Multidimensional patterns of metabolic response in abiotic stressinduced growth of Arabidopsis thaliana
Brijesh S. Yadav1, Tamar Lahav2, Eli Reuveni2, Daniel A. Chamovitz1 and Shiri Freilich2
1
Department of Molecular Biology and Ecology of Plants, George S. Wise Faculty of Life Sciences,
Tel Aviv University, Israel; 2Newe-Ya'ar Research Center, Institute of Plant Sciences, Agricultural
Research Organization, PO Box 1021, Ramat Yishay, 30095, Israel.
Correspondence: Daniel Chamovitz, [email protected]; Shiri Freilich, [email protected]
Expression pattern of differentially expressed down-regulated genes in abiotic stresses
The number of differentially expressed down-regulated genes was lower in comparison to the number
of up-regulated genes in all 96 experiments. Constant increases over time in the number of induced
genes as well as pathways were detected in cold, salt and osmotic stress in both the tissues (Figure A1).
There was insignificant response in drought, genotoxic and wound stress while UV stress induced only
in shoot tissue at 3 to 6 hours and heat in both the tissues after 3 hours.
Figure A1. Heat maps illustrating the number of differentially downregulated genes or significantly
enriched pathways. A. Numbers of downregulated gene. B. Number of pathways significantly enriched
with downregulated genes.
Identification of pathways enriched in differentially expressed down-regulated genes across
experiment:
The induced patterns of down-regulated pathway were also different from upregulated ones. Osmotic
and salt response started late in both tissues followed by amino acid and secondary metabolites
pathways (Figure A2). Drought stress response were started early by shoot tissue with cell wall,
hormonal and RNA pathways while secondary metabolites started first in roots. The cell wall, amino
acid and secondary metabolic pathways form the primary response in genotoxic root. Amino acid and
secondary metabolic pathways were key contributors in heat, while cold stress started late by amino
acid and secondary metabolic pathways. The cell wall and secondary metabolites were observed in
wound and UV-b stress.
Figure A2. A. Scaled distribution of the number of pathways significantly enriched with down-
regulated genes across key MapMan categories. For each stress condition, the data point with the
largest number of pathways was defined as 1 (dark red), to which all other points in the same
conditions were scaled (color bar on right). B. Scaled distribution of pathways in root tissue. C. Scaled
distribution of pathways in shoot tissue.
Using various cut-offs for the construction of pathway network:
The score describing the similarities in the enrichment profile of pathways across experiments was
designed to range between 0 to 1. A score of 1 signifies a perfect match between the enrichment
profiles of two pathways across experiments; a score of 0 signifies a completely non-overlapping
pathway profile. Associations patterns were considered under various cutoffs within score range (>=0,
>=0.2, >=0.4, >=0.6, >=0.8, >=1). To limit associations to a cut-off which is both robust (that is – the
majority of associations are not cut-off dependent) and yet comprehensive, we first counted the number
of associations as a function of the association cut-off. For simplicity, we focused on categories whose
biological interpretation is relatively straightforward: RNA categories were considered at the third level
(specific TFs), hormones and secondary metabolites at the 2nd level (specific compounds), and amino
acids at the fourth level (specific amino acids). Overall, 3387 associations with a score higher than 0
were detected. We observe that associations with a score 0.6 and higher are robust and most of them
are detected also when using a higher cut-off (Figure A3). The biological feasibility of associations lost
when using the stringent cut-off (0.8 instead of 0.6) was manually examined through surveying the
scientific literature for evidence in support of the associations in plant stress conditions (Table 1).
Associations lost choosing the stringent cut off include the links between MYB transcription factors
and glucosinolate biosynthesis (secondary metabolites) in the root, a link whose biological significance
is supported by the reported role of MYB proteins as regulators of glucosinolate biosynthesis in
Arabidopsis stress response (Hirai et al. 2007). Similarly – the biological significance of the link
between jasmonic acid and aromatic amino acids, detected under a cutoff 0.6 but not 0.8, is supported
by evidence for the role of jasomonate as regulates tryptophan biosynthesis (Dombrecht et al. 2007).
Overall, 14 out of 31 associations filtered out when using the stricter threshold are supported whereas
the non-supported associations provide a source of predictions for novel interactions (Table 1).
