Old dogs and new tricks in antimicrobial discovery

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ScienceDirect Old dogs and new tricks in antimicrobial discovery Mark S Butler1, Mark AT Blaskovich1, Jeremy G Owen2 and Matthew A Cooper1 The discovery of new antibiotics is crucial if we are to avoid a future in which simple infections once again lead to death. New antibiotics were traditionally discovered by analyzing extracts from cultured soil-derived microbes. However, in the last few years only a few new antibiotic classes have been identified using this method. Attempts to apply target-based screening approaches to antibiotic discovery have also proven to be unproductive. In this article, we describe how DNA sequencing and bioinformatic techniques are revolutionizing natural product discovery leading to new natural product antibiotics sourced from both cultivated and uncultivated microbes. New chemical structures are also being ‘crowd sourced’ from chemists around the world, and ‘forgotten’ antibiotics repositioned for clinical trials after chemical or biochemical modification of the original natural product. Collectively such approaches have the potential to revamp antibiotic lead discovery and re-invigorate the antibiotic pipeline. Addresses 1 Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane 4072, Australia 2 School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand Corresponding author: Cooper, Matthew A ([email protected])

Current Opinion in Microbiology 2016, 33:25–34 This review comes from a themed issue on Antimicrobials

and bioactivity-guided isolation. However, after several more decades of intensive application this strategy has met with prohibitively diminishing returns, even with improvements in dereplication. For example, while reflecting on natural product screening at Eli Lilly, Baltz proposed that the frequency of rediscovering vancomycin, erythromycin and daptomycin producing strains was 1500, 50 and 1 in 10 000 000 respectively [3,4]. It is now well established that for many environmental microbial communities, only a small fraction of the species present can be readily cultivated in a laboratory setting [5]. Moreover, the explosion in sequenced microbial genomes has led to the realization that even among species that have been cultivated, only a fraction of the molecules encoded in an organism’s genome are detectable under laboratory cultivation conditions [6]. These observations have inspired a growing number of research groups to switch the discovery paradigm from ‘microbial metabolite’ to ‘biosynthetic gene cluster’. In this review, we have highlighted the application of genome mining and metagenome mining for discovery of new antibiotics. We also present other methods that have been re-invigorated with cutting edge technological advances such as crowd sourcing academic chemical compound collections and semisynthetic engineering of ‘forgotten’ antibiotics. Together, these techniques have the potential to revitalise the antimicrobial discovery pipeline.

Edited by Mike Pucci and Thomas Dougherty

Biosynthetic gene cluster mining for discovery of antimicrobials http://dx.doi.org/10.1016/j.mib.2016.05.011

Genome mining approaches

1369-5274/# 2016 Elsevier Ltd. All rights reserved.

The availability of the first complete genome sequences for Streptomycetes in the early 2000s revealed a wealth of biosynthetic diversity that had previously been hidden [7,8]. Recent systematic surveys of 1000s of sequenced microbial genomes have confirmed that silent clusters generally outnumber those expressed during laboratory culture [6,9–12], and have shown that there is enormous untapped natural products potential encoded in bacterial genomes. Genome mining approaches begin with identification of a cryptic biosynthetic gene cluster (BGC) for which there is no known associated compound. Knowledge of this cryptic BGC is then used to guide identification and isolation of the metabolite it encodes (Figure 1). Genome mining techniques have been used to discover a number of new antibiotics. For example, haloduracin (Figure 2) is an antibiotic complex comprising two separate ribosomally encoded and post translationally modified peptides (RiPPs). The BGC for this antibiotic was identified by genome mining, and the compounds generated via

Introduction Antibiotic-resistant bacteria are a serious and growing threat to human health and national healthcare systems. In particular, Multi-Drug Resistant (MDR) Gram-negative strains of Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii and Pseudomonas aeruginosa are of grave concern [1,2]. After the Second World War scientists were inspired by the development of penicillin and streptomycin and began intensive studies to identify new anti-infective drugs from soil-derived microorganisms. Over the next two decades, which are now referred to as the ‘Golden Age of Antibiotics’, most antibiotic classes were discovered using this strategy of laboratory culture www.sciencedirect.com

