Central Carbon Metabolism in Candida albicans Biofilms Is Altered by Dimethyl Sulfoxide.

The effect of dimethyl sulfoxide (DMSO) on fungal metabolism has not been well studied. This study aimed to evaluate, by metabolomics, the impact of DMSO on the central carbon metabolism of Candida albicans. Biofilms of C. albicans SC5314 were grown on paper discs, using minimum mineral (MM) medium, in a dynamic continuous flow system. The two experimental conditions were control and 0.03% DMSO (v/v). After 72 h of incubation (37 °C), the biofilms were collected and the metabolites were extracted. The extracted metabolites were subjected to gas chromatography-mass spectrometry (GC/MS). The experiment was conducted using five replicates on three independent occasions. The GC/MS analysis identified 88 compounds. Among the 88 compounds, the levels of 27 compounds were markedly different between the two groups. The DMSO group exhibited enhanced levels of putrescine and glutathione and decreased levels of methionine and lysine. Additionally, the DMSO group exhibited alterations in 13 metabolic pathways involved in primary and secondary cellular metabolism. Among the 13 altered pathways, seven were downregulated and six were upregulated in the DMSO group. These results indicated a differential intracellular metabolic profile between the untreated and DMSO-treated biofilms. Hence, DMSO was demonstrated to affect the metabolic pathways of C. albicans. These results suggest that DMSO may influence the results of laboratory tests when it is used as a solvent. Hence, the use of DMSO as a solvent must be carefully considered in drug research, as the effect of the researched drugs may not be reliably translated into clinical practice.

Assessment of ethanol tolerance of Kluyveromyces marxianus CCT 7735 selected by adaptive laboratory evolution.

Kluyveromyces marxianus CCT 7735 shows potential for producing ethanol from lactose; however, its low ethanol tolerance is a drawback for its industrial application. The first aim of this study was to obtain four ethanol-tolerant K. marxianus CCT 7735 strains (ETS1, ETS2, ETS3, and ETS4) by adaptive laboratory evolution. The second aim was to select among them the strain that stood out and to evaluate metabolic changes associated with the improved ethanol tolerance in this strain. The ETS4 was selected for displaying a specific growth rate higher than the parental strain under ethanol stress (122%) and specific ethanol production rate (0.26 g/g/h) higher than those presented by the ETS1 (0.22 g/g/h), ETS2 (0.17 g/g/h), and ETS3 (0.17 g/g/h) under non-stress condition. Further analyses were performed with the ETS4 in comparison with its parental strain in order to characterize metabolic changes. Accumulation of valine and metabolites of the citric acid cycle (isocitric acid, citric acid, and cis-aconitic acid) was observed only in the ETS4 subjected to ethanol stress. Their accumulation in this strain may have been important to increase ethanol tolerance. Furthermore, the contents of fatty acid methyl esters and ergosterol were higher in the ETS4 than in the parental strain. These differences likely contributed to enhance ethanol tolerance in the ETS4. KEY POINTS: • K. marxianus ethanol-tolerant strains were selected by adaptive laboratory evolution. • Valine and metabolites of the TCA cycle were accumulated in the ETS4. • High contents of fatty acids and ergosterol contributed to enhance ethanol tolerance.

Hair metabolomics: identification of fetal compromise provides proof of concept for biomarker discovery.

Analysis of the human metabolome has yielded valuable insights into health, disease and toxicity. However, the metabolic profile of complex biological fluids such as blood is highly dynamic and this has limited the discovery of robust biomarkers. Hair grows relatively slowly, and both endogenous compounds and environmental exposures are incorporated from blood into hair during growth, which reflects the average chemical composition over several months. We used hair samples to study the metabolite profiles of women with pregnancies complicated by fetal growth restriction (FGR) and healthy matched controls. We report the use of GC-MS metabolite profiling of hair samples for biomarker discovery. Unsupervised statistical analysis showed complete discrimination of FGR from controls based on hair composition alone. A predictive model combining 5 metabolites produced an area under the receiver-operating curve of 0.998. This is the first study of the metabolome of human hair and demonstrates that this biological material contains robust biomarkers, which may lead to the development of a sensitive diagnostic tool for FGR, and perhaps more importantly, to stable biomarkers for a range of other diseases.

An exometabolomics approach to monitoring microbial contamination in microalgal fermentation processes by using metabolic footprint analysis.

