Microbial metabolites in health and disease: Navigating the unknown in search of function
2017; Elsevier BV; Volume: 292; Issue: 21 Linguagem: Inglês
10.1074/jbc.r116.752899
ISSN1083-351X
AutoresKristina Martinez, Vanessa Leone, Eugene B. Chang,
Tópico(s)Probiotics and Fermented Foods
ResumoThe gut microbiota has been implicated in the development of a number of chronic gastrointestinal and systemic diseases. These include inflammatory bowel diseases, irritable bowel syndrome, and metabolic (i.e. obesity, non-alcoholic fatty liver disease, and diabetes) and neurological diseases. The advanced understanding of host-microbe interactions has largely been due to new technologies such as 16S rRNA sequencing to identify previously unknown microbial communities and, more importantly, their functional characteristics through metagenomic sequencing and other multi-omic technologies, such as metatranscriptomics, metaproteomics, and metabolomics. Given the vast array of newly acquired knowledge in the field and technological advances, it is expected that mechanisms underlying several disease states involving the interactions between microbes, their metabolites, and the host will be discovered. The identification of these mechanisms will allow for the development of more precise therapies to prevent or manage chronic disease. This review discusses the functional characterization of the microbiome, highlighting the advances in identifying bioactive microbial metabolites that have been directly linked to gastrointestinal and peripheral diseases. The gut microbiota has been implicated in the development of a number of chronic gastrointestinal and systemic diseases. These include inflammatory bowel diseases, irritable bowel syndrome, and metabolic (i.e. obesity, non-alcoholic fatty liver disease, and diabetes) and neurological diseases. The advanced understanding of host-microbe interactions has largely been due to new technologies such as 16S rRNA sequencing to identify previously unknown microbial communities and, more importantly, their functional characteristics through metagenomic sequencing and other multi-omic technologies, such as metatranscriptomics, metaproteomics, and metabolomics. Given the vast array of newly acquired knowledge in the field and technological advances, it is expected that mechanisms underlying several disease states involving the interactions between microbes, their metabolites, and the host will be discovered. The identification of these mechanisms will allow for the development of more precise therapies to prevent or manage chronic disease. This review discusses the functional characterization of the microbiome, highlighting the advances in identifying bioactive microbial metabolites that have been directly linked to gastrointestinal and peripheral diseases. The gut microbiota has been implicated in the development of many chronic and systemic diseases, including inflammatory bowel diseases, metabolic disease, and neurological disorders. Over the past 15 years, the field of microbiome research has grown exponentially, in part because of new technologies, particularly 16S rRNA amplicon sequencing, which has led to the identification of previously unidentified members of the gut microbial community as well as an advanced understanding of their functional characteristics through shotgun metagenomic sequencing, and other multi-omic technologies such as metatranscriptomics, metaproteomics, and metabolomics. Although much has been uncovered about gut microbiota, whether or not it plays a causal role in the development of disease remains unclear. Given the vast array of newly gained knowledge in the field, it is likely that the mechanistic role of gut microbes and their microbial metabolites underlying several disease states will be discovered. With the identification of these mechanisms, development of personalized, precise therapies to improve or prevent chronic disease may become a reality. The goal of this review is to briefly discuss the functional capacity of the microbiome, challenges associated with multi-omic technologies, and recent advances in identifying microbial metabolites that have been directly linked to gastrointestinal and peripheral diseases. The gut microbiota consists of over 10 trillion microbial cells and is a primary source of thousands of small molecules and other bioactive compounds that can trigger both host metabolic and immune pathways. The human gut microbiota also contains about 1000 different