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Article

Transcriptional Profile Corroborates that bml Mutant Plays likely Role in Premature Leaf Senescence of Rice (Oryza sativa L.)

by
Delara Akhter
1,2,
Ran Qin
1,
Ujjal Kumar Nath
3,
Jamal Eshag
1,
Xiaoli Jin
1 and
Chunhai Shi
1,*
1
Department of Agronomy, Zhejiang University, Hangzhou 310027, China
2
Department of Genetics and Plant Breeding, Sylhet Agricultural University, Sylhet 3100, Bangladesh
3
Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(7), 1708; https://doi.org/10.3390/ijms20071708
Submission received: 24 March 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 5 April 2019
(This article belongs to the Section Molecular Plant Sciences)
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Abstract

:
Leaf senescence is the last period of leaf growth and a dynamic procedure associated with its death. The adaptability of the plants to changing environments occurs thanks to leaf senescence. Hence, transcriptional profiling is important to figure out the exact mechanisms of inducing leaf senescence in a particular crop, such as rice. From this perspective, leaf samples of two different rice genotypes, the brown midrib leaf (bml) mutant and its wild type (WT) were sampled for transcriptional profiling to identify differentially-expressed genes (DEGs). We identified 2670 DEGs, among which 1657 genes were up- and 1013 genes were down-regulated. These DEGs were enriched in binding and catalytic activity, followed by the single organism process and metabolic process through gene ontology (GO), while the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the DEGs were related to the plant hormone signal transduction and photosynthetic pathway enrichment. The expression pattern and the clustering of DEGs revealed that the WRKY and NAC family, as well as zinc finger transcription factors, had greater effects on early-senescence of leaf compared to other transcription factors. These findings will help to elucidate the precise functional role of bml rice mutant in the early-leaf senescence.

1. Introduction

Leaf senescence is the ending period of leaf growth in the vegetative cycle, and acts as an indicator of physiological maturity of the harvest product. However, premature leaf senescence may mislead the growers to harvest too early. Many reports have pointed out that it plays adaptive roles in plants in changing environments by fine tuning the regulatory genes [1,2]. The majority of these genes are involved in (1) recycling and re-using the nutrients from senescent leaves for newly developed organs, and (2) enhancing tolerance against biotic or abiotic stresses [3,4,5].
Leaf senescence is a vital agronomic attribute for crop yield and quality [6,7,8]. A wide range of physiological processes occur in the leaves during the process of senescence. The rate of assimilation reduces more rapidly with the increase of catabolism, such as degradation of the chloroplast, reducing photosynthesis, and the degradation of macromolecules [9]. The senescence process might also be affected by internal as well as the external factors. The latter case is environmental conditions, e.g. soil moisture, light, temperature, nutrient content and pathogens [10,11]. Therefore, it is important to understand the internal factors that are responsible for leaf senescence, as this knowledge could help to elucidate the basic molecular mechanism. Exploration of that mechanism will provide a means for controlling leaf senescence in crop plants [2].
Many studies have been carried out on senescence-associated genes (SAGs) in different plant species [9,12,13,14], and currently, the database of leaf senescence comprises some 130 SAGs [15]. However, the findings have not provided a complete explanation of the molecular mechanisms of leaf senescence in monocots like, rice, wheat, maize, sorghum and barley. It has been established that the chlorophyll (Chl) metabolism and degradation pathways are involved in several SAGs of leaf senescence [16]. During chlorophyll degradation, Chlb is changed to Chla in the first leaf, wherein NYC1 [17] and NOL [18] are involved in chlorophyll-b degradation. In A. thaliana, the gene PAO (Pheophorbide A Oxygenase) takes part in the degradation of chlorophyll, leading to leaf senescence [19]. PPR (pentatricopeptide repeat) genes control chloroplast gene expression, and eventually, senescence of leaves in rice [20]. Plant hormones like MeJA (methyljasmonate) and JA (jasmonate) have been recognized as leaf senescence enhancing substances for rice [21]. The gene OsCOL1b (CORONATINE INSENSITVE 1b) encodes the JA receptor of Arabidopsis, and the mutant Oscoi1b delays leaf senescence and shows insensitivity to MeJA [22]. Two important genes, OsPME and OsTSD2 [23], encode pectin-esterase and methyltransferase respectively were identified through mGWAS (metabolite-based genome-wide association study) [23]. Besides JA, IAA also plays a complex role in leaf senescence regulation. Leaf senescence can be delayed by exogenous IAA application [24]. Leaf senescence could be positively regulated by auxin-responsive genes such as SAUR (small auxin-up RNA) [24,25,26,27]. The gene OsPLS1 encodes a vacuolar H+-ATPase subunit A1, and negatively regulates leaf senescence at the beginning [28].
Leaf senescence in rice is regulated by several transcription factors (TFs); many of them belong to WRKY transcription factor [29,30]. In Arabidopsis, the AtWRKY70, AtWRKY53, AtWRKY54 genes are engaged in the signaling pathway of SA [31] which induces cell death and leaf senescence [30,31,32]. The over-expression of OsWRKY42 exhibits ROS accumulation and accelerates leaf senescence through repressing OsMT1d in rice [33]. The chlorophyll metabolic pathway involved several GATA-zinc finger TFs, like GNC (GATA nirate-inducible carbon-metabolism) [34], degrades the chlorophyll in A. thaliana [34,35]. The over-expression of OsGATA12 reduces degradation of chlorophyll and delays leaf senescence, reducing the number of leaves and tillers, as well as enhancing yields in rice [36]. In addition, the GPC-B1 (grain protein content-B1) and OsNAC106 (N-acetylcysteine106) genes are senescence-linked transcription factors [37]. Among them, GPC-B1 accelerates leaf senescence [37], while ONAC106 regulates leaf senescence inversely [38]. The transporter family genes OsSWEET5 (sugar transporter family5) [39] and Yr18/Lr34/Pm38 from the ABC-transporter family also induce leaf tip senescence [40] in rice.
Transcriptomic analysis will help to identify the genes associated with leaf senescence, because the transcriptional profiling of the leaf senescence genes in rice is still under exploration. Therefore, the identification of these genes through RNA-Seq (RNA sequencing) is crucial to understanding the molecular mechanisms of leaf senescence and to developing stress-adaptive rice cultivars in order to achieve higher yields. RNA-Seq is a quantification technology of transcripts which provides precise levels of transcripts measurements; therefore, it has been successfully and widely used for profiling transcripts with annotations, and in the identification of genes in different plant species [41,42,43,44,45,46].
In the present study, RNA-sequencing analysis was accomplished with a mutant of early-leaf senescence, bml mutant, and its WT variety, Zhenong 34, at an early-tillering stage. In our previous study, this bml mutant was identified and reported in the mutational rice population developed by ethyl methane sulfonate (EMS) mutagenesis. Such a mutant was differentiated by the phenotypic character of the early-senescence in leaf from the M2 population [47]. Herein, we performed transcriptomic profiling of that bml mutant Vs WT to identify the differentially-expressed genes and post-translation modifications in order to elucidate the mechanisms involved in leaf senescence. GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and KOG/COG (Clusters of Orthologous Groups) analyses were also performed at an early-tillering stage. This transcriptome data will provide information necessary to understand leaf senescence in rice.

