The target of rapamycin kinase affects biomass accumulation and cell cycle progression by altering carbon/nitrogen balance in synchronized Chlamydomonas reinhardtii cells

Several metabolic processes tightly regulate growth and biomass accumulation. A highly conserved protein complex containing the target of rapamycin (TOR) kinase is known to integrate intra- and extracellular stimuli controlling nutrient allocation and hence cellular growth. Although several functions of TOR have been described in various heterotrophic eukaryotes, our understanding lags far behind in photosynthetic organisms. In the present investigation, we used the model alga Chlamydomonas reinhardtii to conduct a time-resolved analysis of molecular and physiological features throughout the diurnal cycle after TOR inhibition. Detailed examination of the cell cycle phases revealed that growth is not only repressed by 50%, but also that significant, non-linear delays in the progression can be observed. By using metabolomics analysis, we elucidated that the growth repression was mainly driven by differential carbon partitioning between anabolic and catabolic processes. Accordingly, the time-resolved analysis illustrated that metabolic processes including amino acid-, starch- and triacylglycerol synthesis, as well RNA degradation, were redirected within minutes of TOR inhibition. Here especially the high accumulation of nitrogen-containing compounds indicated that an active TOR kinase controls the carbon to nitrogen balance of the cell, which is responsible for biomass accumulation, growth and cell cycle progression.

Dynamics of lipids and metabolites during the cell cycle of Chlamydomonas reinhardtii

Metabolites and lipids are the final products of enzymatic processes, distinguishing the different cellular functions and activities of single cells or whole tissues. Understanding these cellular functions within a well-established model system requires a systemic collection of molecular and physiological information. In the current report, the green alga Chlamydomonas reinhardtii was selected to establish a comprehensive workflow for the detailed multi-omics analysis of a synchronously growing cell culture system. After implementation and benchmarking of the synchronous cell culture, a two-phase extraction method was adopted for the analysis of proteins, lipids, metabolites and starch from a single sample aliquot of as little as 10-15 million Chlamydomonas cells. In a proof of concept study, primary metabolites and lipids were sampled throughout the diurnal cell cycle. The results of these time-resolved measurements showed that single compounds were not only coordinated with each other in different pathways, but that these complex metabolic signatures have the potential to be used as biomarkers of various cellular processes. Taken together, the developed workflow, including the synchronized growth of the photoautotrophic cell culture, in combination with comprehensive extraction methods and detailed metabolic phenotyping has the potential for use in in-depth analysis of complex cellular processes, providing essential information for the understanding of complex biological systems.

Organization and evolution of brain lipidome revealed by large-scale analysis of human, chimpanzee, macaque, and mouse tissues

Lipids are prominent components of the nervous system. Here we performed a large-scale mass spectrometry-based analysis of the lipid composition of three brain regions as well as kidney and skeletal muscle of humans, chimpanzees, rhesus macaques, and mice. The human brain shows the most distinct lipid composition: 76% of 5,713 lipid compounds examined in our study are either enriched or depleted in the human brain. Concentration levels of lipids enriched in the brain evolve approximately four times faster among primates compared with lipids characteristic of non-neural tissues and show further acceleration of change in human neocortical regions but not in the cerebellum. Human-specific concentration changes are supported by human-specific expression changes for corresponding enzymes. These results provide the first insights into the role of lipids in human brain evolution.

Exceptional evolutionary divergence of human muscle and brain metabolomes parallels human cognitive and physical uniqueness

Accelerated evolution of the human brain and muscle metabolomes reflects our unique cognitive and physical capacities.

Metabolite concentrations reflect the physiological states of tissues and cells. However, the role of metabolic changes in species evolution is currently unknown. Here, we present a study of metabolome evolution conducted in three brain regions and two non-neural tissues from humans, chimpanzees, macaque monkeys, and mice based on over 10,000 hydrophilic compounds. While chimpanzee, macaque, and mouse metabolomes diverge following the genetic distances among species, we detect remarkable acceleration of metabolome evolution in human prefrontal cortex and skeletal muscle affecting neural and energy metabolism pathways. These metabolic changes could not be attributed to environmental conditions and were confirmed against the expression of their corresponding enzymes. We further conducted muscle strength tests in humans, chimpanzees, and macaques. The results suggest that, while humans are characterized by superior cognition, their muscular performance might be markedly inferior to that of chimpanzees and macaque monkeys.

Physiological processes that maintain our tissues' functionality involve the generation of multiple products and intermediates known as metabolites—small molecules with a weight of less than 1,500 Daltons. Changes in concentrations of these metabolites are thought to be closely related to changes in phenotype. Here, we assessed concentrations of more than 10,000 metabolites in three brain regions and two non-neural tissues (skeletal muscle and kidney) of humans, chimpanzees, macaque monkeys, and mice using mass spectrometry-based approaches. We found that the evolution of the metabolome largely reflects genetic divergence between species and is not greatly affected by environmental factors. In the human lineage, however, we observed an exceptional acceleration of metabolome evolution in the prefrontal cortical region of the brain and in skeletal muscle. Based on additional behavioral tests, we further show that metabolic changes in human muscle seem to be paralleled by a drastic reduction in muscle strength. The observed rapid metabolic changes in brain and muscle, together with the unique human cognitive skills and low muscle performance, might reflect parallel mechanisms in human evolution.

