-friendly interface, we also developed a standalone GUI (graphical user interface) software for TIGERi and it is freely available at https://github.com/namshik/tigeri/.
Conclusions
We therefore believe that our computational approach can identify new members of networks and new interactions between members that are supported by published data but have not been integrated into the existing network models. Moreover, ones who want to analyze their own data with TIGERi could use the software without any command line experience. This work could therefore accelerate researches in transcriptional gene regulation in higher eukaryotes.
... For instance, the IL1403 sequence has revealed the presence of some unexpected pathways, e.g., on competence develop- ment and some expected but interesting traits, such as respiration ability (Duwat et al. 2001), as well as on horizontal gene transfer ( Bolotin et al. 2001). Apart from the mining approaches, which in essence will primarily place genes in functional categories and al- low to compare gene organizations in various related and unrelated species, it is exciting to see how fast a number of experimental approaches have evolved to enable transcriptome analysis, genomotyping, pro- teome analysis, high-throughput screening and high- throughput structural biology in microbial systems (Kuipers 2000;Paton et al. 2000;Sebaihia et al. 2001;Tomita 2001;Ye et al. 2001;Zheng et al. 2001;Oliver et al. 2002). Since there is a huge interest to identify lactococci with desired properties or to construct such strains by genetic modification, it is essential to know how the function of each individual gene/protein is re- lated to its expression/production during growth and in stationary phase in various environments. ...
... Other genome sequencing programs are in progress, i.e., cotton, soya, sunflower, etc. Access to genomics information permits to characterize gene expression under different conditions, thus establishing with precision gene function, a research field referred to as functional genomics. Functional genomics aims at quantitatively determining the spatial and temporal accumulation of specific mRNA, proteins, and metabolites using high-throughput technologies (Oliver et al. 2002). ...
Plant natural products are useful compounds, such as pigments, fragrances, pharmaceuticals used for the treatment of several human diseases. Development of plant manipulation techniques, such as particle bombardment, Agrobacterium-mediated transformation, vacuum infiltration, agrodrench, viral vector, protoplasts fusion and ultrasound, as well as recombinant DNA, and genetic technologies applied toward metabolic engineering of bioactive plant natural products are presented, together with different genetic engineering methods, such as overexpression of transgenes, multiple expression of transgenes, gene silencing, and transcription factors as powerful tools for the engineering of biosynthetic pathways. Future perspectives and the potential of different approaches are presented to highlight how a better understanding of secondary metabolite pathways represents a direct successful highway to genetic manipulation of desired metabolic pathways.
... With the advent of high-throughput measurement techniques such as transcriptome by microarray and proteome by mass spectrometry, the omics, which mean comprehensive analysis of a specific layer in a cellular system and are emerging as essential methodological approaches for molecular biology and systems biology, have been accumulated rapidly and make it possible to capture the entire snapshot of cell-wide activity [1,2]. The increase in data acquisition has lead to a demand for practical and effective data mining methods for in silico analysis. ...
... Metabolomics analysis has emerged in recent years as a promising technology to identify metabolic networks in living cells 11,40 . Metabolomics studies have progressed, especially using gas chromatography-mass spectrometry (GC/MS), following enormous efforts to develop methodological standards and informing numerous metabolites in plants 11,46 . ...
Soybean is a crop known to be susceptible to flooding and enhancing flooding tolerance may be a workable strategy to improve soybean production. To elucidate the effects of flooding on soybean metabolism, metabolite alterations in seedlings during flooding treatment were identified using capillary electrophoresis-mass spectrometry (CE/MS). The principal component analysis (PCA) of soybean seedlings revealed that the first component accounted for 62.2% of total variance, and the alteration of metabolites in control and flooding treatments appears to be separated by this component. Furthermore, comparison of the metabolic loading scores in the first component of PCA show that the significant metabolites for the first component were alanine (Ala), gamma-aminobutyric acid (GABA), citrate, fumarate and malate. Quantitative analysis revealed that the total soluble sugar content of seedlings in both control and flooding treatments had declined and was lower in the latter than the former. Phosphoenolpyruvate, pyruvate and lactate, which belong to glycolytic and fermentation pathways, increased transiently, but decreased 3 to 4 days after treatment. Citrate, 2-oxoglutarate, succinate, fumarate, Ala, and GABA, which are related to the TCA cycle and amino acid metabolism, accumulated during flooding treatment. These results suggest that metabolism associated with the TCA cycle, the Ala synthetic pathway, and the GABA shunt may be strongly influenced by flooding during soybean germination.
... Characterization of the genetic mechanisms relative to the improved producing capability is very important. The fast accumulation of omics data, including genomics, transcriptomics, proteomics, metabolomics, and fluxomics, has provided foundation for the understanding of the genetic mechanisms in depth [115][116][117][118][119], which is crucial for further round of engineering to obtain the next-generation cell factories. ...
Production of bulk chemicals from renewable biomass has been proved to be sustainable and environmentally friendly. Escherichia coli is the most commonly used host strain for constructing cell factories for production of bulk chemicals since it has clear physiological and genetic characteristics, grows fast in minimal salts medium, uses a wide range of substrates, and can be genetically modified easily. With the development of metabolic engineering, systems biology, and synthetic biology, a technology platform has been established to construct E. coli cell factories for bulk chemicals production. In this chapter, we will introduce this technology platform, as well as E. coli cell factories successfully constructed for production of organic acids and alcohols.
Graphical Abstract
... After the establishment of technologies for high-throughput DNA sequencing (genomics), gene expression analysis (transcriptomics), and protein analysis (proteomics), the remaining functional genomics challenge is that of metabolomics [2,3,4]. Metabolism is the term coined for essentially comprehensive, non biased, high-throughput analyses of complex metabolite mixtures typical of plant extracts [5,6,7,8]. This potentially holistic approach to metabolome analysis is driven primarily by recent advances in mass spectrometry technology and by goals of functional genomics research [9,10,11]. ...
... Metabolism is the term coined for essentially comprehensive, non biased, high-throughput analyses of complex metabolite mixtures typical of plant extracts [5,6,7,8]. This potentially holistic approach to metabolome analysis is driven primarily by recent advances in mass spectrometry technology and by goals of functional genomics research [9,10,11]. ...
ARTICLE INFO ABSTRACT Article history: Received on: 22/09/2013 Revised on: 6/10/2013 Accepted on: 20/10/2013 Available Online: 30/10/2013 Metabolomics plays significant roles in plant biology including growth, development and stress resistance. Plant produces diverse array of metabolites (approximately 200,000 to 1,000,000); hence metabolomic study is of great importance in plant biology. Due to presence of diverse array of metabolites in plants, it posses greatest challenge to indentify and quantify them correctly. Very significant improvement has been made in the field of plant metabolomics, but uniform annotation of metabolite signals in database and informatics of international standardization remains a challenge. The advancement of metabolomics largely depends upon increase in separation efficiencies and identification of individual metabolites. Fluxome and metabolomic QTL (mQTL) are very important missing link in plant metabolomics. Now these days, metabolomics is a part of system biology and metabolomics in combination with system biology approach will lead to unbiased acquisition of mass spectrometric data from diverse array of samples. To overcome different challenges, development of improved technology for detection and identification of metabolite in complex plant tissue and dissemination of metabolomic research data will be very helpful.
... The development of publicly-available genomic, transcriptomic, and more recently, metabolomic, flux and proteomic data sets for model organisms has accelerated the understanding of metabolism and metabolic networks [2,[8][9][10][11][12][13][14]. Analogous data sets for medicinal plants will similarly revolutionize how researchers approach, decipher, and model the accumulation of medicinal compounds, and consequently enable the more effective development and utilization of medicinally active plant metabolites. ...
