3.3 Root tissue metabolites
The Partial Least Squares Discrimination Analysis (PLS-DA) model
separated the samples from the two planting treatments. PLS-DA is a
supervised discriminant analysis statistical method, which uses partial
least squares regression (Boulesteix et al., 2007) to establish the
relationship model between metabolite expression and sample category.
PC1 and PC2 were the scores of the test samples in the first and second
principal components, respectively. From Fig. 7, it was clear that the
two planting density treatments had a certain degree of differentiation;
hence, the subsequent data analysis was reliable.
The threshold values of Variable Importance in the Projection (VIP)
>1.0, Fold Change (FC) >1.5 or
<0.667 and P value< 0.05 were set as the screening
criteria for significantly different metabolites. A total of 51
metabolites with significant differences were found. Plotting all
metabolites of the low-density (L) vs high-density (H) treatments in the
volcano map can help us quickly find the differences in expression of
root tissue metabolites. Among the 51 different metabolites, 28 were
significantly up-regulated and 23 were significantly down-regulated,
with a relatively small difference (Fig. 7b). We selected the top 10
metabolites with the largest differences for the annotation study. These
top 10 differential metabolites were: schisandrin C (up), s7p (down),
pelargonidin chloride (down), N-feruloylspermidine (up), tyrosol (down),
acetylharpagide (down), KMH (up), kanamycin (up), 3-ureidopropionic acid
(up), and eicosapentaenoic acid (down) (Fig. 7c).
All the differentially expressed metabolites in the low-density vs
high-density treatments were put into the KEGG database for annotations.
The 14 annotated differential metabolites and the involved metabolic
pathways are listed in Table 3. There were five up-regulated and nine
down-regulated metabolites.
It can be seen from Fig. 7c that the metabolic pathways with the lowest
P values was biosynthesis of unsaturated fatty acids, and the
differential metabolites within the pathways were
eicosapentaenoic acid and
docosahexaenoic acid. Both eicosapentaenoic
and docosahexaenoic acids were
down-regulated.
The metabolite
S-adenosylmethionine was the most
enriched metabolite in all metabolic pathways, so we focused on it.
The mean relative content of
S-adenosylmethionine was 161 × 105 in the low-density
treatment (LD) and 71 × 105 in the high-density
treatment (HD), indicating the content of S-adenosylmethionine in the LD
was twice that in the HD. The mean relative content of docosahexaenoic
acids was 2971 × 105 in the LD treatment and 7183 ×
105 in the HD treatment, i.e., the content of
docosahexaenoic acids in the HD treatment was more than twice that in
the LD treatment.