Unusual cyanobacterial TCA cycles: not broken just different

As a fundamental energy-conserving process common to all living organisms, respiration is responsible for the oxidation of respiratory substrates to drive ATP synthesis. Accordingly, it has long been accepted that a complete tricarboxylic acid (TCA) cycle is necessary for respiratory energy production. Cyanobacteria, similar to some other prokaryotes, appeared to have an incomplete TCA cycle because they lack the enzyme 2-oxoglutarate dehydrogenase (OGDH). However, it has recently been reported that the cycle can be completed by the action of two alternative enzymes. In this opinion article, we discuss the progress being made to elucidate the nature of the TCA cycles in cyanobacteria and plants and outline open questions concerning the functional significance of this unusual metabolic feature in a broader evolutionary context.

High-resolution plant metabolomics: from mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions

The main goal of metabolomics is the comprehensive qualitative and quantitative analysis of the time- and space-resolved distribution of all metabolites present in a given biological system. Because metabolite structures, in contrast to transcript and protein sequences, are not directly deducible from the genomic DNA sequence, the massive increase in genomic information is only indirectly of use to metabolomics, leaving compound annotation as a key problem to be solved by the available analytical techniques. Furthermore, as metabolites vary widely in both concentration and chemical behavior, there is no single analytical procedure allowing the unbiased and comprehensive structural elucidation and determination of all metabolites present in a given biological system. In this review the different approaches for targeted and non-targeted metabolomics analysis will be described with special emphasis on mass spectrometry-based techniques. Particular attention is given to approaches which can be employed for the annotation of unknown compounds. In the second part, the different experimental approaches aimed at tissue-specific or subcellular analysis of metabolites are discussed including a range of non-mass spectrometry based technologies.

Unraveling retrograde signaling pathways: finding candidate signaling molecules via metabolomics and systems biology driven approaches

A tight coordination of biological processes between cellular compartments and organelles is crucial for the survival of any eukaryotic organism. According to cellular requirements, signals can be generated within organelles, such as chloroplasts and mitochondria, modulating the nuclear gene expression in a process called retrograde signaling. Whilst many research efforts have been focused on dissecting retrograde signaling pathways using biochemical and genetics approaches, metabolomics and systems biology driven studies have illustrated their great potential for hypotheses generation and for dissecting signaling networks in a rather unbiased or untargeted fashion. Recently, integrative genomics approaches, in which correlation analysis has been applied on transcript and metabolite profiling data of Arabidopsis thaliana, revealed the identification of metabolites which are putatively acting as mediators of nuclear gene expression. Complimentary, the continuous technological developments in the field of metabolomics per se has further demonstrated its potential as a very suitable readout to unravel metabolite-mediated signaling processes. As foundation for these studies here we outline and discuss recent advances in elucidating retrograde signaling molecules and pathways with an emphasis on metabolomics and systems biology driven approaches.

Identification of enzyme activity quantitative trait loci in a Solanum lycopersicum x Solanum pennellii introgression line population

Activities of 28 enzymes from central carbon metabolism were measured in pericarp tissue of ripe tomato fruits from field trials with an introgression line (IL) population generated by introgressing segments of the genome of the wild relative Solanum pennellii (LA0716) into the modern tomato cultivar Solanum lycopersicum M82. Enzyme activities were determined using a robotized platform in optimized conditions, where the activities largely reflect the level of the corresponding proteins. Two experiments were analyzed from years with markedly different climate conditions. A total of 27 quantitative trait loci were shared in both experiments. Most resulted in increased enzyme activity when a portion of the S. lycopersicum genome was substituted with the corresponding portion of the genome of S. pennellii. This reflects the change in activity between the two parental genotypes. The mode of inheritance was studied in a heterozygote IL population. A similar proportion of quantitative trait loci (approximately 30%) showed additive, recessive, and dominant modes of inheritance, with only 5% showing overdominance. Comparison with the location of putative genes for the corresponding proteins indicates a large role of trans-regulatory mechanisms. These results point to the genetic control of individual enzyme activities being under the control of a complex program that is dominated by a network of trans-acting genes.