Specialized compounds from photosynthetic organisms serve as rich resources for drug development. From aspirin to atropine, plant-derived natural products have had a profound impact on human health. Technological advances provide new opportunities to access these natural products in a metabolic context. Here, we describe a database and platform for storing, visualizing and statistically analyzing metabolomics data from fourteen medicinal plant species. The metabolomes and associated transcriptomes (RNAseq) for each plant species, gathered from up to twenty tissue/organ samples that have experienced varied growth conditions and developmental histories, were analyzed in parallel. Three case studies illustrate different ways that the data can be integrally used to generate testable hypotheses concerning the biochemistry, phylogeny and natural product diversity of medicinal plants. Deep metabolomics analysis of Camptotheca acuminata exemplifies how such data can be used to inform metabolic understanding of natural product chemical diversity and begin to formulate hypotheses about their biogenesis. Metabolomics data from Prunella vulgaris, a species that contains a wide range of antioxidant, antiviral, tumoricidal and anti-inflammatory constituents, provide a case study of obtaining biosystematic and developmental fingerprint information from metabolite accumulation data in a little studied species. Digitalis purpurea, well known as a source of cardiac glycosides, is used to illustrate how integrating metabolomics and transcriptomics data can lead to identification of candidate genes encoding biosynthetic enzymes in the cardiac glycoside pathway. Medicinal Plant Metabolomics Resource (MPM) [1] provides a framework for generating experimentally testable hypotheses about the metabolic networks that lead to the generation of specialized compounds, identifying genes that control their biosynthesis and establishing a basis for modeling metabolism in less studied species. The database is publicly available and can be used by researchers in medicine and plant biology.
... Proteomics analyzes the characters of proteins at the proteome level. Since the post-gnome era is coming, functional genomics that quantitatively determine the spatial and temporal accumulation patterns of specific mRNA, proteins and important metabolites becomes the focus of researches [155]. The proteomics approach clearly is an important component for functional genomics research. ...
The complex nature and polygenic determination of most bone disorders require new approaches to search for genes and genetic mechanisms underlying these diseases. The present article overviews powerful and promising methodologies, which have been used to study these disorders. One of the most commonly used approaches is that of candidate gene association study, which seeks to test the association between a particular genetic variant (i.e. allele) and a specific phenotypes. These candidate genes are identified a priori based on known biologic function of gene product. As a complement of the candidate gene approach, the whole-genome scan studies employ polymorphic makers throughout the human genome to search genomic regions responsible for determining a trait of interest by linkage and / or linkage disequilibrium analyses. High-throughput methods for differential gene expression profiling are another powerful approach for searching genes underlying complex traits. Depending on their design, these methods allow researchers to get a snapshot of either the whole genome or any part. Importantly, in contrast to the various linkage or linkage disequilibrium tests, gene expression profiling provides information about how genes contribute to a trait. These methods of gene expression analysis are a rapidly developing field of functional genomics. It has a very high potential for applications in bone field, including diagnostics, prevention, and treatment of complex bone disorders. Genes usually function via the protein level. Based on the two-dimensional polyacrylamide gel electrophoresis, mass spectrometry and the yeast two-hybrid techniques, proteomics becomes a potentially useful tool for identifying genes and gene functions underlying complex traits. Focused on protein expression profiles and protein-protein interactions, proteomics turns out to be a complement to functional genomics and one of the best ways to clarify complicated biochemical mechanisms underlying complex bone disorders. Promisingly, proteomics may be eventually applied to seeking and screening genes and drug targets for complex bone disorders.
Goats can provide meat, milk and skins for humans and are livestock with high economic benefits. However, despite their economic significance, the comprehensive analysis of goats’ serum metabolic profile and its intricate alterations throughout their developmental journey remains conspicuously absent. To investigate the stage-specificity and dynamic change characteristics of metabolites during the growth and development of goats, this study compared the alterations in serum hormone levels and serum biochemical markers across different developmental stages of female goats (1, 60, 120 and 180 days old; n = 5). Additionally, a serum untargeted LC–MS metabolomics analysis was conducted. A total of 504 DAMs were identified with age. The results indicated that PE, PC, Lyso-PE, Lyso-PC and FAFHA may play important roles in lipid metabolism in goats after birth. Weighted gene co-expression network analysis (WGCNA) identified two metabolite modules (Turquoise and Yellow) and key metabolites within these modules that were significantly associated with phenotypic features. l-carnitine may be a metabolite related to muscle development in goats. The findings of this study demonstrate notable variations in serum metabolites across distinct developmental phases in goats. Lipids and organic acids play important roles in different developmental stages of goats.
The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.
Aroma is a complex attribute governed all the way through a complicated network of regulatory mechanisms of metabolic pathways at various levels. Abiotic factors of an environment like temperature, photoperiod and humidity seem to be more prominent signals as they influence chemical, biochemical and molecular responses. Molecular events are also important to understand the role of various proteins and nucleic acids in this complex regulation of aroma character visa vis role of structural genes/ promoters which are found either inducible or constitutive with respect to a specific macro/ micro environment. On the one side many molecules including different esters derived from lipids and or other secondary metabolites specially 2-Acetyl-1-Pyrroline (2-AP) had been reported as a major compound responsible for aroma based on organoleptic sensory evaluation test involving Human subjects providing them wide popular common food preparations. Many plants mainly Pandanus amaryllifolius Roxb had also reported for appreciable amount of 2-AP. Oryza sativa L. had been widely accepted for the presence of 2-AP including basmati, non-basmati varieties and other scented landraces. The biogenesis of this molecule is complex. However, precursor and product relationship has been established differently. One such relationship for 2-AP had been established from plant stress-related imino acid (proline) through its one of the derivative 2-hydroxy-1-pyrolline. Although such molecules had been shown derived from carbon and nitrogen metabolism. Deletion in the betaine aldehyde dehydrogenase 2 (BADH2) gene leads in the accumulation of Δ 1-pyrroline which reacts non-enzymatically with Review Article Volume 3 Issue 1: 000111. Copyright© Gaur AK, et al. 2 methylglyoxal in order to form 2-AP. Due to non-functionality of this gene which regulates the synthesis of γ-amino butyric acid (GABA), plant species synthesizing 2-AP suffers for the yield losses and susceptibility to biotic and abiotic stresses. Since, BADH2gene is under regulation differing from BADH1 gene those are coupled with 2-AP synthesis, however, BADH2 gene had been proven as a gene of metabolic diversion with reference to Oryza species. Thus, present review focus on 2-AP in relation with stress and aroma biology which in turn will help to understand the productivity and aesthetic value of rice which is a major staple food of majority of global human population.
Elevated serum interleukins (IL-6, IL-1β) over baseline concentration help in blastocyst adhesion to the uterine endometrium in the early phase of pregnancy. A nano PLA (Piperolactam A)-HPBCD (2-hydroxy-propyl-β-cyclodextrin) inclusion complex was developed as an interleukin down-regulator that exhibited 100% anti-implantation activity in rodents at a dose as low as 2.5-5.0 mg/kg. On metabolomics study, among major glyco-lipo-protein metabolites, only serum low-density lipoprotein (LDL) or very low-density lipoprotein (VLDL) levels revealed alteration by the formulation. Administration of PLA-HPBCD did not cause changes in serum estrogen and progesterone levels. However, IL-6 and IL-1β failed to increase post PLA-HPBCD administration; hence, it is assumed to be the mode of the drug's abortifacient action. In addition, absence of signs of either acute or chronic toxicity suggests the formulation was considerably non-toxic. Therefore, the nano-PLA conjugate promises as a non-steroidal contraceptive lead apart from ormeloxifene, the only non-steroidal anti-fertility agent currently available globally.
Heavy metal contamination is a significant environmental issue. Using bacteria for removal and reduction of heavy metals is an attractive alternative owing to its low-cost and eco-friendly properties. However, the mechanisms of resistance to and reduction of Ag(I), Pd(II), and Se(IV), especially in the same strain, remain unclear. Here, Pantoea sp. IMH was examed for its reduction of Ag(I), Pd(II), and Se(IV) to nanoparticles (NPs), and the molecular mechanism was investigated by transcriptome analysis. The results revealed that genes encoding binding, transport, catalytic activity, and metabolism were differentially expressed in cells exposed to Ag(I), Pd(II), and Se(IV). The same resistance mechanisms for all metals included multiple stress resistance protein BhsA and glutathione detoxification metabolism. However, zinc transport protein and sulfate metabolism played an important role in the resistance to cationic metals (Ag+ and Pd2+), while the oxalate transporter and arsenic resistance mechanisms were specifically involved in the resistance to and reduction of anion (SeO32-). In addition, Ag(I) was speculated to be reduced to AgNPs by glucose and cytochrome CpxP was involved in Pd(II) reduction. Our results provided new clues on the mechanisms of resistance to and reduction of Ag(I), Pd(II), and Se(IV).
