FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2807980901> ?p ?o ?g. }

• W2807980901 endingPage "736" @default.
• W2807980901 startingPage "725" @default.
• W2807980901 abstract "Natural products have long played a pivotal role in the development of therapeutics for a variety of diseases. Traditionally, soil and marine environments have provided a rich reservoir from which diverse chemical scaffolds could be discovered. Recently, the human microbiome has been recognized as a promising niche from which secondary metabolites with therapeutic potential have begun to be isolated. In this Review, we address how the expansive history of identifying bacterial natural products in other environments is informing the approaches being brought to bear on the study of the human microbiota. We also touch on how these tools can lead to insights about microbe-microbe and host-microbe interactions and help generate biological hypotheses that may lead to developments of new therapeutic modalities. Natural products have long played a pivotal role in the development of therapeutics for a variety of diseases. Traditionally, soil and marine environments have provided a rich reservoir from which diverse chemical scaffolds could be discovered. Recently, the human microbiome has been recognized as a promising niche from which secondary metabolites with therapeutic potential have begun to be isolated. In this Review, we address how the expansive history of identifying bacterial natural products in other environments is informing the approaches being brought to bear on the study of the human microbiota. We also touch on how these tools can lead to insights about microbe-microbe and host-microbe interactions and help generate biological hypotheses that may lead to developments of new therapeutic modalities. A rapidly growing number of studies suggest a role for the human microbiome in complex pathophysiological processes ranging from the regulation of the immune system to the development of the brain and the central nervous system (Kau et al., 2011Kau A.L. Ahern P.P. Griffin N.W. Goodman A.L. Gordon J.I. Human nutrition, the gut microbiome and the immune system.Nature. 2011; 474: 327-336Crossref PubMed Scopus (1802) Google Scholar, Smith, 2015Smith P.A. The tantalizing links between gut microbes and the brain.Nature. 2015; 526: 312-314Crossref PubMed Scopus (125) Google Scholar). Mouse models highlight the necessity of native bacterial ecology for normal physiologic functions and demonstrate that dysbiosis is associated with diseases like obesity, cancer, diabetes, and colitis among others (Bongers et al., 2014Bongers G. Pacer M.E. Geraldino T.H. Chen L. He Z. Hashimoto D. Furtado G.C. Ochando J. Kelley K.A. Clemente J.C. et al.Interplay of host microbiota, genetic perturbations, and inflammation promotes local development of intestinal neoplasms in mice.J. Exp. Med. 2014; 211: 457-472Crossref PubMed Scopus (60) Google Scholar, Garrett et al., 2007Garrett W.S. Lord G.M. Punit S. Lugo-Villarino G. Mazmanian S.K. Ito S. Glickman J.N. Glimcher L.H. Communicable ulcerative colitis induced by T-bet deficiency in the innate immune system.Cell. 2007; 131: 33-45Abstract Full Text Full Text PDF PubMed Scopus (752) Google Scholar, Ridaura et al., 2013Ridaura V.K. Faith J.J. Rey F.E. Cheng J. Duncan A.E. Kau A.L. Griffin N.W. Lombard V. Henrissat B. Bain J.R. et al.Gut microbiota from twins discordant for obesity modulate metabolism in mice.Science. 2013; 341: 1241214Crossref PubMed Scopus (2423) Google Scholar, Smith et al., 2013Smith M.I. Yatsunenko T. Manary M.J. Trehan I. Mkakosya R. Cheng J. Kau A.L. Rich S.S. Concannon P. Mychaleckyj J.C. et al.Gut microbiomes of Malawian twin pairs discordant for kwashiorkor.Science. 