Dynamic changes in transcriptome in rice compatible and
incompatible interactions with M. oryzae are different
To gain molecular insight into the mechanisms involved in the resistance
to blast disease in Hui1586, in-depth comprehensive transcriptome
profiles compared with susceptible Nipponbare were achieved through
RNA-seq experiments. The two-week-old rice plants were spray inoculated
with spores of M. oryzae Guy11 or H2O (mock
treatment), and then leaf samples with 3 biological replicates were
collected at 12, 24, 36 and 48 hpi for RNA-seq. The mock for each time
point was processed to ensure appropriate comparisons. In total, 48
samples were subjected to RNA-seq, resulting in 6.8-gigabyte 100-bp
paired-end reads per sample on average.
To visualize the variation as well as the similarity for all samples, we
performed a principal component analysis (PCA) on the normalized FPKM
(fragments per kilobase of transcript per million mapped reads) values
of all the detected genes. The PCA plot showed that the data for three
biological replicates were clustered closely and were separated by the
time point, treatments and genotypes (Fig. 2A). Among the data for the
mock treatment, the 12, 24, 36, and 48 h samples were clustered away
from each other, indicating that the circadian clock is a major factor
affecting gene expression. At 12 h, the M. oryzae treatments were
clustered away from the mock treatment in both susceptible Nipponbare
and resistant Hui1586. However, at the late time points of 24, 36, and
48 h, the M. oryzae and mock treatments were clustered closely on
Hui1586, while they were clustered away on Nipponbare, indicatingM. oryzae induced large transcriptome changes in the susceptible
Nipponbare at all time points but high-amplitude transcriptome changes
only at the early infection stage in resistant Hui1586.
A hierarchical clustering analysis was conducted on the FPKM values of
all detected genes. The samples were clustered into 4 larger groups
according to the time point (Fig. 2B), again showing that the circadian
clock is a major regulator in the rice transcriptome. In each large
group, the H2O treatments were clustered into the same
subgroup, except the 36 hpi samples of H2O-treated
Hui1586, which were clustered with M. oryzae- treated Hui1586,
suggesting the occurrence of small transcriptional changes in Hui1586 at
36 hpi with M. oryzae treatment. The 24, 36, and 48-hpi samples
of M. oryzae -treated Nipponbare were clustered away from the mock
samples (Fig. 2B), indicating that M. oryzae persistently induced
strong transcriptional changes in the susceptible Nipponbare.
Then, we identified differentially expressed genes (DEGs; fold change
>2; P < 0.001) between the fungal and mock
treatment in Nipponbare or Hui1586 at each time point. A large number of
DEGs in Nipponbare were identified, especially at 12 h (4680 upregulated
and 2045 downregulated) and 36 h (3347 upregulated and 3653
downregulated). While Guy11 induced more dramatic transcriptional
changes (6808 upregulated and 2895 downregulated genes) in the resistant
Hui1586 at 12 h, the numbers of DEGs markedly decreased at 24, 36 and 48
h (Fig. 2C). In total, 12333 and 12147 DEGs, accounting for
approximately 25% of the rice genes, were identified respectively in
Nipponbare and Hui1586 in the 48 h time-series of the fungal infection.
Among these DEGs, 8211 were commonly regulated in both cultivars (Fig.
2D and Supplemental dataset 1). Together, the data indicated thatM. oryzae infection induced dramatic and dynamic transcriptional
reprogramming in rice. Remarkably, the resistant Hui1586 mounted faster
and stronger transcriptional reprogramming at the early infection stage
than the susceptible Nipponbare, which likely restricted the fungal
infection more efficiently and resulted in smaller transcriptional
changes at the later time points. In contrast, Guy11 could successfully
infect and constitutively multiply on the susceptible Nipponbare,
inducing continuous dramatic transcriptional changes within all
infection stages.