Paulownia witches’ broom (PaWB) disease caused by phytoplasma is a fatal disease that leads to considerable economic losses. Although there are a few reports describing studies of PaWB pathogenesis, the molecular mechanisms underlying phytoplasma pathogenicity in Paulownia trees remain uncharacterized. In this study, after building a transcriptome database containing 67,177 sequences, we used isobaric tags for relative and absolute quantification (iTRAQ) to quantify and analyze the proteome-level changes among healthy P. fortunei (PF), PaWB-infected P. fortunei (PFI), and PaWB-infected P. fortunei treated with 20 mg L−1 or 60 mg L−1 methyl methane sulfonate (MMS) (PFI-20 and PFI-60, respectively). A total of 2,358 proteins were identified. We investigated the proteins profiles in PF vs. PFI (infected process) and PFI-20 vs. PFI-60 (recovered process), and further found that many of the MMS-response proteins mapped to “photosynthesis” and “ribosome” pathways. Based on our comparison scheme, 36 PaWB-related proteins were revealed. Among them, 32 proteins were classified into three functional groups: (1) carbohydrate and energy metabolism, (2) protein synthesis and degradation, and (3) stress resistance. We then investigated the PaWB-related proteins involved in the infected and recovered processes, and discovered that carbohydrate and energy metabolism was inhibited, and protein synthesis and degradation decreased, as the plant responded to PaWB. Our observations may be useful for characterizing the proteome-level changes that occur at different stages of PaWB disease. The data generated in this study may serve as a valuable resource for elucidating the pathogenesis of PaWB disease during phytoplasma infection and recovery stages.
Phospholipids, predominantly phosphoglycerides, have long been recognized as a major component of cell and organellar membranes where they form the phospholipid bilayer. The phospholipid bilayer creates the permeability barrier of all cells and serves as a matrix for a large number of proteins involved in diverse cellular functions including cellular communication, replication, trafficking etc. Although devoid of any catalytic activity, the phospholipids are in a constant state of flux within the membrane and play a significant role in regulating biochemical signaling and the activity of the proteins associated with the membranes (Dowhan, 1997).
Moderate correlations between transcriptome and proteome were found in most studies. According to different data types, current studies could be divided into four categories. They are comparison on single point, comparison between two differential points, comparison among multiple time-sequence points and comparison among multiple non-time-sequence points. In addition to the experimental error and the different datasets, the post-transcription restriction and regulatory would affect the correlation very much. Different results might be got for the different genes, cells, organs, organisms and even the different developmental phases. Because of the differentia and complementary between them, parallel studies of transcriptome and proteome trended to be performed, which could get the panorama screen of gene expression and discover the genes regulated in post-transcription.
Arisaematis Rhizoma (AR), a famous traditional Chinese medicine, has been widely used in Asia over thousands of years. Recorded with obvious toxicity, Arisaematis Rhizoma has been used in clinic safely even after long time oral administration. Therefore, it is important to assess the toxicity of Arisaematis Rhizoma dynamically and holistically. In this study, a 1H NMR-based metabolomics approach has been applied to investigate the toxicological effects of AR and mechanisms underlied after intragastrical administration (dosing at 0, 0.5 and 1 g/kg body weight) for 30 days on male Sprague-Dawley rats. The 1H NMR profiles were analyzed by multivariate pattern recognition techniques to denote metabolic variations induced by Arisaematis Rhizoma:13 metabolites in urine were significantly altered in AR treated rats, suggesting disturbances in energy metabolism, perturbation on gut microflora environment, membrane damage, folate deficiency and injury of kidney. Oral administration of crude AR generally was found to be generally safe, with only slight renal toxicity. Therefore, precautions should be paid to monitor the potential nephrotoxicity of AR in its clinical use. Metabonomics approach provided a promising tool for the study and better understanding of TCM-induced toxicity dynamically and holistically.
Ricin, a large, water soluble toxic glycoprotein, is distributed majorly in the kernels of castor beans (the seeds of Ricinus communis L.) and has been used in traditional Chinese medicine (TCM) or other folk remedies throughout the world. The toxicity of crude ricin (CR) from castor bean kernels was investigated for the first time using an NMR-based metabolomic approach complemented with histopathological inspection and clinical chemistry. The chronic administration of CR could cause kidney and lung impairment, spleen and thymus dysfunction and diminished nutrient intake in rats. An orthogonal signal correction partial least-squares discriminant analysis (OSC-PLSDA) of metabolomic profiles of rat biofluids highlighted a number of metabolic disturbances induced by CR. Long-term CR treatment produced perturbations on energy metabolism, nitrogen metabolism, amino acid metabolism and kynurenine pathway, and evoked oxidative stress. These findings could explain well the CR induced nephrotoxicity and pulmonary toxicity, and provided several potential biomarkers for diagnostics of these toxicities. Such a (1)H NMR based metabolomics approach showed its ability to give a systematic and holistic view of the response of an organism to drugs and is suitable for dynamic studies on the toxicological effects of TCM.
Preclinical Research
Properties related to individual or bioactive compounds that constitute the matrix of pharmaceutical and natural drug products (e.g., essential oils, terpenoids, flavonoids, volatile compounds, and other chemicals) are present at low concentrations (e.g., parts per million or parts per billion). Classical separation, chromatographic, and spectrometric techniques such as high‐performance liquid chromatography, gas chromatography, liquid chromatography, and mass spectrometry have been used for the elucidation of isolated compounds in research and development ( R&D ) of drugs and pharmaceutical products. Hence, the use of standard separation, chromatographic, and spectrometric methods were found useful for fingerprinting and comparing natural and synthetic samples, as well as for identifying single active compounds. However, these methods are time consuming and require some level of preprocessing of the sample before analysis. Over the last four decades, infrared ( IR ) spectroscopy became one of the most attractive and used methods for analysis of agricultural‐related products and plant materials providing simultaneous, rapid, and nondestructive tool to quantify major constituents in such samples. This review describes the benefits and limitations of IR spectroscopy combined with multivariate data analysis for high‐throughput screening and R&D of natural drugs and pharmaceutical products.
The isolation of the essential oil of whole fresh leaves of Citrus lemon by steam distillation is described. The chemical composition of the oil was investigated by means of Gas-Liquid Chromatography (GLC), Column Chromatography (CC) and coupled Gas Chromatography-Mass Spectrometry (GC-MS). The 27 most important volatile components were identified. The volatile components were identified by comparing their retention times of GC chromatograph with those of literature. Further identification was done by GC- MS. The components of the oil, percentage of each constituent, their RI values and their Eight Peak Index were also summarized and reviewed with standard available literature.
Forty-day-old Atriplex halimus seedlings were treated with either NaCl (50, 300 and 550 mM) for the subsequent 30 days or PEG for the following 3, 6 and 10 days. Shoot fresh and dry weights were significantly increased by 50 mM NaCl; nevertheless, the other concentrations had no effect. However, the growth was reduced by drought only after 10 days. Meanwhile, Na+ was accumulated in treated plants; the magnitude of accumulation was highest with high NaCl concentration or PEG for 10 days. The metabolite profiles showed discrimination particularly up-regulation of the amino acids proline, valine, isoleucine, and methionine. Moreover, the macro analysis revealed that NaCl- and PEG-treated plants shared 10 % of the metabolites in the positive mode, however, 87 % were unique to NaCl and 46 % were unique to PEG whereas in the negative mode, 8 % were in share while 90 or 53 % were restricted to NaCl or PEG, respectively. Additionally, sucrose in particular was significantly increased up to threefold and fivefold by 300 and 550 mM NaCl, respectively and up to 2.5-fold by drought for 10 days, nevertheless, the other sugar fractions remained largely unchanged. Also, proline was significantly increased by only the high NaCl concentrations and the long-term drought, nonetheless, the other treatments led, if any, to decreases. These results conclude that NaCl affects the metabolite profiles more than PEG and these metabolites might contribute to osmotic adjustments to act as osmoprotectants rather than osmolytes. These changes of metabolomics might function in many resistance and stress responses.