2013; 339: 548-554Crossref PubMed Scopus (814) Google Scholar). The potential future impact of human microbiome research on human health is evident by the recent surge of clinical trials and venture capital spending in the field (Gormley, 2016Gormley, B. (2016). Microbiome companies attract big investments. Wall Street Journal, September 18, 2016. https://www.wsj.com/articles/microbiome-companies-attract-big-investments-1474250460.Google Scholar, Marchesi et al., 2016Marchesi J.R. Adams D.H. Fava F. Hermes G.D.A. Hirschfield G.M. Hold G. Quraishi M.N. Kinross J. Smidt H. Tuohy K.M. et al.The gut microbiota and host health: a new clinical frontier.Gut. 2016; 65: 330-339Crossref PubMed Scopus (1334) Google Scholar). Currently, more than 1,200 clinical trials can be found in the National Institutes of Health clinical trials database using the search query for “gut microbiome.” Just over half of these are active or are actively recruiting patients (https://clinicaltrials.gov). Despite mounting evidence linking host-associated bacteria to normal development and disease in animal models and correlative evidence in humans, the mechanisms by which specific bacterial functions affect mammalian or microbiome physiology (i.e., effector functions) remain largely undefined. The central dogma of molecular biology, traditionally outlined as the transfer of information from DNA to RNA to protein, stops short of the ultimate end point of much of the information flow in a biological system. In a significant fraction of cellular processes, the end point of information flow is not a protein but is, instead, a small molecule. This is especially true for bacteria which rely heavily on low-molecular-weight compounds (i.e., small molecules or natural products) to interact with their surroundings. The systematic characterization of small molecules produced by human-associated bacteria will undoubtedly be key to deciphering the mechanistic details of the role the human microbiome plays in our health and disease. The study of biologically active natural products made by bacteria associated with other ecosystems (e.g., soils and marine environments) has traditionally been a very effective gateway to identify small molecules that have proved useful as therapeutics and as tools for modulating complex biological systems (Cragg et al., 1997Cragg G.M. Newman D.J. Snader K.M. Natural products in drug discovery and development.J. Nat. Prod. 1997; 60: 52-60Crossref PubMed Scopus (1220) Google Scholar, Harvey et al., 2015Harvey A.L. Edrada-Ebel R. Quinn R.J. The re-emergence of natural products for drug discovery in the genomics era.Nat. Rev. Drug Discov. 2015; 14: 111-129Crossref PubMed Scopus (1584) Google Scholar, Knight et al., 2003Knight V. Sanglier J.J. DiTullio D. Braccili S. Bonner P. Waters J. Hughes D. Zhang L. Diversifying microbial natural products for drug discovery.Appl. Microbiol. Biotechnol. 2003; 62: 446-458Crossref PubMed Scopus (150) Google Scholar, Newman et al., 2000Newman D.J. Cragg G.M. Snader K.M. The influence of natural products upon drug discovery.Nat. Prod. Rep. 2000; 17: 215-234Crossref PubMed Scopus (1044) Google Scholar, Schreiber et al., 2002Schreiber S.L. Nicolaou K.C. Davies K. Diversity-oriented organic synthesis and proteomics. New frontiers for chemistry & biology.Chem. Biol. 2002; 9: 1-2Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar). In fact, approximately 80% of medicines identified up to 1996 were either directly derived from or inspired by natural products (Sneader, 1996Sneader W. Drug Prototypes and Their Exploitation. Wiley, 1996Google Scholar), and half of all drugs approved since 1994 have their origins in natural products (Butler et al., 2017Butler M.S. Blaskovich M.A. Cooper M.A. Antibiotics in the clinical pipeline at the end of 2015.J. Antibiot. 2017; 70: 3-24Crossref PubMed Scopus (241) Google Scholar, Newman and Cragg, 2016Newman D.J. Cragg G.M. Natural products as sources of new drugs from 1981 to 2014.J. Nat. Prod. 2016; 79: 629-661Crossref PubMed Scopus (3825) Google Scholar). The exploration of human-associated bacteria for the production of small molecules with therapeutic potential is still very much in its infancy. Whether these bacteria will prove to be a gold mine of novel therapeutic molecules, as has been the case for bacteria from most other ecosystems, remains to be seen. In addition to providing a source for new natural products discovery, studying the metabolites produced by human-associated bacteria can reveal the language of bacteria-host communication. Such knowledge has scientific merit in itself but can also be used to guide new therapies that seek to modulate the human microbiota. The development of therapeutic prebiotics, which are dietary compounds not digestible by human enzymes that serve as substrates for beneficial microbial conversions (Holscher, 2017Holscher H.D. Dietary fiber and prebiotics and the gastrointestinal microbiota.Gut Microbes. 