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26 Antimicrobials

Figure 1

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activity. The nonribiosomal peptide taromycin A (Figure 1) was recently isolated by genome mining of a marine actinomycete [15]. Taromycin A, a halogenated lipopeptide antibiotic with a similar structure to daptomycin (Figure 5), was isolated by cloning of the biosynthetic pathway, deletion of a repressor element and introduction of the refactored BGC into a heterologous host [15]. A recent survey of 10 000 actinomycete genomes used degenerate PCR followed by shotgun sequencing of 403 strains to identify new phosphonate natural product BGCs [11]. This survey led to the production of 11 new metabolites, two of which possessed antimicrobial activity.

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Cluster mining for antibiotic discovery. (a) Metagenome libraries or genome sequence databases are mined for BGCs. Computational approaches to dereplication can be applied at this stage to prioritize clusters for study. (b) Sequence data aid in identification of previously cryptic metabolites arising from a BGC, either by comparison to a negative ( BGC) reference point, or by using predicted physiochemical properties to guide isolation. (c) Once identified, new metabolites are isolated in sufficient quantities for structure determination and assessment of biological activity.

the enzymatic modification of a synthetic peptide using pathway encoded enzymes [13]. Haloduracin was subsequently found to bind lipid II, preventing transglycosylation and inhibiting cell wall biosynthesis in Gram-positive bacteria [14]. Lactocillin (Figure 1) is another RiPP antibiotic targeting Gram-positive organisms that was recently isolated using a genome mining approach [9]. The BGC for this compound was observed in the genome of a human vaginal isolate of Lactobacillus gasseri, with a negative reference HPLC chromatogram allowing identification of the encoded metabolite through knocking out this BGC. This allowed the isolation of sufficient material for structure elucidation and assessment of antimicrobial Current Opinion in Microbiology 2016, 33:25–34

For many environmental microbial communities, only a small fraction of the species present can be readily cultivated in a laboratory setting [5]. In metagenomic natural products discovery, access to the uncultivated majority is obtained by extracting genomic DNA directly from the consortia of microbial species present in an environmental sample and propagating this as large insert libraries [16]. The archived genetic material — representing 100s to 1000s of unique genomes — can then be interrogated for BGCs encoding new natural products [17]. Once recovered, these genomic blueprints are delivered to a heterologous expression host that is able to read the new instructions and build the new compounds they specify [18–21]. There have been a number of new glycopeptide [22–24] and aromatic polyketide antibiotics discovered using metagenome mining approaches [19,25,26]. One of the key observations from recent metagenome mining studies is that BGCs encoding compound classes commonly distributed among the genomes of uncultivated bacteria are rarely found in cultivable bacteria [24]. Metagenome mining allows access an enormous diversity of BGCs, without the need to accrue large culture collections; however, this immense diversity also presents a formidable challenge in terms of identifying and recovering a specific BGC of interest within a much larger pool of genetic diversity. One promising strategy for dealing with the immense complexity of metagenomes is using natural products sequence tags (NPSTs) to guide recovery of BGCs encoding specific classes of compound [24,27,28,29]. The underlying principle of this approach is that the gene content and chemical output of a small molecule biosynthetic gene cluster can be accurately predicted using a small amount of sequence data derived from a conserved biosynthetic motif, a procedure analogous to the prediction of the phylogeny of an unknown organism from 16S rRNA sequence tags. This biosynthetic mapping approach can be applied to an arrayed metagenome library to pinpoint the location of target BGC [24], or to hundreds of soils in parallel in order to ascertain www.sciencedirect.com

Old dogs and new tricks in antimicrobial discovery Butler et al. 27

Figure 2

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Structures of haloduracin a and b, lactocillin and taromycin A (blue colour denotes difference to daptomycin).

the likely biosynthetic capacity of many metagenomes, with a minimal investment in sequencing [27,28].