2. Results

2.1. Phenotypic Characterization

No observable differences were found between the bml mutant and WT in the seedling to tillering stage [47]. Senescence was found to start in the bottom leaves at the early-heading stage in the bml mutant, wherein the other leaves then became yellow with progressive senescence (Figure 1A). The midrib of the leaves in the bml mutant started to turn brown, and lesion phenotypes appeared on the leaf blade, gradually covering the whole leaf (Figure 1A,B). Leaf senescence was accelerated more in the bml mutant during the late-heading and grain filling stages, compared to WT. Our previous study showed that the bml plants demonstrated slower growth, with significantly reduced numbers of tillers, shorter panicle lengths, lower seed-settings, fewer grains in the panicles, lower 1000-grain weights, and smaller seed sizes (length and width) compared to WT [47].
Moreover, genetic analysis indicated that the early-senescence of leaf was controlled by a single recessive gene [47]. The mutants showed abnormal leaf cells with degraded chloroplasts and less chlorophyll, a reduced photosynthetic rate (Pn), less stomatal conductance (Gs) and intercellular CO2 concentration (Ci), reduced transpiration rate (Tr) and Fv/Fm (maximal quantum yield of PSII) compared to WT at the heading stage [47].

2.2. RNA-Seq Analysis

RNA-seq was performed for the transcriptomic profiling of bml mutant using leaf samples collected from three replicates of bml mutant and WT plants. The cDNA of WT and bml leaf samples were sequenced using Illumina technology to determine the probable DEGs and the pathways involved in the process of early-leaf senescence in the bml mutant. On average, 60,545,108 bp and 62,982,574 bp raw reads were retrieved from the RNA-seq data for the bml mutant and WT, respectively (Table 1). After removing the adaptor, ambiguous nucleotides, and low-quality sequences, approximately 56,637,709 bp and 61,147,444 bp clean reads were obtained from the WT and bml mutant, respectively, which covered an average length of 147.1 to 147.5 bp. The GC content of the clean reads ranged from 54.71 to 55.60% in different libraries, and the Q30 percentage exceeded 94.9% (Table 1).
A total of 110,715,792 clean reads were generated by sequencing six cDNA libraries. All reads were classified into three categories: total mapped, multiple mapped and uniquely mapped. Of all the reads, 94.01–94.27% read were totally mapped, 91–93% were uniquely mapped, and 2–3% were mapped in the genome (Table 2). A summary of the sequencing data, alignment statistics of the reads to the reference gene, alignment statistics to reference genome and QC items for each sample are given in Table 1 and Table 2.
A correlation of a heat map was constructed using the correlation coefficients of replicated samples of the bml mutant and WT (Figure 2). The correlation coefficient among bml samples was 0.95 (bml1, bml2 and bml3), while that among the WT samples was 0.94 (WT1, WT2 and WT3), which had a tendency for unity. These results revealed that the bml group (bml1, bml2 and bml3) differed significantly from the WT group (WT1, WT2 and WT3).
The average numbers of SNPs were 96,368 and 96,228 in the bml mutant and WT respectively, whereas INDELs were 10,861 and 11,020, respectively. SNP-affected regions were divided into two categories, i.e., genic (UTR and CDS) and inter-genic regions. A major part of the SNP-affected regions was covered by the genic region for both the bml mutant and WT (Table S2). Two kinds of nucleotide substitutions were identified at homozygote polymorphic SNPs due to transversions (A/C, A/T, C/G, G/A and G/T) and transitions (A/G, G/A, T/C and C/T). The occurrence of SNPs in transitions was more frequent for both the bml mutant and the WT (Table S3).