Toward the storage metabolome: profiling the barley vacuole

While recent years have witnessed dramatic advances in our capacity to identify and quantify an ever-increasing number of plant metabolites, our understanding of how metabolism is spatially regulated is still far from complete. In an attempt to partially address this question, we studied the storage metabolome of the barley (Hordeum vulgare) vacuole. For this purpose, we used highly purified vacuoles isolated by silicon oil centrifugation and compared their metabolome with that found in the mesophyll protoplast from which they were derived. Using a combination of gas chromatography-mass spectrometry and Fourier transform-mass spectrometry, we were able to detect 59 (primary) metabolites for which we know the exact chemical structure and a further 200 (secondary) metabolites for which we have strong predicted chemical formulae. Taken together, these metabolites comprise amino acids, organic acids, sugars, sugar alcohols, shikimate pathway intermediates, vitamins, phenylpropanoids, and flavonoids. Of the 259 putative metabolites, some 12 were found exclusively in the vacuole and 34 were found exclusively in the protoplast, while 213 were common in both samples. When analyzed on a quantitative basis, however, there is even more variance, with more than 60 of these compounds being present above the detection limit of our protocols. The combined data were also analyzed with respect to the tonoplast proteome in an attempt to infer specificities of the transporter proteins embedded in this membrane. Following comparison with recent observations made using nonaqueous fractionation of Arabidopsis (Arabidopsis thaliana), we discuss these data in the context of current models of metabolic compartmentation in plants.

High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions

The time-resolved response of Arabidopsis thaliana towards changing light and/or temperature at the transcriptome and metabolome level is presented. Plants grown at 21°C with a light intensity of 150 μE m⁻² sec⁻¹ were either kept at this condition or transferred into seven different environments (4°C, darkness; 21°C, darkness; 32°C, darkness; 4°C, 85 μE m⁻² sec⁻¹; 21 °C, 75 μE m⁻² sec⁻¹; 21°C, 300 μE m⁻² sec⁻¹ ; 32°C, 150 μE m⁻² sec⁻¹). Samples were taken before (0 min) and at 22 time points after transfer resulting in (8×) 22 time points covering both a linear and a logarithmic time series totaling 177 states. Hierarchical cluster analysis shows that individual conditions (defined by temperature and light) diverge into distinct trajectories at condition-dependent times and that the metabolome follows different kinetics from the transcriptome. The metabolic responses are initially relatively faster when compared with the transcriptional responses. Gene Ontology over-representation analysis identifies a common response for all changed conditions at the transcriptome level during the early response phase (5-60 min). Metabolic networks reconstructed via metabolite-metabolite correlations reveal extensive environment-specific rewiring. Detailed analysis identifies conditional connections between amino acids and intermediates of the tricarboxylic acid cycle. Parallel analysis of transcriptional changes strongly support a model where in the absence of photosynthesis at normal/high temperatures protein degradation occurs rapidly and subsequent amino acid catabolism serves as the main cellular energy supply. These results thus demonstrate the engagement of the electron transfer flavoprotein system under short-term environmental perturbations.

A topological map of the compartmentalized Arabidopsis thaliana leaf metabolome

BACKGROUND

The extensive subcellular compartmentalization of metabolites and metabolism in eukaryotic cells is widely acknowledged and represents a key factor of metabolic activity and functionality. In striking contrast, the knowledge of actual compartmental distribution of metabolites from experimental studies is surprisingly low. However, a precise knowledge of, possibly all, metabolites and their subcellular distributions remains a key prerequisite for the understanding of any cellular function.

METHODOLOGY/PRINCIPAL FINDINGS

Here we describe results for the subcellular distribution of 1,117 polar and 2,804 lipophilic mass spectrometric features associated to known and unknown compounds from leaves of the model plant Arabidopsis thaliana. Using an optimized non-aqueous fractionation protocol in conjunction with GC/MS- and LC/MS-based metabolite profiling, 81.5% of the metabolic data could be associated to one of three subcellular compartments: the cytosol (including the mitochondria), vacuole, or plastids. Statistical analysis using a marker-'free' approach revealed that 18.5% of these metabolites show intermediate distributions, which can either be explained by transport processes or by additional subcellular compartments.

CONCLUSION/SIGNIFICANCE

Next to a functional and conceptual workflow for the efficient, highly resolved metabolite analysis of the fractionated Arabidopsis thaliana leaf metabolome, a detailed survey of the subcellular distribution of several compounds, in the graphical format of a topological map, is provided. This complex data set therefore does not only contain a rich repository of metabolic information, but due to thorough validation and testing by statistical methods, represents an initial step in the analysis of metabolite dynamics and fluxes within and between subcellular compartments.

Analysis of the compartmentalized metabolome - a validation of the non-aqueous fractionation technique

With the development of high-throughput metabolic technologies, a plethora of primary and secondary compounds have been detected in the plant cell. However, there are still major gaps in our understanding of the plant metabolome. This is especially true with regards to the compartmental localization of these identified metabolites. Non-aqueous fractionation (NAF) is a powerful technique for the determination of subcellular metabolite distributions in eukaryotic cells, and it has become the method of choice to analyze the distribution of a large number of metabolites concurrently. However, the NAF technique produces a continuous gradient of metabolite distributions, not discrete assignments. Resolution of these distributions requires computational analyses based on marker molecules to resolve compartmental localizations. In this article we focus on expanding the computational analysis of data derived from NAF. Along with an experimental workflow, we describe the critical steps in NAF experiments and how computational approaches can aid in assessing the quality and robustness of the derived data. For this, we have developed and provide a new version (v1.2) of the BestFit command line tool for calculation and evaluation of subcellular metabolite distributions. Furthermore, using both simulated and experimental data we show the influence on estimated subcellular distributions by modulating important parameters, such as the number of fractions taken or which marker molecule is selected. Finally, we discuss caveats and benefits of NAF analysis in the context of the compartmentalized metabolome.