The early detection of microbial contamination is crucial to avoid process failure and costly delays in fermentation industries. However, traditional detection methods such as plate counting and microscopy are labor-intensive, insensitive, and time-consuming. Modern techniques that can detect microbial contamination rapidly and cost-effectively are therefore sought. In the present study, we propose gas chromatography-mass spectrometry (GC-MS)-based metabolic footprint analysis as a rapid and reliable method for the detection of microbial contamination in fermentation processes. Our metabolic footprint analysis detected statistically significant differences in metabolite profiles of axenic and contaminated batch cultures of microalgae as early as 3 h after contamination was introduced, while classical detection methods could detect contamination only after 24 h. The data were analyzed by discriminant function analysis and were validated by leave-one-out cross-validation. We obtained a 97% success rate in correctly classifying samples coming from contaminated or axenic cultures. Therefore, metabolic footprint analysis combined with discriminant function analysis presents a rapid and cost-effective approach to monitor microbial contamination in industrial fermentation processes.

Metab: an R package for high-throughput analysis of metabolomics data generated by GC-MS.

MOTIVATION: The Automated Mass Spectral Deconvolution and Identification System (AMDIS) is freeware extensively applied in metabolomics. However, datasets processed by AMDIS require extensive data correction, filtering and reshaping to create reliable datasets for further downstream analysis. Performed manually, these processes are laborious and extremely time consuming. Furthermore, manual corrections increase the chance of human error and can introduce additional technical variability to the data. Thus, an automated pipeline for curating GC-MS data is urgently needed. RESULTS: We present the Metab R package designed to automate the pipeline for analysis of metabolomics GC-MS datasets processed by AMDIS. AVAILABILITY: The Metab package, the AMDIS library and the reference ion library are available at www.metabolomics.auckland.ac.nz/index.php/downloads. CONTACT: k.ruggiero@auckland.ac.nz.

Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity.

MOTIVATION: Metabolomics is one of the most recent omics-technologies and uses robust analytical techniques to screen low molecular mass metabolites in biological samples. It has evolved very quickly during the last decade. However, metabolomics datasets are considered highly complex when used to relate metabolite levels to metabolic pathway activity. Despite recent developments in bioinformatics, which have improved the quality of metabolomics data, there is still no straightforward method capable of correlating metabolite level to the activity of different metabolic pathways operating within the cells. Thus, this kind of analysis still depends on extremely laborious and time-consuming processes. RESULTS: Here, we present a new algorithm Pathway Activity Profiling (PAPi) with which we are able to compare metabolic pathway activities from metabolite profiles. The applicability and potential of PAPi was demonstrated using a previously published data from the yeast Saccharomyces cerevisiae. PAPi was able to support the biological interpretations of the previously published observations and, in addition, generated new hypotheses in a straightforward manner. However, PAPi is time consuming to perform manually. Thus, we also present here a new R-software package (PAPi) which implements the PAPi algorithm and facilitates its usage to quickly compare metabolic pathways activities between different experimental conditions. Using the identified metabolites and their respective abundances as input, the PAPi package calculates pathways' Activity Scores, which represents the potential metabolic pathways activities and allows their comparison between conditions. PAPi also performs principal components analysis and analysis of variance or t-test to investigate differences in activity level between experimental conditions. In addition, PAPi generates comparative graphs highlighting up- and down-regulated pathway activity. AVAILABILITY: These datasets are available in http://www.4shared.com/file/hTWyndYU/extra.html and http://www.4shared.com/file/VbQIIDeu/intra.html. PAPi package is available in: http://www.4shared.com/file/s0uIYWIg/PAPi_10.html CONTACT: s.villas-boas@auckland.ac.nz SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The potential of metabolomics tools in bioremediation studies.

As a post-genomics tool, metabolomics is a young and vibrant field of science in its exponential growth phase. Metabolome analysis has become very popular recently, and novel techniques for acquiring and analyzing metabolomics data continue to emerge that are useful for a variety of biological studies. The bioremediation field has a lot to gain from the advances in this emerging area. Thus, this review article focuses on the potential of various experimental and conceptual approaches developed for metabolomics to be applied in bioremediation research, such as strategies for elucidation of biodegradation pathways using isotope distribution analysis and molecular connectivity analysis, the assessment of mineralization process using metabolic footprinting analysis, and the improvement of the biodegradation process via metabolic engineering. We demonstrate how the use of metabolomics tools can significantly extend and enhance the power of existing bioremediation approaches by providing a better overview of the biodegradation process.

A comparison of direct infusion MS and GC-MS for metabolic footprinting of yeast mutants.