2.3. Identifications of DEGs

The DEGs between bml mutant and WT were figured out at the early-tillering stage to explore the genes involved in leaf senescence of bml mutant (Table S4). A volcano plot of all genes was drawn to characterize the distribution of DEGs in the selection at threshold level (Figure 3A). A total number of 2670 DEGs was identified, among them 1657 genes were up-regulated and 1013 genes were down-regulated in bml Vs WT leaf. Moreover, we also detected the specific DEGs by the Venn diagrams and found 1858 genes as commonality in all the samples (Figure 3B).

2.4. GO Enrichment Analysis

Gene Ontology (GO) is an international standardized gene functional classification system that defines the genes according to three terms: molecular function, cellular component and biological process. The enriched GO terms of down- and up-regulated DEGs was analyzed and presented as bar-plot. A total 16,510 genes was assigned in GO and 1503 (9.10%) DEGs annotated significantly in the GO database, among them 537 and 966 genes were regulated down- and up-, respectively. These genes were assigned to 50 subcategories (Figure 4 and Table S5). Among them, a majority of the genes were categorized as biological process (44.89%) followed by molecular (32.65%) and cellular functions (24.48%) (Figure 4 and Table S5). A higher proportion of the genes were involved in biological function because the leaf senescence is a physiological process, which could bring the changes in many biological processes and regulated by the genes and perceived the signals in the early-tillering stage for enhancing the leaf senescence at early heading stage. In the category of biological process, the majority of the genes were assigned in the metabolic subcategory followed by single organism and cellular process. A significant proportion of the clusters were classified as binding, followed by catalytic activity with transcription factor activity and sequence specific DNA binding but no RNA binding in the molecular function category (Figure 4).
For cellular component subcategory, the largest portion of the genes was subcategorized as the involvement in membrane followed by cell with cell part, membrane part and organelle part. A few genes were assigned to the rhythmic process, immune system process, extracellular matrix and mettalochaperon activity categories. The scatter plot of the DEGs (up- and down-regulated) also showed that maximum number of DEGs involved in binding and catalytic activity followed by the single organism process and the single organism metabolic process (Supplementary Figure S1).

2.5. KEGG and KOG Pathway Analysis of DEGs

KEGG analysis with DEGs was executed for further investigating the pathway involved in leaf senescence of the bml mutant at the early-tillering stage. The unigenes orientations of metabolic pathways were investigated through bar-plot analysis in the KEGG database. Among 5226 assigned genes, only 527 genes were found to be involved in five pathways, such as the cellular, environmental and genetic information processing as well as in the metabolism and organismal systems (Figure 5A and Table S6).
The scatter plot analyses of the significant KEGG enrichment showed the function and highly differentially expressed genes are represented by the size of the dots upon each function (Figure 5B and Table S7). Here, we selected only top 30 KEGGs based on the highest level of KEGG enrichment. Among them, ko04075 (plant hormone signal transduction), ko01230 (biosynthesis of amino acid), ko00940 (phenylpropanoid biosynthesis), ko00710 (carbon fixation in photosynthetic organisms), ko00195 (photosynthesis) and ko00480 (glutathione metabolism) DEGs had significant enrichment in KEGG pathways related to the leaf senescence. It also indicated that the genetic signal of early leaf senescence might be perceived in the earlytillering stage of bml mutant plants through alteration of hormonal signal transduction and photosynthesis.
The most relevant biological pathways for early-leaf senescence of the bml mutant could be identified by enrichment analysis. Functions of uniqueness were classified according to COG/KOG databases using orthologous gene products. A total of 10106 non-redundant uni-genes was found in KOG analysis, among which 929 genes showed differential expression. Unique DEGs were divided into 26 KOG sub-functional groups (Figure 6); it was determined that the maximum of them was involved in the signal transduction mechanism followed by general function prediction, post-translational modification, protein turnover and chaperone with secondary metabolite biosynthesis, transport, as well as catabolism. Few unique DEGs were found to involve in cell motility.

2.6. Confirmation of Gene Expression Profile through qRT-PCR

The qRT-PCR was executed to validate the RAN-seq data using 25 randomly selected genes relevant to leaf senescence (Table 3). The gene expression pattern was similar to the transcriptome data of RNA-seq (Table 3), indicating the consistency and reliability of the transcriptomes.
bml showed the senescence and lesion mimic phenotype and functions related to hormone signaling and photosynthesis pathways and transcription factor genes. In this regard, we prioritized the identification of the differentially-expressed genes involved in early-leaf senescence (Table 3). In the early-leaf senescence of rice mutant bml, DEGs were enriched in plant hormone signal transduction, photosynthesis, and photosynthesis with antenna proteins pathways including WRKY, NAC, GATA transcription factors, chlorophyll metabolism as well as different biological processes. We also identified 22 DEGs orthologs of the rice genes related to abscicic acid (ABA; 6 genes), indole acetic acid (IAA; 6 genes), jasmonic acid (JA; 2 genes), and salicylic acid (SA; 8 genes) responses (Table S4). Among them, the orthologs of IAA26 (LOC_Os02g57250) and IAA13 (LOC_Os01g09450) were down-regulated in the bml mutant. The relative expression of the genes of ABA signal transduction was increased in bml compared to WT. PR1 family-related genes are involved in the SA signaling pathway; therefore, we found that SA-related LOC_Os01g28450, LOC_Os01g09800 and LOC_Os07g03730 genes were significantly up-regulated in the bml mutant compared to WT. JA signaling genes, e. g. JAZ10 (LOC_Os03g08330) and JAZ5 (LOC_Os04g32480) were also up-regulated in the bml mutant (Table 3). The results confirmed that the gene expression was matched and steady with the transcript analysis accomplished through RNA-seq.