Metabolomic and transcriptomic stress response of Escherichia coli

GC-MS-based analysis of the metabolic response of Escherichia coli exposed to four different stress conditions reveals reduction of energy expensive pathways.

Time-resolved response of E. coli to changing environmental conditions is more specific on the metabolite as compared with the transcript level.

Cease of growth during stress response as compared with stationary phase response invokes similar transcript but dissimilar metabolite responses.

Condition-dependent associations between metabolites and transcripts are revealed applying co-clustering and canonical correlation analysis.

The response of biological systems to environmental perturbations is characterized by a fast and appropriate adjusting of physiology on every level of the cellular and molecular network.

Stress response is usually represented by a combination of both specific responses, aimed at minimizing deleterious effects or repairing damage (e.g. protein chaperones under temperature stress) and general responses which, in part, comprise the downregulation of genes related to translation and ribosome biogenesis. This in turn is reflected by growth cessation or reduction observed under essentially all stress conditions and is an important strategy to adjust cellular physiology to the new condition.

E. coli has been intensively investigated in relation to stress responses. Thus far, however, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. To better understand system response to perturbation, we designed a time-resolved experiment to compare and integrate metabolic and transcript changes of E. coli using four stress conditions including non-lethal temperature shifts, oxidative stress, and carbon starvation relative to cultures grown under optimal conditions covering both states before and directly after stress application, resumption of growth after stress-induced lag phase, and finally the stationary phase.

Metabolic changes occurring after stress application were characterized by a reduction in metabolites of central metabolism (TCA cycle and glycolysis) as well as an increase in free amino acids. Whereas the latter is probably due to protein degradation and stalling of translation, the former supports and extends conclusions based on transcriptome data demonstrating a major decrease in energy-consuming processes as a general stress response. Further comparative analysis of the response on the metabolome and transcriptome, however, revealed in addition to these similarities major differences. Thus, the response on the metabolome displayed a significantly higher specificity towards the specific stress as compared with the transcriptome. Further, when comparing the metabolome of cells ceasing growth due to stress application with cells ceasing growth due to reaching stationary phase the metabolome response differed to a significant extent between both growth arrest phases, whereas the transcriptome response showed significant overlap again, suggesting that the response of E. coli on the metabolome level displays a higher level of significance as compared with the transcriptome level.

Subsequently, both data sets were jointly analyzed using co-clustering and canonical correlation approaches to identify coordinated changes on the transcriptome and the metabolite level indicative metabolite–transcript associations. A first outcome of this study was that no association was preserved during all conditions analyzed but rather condition-specific associations were observed. One set of associations found was between metabolites from the oxidative pentose phosphate pathway such as glc-6-P, 6-P-gluconic acid, ribose-5-P, and E-4-P and metabolites from the glycolytic pathway (3PGA and PEP in addition to glc-6-P with two genes encoding pathway enzymes, that is rpe encoding ribulose phosphate 3-epimerase and pps encoding PEP synthase.

A second example comprises metabolites of the TCA cycle such as pyruvic acid, 2-ketoglutaric acid, fumaric acid, malic acid, and succinic acid and the mqo gene encoding malate-quinone oxidoreductase (MQO). MQO catalyses the irreversible oxidation of malate to oxaloacetate that in turn regulates the activity of citrate synthase, which is a major rate determining enzyme of the TCA cycle. The strong association between mqo gene expression and multiple members of the TCA cycle as well as pyruvate suggest mqo expression to have a major function for the regulation of the TCA cycle, which need to be experimentally validated.

Multiple further associations identified show on the one hand the power of integrative systems oriented approaches for developing new hypothesis, on the other hand their condition-dependent behavior shows the extreme flexibility of the biological systems studied thus requesting a much more intense effort toward parallel analysis of biological systems under several environmental conditions.

Environmental fluctuations lead to a rapid adjustment of the physiology of Escherichia coli, necessitating changes on every level of the underlying cellular and molecular network. Thus far, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. Here, we incorporate the metabolite composition together with gene expression data to provide a more comprehensive insight on system level stress adjustments by describing detailed time-resolved E. coli response to five different perturbations (cold, heat, oxidative stress, lactose diauxie, and stationary phase). The metabolite response is more specific as compared with the general response observed on the transcript level and is reflected by much higher specificity during the early stress adaptation phase and when comparing the stationary phase response to other perturbations. Despite these differences, the response on both levels still follows the same dynamics and general strategy of energy conservation as reflected by rapid decrease of central carbon metabolism intermediates coinciding with downregulation of genes related to cell growth. Application of co-clustering and canonical correlation analysis on combined metabolite and transcript data identified a number of significant condition-dependent associations between metabolites and transcripts. The results confirm and extend existing models about co-regulation between gene expression and metabolites demonstrating the power of integrated systems oriented analysis.