Figure A3. Pairwise pathways association with different cutoff values. X axis represent cutoff values
and Y axis represent number of associate pathways.
Table 1. Scientific literature supporting detected associations. Associations listed between
categories A and B below were detected under 0.6 < cutoff < 0.8. Literature support requires evidence
for regulatory/biochemical connection in plant stress response.
Category A
Category B
Supporting reference
RNA.regulation of
transcription.PHOR1.
RNA.regulation of
transcription.PHOR1.
hormone metabolism.ethylene.
RNA.regulation of
transcription.C3H zinc finger
family.
RNA.regulation of
transcription.C3H zinc finger
family.
RNA.regulation of
transcription.C3H zinc finger
family.
RNA.regulation of
transcription.GRAS transcription
factor family.
RNA.regulation of
transcription.GRAS transcription
factor family.
RNA.regulation of
transcription.C3H zinc finger
family.
RNA.regulation of
transcription.C3H zinc finger
family.
RNA.regulation of
transcription.GRAS transcription
factor family.
RNA.regulation of
transcription.GRAS transcription
factor family.
RNA.regulation of
transcription.MYB domain
transcription factor family.
hormone metabolism.gibberelin.
hormone metabolism.gibberelin.
amino acid
metabolism.synthesis.aromatic
aa.phenylalanine and tyrosine.
hormone metabolism.jasmonate.
amino acid metabolism.degradation.aspartate
family.methionine.
amino acid metabolism. degradation.branchedchain group.shared.
secondary metabolism.phenylpropanoids.
amino acid metabolism.degradation.aspartate
family.methionine.
Not available
secondary metabolism. glucosinolate
Not available
amino acid metabolism.degradation.branchedchain group.shared.
Not available
amino acid metabolism.degradation.aspartate
family.methionine.
Not available
amino acid metabolism.degradation.branchedchain group.shared.
Not available
secondarymetabolism.phenylpropanoids.
Not available
secondary metabolism. glucosinolate
(Gao et al. 2014)
secondary metabolism.phenylpropanoids.
Not available
Secondary metabolism. glucosinolate
Not available
amino acid metabolism.synthesis.aromatic
aa.tryptophan.
(Li and Lu 2014)
secondary metabolism.phenylpropanoids.
secondary metabolism. glucosinolate
secondary metabolism. glucosinolate
(Cheng et al. 2015)
Not available
(Casati and Walbot 2005)
amino acid metabolism.synthesis.aromatic
aa.tryptophan.
secondary metabolism.flavonoids.
(Tzin and Galili 2010)
secondary metabolism.isoprenoids.
(Yamauchi 2010)
secondary metabolism. glucosinolate
(Halkier and Gershenzon
2006)
secondary metabolism.phenylpropanoids.
(Mitsunami et al. 2014)
amino acid
metabolism.synthesis.branched
chain group.common.
amino acid
metabolism.degradation.branchedchain group.shared.
amino acid
metabolism.degradation.branchedchain group.shared.
RNA.regulation of
transcription.MYB-related
transcription factor family.
Not available
(Saltveit 2004)
Not available
Not available
RNA.regulation of
transcription.MYB-related
transcription factor family.
RNA.regulation of
transcription.PHOR1.
amino acid
metabolism.synthesis.aromatic
aa.tryptophan.
RNA.regulation of
transcription.C2C2(Zn) DOF zinc
finger family.
amino acid
metabolism.degradation.aromatic
aa.tyrosine.
hormone metabolism.abscisic acid.
hormone metabolism.abscisic acid.
hormone metabolism.abscisic acid.
secondary metabolism. glucosinolate
(Frerigmann and Gigolashvili
2014)
secondary metabolism.phenylpropanoids.
Not available
secondary metabolism.flavonoids.
(Petridis et al. 2016)
secondary metabolism.phenylpropanoids.
Not available
secondary metabolism.wax.
Not available
amino acid metabolism.synthesis.aspartate
family.asparagine.
secondary metabolism.flavonoids.
amino acid metabolism.degradation.aspartate
family.methionine.
(Wang et al. 2005)
(Nguyen et al. 2016)
(Liu et al. 2015)
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