Mesothelial Cell HIF1 alpha Expression Is Metabolically Downregulated by Metformin to Prevent Oncogenic Tumor-Stromal Crosstalk

The tumor microenvironment (TME) plays a pivotal role in cancer progression, and, in ovarian cancer (OvCa), the primary TME is the omentum. Here, we show that the diabetes drug metformin alters mesothelial cells in the omental microenvironment. Metformin interrupts bidirectional signaling between tumor and mesothelial cells by blocking OvCa cell TGF-beta signaling and mesothelial cell production of CCL2 and IL-8. Inhibition of tumor-stromal crosstalk by metformin is caused by the reduced expression of the tricarboxylic acid (TCA) enzyme succinyl CoA ligase (SUCLG2). Through repressing this TCA enzyme and its metabolite, succinate, metformin activated prolyl hydroxylases (PHDs), resulting in the degradation of hypoxia-inducible factor 1 alpha (HIF1 alpha) in mesothelial cells. Disruption of HIF1 alpha-driven IL-8 signaling in mesothelial cells by metformin results in reduced OvCa invasion in an organotypic 3D model. These findings indicate that tumor-promoting signaling between mesothelial and OvCa cells in the TME can be targeted using metformin

Proteolytic profile of Treponema vincentii ATCC 35580 with special reference to collagenolytic and arginine aminopeptidase activity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73401/1/j.1399-302X.1988.tb00096.x.pd

Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae

Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The TP309 cultivar is susceptible to infection by Xoo strain PXO99. A transgenic variety, TP309_Xa21, expresses the pattern recognition receptor Xa21, and is resistant. PXO99△raxST, a strain lacking the raxST gene, is able to overcome Xa21-mediated immunity. We used a single extraction solvent to demonstrate comprehensive metabolomics and transcriptomics profiling under sample limited conditions, and analyze the molecular responses of two rice lines challenged with either PXO99 or PXO99△raxST. LC–TOF raw data file filtering resulted in better within group reproducibility of replicate samples for statistical analyses. Accurate mass match compound identification with molecular formula generation (MFG) ranking of 355 masses was achieved with the METLIN database. GC–TOF analysis yielded an additional 441 compounds after BinBase database processing, of which 154 were structurally identified by retention index/MS library matching. Multivariate statistics revealed that the susceptible and resistant genotypes possess distinct profiles. Although few mRNA and metabolite differences were detected in PXO99 challenged TP309 compared to mock, many differential changes occurred in the Xa21-mediated response to PXO99 and PXO99△raxST. Acetophenone, xanthophylls, fatty acids, alkaloids, glutathione, carbohydrate and lipid biosynthetic pathways were affected. Significant transcriptional induction of several pathogenesis related genes in Xa21 challenged strains, as well as differential changes to GAD, PAL, ICL1 and Glutathione-S-transferase transcripts indicated limited correlation with metabolite changes under single time point global profiling conditions

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided

Metabolomics demonstrates divergent responses of two Eucalyptus species to water stress

Past studies of water stress in Eucalyptus spp. generally highlighted the role of fewer than five “important” metabolites, whereas recent metabolomic studies on other genera have shown tens of compounds are affected. There are currently no metabolite profiling data for responses of stress-tolerant species to water stress. We used GC–MS metabolite profiling to examine the response of leaf metabolites to a long (2 month) and severe (Ψpredawn < −2 MPa) water stress in two species of the perennial tree genus Eucalyptus (the mesic Eucalyptus pauciflora and the semi-arid Eucalyptus dumosa). Polar metabolites in leaves were analysed by GC–MS and inorganic ions by capillary electrophoresis. Pressure–volume curves and metabolite measurements showed that water stress led to more negative osmotic potential and increased total osmotically active solutes in leaves of both species. Water stress affected around 30–40% of measured metabolites in E. dumosa and 10–15% in E. pauciflora. There were many metabolites that were affected in E. dumosa but not E. pauciflora, and some that had opposite responses in the two species. For example, in E. dumosa there were increases in five acyclic sugar alcohols and four low-abundance carbohydrates that were unaffected by water stress in E. pauciflora. Re-watering increased osmotic potential and decreased total osmotically active solutes in E. pauciflora, whereas in E. dumosa re-watering led to further decreases in osmotic potential and increases in total osmotically active solutes. This experiment has added several extra dimensions to previous targeted analyses of water stress responses in Eucalyptus, and highlights that even species that are closely related (e.g. congeners) may respond differently to water stress and re-waterin

The registry of the German Network for Systemic Scleroderma: frequency of disease subsets and patterns of organ involvement

Objective. Systemic sclerosis (SSc) is a rare, heterogeneous disease, which affects different organs and therefore requires interdisciplinary diagnostic and therapeutic management. To improve the detection and follow-up of patients presenting with different disease manifestations, an interdisciplinary registry was founded with contributions from different subspecialties involved in the care of patients with SSc

Software platform virtualization in chemistry research and university teaching

<p>Abstract</p> <p>Background</p> <p>Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software. Software platform virtualization allows a single guest operating system to execute multiple other operating systems on the same computer. We apply and discuss the use of virtual machines in chemistry research and teaching laboratories.</p> <p>Results</p> <p>Virtual machines are commonly used for cheminformatics software development and testing. Benchmarking multiple chemistry software packages we have confirmed that the computational speed penalty for using virtual machines is low and around 5% to 10%. Software virtualization in a teaching environment allows faster deployment and easy use of commercial and open source software in hands-on computer teaching labs.</p> <p>Conclusion</p> <p>Software virtualization in chemistry, mass spectrometry and cheminformatics is needed for software testing and development of software for different operating systems. In order to obtain maximum performance the virtualization software should be multi-core enabled and allow the use of multiprocessor configurations in the virtual machine environment. Server consolidation, by running multiple tasks and operating systems on a single physical machine, can lead to lower maintenance and hardware costs especially in small research labs. The use of virtual machines can prevent software virus infections and security breaches when used as a sandbox system for internet access and software testing. Complex software setups can be created with virtual machines and are easily deployed later to multiple computers for hands-on teaching classes. We discuss the popularity of bioinformatics compared to cheminformatics as well as the missing cheminformatics education at universities worldwide.</p

The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets

<p>Abstract</p> <p>Background</p> <p>Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.</p> <p>Description</p> <p>Here, we present MetabolomeExpress, a new File Transfer Protocol (FTP) server and web-tool for the online storage, processing, visualisation and statistical re-analysis of publicly submitted GC/MS metabolomics datasets. Users may search a quality-controlled database of metabolite response statistics from publicly submitted datasets by a number of parameters (eg. metabolite, species, organ/biofluid etc.). Users may also perform meta-analysis comparisons of multiple independent experiments or re-analyse public primary datasets via user-friendly tools for t-test, principal components analysis, hierarchical cluster analysis and correlation analysis. They may interact with chromatograms, mass spectra and peak detection results via an integrated raw data viewer. Researchers who register for a free account may upload (via FTP) their own data to the server for online processing via a novel raw data processing pipeline.</p> <p>Conclusions</p> <p>MetabolomeExpress <url>https://www.metabolome-express.org</url> provides a new opportunity for the general metabolomics community to transparently present online the raw and processed GC/MS data underlying their metabolomics publications. Transparent sharing of these data will allow researchers to assess data quality and draw their own insights from published metabolomics datasets.</p

The Complex Genetic Architecture of the Metabolome

Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype–metabolite associations distributed non-randomly within the genome. These clusters of genotype–metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype–metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable