Biology:Metabolic gene cluster

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Not to be confused with Gene cluster, Human genetic clustering, or Cluster genealogy.

Metabolic gene clusters or biosynthetic gene clusters (BGC) 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], most fungal[5], and plant[6] genomes (although there is contradicting evidence on plant[7] 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,[8] 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.

Motivating examples

Nutrient acquisition (transport and degredation):

Cofactor/vitamin biosynthesis:[lower-alpha 1]

Secondary metabolite production:

  • PsiD/M/H/K/T for psilocybin biosynthesis, which has also seen horizontal gene transfer.[11]
  • Various nonribosomal peptide synthase (NRPS) and polyketide synthase (PKS) clusters, such as those for ciclosporin and microcystins.

Evolution

The origin and evolution of metabolic gene clusters have been debated since the 1990s.[12][13] It has since been demonstrated that metabolic gene clusters can arise in a genome by genome rearrangement, gene duplication, or horizontal gene transfer,[14] and some metabolic clusters have evolved convergently in multiple species.[15] Horizontal gene cluster transfer has been linked to ecological niches in which the encoded pathways are thought to provide a benefit.[16] 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.[17]

Bioinformatic tools

Tools based on rules

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.[18] 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,[19] 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).

Identification

AntiSMASH is a framework for detecting BGCs. It uses profile hidden Markov models (HMM) to identify protein families corresponding to biosynthetic functions or smaller amino-acid patterns for motifs too short for HMM. Manually-curated rules then describe how specific types of BGCs can be inferred by co-occurrence of certain families/motifs. It features advanced detection and annotation for NRPS and PKS clusters.[20]

PRISM uses a mixture of HMM, motifs, BLAST, and machine learning to identify protein domains involved in biosynthetic functions. Clusters are identified in a rule-based approach. Each cluster is then examined to predict the structure of their products in a combinatorial, graph-based fashion.[21]

Clustering

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[22] 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.[22]

Tools based on machine learning

Databases

Minimum Information about a Biosynthetic Gene cluster (MIBiG)
MIBiG is a data standard that describes the minimum amount of information a researcher should provide about a newly-discovered BiG as well as a database that records BiGs according to this standard.[23] MIBiG is regularly updated by a group of curators. As of September 2026, the latest revision is MIBiG 4.0 of January 2025.[24]
AntiSMASH database
Pre-calculated antiSMASH results.
BiG-FAM
BiG-FAM is also known as the Biosynthetic Gene Cluster Family (GCF) database. It combines the BGCs from MIBiG and the results of running antiSMASH on available genomes. These genomes are then clustered with BiG-SLiCE. As of 2021 it includes 1,225,071 BGCs from 209,206 genomes (21,678 species) organized into 29,955 families.[25]

Notes

  1. ↑ A vitamin is, technically speaking, a cofactor or other kind of material necessary for life that can only be acquired from the surroundings. When an organism can synthesize a cofactor to the point that it can be independent of outside input of this molecule, this cofactor stops being a vitamin for it.

