Transmission electron microscopic observation of chloroplast structure
In this study, transmission electron microscopy was used to analyze leaf phenotypic characteristics and compare the differences on chloroplasts structural of early indica rice seedlings after 3 days between LT and LTF. The results showed that chloroplasts were regular boat shaped or spindle shaped and thylakoid lamellae clear arranged close to the inner wall of cells in CK (Fig. 1C). Compared with CK, chloroplasts began to degrade, shaped distorted and loosely structured in LTF (Fig. 1B), however, chloroplasts had been severely degraded, thylakoid lamellae were seriously damaged and osmiophilic body increased gradually in LT (Fig. 1A). The damage to chloroplast structure in LTF was less than that in LT. These results showed that chloroplast were affected to some extent after low temperature stress, and flooding could alleviate low temperature damage on chloroplast structure.
Analysis of photosynthesis activity andendogenous hormone content
Rubisco is the key enzyme of photosynthetic carbon metabolism in C3 plants, which is positively correlated with the photosynthetic rate (Yu et al. 2012). Under LT stress, the balance of plant cell free radical production and elimination system is damaged, chlorophyll synthesis and energy metabolism are affected (Wang et al. 2006). This study showed that the rubisco content of LT was significantly decreased by 26.97% (P < 0.05) compared to CK, but there was no significant difference between LTF and CK (Fig. 2A). The PEPCK activity, chlorophyll content and ATP content of LT and LTF were significantly decreased (P < 0.05) compared with CK, and those indexs were lower in LT than in LTF (Fig. 2B; Fig. 2C; Fig. 2D). Endogenous hormones are signal molecules produced in the process of plant metabolism, which play an important role in plant growth and environmental response (Sakamoto T. 2006). Compared with CK, the GA3 content of pro-growth hormones in LT and LTF decreased significantly (Fig. 2E). In contrast, the ABA content of anti-growth hormones in LT and LTF significantly increased by 35.71% and 16.67% (P < 0.05), respectively (Fig. 2F). There were the same trends on GA3 and ABA between LT and LTF, which reached a significant level. These results indicated that flooding could improve photosynthesis activity and endogenous hormone content under low temperature stress of direct-seeded early indica rice at seedling stage.
Analysis of soluble protein content, antioxidase and osmotic regulatory substances
Soluble protein is an important osmotic regulator that affects the osmotic potential of cells (Zhu et al. 2015). After plants were stressed, plant cell membrane would be damaged by free radicals and reactive oxygen species, resulting in membrane lipid peroxidation and protein activity affected (Vighi et al. 2017; Hu et al. 2017). This study showed that the content of soluble protein and MDA in LT and LTF increased significantly (P < 0.05) compared with CK, and LT also significantly increased soluble protein and MDA contrast to LTF (Fig. 3A; Fig. 3B). In addition, LT and LTF significantly increased the activities of SOD and POD (P < 0.05) compared with CK, and there were no significant differences between LT and LTF, although SOD and POD were higher in LT than in LTF (Fig. 3C; Fig. 3D).
Identification of DEPs
To evaluate the reliability of the data generated by proteomic analysis, the Pearson correlation coefficient was calculated for each of three samples, which indicated good reproducibility of the three biological replicates in each treatment (Fig. 4A). In addition, a total of 412489 spectrums were detected, 236880 of which could be matched to peptides in the database and 28934 were unique peptides. In total, 5639 proteins could be identified and 4518 proteins were experimentally quantified (Table 1).
Proteins with a fold change (FC) > 1.5 or (FC) < 0.67 (P < 0.05) between the treatment (LT, LTF) and control groups (CK) were regarded as DEPs, and DEPs were hence considered as low temperature and low temperature flooding responsive proteins at the seedling stage. There were 567 DEPs between LT and CK, 239 DEPs between LTF and CK, and 235 DEPs between LTF and LT. The number of up-regulated and down-regulated DEPs was shown in Fig. 4B and the three groups had 16 DEPs in common (Fig. 4C).
In this study, 72818 transcripts and 5639 proteins were identified by quantitative transcriptome and proteome studies. 4983 genes were identified at both transcriptome and proteome levels (Fig. 4D). The correlation coefficient between transcripts and proteins in LT and CK treatment groups was 0.19, that in LTF and CK treatment groups was 0.25, and that in LT and LTF treatment groups was 0.22. It indicates that the correlation degree of samples in each treatment group is low, and they are basically consistent with the expectation (Fig. 5).
Gene functional description and GO analysis
To annotate the function of low temperature flooding responsive proteins, the protein IDs were searched in the NCBI database (https:// www.ncbi.nlm.nih.gov/) and / or the UniProt database (http://www. uniprot.org/). For the DEPs between LT and CK, 197 up-regulated proteins and 369 down-regulated proteins showed annotated functions, and 1 down-regulated protein remained uncharacterized (Additional file 1: Dataset S1). For the DEPs between LTF and CK, both 114 up-regulated proteins and 125 down-regulated proteins could be annotated with functions (Additional file 2: Dataset S2). For the DEPs between LTF and LT, both 154 up-regulated proteins and 81 down-regulated proteins showed annotated functions (Additional file 3: Dataset S3).
