Microcystis is a freshwater cyanobacterium frequently forming nuisance blooms in the summer months. The genus belongs to the predominant producers of the potent hepatotoxin microcystin. The success of Microcystis and its remarkable resistance to high light conditions are not well understood. Here, we have compared the metabolic response of Microcystis aeruginosa?PCC7806, its microcystin-deficient ?mcyB mutant (Mut) and the cyanobacterial model organism Synechocystis?PCC6803 to high light exposure of 250??mol?photons?m(-2) ?s(-1) using GC/MS-based metabolomics. Microcystis wild type and Mut show pronounced differences in their metabolic reprogramming upon high light. Seventeen per?cent of the detected metabolites showed significant differences between the two genotypes after high light exposure. Whereas the microcystin-producing wild type shows a faster accumulation of glycolate upon high light illumination, loss of microcystin leads to an accumulation of general stress markers such as trehalose and sucrose. The study further uncovers differences in the high light adaptation of the bloom-forming cyanobacterium Microcystis and the model cyanobacterium Synechocystis. Most notably, Microcystis invests more into carbon reserves such as glycogen after high light exposure. Our data shed new light on the lifestyle of bloom-forming cyanobacteria, the role of the widespread toxin microcystin and the metabolic diversity of cyanobacteria.
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.
Every biological organism relies for its proper function on interactions between a multitude of molecular entities like RNA, proteins, and metabolites. The comprehensive measurement and the analysis of all these entities would therefore provide the basis for our functional and mechanistic understanding of most biological processes. Next to their amount and identity, it is most crucial to also gain information about the subcellular distribution and the flux of the measured compounds between the cellular compartments. That is, we want to understand not only the individual functions of cellular components but also their functional implications within the whole organism. While the analysis of macromolecules like DNA, RNA, and proteins is quite established and robust, analytical techniques for small metabolites, which are prone to diffusion and degradation processes, provide a host of unsolved challenges. The major limitations here are the metabolite conversion and relocation processes. In this protocol we describe a methodological workflow which includes a nonaqueous fractionation method, a fractionated two-phase liquid/liquid extraction protocol, and a software package, which together allow extracting and analyzing starch, proteins, and especially polar and lipophilic metabolites from a single sample towards the estimation of their subcellular distributions.
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.
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.
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 ?E 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 ?E m?² sec?¹; 21 °C, 75 ?E m?² sec?¹; 21°C, 300 ?E m?² sec?¹ ; 32°C, 150 ?E 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.
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.
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.
Here we describe an integrative protocol for metabolite extraction and the measurement of three cellular constituents, chlorophyll a, total protein, and glycogen from the same small volume of cyanobacterial cultures that can be used as alternative sample amount parameters for data adjustment in comparative metabolome studies. We conducted recovery experiments to assess the robustness and reproducibility of the measurements obtained for the cellular constituents. Also, we have chosen three profile-intrinsic parameters derived from gas chromatography-mass spectrometry (GC/MS) data in order to test their utility for spectral data adjustment. To demonstrate the relevance of these six parameters, we analyzed three cyanobacteria with greatly different morphologies, comprising a unicellular, a filamentous, and a filamentous biofilm-forming strain. Comparative analysis of GC/MS data from cultures grown under standardized conditions indicated that adjustment of the corresponding metabolite profiles by any of the measured cellular constituents or chosen intrinsic parameters led to similar results with respect to sample cohesion and strain separation. Twenty-one metabolites significantly enriched for the carbohydrate and amine superclasses are mainly responsible for strain separation, with a majority of the remaining metabolites contributing to sample group cohesion. Therefore, we conclude that any of the parameters tested in this study can be used for spectral data adjustment of cyanobacterial strains grown under controlled conditions. However, their use for the differentiation between different stresses or physiological states within a strain remains to be shown. Interestingly, both the adjustment approaches and statistical tests applied effected the detection of metabolic differences and their patterns among the analyzed strains.
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.
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.
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.
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.
Metabolomics is the comprehensive analysis of the small molecules that compose an organisms 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.
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.
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.
Mitochondria are tightly linked to cellular nutrient sensing, and provide not only energy, but also intermediates for the de novo synthesis of cellular compounds including amino acids. Mitochondrial metabolic enzymes as generators and/or targets of signals are therefore important players in the distribution of intermediates between catabolic and anabolic pathways. The highly regulated 2-oxoglutarate dehydrogenase complex (OGDHC) participates in glucose oxidation via the tricarboxylic acid cycle. It occupies an amphibolic branch point in the cycle, where the energy-producing reaction of the 2-oxoglutarate degradation competes with glutamate (Glu) synthesis via nitrogen incorporation into 2-oxoglutarate. To characterize the specific impact of the OGDHC inhibition on amino acid metabolism in both plant and animal mitochondria, a synthetic analog of 2-oxoglutarate, namely succinyl phosphonate (SP), was applied to living systems from different kingdoms, both in situ and in vivo. Using a high-throughput mass spectrometry-based approach, we showed that organisms possessing OGDHC respond to SP by significantly changing their amino acid pools. By contrast, cyanobacteria which lack OGDHC do not show perturbations in amino acids following SP treatment. Increases in Glu, 4-aminobutyrate and alanine represent the most universal change accompanying the 2-oxoglutarate accumulation upon OGDHC inhibition. Other amino acids were affected in a species-specific manner, suggesting specific metabolic rearrangements and substrate availability mediating secondary changes. Strong perturbation in the relative abundance of amino acids due to the OGDHC inhibition was accompanied by decreased protein content. Our results provide specific evidence of a considerable role of OGDHC in amino acid metabolism.
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.
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.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.