2.7. Cluster Analysis of DEGs

We performed a cluster analysis using the expression values of the DEGs to identify the genes directly connected to leaf senescence. Hierarchical clustering was performed considering 10% cut-off of the tree constructed from DEGs by the R package (Figure 7).
In total, 4101 genes which exhibited diverse expression patterns between the bml mutant and WT were classified into 10 sub-clusters, (Figure 8 and Table S8). Six clusters showed the changes of gene expression, while 4 exhibited genes with up-regulation, and 2 contained genes with down-regulation in bml. We found that the genes presented in the clusters were associated with senescence, and most genes showed up-regulation in the bml mutant. Among the up-regulated genes, 139 encoded kinase family proteins, and 19 belonged to receptor-like genes. Additionally, 55 genes were annotated as transcription factors or DNA-binding factors, 17 with WRKY transcription factors, while the rest showed orthology to bZIP, NAC and zinc finger transcription factors (Table S8).

3. Discussion

A rice mutant, bml, showed the effect on plant growth and development. By genetic analysis, it was concluded that early-leaf senescence was governed by the recessive bml gene [47]. RNA-Seq analyses were performed to determine whether the changes of the transcriptome of senescence-associated genes, rather than solely the bml recessive gene, started at the beginning of tillering, by comparing the sequence data of bml and WT of rice at early-tillering stage. It was kept in mind that RNA-Seq analysis helps with precise the transcript profiling, annotation and identification of genes in plants [41,43,46].
Most of the correlation coefficients of our bml samples were 0.95, while that of the WT samples was 0.94, which were approaching unity, indicating a higher level of similarity in the expression patterns among the samples [45]. These results revealed high sample repeatability, and reflected significant differences between the bml and WT groups.
A total of 25 genes were randomly selected from the identified DEGs for testing of their expression at the early-tillering stage by qRT-PCR. The qRT-PCR results showed that the genes were expressed differentially, and they confirmed the trustworthiness of our transcriptome data. The identification of DEGs in diverse transcripts was very important for finding the differentially-expressed genes between the samples, and for carrying out an additional functional analysis of the outcomes [45,48]. Among the 2670 DEGs, 1657 and 1013 genes showed up- and down-regulation respectively. About 50 enriched GO terms were detected by the GO enrichment analysis of the DEGs at the early-tillering stage. Moreover, KOG analysis of DEGs showed the involvement of uni-genes in the signal transduction mechanism, which is relevant to early-leaf senescence, and which was recognized at the early-heading stage; these results were confirmed by Zhang et al. [32]. Mainly, the WRKY transcription factor genes were significantly up-regulated in the bml mutant, indicating their direct involvement in early-leaf senescence. Similarly, the orthologous genes of AtWRKY40, AtWRKY53 and AtWRKY70 transcription factors were engaged in SA signaling transduction pathways in leaf senescence [31,32,49].
The functional activity of WRKY transcription factors occurs in the upstream of the regulatory network of leaf senescence. By comparing the expression profiles of the differential genes of bml mutant and WT, we deduced that the gene encoded protein kinase WRKY transcription factor, GATA transcription factors, bZIP, NAC and zinc finger transcription factors play potential roles in the leaf senescence process, which is in agreement with a previous study by Leng et al. [5]. We also found that down-regulated DEGs were enriched in GO terms such as photosystem I, photosystem II and chlorophyll metabolism at the early-tillering stage (Table S5); those were reported as the factor of degenerative phase in earlier studies [50]. On the process of senescence, the leaf cells pass through a remarkable transition and systematic deterioration of different cellular structures [29]. We found several differentially-expressed genes in the bml mutant related to chlorophyll metabolism and which contribute to leaf senescence.
The expression of orthologs of chloroplast precursor-relative and TPR (tetratricopeptide repeat) like super family genes LOC_Os08g09270, LOC_Os06g02120 and LOC-Os01g25600 were down-regulated in the bml mutant (Table S4). In the bml mutant, the genes of grana degradation, photosynthesis pathway, photosystem I and photosystem II (Photosynthesis - antenna proteins pathway) were shown to be down-regulated, which supports our results by the down-regulation of NYC1, ATFD3, ATLFNR2, LHCA5 and LHCA2 genes [18,51,52] which causes early-leaf senescence. The orthologs of ATCOL2 (a GATA transcription factor) were down-regulated, which can influence leaf senescence by regulating chlorophyll accumulation [35,36]. Pentatricopeptide repeat (PPR) super family proteins can affect the content of chloroplast through regulating the gene expression of the chloroplast, e.g., OspTAC2 gene in rice [20] and other PPR, PPR53 genes might also change the gene expression of chloroplast and influence the development of leaf in maize [53]. We found several PPR-like genes in our transcriptome profiles, such as LOC_Os01g48380, LOC_Os06g07550, LOC_Os10g10170 and LOC_Os06g09880, which were down-regulated in the bml mutant and might play a role in the process of early leaf senescence in rice.
KEGG pathway analysis showed that leaf senescence is affected by the plant hormone signaling pathway. A prior study reported that SA, JA, ABA and ET accelerate leaf senescence, while IAA, GA and CK were found to delay its development [54]. In the present study, we found IAA related genes, such as the LOC_Os02g57250 and LOC_Os01g09450 as orthologs of IAA26 and IAA13, respectively, were down-regulated in the bml mutant. The expression of ABA hormone signal transduction pathway genes increased in the bml mutant. The PR1 family-related genes, such as LOC_Os01g28450, LOC_Os01g09800, and LOC_Os07g03730 involved in SA signaling pathway showed significant up-regulation in the bml mutant. JA signaling genes, such as JAZ10 (LOC_Os03g08330) and JAZ5 (LOC_Os04g32480), also showed up-regulation in the bml mutant (Table 3), which should enhance leaf senescence. These results revealed that leaf senescence in the bml mutant might be controlled by different hormones, as stated in a previous study of wheat [32].
The cluster analysis of DEG expression patterns gave us an idea and the opportunity to classify and identify senesce-associated genes (SAGs) in rice. We gave more emphasis to clusters in which gene expression patterns showed obvious up- and/or down regulation in the bml mutant compared to WT (Figure 8 and Table S8). Many types of protein kinases genes, like receptor-like protein kinase, transcription factors and transporter proteins, were significantly enriched. The number of WRKY family, NAC and zinc finger transcription factor genes was higher than that of other transcription factor genes. A previous report revealed that many transcription factor genes were connected to leaf senescence in Arabidopsis [30], and might play significant roles in the early-senescence of rice. So, we concluded that signal transduction and substance transport were associated, and that the perceived the genetical signals at the early-tillering stage of bml rice plants contribute to early leaf senescence.
We explored and profiled the transcriptomes of bml mutant and WT plants at the early-tillering stage to determine and annotate the transcripts linked to leaf senescence. The GO enrichment, the KEGG pathway of DEGs and the KOG/COG were analyzed at the early-tillering stage. Multiple pathways were found to be involved in the leaf senescence process in rice. Among them, plant hormone signal transduction, photosynthesis, photosynthesis antenna protein pathways played predominant roles in the early leaf senescence process. These pathways’ genes might be evacuated in chlorophyll break down or degradation; the accumulation of antioxidant activity and ROS molecules play a direct role in leaf senescence. Our results confirmed the transcriptional factor and signal transduction genes which bring about changes in the regulation of different hormones which accelerate the early senescence process of leaf in rice. Therefore, in our future breeding programs, we could emphasize those genes to ensure delayed senescence by increasing the duration of photosynthesis and higher yields in rice.

