Transcriptome Analysis in Plants

**39**

**1. Introduction**

**Chapter 4**

Sterility

**Abstract**

Plant Comparative

*Xiangyuan Wan and Ziwen Li*

development and plant male sterility.

Transcriptomics Reveals

Functional Mechanisms and Gene

Gene transcription and transcriptional regulation are crucial biological processes

in all cellular life. Through the next-generation sequencing (NGS) technology, transcriptome data from different tissues and developmental stages can be easily obtained, which provides us a powerful tool to reveal the transcriptional landscape of investigated tissue(s) at special developmental stage(s). Anther development is an important process not only for sexual plant reproduction but also for genic male sterility (GMS) used in agriculture production. Plant comparative transcriptomics has been widely used to uncover molecular mechanism of GMS. Here, we focused on researches of anther developmental process and plant GMS genes by using comparative transcriptomics method. In detail, the contents include the following: (1) we described the commonly used flowchart in comparative transcriptomics; (2) we summarized the comparative strategies used to analyze transcriptome data; (3) we presented a case study on a maize GMS gene, *ZmMs33*; (4) we described the methods and results previously reported on gene co-expression and gene regulatory networks; (5) we presented the workflow of a case study on gene regulatory network reconstruction. The further development of comparative transcriptomics will provide us more powerful theoretical and application tools to investigate molecular mechanism underlying anther

**Keywords:** plant comparative transcriptomics, gene regulatory network,

Gene transcription is an important biological process by which genetic information stored within DNA molecules is transmitted to RNA molecules according to the "genetic central dogma" in molecular biology [1]. After completion of the human genome project, the researchers began to reveal the transcriptional

anther development, genic male sterility, molecular mechanism

Regulatory Networks Involved in

Anther Development and Male

#### **Chapter 4**

## Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory Networks Involved in Anther Development and Male Sterility

*Xiangyuan Wan and Ziwen Li*

### **Abstract**

Gene transcription and transcriptional regulation are crucial biological processes in all cellular life. Through the next-generation sequencing (NGS) technology, transcriptome data from different tissues and developmental stages can be easily obtained, which provides us a powerful tool to reveal the transcriptional landscape of investigated tissue(s) at special developmental stage(s). Anther development is an important process not only for sexual plant reproduction but also for genic male sterility (GMS) used in agriculture production. Plant comparative transcriptomics has been widely used to uncover molecular mechanism of GMS. Here, we focused on researches of anther developmental process and plant GMS genes by using comparative transcriptomics method. In detail, the contents include the following: (1) we described the commonly used flowchart in comparative transcriptomics; (2) we summarized the comparative strategies used to analyze transcriptome data; (3) we presented a case study on a maize GMS gene, *ZmMs33*; (4) we described the methods and results previously reported on gene co-expression and gene regulatory networks; (5) we presented the workflow of a case study on gene regulatory network reconstruction. The further development of comparative transcriptomics will provide us more powerful theoretical and application tools to investigate molecular mechanism underlying anther development and plant male sterility.

**Keywords:** plant comparative transcriptomics, gene regulatory network, anther development, genic male sterility, molecular mechanism

### **1. Introduction**

Gene transcription is an important biological process by which genetic information stored within DNA molecules is transmitted to RNA molecules according to the "genetic central dogma" in molecular biology [1]. After completion of the human genome project, the researchers began to reveal the transcriptional

landscape of all genes in a genome to further investigate the functional mechanisms underlying phenotypic variations at a genome-wide transcriptional level. Therefore, biological studies on high-throughput omics data run from the genomic level into the transcriptomic level. Transcriptome data includes biological information of gene transcriptional activities in a certain cell, a tissue, or an individual (a population of cells) and even in a pool of samples under a certain developmental stage, an environmental condition, or an experimental treatment. Compared with other omics data (e.g., data of genome, epigenome, proteome, metabolome, or phenome), the primary characteristic of transcriptome data is that it includes temporal–spatial bioinformation affected by diverse developmental stages, tissue types, and internal/external environment events. Therefore, transcriptome data is more complex than genome data.