2017; 8: 172-184Crossref PubMed Scopus (719) Google Scholar), could prosper from a more intricate understanding of bacterial metabolic processes in the gut (Martens et al., 2011Martens E.C. Lowe E.C. Chiang H. Pudlo N.A. Wu M. McNulty N.P. Abbott D.W. Henrissat B. Gilbert H.J. Bolam D.N. Gordon J.I. Recognition and degradation of plant cell wall polysaccharides by two human gut symbionts.PLoS Biol. 2011; 9: e1001221Crossref PubMed Scopus (510) Google Scholar). Furthermore, therapeutic administration of live bacteria into patients has been used since the mid-20th century for a handful of diseases with limited mechanistic understanding (O’Toole et al., 2017O’Toole P.W. Marchesi J.R. Hill C. Next-generation probiotics: the spectrum from probiotics to live biotherapeutics.Nat. Microbiol. 2017; 2: 17057Crossref PubMed Scopus (399) Google Scholar). Therapeutic preparations containing live microorganisms, which are now referred to as live biotherapeutic products (LBPs) by the U.S. Food and Drug Administration (FDA), have been subject to increased regulatory measures over the last decade, signifying a concerted effort to more strictly define how these bacteria function in the human body (Dreher-Lesnick et al., 2017Dreher-Lesnick S.M. Stibitz S. Carlson Jr., P.E. U.S. regulatory considerations for development of live biotherapeutic products as drugs.Microbiol. Spectr. 2017; 5: 5Crossref Scopus (17) Google Scholar). Thus, defining the chemical crosstalk of human-associated bacteria could promote an informed application of alternative therapies, such as prebiotics and LBPs, in addition to the discovery of discrete therapeutic natural products. In this Review, we will summarize the methodologies that have been used to mine the human microbiome for metabolites demonstrating biological function (functional metabolites) that might ultimately have therapeutic utility. This is not intended to be a comprehensive review of the small molecules characterized from the microbiome, which have been extensively reviewed elsewhere (Donia and Fischbach, 2015Donia M.S. Fischbach M.A. HUMAN MICROBIOTA. Small molecules from the human microbiota.Science. 2015; 349: 1254766Crossref PubMed Scopus (425) Google Scholar, Mousa et al., 2017Mousa W.K. Athar B. Merwin N.J. Magarvey N.A. Antibiotics and specialized metabolites from the human microbiota.Nat. Prod. Rep. 2017; 34: 1302-1331Crossref PubMed Google Scholar, Sharon et al., 2014Sharon G. Garg N. Debelius J. Knight R. Dorrestein P.C. Mazmanian S.K. Specialized metabolites from the microbiome in health and disease.Cell Metab. 2014; 20: 719-730Abstract Full Text Full Text PDF PubMed Scopus (352) Google Scholar). Notably, two of the most studied bacterial molecules, colibactin and short-chain fatty acids (SCFAs), have been extensively reviewed (Balskus, 2015Balskus E.P. Colibactin: understanding an elusive gut bacterial genotoxin.Nat. Prod. Rep. 2015; 32: 1534-1540Crossref PubMed Google Scholar, Tan et al., 2014Tan J. McKenzie C. Potamitis M. Thorburn A.N. Mackay C.R. Macia L. The role of short-chain fatty acids in health and disease.Adv. Immunol. 2014; 121: 91-119Crossref PubMed Scopus (1208) Google Scholar) and are not discussed here. Instead, we hope to broadly describe the approaches being used by researchers to bridge the gap between bacteria and host pathophysiology through the discovery of functional bacterial metabolites. Systematically identifying small molecules produced by host-associated bacteria will be fundamental to the characterization of the human microbiome. Currently, we have only a glimpse of the full scope of metabolites; however, the studies below suggest a bright future for discovery. The mining of the human microbiome for bioactive small molecules has largely come about in the post-genomics era. Consequently, the approaches being most heavily used in these studies tend to first leverage either molecular biology methods or sequence data rather than analytical chemistry to identify natural products or natural product gene clusters of interest. The availability of rapid and inexpensive sequencing of entire bacterial genomes and the development of targeted metagenomic sequencing approaches have enabled researchers to access molecules even if their biosynthetic genes are silent or their bacterial source is recalcitrant to cultivation (Milshteyn et al., 2014Milshteyn A. Schneider J.S. Brady S.F. Mining the metabiome: identifying novel natural products from microbial communities.Chem. Biol. 2014; 21: 1211-1223Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar, Rutledge and Challis, 2015Rutledge P.J. Challis G.L. Discovery of microbial natural products by activation of silent biosynthetic gene clusters.Nat. Rev. Microbiol. 