4. Materials and Methods

4.1. Plant Materials

Two rice genotypes, i.e., the wild form of Zhenong 34 and its mutant of brown midrib leaf (bml) from Oryza sativa L. ssp. Indica, were used for RNA-sequencing. The bml is the mutant of Zhenong 34 produced by ethyl methane sulfonate (EMS) mutagenesis. The mutant plants were selected by gene mapping according to a bulk segregant analysis (BSA) method, as well as being differentiated phenotypically and recognized as early-senescence of leaf compared to WT in the M2 population [47]. Plants were grown in rice fields at the Zhejiang University in Hangzhou, China (30°15′49″ N, 120°7′15″ E), during March–July 2017; the average temperature was 14 °C to 33 °C, with 60 to 70% R.H. The leaf tissues were collected from the plant seedlings (25 days after sowing and just before transplanting), early tillering (15 days after transplanting) and early booting (45 days after sowing) stages, 10 leaves from 10 plants at same growing stage of the WT and bml plants were collected as biological replicates and pooled together before being immersed in liquid nitrogen. All samples were stored in a refrigerator at −80 °C until RNA extraction.

4.2. RNA Isolation, Library Preparation and Sequencing

Total RNA was extracted using the total RNA Extractor (Trizol) kit (B511311, Sangon, China) according to the manufacturer’s protocol. The quality and quantity of the extracted RNA were assessed using Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA) and NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). High-quality RNA samples were subsequently submitted to the Sangon Biotech Co. Ltd., Shanghai, China for sequencing and library preparation.
The mRNA was purified from the total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using the divalent cations under an elevated temperature in a VAHTSTM First-Strand Synthesis Reaction Buffer (5×). First strand cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). Second strand cDNA synthesis was subsequently performed using DNA polymerase I and RNase H. The remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3´ ends of the DNA fragments, the adaptor was ligated in order to prepare the libraries. The adapter primer sequences were used as the forward primer: 5’AGATCGGAAGAGCACACGTCTGAAC3´, and the reverse primer: 5´AGATCGGAAGAGCGTCGTGTAGGGA3´. The library quality was assessed on the Agilent Bioanalyzer 2100 system (Agilent Technologies). A total of six libraries, including three from the bml mutant and three from WT, were generated. Paired-end sequencing of the libraries was performed using an Illumina HiSeqTM2000 (Illumina, San Diego, CA, USA).

4.3. Data Filtering, Reads Mapping and RNA-Seq Data Analysis

Raw reads of primary sequencing were collected from the Illumina HiSeqTM 2000 and filtered (before downstream analysis for reducing data noise) to retrieve the clear reads. During the filtration of the data, adapter, poly-N, as well as low-quality reads were removed from the raw data to ensure the reliability of the analysis. The clean reads were stored in the FASTQ format [55], and Bowtie 2 [56] was used to map clean reads to the reference gene. HISAT2 (version 2.1.0) [57] was used with the parameters: hisat2-mm-dta-p 10-x genome.fa-known-splicesite-infile genes.ss-1 R1.fastq.gz-2 R2.fastq.gz to align the clean reads with the reference genome of the Nipponbare subspecies of rice (Oryza_sativa.v7.0; http://rice.plantbiology.msu.edu/) with an ideal match or one mismatch. RSEM, a precise and accessible software tool, was used to determine the transcript isoforms of the same gene [58].