Enzyme activity profiles during fruit development in tomato cultivars and Solanum pennellii

Enzymes interact to generate metabolic networks. The activities of more than 22 enzymes from central metabolism were profiled during the development of fruit of the modern tomato cultivar Solanum lycopersicum 'M82' and its wild relative Solanum pennellii (LA0716). In S. pennellii, the mature fruit remains green and contains lower sugar and higher organic acid levels. These genotypes are the parents of a widely used near introgression line population. Enzymes were also profiled in a second cultivar, S. lycopersicum 'Moneymaker', for which data sets for the developmental changes of metabolites and transcripts are available. Whereas most enzyme activities declined during fruit development in the modern S. lycopersicum cultivars, they remained high or even increased in S. pennellii, especially enzymes required for organic acid synthesis. The enzyme profiles were sufficiently characteristic to allow stages of development and cultivars and the wild species to be distinguished by principal component analysis and clustering. Many enzymes showed coordinated changes during fruit development of a given genotype. Comparison of the correlation matrices revealed a large overlap between the two modern cultivars and considerable overlap with S. pennellii, indicating that despite the very different development responses, some basic modules are retained. Comparison of enzyme activity, metabolite profiles, and transcript profiles in S. lycopersicum 'Moneymaker' revealed remarkably little connectivity between the developmental changes of transcripts and enzymes and even less between enzymes and metabolites. We discuss the concept that the metabolite profile is an emergent property that is generated by complex network interactions.

Combined transcript and metabolite profiling of Arabidopsis grown under widely variant growth conditions facilitates the identification of novel metabolite-mediated regulation of gene expression

Regulation of metabolism at the level of transcription and its corollary metabolite-mediated regulation of transcription are well-documented mechanisms by which plants adapt to circumstance. That said the function of only a minority of transcription factor networks are fully understood and it seems likely that we have only identified a subset of the metabolites that play a mediator function in the regulation of transcription. Here we describe an integrated genomics approach in which we perform combined transcript and metabolite profiling on Arabidopsis (Arabidopsis thaliana) plants challenged by various environmental extremes. We chose this approach to generate a large variance in the levels of all parameters recorded. The data was then statistically evaluated to identify metabolites whose level robustly correlated with those of a particularly large number of transcripts. Since correlation alone provides no proof of causality we subsequently attempted to validate these putative mediators of gene expression via a combination of statistical analysis of data available in publicly available databases and iterative experimental evaluation. Data presented here suggest that, on adoption of appropriate caution, the approach can be used for the identification of metabolite mediators of gene expression. As an exemplary case study we document that in plants, as in yeast (Saccharomyces cerevisiae) and mammals, leucine plays an important role as a regulator of gene expression and provide a leucine response gene regulatory network.

Co-expression tools for plant biology: opportunities for hypothesis generation and caveats

Gene co-expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co-expression analysis asks the question 'what are the genes that are co-expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?'. Genes that are highly co-expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co-expression results, calculation of co-expression scores and P values, and the influence of data sets used for co-expression analysis. Finally, examples from the literature will be presented, wherein co-expression has been used to corroborate and discover various aspects of plant biology.

Assessment of sampling strategies for gas chromatography-mass spectrometry (GC-MS) based metabolomics of cyanobacteria

Metabolomics is the comprehensive analysis of the small molecules that compose an organism's metabolism. The main limiting step in microbial metabolomics is the requirement for fast and efficient separation of microbes from the culture medium under conditions in which metabolism is rapidly halted. In this article we compare three different sampling strategies, quenching, filtering, and centrifugation, for arresting the metabolic activities of two morphologically diverse cyanobacteria, the unicellular Synechocystis sp. PCC 6803 and the filamentous Nostoc sp. PCC 7120 for GC-MS analysis. We demonstrate that each sampling technique produces internally consistent and reproducible data, however, cold methanol-water quenching caused leakage and substantial loss of metabolites from various compound classes, while fast filtering and centrifugation produced quite similar metabolite pool sizes, even for metabolites with predicted high turnover. This indicates that cyanobacterial metabolic pools, as measured by GC-MS, do not show high turnover under standard growing conditions. As well, using stable (13)C labeling we show the biological origin of some of the consistently observed unknown analytes. With the development of these techniques, we establish the basis for broad scale comparative metabolite profiling of cyanobacteria.