bacterial species with defined functions allowing them to thrive and create a niche in the midst of others with redundant or competing functions. Microbial ecosystems maintain homeostasis through a tight balance of cell-to-cell signaling and release of antimicrobial peptides to control neighboring bacterial clades allowing for their persistence in the confines of the human host. In addition to securing community dynamics with neighboring microbes, gut microbes also communicate with the human host in either a symbiotic or deleterious fashion, the latter contributing to the development of human disease. Advances in metagenomics and metabolomics have led to the discovery of thousands of microbe-derived small molecules as well as the genes associated with their production. Although the advances in technology and wealth of big data sets have expanded our knowledge and appreciation for the contribution of gut microbes, the challenge remains in identifying small molecules that elicit a biological effect upon the host, the physiological levels necessary to do so, and how to assess the physiological impact of the targeted molecule (1Donia M.S. Fischbach M.A. Human microbiota. Small molecules from the human microbiota.Science. 2015; 3491254766Crossref PubMed Scopus (429) Google Scholar). The following section will describe the basis of these technologies and discuss the strengths and challenges with these methods, particularly the challenge of incorporating and understanding these datasets simultaneously. Many research studies that correlate the involvement of the microbiome with disease states in animal models and humans have relied heavily on the use of 16S rRNA marker gene amplicon-sequencing platforms. Although this technology has expanded our knowledge of gut microbial diversity and provides a relatively accurate fingerprint of phylogenetic community membership and structure, little can be learned about the functional properties of the community. In an attempt to predict function based on 16S rRNA sequences, tools have been designed to compare 16S marker gene sequences to reference gene databases (PICRUSt) (2Langille M.G. Zaneveld J. Caporaso J.G. McDonald D. Knights D. Reyes J.A. Clemente J.C. Burkepile D.E. Vega Thurber R.L. Knight R. Beiko R.G. Huttenhower C. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.Nat. Biotechnol. 2013; 31: 814-821Crossref PubMed Scopus (5793) Google Scholar). However, the reference genome database used is a limiting factor if it does not accurately reflect gene functions for the microbial community of interest, particularly in the case of rare members of the gut microbial community (3Sharpton T.J. An introduction to the analysis of shotgun metagenomic data.Front. Plant Sci. 2014; 5: 209Crossref PubMed Scopus (317) Google Scholar). To gain more accurate insights into microbial community gene function, high throughput shotgun metagenomic sequencing has become an important tool, as it avoids many biases introduced by amplicon sequencing due to its untargeted nature. Shotgun metagenomic sequencing allows for an in-depth characterization of known microbial genes as well as identification of novel genetic microbial material. Following DNA shearing and template amplification, the short reads that are obtained can either be mapped to reference genomes or undergo de novo assembly, and functional annotation can be performed using specialized analyses platforms. For mapping purposes, off-line platforms such as HUMAnN (4Abubucker S. Segata N. Goll J. Schubert A.M. Izard J. Cantarel B.L. Rodriguez-Mueller B. Zucker J. Thiagarajan M. Henrissat B. White O. Kelley S.T. Methé B. Schloss P.D. Gevers D. et al.Metabolic reconstruction for metagenomic data and its application to the human microbiome.PLoS Comput. Biol. 2012; 8e1002358Crossref PubMed Scopus (706) Google Scholar) and on-line platforms, including MG-RAST (5Meyer F. Paarmann D. D'Souza M. Olson R. Glass E.M. Kubal M. Paczian T. Rodriguez A. Stevens R. Wilke A. Wilkening J. Edwards R.A. The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes.BMC Bioinformatics. 2008; 9: 386Crossref PubMed Scopus (2575) Google Scholar) or JCVI Metagenomics Reports (METAPREP) (6Goll J. Rusch D.B. Tanenbaum D.M. Thiagarajan M. Li K. Methé B.A. Yooseph S. METAREP: JCVI metagenomics reports–an open source tool for high-performance comparative metagenomics.Bioinformatics. 