2015; 13: 509-523Crossref PubMed Scopus (565) Google Scholar). Here, we present examples of the various complementary microbiome mining approaches that have led to the characterization of discrete bioactive natural products with potential therapeutic applications. These approaches fall into the broad categories of functional metagenomics, which is aimed at identifying metabolites from metagenomic libraries that show biological activity in functional assays (Figure 1), and sequence-based metagenomics (Figure 2), which relies on (meta)genomic sequence information to guide the discovery of biologically active metabolites. After presenting these approaches, we will highlight alternative paths that fall outside of the distinction of (meta)genomics, particularly those that rely on chemical or functional analysis of secreted metabolites from laboratory cultivation of bacteria without a priori genetic hypotheses (Figure 4).Figure 2Sequence-Based (Meta)genomics WorkflowsShow full caption(A) Sequence-based (meta)genomics leverages the power of predictive bioinformatics tools to identify biosynthetic gene clusters in the available sequence data and guide the targeted discovery of bacterial metabolites. This approach was used to discover the novel antibiotic lactocillin and various dipeptide aldehydes with implications in mammalian protease inhibition.(B) In the synthetic-bioinformatic natural product (syn-BNP) workflow, specific chemical structures of natural products are predicted from analyses of biosynthetic gene clusters, and these molecules are chemically synthesized and subsequently tested for biological activity, such as antibiosis in the case of the humimycins.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) Sequence-based (meta)genomics leverages the power of predictive bioinformatics tools to identify biosynthetic gene clusters in the available sequence data and guide the targeted discovery of bacterial metabolites. This approach was used to discover the novel antibiotic lactocillin and various dipeptide aldehydes with implications in mammalian protease inhibition. (B) In the synthetic-bioinformatic natural product (syn-BNP) workflow, specific chemical structures of natural products are predicted from analyses of biosynthetic gene clusters, and these molecules are chemically synthesized and subsequently tested for biological activity, such as antibiosis in the case of the humimycins. The development of sequencing technologies that revolutionized how we study bacteria has also brought with it the recognition that culture-based studies have barely scratched the surface of the bacterial diversity in the environment, with the vast majority of bacteria not being amenable to culturing techniques (Rappé and Giovannoni, 2003Rappé M.S. Giovannoni S.J. The uncultured microbial majority.Annu. Rev. Microbiol. 2003; 57: 369-394Crossref PubMed Scopus (1406) Google Scholar). To overcome this limitation, methods have been developed to capture total bacterial DNA from an environment or a biological sample in the form of metagenomic cosmid or fosmid DNA libraries. These methods enable culture-independent studies of the secondary metabolite biosynthesis pathways encoded by bacteria represented in the metagenome (Brady, 2007Brady S.F. Construction of soil environmental DNA cosmid libraries and screening for clones that produce biologically active small molecules.Nat. Protoc. 2007; 2: 1297-1305Crossref PubMed Scopus (169) Google Scholar). Individual clones in these libraries can then be screened for the production of clone-specific metabolites in diverse assays (Brady and Clardy, 2000Brady S.F. Clardy J. Long-chain N-acyl amino acid antibiotics isolated from heterologously expressed environmental DNA.J. Am. Chem. Soc. 2000; 122: 12903-12904Crossref Scopus (128) Google Scholar, Lim et al., 2005Lim H.K. Chung E.J. Kim J.C. Choi G.J. Jang K.S. Chung Y.R. Cho K.Y. Lee S.W. Characterization of a forest soil metagenome clone that confers indirubin and indigo production on Escherichia coli.Appl. Environ. Microbiol. 