Metabolic gene clusters or biosynthetic gene clusters are tightly linked sets of mostly non-homologousgenes participating in a common, discrete metabolic pathway. The genes are in physical vicinity to each other on the genome, and their expression is often coregulated.[1][2][3] Metabolic gene clusters are common features of bacterial[4] and most fungal[5] genomes. They are less often found in other[6] organisms. They are most widely known for producing secondary metabolites, the source or basis of most pharmaceutical compounds, natural toxins, chemical communication, and chemical warfare between organisms. Metabolic gene clusters are also involved in nutrient acquisition, toxin degradation,[7] antimicrobial resistance, and vitamin biosynthesis.[5] Given all these properties of metabolic gene clusters, they play a key role in shaping microbial ecosystems, including microbiome-host interactions. Thus several computational genomics tools have been developed to predict metabolic gene clusters.
Bioinformatic tools have been developed to predict, and determine the abundance and expression of, this kind of gene cluster in microbiome samples, from metagenomic data.[8] Since the size of metagenomic data is considerable, filtering and clusterization thereof are important parts of these tools. These processes can consist of dimensionality -reduction techniques, such as Minhash,[9] and clusterization algorithms such as k-medoids and affinity propagation. Also several metrics and similarities have been developed to compare them.
Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs). BiG-SLiCE (Biosynthetic Genes Super-Linear Clustering Engine), a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion.
Satria et al., 2021[10] across BiG-SLiCE demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential, opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry.[10]
tools based on machine learningedit
Evolutionedit
The origin and evolution of metabolic gene clusters have been debated since the 1990s.[11][12] It has since been demonstrated that metabolic gene clusters can arise in a genome by genome rearrangement, gene duplication, or horizontal gene transfer,[13] and some metabolic clusters have evolved convergently in multiple species.[14] Horizontal gene cluster transfer has been linked to ecological niches in which the encoded pathways are thought to provide a benefit.[15] It has been argued that clustering of genes for ecological functions results from reproductive trends among organisms, and goes on to contribute to accelerated adaptation by increasing refinement of complex functions in the pangenome of a population.[16]
Referencesedit
^Schläpfer P, Zhang P, Wang C, Kim T, Banf M, Chae L, et al. (April 2017). "Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants". Plant Physiology. 173 (4): 2041–2059. doi:10.1104/pp.16.01942. PMC5373064. PMID 28228535.
^Miller BL, Miller KY, Roberti KA, Timberlake WE (January 1987). "Position-dependent and -independent mechanisms regulate cell-specific expression of the SpoC1 gene cluster of Aspergillus nidulans". Molecular and Cellular Biology. 7 (1): 427–34. doi:10.1128/MCB.7.1.427. PMC365085. PMID 3550422.
^Banf M, Zhao K, Rhee SY (September 2019). "METACLUSTER-an R package for context-specific expression analysis of metabolic gene clusters". Bioinformatics. 35 (17): 3178–3180. doi:10.1093/bioinformatics/btz021. PMC6735823. PMID 30657869.
^Cimermancic P, Medema MH, Claesen J, Kurita K, Wieland Brown LC, Mavrommatis K, et al. (July 2014). "Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters". Cell. 158 (2): 412–421. doi:10.1016/j.cell.2014.06.034. PMC4123684. PMID 25036635.
^ abSlot JC (2017). "Fungal Gene Cluster Diversity and Evolution". Fungal Phylogenetics and Phylogenomics. Advances in Genetics. Vol. 100. pp. 141–178. doi:10.1016/bs.adgen.2017.09.005. ISBN978-0-12-813261-6. PMID 29153399.
^Wisecaver JH, Borowsky AT, Tzin V, Jander G, Kliebenstein DJ, Rokas A (May 2017). "A Global Coexpression Network Approach for Connecting Genes to Specialized Metabolic Pathways in Plants". The Plant Cell. 29 (5): 944–959. doi:10.1105/tpc.17.00009. PMC5466033. PMID 28408660.
^Gluck-Thaler E, Slot JC (June 2018). "Specialized plant biochemistry drives gene clustering in fungi". The ISME Journal. 12 (7): 1694–1705. Bibcode:2018ISMEJ..12.1694G. doi:10.1038/s41396-018-0075-3. PMC6018750. PMID 29463891.
^Pascal-Andreu V, Augustijn H, van den Berg K, van der Hooft J, Fischbach M, Medema M (2020). "BiG-MAP: an automated pipeline to profile metabolic gene cluster abundance and expression in microbiomes" (PDF). bioRxiv. 6 (5): e00937-21. doi:10.1101/2020.12.14.422671. PMC8547482. PMID 34581602.
^Ondov B, Treangen T, Melsted P, Mallonee A, Bergman N, Koren S, et al. (2016). "Mash: fast genome and metagenome distance estimation using MinHash". Genome Biology. 17 (32): 14. doi:10.1186/s13059-016-0997-x. PMC4915045. PMID 27323842.
