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The Plant Cell, Vol. 29: 915, May 2017, www.plantcell.org ã 2017 ASPB.
IN BRIEF
Chasing Scattered Genes: Identifying Specialized Metabolite
Pathway Genes through Global Coexpression Analysis
OPEN
Plants produce scores of specialized metabolites (SMs) to attract or repel the organisms
around them and to cope with life in a variable
environment. For thousands of years, we have
been exploiting these compounds to feed, heal,
and anoint us. Many more SMs remain to be
discovered: The chemical constituents of only
15% of the estimated 350,000 plant species on
Earth have thus far been explored (Wurtzel and
Kutchan, 2016). Since SMs are generally not
required for plant growth or reproduction, the
underlying genes and pathways leading to their
production have diversified greatly over time
and are not well conserved among species,
making them difficult to identify through standard homology searches. However, genes
within an SM pathway can be identified through
their shared regulatory network, since the successful production of an SM requires the underlying genes to be expressed at the right time
and place. Searches for coexpressed genes
from global gene expression data have shed
light on SM pathways in various plants, but
technical constraints and limited data have
hampered such analyses.
In addition to their tight regulation, genes in
SM pathways, at least in bacteria and fungi,
are often clustered together in the genome,
forming biosynthetic gene clusters (BGCs).
Powerful tools are used to identify BGCs and
to predict their involvement in SM pathways.
While most known plant SM pathway genes
are dispersed across the genome, several
plant BGCs have been identified and many
more have been predicted (Schlapfer et al.,
2017), and the idea that SM pathway genes in
plants tend to be clustered together has been
gaining traction. If this is not the case, however, techniques for finding plant SM genes
based on chromosomal proximity, an easyto-detect feature, would fail to uncover most
SM pathways, prompting Wisecaver et al.
(2017) to investigate this issue using data
from eight model plant species.
Based on the assumption that genes in an
SM pathway form tightly associated coexpression modules, the authors used pairwise
OPEN
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Global coexpression analysis reveals most metGSL biosynthesis genes in Arabidopsis. Network map
of a typical coexpression module involved in metGSL biosynthesis. Nodes in the map represent genes,
and edges connecting two genes represent the weight (transformed MR score) for the association. The
map was drawn using a Fruchterman-Reingold force-directed layout with the igraph R package (http://
igraph.org). (Reprinted from Wisecaver et al. [2017], Figure 3A.)
measurements of gene coexpression data
from hundreds to thousands of experiments
to construct mutual rank (MR)-based coexpression networks. Genes were then assigned
to modules of tightly coexpressed genes using the ClusterONE tool. Focusing on small
(<50 gene) modules to reflect the typical size of
an SM pathway, the authors looked for modules containing SM pathway genes in the Pfam
database, finding the fewest such modules in
the green alga, Chlamydomonas reinhardtii,
and the most in the mustard, Brassica rapa.
Many modules (;15 to just over 50%) contained two or more known SM biosynthetic
genes and genes enriched in SM-related functional categories, as well as many experimentally validated SM pathways. For example, this
analysis identified almost all genes involved in
the methionine-derived aliphatic glucosinolate
(metGSL) biosynthesis pathway and associated biochemical processes in Arabidopsis
thaliana (see figure). This approach also revealed all six functionally characterized SM
pathways known to form BGCs in the eight
plant genomes examined. However, an examination of predicted but not experimentally
validated BGCs suggested that these clustered genes are not coexpressed and do not
form coexpression modules and might therefore not correspond to functional SM pathways
after all. Thus, proximity might not be a reliable
index for identifying SM pathways, since most
are likely scattered, not clustered. Instead, SM
pathways manage to produce their highly coveted products through coordinated expression of their genes, a trait that can now be
exploited to facilitate their discovery.
Jennifer Lockhart
Science Editor
[email protected]
ORCID ID: 0000-0002-1394-8947
REFERENCES
Schlapfer, P., Zhang, P., Wang, C., Kim, T.,
Banf, M., Chae, L., Dreher, K., Chavali, A.K.,
Nilo-Poyanco, R., Bernard, T., Kahn, D., and
Rhee, S.Y. (2017). Genome-wide prediction
of metabolic enzymes, pathways and gene
clusters in plants. Plant Physiol. 173: 2071–
2059.
Wisecaver, J.H., Borowsky, A.T., Tzin, V., Jander,
G., Kliebenstein, D.J., and Rokas, A. (2017). A
global coexpression network approach for
connecting genes to specialized metabolic
pathways in plants. Plant Cell 29: 944–
959.
Wurtzel, E.T., and Kutchan, T.M. (2016). Plant
metabolism, the diverse chemistry set of the
future. Science 353: 1232–1236.
Chasing Scattered Genes: Identifying Specialized Metabolite Pathway Genes through Global
Coexpression Analysis
Jennifer Lockhart
Plant Cell 2017;29;915; originally published online April 13, 2017;
DOI 10.1105/tpc.17.00303
This information is current as of August 10, 2017
Supplemental Data
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References
This article cites 3 articles, 2 of which can be accessed free at:
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