2005; 71: 7768-7777Crossref PubMed Scopus (113) Google Scholar, MacNeil et al., 2001MacNeil I.A. Tiong C.L. Minor C. August P.R. Grossman T.H. Loiacono K.A. Lynch B.A. Phillips T. Narula S. Sundaramoorthi R. et al.Expression and isolation of antimicrobial small molecules from soil DNA libraries.J. Mol. Microbiol. Biotechnol. 2001; 3: 301-308PubMed Google Scholar, Owen et al., 2012Owen J.G. Robins K.J. Parachin N.S. Ackerley D.F. A functional screen for recovery of 4′-phosphopantetheinyl transferase and associated natural product biosynthesis genes from metagenome libraries.Environ. Microbiol. 2012; 14: 1198-1209Crossref PubMed Scopus (44) Google Scholar, Wang et al., 2000Wang G.-Y.-S. Graziani E. Waters B. Pan W. Li X. McDermott J. Meurer G. Saxena G. Andersen R.J. Davies J. Novel natural products from soil DNA libraries in a streptomycete host.Org. Lett. 2000; 2: 2401-2404Crossref PubMed Scopus (216) Google Scholar). The functional metagenomic approach aims to provide access to bacterial metabolites by screening metagenomic libraries for bioactivities of interest in a high-throughput fashion (Figure 1A). Once a bioactive library clone is identified in a functional screen, it can be isolated and sequenced to identify the gene, or a collection of genes, responsible for the production of the bioactive metabolite. This ability to connect the metabolite directly with its biosynthetic components is one of the major strengths of functional metagenomics. In the context of the human microbiome, where a large fraction of the bacterial species has been fully sequenced, this can also allow the researchers to simultaneously connect the bioactive metabolite with its function in the host and its producer in the microbiome. Although there are as many ways to employ functional metagenomics as there are screening methods, the most productive screening approaches to date using human microbiome metagenomic libraries have involved screening culture broth filtrates from individually arrayed metagenomic clones against human cell reporter assays. In the earliest such effort, Lakhdari and co-workers used a nuclear factor-κB (NF-κB) activation screen to identify 171 clones in a 2,640-clone human gut microbiome fosmid library that appeared to modulate the activity of the NF-κB reporter (Lakhdari et al., 2010Lakhdari O. Cultrone A. Tap J. Gloux K. Bernard F. Ehrlich S.D. Lefèvre F. Doré J. Blottière H.M. Functional metagenomics: a high throughput screening method to decipher microbiota-driven NF-κB modulation in the human gut.PLoS One. 2010; 5: 5Crossref Google Scholar). NF-κB is a rapidly inducible transcription factor that plays a broad role in innumerable cellular responses (Zhang et al., 2017Zhang Q. Lenardo M.J. Baltimore D. 30 years of NF-κB: a blossoming of relevance to human pathobiology.Cell. 2017; 168: 37-57Abstract Full Text Full Text PDF PubMed Scopus (1091) Google Scholar). Importantly, it is intimately involved in the immuno-inflammatory response in the gut, making it a good candidate to detect a broad range of microbe-host interactions (Hayden et al., 2006Hayden M.S. West A.P. Ghosh S. NF-kappaB and the immune response.Oncogene. 2006; 25: 6758-6780Crossref PubMed Scopus (932) Google Scholar). In a more recent study, Cohen et al., 2015Cohen L.J. Kang H.S. Chu J. Huang Y.H. Gordon E.A. Reddy B.V. Ternei M.A. Craig J.W. Brady S.F. Functional metagenomic discovery of bacterial effectors in the human microbiome and isolation of commendamide, a GPCR G2A/132 agonist.Proc. Natl. Acad. Sci. USA. 2015; 112: E4825-E4834Crossref PubMed Scopus (109) Google Scholar used an NF-κB-driven GFP reporter construct to identify a number of host-associated bacteria effector genes (Cbegs) from cosmid metagenomic libraries constructed using DNA extracted from human stool samples. For this study, a total of ∼75,000 individually arrayed cosmid clones hosted in E. coli were grown in lysogeny broth medium, and filter-sterilized spent culture broth was then applied to human cells carrying an NF-κB reporter. Library clones that triggered the reporter were then subjected to transposon mutagenesis to identify the genes responsible. Identified effector genes fell into three broad categories: hydrolases (catabolic), transferases (anabolic), and binding proteins. An in-depth investigation of one identified effector gene revealed that it encoded the production of a novel long-chain N-acyl amide, commendamide (Figure 1A), which is structurally similar to endogenous human metabolites that are known to activate G-protein-coupled receptors (GPCRs) (Hanuš et al., 2014Hanuš L. Shohami E. Bab I. Mechoulam R. N-acyl amino acids and their impact on biological processes.Biofactors. 