2010; 26: 2631-2632Crossref PubMed Scopus (86) Google Scholar), can be utilized. Assembly programs include khmer (7Crusoe M.R. Alameldin H.F. Awad S. Boucher E. Caldwell A. Cartwright R. Charbonneau A. Constantinides B. Edvenson G. Fay S. Fenton J. Fenzl T. Fish J. Garcia-Gutierrez L. Garland P. et al.The khmer software package: enabling efficient nucleotide sequence analysis.F1000Res. 2015; 4: 900Crossref PubMed Scopus (212) Google Scholar) and novel interfaces, such as A'nvio (8Eren A.M. Esen Ö.C. Quince C. Vineis J.H. Morrison H.G. Sogin M.L. Delmont T.O. Anvi'o: an advanced analysis and visualization platform for ‘omics data.PeerJ. 2015; 3e1319Crossref PubMed Scopus (802) Google Scholar). Although bias is avoided using this technique, if sequencing depth and coverage are insufficient, the reads cannot be properly assembled, and gene assignment based on known reference genomes cannot be completed. In addition, contaminating host DNA can overwhelm the sequencing output, inhibiting amplification of microbial sequences (3Sharpton T.J. An introduction to the analysis of shotgun metagenomic data.Front. Plant Sci. 2014; 5: 209Crossref PubMed Scopus (317) Google Scholar). In this case, samples such as this would require more sequencing depth and coverage, which can be cost-prohibitive, or require strategies to eliminate host DNA contamination and enrich for microbial DNA (3Sharpton T.J. An introduction to the analysis of shotgun metagenomic data.Front. Plant Sci. 2014; 5: 209Crossref PubMed Scopus (317) Google Scholar). Despite these limitations, the information gleaned from shotgun metagenomics has led to the identification of unique microbial strain-specific genes, particularly in the context of human disease (4Abubucker S. Segata N. Goll J. Schubert A.M. Izard J. Cantarel B.L. Rodriguez-Mueller B. Zucker J. Thiagarajan M. Henrissat B. White O. Kelley S.T. Methé B. Schloss P.D. Gevers D. et al.Metabolic reconstruction for metagenomic data and its application to the human microbiome.PLoS Comput. Biol. 2012; 8e1002358Crossref PubMed Scopus (706) Google Scholar). Shotgun metagenomics provides a fingerprint of the gene content and functional capacity of the microbial community. However, it cannot be used to assess the activity of microbial gene expression. Combining shotgun metagenomic sequencing data with metatranscriptomic shotgun sequencing provides advantages for identification of the active microbial genome under differing conditions or disease states. Initially, total RNA from the microbial community is isolated and enriched for RNA (mRNA, lincRNA, and microRNA) followed by fragmentation. RNA is then converted to cDNA via reverse transcriptase with either random hexamers or oligo(dT) primers. Libraries are then constructed and sequenced (9Bashiardes S. Zilberman-Schapira G. Elinav E. Use of metatranscriptomics in microbiome research.Bioinform. Biol. Insights. 2016; 10: 19-25Crossref PubMed Scopus (223) Google Scholar). Although this technology offers more precise characterization of the activity of the microbial whole genome, many technical issues can limit its utility. Collection and storage of gut microbial samples to maintain RNA integrity can be challenging, which can lead to an insufficient yield of quality microbial RNA for downstream purification strategies. For instance, in the process of enriching for mRNA, ribosomal RNA is eliminated, which constitutes nearly 90% of total RNA. Similar strategies to those used for shotgun metagenomics can be used for downstream data analysis with metatranscriptomic sequences. By applying these tools to the metatranscriptomic sequences, taxonomy can be assigned along with their actively expressed gene functions (9Bashiardes S. Zilberman-Schapira G. Elinav E. Use of metatranscriptomics in microbiome research.Bioinform. Biol. Insights. 2016; 10: 19-25Crossref PubMed Scopus (223) Google Scholar). This application can improve existing annotations, which remains a limiting factor of the utility of these technologies when inferring functionality of specific microbes. By coupling shotgun metagenomic and metatranscriptomic sequencing, greater insights are possible with regard to functional genetic material and activity within the gut microbial community. Several bioinformatic tools can serve dual functions and integrate the two complementary strategies, including HUMAnN and Anvi'o (4Abubucker S. Segata N. Goll J. Schubert A.M. Izard J. Cantarel B.L. Rodriguez-Mueller B. Zucker J. Thiagarajan M. Henrissat B. White O. Kelley S.T. Methé B. Schloss P.D. Gevers D. et al.Metabolic reconstruction for metagenomic data and its application to the human microbiome.PLoS Comput. Biol. 2012; 8e1002358Crossref PubMed Scopus (706) Google Scholar, 8Eren A.M. Esen Ö.C. Quince C. Vineis J.H. Morrison H.G. Sogin M.L. Delmont T.O. Anvi'o: an advanced analysis and visualization platform for ‘omics data.PeerJ. 