Computational tools for biosynthetic gene cluster discovery Both genome and metagenome mining techniques are dependent on computational strategies for identifying BGCs and for providing useful annotations. There are now a large collection of automated tools available that perform these tasks [29–32], of which antiSMASH [30] has been most widely adopted by the genomics driven discovery community. AntiSMASH identifies BGCs, determines www.sciencedirect.com

their enzymatic components, generates structure predictions for encoded metabolites and compares query clusters to a reference collection to identify relatives. The intuitive and integrated suite of tools encapsulated by antiSMASH allow powerful analyses to be performed with relative ease, and have been instrumental in the development of the field. More recently, another integrated algorithmic tool kit for genome mining called PRISM was released [32]. While broadly similar to antiSMASH in terms of work flow, PRISM offers a number of useful new features that improve the scope and accuracy of structure predictions for NRPS (non-ribosomal peptide synthetases) and PKS Current Opinion in Microbiology 2016, 33:25–34

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(polyketide synthetases) derived natural products. Of particular interest is the ability to generate not just one, but a collection of structure predictions, that reflect all of the possible permutations of enzymatic module ordering, allowing better handling of cases where an unambiguous co-linear prediction is not possible. PRISM is also able to generate structure predictions for iterative type II polyketides, and infer the identity of deoxysugars that might be linked to a scaffold. Finally it offers the ability to search for both related gene clusters, and known natural products that are similar to the predicted product of a query, providing a powerful means for dereplication using only DNA sequence data. Identification of BGCs and bioinformatic prediction of their products is particularly powerful when used in conjunction with LC–MS/MS data, as predicted

structures can be searched against fragmentation patterns in MSn data [33]. This approach is applicable to natural products that fragment in a predictable manner such as those containing peptide bonds [34,35] or deoxysugars [36,37]. There are now software packages that perform the automated analysis of paired m/z and DNA sequence data to assign putative molecule fragmentation patterns to cryptic BGCs [37,38].

Semisynthetic engineering of old lipopeptide and glycopeptide antibiotics Natural product-derived antibiotics can be semi-synthetically altered using various chemical and enzymatic techniques to produce new drugs that can help to overcome resistance, improve pharmacokinetic properties, broaden

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Structures of semi-synthetic glycopeptide telavancin, dalbavancin and oritavancin with the synthetic modifications in blue. Current Opinion in Microbiology 2016, 33:25–34

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Old dogs and new tricks in antimicrobial discovery Butler et al. 29

the spectra of activity and/or reduce side effects [39,40]. This strategy can be traced back to 1957 with the discovery that 6-aminopenicillanic acid could be produced in large amounts using fermentation and then used as a precursor to synthesize semi-synthetic penicillin antibiotics [41].

synthesized from the acid degradation product Factor A3 [53]. Novartis synthesized LFF571 [54,55] that reached Phase II clinical testing for the treatment of C. difficile infections, while Lepetit synthesized a series of amide analogues. One of these, CB-06-01 (NAI003) [56], is being evaluated by Cassiopea S.p.A. in a Phase II clinical trial for the treatment of acne. Other notable semi-synthetic antibiotics include clarithromycin and azithromycin, which are derived from the macrolide antibiotic erythromycin, tetracycline-type derivatives doxycycline, minocycline and tigecycline, and rifampicin derived from rifamycin-type antibiotics [40].

Chemical semi-synthesis approaches The first glycopeptide, vancomycin, was isolated in 1953, evaluated in patients in a semi-pure form from 1955 and approved in 1958 by the FDA [42]. The use of vancomycin waned due to nephrotoxicity issues and the availability of alternative more convenient (orally available) antibiotics but the rise of methicillin-resistant Staphylococcus aureus (MRSA) in the 1980s led to a resurgence in its use. Three semi-synthetic glycopeptide derivatives (Figure 3) have been approved for clinical use: telavancin [43] in 2009, and dalbavancin [44] and oritavancin [45] in 2014. The ease of modification of the C-terminus of vancomycin has led to a number of other programs developing semisynthetic analogues with reported in vivo activity [46–49].

