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Title: Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight weremore » characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.« less
Authors:
ORCiD logo [1] ;  [1] ; ORCiD logo [1] ;  [1] ;  [1] ; ORCiD logo [1] ; ORCiD logo [1] ;  [1] ;  [1] ;  [2] ; ORCiD logo [2] ; ORCiD logo [1]
  1. Carnegie Inst. of Science, Stanford, CA (United States). Plant Biology Dept.
  2. Univ. of Lyon, Lyon (France). Lab. of Biometry and Evolutionary biology and French National Center for Scientific Research
Publication Date:
Grant/Contract Number:
SC0008769; IOS-1026003; DBI-0640769; 1U01GM110699-01A1
Type:
Accepted Manuscript
Journal Name:
Plant Physiology (Bethesda)
Additional Journal Information:
Journal Name: Plant Physiology (Bethesda); Journal Volume: 173; Journal Issue: 4; Journal ID: ISSN 0032-0889
Publisher:
American Society of Plant Biologists
Research Org:
Donald Danforth Plant Science Center, St. Louis, MO (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23). Biological Systems Science Division; National Science Foundation (NSF); National Institutes of Health (NIH), Bethesda, MD (United States); National Commission for Scientific and Technological Research (CONICYT); Swiss National Science Foundation (SNSF); Alexander Humboldt Foundation
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES
OSTI Identifier:
1423882

Schläpfer, Pascal, Zhang, Peifen, Wang, Chuan, Kim, Taehyong, Banf, Michael, Chae, Lee, Dreher, Kate, Chavali, Arvind K., Nilo-Poyanco, Ricardo, Bernard, Thomas, Kahn, Daniel, and Rhee, Seung Y.. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants. United States: N. p., Web. doi:10.1104/pp.16.01942.
Schläpfer, Pascal, Zhang, Peifen, Wang, Chuan, Kim, Taehyong, Banf, Michael, Chae, Lee, Dreher, Kate, Chavali, Arvind K., Nilo-Poyanco, Ricardo, Bernard, Thomas, Kahn, Daniel, & Rhee, Seung Y.. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants. United States. doi:10.1104/pp.16.01942.
Schläpfer, Pascal, Zhang, Peifen, Wang, Chuan, Kim, Taehyong, Banf, Michael, Chae, Lee, Dreher, Kate, Chavali, Arvind K., Nilo-Poyanco, Ricardo, Bernard, Thomas, Kahn, Daniel, and Rhee, Seung Y.. 2017. "Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants". United States. doi:10.1104/pp.16.01942. https://www.osti.gov/servlets/purl/1423882.
@article{osti_1423882,
title = {Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants},
author = {Schläpfer, Pascal and Zhang, Peifen and Wang, Chuan and Kim, Taehyong and Banf, Michael and Chae, Lee and Dreher, Kate and Chavali, Arvind K. and Nilo-Poyanco, Ricardo and Bernard, Thomas and Kahn, Daniel and Rhee, Seung Y.},
abstractNote = {Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.},
doi = {10.1104/pp.16.01942},
journal = {Plant Physiology (Bethesda)},
number = 4,
volume = 173,
place = {United States},
year = {2017},
month = {4}
}