2014; 40: 381-388Crossref PubMed Scopus (42) Google Scholar). Fittingly, commendamide was found to activate a single receptor, GPR132/G2A, in a screen of 242 GPCRs using a cell-based assay. This receptor is believed to be activated endogenously by lysophosphatidylcholine and oxidized long-chain fatty acids and has been implicated in a variety of immune cell functions (Kabarowski, 2009Kabarowski J.H. G2A and LPC: regulatory functions in immunity.Prostaglandins Other Lipid Mediat. 2009; 89: 73-81Crossref PubMed Scopus (107) Google Scholar). In a follow-up study, Cohen et al. used bioinformatics and targeted gene synthesis to systematically heterologously express phylogenetically diverse N-acyl amide synthase genes found the Human Microbiome Project (HMP) datasets (Figure 1B) (Cohen et al., 2017Cohen L.J. Esterhazy D. Kim S.H. Lemetre C. Aguilar R.R. Gordon E.A. Pickard A.J. Cross J.R. Emiliano A.B. Han S.M. et al.Commensal bacteria make GPCR ligands that mimic human signalling molecules.Nature. 2017; 549: 48-53Crossref PubMed Scopus (252) Google Scholar, Human Microbiome Project Consortium, 2012Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (6942) Google Scholar). Detailed analysis of extracts derived from E. coli cultures transformed with these constructs revealed six distinct N-acyl amide families that differed by amine head group and fatty acid tail. Individual N-acyl amides were found to interact with specific GPCRs known to regulate gastrointestinal tract physiology, and it was observed that human microbiota-encoded N-acyl amides bear structural similarity to endogenous GPCR-active ligands (Cohen et al., 2015Cohen L.J. Kang H.S. Chu J. Huang Y.H. Gordon E.A. Reddy B.V. Ternei M.A. Craig J.W. Brady S.F. Functional metagenomic discovery of bacterial effectors in the human microbiome and isolation of commendamide, a GPCR G2A/132 agonist.Proc. Natl. Acad. Sci. USA. 2015; 112: E4825-E4834Crossref PubMed Scopus (109) Google Scholar). The clearest overlap in structure and function between bacterial and human ligands was for the activators of endocannabinoid receptor GPR119. The N-acyl serinol structures isolated in this study only differed from 2-oleoyl glycerol by the presence of an amide instead of an ester and from oleoylethanolamide by the presence of an additional ethanol substituent (Figure 1B). Mouse- and cell-based models demonstrate that bacterial GPR119 agonists regulate metabolic hormones and glucose homeostasis as efficiently as the endogenous GPR119 ligands. Colonization of mice with E. coli engineered to produce N-acyl serinol altered glucose homeostasis in these animals at levels similar to what is seen for synthetic GPR119 agonists. This, and other examples, suggests that mimicry of endogenous signaling molecules may be common in host-associated bacteria and that manipulation of microbiota biosynthetic genes provides a new small-molecule therapeutic modality—microbiome-biosynthetic gene therapy (Cohen et al., 2015Cohen L.J. Kang H.S. Chu J. Huang Y.H. Gordon E.A. Reddy B.V. Ternei M.A. Craig J.W. Brady S.F. Functional metagenomic discovery of bacterial effectors in the human microbiome and isolation of commendamide, a GPCR G2A/132 agonist.Proc. Natl. Acad. Sci. USA. 2015; 112: E4825-E4834Crossref PubMed Scopus (109) Google Scholar). Taken together, these two studies provide a clear example of how functional metagenomics can not only uncover novel molecular mechanisms underlying microbiome-host interactions, but also lead to identification of new therapeutic targets and strategies. Furthermore, using patient-specific source material for metagenomic library construction allows one to take a targeted approach to intelligently design screening strategies. Functional metagenomics still faces a number of challenges, including the ability to capture large biosynthetic gene clusters (BGCs) on a single clone and the expression efficiency of heterologous hosts (Gabor et al., 2004Gabor E.M. Alkema W.B. Janssen D.B. Quantifying the accessibility of the metagenome by random expression cloning techniques.Environ. Microbiol. 