Combined enzymatic and semi-synthesis approaches The acyl fatty acid tail in some lipopeptides has been known to cause toxicity issues that include human blood hemolysis. A generalized replacement strategy has been developed where any free amines are protected, the fatty acid removed with a deacylase enzyme, a new fatty acid coupled to the unmasked free amine and the protection groups removed to yield new lipopeptide analogues.

The antibiotic GE2270A (Figure 4), which was first described by the Lepetit Research Center in the early 1990s, has broad spectrum Gram-positive activity [50] and a rare mode of action through inhibition of elongation factor Tu [51,52]. GE2770A was too insoluble suitable for clinical development and more soluble derivatives were

A21978C is an antibiotic complex of cyclic peptides with different fatty acid tails. Biological evaluation of the components identified that the n-decanoic analogue, now called daptomycin (Figure 5), had the best safety profile [57]; Cubist Pharmaceuticals completed the clinical development with approval granted in 2003. Cubist

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Structures of GE-2770A and Factor A3 and the semi-synthetic derivatives LFF-571 and CB-06-01.

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has applied the deacylase method to synthesize daptomycin analogues with improved activity against C. difficile. One of these, surotomycin [58,59], is in late stage Phase III clinical trials. Similarly, the deacylation method was used by MIGENIX to synthesize the amphomycin analogue MX-2401 (Figure 5) that showed the same spectra of activity as amphomycin but with a longer half-life and no human blood haemolysis liability [60,61]. MX-2401 reached late stage preclinical development but never entered clinical trials. The deacylase method was also used to synthesize the polymyxin derivative CB182,804 (Figure 6) that started a Phase I clinical trial in 2009 but was discontinued. More recently, a total synthesis approach was applied to prepare a series of polymyxin analogues with variations at positions within the peptide itself, identifying derivatives with improved activity and reduced potential for nephrotoxicity [62]. Many lead compounds that were discarded during the ‘golden age’ of discovery for reasons such as poor Current Opinion in Microbiology 2016, 33:25–34

pharmacokinetic profile might warrant re-examination in the current climate of antibiotic resistance. For example, the telomycins and griselimycins, depsipeptides originally isolated from Streptomyces spp. in 1960s and 1950s respectively, have both been the subject of recent studies [63–65]. Improved acyl telomycins (Figure 6) were generated by deletion of a pathway encoded deacylase gene or synthetic acylation of the nucleus [63]. In a another study, a retro-biosynthetic algorithm was developed and applied to a large collection of antibiotic structures, resulting in the identification of the telomycins as possibly possessing a new mode of action [64]. This assumption was subsequently supported biochemically, suggesting the telomycins exert their activity via interaction with membrane cardioliplin phospholipids [64]. The griselimycins were found to exert their potent antimycobacterial activity via high affinity binding to the DNA clamp protein DnaN, and new analogues such as cyclohexylgriselimycin (Figure 6) with improved potency and pharmacokinetic profile were synthesized [65]. www.sciencedirect.com

Old dogs and new tricks in antimicrobial discovery Butler et al. 31

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Structures of the polymyxin B analogue CB-182,804, cyclohexylgriselimycin and the acyl telomycin derivative.

Community for open antimicrobial drug discovery (CO-ADD) An alternative approach to screening the natural product universe for new antibiotics is to screen the synthetic chemical universe. Pharmaceutical companies have attempted to employ this approach by testing their large compound libraries, but in general have had little success. However, the types of compounds contained in these libraries are generally designed to comply with a set of ‘drug-like’ physicochemical property rules to maximize their likelihood of oral bioavailability, and vetted to ensure that reactive functional groups are not included. In contrast, most antibiotics do not comply with oral availability parameters such as the Lipinski ‘Rule of Five’ or number of rotatable bonds, and many contain moieties generally considered to be undesirable (i.e. labile lactam in b-lactam antibiotics, dichloromethylketone in chloramphenicol, epoxide in fosfomycin) [66]. Therefore, synthetic chemical www.sciencedirect.com