^ abKautsar SA, van der Hooft JJ, de Ridder D, Medema MH (13 January 2021). "BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters". GigaScience. 10 (1): giaa154. doi:10.1093/gigascience/giaa154. PMC7804863. PMID 33438731.
^Lawrence JG, Roth JR (1996-08-01). "Selfish Operons: Horizontal Transfer May Drive the Evolution of Gene Clusters". Genetics. 143 (4): 1843–1860. doi:10.1093/genetics/143.4.1843. ISSN 0016-6731. PMC1207444. PMID 8844169.
^Pál C, Hurst LD (2004-06-01). "Evidence against the selfish operon theory". Trends in Genetics. 20 (6): 232–234. doi:10.1016/j.tig.2004.04.001. PMID 15145575.
^Slot JC, Rokas A (2010-06-01). "Multiple GAL pathway gene clusters evolved independently and by different mechanisms in fungi". Proceedings of the National Academy of Sciences. 107 (22): 10136–10141. Bibcode:2010PNAS..10710136S. doi:10.1073/pnas.0914418107. PMC2890473. PMID 20479238.
^Greene GH, McGary KL, Rokas A, Slot JC (January 2014). "Ecology drives the distribution of specialized tyrosine metabolism modules in fungi". Genome Biology and Evolution. 6 (1): 121–132. doi:10.1093/gbe/evt208. ISSN 1759-6653. PMC3914699. PMID 24391152.
^Slot JC, Gluck-Thaler E (2019-10-01). "Metabolic gene clusters, fungal diversity, and the generation of accessory functions". Current Opinion in Genetics & Development. 58–59: 17–24. doi:10.1016/j.gde.2019.07.006. ISSN 0959-437X. PMID 31466036. S2CID 201674539.
January 01, 1970
metabolic, gene, cluster, confused, with, gene, cluster, operon, biosynthetic, gene, clusters, tightly, linked, sets, mostly, homologous, genes, participating, common, discrete, metabolic, pathway, genes, physical, vicinity, each, other, genome, their, express. Not to be confused with gene cluster or operon Metabolic gene clusters or biosynthetic gene clusters are tightly linked sets of mostly non homologous genes participating in a common discrete metabolic pathway The genes are in physical vicinity to each other on the genome and their expression is often coregulated 1 2 3 Metabolic gene clusters are common features of bacterial 4 and most fungal 5 genomes They are less often found in other 6 organisms They are most widely known for producing secondary metabolites the source or basis of most pharmaceutical compounds natural toxins chemical communication and chemical warfare between organisms Metabolic gene clusters are also involved in nutrient acquisition toxin degradation 7 antimicrobial resistance and vitamin biosynthesis 5 Given all these properties of metabolic gene clusters they play a key role in shaping microbial ecosystems including microbiome host interactions Thus several computational genomics tools have been developed to predict metabolic gene clusters Contents 1 Databases 2 Bioinformatic tools 2 1 Tools based on rules 2 2 tools based on machine learning 3 Evolution 4 ReferencesDatabases editMIBiG BiG FAMBioinformatic tools editTools based on rules edit Bioinformatic tools have been developed to predict and determine the abundance and expression of this kind of gene cluster in microbiome samples from metagenomic data 8 Since the size of metagenomic data is considerable filtering and clusterization thereof are important parts of these tools These processes can consist of dimensionality reduction techniques such as Minhash 9 and clusterization algorithms such as k medoids and affinity propagation Also several metrics and similarities have been developed to compare them Genome mining for biosynthetic gene clusters BGCs has become an integral part of natural product discovery The gt 200 000 microbial genomes now publicly available hold information on abundant novel chemistry One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs which allows identification of cross species patterns that can be matched to the presence of metabolites or biological activities However current tools are hindered by a bottleneck caused by the expensive network based approach used to group these BGCs into gene cluster families GCFs BiG SLiCE Biosynthetic Genes Super Linear Clustering Engine a tool designed to cluster massive numbers of BGCs By representing them in Euclidean space BiG SLiCE can group BGCs into GCFs in a non pairwise near linear fashion Satria et al 2021 10 across BiG SLiCE demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs As more genomes are sequenced from understudied taxa more information can be mined to highlight their potentially novel chemistry 10 tools based on machine learning editEvolution editThe origin and evolution of metabolic