Metabolic profiling of the fission yeast S. pombe: quantification of compounds under different temperatures and genetic perturbation

Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks

Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.

Metabolome analysis of gram-positive bacteria such as Staphylococcus aureus by GC-MS and LC-MS

The field of metabolomics has become increasingly important in the context of functional genomics. Together with other "omics" data, the investigation of the metabolome is an essential part of systems biology. Beside the analysis of human and animal biofluids, the investigation of the microbial physiology by methods of metabolomics has gained increased attention. For example, the analysis of metabolic processes during growth or virulence factor expression is crucially important to understand pathogenesis of bacteria. Common bioanalytical techniques for metabolome analysis include liquid and gas chromatographic methods coupled to mass spectrometry (LC-MS and GC-MS) and spectroscopic approaches such as NMR. In order to achieve metabolome data representing the physiological status of a microorganism, well-verified protocols for sampling and analysis are necessary. This chapter presents a detailed protocol for metabolome analysis of the Gram-positive bacterium Staphylococcus aureus. A detailed manual for cell sampling and metabolite extraction is given, followed by the description of the analytical procedures GC-MS and LC-MS. The advantages and limitations of each experimental setup are discussed. Here, a guideline specified for S. aureus metabolomics and information for important protocol steps are presented, to avoid common pitfalls in microbial metabolome analysis.

AMDORAP: non-targeted metabolic profiling based on high-resolution LC-MS

Background

Liquid chromatography-mass spectrometry (LC-MS) utilizing the high-resolution power of an orbitrap is an important analytical technique for both metabolomics and proteomics. Most important feature of the orbitrap is excellent mass accuracy. Thus, it is necessary to convert raw data to accurate and reliable m/z values for metabolic fingerprinting by high-resolution LC-MS.

Results

In the present study, we developed a novel, easy-to-use and straightforward m/z detection method, AMDORAP. For assessing the performance, we used real biological samples, Bacillus subtilis strains 168 and MGB874, in the positive mode by LC-orbitrap. For 14 identified compounds by measuring the authentic compounds, we compared obtained m/z values with other LC-MS processing tools. The errors by AMDORAP were distributed within ±3 ppm and showed the best performance in m/z value accuracy.

Conclusions

Our method can detect m/z values of biological samples much more accurately than other LC-MS analysis tools. AMDORAP allows us to address the relationships between biological effects and cellular metabolites based on accurate m/z values. Obtaining the accurate m/z values from raw data should be indispensable as a starting point for comparative LC-orbitrap analysis. AMDORAP is freely available under an open-source license at http://amdorap.sourceforge.net/.

Specific biomarkers for stochastic division patterns and starvation-induced quiescence under limited glucose levels in fission yeast

Glucose as a source of energy is centrally important to our understanding of life. We investigated the cell division–quiescence behavior of the fission yeast Schizosaccharomyces pombe under a wide range of glucose concentrations (0–111 mm). The mode of S. pombe cell division under a microfluidic perfusion system was surprisingly normal under highly diluted glucose concentrations (5.6 mm, 1/20 of the standard medium, within human blood sugar levels). Division became stochastic, accompanied by a curious division-timing inheritance, in 2.2–4.4 mm glucose. A critical transition from division to quiescence occurred within a narrow range of concentrations (2.2–1.7 mm). Under starvation (1.1 mm) conditions, cells were mostly quiescent and only a small population of cells divided. Under fasting (0 mm) conditions, division was immediately arrested with a short chronological lifespan (16 h). When cells were first glucose starved prior to fasting, they possessed a substantially extended lifespan (∼14 days). We employed a quantitative metabolomic approach for S. pombe cell extracts, and identified specific metabolites (e.g. biotin, trehalose, ergothioneine, S-adenosyl methionine and CDP-choline), which increased or decreased at different glucose concentrations, whereas nucleotide triphosphates, such as ATP, maintained high concentrations even under starvation. Under starvation, the level of S-adenosyl methionine increased sharply, accompanied by an increase in methylated amino acids and nucleotides. Under fasting, cells rapidly lost antioxidant and energy compounds, such as glutathione and ATP, but, in fasting cells after starvation, these and other metabolites ensuring longevity remained abundant. Glucose-starved cells became resistant to 40 mm H₂O₂ as a result of the accumulation of antioxidant compounds.