Analysis of cytosolic and plastidic serine acetyltransferase mutants and subcellular metabolite distributions suggests interplay of the cellular compartments for cysteine biosynthesis in Arabidopsis

In plants, the enzymes for cysteine synthesis serine acetyltransferase (SAT) and O-acetylserine-(thiol)-lyase (OASTL) are present in the cytosol, plastids and mitochondria. However, it is still not clearly resolved to what extent the different compartments are involved in cysteine biosynthesis and how compartmentation influences the regulation of this biosynthetic pathway. To address these questions, we analysed Arabidopsis thaliana T-DNA insertion mutants for cytosolic and plastidic SAT isoforms. In addition, the subcellular distribution of enzyme activities and metabolite concentrations implicated in cysteine and glutathione biosynthesis were revealed by non-aqueous fractionation (NAF). We demonstrate that cytosolic SERAT1.1 and plastidic SERAT2.1 do not contribute to cysteine biosynthesis to a major extent, but may function to overcome transport limitations of O-acetylserine (OAS) from mitochondria. Substantiated by predominantly cytosolic cysteine pools, considerable amounts of sulphide and presence of OAS in the cytosol, our results suggest that the cytosol is the principal site for cysteine biosynthesis. Subcellular metabolite analysis further indicated efficient transport of cysteine, gamma-glutamylcysteine and glutathione between the compartments. With respect to regulation of cysteine biosynthesis, estimation of subcellular OAS and sulphide concentrations established that OAS is limiting for cysteine biosynthesis and that SAT is mainly present bound in the cysteine-synthase complex.

Analysis of cytosolic and plastidic serine acetyltransferase mutants and subcellular metabolite distributions suggests interplay of the cellular compartments for cysteine biosynthesis in Arabidopsis

In plants, the enzymes for cysteine synthesis serine acetyltransferase (SAT) and O-acetylserine-(thiol)-lyase (OASTL) are present in the cytosol, plastids and mitochondria. However, it is still not clearly resolved to what extent the different compartments are involved in cysteine biosynthesis and how compartmentation influences the regulation of this biosynthetic pathway. To address these questions, we analysed Arabidopsis thaliana T-DNA insertion mutants for cytosolic and plastidic SAT isoforms. In addition, the subcellular distribution of enzyme activities and metabolite concentrations implicated in cysteine and glutathione biosynthesis were revealed by non-aqueous fractionation (NAF). We demonstrate that cytosolic SERAT1.1 and plastidic SERAT2.1 do not contribute to cysteine biosynthesis to a major extent, but may function to overcome transport limitations of O-acetylserine (OAS) from mitochondria. Substantiated by predominantly cytosolic cysteine pools, considerable amounts of sulphide and presence of OAS in the cytosol, our results suggest that the cytosol is the principal site for cysteine biosynthesis. Subcellular metabolite analysis further indicated efficient transport of cysteine, gamma-glutamylcysteine and glutathione between the compartments. With respect to regulation of cysteine biosynthesis, estimation of subcellular OAS and sulphide concentrations established that OAS is limiting for cysteine biosynthesis and that SAT is mainly present bound in the cysteine-synthase complex.

Transcription factors relevant to auxin signalling coordinate broad-spectrum metabolic shifts including sulphur metabolism

A systems approach has previously been used to follow the response behaviour of Arabidopsis thaliana plants upon sulphur limitation. A response network was reconstructed from a time series of transcript and metabolite profiles, integrating complex metabolic and transcript data in order to investigate a potential causal relationship. The resulting scale-free network allowed potential transcriptional regulators of sulphur metabolism to be identified. Here, three sulphur-starvation responsive transcription factors, IAA13, IAA28, and ARF-2 (ARF1-Binding Protein), all of which are related to auxin signalling, were selected for further investigation. IAA28 overexpressing and knock-down lines showed no major morphological changes, whereas IAA13- and ARF1-BP-overexpressing plants grew more slowly than the wild type. Steady-state metabolite levels and expression of pathway-relevant genes were monitored under normal and sulphate-depleted conditions. For all lines, changes in transcript and metabolite levels were observed, yet none of these changes could exclusively be linked to sulphur stress. Instead, up- or down-regulation of the transcription factors caused metabolic changes which in turn affected sulphur metabolism. Auxin-relevant transcription factors are thus part of a complex response pattern to nutrient starvation that serve as coordinators of the metabolic shifts driving sulphur homeostasis rather then as direct effectors of the sulphate assimilation pathway. This study provides the first evidence ever presented that correlates auxin-related transcriptional regulators with primary plant metabolism.

Methods, applications and concepts of metabolite profiling: primary metabolism

In the 1990s the concept of a comprehensive analysis of the metabolic complement in biological systems, termed metabolomics or alternately metabonomics, was established as the last of four cornerstones for phenotypic studies in the post-genomic era. With genomic, transcriptomic, and proteomic technologies in place and metabolomic phenotyping under rapid development all necessary tools appear to be available today for a fully functional assessment of biological phenomena at all major system levels of life. This chapter attempts to describe and discuss crucial steps of establishing and maintaining a gas chromatography/electron impact ionization/ mass spectrometry (GC-EI-MS)-based metabolite profiling platform. GC-EI-MS can be perceived as the first and exemplary profiling technology aimed at simultaneous and non-biased analysis of primary metabolites from biological samples. The potential and constraints of this profiling technology are among the best understood. Most problems are solved as well as pitfalls identified. Thus GC-EI-MS serves as an ideal example for students and scientists who intend to enter the field of metabolomics. This chapter will be biased towards GC-EI-MS analyses but aims at discussing general topics, such as experimental design, metabolite identification, quantification and data mining.