Recent technical advances in mass spectrometry (MS) have propelled this technology to the forefront of methods employed in metabolome analysis. Here, we compare two distinct analytical approaches based on MS for their potential in revealing specific metabolic footprints of yeast single-deletion mutants. Filtered fermentation broth samples were analyzed by GC-MS and direct infusion ESI-MS. The potential of both methods in producing specific and, therefore, discriminant metabolite profiles was evaluated using samples from several yeast deletion mutants grown in batch-culture conditions with glucose as the carbon source. The mutants evaluated were cat8, gln3, ino2, opi1, and nil1, all with deletion of genes involved in nutrient sensing and regulation. From the analysis, we found that both methods can be used to classify mutants, but the classification depends on which metabolites are measured. Thus, the GC-MS method is good for classification of mutants with altered nitrogen regulation as it primarily measures amino acids, whereas this method cannot classify mutants involved in regulation of phospholipids metabolism as well as the direct infusion MS (DI-MS) method. From the analysis, we find that it is possible to discriminate the mutants in both the exponential and stationary growth phase, but the data from the exponential growth phase provide more physiological relevant information. Based on the data, we identified metabolites that are primarily involved in discrimination of the different mutants, and hereby providing a link between high-throughput metabolome analysis, strain classification, and physiology.

Metabolite profiling for analysis of yeast stress response during very high gravity ethanol fermentations.

A laboratory strain and an industrial strain of Saccharomyces cerevisiae were grown at high substrate concentration, so-called very high gravity (VHG) fermentation. Simultaneous saccharification and fermentation (SSF) was applied in a batch process using 280 g/L maltodextrin as carbon source. It was shown that known ethanol and osmotic stress responses such as decreased growth rate, lower viability, higher energy consumption, and intracellular trehalose accumulation occur in VHG SSF for both strains when compared with standard laboratory medium (20 g/L glucose). The laboratory strain was the most affected. GC-MS metabolite profiling was applied for assessing the yeast stress response influence on cellular metabolism. It was found that metabolite profiles originating from different strains and/or fermentation conditions were unique and could be distinguished with the help of multivariate data analysis. Several differences in the metabolic responses to stressing conditions were revealed, particularly the increased energy consumption of stressed cells was also reflected in increased intracellular concentrations of pyruvate and related metabolites.

Biosynthesis of glyoxylate from glycine in Saccharomyces cerevisiae.

Glyoxylate biosynthesis in Saccharomyces cerevisiae is traditionally mainly ascribed to the reaction catalyzed by isocitrate lyase (Icl), which converts isocitrate to glyoxylate and succinate. However, Icl is generally reported to be repressed by glucose and yet glyoxylate is detected at high levels in S. cerevisiae extracts during cultivation on glucose. In bacteria there is an alternative pathway for glyoxylate biosynthesis that involves a direct oxidation of glycine. Therefore, we investigated the glycine metabolism in S. cerevisiae coupling metabolomics data and (13)C-isotope-labeling analysis of two reference strains and a mutant with a deletion in a gene encoding an alanine:glyoxylate aminotransferase. The strains were cultivated on minimal medium containing glucose or galactose, and (13)C-glycine as sole nitrogen source. Glyoxylate presented (13)C-labeling in all cultivation conditions. Furthermore, glyoxylate seemed to be converted to 2-oxovalerate, an unusual metabolite in S. cerevisiae. 2-Oxovalerate can possibly be converted to 2-oxoisovalerate, a key precursor in the biosynthesis of branched-chain amino acids. Hence, we propose a new pathway for glycine catabolism and glyoxylate biosynthesis in S. cerevisiae that seems not to be repressed by glucose and is active under both aerobic and anaerobic conditions. This work demonstrates the great potential of coupling metabolomics data and isotope-labeling analysis for pathway reconstructions.

Intracellular metabolite profiling of Fusarium oxysporum converting glucose to ethanol.

The filamentous fungus Fusarium oxysporum is known for its ability to produce ethanol by simultaneous saccharification and fermentation (SSF) of cellulose. However, the conversion rate is low and significant amounts of acetic acid are produced as a by-product. In this study, the growth characteristics of F. oxysporum were evaluated in a minimal medium using glucose as the sole carbon source in aerobic, anaerobic and oxygen-limited batch cultivations. Under aerobic conditions the maximum specific growth rate was found to be 0.043 h(-1), and the highest ethanol yield (1.66 mol/mol) was found under anaerobic conditions. During the different phases of the cultivations, the intracellular profiles were determined under aerobic and anaerobic conditions. The profiles of the phosphorylated intermediates indicated that there was a high glycolytic flux at anaerobic growth conditions, characterized by high efflux of glyceraldehyde-3-phosphate (G3P) and fructose-6-phosphate (F6P) from the pentose phosphate pathway (PPP) to the Embden-Meyerhof-Parnas (EMP) pathway, resulting in the highest ethanol production under these conditions. The amino acid profile clearly suggests that the TCA cycle was primarily active under aerobic cultivation. On the other hand, the presence of high levels of gamma-amino-n-butyric acid (GABA) under anaerobic conditions suggests a functional GABA bypass and a possible block in the TCA cycle at these conditions.