Transcriptomic studies usually focus on the transcriptional content and gene regulations in a genome. Gene expression microarray (GEM) is an early developed but still-utilized biotechnology by which the genome-wide transcription information can be obtained for genome-sequenced or transcriptional loci available species. In 1995, Schena et al. monitored expression levels of 48 genes by GEM in *Arabidopsis thaliana* [2], and then GEM was gradually and widely used for the estimation of gene expression levels. Until 2013, the amount of transcripts monitored by one microarray had been reached to more than 285,000 in human transcriptomics studies (the human transcriptome array). GEM is a hybrid-based method, while the sequencing-based method has been developed much faster and became one of the most commonly used biotechnologies in scientific studies and applications related to disease diagnosis [3]. Serial analysis of gene expression (SAGE) proposed by Velculescu et al. [4] and massively parallel signature sequencing (MPSS) reported by Brenner et al. [5] are two earlier developed sequencing-based methods to estimate the transcription information at a genome level. Nowadays, the majority of transcriptome data are generated by the NGS-based RNA sequencing (RNA-seq). RNA-seq technology combining with the following developed comparative transcriptomics analysis flowchart that is mainly based on digital gene expression profile (DGEP) is a commonly used research strategy in biological studies at molecular and genomic levels.

Anther is an important organ in sexual plant reproduction. Anther development is a dynamic process from the identity of the stamen to the production of mature pollen grains. During this period, two-thirds of protein-coding genes are transcribed, and more than 6% of them are anther specific (a reanalyzed result based on [6]). Thus, the anther transcriptome is specific and complex compared with transcriptomes of other plant organs. Plant comparative transcriptomics is an effective strategy used to investigate the molecular mechanism underlying anther developmental process. The comparative method based on anther transcriptomes can be performed between different genotypes, different developmental stages, different types of anther cells, and different biotic or abiotic treatments and even between different plant species. Consequently, differentially expressed genes (DEGs) are identified from above comparisons. Based on the comparison results, functionally important coding genes and noncoding transcripts including long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and other small RNAs could be uncovered. However, the goal of plant comparative transcriptomics is not only to identify DEGs but also to reconstruct gene regulatory relationships of the upstream regulators and the downstream regulated targets of the investigated genes. In this review, based on anther transcriptomes, we first summarized the research workflow commonly used in the experimental design and data analyses in plant transcriptomics studies, and then we described several types of comparison strategies in comparative transcriptomics using anther transcriptome data as the analyzed example. In the following

**41**

**Figure 1.**

*A flowchart of comparative transcriptomics analysis.*

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

section, we generally discussed gene regulatory and co-expression networks used to investigate the molecular foundation of developing anther in a network-based perspective. Additionally, we described two case studies in our laboratory to explain the detailed analysis processes and applications of comparative transcriptomics in

In comparative transcriptomics, the commonly used pipeline to identify potential functional genes and to reveal the gene functions, as well as to investigate the regulatory relationships between these genes, includes five aspects. They are data preparation, DGEP analysis, DEG analysis, gene set enrichment (GSE) analysis, and gene regulatory network (GRN) analysis, respectively (**Figure 1**). These five aspects are closely connected in the whole pipeline, and the corresponding analyses

The basic application of comparative transcriptomics is to obtain a transcriptional landscape of the investigated biological sample. It is composed of not only the estimated transcription levels of annotated transcribed loci along the genomes (the known genomic loci with reported or predicted transcription abilities) but also the identification of novel transcribed loci (the stably transcribed loci not annotated or identified in previous studies). More importantly, in current biological studies, transcribed loci identified by researchers include not only the proteincoding genes but also lncRNAs and other noncoding RNAs. Both GEM and RNA-seq technologies can be used to uncover the genome-wide profiles of transcription levels of annotated genes. However, the identification of novel transcribed loci can be only effectively performed by RNA-seq method and the following DGEP analysis. This is one reason why RNA-seq is more commonly used in transcriptomics studies. Moreover, GEM method depends on hybridization probes that are designed based on known whole genome sequence or an appreciable set of sequenced transcripts

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

**2. Comparative analysis using transcriptome data**

mainly depend on data management skills in bioinformatics.

plant GMS gene studies.