screening historically appears to have been looking in the wrong chemical space (Figure 7). To overcome this issue, the CO-ADD initiative has targeted the vast diversity of compounds produced by academic synthetic chemists, enticing their submission by offering free screening as a public service funded by the Wellcome Trust and the University of Queensland [66,67]. It is hoped that this collection of compounds, unfettered by constraints of ‘drug-like’ properties and ease of synthesis, may contain a much richer collection of structurally diverse, three dimensional and complex molecules than has previously been tested. Over 50 000 compounds have been submitted and tested to date, validating the strong interest in participating in an open-source effort to identify new compounds. It is still too early to assess whether this approach will be fruitful in producing novel antibiotic candidates, but the active compound ‘hit’ rate is significantly higher than from a commercial collection assessed in the same screen. Current Opinion in Microbiology 2016, 33:25–34

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Comparison of physicochemical properties of academic compounds with reported antimicrobial activity (ChEMBL, MIC  10 mM) against Grampositive (blue) or Gram-negative (red) activity, compared to the much more limited chemical space occupied by collated commercial screening libraries (green).

Conclusion The lack of treatment options for MDR bacteria, such as E. coli, A. baumannii, P. aeruginosa, and K. pneumoniae, and the emergence of extremely resistant isolates underline the urgent need for new antibiotics active against serious Gram negative infections. While most ‘superbugs’ are prevalent in hospital settings, they are increasingly becoming disseminated in the wider community: for example the NDM-1 gene responsible for resistance to carbapenem antibiotics is now being reported in community-acquired infections and is rapidly spreading. In 2010, 33% of European P. aeruginosa isolates were resistant to one or more of the five major antibiotic classes, while 15% were resistant to three or more, with the most common pattern (52%) consisting of resistance to all five antibiotic classes [68]. With the failure of the market model for antibiotic development in the pharmaceutical industry (note Pfizer’s recent exit from the field, Astra Zeneca’s exit from antibiotic research via spinout of a small company called Entasis Therapeutics, and the release of the Cubist research team after its acquisition by Merck, which collectively has resulted in a huge loss of accumulated antibiotic discovery wisdom), we now face a ‘perfect storm’ of a diminished pipeline of new antibiotics together with increasing bacterial resistance to antibiotics and concomitant increasing morbidity and mortality. The new strategies outlined in this review offer a potential path Current Opinion in Microbiology 2016, 33:25–34

forward to overcome these dangers and maintain the lifesaving capabilities of these invaluable drugs.

Acknowledgements We thank Johannes Zuegg (IMB, UQ) for the compilation of data used for Figure 7. MAC is supported by a NHMRC Principal Research Fellowship APP1059354. MSB and MAB are supported by the University of Queensland, the Institute for Molecular Bioscience’s Center for Superbug Solutions and the Community for Open Antimicrobial Discovery via a Strategic Award WT141107 from the Wellcome Trust, UK.

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2.

Butler MS, Blaskovich MA, Cooper MA: Antibiotics in the clinical pipeline in 2013. J Antibiot 2013, 66:571-591.

3.

Baltz RH: Marcel Faber roundtable: is our antibiotic pipeline unproductive because of starvation, constipation or lack of inspiration? J Ind Microbiol Biotechnol 2006, 33:507-513.

4.

Carrano L, Marinelli F: The relevance of chemical dereplication in microbial natural product screening. J Appl Bioanal 2015, 1:55-67.

5.

Tringe SG, von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, Podar M, Short JM, Mathur EJ, Detter JC et al.: Comparative metagenomics of microbial communities. Science 2005, 308:554-557. www.sciencedirect.com

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Cimermancic P, Medema MH, Claesen J, Kurita K, Wieland Brown LC, Mavrommatis K, Pati A, Godfrey PA, Koehrsen M, Clardy J et al.: Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters. Cell 2014, 158:412-421.

7.

Bentley SD, Chater KF, Cerdeno-Tarraga AM, Challis GL, Thomson NR, James KD, Harris DE, Quail MA, Kieser H, Harper D et al.: Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2). Nature 2002, 417:141-147.

8.

