Analytica Chimica Acta 805 (2013) 70–79 Contents lists available at ScienceDirect Analytica Chimica Acta jou rn al h om epage: www.elsevier.com/locate/aca Mass Spectrometry Chemical dereplication of marine actinomycetes by liquid chromatography–high resolution mass spectrometry profiling and statistical analysis David Forner a,1 , Fabrice Berrué a,b,1 , Hebelin Correa a , Katherine Duncan c , Russell G. Kerr a,b,c,∗ a Nautilus Biosciences Canada Inc., Charlottetown PEI, Canada C1A 4P3 b Department of Chemistry, University of Prince Edward Island, Charlottetown PEI, Canada C1A 4P3 c Department of Biomedical Sciences, Atlantic Veterinary College, Charlottetown PEI, Canada C1A 4P3 h i g h l i g h t s • Novel methodology to chemically dereplicate microbial strains • Reproducible metabolic fingerprints using LC–HRMS • Statistical tools highlight unique strains and putatively novel compounds g r a p h i c a l a b s t r a c t a r t i c l e i n f o Article history: Received 26 June 2013 Received in revised form 2 October 2013 Accepted 11 October 2013 Available online 21 October 2013 Keywords: Cluster analysis Metabolomics Actinobacteria Chemical dereplication Natural products a b s t r a c t Discovery of novel bioactive metabolites from marine bacteria is becoming increasingly challenging, and the development of novel approaches to improve the efficiency of early steps in the microbial drug dis- covery process is therefore of interest. For example, current protocols for the taxonomic dereplication of microbial strains generally use molecular tools which do not take into consideration the ability of these selected bacteria to produce secondary metabolites. As the identification of novel chemical enti- ties is one of the key elements driving drug discovery programs, this study reports a novel methodology to dereplicate microbial strains by a metabolomics approach using liquid chromatography–high resolu- tion mass spectrometry (LC–HRMS). In order to process large and complex three dimensional LC–HRMS datasets, the reported method uses a bucketing and presence–absence standardization strategy in addi- tion to statistical analysis tools including principal component analysis (PCA) and cluster analysis. From a closely related group of Streptomyces isolated from geographically varied environments, we demon- strated that grouping bacteria according to the chemical diversity of produced metabolites is reproducible and provides greatly improved resolution for the discrimination of microbial strains compared to current molecular dereplication techniques. Importantly, this method provides the ability to identify putative novel chemical entities as natural product discovery leads. © 2013 Elsevier B.V. All rights reserved. ∗ Corresponding author. Tel.: +1 902 566 0565; fax: +1 902 566 7445. E-mail address: rkerr@upei.ca (R.G. Kerr). 1 The authors equally contributed to the manuscript. 1. Introduction Natural products have been a source of inspiration for the devel- opment of novel therapeutic agents for decades. Over the past 30 years, natural products, or their derivatives, have accounted for nearly 75% of all new antibacterial and 60% of new anticancer 0003-2670/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.aca.2013.10.029