#### *Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

section, we generally discussed gene regulatory and co-expression networks used to investigate the molecular foundation of developing anther in a network-based perspective. Additionally, we described two case studies in our laboratory to explain the detailed analysis processes and applications of comparative transcriptomics in plant GMS gene studies.

#### **2. Comparative analysis using transcriptome data**

In comparative transcriptomics, the commonly used pipeline to identify potential functional genes and to reveal the gene functions, as well as to investigate the regulatory relationships between these genes, includes five aspects. They are data preparation, DGEP analysis, DEG analysis, gene set enrichment (GSE) analysis, and gene regulatory network (GRN) analysis, respectively (**Figure 1**). These five aspects are closely connected in the whole pipeline, and the corresponding analyses mainly depend on data management skills in bioinformatics.

The basic application of comparative transcriptomics is to obtain a transcriptional landscape of the investigated biological sample. It is composed of not only the estimated transcription levels of annotated transcribed loci along the genomes (the known genomic loci with reported or predicted transcription abilities) but also the identification of novel transcribed loci (the stably transcribed loci not annotated or identified in previous studies). More importantly, in current biological studies, transcribed loci identified by researchers include not only the proteincoding genes but also lncRNAs and other noncoding RNAs. Both GEM and RNA-seq technologies can be used to uncover the genome-wide profiles of transcription levels of annotated genes. However, the identification of novel transcribed loci can be only effectively performed by RNA-seq method and the following DGEP analysis. This is one reason why RNA-seq is more commonly used in transcriptomics studies. Moreover, GEM method depends on hybridization probes that are designed based on known whole genome sequence or an appreciable set of sequenced transcripts

#### **Figure 1.**

*A flowchart of comparative transcriptomics analysis.*

*Transcriptome Analysis*

more complex than genome data.

ies at molecular and genomic levels.

landscape of all genes in a genome to further investigate the functional mechanisms underlying phenotypic variations at a genome-wide transcriptional level. Therefore, biological studies on high-throughput omics data run from the genomic level into the transcriptomic level. Transcriptome data includes biological information of gene transcriptional activities in a certain cell, a tissue, or an individual (a population of cells) and even in a pool of samples under a certain developmental stage, an environmental condition, or an experimental treatment. Compared with other omics data (e.g., data of genome, epigenome, proteome, metabolome, or phenome), the primary characteristic of transcriptome data is that it includes temporal–spatial bioinformation affected by diverse developmental stages, tissue types, and internal/external environment events. Therefore, transcriptome data is

Transcriptomic studies usually focus on the transcriptional content and gene regulations in a genome. Gene expression microarray (GEM) is an early developed but still-utilized biotechnology by which the genome-wide transcription information can be obtained for genome-sequenced or transcriptional loci available species. In 1995, Schena et al. monitored expression levels of 48 genes by GEM in *Arabidopsis thaliana* [2], and then GEM was gradually and widely used for the estimation of gene expression levels. Until 2013, the amount of transcripts monitored by one microarray had been reached to more than 285,000 in human transcriptomics studies (the human transcriptome array). GEM is a hybrid-based method, while the sequencing-based method has been developed much faster and became one of the most commonly used biotechnologies in scientific studies and applications related to disease diagnosis [3]. Serial analysis of gene expression (SAGE) proposed by Velculescu et al. [4] and massively parallel signature sequencing (MPSS) reported by Brenner et al. [5] are two earlier developed sequencing-based methods to estimate the transcription information at a genome level. Nowadays, the majority of transcriptome data are generated by the NGS-based RNA sequencing (RNA-seq). RNA-seq technology combining with the following developed comparative transcriptomics analysis flowchart that is mainly based on digital gene expression profile (DGEP) is a commonly used research strategy in biological stud-