gene clusters have been debated since the 1990s 11 12 It has since been demonstrated that metabolic gene clusters can arise in a genome by genome rearrangement gene duplication or horizontal gene transfer 13 and some metabolic clusters have evolved convergently in multiple species 14 Horizontal gene cluster transfer has been linked to ecological niches in which the encoded pathways are thought to provide a benefit 15 It has been argued that clustering of genes for ecological functions results from reproductive trends among organisms and goes on to contribute to accelerated adaptation by increasing refinement of complex functions in the pangenome of a population 16 References edit Schlapfer P Zhang P Wang C Kim T Banf M Chae L et al April 2017 Genome Wide Prediction of Metabolic Enzymes Pathways and Gene Clusters in Plants Plant Physiology 173 4 2041 2059 doi 10 1104 pp 16 01942 PMC 5373064 PMID 28228535 Miller BL Miller KY Roberti KA Timberlake WE January 1987 Position dependent and independent mechanisms regulate cell specific expression of the SpoC1 gene cluster of Aspergillus nidulans Molecular and Cellular Biology 7 1 427 34 doi 10 1128 MCB 7 1 427 PMC 365085 PMID 3550422 Banf M Zhao K Rhee SY September 2019 METACLUSTER an R package for context specific expression analysis of metabolic gene clusters Bioinformatics 35 17 3178 3180 doi 10 1093 bioinformatics btz021 PMC 6735823 PMID 30657869 Cimermancic P Medema MH Claesen J Kurita K Wieland Brown LC Mavrommatis K et al July 2014 Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters Cell 158 2 412 421 doi 10 1016 j cell 2014 06 034 PMC 4123684 PMID 25036635 a b Slot JC 2017 Fungal Gene Cluster Diversity and Evolution Fungal Phylogenetics and Phylogenomics Advances in Genetics Vol 100 pp 141 178 doi 10 1016 bs adgen 2017 09 005 ISBN 978 0 12 813261 6 PMID 29153399 Wisecaver JH Borowsky AT Tzin V Jander G Kliebenstein DJ Rokas A May 2017 A Global Coexpression Network Approach for Connecting Genes to Specialized Metabolic Pathways in Plants The Plant Cell 29 5 944 959 doi 10 1105 tpc 17 00009 PMC 5466033 PMID 28408660 Gluck Thaler E Slot JC June 2018 Specialized plant biochemistry drives gene clustering in fungi The ISME Journal 12 7 1694 1705 Bibcode 2018ISMEJ 12 1694G doi 10 1038 s41396 018 0075 3 PMC 6018750 PMID 29463891 Pascal Andreu V Augustijn H van den Berg K van der Hooft J Fischbach M Medema M 2020 BiG MAP an automated pipeline to profile metabolic gene cluster abundance and expression in microbiomes PDF bioRxiv 6 5 e00937 21 doi 10 1101 2020 12 14 422671 PMC 8547482 PMID 34581602 Ondov B Treangen T Melsted P Mallonee A Bergman N Koren S et al 2016 Mash fast genome and metagenome distance estimation using MinHash Genome Biology 17 32 14 doi 10 1186 s13059 016 0997 x PMC 4915045 PMID 27323842 a b Kautsar SA van der Hooft JJ de Ridder D Medema MH 13 January 2021 BiG SLiCE A highly scalable tool maps the diversity of 1 2 million biosynthetic gene clusters GigaScience 10 1 giaa154 doi 10 1093 gigascience giaa154 PMC 7804863 PMID 33438731 Lawrence JG Roth JR 1996 08 01 Selfish Operons Horizontal Transfer May Drive the Evolution of Gene Clusters Genetics 143 4 1843 1860 doi 10 1093 genetics 143 4 1843 ISSN 0016 6731 PMC 1207444 PMID 8844169 Pal C Hurst LD 2004 06 01 Evidence against the selfish operon theory Trends in Genetics 20 6 232 234 doi 10 1016 j tig 2004 04 001 PMID 15145575 Reynolds HT Vijayakumar V Gluck Thaler E Korotkin HB Matheny PB Slot JC 2018 Horizontal gene cluster transfer increased hallucinogenic mushroom diversity Evolution Letters 2 2 88 101 doi 10 1002 evl3 42 ISSN 2056 3744 PMC 6121855 PMID 30283667 Slot JC Rokas A 2010 06 01 Multiple GAL pathway gene clusters evolved independently and by different mechanisms in fungi Proceedings of the National Academy of Sciences 107 22 10136 10141 Bibcode 2010PNAS 10710136S doi 10 1073 pnas 0914418107 PMC 2890473 PMID 20479238 Greene GH McGary KL Rokas A Slot JC January 2014 Ecology drives the distribution of specialized tyrosine metabolism modules in fungi Genome Biology and Evolution 6 1 121 132 doi 10 1093 gbe evt208 ISSN 1759 6653 PMC 3914699 PMID 24391152 Slot JC Gluck Thaler E 2019 10 01 Metabolic gene clusters fungal diversity and the generation of accessory functions Current Opinion in Genetics amp Development 58 59 17 24 doi 10 1016 j gde 2019 07 006 ISSN 0959 437X PMID 31466036 S2CID 201674539 Retrieved from https en wikipedia org w index php title Metabolic gene cluster amp oldid 1217358746, wikipedia, wiki, book, books, library,