Ikeda H, Ishikawa J, Hanamoto A, Shinose M, Kikuchi H, Shiba T, Sakaki Y, Hattori M, Omura S: Complete genome sequence and comparative analysis of the industrial microorganism Streptomyces avermitilis. Nat Biotechnol 2003, 21:526-531.

9.

Donia MS, Cimermancic P, Schulze CJ, Wieland Brown LC, Martin J, Mitreva M, Clardy J, Linington RG, Fischbach MA: A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics. Cell 2014, 158:1402-1414.

10. Doroghazi JR, Albright JC, Goering AW, Ju KS, Haines RR, Tchalukov KA, Labeda DP, Kelleher NL, Metcalf WW: A roadmap for natural product discovery based on large-scale genomics and metabolomics. Nat Chem Biol 2014, 10:963-968. 11. Ju KS, Gao J, Doroghazi JR, Wang KK, Thibodeaux CJ, Li S, Metzger E, Fudala J, Su J, Zhang JK, Lee J et al.: Discovery of phosphonic acid natural products by mining the genomes of 10,000 actinomycetes. Proc Natl Acad Sci U S A 2015, 112:12175-12180. 12. Wang H, Fewer DP, Holm L, Rouhiainen L, Sivonen K: Atlas of nonribosomal peptide and polyketide biosynthetic pathways reveals common occurrence of nonmodular enzymes. Proc Natl Acad Sci U S A 2014, 111:9259-9264. 13. McClerren AL, Cooper LE, Quan C, Thomas PM, Kelleher NL, van der Donk WA: Discovery and in vitro biosynthesis of haloduracin, a two-component lantibiotic. Proc Natl Acad Sci U S A 2006, 103:17243-17248. 14. Oman TJ, Lupoli TJ, Wang TS, Kahne D, Walker S, van der Donk WA: Haloduracin a binds the peptidoglycan precursor lipid II with 2:1 stoichiometry. J Am Chem Soc 2011, 133:17544-17547. 15. Yamanaka K, Reynolds KA, Kersten RD, Ryan KS, Gonzalez DJ,  Nizet V, Dorrestein PC, Moore BS: Direct cloning and refactoring of a silent lipopeptide biosynthetic gene cluster yields the antibiotic taromycin A. Proc Natl Acad Sci U S A 2014, 111:1957-1962. An excellent example of genome mining, in which a new variant of daptomycin was discovered by examining the genome sequence of a marine actinomycete. 16. Brady SF: Construction of soil environmental DNA cosmid libraries and screening for clones that produce biologically active small molecules. Nat Protoc 2007, 2:1297-1305. 17. Milshteyn A, Schneider JS, Brady SF: Mining the metabiome: identifying novel natural products from microbial communities. Chem Biol 2014, 21:1211-1223. 18. Chang FY, Brady SF: Discovery of indolotryptoline antiproliferative agents by homology-guided metagenomic screening. Proc Natl Acad Sci U S A 2013, 110:2478-2483. 19. Feng Z, Kallifidas D, Brady SF: Functional analysis of  environmental DNA-derived type ii polyketide synthases reveals structurally diverse secondary metabolites. Proc Natl Acad Sci U S A 2011, 108:12629-12634. An excellent demonstration of the power of metagenome mining, in which a number of new aromatic polyketides, including new antibiotics, were discovered. 20. Feng Z, Kim JH, Brady SF: Fluostatins produced by the heterologous expression of a TAR reassembled environmental DNA derived type ii PKS gene cluster. J Am Chem Soc 2010, 132:11902-11903. 21. Kang HS, Brady SF: Arimetamycin A: improving clinically relevant families of natural products through sequenceguided screening of soil metagenomes. Angew Chem Int Ed Engl 2013, 52:11063-11067. www.sciencedirect.com