2015; 3e1319Crossref PubMed Scopus (802) Google Scholar). Despite gaining an overall snapshot of the genetic composition and activity of specific genes within the gut microbial community, actual metabolic output remains unknown. Although some limitations exist, further exploration of microbially derived metabolites via metabolomics may provide the most valuable insights into gut microbial functional capacity and its impact on the host in health and disease. To determine the contribution of gut microbes to the host metabolism, one strategy has been to compare tissues of germ-free (GF) mice raised in the complete absence of microbiota to their conventionally raised or conventionalized counterparts. Although this tool has provided a great deal of insight, several limitations must first be recognized. First, it is acknowledged that GF mice exhibit several developmental abnormalities and altered structural features of the gastrointestinal tract, including increased transit time, enlarged cecum, shorter villi, and a thinner intestinal wall. Germ-free mice also display differences in other physiological features such as altered metabolism and reduced cardiac output (10Coates M.E. Gnotobiotic animals in research: their uses and limitations.Lab. Anim. 1975; 9: 275-282Crossref PubMed Scopus (28) Google Scholar, 11Al-Asmakh M. Zadjali F. Use of germ-free animal models in microbiota-related research.J. Microbiol. Biotechnol. 2015; 25: 1583-1588Crossref PubMed Scopus (141) Google Scholar, 12Nicklas W. Keubler L. Bleich A. Maintaining and monitoring the defined microbiota status of gnotobiotic rodents.ILAR J. 2015; 56: 241-249Crossref PubMed Scopus (36) Google Scholar). 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Beyond the use of GF mice, recent technological advances in the discovery of metabolites (or small molecules) have helped to advance our understanding of the contribution of gut microbes to the host metabolome. It is well accepted that characterization of the human metabolome can provide much insight into determining states of health or disease. Thus far, identification of metabolites is achieved through the use of advanced targeted and untargeted analytical chemistry techniques, including nuclear magnetic resonance spectrometry (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS). These technologies, when used as complementary approaches, can yield vast amounts of information about the composition of both inorganic and organic compounds potentially derived from microbes, such as amino acids, lipids, sugars, biogenic amines, and organic acids, including volatile organic compounds (VOCs), ribosomally synthesized and post-translationally modified peptides, glycolipids, oligosaccharides, terpenoids or secondary bile acids, nonribosomal peptides, and polyketides (1Donia M.S. Fischbach M.A. Human microbiota. Small molecules from the human microbiota.Science. 2015; 3491254766Crossref PubMed Scopus (429) Google Scholar). VOCs, detected via GC-MS or selected ion flow tube-mass spectrometry (SIFT-MS), have been investigated for use as sensitive screening markers for inflammatory bowel disease (IBD). For example, VOCs were reported to distinguish IBD patients from healthy controls as well as Crohn's disease (CD) from ulcerative colitis (UC) patients. Specifically, dimethyl sulfide and hydrogen sulfide, produced by several classes of bacteria, were significantly different in CD versus UC patients (18Hicks L.C. Huang J. Kumar S. Powles S.T. Orchard T.R. Hanna G.B. Williams H.R. Analysis of exhaled breath volatile organic compounds in inflammatory bowel disease: a pilot study.J. Crohns Colitis. 2015; 9: 731-737Crossref PubMed Scopus (44) Google Scholar). However, in this study, the authors were not able to determine the precise contribution of microbes alone, as the host can also produce these specific metabolites. In addition to this technology, further advances in matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MALDI-TOF) have allowed for the detection and imaging of specific metabolites in healthy and disease states (19Singhal N. Kumar M. Kanaujia P.K. Virdi J.S. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis.Front. 