4.4. Transcriptomic Analysis

DESeq2 (version 1.12.4) was used to determine the DEGs between two samples. Genes were considered as differentially- and significantly-expressed if q-value < 0.001 and |Fold Change| > 2. Expression values of zero for the samples were treated as 0.01 during normalization, because zero could not be plotted onto the log plot.
Next, DEGs were mapped to the GO terms (biological functions) in the database, the number of genes was calculated in every term, and a hypergeometric test was performed to identify any significantly enriched GO terms for the genes list in the background of the reference list. KEGG pathway analysis identified significantly enriched metabolic pathways and signal transduction pathways in DEGs compared to a reference genome.

4.5. Quantitative Real-Time PCR

For RNA-seq data validation, 25 genes were randomly selected from DEGs and tested for their expression levels at the early-tillering stage using quantitative real time PCR (qRT-PCR). Gene-specific primers were designed using the Primer 5 software (Table S1), with the parameters of 100–500 bp PCR product size, 20 ± 2 bp primer length, melting temperature 50–62 °C, GC content 40–60% and run in automatic search mode. The qRT-PCR was performed in the Roche light cycler 480 real-time system (Roche, Germany) using Ex TaqII (Takara, Tokyo, Japan) according to the manufacturer’s instructions, with five technical replicates of 10 μL reaction mixture. Then, 10 μL reaction mixture was mixed with 5µl 2× quanti speed SYBR mix with 1 µL (10 pmol) for each of the forward (F) and reverse (R) gene specific primers, 1 µL template cDNA (50ng) and 2 µL distilled-deionized water (ddH2O). qRT-PCR was performed following denaturation at 95°C for 30s, 40 cycles of denaturation at 95 °C for 5 s, annealing at 55 °C for 20 s, and extension at 72 °C for 10 s. To calculate the expression, the OsActin reference gene was used as an internal control [59]. The relative expression was calculated by the 2-ΔΔCT method [60] using the cq values normalized by the cq of the rice OsActin gene.

Supplementary Materials

Supplementary materials can be found at https://www.mdpi.com/1422-0067/20/7/1708/s1.

Author Contributions

D.A. and C.S. designed and developed the experiments. D.A. performed the main experiments and drafted the manuscript. C.S. identified and provided the populations of the bml mutant for genetic study and gene mapping. R.Q., J.E. and X.J. helped to carry out the experiments and collected some data. U.K.N. analyzed the data and edited the manuscript. All authors have read and approved the final manuscript.

Funding

This study was supported by grants from the Science and Technology Office of Zhejiang Province (2012C12901-2, 2016C32G2010016 and 2016C02050-6) and a grant from National Key Research and Development of China (2017YFD0100300-5).

Acknowledgments

We thanked Jocelyn M. Losh (email: [email protected]), College of Life Science, Zhejiang University, China for critical reviewing and editing the manuscript. Special Thanks to Li Junying, Analysis Centre of Agribiologic and Environmental Science, Lab of Electron Microscope, Zhejiang University, China for helping partial analysis of transcriptome data.

Conflicts of Interest

The authors declared no conflict of interest.