Clostridium thermocellum is a thermophilic bacterium that converts biomass to ethanol directly; however, high sensitivity of this bacterium toward ethanol limits its commercial utility. To elucidate the effect of ethanol on the growth of this bacterium a metabolite analysis of C. thermocellum was performed. The hypothesis of the project was that exogenous ethanol alters the metabolite profile of C. thermocellum. For metabolite analysis, capillary electrophoresis-electrospray ionization-mass spectrometry method (CE-ESI-MS) was developed due to highly polar and charged nature of metabolites. To increase the sensitivity of CE-ESI-MS, several parameters at the ESI interface were optimized. The application of 50% isopropanol as a sheath liquid increased sensitivity for metabolite analysis dramatically. Trimethylamine acetate (pH 10) was used as background electrolyte (BGE) due to its ability to separate the structural isomers of glucose phosphate.
For metabolite sample preparation, novel methods for quenching and CE compatible metabolite extraction protocols were developed. Newly developed protocols were applied to metabolite analysis of wild type (WT) and ethanol adapted (EA) strains of C. thermocellum grown in batch cultures. Significant differences were found in key intracellular metabolites such as NAD+ and pyruvic acid. Intracellular concentrations of NAD+ were low in EA cells compared to WT cells and pyruvic acid was only detected in EA cells. To further understand the effect of ethanol on metabolite fluxes, WT and EA cells were grown in increasing concentrations of ethanol and the metabolite profile for each ethanol treatment was obtained. Significant changes were found in intracellular metabolite concentrations. Metabolic data showed that the glycolysis process in WT cells was obstructed due to exogenous ethanol which was evident from accumulation of G6P. On the other hand, no such accumulation of G6P was observed in the EA strain; however pyruvate began to accumulate in EA strain. These changes in intracellular metabolite concentrations due to perturbation of exogenous ethanol supported the hypothesis. Also, this investigation revealed a correlation between ethanol and metabolite profile changes and was able to explain a possible mechanism of growth inhibition of C. thermocellum which will certainly help genetic engineers to develop superior strains of C. thermocellum for commercial cellulosic ethanol production.
Background:
Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.
Objective:
The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.
Methods:
Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.
Results/conclusion:
The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.
Substrates, intermediary metabolites and products, the so called “global metabolite pool”, constitute a living organism’s metabolome. Variations and changes in components of the metabolome reflect adaptation of an organism to its microenvironment, as defined by substrate availability and hormonal milieu, through altered gene expression and through the activation of signaling cascades (Tweeddale et al., 1998). The major regulatory components of cell function, the genome, transcriptome and proteome, ultimately act on the metabolome resulting in the expression of a specific phenotype. The close interactions among these components establish a rationale for integrating functional genomics, proteomics and metabolomics, as a means to study the complete intracellular signal processing apparatus that regulates metabolic adaptation, phenotype and ultimately cellular function (Kell and King, 2000).
Covering: up to 2013Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.
Tolerance of anoxia in maize root tips is greatly improved when seedlings are pretreated with 2 to 4 h of hypoxia. We describe the patterns of protein synthesis during hypoxic acclimation and anoxia. We quantified the incorporation of [35S]methionine into total protein and 262 individual proteins under different oxygen tensions. Proteins synthesized most rapidly under normoxic conditions continued to account for most of the proteins synthesized during hypoxic acclimation, while the production of a very few proteins was selectively enhanced. When acclimated root tips were placed under anoxia, protein synthesis was depressed and no “new” proteins were detected. We present evidence that protein synthesis during acclimation, but not during subsequent anoxia, is crucial for acclimation. The complex and quantitative changes in protein synthesis during acclimation necessitate identification of large numbers of individual proteins. We show that mass spectrometry can be effectively used to identify plant proteins arrayed by two-dimensional gel electrophoresis. Of the 48 protein spots analyzed, 46 were identified by matching to the protein database. We describe the expression of proteins involved in a wide range of cellular functions, including previously reported anaerobic proteins, and discuss their possible roles in adaptation of plants to low-oxygen stress.
We assessed the quantitative performances of a classical method for profiling urinary organic acids: ethyl acetate extraction/oxime-trimethylsilyl derivatization/GC-MS. Twenty-seven acids were quantified on the basis of specific ions in both scan and selected-ion monitoring modes. We found that the tuning of the mass detector severely affects the calibration factors, being critical to achieve quantitative results, and we propose a practical procedure for reproducible tuning. Of seven compounds tested, tropic acid was retained as the internal standard suitable for most of the acids of clinical interest; a second internal standard, 2-ketocaproic acid, was used in quantifying keto-acids. The within-day and total relative standard deviations (CVs), estimated from scan-mode analyses of urine, ranged from 2.6% to 12.7% and from 4.2% to 11.8%, respectively. Curvilinear relationships between analytical response and concentration were observed for most of the acids investigated.
We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: alpha factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu
Systematic gene expression analyses provide comprehensive information about the transcriptional response to different environmental and developmental conditions. With enough gene expression data points, computational biologists may eventually generate predictive computer models of transcription regulation. Such models will require computational methodologies consistent with the behavior of known biological systems that remain tractable. We represent regulatory relationships between genes as linear coefficients or weights, with the "net" regulation influence on a gene's expression being the mathematical summation of the independent regulatory inputs. Test regulatory networks generated with this approach display stable and cyclically stable gene expression levels, consistent with known biological systems. We include variables to model the effect of environmental conditions on transcription regulation and observed various alterations in gene expression patterns in response to environmental input. Finally, we use a derivation of this model system to predict the regulatory network from simulated input/output data sets and find that it accurately predicts all components of the model, even with noisy expression data.
Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
The use of HPLC-ICP-MS for the profiling and quantification of the metabolites of 4-bromoaniline following reversed-phase gradient chromatography is demonstrated. In the 0-8 h post dose sample, which contained the highest concentrations of compound-related material, it was possible to detect at least 16 metabolites of the compound. The methodology described offers the possibility of obtaining metabolite profiles and quantification for drugs and other xenobiotics in biological fluids and excreta without the requirement for radiolabelled tracers.
New technologies, in combination with increasing amounts of plant genome sequence data, have opened up incredible experimental possibilities to identify the total set of chloroplast proteins (the chloroplast proteome) as well as their expression levels and post-translational modifications in a global manner. This is summarized under the term 'proteomics' and typically involves two-dimensional electrophoresis or chromatography, mass spectrometry and bioinformatics. Complemented with nucleotide-based global techniques, proteomics is expected to provide many new insights into chloroplast biogenesis, adaptation and function.
Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of "metabolic phenotypes" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.
To provide a broad analysis of gene expression in developing Arabidopsis seeds, microarrays have been produced that display approximately 2,600 seed-expressed genes. DNA for genes spotted on the arrays were selected from >10,000 clones partially sequenced from a cDNA library of developing seeds. Based on a series of controls, sensitivity of the arrays was estimated at one to two copies of mRNA per cell and cross hybridization was estimated to occur if closely related genes have >70% to 80% sequence identity. These arrays have been hybridized in a series of experiments with probes derived from seeds, leaves, and roots of Arabidopsis. Analysis of expression ratios between the different tissues has allowed the tissue-specific expression patterns of many hundreds of genes to be described for the first time. Approximately 25% of the 2, 600 genes were expressed at ratios > or =2-fold higher in seeds than leaves or roots and 10% at ratios > or =10. Included in this list are a large number of proteins of unknown function, and potential regulatory factors such as protein kinases, phosphatases, and transcription factors. The Arabidopsis arrays were also found to be useful for transcriptional profiling of mRNA isolated from developing oilseed rape (Brassica napus) seeds and expression patterns correlated well between the two species.
Metabolic profiling using gas chromatography-mass spectrometry technologies is a technique whose potential in the field of functional genomics is largely untapped. To demonstrate the general usefulness of this technique, we applied to diverse plant genotypes a recently developed profiling protocol that allows detection of a wide range of hydrophilic metabolites within a single chromatographic run. For this purpose, we chose four independent potato genotypes characterized by modifications in sucrose metabolism. Using data-mining tools, including hierarchical cluster analysis and principle component analysis, we were able to assign clusters to the individual plant systems and to determine relative distances between these clusters. Extraction analysis allowed identification of the most important components of these clusters. Furthermore, correlation analysis revealed close linkages between a broad spectrum of metabolites. In a second, complementary approach, we subjected wild-type potato tissue to environmental manipulations. The metabolic profiles from these experiments were compared with the data sets obtained for the transgenic systems, thus illustrating the potential of metabolic profiling in assessing how a genetic modification can be phenocopied by environmental conditions. In summary, these data demonstrate the use of metabolic profiling in conjunction with data-mining tools as a technique for the comprehensive characterization of a plant genotype.
A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.
Motivation and results:
A relational schema is described for capturing highly parallel gene expression experiments using different technologies. This schema grew out of efforts to build a database for collaborators working on different biological systems and using different types of platforms in their gene expression experiments as well as different types of image quantification software. The tables are conceptually organized into three categories of information: Platform, Experiment (which includes image scanning and quantification), and Data. The strengths of the schema are: (i) integrating information on array elements using a gene index; (ii) describing samples using ontologies; (iii) reducing an experiment to a single RNA source for precise descriptions yet not losing the relationships between experiments done at the same time or for the same project; and (iv) maintaining both raw and processed (e.g. cleansed and normalized) data and recording how the data is processed. The result is a novel schema, which can hold both array and non-array data, is extensible for detailed experimental descriptions that are precise and consistent, and allows for meaningful comparisons of genes between experiments.
Analytical methods for probing plant metabolism are taking on new significance in the era of functional genomics and metabolic engineering. Among the available methods, nuclear magnetic resonance (NMR) spectroscopy is a technique that can provide insights into the integration and regulation of plant metabolism through a combination of in vivo and in vitro measurements. Thus NMR can be used to identify, quantify, and localize metabolites, to define the intracellular environment, and to explore pathways and their operation. We review these applications and their significance from a metabolic perspective. Topics of current interest include applications of NMR to metabolic flux analysis, metabolite profiling, and metabolite imaging. These and other areas are discussed in relation to NMR investigations of intermediary carbon and nitrogen metabolism. We conclude that metabolic NMR has a continuing role to play in the development of a quantitative understanding of plant metabolism and in the characterization of metabolic phenotypes.
We investigated the role of apoptosis (programed cell death) in the pathogenesis of chronic rejection.
Epicardial coronary arteries from cardiac allografts with chronic rejection were examined for apoptosis by the TUNEL assay. Double labeling was carried out using anti-CD3, anti-CD68, and anti-von Willenbrand factor (vWF) monoclonal antibodies. Additional immunostaining was carried using anti-Fas, anti-Fas-L, and anti-Bcl-2 monoclonal antibodies. Apoptosis-associated oligonucleosomal DNA degradation was assessed by DNA agarose gel electrophoresis. The transcription level of apoptosis-related caspase genes were determined using microarrays.
Apoptotic cells (TUNEL+) were detected within the arterial wall and in perivascular areas. Double labeling demonstrated that apoptotic cells included T cells (CD3+), monocyte/macrophages (CD68+), and vascular endothelial cells (VWF+). Numbers and densities of TUNEL+ cells did not correlate with the degree of arterial stenosis. Apoptosis-associated oligonucleosomal DNA degradation was assessed by agarose gel electrophoresis of DNA, which showed DNA fragments of approximately 180 bp and multimers thereof (DNA laddering gel), which are characteristic for DNA fragmentation in apoptotic cells. Microarray analysis demonstrated that the apoptosis related caspases 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, were all transcribed (caspases 8, 9, and 10 were highly up-regulated). These results are consistent with the involvement of apoptosis in chronic rejection. Immunoreactivity for Fas/Fas-L was present at the sites of apoptotic cells. Immunoreactivity for Bcl-2 was present in areas with very few apoptotic cells.
Apoptotic cells include T cells, monocyte/macrophages, and endothelial cells. Apoptosis, likely through the Fas/Fas-L system, is involved in the pathogenesis of chronic rejection in cardiac allografts.
Starvation for amino acids induces Gcn4p, a transcriptional activator of amino acid biosynthetic genes in Saccharomyces cerevisiae. In an effort to identify all genes regulated by Gcn4p during amino acid starvation, we performed cDNA microarray analysis. Data from 21 pairs of hybridization experiments using two different strains derived from S288c revealed that more than 1,000 genes were induced, and a similar number were repressed, by a factor of 2 or more in response to histidine starvation imposed by 3-aminotriazole (3AT). Profiling of a gcn4Delta strain and a constitutively induced mutant showed that Gcn4p is required for the full induction by 3AT of at least 539 genes, termed Gcn4p targets. Genes in every amino acid biosynthetic pathway except cysteine and genes encoding amino acid precursors, vitamin biosynthetic enzymes, peroxisomal components, mitochondrial carrier proteins, and autophagy proteins were all identified as Gcn4p targets. Unexpectedly, genes involved in amino acid biosynthesis represent only a quarter of the Gcn4p target genes. Gcn4p also activates genes involved in glycogen homeostasis, and mutant analysis showed that Gcn4p suppresses glycogen levels in amino acid-starved cells. Numerous genes encoding protein kinases and transcription factors were identified as targets, suggesting that Gcn4p is a master regulator of gene expression. Interestingly, expression profiles for 3AT and the alkylating agent methyl methanesulfonate (MMS) overlapped extensively, and MMS induced GCN4 translation. Thus, the broad transcriptional response evoked by Gcn4p is produced by diverse stress conditions. Finally, profiling of a gcn4Delta mutant uncovered an alternative induction pathway operating at many Gcn4p target genes in histidine-starved cells.
To better understand seed germination, a complex developmental process, we developed a proteome analysis of the model plant Arabidopsis for which complete genome sequence is now available. Among about 1,300 total seed proteins resolved in two-dimensional gels, changes in the abundance (up- and down-regulation) of 74 proteins were observed during germination sensu stricto (i.e. prior to radicle emergence) and the radicle protrusion step. This approach was also used to analyze protein changes occurring during industrial seed pretreatments such as priming that accelerate seed germination and improve seedling uniformity. Several proteins were identified by matrix-assisted laser-desorption ionization time of flight mass spectrometry. Some of them had previously been shown to play a role during germination and/or priming in several plant species, a finding that underlines the usefulness of using Arabidopsis as a model system for molecular analysis of seed quality. Furthermore, the present study, carried out at the protein level, validates previous results obtained at the level of gene expression (e.g. from quantitation of differentially expressed mRNAs or analyses of promoter/reporter constructs). Finally, this approach revealed new proteins associated with the different phases of seed germination and priming. Some of them are involved either in the imbibition process of the seeds (such as an actin isoform or a WD-40 repeat protein) or in the seed dehydration process (e.g. cytosolic glyceraldehyde-3-phosphate dehydrogenase). These facts highlight the power of proteomics to unravel specific features of complex developmental processes such as germination and to detect protein markers that can be used to characterize seed vigor of commercial seed lots and to develop and monitor priming treatments.
As a first step in establishing a proteome database for maize, we have embarked on the identification of the leaf proteins resolved on two-dimensional (2-D) gels. We detected nearly 900 spots on the gels with a pH 4–7 gradient and over 200 spots on the gels with a pH 6–11 gradient when the proteins were visualized with colloidal Coomassie blue. Peptide mass fingerprints for 300 protein spots were obtained with matrix assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometer and 149 protein spots were identified using the protein databases. We also searched the pdbEST databases to identify the leaf proteins and verified 66% of the protein spots that had been identified using the protein databases. Sixty-seven additional protein spots were identified from expressed sequence tags (ESTs). Many abundant leaf proteins are present in multiple spots. Functions of over 50% of the abundant leaf proteins are either unknown or hypothetical. Our results show that EST databases in conjunction with peptide mass fingerprints can be used for identifying proteins from organisms with incomplete genome sequence information.
The broad category of antibiotics encompasses some of the most widely prescribed pharmaceuticals in the world. As is the case with any pharmaceutical, an antibiotic must be characterized in terms of its potency and the presence and quantity of impurities. Additionally, any residue or metabolite that may be present as a result of its use must be monitored. Many capillary electrophoretic techniques have been utilized in the analysis of antibiotics, addressing the various aspects of quantifying, profiling, and monitoring. Some of the more recent applications are summarized in this review article.