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments

Background

Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis.

Results

Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.

PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.

PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at .

Conclusion

PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Enhancing Vacuolar Sucrose Cleavage Within the Developing Potato Tuber has only Minor Effects on Metabolism

Modification of tuber carbohydrate metabolism by the tuber-specific expression of a yeast invertase targeted to the cytosol or apoplast has previously been demonstrated to have diverse effects on tuber growth and metabolism. In the current study, we generated plants exhibiting tuber-specific expression of the same enzyme targeted to the vacuole. Enzymatic analysis of the carbohydrate levels of the tuber revealed dramatic decreases in sucrose content coupled with large increases in the levels of glucose and hexose phosphates, but unaltered starch content in the transformants. Analysis of the key enzyme of glycolysis suggests that this pathway is down-regulated in the transformants. Despite these changes in metabolite pools and enzyme activity, few consistent changes could be observed in the estimated metabolic fluxes following incubation of isolated tuber discs in labelled glucose. The analysis of the relative levels of a wide range of metabolites using a gas chromatography-mass spectrometry (GC-MS)-based metabolite profiling method revealed large changes in the levels of fructose and decreases in a range of other sugars, but very few changes in the contents of organic and amino acids. This metabolic profile is remarkably consistent with that obtained following expression of the invertase in the apoplastic compartment, providing circumstantial evidence for the endocytotic trafficking of sugars within potato tuber parenchyma. Finally, the results of this study are compared with those from other plant species and the relative roles of the vacuolar isoform of the enzyme are contrasted.

Inferring hypotheses on functional relationships of genes: Analysis of the Arabidopsis thaliana subtilase gene family

The gene family of subtilisin-like serine proteases (subtilases) in Arabidopsis thaliana comprises 56 members, divided into six distinct subfamilies. Whereas the members of five subfamilies are similar to pyrolysins, two genes share stronger similarity to animal kexins. Mutant screens confirmed 144 T-DNA insertion lines with knockouts for 55 out of the 56 subtilases. Apart from SDD1, none of the confirmed homozygous mutants revealed any obvious visible phenotypic alteration during growth under standard conditions. Apart from this specific case, forward genetics gave us no hints about the function of the individual 54 non-characterized subtilase genes. Therefore, the main objective of our work was to overcome the shortcomings of the forward genetic approach and to infer alternative experimental approaches by using an integrative bioinformatics and biological approach. Computational analyses based on transcriptional co-expression and co-response pattern revealed at least two expression networks, suggesting that functional redundancy may exist among subtilases with limited similarity. Furthermore, two hubs were identified, which may be involved in signalling or may represent higher-order regulatory factors involved in responses to environmental cues. A particular enrichment of co-regulated genes with metabolic functions was observed for four subtilases possibly representing late responsive elements of environmental stress. The kexin homologs show stronger associations with genes of transcriptional regulation context. Based on the analyses presented here and in accordance with previously characterized subtilases, we propose three main functions of subtilases: involvement in (i) control of development, (ii) protein turnover, and (iii) action as downstream components of signalling cascades. Supplemental material is available in the Plant Subtilase Database (PSDB) (http://csbdb.mpimp-golm.mpg.de/psdb.html) , as well as from the CSB.DB (http://csbdb.mpimp-golm.mpg.de).

The first complete plant genome sequence was available for Arabidopsis thaliana, a common weed. The number of genes in the Arabidopsis genome is estimated to be around 25,000. The functions of most of these gene are, however, still unknown. Many genes are grouped into gene families due to conserved sequences and predicted protein structures. In this article, the large subtilisin-like serine protease (subtilase) family of Arabidopsis is analysed. Although 56 subtilase genes have been identified in Arabidopsis, the function of only two subtilases is known. Analysis of mutants has revealed no further hints about the function of the other 54 subtilases. Here the authors present a novel approach to infer hypotheses about functions of the subtilase genes using computational analysis. Based on the analyses presented here and in accordance with previously characterized subtilases, they propose three main functions of subtilases: involvement in (i) control of development, (ii) protein degradation, and (iii) signalling. The results presented can be used to direct further analysis to elucidate functions of subtilases in plants.

Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of corresponding genes, and comparison with known responses

MapMan is a user-driven tool that displays large genomics datasets onto diagrams of metabolic pathways or other processes. Here, we present new developments, including improvements of the gene assignments and the user interface, a strategy to visualize multilayered datasets, the incorporation of statistics packages, and extensions of the software to incorporate more biological information including visualization of corresponding genes and horizontal searches for similar global responses across large numbers of arrays.