References

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Figure 1. Phenotypes of bml mutant and WT rice plants. (A). Phenotype observed at the heading stage in bml mutant compared to WT. Bar = 10 cm. (B). Magnified view of the part of flag leaf from the top of the plant displayed in A. Bar = 5 cm.
Figure 1. Phenotypes of bml mutant and WT rice plants. (A). Phenotype observed at the heading stage in bml mutant compared to WT. Bar = 10 cm. (B). Magnified view of the part of flag leaf from the top of the plant displayed in A. Bar = 5 cm.
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Figure 2. Heat-map diagram of inter-sample correlation analysis. The color block represents the correlation index value. The grayer the color, the lower the correlation index between samples and higher intensity of the color represents the higher correlation coefficient.
Figure 2. Heat-map diagram of inter-sample correlation analysis. The color block represents the correlation index value. The grayer the color, the lower the correlation index between samples and higher intensity of the color represents the higher correlation coefficient.
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Figure 3. Volcano map and Venn diagram of DEGs. (A). DEGs are shown by volcano map between bml and WT at early-tillering stage. The horizontal axis is the fold-change (log2(B/A)) value of the differential expression of the gene in different groups of samples, and the vertical axis is the statistical significance level with p value representing the changes in gene expression. Each point in the figure represents a gene, in which red indicates up-regulated genes, green indicates down-regulated genes, and black indicates non-differentiated genes. (B). DEGs between bml mutant and WT at early-tillering stage are plotted by Venn diagram. The comparison between different groups is represented by different colors, and the numbers in the figure represent the number of specific or shared DEGs. The overlap region indicates the number of differentially expressed genes shared by different comparison groups, and the non-overlapping region indicates the number of DEGs unique between the different comparison groups.
Figure 3. Volcano map and Venn diagram of DEGs. (A). DEGs are shown by volcano map between bml and WT at early-tillering stage. The horizontal axis is the fold-change (log2(B/A)) value of the differential expression of the gene in different groups of samples, and the vertical axis is the statistical significance level with p value representing the changes in gene expression. Each point in the figure represents a gene, in which red indicates up-regulated genes, green indicates down-regulated genes, and black indicates non-differentiated genes. (B). DEGs between bml mutant and WT at early-tillering stage are plotted by Venn diagram. The comparison between different groups is represented by different colors, and the numbers in the figure represent the number of specific or shared DEGs. The overlap region indicates the number of differentially expressed genes shared by different comparison groups, and the non-overlapping region indicates the number of DEGs unique between the different comparison groups.
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Figure 4. Differential gene ontology (GO) analysis of functional enrichment with up- and down-regulated DEGs between bml mutant and WT at early-tillering stage. The horizontal axis is the functional classification and the vertical axis is the number of genes in the classification (right) and the percentage of the total number of genes on the annotation (left). Different colors represent different classifications. Light colors on the histogram and axis represent differential genes, and dark colors represent all genes.
Figure 4. Differential gene ontology (GO) analysis of functional enrichment with up- and down-regulated DEGs between bml mutant and WT at early-tillering stage. The horizontal axis is the functional classification and the vertical axis is the number of genes in the classification (right) and the percentage of the total number of genes on the annotation (left). Different colors represent different classifications. Light colors on the histogram and axis represent differential genes, and dark colors represent all genes.
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Figure 5. KEGG (Kyoto encyclopedia of genes and genomes) analyses of DEGs between bml and WT at early-tillering stage by barplot and scatter diagram. (A). KEGG analysis of DEGs through barplot. The horizontal axis is the functional classification, and the vertical axis is the number of genes in the classification (right) and the percentage of the total number of genes on the annotation (left). Different colors represent different classifications: light colors on the histogram and axis represent differential genes, and dark colors represent all genes. (B). Scatter plot of significant enrichment function. The vertical axis represents functional annotation information, and the horizontal axis represents the function of the Rich Factor (the number of differential genes annotated to the function divided by the number of genes annotated to the function). The size of the Q value is represented by the color of the dot; the smaller the Q value, the closer the color is to red, and the more the differential genes contained under each function are represented by the size of the dots. Only the top 30 GOs with the highest levels of enrichment were selected.
Figure 5. KEGG (Kyoto encyclopedia of genes and genomes) analyses of DEGs between bml and WT at early-tillering stage by barplot and scatter diagram. (A). KEGG analysis of DEGs through barplot. The horizontal axis is the functional classification, and the vertical axis is the number of genes in the classification (right) and the percentage of the total number of genes on the annotation (left). Different colors represent different classifications: light colors on the histogram and axis represent differential genes, and dark colors represent all genes. (B). Scatter plot of significant enrichment function. The vertical axis represents functional annotation information, and the horizontal axis represents the function of the Rich Factor (the number of differential genes annotated to the function divided by the number of genes annotated to the function). The size of the Q value is represented by the color of the dot; the smaller the Q value, the closer the color is to red, and the more the differential genes contained under each function are represented by the size of the dots. Only the top 30 GOs with the highest levels of enrichment were selected.
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Figure 6. KOG/COG (Clusters of Orthologous Groups) analyses of DEGs between bml mutant and WT at early-tillering stage. The horizontal axis is the functional classification, and the vertical axis is the number of genes in the classification (right) and the percentage of the total number of genes on the annotation (left). Different colors represent different classifications. Light colors on the histogram and axis represent differential genes, and dark colors represents all genes.
Figure 6. KOG/COG (Clusters of Orthologous Groups) analyses of DEGs between bml mutant and WT at early-tillering stage. The horizontal axis is the functional classification, and the vertical axis is the number of genes in the classification (right) and the percentage of the total number of genes on the annotation (left). Different colors represent different classifications. Light colors on the histogram and axis represent differential genes, and dark colors represents all genes.