Metabolic profiling using gas chromatography‐mass spectrometry technologies is a technique whose potential in the field of functional genomics is largely untapped. To demonstrate the general usefulness of this technique, we applied to diverse plant genotypes a recently developed profiling protocol that allows detection of a wide range of hydrophilic metabolites within a single chromatographic run. For this purpose, we chose four independent potato genotypes characterized by modifications in sucrose metabolism. Using data-mining tools, including hierarchical cluster analysis and principle component analysis, we were able to assign clusters to the individual plant systems and to determine relative distances between these clusters. Extraction analysis allowed identification of the most important components of these clusters. Furthermore, correlation analysis revealed close linkages between a broad spectrum of metabolites. In a second, complementary approach, we subjected wild-type potato tissue to environmental manipulations. The metabolic profiles from these experiments were compared with the data sets obtained for the transgenic systems, thus illustrating the potential of metabolic profiling in assessing how a genetic modification can be phenocopied by environmental conditions. In summary, these data demonstrate the use of metabolic profiling in conjunction with datamining tools as a technique for the comprehensive characterization of a plant genotype.
Arabidopsis amp1 mutants show pleiotropic phenotypes, including altered shoot apical meristems, increased cell proliferation, polycotyly, constitutive photomorphogenesis, early flowering time, increased levels of endogenous cytokinin, and increased cyclin cycD3 expression. We have isolated the AMP1 gene by map-based cloning. The AMP1 cDNA encodes a 706;–amino acid polypeptide with significant similarity to glutamate carboxypeptidases. The AMP1 mRNA was expressed in all tissues examined, with higher expression in roots, stems, inflorescences, and siliques. Microarray analysis identified four mRNA species with altered expression in two alleles of amp1, including upregulation of CYP78A5, which has been shown to mark the shoot apical meristem boundary. The similarity of the AMP1 protein to glutamate carboxypeptidases, and in particular to N-acetyl α-linked acidic dipeptidases, suggests that the AMP1 gene product modulates the level of a small signaling molecule that acts to regulate a number of aspects of plant development, in particular the size of the apical meristem.
To better understand seed germination, a complex developmental process, we developed a proteome analysis of the model plant Arabidopsis for which complete genome sequence is now available. Among about 1,300 total seed proteins resolved in two-dimensional gels, changes in the abundance (up- and down-regulation) of 74 proteins were observed during germination sensu stricto (i.e. prior to radicle emergence) and the radicle protrusion step. This approach was also used to analyze protein changes occurring during industrial seed pretreatments such as priming that accelerate seed germination and improve seedling uniformity. Several proteins were identified by matrix-assisted laser-desorption ionization time of flight mass spectrometry. Some of them had previously been shown to play a role during germination and/or priming in several plant species, a finding that underlines the usefulness of using Arabidopsis as a model system for molecular analysis of seed quality. Furthermore, the present study, carried out at the protein level, validates previous results obtained at the level of gene expression (e.g. from quantitation of differentially expressed mRNAs or analyses of promoter/reporter constructs). Finally, this approach revealed new proteins associated with the different phases of seed germination and priming. Some of them are involved either in the imbibition process of the seeds (such as an actin isoform or a WD-40 repeat protein) or in the seed dehydration process (e.g. cytosolic glyceraldehyde-3-phosphate dehydrogenase). These facts highlight the power of proteomics to unravel specific features of complex developmental processes such as germination and to detect protein markers that can be used to characterize seed vigor of commercial seed lots and to develop and monitor priming treatments.
Tolerance of anoxia in maize root tips is greatly improved when seedlings are pretreated with 2 to 4 h of hypoxia. We describe the patterns of protein synthesis during hypoxic acclimation and anoxia. We quantified the incorporation of [³⁵S]methionine into total protein and 262 individual proteins under different oxygen tensions. Proteins synthesized most rapidly under normoxic conditions continued to account for most of the proteins synthesized during hypoxic acclimation, while the production of a very few proteins was selectively enhanced. When acclimated root tips were placed under anoxia, protein synthesis was depressed and no “new” proteins were detected. We present evidence that protein synthesis during acclimation, but not during subsequent anoxia, is crucial for acclimation. The complex and quantitative changes in protein synthesis during acclimation necessitate identification of large numbers of individual proteins. We show that mass spectrometry can be effectively used to identify plant proteins arrayed by two-dimensional gel electrophoresis. Of the 48 protein spots analyzed, 46 were identified by matching to the protein database. We describe the expression of proteins involved in a wide range of cellular functions, including previously reported anaerobic proteins, and discuss their possible roles in adaptation of plants to low-oxygen stress.
Background. We investigated the role of apoptosis (programed cell death) in the pathogenesis of chronic rejection.
Methods. Epicardial coronary arteries from cardiac allografts with chronic rejection were examined for apoptosis by the TUNEL assay. Double labeling was carried out using anti-CD3, anti-CD68, and anti-von Willenbrand factor (vWF) monoclonal antibodies. Additional immunostaining was carried using anti-Fas, anti-Fas-L, and anti-Bcl-2 monoclonal antibodies. Apoptosis-associated oligonucleosomal DNA degradation was assessed by DNA agarose gel electrophoresis. The transcription level of apoptosis-related caspase genes were determined using microarrays.
Results. Apoptotic cells (TUNEL+) were detected within the arterial wall and in perivascular areas. Double labeling demonstrated that apoptotic cells included T cells (CD3+), monocyte/macrophages (CD68+), and vascular endothelial cells (VWF+). Numbers and densities of TUNEL+ cells did not correlate with the degree of arterial stenosis. Apoptosis-associated oligonucleosomal DNA degradation was assessed by agarose gel electrophoresis of DNA, which showed DNA fragments of approximately 180 bp and multimers thereof (DNA laddering gel), which are characteristic for DNA fragmentation in apoptotic cells. Microarray analysis demonstrated that the apoptosis related caspases 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, were all transcribed (caspases 8, 9, and 10 were highly up-regulated). These results are consistent with the involvement of apoptosis in chronic rejection. Immunoreactivity for Fas/Fas-L was present at the sites of apoptotic cells. Immunoreactivity for Bcl-2 was present in areas with very few apoptotic cells.
Conclusions. Apoptotic cells include T cells, monocyte/macrophages, and endothelial cells. Apoptosis, likely through the Fas/Fas-L system, is involved in the pathogenesis of chronic rejection in cardiac allografts.
This article reviews recent developments in the characterization of antibiotics. Many capillary electrophoretic techniques have been utilized in their analyses, addressing various aspects of quantifying, profiling and monitoring. Sensitive electrochemical and laser-induced fluorescence detection systems have been utilized, demonstrating trace level determinations in clinical settings and in environmental samples. Different sample introduction methods have been explored, enhancing detection sensitivity, or reducing or eliminating sample manipulation prior to injection.
Recent advances in cDNA microarray technology have made it possible to analyze expression of more than 8000 genes. Using this technology, gene expression in the hippocampus containing neurofibrillary tangle-associated lesions from an Alzheimer's disease (AD) patient was compared with expression in the parietal cortex from the same patient that lacked these lesions. We also compared gene expression using a control brain. The top 20 named genes significantly up-regulated or down-regulated only in the AD brain were determined. The most up-regulated gene proved to be calcineurin Aβ mRNA (CAβ). In situ hybridization histochemistry revealed that CAβ was significantly up-regulated in pyramidal neurons of the hippocampus in the AD brain. RT-PCR analysis revealed that CAβ was up-regulated in the hippocampus from two out of three AD brains while there were no changes in three control brains. Our study suggests that CAβ may play a crucial role in the pathophysiological mechanisms in AD.
The marking of a place in a PN may correspond to the state of a device, e.g. a machine is or is not available. This marking can be compared to a Boolean variable. A marking can also be associated with an integer, e.g. the number of parts in the input buffer of a machine. In this second case, the number of tokens may be a large number. This may result in such a large number of reachable markings that a limit is formed for use of PNs. A number of authors studying production systems have modeled a number of parts by a real number, an approximation which generally proves very satisfactory. Why not then in a PN?
The continuous Petri net is a model in which the number of marks in the places are real numbers instead of integers. The motivation is explained in Section 4.1 and the model is presented in Section 4.2. Then, hybrid PNs containing a “discrete part” and a “continuous part” are defined in Section 4.3. Properties of continuous and hybrid PNs are presented in Section 4.4. Finally, Section 4.5 is devoted to a model called extended hybrid PN.
All the models in this chapter are autonomous, i.e., not dependent on time or on the environment.
