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Cyclo(tetrahydroxybutyrate) production is sufficient to distinguish between Xenorhabdus and Photorhabdus isolates in Thailand
Authors:Nicholas J. Tobias  César Parra-Rojas  Yan-Ni Shi  Yi-Ming Shi  Svenja Simonyi  Aunchalee Thanwisai  Apichat Vitta  Narisara Chantratita  Esteban A. Hernandez-Vargas  Helge B. Bode
Affiliation:1. Molekulare Biotechnologie, Goethe-Universität Frankfurt, Frankfurt am Main, Germany;2. Department of Microbiology and Parasitology, Faculty of Medical Science, Naresuan University, Phitsanulok, 65000 Thailand;3. Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400 Thailand;4. Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438, Frankfurt am Main, Germany
Abstract:Bacteria of the genera Photorhabdus and Xenorhabdus produce a plethora of natural products to support their similar symbiotic life cycles. For many of these compounds, the specific bioactivities are unknown. One common challenge in natural product research when trying to prioritize research efforts is the rediscovery of identical (or highly similar) compounds from different strains. Linking genome sequence to metabolite production can help in overcoming this problem. However, sequences are typically not available for entire collections of organisms. Here, we perform a comprehensive metabolic screening using HPLC-MS data associated with a 114-strain collection (58 Photorhabdus and 56 Xenorhabdus) across Thailand and explore the metabolic variation among the strains, matched with several abiotic factors. We utilize machine learning in order to rank the importance of individual metabolites in determining all given metadata. With this approach, we were able to prioritize metabolites in the context of natural product investigations, leading to the identification of previously unknown compounds. The top three highest ranking features were associated with Xenorhabdus and attributed to the same chemical entity, cyclo(tetrahydroxybutyrate). This work also addresses the need for prioritization in high-throughput metabolomic studies and demonstrates the viability of such an approach in future research.
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