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Microbiology (2008), 154, 1555–1569 DOI 10.1099/mic.0.2008/018523-0 SGM Special Lecture Fleming Prize Lecture 2007 Delivered at the 160th meeting of the SGM, 27 March 2007 Correspondence Gregory L. Challis G.L.Challis@warwick.ac.uk Mining microbial genomes for new natural products and biosynthetic pathways Gregory L. Challis Department of Chemistry, University of Warwick, Coventry CV4 7AL, UK Analyses of microbial genome sequences have revealed numerous examples of ‘cryptic’ or ‘orphan’ biosy
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  SGM SpecialLecture Fleming Prize Lecture2007 Delivered at the 160th meeting of the SGM, 27 March 2007  Correspondence Gregory L. ChallisG.L.Challis@warwick.ac.uk Mining microbial genomes for new natural productsand biosynthetic pathways Gregory L. Challis Department of Chemistry, University of Warwick, Coventry CV4 7AL, UK Analyses of microbial genome sequences have revealed numerous examples of ‘cryptic’ or‘orphan’ biosynthetic gene clusters, with the potential to direct the production of novel, structurallycomplex natural products. This article summarizes the various methods that have been developedfor discovering the products of cryptic biosynthetic gene clusters in microbes and gives anaccount of my group’s discovery of the products of two such gene clusters in the modelactinomycete Streptomyces coelicolor  M145. These discoveries hint at new mechanisms, rolesand specificities for natural product biosynthetic enzymes. Our efforts to elucidate these aredescribed. The identification of new secondary metabolites of S. coelicolor  raises the question:what is their biological function? Progress towards answering this question is also summarized. Introduction Alexander Fleming’s discovery of penicillin in 1928 and itssubsequent development into a medicine by Florey andChain in the 1940s provided the foundation for devel-opment of microbial natural products as a cornerstone of new drug discovery in the 20th century (Fleming, 1929;Chain et al. , 1940). By the end of the century many microbial natural products had found their way into theclinic as antibacterial, antifungal, antiparasitic, anticancerand immunosuppressive agents. Yet the turn of the century also witnessed a mass withdrawal of large pharmaceuticalcompanies from new microbial natural product discovery and microbial natural products research (Koehn & Carter,2005). Several factors spurred this retreat, includingrediscovery of known natural products with high fre-quency, the technical challenges associated with purifica-tion and structure elucidation of natural products frommicrobial fermentations and the advent of combinatorialchemistry, which promised to provide a wealth of new compounds to screen for biological activity. At the very time natural product discovery programmes were rampingdown in the late 1990s, large-scale microbial genomesequencing began to ramp up, fuelled by the pioneeringapplication of whole-genome shotgun sequencing to Haemophilus influenzae  , published in 1995, which demon-strated that microbial genome sequences could be obtainedwith hitherto unimagined rapidity (Fleischmann et al. ,1995). At present there are more than 580 completemicrobial genome sequences. Dramatic and sustainedincreases in our understanding of the genetics andenzymology of microbial natural product biosynthesisthroughout the 1990s have also facilitated the identificationand analysis of gene clusters likely to encode naturalproduct biosynthetic pathways in sequenced microbialgenomes (Fischbach & Walsh, 2006).It was extremely fortunate that the Wellcome Trust fundedme to carry out research as a postdoctoral fellow in theDepartment of Genetics at the John Innes Centre in 2000–2001, just as the Streptomyces coelicolor  A3(2) genomesequencing project was nearing completion. S. coelicolor  was one of the first sequenced microbes in which it wasrecognized that there are many more gene clustersencoding natural product-like biosynthetic pathways thanthere are known natural products of the organism (Bentley  et al. , 2002). Similar observations have now been reportedfor several diverse sequenced micro-organisms (Bok  et al. ,2006; Ikeda et al. , 2003; Keller et al. , 2005; Oliynyk  et al. ,2007; Omura et al. , 2001; Paulsen et al. , 2005; Udwary  et al. , 2007). This discovery provided a strong counter-argument to the idea that few novel compounds remain tobe discovered from natural sources and suggested that thewithdrawal of big pharmaceutical companies from naturalproduct drug discovery was premature. At