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Fenollar F. MALDI-TOF identification of the human gut microbiome in people with and without diarrhea in Senegal.PLoS ONE. 2014; 9e87419Crossref PubMed Scopus (41) Google Scholar). Coupled with the ability to obtain pure cultures of specific microbes, the spectra peak output from techniques such as this can then be compared with existing databases, including MetaCyc (23Caspi R. Foerster H. Fulcher C.A. Kaipa P. Krummenacker M. Latendresse M. Paley S. Rhee S.Y. Shearer A.G. Tissier C. Walk T.C. Zhang P. Karp P.D. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases.Nucleic Acids Res. 2008; 36: D623-D631Crossref PubMed Scopus (550) Google Scholar), Human Metabolome Database (HMDB) (24Wishart D.S. Jewison T. Guo A.C. Wilson M. Knox C. Liu Y. Djoumbou Y. Mandal R. Aziat F. Dong E. Bouatra S. Sinelnikov I. Arndt D. Xia J. Liu P. et al.HMDB 3.0–The Human Metabolome Database in 2013.Nucleic Acids Res. 2013; 41: D801-D807Crossref PubMed Scopus (2287) Google Scholar), SetupX, and BinBase to aid in the identification and perhaps source of specific metabolites (25Wishart D.S. Current progress in computational metabolomics.Brief Bioinform. 2007; 8: 279-293Crossref PubMed Scopus (170) Google Scholar). Although these technologies provide a great deal of information, computational tools to gain insights into large-scale datasets are prohibitive to discovery. To combat this, a large body of evidence as well as analytical tools and algorithms to identify biosynthetic gene clusters (BGCs) and the associated microbial metabolome have been developed and vetted by Medema and Fischbach (26Medema M.H. Fischbach M.A. Computational approaches to natural product discovery.Nat. Chem. Biol. 2015; 11: 639-648Crossref PubMed Scopus (288) Google Scholar). Another tool that may be especially useful in identifying microbe-derived bioactive compounds and coupling them to BGCs is the Integrated Microbial Genomes-Atlas of Microbial Gene Clusters (IMP-ABC), which is a publicly available database of biosynthetic gene clusters and predicted secondary metabolites that is based on a collection of thousands of published isolated genomes and metagenomes (27Hadjithomas M. Chen I.-M. Chu K. Ratner A. Palaniappan K. Szeto E. Huang J. Reddy T.B. Cimermančič P. Fischbach M.A. Ivanova N.N. Markowitz V.M. Kyrpides N.C. Pati A. IMG-ABC: a knowledge base to fuel discovery of biosynthetic gene clusters and novel secondary metabolites.MBio. 2015; 6e00932Crossref PubMed Scopus (76) Google Scholar). This tool is expected to allow researchers to identify novel gene clusters from single isolates where whole genome sequencing has been performed or via metagenomes that may generate biologically active small molecules that impact host health. Metabolomic research can also allow for better insight into the contribution of microbes to xenobiotic metabolism and the secondary metabolites they produce as a result that may have a significant impact on the host. Recent work has highlighted that the gut microbiota is a key contributor to regulating host drug metabolism because it is the first to interact with ingested xenobiotics prior to transport to the liver via portal circulation. Using RNA-seq to examine the community microbial metatranscriptome, Maurice et al. (28Maurice C.F. Haiser H.J. Turnbaugh P.J. Xenobiotics shape the physiology and gene expression of the active human gut microbiome.Cell. 2013; 152: 39-50Abstract Full Text Full Text PDF PubMed Scopus (573) Google Scholar) showed that following short-term xenobiotic exposure, microbes exhibited altered gene expression pathways associated with tRNA biosynthesis, translation, vitamin biosynthesis, phosphate transport, the pentose phosphate pathway, and not surprisingly, xenobiotic metabolism/biodegradation using KEGG pathway abundance analysis coupled with HUMAnN and LEfSe. This xenobiotic metabolism can alter availability of specific drugs to the host. For instance, digoxin, a cardiac glycoside used to treat heart failure and arrhythmias, exhibits poor efficacy in some patients, due in part to gut microbial composition. Using several approaches, Haiser et al. (29Haiser H.J. Gootenberg D.B. Chatman K. Sirasani G. Balskus E.P. Turnbaugh P.J. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta.Science. 