22. Banik JJ, Craig JW, Calle PY, Brady SF: Tailoring enzyme-rich environmental DNA clones: a source of enzymes for generating libraries of unnatural natural products. J Am Chem Soc 2010, 132:15661-15670. 23. Bauer JD, King RW, Brady SF: Utahmycins A and B, azaquinones produced by an environmental DNA clone. J Nat Prod 2010, 73:976-979. 24. Owen JG, Reddy BV, Ternei MA, Charlop-Powers Z, Calle PY, Kim JH, Brady SF: Mapping gene clusters within arrayed metagenomic libraries to expand the structural diversity of biomedically relevant natural products. Proc Natl Acad Sci U S A 2013, 110:11797-11802. 25. Feng Z, Chakraborty D, Dewell SB, Reddy BV, Brady SF: Environmental DNA-encoded antibiotics fasamycins A and B inhibit FabF in type II fatty acid biosynthesis. J Am Chem Soc 2012, 134:2981-2987. 26. Kallifidas D, Kang HS, Brady SF: Tetarimycin A, an MRSA-active antibiotic identified through induced expression of environmental DNA gene clusters. J Am Chem Soc 2012, 134:19552-19555. 27. Charlop-Powers Z, Owen JG, Reddy BV, Ternei MA, Brady SF: Chemical-biogeographic survey of secondary metabolism in soil. Proc Natl Acad Sci U S A 2014, 111:3757-3762. 28. Owen JG, Charlop-Powers Z, Smith AG, Ternei MA, Calle PY,  Reddy BV, Montiel D, Brady SF: Multiplexed metagenome mining using short DNA sequence tags facilitates targeted discovery of epoxyketone proteasome inhibitors. Proc Natl Acad Sci U S A 2015, 112:4221-4226. Demonstrates the use of short sequence tags for targeted discovery of natural products by mining many soil metagenomes in parallel. 29. Reddy BV, Milshteyn A, Charlop-Powers Z, Brady SF: eSNaPD: a versatile, web-based bioinformatics platform for surveying and mining natural product biosynthetic diversity from metagenomes. Chem Biol 2014, 21:1023-1033. 30. Weber T, Blin K, Duddela S, Krug D, Kim HU, Bruccoleri R, Lee SY, Fischbach MA, Muller R, Wohlleben W et al.: AntiSMASH 3.0 — a comprehensive resource for the genome mining of biosynthetic gene clusters. Nucleic Acids Res 2015, 43:W237-W243. 31. Ziemert N, Podell S, Penn K, Badger JH, Allen E, Jensen PR: The natural product domain seeker NaPDoS: a phylogeny based bioinformatic tool to classify secondary metabolite gene diversity. PLoS One 2012, 7:e34064. 32. Skinnider MA, Dejong CA, Rees PN, Johnston CW, Li H, Webster AL, Wyatt MA, Magarvey NA: Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM). Nucleic Acids Res 2015, 43:9645-9662. 33. Kersten RD, Yang YL, Xu Y, Cimermancic P, Nam SJ, Fenical W,  Fischbach MA, Moore BS, Dorrestein PC: A mass spectrometryguided genome mining approach for natural product peptidogenomics. Nat Chem Biol 2011, 7:794-802. This paper demonstrates the combined use of tandem mass spectra and genome sequence data to discover new peptide natural products. 34. Goering AW, McClure RA, Doroghazi JR, Albright JC, Haverland NA, Zhang Y, Ju K-S, Thomson RJ, Metcalf WW, Kelleher NL: Metabologenomics. Correlation of microbial gene clusters with metabolites drives discovery of a nonribosomal peptide with an unusual amino acid monomer. ACS Cent Sci 2016, 2:99-108. 35. Duncan KR, Crusemann M, Lechner A, Sarkar A, Li J, Ziemert N, Wang M, Bandeira N, Moore BS, Dorrestein PC, Jensen PR: Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora species. Chem Biol 2015, 22:460-471. 36. Kersten RD, Ziemert N, Gonzalez DJ, Duggan BM, Nizet V, Dorrestein PC, Moore BS: Glycogenomics as a mass spectrometry-guided genome-mining method for microbial glycosylated molecules. Proc Natl Acad Sci U S A 2013, 110:E4407-E4416. Current Opinion in Microbiology 2016, 33:25–34

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