Anther is an important organ in sexual plant reproduction. Anther development is a dynamic process from the identity of the stamen to the production of mature pollen grains. During this period, two-thirds of protein-coding genes are transcribed, and more than 6% of them are anther specific (a reanalyzed result based on [6]). Thus, the anther transcriptome is specific and complex compared with transcriptomes of other plant organs. Plant comparative transcriptomics is an effective strategy used to investigate the molecular mechanism underlying anther developmental process. The comparative method based on anther transcriptomes can be performed between different genotypes, different developmental stages, different types of anther cells, and different biotic or abiotic treatments and even between different plant species. Consequently, differentially expressed genes (DEGs) are identified from above comparisons. Based on the comparison results, functionally important coding genes and noncoding transcripts including long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and other small RNAs could be uncovered. However, the goal of plant comparative transcriptomics is not only to identify DEGs but also to reconstruct gene regulatory relationships of the upstream regulators and the downstream regulated targets of the investigated genes. In this review, based on anther transcriptomes, we first summarized the research workflow commonly used in the experimental design and data analyses in plant transcriptomics studies, and then we described several types of comparison strategies in comparative transcriptomics using anther transcriptome data as the analyzed example. In the following

**40**

(e.g., expressed sequence tags) of the investigated species, which restrict its application on some species without whole genome information or sequence resource. On the contrary, the sequencing-based method of RNA-seq can be applicable for species without sequenced genomes. This is another reason for the popularity of RNA-seq. In genome available species, RNA-seq data should be firstly mapped to the reference genome (**Figure 1**).

A gene with its transcription levels significantly different between two groups of samples is defined as a DEG under a certain comparison condition (**Figure 1**). It is notable that the concept of DEG specially represents the expression changes of protein-coding genes at the earlier stages of expression data analysis. However, along with the rapid development of molecular biology and the deeper understanding on the functional element on the genome, the concept of DEG has been expanded to noncoding transcripts, for example, the differentially expressed (DE) miRNA and the DE lncRNA. Furthermore, if both coding and noncoding transcripts are considered in the comparative analysis of transcriptome data, transcriptional alterations between control and treated samples should be defined as DE transcribed loci or DE loci. Thus, DE loci is a broad concept used to describe transcriptional alterations of genetic element. There are several strategies for comparing transcriptomes from different research subjects to identify DE loci (described in Section 3, "Plant comparative transcriptomics in anther").

Identified DEG set or DE loci should be appropriately annotated with functional descriptions to determine which biological process or pathway these DEGs are involved in. In comparative transcriptomics, this step is a critical bridge linking transcriptional changes to gene functions and even gene regulation networks. Two commonly utilized methods to annotate DEGs consist of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway gene enrichment analyses. Both of them belong to GSE analysis (**Figure 1**). The GO database includes tens of thousands of GO terms, and each GO term contains several genes with the same biological function. Each gene has three functional aspects, including molecular function (the molecular activities of gene products), cellular component (the cellular locations of functional gene products), and biological process (the gene products' molecular functions with biological process). GSE analysis based on GO database provides some basic functional descriptions for the investigated DEG set. KEGG analysis is a pathway-based enrichment method. The KEGG database has accumulated hundreds of metabolism pathways in plants, animals, and other species. Thus, KEGG analysis can reveal significant pathways the DEGs participated in. GO-based methods can annotate more genes than KEGG-based method, as the GO terms are more flexible and include a larger number of genes. On the other hand, because most metabolic pathways are conserved across species and more significant in biological processes, annotated results obtained from KEGG-based method may be more conserved and stable. In comparative transcriptomics, GO- and KEGGbased analyses are together utilized in gene function studies.

The locations of transcribed loci on the genome, their transcription levels, and the changed expression can be identified through comparative transcriptomics analysis. The detected DEG set represents a functional gene set related to the function of investigated gene, the phenotype variation, the stress resistance ability, or the development process. Furthermore, gene regulation relationships are the underlining molecular mechanism of altered transcriptomes, and novel gene regulatory networks could be uncovered by comparative transcriptomics analysis (**Figure 1**). Several types of gene regulatory relationships and the reconstructions of gene regulatory networks based on plant comparative transcriptomics are described and discussed in Section 5 ("Gene co-expression and regulatory networks reconstructed by comparative transcriptomics method").