2013; 341: 295-298Crossref PubMed Scopus (398) Google Scholar) showed that specific strains of Eggerthella lenta could reduce digoxin, inhibiting its action in the host, and could be prevented by the presence of high levels of arginine. In another example, bioavailability of the Parkinson's therapy levidopa (l-dopa) was reduced in patients colonized with Helicobacter pylori in the stomach. Eradication of H. pylori led to significant increases in l-dopa levels in patient serum and improved efficacy (30Niehues M. Hensel A. In vitro interaction of l-dopa with bacterial adhesins of Helicobacter pylori: an explanation for clinical differences in bioavailability?.J. Pharm. Pharmacol. 2009; 61: 1303-1307Crossref PubMed Google Scholar). In addition, microbial metabolism of the colon cancer treatment, irinotecan, was found to induce gastrointestinal toxicity via β-glucuronidase activity. Blocking β-glucuronidase with an inhibitor greatly reduced the gastrointestinal side effects (31Carmody R.N. Turnbaugh P.J. Host-microbial interactions in the metabolism of therapeutic and diet-derived xenobiotics.J. Clin. Invest. 2014; 124: 4173-4181Crossref PubMed Scopus (186) Google Scholar). In the next section, bacterial metabolites will be examined through the lens of their biological significance related to gastrointestinal, neurological, and metabolic disease. Major metabolites related to these conditions will be highlighted, and a list of these metabolites can be found in Table 1.TABLE 1Altered metabolites in gastrointestinal and systemic diseases Open table in a new tab The challenge in using multi-omic approaches is developing tools to identify the best candidate metabolites and microbes for further study. The best inferences may come from incorporating and simultaneously assessing results from metagenomic/metabolomics or metagenomic/metatranscriptomic datasets, for example. This approach will allow the overlay and prediction of which metabolites correspond to a bacterial gene/gene transcript, thus strengthening the ability to target a dynamic flow of events (32McHardy I.H. Goudarzi M. Tong M. Ruegger P.M. Schwager E. Weger J.R. Graeber T.G. Sonnenburg J.L. Horvath S. Huttenhower C. McGovern D.P. Fornace Jr., A.J. Borneman J. Braun J. Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships.Microbiome. 2013; 1: 17Crossref PubMed Scopus (192) Google Scholar). These capabilities are in their infancy, and more advances in data processing are on the horizon. IBD includes UC and CD. Ulcerative colitis is localized only to the colon and characterized by superficial ulcerative inflammation, whereas CD occurs sporadically along the length of the GI tract and is characterized by transmural inflammation penetrating through the epithelium. Both can be debilitating during flare-ups, persist throughout life, and may require surgical resection of regions of the GI tract if remission is not achieved using standard therapies. Understanding host-microbe interactions underlying the disease could lead to strategies to alleviate symptoms or prevent relapse for these individuals. It is appreciated that gut microbes play an intimate role in the development of IBD. Indeed, the GI tract contains the majority of microbes in the body and is a major site of host-microbe interactions. UC and CD are associated with decreased microbial diversity based on phylogenetic measurements through 16S rRNA sequencing as well as metagenomic analysis (33Mondot S. Lepage P. The human gut microbiome and its dysfunctions through the meta-omics prism.Ann. N.Y. Acad. Sci. 2016; 1372: 9-19Crossref PubMed Scopus (32) Google Scholar). Another hallmark of IBD is microbial dysbiosis, which is characterized by the decreased abundance of commensals and increased abundance of pathogenic microbes, as well as reduced SCFA levels (34Leone V. Chang E.B. Devkota S. Diet, microbes, and host genetics: the perfect storm in inflammatory bowel diseases.J. Gastroenterol. 2013; 48: 315-321Crossref PubMed Scopus (101) Google Scholar). Commensal gut microbes perform a variety of functions important to the host, including protection from pathogens through secretion of antimicrobial molecules and improved barrier function through the production of bioactive metabolites, including SCFAs. However, pathogenic microbes secrete detrimental metabolites such as hydrogen sulfide and bile acid derivatives that may exacerbate inflammatory states (35Devkota S. Wang Y. Musch M.W. Leone V. Fehlner-Peach H. Nadimpalli A. Antonopoulos D.A. Jabri B. Chang E.B. Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10−/− mice.Nature. 2012; 487: 104-108Crossref PubMed Scopus (1230) Google Scholar). Through metabolomic
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