the inception of my independent academic career, we set out to investigatethe so-called ‘cryptic’ or ‘orphan’ natural product biosyn-thetic gene clusters found within the genomes of  S.coelicolor  and other sequenced microbes that encodenatural product biosynthesis-like proteins not associatedwith the production of known metabolites. Over the past 6 years numerous groups around the world have focused onsimilar objectives and today ‘genome mining’ for new natural products and biosynthetic pathways has become adynamic and rapidly advancing field (Corre & Challis,2007; Challis, 2008; Gross, 2007; Wilkinson & Micklefield,2007). Abbreviations: A, adenylation; ACP, acyl carrier protein; CDA, calcium-dependent antibiotic; FAS, fatty acid synthase; fhOrn, N  5 -formyl- N  5 -hydroxyornithine; hOrn, N  5 -hydroxyornithine; NRPS, nonribosomal pep-tide synthetase; PCP, peptidyl carrier protein; PKS, polyketide synthase;TE, thioesterase. Microbiology  (2008), 154, 1555–1569 DOI 10.1099/mic.0.2008/018523-02008/018523 G 2008 SGM Printed in Great Britain 1555  In the first part of this article I will briefly review thedifferent approaches various groups have taken fordiscovering the metabolic products of cryptic naturalproduct biosynthetic gene clusters. An account of ourdiscovery of new metabolites of  S. coelicolor  follows,along with a discussion of the investigations by us andothers of the biosynthesis and biological functions of thesemetabolites. Strategies for identifying the metabolic productsof cryptic gene clusters Many microbial natural products, in particular complex polyketides and nonribosomal peptides, are assembled by biosynthetic assembly lines involving modular mega-synthases and synthetases (Fischbach & Walsh, 2006). Inmany cases the number of modules in the assembly linecorresponds exactly to the number of metabolic buildingblocks incorporated into the final product, although severalexceptions to this paradigm have emerged in recent years(Haynes & Challis 2007). The presence or absence of domains with ‘tailoring’ activities in individual modulesoften allows prediction of the way in which an initially selected metabolic building block gets modified during theprocess of its incorporation into the natural product(Fischbach & Walsh, 2006). Models that predict thestereochemical outcome of some of these tailoring reac-tions, e.g. ketoreduction, have also emerged recently (Caffrey, 2003; Reid et al. , 2003). Models that predict thesubstrate specificity of the adenylation (A) and acyltrans-ferase (AT) domains responsible for building block selection in each module of nonribosomal peptidesynthetase (NRPS) and polyketide synthase (PKS) assembly lines have also been reported and continue to be developed(Haydock  et al. , 1995; Banskota et al. , 2006a, b; Stachelhaus et al. , 1999; Challis et al. , 2000; Rausch et al. , 2005). Insightinto the structural features of the metabolic products of cryptic biosynthetic assembly lines can often be derived by application of the above bioinformatics analyses (Banskota et al. , 2006a, b; Bentley  et al. , 2002; Challis & Ravel, 2000;Chen et al. , 2007; de Bruijn et al. , 2007; McAlpine et al. ,2005; Minowa et al. , 2007; Nguyen et al. , 2008; Paulsen et al. , 2005; Sudek  et al. , 2006; Tohyama et al. , 2004; Udwary  et al. , 2007; Zirkle et al. , 2004). Such structural insights canlead to the prediction of putative physico-chemicalproperties of a metabolic product of a cryptic biosyntheticsystem. The search of fermentation broths for products of cryptic pathways can be narrowed to target only metaboliteswith the predicted physico-chemical properties, thussimplifying the analytical challenge (Fig.1). Several new metabolic products of crypticbiosyntheticgeneclusters havebeen discovered using such methodologies (Lautru et al. ,2005; McAlpine et al. , 2005; Banskota et al. , 2006a, b).Two other approaches that have been applied to crypticbiosynthetic systems where the substrates of enzymes in thepathways can be predicted are the ‘genomisotopicapproach’ and in vitro  reconstitution. In the genomisotopicapproach, stable-isotope-labelled putative precursors of themetabolic product are fed to the organism containing thecryptic biosynthetic gene cluster and 2D NMR experimentsare used to screen extracts of the fermentation broth toidentify metabolites containing the labelled precursors(Fig. 2; Gross et al. , 2007). NMR detection of the labelledmetabolites can be used to guide fractionation of theextracts to facilitate their purification. This approachhas been applied to isolation of the orfamides, novelmacrocyclic