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Figure 7. Clustering heat map of differential gene expression. Each row in the Figure represents a gene, each column represents a sample, and the color indicates the amount of expression of the gene in the sample. Red indicates that the gene is expressed in the sample, and green indicates that the expression level is low. The left side is a tree diagram of gene clustering. The closer the two gene branches are, the closer they are. The upper part is the tree diagram of the sample cluster, and the lower part is the name of the sample; the two samples are separated. The closer it is, the closer the expression patterns of all the genes in these two samples are, i.e., the closer the trend of gene expression changes.
Figure 7. Clustering heat map of differential gene expression. Each row in the Figure represents a gene, each column represents a sample, and the color indicates the amount of expression of the gene in the sample. Red indicates that the gene is expressed in the sample, and green indicates that the expression level is low. The left side is a tree diagram of gene clustering. The closer the two gene branches are, the closer they are. The upper part is the tree diagram of the sample cluster, and the lower part is the name of the sample; the two samples are separated. The closer it is, the closer the expression patterns of all the genes in these two samples are, i.e., the closer the trend of gene expression changes.
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Figure 8. Differential gene module expression trends by the line chart. The expression trend line graph of each sub-module, the horizontal axis is the sample, and the vertical axis is the expression level of the gene in the sample. Each line in the figure represents a gene, and the colored lines represent the mean of a set of genes. Each graph shows one type of expression pattern, a trend that reflects changes in the expression levels of this group of genes; (A,C,F) up-regulated pattern of gene in the bml mutants but down-regulated in WT, (B,D) up-regulated pattern of gene in the WT but down-regulated in bml mutants and (E) genes had no response due to mutation.
Figure 8. Differential gene module expression trends by the line chart. The expression trend line graph of each sub-module, the horizontal axis is the sample, and the vertical axis is the expression level of the gene in the sample. Each line in the figure represents a gene, and the colored lines represent the mean of a set of genes. Each graph shows one type of expression pattern, a trend that reflects changes in the expression levels of this group of genes; (A,C,F) up-regulated pattern of gene in the bml mutants but down-regulated in WT, (B,D) up-regulated pattern of gene in the WT but down-regulated in bml mutants and (E) genes had no response due to mutation.
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Table
Table 1. Reads summary of samples by Illumina deep RNA sequencing.
Table 1. Reads summary of samples by Illumina deep RNA sequencing.
Sample NameRaw ReadsAverage Raw Read Length (bp)Clean ReadsAverage Clean Read Length (bp)Clean Base (bp)Error (%)Q20 (%)Q30 (%)GC Content (%)
WT60,545,10815056,637,709147.1030,9650.0098.4494.6454.71
Bml62,982,57415061, 147, 444147.5043,1800.0098.5994.9955.60
Table
Table 2. Alignment statistics summary of RNA-seq and mapping results.
Table 2. Alignment statistics summary of RNA-seq and mapping results.
Sample NameTotal ReadsTotal Mapped (%)Multiple Mapped (%)Uniquely Mapped (%)Non-Splice Reads (%)Splice Reads (%)Reads Mapped in Proper Pairs (%)
WT5357939894.013.0091.0061.4629.5587.31
bml5713639494.272.5693.4563.3628.3388.06
Table
Table 3. Relative expression of 25 randomly-selected genes for comparison between the bml mutant and WT group with respect to RNA- seq and qRT PCR.
Table 3. Relative expression of 25 randomly-selected genes for comparison between the bml mutant and WT group with respect to RNA- seq and qRT PCR.
Gene IDGene Namebml vs. WTGene DescriptionPathway/Function Involved
RNA-Seq log2 Fold ChangeqRT PCR Fold Change
LOC_Os01g12710NYC1−1.509−0.880 ± 0.02NAD(P)-binding Rossmann-fold superfamily proteinPorphyrin and chlorophyll metabolism
LOC_Os02g52650LHCA5−1.266−569 ± 0.207Photosystem I light harvesting complex gene 5Photosynthesis-antenna proteins
LOC_Os09g26810LHCA2.1−1.0265−0.879 ± 0.0890Photosystem I light harvesting complex gene 6Photosynthesis-antenna proteins
LOC_Os06g44010ATWRKY403.7223.455 ± 0.418WRKY DNA-binding protein 40WRKY transcription factor
LOC_Os12g02470ATWRKY536.5125.360 ± 0.1837WRKY family transcription factorWRKY transcription factor
LOC_Os11g02540ATWRKY704.6692.862 ± 0.2069WRKY DNA-binding protein 70WRKY transcription factor
LOC_Os07g04560anac0421.5662.641 ± 0.0344NAC domain containing protein 42NAC transcription factor
LOC_Os10g42130anac0712.6302.798 ± 0.0182NAC domain containing protein 71NAC transcription factor
LOC_Os09g36200ATNYE1.4371.375 ± 0.0246Non-yellowing 1NAC transcription factor
LOC_Os01g21250AtLEA54.1823.947 ± 0.0286Senescence-linked gene 21Biological process
LOC_Os03g0529SAG121.0433.139 ± 0.0128Senescence-associated gene 12Proteolysis (Biological process)
LOC_Os06g16370ATCOL2−2.334−0.194 ± 0.0378CONSTANS-like 2GATA transcription factor
LOC_Os03g08330JAZ101.3360.893 ± 0.0239Jasmonate-zim-domain protein 1Plant hormone signal transduction
LOC_Os01g09450IAA26−3.341−1.985 ± 0.0462Phytochrome-associated protein 1Plant hormone signal transduction
LOC_Os02g57250IAA13−1.316−0.639 ± 0.0302Auxin-induced protein 13Plant hormone signal transduction
LOC_Os01g28450ATPR16.0955.015 ± 0.0108Pathogenesis-related gene 1Plant hormone signal transduction
LOC_Os07g03730PR1a10.4204.106 ± 0.105Pathogenesis-related gene 1Plant hormone signal transduction
LOC_Os01g09800ATNPR11.1401.893 ± 0.1017Pathogenesis-related gene 1Plant hormone signal transduction
LOC_Os04g32480JAZ53.7462.084 ± 0.143Jasmonate-zim-domain protein 10Plant hormone signal transduction
LOC_Os09g29660ABCG111.8683431.901 ± 0.041ABC super familyABC transporter
LOC_Os07g48260ATWRKY543.9806984.035 ± 0.051WRKY DNA-binding protein 54WRKY transcription factor
LOC_Os01g60700ATSIK1.0361381.241 ± 0.312Serine/threonine protein kinaseProtein kinase superfamily protein
LOC_Os04g33630ATFD3−1.74014−0.934 ± 0.063Ferredoxin 3Photosynthesis related
LOC_Os09g36250AtMYB421.86931.012 ± 0.082MYB domain protein 42MYB domain transcripton factor
LOC_Os04g43070AMT1;11.99461691.512 ± 0.101Ammonium transporter 1;1Ammonium transporter

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MDPI and ACS Style

Akhter, D.; Qin, R.; Nath, U.K.; Eshag, J.; Jin, X.; Shi, C. Transcriptional Profile Corroborates that bml Mutant Plays likely Role in Premature Leaf Senescence of Rice (Oryza sativa L.). Int. J. Mol. Sci. 2019, 20, 1708. https://doi.org/10.3390/ijms20071708

AMA Style

Akhter D, Qin R, Nath UK, Eshag J, Jin X, Shi C. Transcriptional Profile Corroborates that bml Mutant Plays likely Role in Premature Leaf Senescence of Rice (Oryza sativa L.). International Journal of Molecular Sciences. 2019; 20(7):1708. https://doi.org/10.3390/ijms20071708

Chicago/Turabian Style

Akhter, Delara, Ran Qin, Ujjal Kumar Nath, Jamal Eshag, Xiaoli Jin, and Chunhai Shi. 2019. "Transcriptional Profile Corroborates that bml Mutant Plays likely Role in Premature Leaf Senescence of Rice (Oryza sativa L.)" International Journal of Molecular Sciences 20, no. 7: 1708. https://doi.org/10.3390/ijms20071708

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