Metabolomic Analysis Reveals Contributions of Citric and Citramalic Acids to Rare Earth Bioleaching by a Paecilomyces Fungus.

Conventional methods for extracting rare earth elements from monazite ore require high energy inputs and produce environmentally damaging waste streams. Bioleaching offers a potentially more environmentally friendly alternative extraction process. In order to better understand bioleaching mechanisms, we conducted an exo-metabolomic analysis of a previously isolated rare earth bioleaching fungus from the genus Paecilomyces (GenBank accession numbers KM874779 and KM 874781) to identify contributions of compounds exuded by this fungus to bioleaching activity. Exuded compounds were compared under two growth conditions: growth with monazite ore as the only phosphate source, and growth with a soluble phosphate source (K2HPO4) added. Overall metabolite profiling, in combination with glucose consumption and biomass accumulation data, reflected a lag in growth when this organism was grown with only monazite. We analyzed the relationships between metabolite concentrations, rare earth solubilization, and growth conditions, and identified several metabolites potentially associated with bioleaching. Further investigation using laboratory prepared solutions of 17 of these metabolites indicated statistically significant leaching contributions from both citric and citramalic acids. These contributions (16.4 and 15.0 mg/L total rare earths solubilized) accounted for a portion, but not all, of the leaching achieved with direct bioleaching (42 ± 15 mg/L final rare earth concentration). Additionally, citramalic acid released significantly less of the radioactive element thorium than did citric acid (0.25 ± 0.01 mg/L compared to 1.18 ± 0.01 mg/L), suggesting that citramalic acid may have preferable leaching properties for a monazite bioleaching process

Bioinformatics methods for metabolomics based biomarker detection in functional genomics studies

The biochemical and physiological functions of a large proportion of the approximately 27,000 protein-encoding genes in the Arabidopsis genome is experimentally undetermined using sequence homology techniques alone. This thesis presents a set of bioinformatics resources including a software platform for data visualization and data analysis that address the key issues in incorporating the metabolomics data for functional genomics studies. Multiple mass spectrometry based metabolomics platforms are combined together to get better coverage of the metabolome. Different strategies for integrating the metabolomics abundance data from multiple platforms are compared to find the ideal method for biomarker discovery. A new method of putatively identifying unknown metabolites by first order partial correlation networks is proposed that uses the existing data to incorporate structurally unknown metabolites. A comprehensive study of 70 single gene knock mutants vs. wild type samples is performed using Random Forest machine learning algorithm and a biomarker database for each of the 70 mutations is built with the key metabolites including the putative identifications of unknown metabolites. A proof-of-concept analysis on the oxoprolinase (oxp1) and gamma-glutamyl transpeptidase (ggt1 and ggt2) single gene knock-out mutants in the glutathione degradation (GSH) pathway of the Arabidopsis confirms the known biology that OXP1 is responsible for conversion of 5-oxoproline (5-OP) to glutamic acid. In addition, ggt1/ggt2 analysis supports the hypothesis that the GGT genes may not be major contributors for the 5-OP production. Also, the lack of biochemical changes in ggt2 mutation supports the previous studies of its low level expression in leaf tissues. The metabolomics database, the biomarker database and the data mining tools are implemented in a web based software suite at www.plantmetabolomics.org

Menthol, a unique urinary volatile compound, is associated with chronic inflammation in interstitial cystitis.

Chronic inflammation is a potential systemic risk factor for many bladder dysfunctions, including interstitial cystitis (IC). However, the underlying mechanism through which a healthy bladder protects itself from inflammatory triggers remains unknown. In this study, we identified odor compounds in urine obtained from IC patients and healthy controls. Using comprehensive solid-phase microextraction-gas chromatography-time-of-flight-mass spectrometry (SPME-GC-TOF-MS) profiling and bioinformatics, we found that levels of urinary volatile metabolites, such as menthol, were significantly reduced in IC patients, compared to healthy controls. In an attempt to understand the mechanistic meaning of our volatile metabolites data and the role of menthol in the immune system, we performed two independent experiments: (a) cytokine profiling, and (b) DNA microarray. Our findings suggest that lipopolysaccharide (LPS)-stimulated inflammatory events, such as the production and secretion of inflammatory cytokines (e.g., TNF-α, IL-6, and IL-1β) and the activation of NF-κB and associated proteins within a large signaling network (e.g., Akt, TLR1, TNFAIP3, and NF-κB), are suppressed by the presence of menthol. These findings broaden our knowledge on the role of urinary menthol in suppressing inflammatory events and provide potential new strategies for alleviating both the odor and inflammation associated with IC

Multi-Omics Analyses Detail Metabolic Reprogramming in Lipids, Carnitines, and Use of Glycolytic Intermediates between Prostate Small Cell Neuroendocrine Carcinoma and Prostate Adenocarcinoma.