lipopeptides predicted to be produced by  Pseudomonas fluorescens  Pf-5 from analysis of its genome Fig. 1. Method for identifying the product(s) of cryptic biosyn-thetic gene clusters by predicting likely physico-chemical prop-erties of the product from sequence analyses. Fig. 2. Principles of the genomisotopic approach for identifyingthe product(s) of cryptic biosynthetic gene clusters. G. L. Challis1556 Microbiology  154  sequence (Gross et al. , 2007). In the in vitro  reconstitutionapproach, the predicted substrates of a biosyntheticenzyme, which has been produced in pure recombinantform, are incubated with it and the structures of theproducts are determined (Fig. 3). Epi-isozizaene is anexample of a new compound that has been identified as theproduct of a cryptic sesquiterpene synthase discovered by the S. coelicolor  genome sequencing project using the in vitro  reconstitution approach (Lin et al. , 2006). Thismetabolite has recently been shown to be an intermediatein the assembly of the known Streptomyces  sesquiterpenealbaflavenone (Zhao et al. , 2008).For some types of biosynthetic system, substrate specificity cannot be predicted with any degree of confidence frombioinformatics analyses. In these cases the directedapproaches described above are not useful for finding themetabolic products of cryptic biosynthetic pathways andmore generic approaches are required. Two related moregeneric approaches that have been used successfully todiscover the products of cryptic biosynthetic gene clustersare gene knockout/comparative metabolic profiling andheterologous gene expression/comparative metabolic pro-filing (Corre & Challis, 2007). The first of these involvesinactivation of a gene within the cryptic biosynthetic genecluster hypothesized to be essential for metabolite biosyn-thesis, followed by comparison of the metabolites in theculture supernatants or extracts of the wild-type organismand the non-producing mutant using an appropriateanalytical technique such as liquid chromatography-massspectrometry (LC-MS). Metabolites present in the wild-type but lacking in the mutant are likely products of thecryptic gene cluster (Fig. 4), which can be isolated andstructurally characterized. Germicidins are an example of metabolites discovered using this strategy (Song et al. ,2006). In the second approach the entire biosynthetic genecluster is cloned, often in a single cosmid or BAC vector,and expressed in a heterologous host. The profile of metabolites in culture supernatants or extracts of theheterologous host containing and lacking the clonedcryptic biosynthetic gene cluster are compared using LC-MS or other appropriate analytical techniques. Metabolitespresent in the host containing the gene cluster, but absentin the host lacking the cluster, are likely products of thecryptic biosynthetic pathway (Fig. 5), which can be purifiedand structurally characterized as in the first approach.CBS40 is an example of a novel metabolite that has beenidentified by this approach (Hornung et al. , 2007). Onepotential obstacle which often has to be overcome in the Fig. 3. The in vitro reconstitution approach for identifying theproduct(s) of cryptic biosynthetic gene clusters. The structure(s) ofthe product(s) resulting from incubation of the predicted sub-strate(s) with the purified enzyme are determined. Fig. 4. The gene knockout/comparative metabolic profilingapproach to identifying the product(s) of cryptic biosynthetic geneclusters. Fig. 5. The heterologous expression/comparative metabolic pro-filing approach to identifying the product(s) of cryptic biosyntheticgene clusters. Fleming Prize Lecture 2007http://mic.sgmjournals.org 1557  heterologous expression/comparative metabolic profilingapproach is that natural product biosynthetic gene clustersare often large ( . 40 kb) and therefore it can be difficult toclone the entire cluster in a single vector. The use of multiple, mutually compatible expression vectors is oneapproach to overcoming this problem (Challis, 2006),although it has yet to be applied to the discovery of new metabolic products of cryptic biosynthetic gene clusters.In the majority of the above approaches a commonproblem can be encountered: that the cryptic biosyntheticgene cluster is not expressed in the wild-type organism orheterologous host in laboratory culture. The exception isthe in vitro  reconstitution approach, which removes thebiosynthetic genes from their natural regulatory context by expressing them under the control of a heterologous (andusually inducible) promoter. However, the in vitro  reconstitution of an entire biosynthetic pathway