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      Metabolomic Analyses of Leishmania Reveal Multiple Species Differences and Large Differences in Amino Acid Metabolism

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          Abstract

          Comparative genomic analyses of Leishmania species have revealed relatively minor heterogeneity amongst recognised housekeeping genes and yet the species cause distinct infections and pathogenesis in their mammalian hosts. To gain greater information on the biochemical variation between species, and insights into possible metabolic mechanisms underpinning visceral and cutaneous leishmaniasis, we have undertaken in this study a comparative analysis of the metabolomes of promastigotes of L. donovani, L. major and L. mexicana. The analysis revealed 64 metabolites with confirmed identity differing 3-fold or more between the cell extracts of species, with 161 putatively identified metabolites differing similarly. Analysis of the media from cultures revealed an at least 3-fold difference in use or excretion of 43 metabolites of confirmed identity and 87 putatively identified metabolites that differed to a similar extent. Strikingly large differences were detected in their extent of amino acid use and metabolism, especially for tryptophan, aspartate, arginine and proline. Major pathways of tryptophan and arginine catabolism were shown to be to indole-3-lactate and arginic acid, respectively, which were excreted. The data presented provide clear evidence on the value of global metabolomic analyses in detecting species-specific metabolic features, thus application of this technology should be a major contributor to gaining greater understanding of how pathogens are adapted to infecting their hosts.

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          Highly sensitive feature detection for high resolution LC/MS

          Background Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features – a reliable feature detection is mandatory. Results We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity. Conclusion The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from
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            Metabolomics: a global biochemical approach to drug response and disease.

            Metabolomics is the study of metabolism at the global level. This rapidly developing new discipline has important potential implications for pharmacologic science. The concept that metabolic state is representative of the overall physiologic status of the organism lies at the heart of metabolomics. Metabolomic studies capture global biochemical events by assaying thousands of small molecules in cells, tissues, organs, or biological fluids-followed by the application of informatic techniques to define metabolomic signatures. Metabolomic studies can lead to enhanced understanding of disease mechanisms and to new diagnostic markers as well as enhanced understanding of mechanisms for drug or xenobiotic effect and increased ability to predict individual variation in drug response phenotypes (pharmacometabolomics). This review outlines the conceptual basis for metabolomics as well as analytical and informatic techniques used to study the metabolome and to define metabolomic signatures. It also highlights potential metabolomic applications to pharmacology and clinical pharmacology.
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              Chromosome and gene copy number variation allow major structural change between species and strains of Leishmania.

              Leishmania parasites cause a spectrum of clinical pathology in humans ranging from disfiguring cutaneous lesions to fatal visceral leishmaniasis. We have generated a reference genome for Leishmania mexicana and refined the reference genomes for Leishmania major, Leishmania infantum, and Leishmania braziliensis. This has allowed the identification of a remarkably low number of genes or paralog groups (2, 14, 19, and 67, respectively) unique to one species. These were found to be conserved in additional isolates of the same species. We have predicted allelic variation and find that in these isolates, L. major and L. infantum have a surprisingly low number of predicted heterozygous SNPs compared with L. braziliensis and L. mexicana. We used short read coverage to infer ploidy and gene copy numbers, identifying large copy number variations between species, with 200 tandem gene arrays in L. major and 132 in L. mexicana. Chromosome copy number also varied significantly between species, with nine supernumerary chromosomes in L. infantum, four in L. mexicana, two in L. braziliensis, and one in L. major. A significant bias against gene arrays on supernumerary chromosomes was shown to exist, indicating that duplication events occur more frequently on disomic chromosomes. Taken together, our data demonstrate that there is little variation in unique gene content across Leishmania species, but large-scale genetic heterogeneity can result through gene amplification on disomic chromosomes and variation in chromosome number. Increased gene copy number due to chromosome amplification may contribute to alterations in gene expression in response to environmental conditions in the host, providing a genetic basis for disease tropism.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                14 September 2015
                2015
                : 10
                : 9
                : e0136891
                Affiliations
                [1 ]Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
                [2 ]Institute of Biomedical and Environmental Health Research, University of the West of Scotland, Paisley, United Kingdom
                [3 ]Instituto de Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal
                National Center for Cell Science, INDIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GDW RAMW DGW AMS GHC. Performed the experiments: GDW RAMW LW TZ AMS. Analyzed the data: GDW RAMW LW TZ DGW AMS GHC. Wrote the paper: GDW RAMW TZ DGW GHC.

                Article
                PONE-D-15-26686
                10.1371/journal.pone.0136891
                4569581
                26368322
                50f05741-74a5-4859-8fdd-ccc744f56c95
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 18 June 2015
                : 9 August 2015
                Page count
                Figures: 4, Tables: 5, Pages: 29
                Funding
                This work was funded by the Scottish Universities Life Sciences Alliance (SULSA; www.sulsa.ac.uk) to DGW and GHC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Research Article
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