**43**

**Figure 2.**

*Comparative transcriptomics strategies.*

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

One of the major subjects of modern molecular biology is to uncover the functions of genes in the genome and reveal the molecular mechanism of phenotypic variation. Gene transcription levels and their changes in different conditions are important information that can reflect the functions and transcriptional regulation relationships of investigated genes. How to estimate the transcription levels of genes and how to obtain the transcriptional landscape of a genome are two major subjects in biological studies on gene expression. DGEP and DEG analyses are powerful tools to solve these questions. In DEG analysis, according to the scientific or application questions, the comparison strategies between investigated biological samples are classified into six types including (1) different genotypes, (2) different developmental stages, (3) different tissues, (4) different cell types, (5) different treatments, and (6) different species (**Figure 2**). Here, as we mainly focus on comparative transcriptomics analysis on the developmental anther tissues and the interspecies analysis on anther transcriptome data being rare, the third and sixth types will not be discussed.

There are two types of genotype-based transcriptome data between wild type (WT) and mutant lines in GMS studies, which are based on whether the causal mutation is known or not (**Table 1**). For transcriptomes of male sterility (MS) lines with known causal mutations, the comparison of transcriptomes between WT and MS lines will identify many DEGs associated with the function loss or expression change of the investigated mutation locus. If the causal mutation has not been identified from the MS line, comparative transcriptomics analyses will provide the researchers important results related to the unsettled genetic difference, such as how many genes are changed in expression levels in the MS lines and what the functions of these genes are, even though the causal mutation candidates can be inferred

The phenotypic differences among tissues and organs (e.g., root, leaf, and flower in plant) due to their differences of transcriptome landscape are well known.

from these genes if the researchers have primary mapping results.

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

**3.1 Different genotypes**

**3.2 Different developmental stages**

**3. Plant comparative transcriptomics in anther**

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

#### **3. Plant comparative transcriptomics in anther**

One of the major subjects of modern molecular biology is to uncover the functions of genes in the genome and reveal the molecular mechanism of phenotypic variation. Gene transcription levels and their changes in different conditions are important information that can reflect the functions and transcriptional regulation relationships of investigated genes. How to estimate the transcription levels of genes and how to obtain the transcriptional landscape of a genome are two major subjects in biological studies on gene expression. DGEP and DEG analyses are powerful tools to solve these questions. In DEG analysis, according to the scientific or application questions, the comparison strategies between investigated biological samples are classified into six types including (1) different genotypes, (2) different developmental stages, (3) different tissues, (4) different cell types, (5) different treatments, and (6) different species (**Figure 2**). Here, as we mainly focus on comparative transcriptomics analysis on the developmental anther tissues and the interspecies analysis on anther transcriptome data being rare, the third and sixth types will not be discussed.

#### **3.1 Different genotypes**

*Transcriptome Analysis*

the reference genome (**Figure 1**).

comparative transcriptomics in anther").

(e.g., expressed sequence tags) of the investigated species, which restrict its application on some species without whole genome information or sequence resource. On the contrary, the sequencing-based method of RNA-seq can be applicable for species without sequenced genomes. This is another reason for the popularity of RNA-seq. In genome available species, RNA-seq data should be firstly mapped to

A gene with its transcription levels significantly different between two groups of samples is defined as a DEG under a certain comparison condition (**Figure 1**). It is notable that the concept of DEG specially represents the expression changes of protein-coding genes at the earlier stages of expression data analysis. However, along with the rapid development of molecular biology and the deeper understanding on the functional element on the genome, the concept of DEG has been expanded to noncoding transcripts, for example, the differentially expressed (DE) miRNA and the DE lncRNA. Furthermore, if both coding and noncoding transcripts are considered in the comparative analysis of transcriptome data, transcriptional alterations between control and treated samples should be defined as DE transcribed loci or DE loci. Thus, DE loci is a broad concept used to describe transcriptional alterations of genetic element. There are several strategies for comparing transcriptomes from different research subjects to identify DE loci (described in Section 3, "Plant