References

  1. ↑ "Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants". Plant Physiology 173 (4): 2041–2059. April 2017. doi:10.1104/pp.16.01942. PMID 28228535. Bibcode: 2017PlanP.173.2041S. 
  2. ↑ "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. January 1987. doi:10.1128/MCB.7.1.427. PMID 3550422. 
  3. ↑ "METACLUSTER-an R package for context-specific expression analysis of metabolic gene clusters". Bioinformatics 35 (17): 3178–3180. September 2019. doi:10.1093/bioinformatics/btz021. PMID 30657869. 
  4. ↑ "Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters". Cell 158 (2): 412–421. July 2014. doi:10.1016/j.cell.2014.06.034. PMID 25036635. Bibcode: 2014Cell..158..412C. 
  5. ↑ 5.0 5.1 "Fungal Gene Cluster Diversity and Evolution". Fungal Phylogenetics and Phylogenomics. Advances in Genetics. 100. 2017. pp. 141–178. doi:10.1016/bs.adgen.2017.09.005. ISBN 978-0-12-813261-6. 
  6. ↑ Polturak, Guy; Osbourn, Anne (2021-07-02). Zipfel, Cyril. ed. "The emerging role of biosynthetic gene clusters in plant defense and plant interactions" (in en). PLOS Pathogens 17 (7). doi:10.1371/journal.ppat.1009698. ISSN 1553-7374. PMID 34214143. 
  7. ↑ "A Global Coexpression Network Approach for Connecting Genes to Specialized Metabolic Pathways in Plants". The Plant Cell 29 (5): 944–959. May 2017. doi:10.1105/tpc.17.00009. PMID 28408660. Bibcode: 2017PlanC..29..944W. 
  8. ↑ "Specialized plant biochemistry drives gene clustering in fungi". The ISME Journal 12 (7): 1694–1705. June 2018. doi:10.1038/s41396-018-0075-3. PMID 29463891. Bibcode: 2018ISMEJ..12.1694G. 
  9. ↑ Zhu, W; Klinman, JP (December 2020). "Biogenesis of the peptide-derived redox cofactor pyrroloquinoline quinone.". Current Opinion in Chemical Biology 59: 93–103. doi:10.1016/j.cbpa.2020.05.001. PMID 32731194. 
  10. ↑ Haft DH (2011). "Bioinformatic evidence for a widely distributed, ribosomally produced electron carrier precursor, its maturation proteins, and its nicotinoprotein redox partners.". BMC Genomics 12. doi:10.1186/1471-2164-12-21. PMID 21223593. 
  11. ↑ "Horizontal gene cluster transfer increased hallucinogenic mushroom diversity". Evolution Letters 2 (2): 88–101. April 2018. doi:10.1002/evl3.42. PMID 30283667. 
  12. ↑ Lawrence, Jeffrey G.; Roth, John R. (1996-08-01). "Selfish Operons: Horizontal Transfer May Drive the Evolution of Gene Clusters" (in en). Genetics 143 (4): 1843–1860. doi:10.1093/genetics/143.4.1843. ISSN 0016-6731. PMID 8844169. PMC 1207444. https://www.genetics.org/content/143/4/1843. 
  13. ↑ Pál, Csaba; Hurst, Laurence D (2004-06-01). "Evidence against the selfish operon theory" (in en). Trends in Genetics 20 (6): 232–234. doi:10.1016/j.tig.2004.04.001. PMID 15145575. https://linkinghub.elsevier.com/retrieve/pii/S0168952504000939. 
  14. ↑ Reynolds, Hannah T.; Vijayakumar, Vinod; Gluck-Thaler, Emile; Korotkin, Hailee Brynn; Matheny, Patrick Brandon; Slot, Jason C. (2018). "Horizontal gene cluster transfer increased hallucinogenic mushroom diversity" (in en). Evolution Letters 2 (2): 88–101. doi:10.1002/evl3.42. ISSN 2056-3744. PMID 30283667. 
  15. ↑ Slot, Jason C.; Rokas, Antonis (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. doi:10.1073/pnas.0914418107. PMID 20479238. Bibcode: 2010PNAS..10710136S. 
  16. ↑ Greene, George H.; McGary, Kriston L.; Rokas, Antonis; Slot, Jason C. (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. PMID 24391152. 
  17. ↑ Slot, Jason C.; Gluck-Thaler, Emile (2019-10-01). "Metabolic gene clusters, fungal diversity, and the generation of accessory functions" (in en). Current Opinion in Genetics & Development 58-59: 17–24. doi:10.1016/j.gde.2019.07.006. ISSN 0959-437X. PMID 31466036. 
  18. ↑ "BiG-MAP: An Automated Pipeline to Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes". mSystems 6 (5): e00937-21. 2021. doi:10.1128/msystems.00937-21. PMID 34581602. 
  19. ↑ "Mash: fast genome and metagenome distance estimation using MinHash". Genome Biology 17 (32): 14. 2016. doi:10.1186/s13059-016-0997-x. PMID 27323842. 
  20. ↑ Blin, Kai; Shaw, Simon; Augustijn, Hannah E; Reitz, Zachary L; Biermann, Friederike; Alanjary, Mohammad; Fetter, Artem; Terlouw, Barbara R et al. (5 July 2023). "antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation". Nucleic Acids Research 51 (W1): W46–W50. doi:10.1093/nar/gkad344. PMID 37140036. 
  21. ↑ Skinnider, Michael A.; Johnston, Chad W.; Gunabalasingam, Mathusan; Merwin, Nishanth J.; Kieliszek, Agata M.; MacLellan, Robyn J.; Li, Haoxin; Ranieri, Michael R. M. et al. (27 November 2020). "Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences". Nature Communications 11 (1). doi:10.1038/s41467-020-19986-1. PMID 33247171. Bibcode: 2020NatCo..11.6058S. 
  22. ↑ 22.0 22.1 Kautsar, Satria A; van der Hooft, Justin J J; de Ridder, Dick; Medema, Marnix H (13 January 2021). "BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters". GigaScience 10 (1). doi:10.1093/gigascience/giaa154. PMID 33438731. 
  23. ↑ Medema, Marnix H; Kottmann, Renzo; Yilmaz, Pelin; Cummings, Matthew; Biggins, John B; Blin, Kai; de Bruijn, Irene; Chooi, Yit Heng et al. (September 2015). "Minimum Information about a Biosynthetic Gene cluster". Nature Chemical Biology 11 (9): 625–631. doi:10.1038/nchembio.1890. PMID 26284661. Bibcode: 2015NatCB..11..625M. 
  24. ↑ Zdouc, Mitja M; Blin, Kai; Louwen, Nico L L; Navarro, Jorge; Loureiro, Catarina; Bader, Chantal D; Bailey, Constance B; Barra, Lena et al. (6 January 2025). "MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration". Nucleic Acids Research 53 (D1): D678–D690. doi:10.1093/nar/gkae1115. PMID 39657789. 
  25. ↑ Kautsar, Satria A; Blin, Kai; Shaw, Simon; Weber, Tilmann; Medema, Marnix H (8 January 2021). "BiG-FAM: the biosynthetic gene cluster families database". Nucleic Acids Research 49 (D1): D490–D497. doi:10.1093/nar/gkaa812. PMID 33010170. 



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