Potential role of metabolomics apporoaches in the area of traditional Chinese medicine: as pillars of the bridge between Chinese and Western medicine

Traditional Chinese medicine (TCM) is a holistic approach to health that attempts to bring the body, mind and spirit into harmony. Entering 21st century, TCM is getting more and more popular in the whole world for improving health condition of human beings and preventing or healing diseases, especially shows great advantages in early intervention, combination therapies and personalized medicine, etc. However, like almost all other ethnopharmacology, TCM also faces severe challenges and suffers from insufficient modern research owing to lack of scientific and technologic approaches, restricts the development of TCM in the world. Fortunately, a novel analytical technique, metabolomics (or metabonomics), adopts a 'top-down' strategy to reflect the function of organisms from terminal symptoms of metabolic network and understand metabolic changes of a complete system caused by interventions in holistic context. Its property consists with the holistic thinking of TCM, may beneficially provide an opportunity to scientifically express the meaning of evidence-based Chinese medicine, such as Chinese medicine syndromes (CMS), preventive treatment, action of Chinese medicine, Chinese medical formulae (CMF) and acupuncture efficacy. This review summarizes potential applications of robust metabolomics apporoaches in the area of traditional oriental medicine, and highlights the key role of metabolomics to resolve special TCM issues.

MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

Background

Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.

Results

A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSⁿ data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms.

Conclusions

MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

Iron-dependent remodeling of fungal metabolic pathways associated with ferrichrome biosynthesis

The fission yeast Schizosaccharomyces pombe excretes and accumulates the hydroxamate-type siderophore ferrichrome. The sib1(+) and sib2(+) genes encode, respectively, a siderophore synthetase and an l-ornithine N(5)-oxygenase that participate in ferrichrome biosynthesis. In the present report, we demonstrate that sib1(+) and sib2(+) are repressed by the GATA-type transcriptional repressor Fep1 in response to high levels of iron. We further found that the loss of Fep1 results in increased ferrichrome production. We showed that a sib1Delta sib2Delta mutant strain exhibits a severe growth defect on iron-poor media. We determined that two metabolic pathways are involved in biosynthesis of ornithine, an obligatory precursor of ferrichrome. Ornithine is produced by hydrolysis of arginine by the Car1 and Car3 proteins. Although car3(+) was constitutively expressed, car1(+) transcription levels were repressed upon exposure to iron, with a concomitant decrease of Car1 arginase activity. Ornithine is also generated by transformation of glutamate, which itself is produced by two separate biosynthetic pathways which are transcriptionally regulated by iron in an opposite fashion. In one pathway, the glutamate dehydrogenase Gdh1, which produces glutamate from 2-ketoglutarate, was repressed under iron-replete conditions in a Fep1-dependent manner. The other pathway involves two coupled enzymes, glutamine synthetase Gln1 and Fe-S cluster-containing glutamate synthase Glt1, which were both repressed under iron-limiting conditions but were expressed under iron-replete conditions. Collectively, these results indicate that under conditions of iron deprivation, yeast remodels metabolic pathways linked to ferrichrome synthesis in order to limit iron utilization without compromising siderophore production and its ability to sequester iron from the environment.

Synergistic roles of the proteasome and autophagy for mitochondrial maintenance and chronological lifespan in fission yeast

Regulations of proliferation and quiescence in response to nutritional cues are important for medicine and basic biology. The fission yeast Schizosaccharomyces pombe serves as a model, owing to the shift of proliferating cells to the metabolically active quiescence (designate G0 phase hereafter) by responding to low nitrogen source. S. pombe G0 phase cells keep alive for months without growth and division. Nitrogen replenishment reinstates vegetative proliferation phase (designate VEG). Some 40 genes required for G0 maintenance were identified, but many more remain to be identified. We here show, using mutants, that the proteasome is required for maintaining G0 quiescence. Functional outcomes of proteasome in G0 and VEG phases appear to be distinct. Upon proteasome dysfunction, a number of antioxidant proteins and compounds responsive to ROS (reactive oxygen species) are produced. In addition, autophagy-mediated destruction of mitochondria occurs, which suppresses the loss of viability by eliminating ROS-generating mitochondria. These defensive responses are found in G0 but not in VEG, suggesting that the main function of proteasome in G0 phase homeostasis is to minimize ROS. Proteasome and autophagy are thus collaborative to support the lifespan of S. pombe G0 phase.