2004; 6: 879-886Crossref PubMed Scopus (188) Google Scholar). These two challenges have hindered the application of functional metagenomics to environmental microbiomes and should not be overlooked in the context of mining the human microbiome. However, they are mitigated somewhat by the fact that a large fraction of the known, bacterially produced effectors of host pathophysiology are encoded by individual genes or small sets of genes that can be captured on individual clones and expressed in E. coli (Donia and Fischbach, 2015Donia M.S. Fischbach M.A. HUMAN MICROBIOTA. Small molecules from the human microbiota.Science. 2015; 349: 1254766Crossref PubMed Scopus (425) Google Scholar). We also know that by varying the hosts employed for heterologous expression, a greater variety of metagenomic clones that actively express secondary metabolites can be identified (Craig et al., 2010Craig J.W. Chang F.Y. Kim J.H. Obiajulu S.C. Brady S.F. Expanding small-molecule functional metagenomics through parallel screening of broad-host-range cosmid environmental DNA libraries in diverse proteobacteria.Appl. Environ. Microbiol. 2010; 76: 1633-1641Crossref PubMed Scopus (150) Google Scholar). As much as 35% to 65% of the bacteria comprising the human microbiome have been amenable to culture. This pool provides many potential heterologous hosts for functional metagenomic studies of the human microbiome (Lagkouvardos et al., 2017Lagkouvardos I. Overmann J. Clavel T. Cultured microbes represent a substantial fraction of the human and mouse gut microbiota.Gut Microbes. 2017; 8: 493-503Crossref PubMed Scopus (56) Google Scholar). Genome mining is a computationally driven small-molecule discovery approach that is based on the predictive power of algorithms developed to identify BGCs in sequence data. Existing algorithms largely leverage data from the conserved sequences of well-characterized classes of BGCs to identify new gene clusters in these families (e.g., antiSMASH, eSNaPD, NP.searcher, ClustScan, MutliGeneBlast) (Li et al., 2009Li M.H. Ung P.M. Zajkowski J. Garneau-Tsodikova S. Sherman D.H. Automated genome mining for natural products.BMC Bioinformatics. 2009; 10: 185Crossref PubMed Scopus (190) Google Scholar, Medema and Fischbach, 2015Medema M.H. Fischbach M.A. Computational approaches to natural product discovery.Nat. Chem. Biol. 2015; 11: 639-648Crossref PubMed Scopus (284) Google Scholar, Medema et al., 2013Medema M.H. Takano E. Breitling R. Detecting sequence homology at the gene cluster level with MultiGeneBlast.Mol. Biol. Evol. 2013; 30: 1218-1223Crossref PubMed Scopus (226) Google Scholar, Reddy et al., 2014Reddy B.V. Milshteyn A. Charlop-Powers Z. Brady S.F. eSNaPD: a versatile, web-based bioinformatics platform for surveying and mining natural product biosynthetic diversity from metagenomes.Chem. Biol. 2014; 21: 1023-1033Abstract Full Text Full Text PDF PubMed Scopus (70) Google Scholar, Starcevic et al., 2008Starcevic A. Zucko J. Simunkovic J. Long P.F. Cullum J. Hranueli D. ClustScan: an integrated program package for the semi-automatic annotation of modular biosynthetic gene clusters and in silico prediction of novel chemical structures.Nucleic Acids Res. 2008; 36: 6882-6892Crossref PubMed Scopus (145) Google Scholar). More recently, efforts have been made to develop algorithms, such as ClusterFinder, capable of identifying as yet unknown BGC classes (Cimermancic et al., 2014Cimermancic P. Medema M.H. Claesen J. Kurita K. Wieland Br" @default.
• W2807980901 created "2018-06-21" @default.
• W2807980901 creator A5041671729 @default.
• W2807980901 creator A5050310376 @default.
• W2807980901 creator A5073186281 @default.
• W2807980901 date "2018-06-01" @default.
• W2807980901 modified "2023-10-16" @default.
• W2807980901 title "Accessing Bioactive Natural Products from the Human Microbiome" @default.
• W2807980901 cites W1202680985 @default.
• W2807980901 cites W1539086439 @default.