A review of mass spectrometry in organic chemistry is given, dealing with advances in instrumentation and computer techniques, selected topics in gas-phase ion chemistry, and applications in such fields as biomedicine, natural-product studies, and environmental pollution analysis. Innovative techniques and instrumentation are discussed, along with chromatographic-mass spectrometric on-line computer techniques, mass spectral interpretation and management techniques, and such topics in gas-phase ion chemistry as electron-impact ionization and decomposition, photoionization, field ionization and desorption, high-pressure mass spectrometry, ion cyclotron resonance, and isomerization reactions of organic ions. Applications of mass spectrometry are examined with respect to bio-oligomers and their constituents, biomedically important substances, microbiology, environmental organic analysis, and organic geochemistry.
Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.
Rapid drug metabolite profiling can be achieved using fast chromatographic separation and fast mass spectrometric scanning without compromising the separation efficiency. Fast chromatographic separations of drug and its metabolites can be achieved by eluting from a short narrow-bore guard cartridge column (20 x 2 mm I.D., 3 microns BDS Hypersil C8) at flow-rate of 1.0 ml/min and with a gradient volume greater than 90 column volumes. The need for chromatographic separation is important for automated data dependent multiple-stage mass spectrometry (MSn) experimentation. The total analysis time of 8 min permits profiling of metabolites in a 96-well plate in 13 h. The narrow chromatographic peaks resulting from the high flow-rate require the use of a mass spectrometer capable of fast scan speed due to the need to perform multiple MS experiments within the same chromatographic analysis. A method has been developed for screening potentially biologically active in vitro microsomal metabolites by affinity binding with a receptor. After separation by centrifugal ultrafiltration, the bound ligands are released and characterized by LC-MS. In vitro microsomal metabolites of tamoxifen, raloxifene and adatanserin were screened for potential biological activity using this method. The in vitro metabolites of tamoxifen captured by the receptor include N-demethyltamoxifen and three species of hydroxytamoxifen; these data are consistent with those from a conventional binding study and bioassay. In addition, both hydroxyraloxifene and dihydroxyraloxifene are also recognized by the receptor. The specificity of the molecular recognition process is illustrated by the absence of binding with control microsomal incubate and with adatanserin and its metabolites. Therefore, active metabolites can be rapidly profiled by fast LC, automated MSn, and receptor binding. This information can be obtained quickly and can add value to the drug discovery process.
Retigabine (D-23129, N-(2-amino-4-(4-fluorobenzylamino)-phenyl) carbamic acid ethyl ester) is a potent anticonvulsant in a variety of animal models. Rats metabolized [14C]retigabine mainly through glucuronidation and acetylation reactions. Glucuronides were detected in incubates with liver microsomes or slices, in plasma, and in bile and feces but were absent in urine (0-24 h) that contained about 2% of the dose as retigabine and approximately 29% of the dose in > 20 metabolites, which are derived mainly from acetylation reactions. About 67% of the radioactivity was excreted into feces, approximately 10% of the dose as glucuronide. The metabolite pattern in the urine (0-24 h) of dogs was comparatively simple in that retigabine (13%), retigabine-N-glucuronide (5%), and retigabine-N-glucoside (1%) were present. In the same 24-h interval, about 39% of unchanged retigabine was excreted into feces. Plasma profiling and spectroscopic analysis (liquid chromatography with tandem mass spectrometry NMR) of two isolated urinary metabolites obtained after single oral dosing of 600 mg retigabine in healthy volunteers indicated that both acetylation and glucuronidation are major metabolic pathways of retigabine in humans. We found that in vitro assays with liver slices from rat and humans reveal the major circulating metabolites in vivo.
An extensive proteomic approach relies on the possibility to visualize and analyze various types of proteins, including hydrophobic proteins which are rarely detectable on two-dimensional electrophoresis (2-DE) gels. In this study, two methods were employed for the purification of hydrophobic proteins from Arabidopsis thaliana leaf plasma membrane (PM) model plants, prior to analysis on 2-DE immobilized pH gradient (IPG) gels. Solubilization efficiency of two detergents, (3-[(3-cholomidopropyl)-1-propanesulfonic acid (CHAPS) and C8phi, were tested for the recovery of hydrophobic proteins. An immunological approach was used to determine the efficiency of the above methods. Fractionation of proteins by Triton X-114 combined with solubilization with CHAPS resulted in the inability to detect hydrophobic proteins on 2-DE gels. The use of C8phi for protein solubilization did not improve this result. On the contrary, after treatment of membranes with alkaline buffer, the solubilization of PM proteins with detergent C8phi permitted the recovery of such proteins on 2-DE gels. The combination of membrane washing and the use of zwitterionic detergent resulted in the resolution of several integral proteins and the disappearance of peripheral proteins. In the resolution of expressed genome proteins, both large pH gradients in the first dimension and various acrylamide concentrations in the second dimension must be used. Notwithstanding, it is important to combine various sample treatments and different detergents in order to resolve soluble and hydrophobic proteins.
Liang, Fuhrman and Somogyi (PSB98, 18-29, 1998) have described an algorithm for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Their results of computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient in order to infer the original Boolean network correctly. This paper gives a mathematical proof for their observation. Precisely, this paper devises a much simpler algorithm for the same problem and proves that, if the indegree of each node (i.e., the number of input nodes to each node) is bounded by a constant, only O(log n) state transition pairs (from 2n pairs) are necessary and sufficient to identify the original Boolean network of n nodes correctly with high probability. We made computational experiments in order to expose the constant factor involved in O(log n) notation. The computational results show that the Boolean network of size 100,000 can be identified by our algorithm from about 100 INPUT/OUTPUT pairs if the maximum indegree is bounded by 2. It is also a merit of our algorithm that the algorithm is conceptually so simple that it is extensible for more realistic network models.
Metabolite profiling is one of the most challenging fields in applied mass spectrometry. Mass spectrometry was used to characterize the metabolites of propranolol, a beta-adrenergic receptor antagonist containing numerous oxidation sites. Propranolol is extensively metabolized, with most metabolites appearing in urine. Urine samples were collected from young adult male Sprague-Dawley rats. Structural identification of various metabolites was performed by LC/MS/MS, using a PE SCIEX triple quadrupole instrument (PE SCIEX API 3000). Metabolites were itemized using several LC/MS/MS techniques, including Q3 full scan and precursor and constant neutral loss experiments. A looped experiment technique revealed the presence of mono- and di-hydroxylated metabolites as well as regio isomers of hydroxy- and dihydroxy-propranolol glucuronides and propranolol glucuronic acid. Propranolol glucuronide was not observed, while the presence of dealkylated metabolites was suggested but not confirmed.
We report a GC/NICI-MS assay and a LC/ESI-MS/MS assay for the analysis of N-acetylcysteine (NAC) conjugates of (E)-2,4-diene VPA (NAC I and NAC II) identified in humans. The assay also includes the analysis of the NAC conjugate of 4,5-epoxy VPA (NAC III), an identified metabolite in rats treated with 4-ene VPA for its use in metabolic studies in animals. The highly sensitive GC/MS assay was designed to monitor selectively the diagnostic and most abundant [M - 181](-) fragment anion of the di-PFB derivatives of NAC I, NAC II, and NAC IV, the internal standard (IS) and the PFB derivative of NAC III. The higher selectivity of LC/MS/MS methodology was the basis for an assay which could identify and quantitate the underivatized conjugates simultaneously using MRM of the diagnostic ions m/z 130 and 123 arising from the CID of their protonated molecular ions [MH](+). The GC/MS assay employed liquid-liquid extraction whereas the LC/MS/MS assay used a solid-phase extraction procedure. Linearity ranges of the calibration curves were 0.10-5.0microg ml(-1) by GC/MS and 0.10-1.0microg ml(-1) by LC/MS/MS for NAC I, NAC II and NAC III (r(2) = 0.999 or better). Both assays were validated for NAC I and NAC II and provided good inter- and intra-assay precision and accuracy for NAC I and NAC II. The LOQ by LC/MS/MS was 0.1microg ml(-1), representing 1 ng of NAC I and NAC II. The same LOQ (0.1microg ml(-1)) was observed by GC/MS and was equivalent to 100 pg of each metabolite. NAC III was detected at concentrations as low as 0.01 microg ml(-1) by both methods. The total urinary excretion of the NAC conjugates in four patients on VPA therapy was determined to be 0.004-0.088% of a VPA dose by GC/MS and 0.004-0. 109% of a VPA dose by LC/MS/MS.