Identification of brassinosteroid-related genes by means of transcript co-response analyses

The comprehensive systems-biology database (CSB.DB) was used to reveal brassinosteroid (BR)-related genes from expression profiles based on co-response analyses. Genes exhibiting simultaneous changes in transcript levels are candidates of common transcriptional regulation. Combining numerous different experiments in data matrices allows ruling out outliers and conditional changes of transcript levels. CSB.DB was queried for transcriptional co-responses with the BR-signalling components BRI1 and BAK1: 301 out of 9694 genes represented in the nasc0271 database showed co-responses with both genes. As expected, these genes comprised pathway-involved genes (e.g. 72 BR-induced genes), because the BRI1 and BAK1 proteins are required for BR-responses. But transcript co-response takes the analysis a step further compared with direct approaches because BR-related non BR-responsive genes were identified. Insights into networks and the functional context of genes are provided, because factors determining expression patterns are reflected in correlations. Our findings demonstrate that transcript co-response analysis presents a valuable resource to uncover common regulatory patterns of genes. Different data matrices in CSB.DB allow examination of specific biological questions. All matrices are publicly available through CSB.DB. This work presents one possible roadmap to use the CSB.DB resources.

GC-MS libraries for the rapid identification of metabolites in complex biological samples

Gas chromatography-mass spectrometry based metabolite profiling of biological samples is rapidly becoming one of the cornerstones of functional genomics and systems biology. Thus, the technology needs to be available to many laboratories and open exchange of information is required such as those achieved for transcript and protein data. The key-step in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations with composite structure. Collections of mass spectra, which comprise frequently observed identified and non-identified metabolites, represent the most effective means to pool the identification efforts currently performed in many laboratories around the world. Here, we describe a platform for mass spectral and retention time index libraries that will enable this process (MSRI; www.csbdb.mpimp-golm.mpg.de/gmd.html). This resource should ameliorate many of the problems that each laboratory will face both for the initial establishment of metabolome analysis and for its maintenance at a constant sample throughput.

GMD@CSB.DB: the Golm Metabolome Database

Metabolomics, in particular gas chromatography-mass spectrometry (GC-MS) based metabolite profiling of biological extracts, is rapidly becoming one of the cornerstones of functional genomics and systems biology. Metabolite profiling has profound applications in discovering the mode of action of drugs or herbicides, and in unravelling the effect of altered gene expression on metabolism and organism performance in biotechnological applications. As such the technology needs to be available to many laboratories. For this, an open exchange of information is required, like that already achieved for transcript and protein data. One of the key-steps in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations from biological samples. Collections of mass spectra, which comprise frequently observed metabolites of either known or unknown exact chemical structure, represent the most effective means to pool the identification efforts currently performed in many laboratories around the world. Here we present GMD, The Golm Metabolome Database, an open access metabolome database, which should enable these processes. GMD provides public access to custom mass spectral libraries, metabolite profiling experiments as well as additional information and tools, e.g. with regard to methods, spectral information or compounds. The main goal will be the representation of an exchange platform for experimental research activities and bioinformatics to develop and improve metabolomics by multidisciplinary cooperation.

AVAILABILITY

http://csbdb.mpimp-golm.mpg.de/gmd.html

CONTACT

Steinhauser@mpimp-golm.mpg.de

SUPPLEMENTARY INFORMATION

http://csbdb.mpimp-golm.mpg.de/

Hypothesis-driven approach to predict transcriptional units from gene expression data

MOTIVATION

A major issue in computational biology is the reconstruction of functional relationships among genes, for example the definition of regulatory or biochemical pathways. One step towards this aim is the elucidation of transcriptional units, which are characterized by co-responding changes in mRNA expression levels. These units of genes will allow the generation of hypotheses about respective functional interrelationships. Thus, the focus of analysis currently moves from well-established functional assignment through comparison of protein and DNA sequences towards analysis of transcriptional co-response. Tools that allow deducing common control of gene expression have the potential to complement and extend routine BLAST comparisons, because gene function may be inferred from common transcriptional control.

RESULTS

We present a co-clustering strategy of genome sequence information and gene expression data, which was applied to identify transcriptional units within diverse compendia of expression profiles. The phenomenon of prokaryotic operons was selected as an ideal test case to generate well-founded hypotheses about transcriptional units. The existence of overlapping and ambiguous operon definitions allowed the investigation of constitutive and conditional expression of transcriptional units in independent gene expression experiments of Escherichia coli. Our approach allowed identification of operons with high accuracy. Furthermore, both constitutive mRNA co-response as well as conditional differences became apparent. Thus, we were able to generate insight into the possible biological relevance of gene co-response. We conclude that the suggested strategy will be amenable in general to the identification of transcriptional units beyond the chosen example of E.coli operons.

AVAILABILITY

The analyses of E.coli transcript data presented here are available upon request or at http://csbdb.mpimp-golm.mpg.de/