As the most common cancer in men, prostate cancer is molecularly heterogeneous. Contributing to this heterogeneity are the poorly understood metabolic adaptations of the two main types of prostate cancer, i.e., adenocarcinoma and small cell neuroendocrine carcinoma (SCNC), the latter being more aggressive and lethal. Using transcriptomics, untargeted metabolomics and lipidomics profiling on LASCPC-01 (prostate SCNC) and LNCAP (prostate adenocarcinoma) cell lines, we found significant differences in the cellular phenotypes of the two cell lines. Gene set enrichment analysis on the transcriptomics data showed 62 gene sets were upregulated in LASCPC-01, while 112 gene sets were upregulated in LNCAP. ChemRICH analysis on metabolomics and lipidomics data revealed a total of 25 metabolite clusters were significantly different. LASCPC-01 exhibited a higher glycolytic activity and lower levels of triglycerides, while the LNCAP cell line showed increases in one-carbon metabolism as an exit route of glycolytic intermediates and a decrease in carnitine, a mitochondrial lipid transporter. Our findings pinpoint differences in prostate neuroendocrine carcinoma versus prostate adenocarcinoma that could lead to new therapeutic targets in each type

Recent developments and application of metabolomics in cancer diseases

      Metabolomics studies provide useful information about health and disease status. Metabolite based investigations on various cancers is a powerful approach to diagnosis, prognosis and therapy of cancer diseases. Recently by using advanced analytical techniques such as NMR and MS and its hyphenation methods, global metabolic profiling of diseases has been possible. It is predictable that international contributions and software developments in the future will lead to accurate instrumental analysis based on  a large number of  human samples that finally will improve validation methods and reach this field from the research phase to the clinical phase. In this review, we also discussed the latest developments in analytical methods, application of data analysis, investigation of useful databases and the curent application of metabolomics in cancer diseases that have led to the identification of related biomarkers. In continuation, we listed biomarkers involved in cancer diseases that have been published during recent years.

Making It Last: Storage Time and Temperature Have Differential Impacts on Metabolite Profiles of Airway Samples from Cystic Fibrosis Patients.

Metabolites of human or microbial origin have the potential to be important biomarkers of the disease state in cystic fibrosis (CF). Clinical sample collection and storage conditions may impact metabolite abundances with clinical relevance. We measured the change in metabolite composition based on untargeted gas chromatography-mass spectrometry (GC-MS) when CF sputum samples were stored at 4°C, -20°C, or -80°C with one or two freeze-thaw cycles. Daily measurements were taken for 1 week and then weekly for 4 weeks (4°C) and 8 weeks (-20°C). The metabolites in samples stored at -20°C maintained abundances similar to those found at-80°C over the course of 8 weeks (average change in Bray-Curtis distance, 0.06 ± 0.04) and were also stable after one or two freeze-thaw cycles. However, the metabolite profiles of samples stored at 4°C shifted after 1 day and continued to change over the course of 4 weeks (average change in Bray-Curtis distance, 0.31 ± 0.12). The abundances of several amino acids and other metabolites increased with time of storage at 4°C but remained constant at -20°C. Storage temperature was a significant factor driving the metabolite composition (permutational multivariate analysis of variance: r2 = 0.32 to 0.49, P < 0.001). CF sputum samples stored at -20°C at the time of sampling maintain a relatively stable untargeted GC-MS profile. Samples should be frozen on the day of collection, as more than 1 day at 4°C impacts the global composition of the metabolites in the sample. IMPORTANCE Metabolomics has great potential for uncovering biomarkers of the disease state in CF and many other contexts. However, sample storage timing and temperature may alter the abundance of clinically relevant metabolites. To assess whether existing samples are stable and to direct future study design, we conducted untargeted GC-MS metabolomic analysis of CF sputum samples after one or two freeze-thaw cycles and storage at 4°C and -20°C for 4 to 8 weeks. Overall, storage at -20°C and freeze-thaw cycles had little impact on metabolite profiles; however, storage at 4°C shifted metabolite abundances significantly. GC-MS profiling will aid in our understanding of the CF lung, but care should be taken in studies using sputum samples to ensure that samples are properly stored

Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome

Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different elevations (1162 m and 1651 m). A total of 317 high and 280 low elevation compounds were detected, many of them known to have sensory and health beneficial properties. The samples were evaluated by two different software. The first, GC Image, used feature-based detection algorithms to identify spectral patterns and peak-regions, leading to tentative identification of 107 compounds. The software produced a composite map illustrating differences in the samples. The second, Ion Analytics, employed spectral deconvolution algorithms to detect target compounds, then subtracted their spectra from the total ion current chromatogram to reveal untargeted compounds. Compound identities were more easily assigned, since chromatogram complexities were reduced. Of the 317 compounds, for example, 34% were positively identified and 42% were tentatively identified, leaving 24% as unknowns. This study demonstrated the targeted/untargeted approach taken simplifies the analysis time for large data sets, leading to a better understanding of the chemistry behind biological phenomena

Automated mass spectrometry-based metabolomics data processing by blind source separation methods

Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i adquirir nous coneixements biològics.Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i adquirir nous coneixements biològics.Una de las principales limitaciones de la metabolómica es la transformación de datos crudos en información biológica. Además, la metabolómica basada en espectrometría de masas genera grandes cantidades de datos complejos caracterizados por la co-elución de compuestos y artefactos experimentales. El objetivo de esta tesis es desarrollar estrategias automatizadas basadas en deconvolución ciega de la señal para mejorar las capacidades de los métodos existentes que tratan las limitaciones de los diferentes pasos del procesamiento de datos en metabolómica. El objetivo de esta tesis es también desarrollar herramientas capaces de ejecutar el flujo de trabajo del procesamiento de datos en metabolómica, que incluye el preprocessamiento de datos, deconvolución espectral, alineamiento e identificación. Como resultado, tres nuevos métodos automáticos para deconvolución espectral basados en deconvolución ciega de la señal fueron desarrollados. Estos métodos fueron incluidos en dos herramientas computacionales que permiten convertir automáticamente datos crudos en información biológica interpretable y por lo tanto, permiten resolver hipótesis biológicas y adquirir nuevos conocimientos biológicos.One of the major bottlenecks in metabolomics is to convert raw data samples into biological interpretable information. Moreover, mass spectrometry-based metabolomics generates large and complex datasets characterized by co-eluting compounds and with experimental artifacts. This thesis main objective is to develop automated strategies based on blind source separation to improve the capabilities of the current methods that tackle the different metabolomics data processing workflow steps limitations. Also, the objective of this thesis is to develop tools capable of performing the entire metabolomics workflow for GC--MS, including pre-processing, spectral deconvolution, alignment and identification. As a result, three new automated methods for spectral deconvolution based on blind source separation were developed. These methods were embedded into two computation tools able to automatedly convert raw data into biological interpretable information and thus, allow resolving biological answers and discovering new biological insights

Codling moth, Cydia pomonella, antennal responses to Metschnikowia pulcherrima and Metschnikowia andauensis synthesised volatiles

Insect-yeast interactions have been known for decades but are poorly understood. To investigate the codling moth (Cydia pomonella ) olfactory connection to yeasts, volatiles from two ascomycete yeasts, Metschnikowia pulcherrima and Metschnikowia andauensis, were analysed using gas chromatography combined with electroantennographic detection (GC-EAD). To be able to detect minute, but possibly behaviourally important responses to volatiles , multiple signals were averaged using a novel time synchronisation technique. Antennally active compounds were identified with GC-MS. Utilizing a wavelet-based peak separation and spectral enhancement algorithm, compounds in otherwise undetectable amounts could be identified. Results showed multiple responses to both yeasts indicating the ability of the moth to detect yeast synthesised volatile compounds