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Transporter genes in biosynthetic gene clusters predict metabolite characteristics and siderophore activity

Alexander Crits-Christoph, Nicholas Bhattacharya, Matthew R. Olm, Yun S. Song, Jillian F. Banfield
doi: https://doi.org/10.1101/2020.06.24.170084
Alexander Crits-Christoph
1Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
2Innovative Genomics Institute, Berkeley, CA, 94704, USA
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Nicholas Bhattacharya
3Department of Mathematics, University of California, Berkeley, CA, USA
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Matthew R. Olm
4Department of Microbiology and Immunology, Stanford University, CA, USA
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Yun S. Song
5Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
6Department of Statistics, University of California, Berkeley, CA, USA
9Chan Zuckerberg Biohub, San Francisco, CA, USA
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Jillian F. Banfield
2Innovative Genomics Institute, Berkeley, CA, 94704, USA
4Department of Microbiology and Immunology, Stanford University, CA, USA
7Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA
8Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
9Chan Zuckerberg Biohub, San Francisco, CA, USA
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  • For correspondence: jbanfield@berkeley.edu
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Abstract

Biosynthetic gene clusters (BGCs) are operonic sets of microbial genes that synthesize specialized metabolites with diverse functions, including siderophores and antibiotics, which often require export to the extracellular environment. For this reason, genes for transport across cellular membranes are essential for the production of specialized metabolites, and are often genomically co-localized with BGCs. Here we conducted a comprehensive computational analysis of transporters associated with characterized BGCs. In addition to known exporters, in BGCs we found many importer-specific transmembrane domains that co-occur with substrate binding proteins possibly for uptake of siderophores or metabolic precursors. Machine learning models using transporter gene frequencies were predictive of known siderophore activity, molecular weights, and a measure of lipophilicity (log P) for corresponding BGC-synthesized metabolites. Transporter genes associated with BGCs were often equally or more predictive of metabolite features than biosynthetic genes. Given the importance of siderophores as pathogenicity factors, we used transporters specific for siderophore BGCs to identify both known and uncharacterized siderophore-like BGCs in genomes from metagenomes from the infant and adult gut microbiome. We find that 23% of microbial genomes from the infant gut have siderophore-like BGCs, but only 3% of those assembled from adult gut microbiomes do. While siderophore-like BGCs from the infant gut are predominantly associated with Enterobactericaee and Staphylococcus, siderophore-like BGCs can be identified from taxa in the adult gut microbiome that have rarely been recognized for siderophore production. Taken together, these results show that consideration of BGC-associated transporter genes can inform predictions of specialized metabolite structure and function.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The author list was submitted out of order.

  • https://github.com/nickbhat/bgc_tran

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 26, 2020.
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Transporter genes in biosynthetic gene clusters predict metabolite characteristics and siderophore activity
Alexander Crits-Christoph, Nicholas Bhattacharya, Matthew R. Olm, Yun S. Song, Jillian F. Banfield
bioRxiv 2020.06.24.170084; doi: https://doi.org/10.1101/2020.06.24.170084
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Transporter genes in biosynthetic gene clusters predict metabolite characteristics and siderophore activity
Alexander Crits-Christoph, Nicholas Bhattacharya, Matthew R. Olm, Yun S. Song, Jillian F. Banfield
bioRxiv 2020.06.24.170084; doi: https://doi.org/10.1101/2020.06.24.170084

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