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Taxonomically Informed Scoring Enhances Confidence in Natural 2 Products Annotation

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Taxonomically Informed Scoring Enhances Confidence in Natural 2 Products Annotation bioRxiv preprint doi: https://doi.org/10.1101/702308; this version posted July 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Taxonomically informed scoring enhances confidence in natural 2 products annotation 3 4 Adriano Rutz1,∆, Miwa Dounoue-Kubo1,2,∆, Simon Ollivier1, Jonathan Bisson3, Mohsen 5 Bagheri1,4, Tongchai Saesong5, Samad Nejad Ebrahimi4, Kornkanok Ingkaninan5, Jean-Luc 6 Wolfender1 and Pierre-Marie Allard1,* 7 ∆equal contribution 8 1School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, CMU - Rue Michel 9 Servet, 1 1211 Geneva 4 – Switzerland 10 2Faculty of Pharmaceutical Sciences, Tokushima Bunri University, 180 Yamashiro-cho, Tokushima, 770-8514 – 11 Japan 12 3Center for Natural Product Technologies, Program for Collaborative Research in the Pharmaceutical Sciences 13 (PCRPS) and Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 14 South Wood Street, Chicago, Illinois 60612, United States 15 4Department of Phytochemistry, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, G.C., 16 Evin, Tehran, Iran. 17 5Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmaceutical Sciences and Center of 18 Excellence for Innovation in Chemistry, Naresuan University, Phitsanulok, Thailand, 65000 19 * Correspondence: 20 Corresponding Author 21 [email protected] 22 23 Keywords: metabolite annotation, chemotaxonomy, taxonomically informed scoring system, 24 natural products chemistry, metabolomics, taxonomic distance. 25 Abstract 26 Mass spectrometry (MS) hyphenated to liquid chromatography (LC)-MS offers unrivalled sensitivity 27 for metabolite profiling of complex biological matrices encountered in natural products (NP) research. 28 With advanced platforms LC, MS/MS spectra are acquired in an untargeted manner on most detected 29 features. This generates massive and complex sets of spectral data that provide valuable structural 30 information on most analytes. To interpret such datasets, computational methods are mandatory. To 31 this extent, computerized annotation of metabolites links spectral data to candidate structures. When 32 profiling complex extracts spectra are often organized in clusters by similarity via Molecular 33 Networking (MN). A spectral matching score is usually established between the acquired data and 34 experimental or theoretical spectral databases (DB). The process leads to various candidate structures 35 for each MS features. At this stage, obtaining high annotation confidence level remains a challenge 36 notably due to the high chemodiversity of specialized metabolomes. bioRxiv preprint doi: https://doi.org/10.1101/702308; this version posted July 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International Taxonomically license. informed metabolite annotation 37 The integration of additional information in a meta-score is a way to capture complementary 38 experimental attributes and improve the annotation process. Here we show that integrating 39 unambiguous taxonomic position of analyzed samples and candidate structures enhances confidence 40 in metabolite annotation. A script is proposed to automatically input such information at various 41 granularity levels (species, genus, and family) and weight the score obtained between experimental 42 spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS- 43 Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking 44 of the candidate structures leading to a systematic enhancement of the recall and precision rates of the 45 tools (1.5 to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of 46 considering taxonomic information in the process of specialized metabolites’ annotation. This requires 47 to access structural data systematically documented with biological origin, both for new and previously 48 reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and 49 their associated metadata (particularly biological sources) is timely and critical for the NP research 50 community. 51 52 1 Introduction 53 Specialized metabolites define the chemical signature of a living organism. Sessile organisms such as 54 plants, sponges and corals, but also microorganisms (bacteria and fungi), are known to biosynthesize 55 a wealth of such chemicals. These molecules can play a role as defense or communication agents 56 (Brunetti et al., 2018). However, the functional role of these metabolites for the producer is not always 57 fully understood. The comprehension of these functions is one of the goals of chemical ecology and 58 recent research illustrates the importance of specialized metabolism (Hoffmann et al., 2018; Schmitt et 59 al., 1995). Throughout history, humans have been relying on plant derived products for a variety of 60 purposes: housing, feeding, clothing and, especially, medication. Our therapeutic arsenal is in fact 61 deeply dependent on the chemistry of natural products (NPs) whether they are used in mixtures, 62 purified forms or for hemi-synthetic drug development. After a period of disregard by the 63 pharmaceutical industry, NPs are the object of a renewed interest (Shen, 2015). Today, technological 64 and methodological advances allow to establish more precise views of the chemo-biologic aspects of 65 life. In the field of NPs, advances in genomics and metagenomics now allow the anticipation of the 66 chemical output directly from environmental DNA (Alanjary et al., 2019; Craig et al., 2009). 67 Developments in metabolite profiling by mass spectrometry (MS) grant access to large volumes of 68 high-quality spectral data from minimal amount of samples and appropriate data analysis workflows 69 allow to efficiently mine such data (Wolfender et al., 2019). Initiatives such as the Global Natural 70 Products Social (GNPS) molecular networking (MN) (Wang et al., 2016) project offer both a living 71 MS repository and the possibility to establish MN organizing MS data. Despite such advancements, 72 metabolite identification remains a major challenge for both NP research and metabolomics (Kind et 73 al., 2018). Metabolite identification of a novel compound requires physical isolation of the analyte 74 followed by complete NMR acquisition and three-dimensional structural establishment via X-ray 75 diffraction or chiroptical techniques. For previously described compounds, metabolite identification 76 implies complete matching of physicochemical properties between the analyte and a standard 77 compound (including chiroptical properties). Metabolite identification is thus a tedious and labor- 78 intensive process, which should ideally be reserved to novel metabolites’ description. Any less 79 complete process should be defined as metabolite annotation. By definition, metabolite annotation 80 can be applied at a higher throughput and offers an effective proxy for the chemical characterization 81 of complex matrices. This process includes dereplication (the annotation of previously described 2 This is a provisional file, not the final typeset article bioRxiv preprint doi: https://doi.org/10.1101/702308; this version posted July 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International Taxonomicall license. y informed metabolite annotation 82 molecules prior to any physical isolation process) and allows focusing isolation and metabolite 83 identification efforts on potentially novel compounds only (Newman, 2017). 84 Given its sensitivity, selectivity and structural determination potential, MS is a tool of choice for 85 metabolite annotation in complex mixtures. High resolution mass spectrometers (HRMS) affording 86 high mass accuracy (ppm range) are now routinely used (Eliuk and Makarov, 2015; Lommen et al., 87 2011; Olsen et al., 2005). As a result of this evolution, unambiguous molecular formula (MF) can be 88 assigned in most cases. However, the isomeric nature of many NPs indicates that MF determination is 89 a necessary but non-sufficient step in the metabolite annotation process. Acquisition of fragmentation 90 spectra (tandem MS (MS/MS) or MSn) offers a way to gain structural insights on analytes. Such 91 analytical approach (physically breaking down the analyte in constitutive building blocks to infer its 92 general structure) is analog to DNA (genomics) or protein sequencing (proteomics). However, 93 compared to those two fields, a notable difference lies within the non-polymeric nature of small 94 metabolites, which partly explains the difficulties encountered in linking a fragmentation spectrum to 95 a chemical structure in metabolomics. The dependence of the fragmentation behavior of analytes on 96 the mass spectrometer geometry adds further complexity to the interpretation of MS data (Johnson and 97 Carlson, 2015). To interpret such data, a common approach consists in comparing experimental
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