To determine the cellular component (CC), molecular function (MF) and biological process (BP) categories of GO for the low temperature and low temperature flooding response proteins, we searched their protein IDs against the GO database (Mi and Al. 2013). GO analysis showed that the DEPs were involved in fourteen subgroups of BP (Fig. 6A), ten subgroups of CC (Fig. 6B), and ten subgroups of MF (Fig. 6C) between LT and CK. The main biological process categories were metabolic process (30%), cellular process (25%), single-organism process (18%), response to stimulus (7%), localization (7%), biological regulation (5%), cellular component organization or biogenesis (4%), other (4%). The cellular component categories were cell (34%), organelle (25%), membrane (24%), and macromolecular complex (12%), other (5%). The molecular function categories were binding (44%), catalytic activity (42%), transporter activity (5%), structural molecule activity (4%), and antioxidant activity (2%), other (3%) (Additional file 4: Figure S1).
GO analysis showed that the DEPs were involved in thirteen subgroups of BP (Fig. 6A), nine subgroups of CC (Fig. 6B), and ten subgroups of MF (Fig. 6C) between LTF and CK. The biological process categories were metabolic process (30%), cellular process (26%), single-organism process (21%), and response to stimulus (7%), biological regulation (5%), cellular component organization or biogenesis (4%), localization (4%), other (3%). The cellular component categories were cell (36%), membrane (28%), organelle (26%), macromolecular complex (4%), and extracellular region (3%), other (3%). The molecular function categories were catalytic activity (47%), binding (44%), structural molecule activity (2%), and transporter activity (2%), other (5%) (Additional file 5: Figure S2).
GO analysis showed that the DEPs were involved in fourteen subgroups of BP (Fig. 6A), eight subgroups of CC (Fig. 6B), and nine subgroups of MF (Fig. 6C) between LTF and LT. The biological process categories were metabolic process (27%), cellular process (19%), single-organism process (17%), localization (10%), response to stimulus (7%), biological regulation (5%), developmental process (3%), multicellular organismal process (3%), cellular component organization or biogenesis (3%), reproduction (3%), other (5%). The cellular component categories were membrane (33%), cell (29%), organelle (23%), and macromolecular complex (12%), other (3%). The molecular function categories were binding (45%), catalytic activity (41%), transporter activity (7%), and structural molecule activity (3%), other (4%) (Additional file 6: Figure S3).
Protein–protein interaction
The functional DEPs of all annotated were used to analyze protein interactions. This revealed that most enzymatic proteins and proteins related to biosynthesis of secondary metabolites, monobactam biosynthesis, metabolic pathways, pentose phosphate pathway, fructose and mannose metabolism, glycolysis / gluconeogenesis, glycine, serine and threonine metabolism, arachidonic acid metabolism, biosynthesis of amino acids, phenylalanine, tyrosine and tryptophan biosynthesis and proteasome related proteins interactions were affected by LT and CK (Additional file 7: Figure S4). Most enzymatic proteins and metabolic pathways, biosynthesis of secondary metabolites, carotenoid biosynthesis, ribosome biogenesis in eukaryotes, glycolysis/gluconeogenesis, glycine, serine and threonine metabolism photosynthesis and thiamine metabolism were observed for the interaction between LTF and CK (Additional file 8: Figure S5). Most enzymatic proteins and photosynthesis-antenna proteins, photosynthesis related proteins interactions were affected by LTF and LT (Additional file 9: Figure S6). In this study, the photosynthesis pathway and energy metabolism pathway were observed for being highly enriched under low temperature and low temperature flooding. This result showed that low temperature flooding played an important role in regulating the photosynthetic capacity of rice leaves. Consistent with our GO analysis findings, the majority of proteins were involved in photosynthesis and metabolic processes. We could focus on proteins related to photosynthesis and metabolism at the proteomic level.
KEGG pathway analysis
All of the DEGs and DEPs were analyzed for the KEGG over-representation of pathways to obtain functional insights into the difference between LT, LTF and CK treatment. The significantly (P < 0.01) enriched KEGG pathways are shown in Table 2. The KEGG pathways (ordered by rank) are monobactam biosynthesis, glycine, serine and threonine metabolism, biosynthesis of secondary metabolites, pentose phosphate pathway, biosynthesis of amino acids, metabolic pathways, arachidonic acid metabolism, glycolysis / gluconeogenesis, proteasome, phenylalanine, tyrosine and tryptophan biosynthesis, fructose and mannose metabolism between LT and CK. The KEGG pathways (ordered by rank) are thiamine metabolism, ribosome biogenesis in eukaryotes, carotenoid biosynthesis, biosynthesis of secondary metabolites, metabolic pathways, glycine, serine and threonine metabolism, glycolysis / gluconeogenesis between LTF and CK. The KEGG pathways (ordered by rank) are photosynthesis, photosynthesis - antenna proteins between LTF and LT.
Analysis of DEGs and DEPs by qRT-PCR
To verify the proteomes and transcriptomes results, eighteen related genes including five up-regulated and thirteen down-regulated were detected. As shown that the mRNA expression of A2XLW5, B8AYU2, A2X822, B8ASV8 and A2XYC2 were down-regulated between LT and CK (Fig. 7A). The mRNA expression levels of A2YM28, A2XKN7, A2WRR8 and A2X8P7 were down-regulated between LTF and CK, B8AZB8, B8BJP8, B8AS16 were significantly up-regulated between LTF and CK (Fig. 7B). In LTF and LT, A2YMZ1, A2YCB9, A2YHC5 and A2YLE6 were down-regulated, and B8B7M5 and A2YP23 were up-regulated (Fig. 7C). Those as mentioned indicated that transcriptomes and proteomes results indeed reflected the relative expression of each gene, in which up-regulated or down-regulated genes in qRT-PCR were completely consistent with transcriptome and proteome trends. Therefore, the transcriptome results were reliable.