usually involves separate overexpression of each gene and puri-fication of the resulting overproduced protein, and many cryptic gene clusters contain multiple putative biosyntheticgenes. Thus, the discovery of a fully elaborated metabolicproduct by this approach is likely to be very laborious. Twoapproaches to address this problem in Aspergillus nidulans  have been reported (Bok  et al. , 2006; Bergmann et al. ,2007). The first involved comparative profiling of geneexpression in cryptic biosynthetic clusters in the wild-type,and mutants with the pleiotropic regulator of secondary metabolism laeA either deleted or overexpressed (Bok  et al. ,2006). Gene clusters that are differentially expressed in themutants compared with the wild-type are identified asputatively involved in secondary metabolic biosynthesis.This approach offers potential for the discovery of new metabolic products of cryptic biosynthetic pathwaysbecause overexpression of  laeA causes increased expressionof some Aspergillus  cryptic gene clusters. However, thispotential has yet to be demonstrated by the discovery of anovel Aspergillus  natural product. The second approachinvolves expression of putative pathway-specific activatorgenes from within silent cryptic biosynthetic gene clustersunder the control of an inducible promoter (Bergmann et al. , 2007). This approach has been shown to causeexpression of a normally silent gene cluster in A. nidulans  upon addition of the inducer. Aspyridones, the metabolicproducts of this gene cluster, were identified by compar-ative metabolic profiling of the wild-type and mutantstrains and spectroscopic analyses showed them to havenovel structures, thus proving the utility of this approachfor discovering new natural products of cryptic biosyn-thetic gene clusters that are not expressed in laboratory cultures (Bergmann et al. , 2007; Fig. 6).The various strategies summarized above for identifyingthe metabolic products of cryptic biosynthetic gene clustershave different strengths and weaknesses, depending on how much can be deduced about the structure of the productsfrom bioinformatics analyses, the size of the gene cluster,and whether the gene cluster is well expressed in laboratory cultures. These factors need to be carefully consideredwhen choosing the best approach to take in attempting toidentify the products of a cryptic biosynthetic gene cluster.Doubtless further approaches will be added to this already impressive array as research activity in this exciting new field continues to increase. Discovery of coelichelin by S. coelicolor  genomemining In the course of a search of microbial genome sequences forgene clusters encoding novel NRPS systems, a cluster of 11genes, one of which encodes a protein that is similar toseveral well-characterized NRPSs, was discovered in thepartially completed genome sequence of  S. coelicolor  M145(Fig. 7; Challis & Ravel, 2000). This gene cluster had notbeen reported to be involved in the biosynthesis of any known secondary metabolites of  S. coelicolor  . Sequenceanalysis of the NRPS-like protein encoded by the cchH  genein this cluster suggested that it contains 10 catalyticdomains, organized into three functional modules (Fig. 7;Challis & Ravel, 2000). The first two modules both containdomains similar to known epimerization (E) domains inother NRPSs, suggesting that these modules incorporate D -amino acids into the product of CchH. Application of thepredictive, structure-based models that Jacques Ravel and Ideveloped as postdoctoral researchers in Craig Townsend’sgroup (Challis et al. , 2000) suggested that the A domains inmodules 1, 2 and 3 of CchH selected the amino acids L - N  5 -formyl- N  5 -hydroxyornithine ( L -fhOrn), L -threonine and L - N  5 -hydroxyornithine ( L -hOrn), respectively, and catalysedtheir ATP-dependent transfer onto the adjacent peptidylcarrier protein (PCP) domains in each module (Challis & Ravel, 2000; Fig. 7). Thus we proposed that CchH catalysedassembly of the novel tripeptide D -fhOrn- D -allo-Thr- L -hOrn, which could be formed as one of two essentially isomeric structures, depending on the regiospecificity of the condensation (C) domain in module 3 of the NRPS Fig. 6. The expression of pathway-specific activator/comparativemetabolic profiling approach to identifying the product(s) of crypticbiosynthetic gene clusters. Placing a pathway-specific activatorgene under the control of an inducible promoter results inactivation of transcription of the normally silent biosynthetic genecluster. G. L. Challis1558 Microbiology  154
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