• W2807980901 cites W1715339469 @default.
• W2807980901 cites W1815204272 @default.
• W2807980901 cites W1827213771 @default.
• W2807980901 cites W184020152 @default.
• W2807980901 cites W1904975951 @default.
• W2807980901 cites W1908709110 @default.
• W2807980901 cites W1948266836 @default.
• W2807980901 cites W1977385550 @default.
• W2807980901 cites W1989179901 @default.
• W2807980901 cites W1993399595 @default.
• W2807980901 cites W1998783353 @default.
• W2807980901 cites W2009170813 @default.
• W2807980901 cites W2009257824 @default.
• W2807980901 cites W2011337131 @default.
• W2807980901 cites W2011700260 @default.
• W2807980901 cites W2013619747 @default.
• W2807980901 cites W2024694894 @default.
• W2807980901 cites W2026658658 @default.
• W2807980901 cites W2037653168 @default.
• W2807980901 cites W2037814086 @default.
• W2807980901 cites W2040539560 @default.
• W2807980901 cites W2041537722 @default.
• W2807980901 cites W2045737039 @default.
• W2807980901 cites W2046097464 @default.
• W2807980901 cites W2051022356 @default.
• W2807980901 cites W2051510344 @default.
• W2807980901 cites W2054049548 @default.
• W2807980901 cites W2055173643 @default.
• W2807980901 cites W2059484264 @default.
• W2807980901 cites W2063158722 @default.
• W2807980901 cites W2063756270 @default.
• W2807980901 cites W2064403224 @default.
• W2807980901 cites W2067278945 @default.
• W2807980901 cites W2075892592 @default.
• W2807980901 cites W2086833302 @default.
• W2807980901 cites W2088226047 @default.
• W2807980901 cites W2092725930 @default.
• W2807980901 cites W2093500216 @default.
• W2807980901 cites W2097478615 @default.
• W2807980901 cites W2098533519 @default.
• W2807980901 cites W2098949773 @default.
• W2807980901 cites W2107658072 @default.
• W2807980901 cites W2108744210 @default.
• W2807980901 cites W2110363791 @default.
• W2807980901 cites W2114330412 @default.
• W2807980901 cites W2114839447 @default.
• W2807980901 cites W2115292537 @default.
• W2807980901 cites W2116607488 @default.
• W2807980901 cites W2117029190 @default.
• W2807980901 cites W2126176053 @default.
• W2807980901 cites W2127134779 @default.
• W2807980901 cites W2128769815 @default.
• W2807980901 cites W2129690444 @default.
• W2807980901 cites W2131642993 @default.
• W2807980901 cites W2131887397 @default.
• W2807980901 cites W2132241088 @default.
• W2807980901 cites W2136520405 @default.
• W2807980901 cites W2139119508 @default.
• W2807980901 cites W2147378425 @default.
• W2807980901 cites W2150365613 @default.
• W2807980901 cites W2155395286 @default.
• W2807980901 cites W2155466666 @default.
• W2807980901 cites W2155897723 @default.
• W2807980901 cites W2156202011 @default.
• W2807980901 cites W2162871918 @default.
• W2807980901 cites W2167415413 @default.
• W2807980901 cites W2171908015 @default.
• W2807980901 cites W2203834014 @default.
• W2807980901 cites W2256742438 @default.
• W2807980901 cites W2463990453 @default.
• W2807980901 cites W2477760291 @default.
• W2807980901 cites W2481379384 @default.
• W2807980901 cites W2533385232 @default.
• W2807980901 cites W2542814600 @default.
• W2807980901 cites W2558941943 @default.
• W2807980901 cites W2571989678 @default.
• W2807980901 cites W2574819148 @default.
• W2807980901 cites W2586155766 @default.
• W2807980901 cites W2605025237 @default.
• W2807980901 cites W2606153738 @default.
• W2807980901 cites W2606877093 @default.
• W2807980901 cites W2608502548 @default.
• W2807980901 cites W2729743256 @default.
• W2807980901 cites W2735584664 @default.
• W2807980901 cites W2745901473 @default.
• W2807980901 cites W2753100975 @default.
• W2807980901 cites W2762948746 @default.
• W2807980901 cites W2764103617 @default.

• next