Proteomics, the large-scale analysis of proteins, will contribute greatly to our understanding of gene function in the post-genomic era. Proteomics can be divided into three main areas: (1) protein micro-characterization for large-scale identification of proteins and their post-translational modifications; (2) 'differential display' proteomics for comparison of protein levels with potential application in a wide range of diseases; and (3) studies of protein-protein interactions using techniques such as mass spectrometry or the yeast two-hybrid system. Because it is often difficult to predict the function of a protein based on homology to other proteins or even their three-dimensional structure, determination of components of a protein complex or of a cellular structure is central in functional analysis. This aspect of proteomic studies is perhaps the area of greatest promise. After the revolution in molecular biology exemplified by the ease of cloning by DNA methods, proteomics will add to our understanding of the biochemistry of proteins, processes and pathways for years to come.
Modeling genetic networks and metabolic networks is an important topic in bioinformatics. We propose a qualitative network model which is a combination of the Boolean network and qualitative reasoning, where qualitative reasoning is a kind of reasoning method well-studied in Artificial Intelligence. We also present algorithms for inferring qualitative networks from time series data and an algorithm for inferring S-systems (synergistic and saturable systems) from time series data, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems.
Previous papers in our Molecular Medicine series have described how the many tools of the molecular biologist are being used to develop practical bedside applications of modern molecular biology. We have discussed molecular diagnostics, gene therapy, and applications in clinical genetics. In this paper we discuss DNA microarray technology which provides a genome-wide profile of gene expression. We then describe some current and potential clinical applications in the disease diagnosis, prognosis and treatment. Particularly exciting is the potential of DNA array technology to provide individualized treatment for a wide variety of clinical conditions.
Using a cDNA microarray, we compared the expression of approximately 8000 genes between two unique, clonally related T cell lines derived from different stages of a progressive T cell lymphoma involving skin. A total of 180 genes was found to be differentially expressed at the RNA level by a factor of fivefold or greater. Compared with the cells from the earlier, clinically indolent stage of the lymphoma, 56 genes were up-regulated, whereas 124 genes were down-regulated in the cells from the advanced, clinically aggressive stage lymphoma. The functions of approximately 65% of these genes are currently unknown. The 22 genes with a known function that were up-regulated in the advanced lymphoma cells included several genes involved in promotion of cell proliferation and survival as well as drug resistance. The 42 functionally characterized genes that were down-regulated in the advanced lymphoma cells included negative regulators of cell activation and cell cycle, and mediators of cell adhesion, apoptosis, and genome integrity. The differential expression identified by the cDNA microarray analysis was confirmed for selected genes by reverse transcription-polymerase chain reaction and Northern blotting. The identified differences in gene expression may be related to the differences in behavior between the early and advanced stages of the T cell lymphoma and point to directions for further investigations into mechanisms of lymphoma progression.
Motivation:
Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied.
Results:
We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure. In general, this algorithm guarantees to eventually find the globally optimal distribution of genes over clusters. We introduce an iterative scheme that serves to evaluate quantitatively the optimal number of clusters for each specific data set. The scheme is based on standard approaches used in regular statistical tests. The basic idea is to organize the search of the optimal number of clusters simultaneously with the optimization of the distribution of genes over clusters. The efficiency of the proposed algorithm has been evaluated by means of a reverse engineering experiment, that is, a situation in which the correct distribution of genes over clusters is known a priori. The employment of this statistically rigorous test has shown that our algorithm places greater than 90% genes into correct clusters. Finally, the algorithm has been tested on real gene expression data (expression changes during yeast cell cycle) for which the fundamental patterns of gene expression and the assignment of genes to clusters are well understood from numerous previous studies.
Transplantation of dopamine-secreting cells harvested from fetal mesencephalon directly into the striatum has had limited success as a therapy for Parkinson's disease. A major problem is that the majority of the cells die during the first 3 weeks following transplantation. Hypoxia in the tissue surrounding the graft is a potential cause of the cell death. We have used subtractive cDNA libraries and microarray analysis to identify the gene expression profile that regulates tolerance to hypoxia. An improved understanding of the molecular basis of hypoxia-tolerance may allow investigators to engineer cells that can survive in the hypoxic environment of the brain parenchyma following transplantation.
Study of the cell will never be complete unless its dynamic behavior is understood. The complex behavior of the cell cannot be determined or predicted unless a computer model of the cell is constructed and computer simulation is undertaken. Rapid accumulation of biological data from genome, proteome, transcriptome and metabolome projects can bring us to the point where it is no longer purely speculative to discuss how to construct virtual cells in silico. This article describes attempts to construct whole cell models. The E-CELL project has completed a couple of virtual cell models, and computer simulations have revealed some biological surprises.
A decade after the discovery of electrospray and matrix-assisted laser desorption ionization (MALDI), methods that finally allowed gentle ionization of large biomolecules, mass spectrometry has become a powerful tool in protein analysis and the key technology in the emerging field of proteomics. The success of mass spectrometry is driven both by innovative instrumentation designs, especially those operating on the time-of-flight or ion-trapping principles, and by large-scale biochemical strategies, which use mass spectrometry to detect the isolated proteins. Any human protein can now be identified directly from genome databases on the basis of minimal data derived by mass spectrometry. As has already happened in genomics, increased automation of sample handling, analysis, and the interpretation of results will generate an avalanche of qualitative and quantitative proteomic data. Protein-protein interactions can be analyzed directly by precipitation of a tagged bait followed by mass spectrometric identification of its binding partners. By these and similar strategies, entire protein complexes, signaling pathways, and whole organelles are being characterized. Posttranslational modifications remain difficult to analyze but are starting to yield to generic strategies.
The recent development of microarray technologies has made possible the simultaneous measurement of mRNA levels for thousands of genes and a new genomic method termed gene expression profiling. The application of this approach to animal models or post-mortem tissue provides a powerful tool for the discovery of novel genes involved in psychiatric disorders. This approach has strengths that are complementary to those of another genomic method for gene discovery, positional cloning. Microarray technologies and their application to post-mortem tissue and animal models of bipolar disorder are reviewed. A novel approach termed convergent functional genomics, which integrates gene profiling and positional cloning in order to rapidly identify candidate disease genes, is also described.
Gene-specific transcription activators are among the main factors which specifically shape the transcriptome profiles. It is tempting to take advantage of their properties to decipher the genome expression circuitry. The advent of microarray technology has offered fantastic opportunities to quickly analyze the expression profiles dictated by specific transcription factors. This review will first focus on the strategies which have been devised to control the activity of transcription factors and in the second part on the microarray experiments which addressed the role of these transcription factors in the genome-wide expression profile. This last part will mainly consider the case of the yeast Saccharomyces cerevisiae genome. All the collected data are available through the on-line database yTAFNET (http://transcriptome.ens.fr/ytafnet/). yTAFNET is designed to help the characterization of connections between the different yeast regulatory networks.
Significant progress has been made in the past year in understanding the mechanism of systemic acquired resistance. Mitogen-activated protein kinase cascades have been implicated as negative regulators of salicyclic acid accumulation and the induction of resistance. The salicylic acid signal is transduced through NPR1, a nuclear-localized protein that interacts with transcription factors that are involved in regulating salicylic-acid-mediated gene expression. Both promoter analyses and genetic studies have shown that gene expression in systemic acquired resistance requires not only the activation of a transcriptional activator(s) but also inhibition of a transcriptional repressor(s). Microarray experiments have been performed to search for those genes whose expression is transcriptionally regulated during systemic acquired resistance and to identify common promoter elements that control these genes.
As a first step in establishing a proteome database for maize, we have embarked on the identification of the leaf proteins resolved on two-dimensional (2-D) gels. We detected nearly 900 spots on the gels with a pH 4-7 gradient and over 200 spots on the gels with a pH 6-11 gradient when the proteins were visualized with colloidal Coomassie blue. Peptide mass fingerprints for 300 protein spots were obtained with matrix assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometer and 149 protein spots were identified using the protein databases. We also searched the pdbEST databases to identify the leaf proteins and verified 66% of the protein spots that had been identified using the protein databases. Sixty-seven additional protein spots were identified from expressed sequence tags (ESTs). Many abundant leaf proteins are present in multiple spots. Functions of over 50% of the abundant leaf proteins are either unknown or hypothetical. Our results show that EST databases in conjunction with peptide mass fingerprints can be used for identifying proteins from organisms with incomplete genome sequence information.