Identified DEG set or DE loci should be appropriately annotated with functional

descriptions to determine which biological process or pathway these DEGs are involved in. In comparative transcriptomics, this step is a critical bridge linking transcriptional changes to gene functions and even gene regulation networks. Two commonly utilized methods to annotate DEGs consist of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway gene enrichment analyses. Both of them belong to GSE analysis (**Figure 1**). The GO database includes tens of thousands of GO terms, and each GO term contains several genes with the same biological function. Each gene has three functional aspects, including molecular function (the molecular activities of gene products), cellular component (the cellular locations of functional gene products), and biological process (the gene products' molecular functions with biological process). GSE analysis based on GO database provides some basic functional descriptions for the investigated DEG set. KEGG analysis is a pathway-based enrichment method. The KEGG database has accumulated hundreds of metabolism pathways in plants, animals, and other species. Thus, KEGG analysis can reveal significant pathways the DEGs participated in. GO-based methods can annotate more genes than KEGG-based method, as the GO terms are more flexible and include a larger number of genes. On the other hand, because most metabolic pathways are conserved across species and more significant in biological processes, annotated results obtained from KEGG-based method may be more conserved and stable. In comparative transcriptomics, GO- and KEGG-

based analyses are together utilized in gene function studies.

by comparative transcriptomics method").

The locations of transcribed loci on the genome, their transcription levels, and the changed expression can be identified through comparative transcriptomics analysis. The detected DEG set represents a functional gene set related to the function of investigated gene, the phenotype variation, the stress resistance ability, or the development process. Furthermore, gene regulation relationships are the underlining molecular mechanism of altered transcriptomes, and novel gene regulatory networks could be uncovered by comparative transcriptomics analysis (**Figure 1**). Several types of gene regulatory relationships and the reconstructions of gene regulatory networks based on plant comparative transcriptomics are described and discussed in Section 5 ("Gene co-expression and regulatory networks reconstructed

**42**

There are two types of genotype-based transcriptome data between wild type (WT) and mutant lines in GMS studies, which are based on whether the causal mutation is known or not (**Table 1**). For transcriptomes of male sterility (MS) lines with known causal mutations, the comparison of transcriptomes between WT and MS lines will identify many DEGs associated with the function loss or expression change of the investigated mutation locus. If the causal mutation has not been identified from the MS line, comparative transcriptomics analyses will provide the researchers important results related to the unsettled genetic difference, such as how many genes are changed in expression levels in the MS lines and what the functions of these genes are, even though the causal mutation candidates can be inferred from these genes if the researchers have primary mapping results.

#### **3.2 Different developmental stages**

The phenotypic differences among tissues and organs (e.g., root, leaf, and flower in plant) due to their differences of transcriptome landscape are well known.

#### **Figure 2.**

*Comparative transcriptomics strategies.*


*a "SD" indicates the raw data is unavailable, while the up- and downregulated genes are listed in the supplemental data (SD) in references cited.*

**45**

anther development.

further revealed.

**3.3 Different types of anther cells**

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

Furthermore, it is a developmental process for most types of plant organs from the organ identity (e.g., meristematic cells) to the final mature organ. Thus, how to reveal the dynamic changes of gene transcription levels and how to explain the morphological alterations regulated by gene expression changes are important tasks

Meiosis is an important step in gametophyte generation process and sexual plant reproduction. Morphologic changes during cell meiosis process have been well described by cellular level investigations, while the molecular level alterations and their corresponding gene regulatory networks are not well understood. Plant transcriptomes are a powerful dataset to estimate the gene expression changes and infer the regulatory roles of key genes. Based on GEM technology, Ma et al. investigated maize anther transcriptomes during seven developmental stages and found that transcriptomes during meiosis stages exhibited the lowest complexity [33]. Hollender et al. surveyed the gene transcription profiles of anther of woodland strawberry (*Fragaria vesca*) from developmental stages 7–12 and identified numerous F-Box genes induced in transcription levels at meiosis stage [34]. Besides, tapetum is the inner cell layer of anther with important functions in anther development and gametocyte maturation. The generation, development, and degradation of tapetum are fine regulated during the anther development, while the regulatory framework and the details are far from complete. Yue et al. identified 243 DEG and 108 stage-specific genes during four anther developmental stages in *Hamelia patens* [35]. Chen et al. investigated the expression of genes involving in tapetum development of male floral bud during eight developmental stages in *Populus tomentosa* [36]. Thus, anther transcriptome data during different developmental stages provide valuable data sources for anther development studies. By the combination of comparative transcriptomics and bioinformatics analyses, more key functional genes and the underlying regulatory mechanisms for anther development will be

The cytological structure of anther consists of four cell layers, including the epidermis, endothecium, middle layer, and tapetum, and the archesporial cells are directly surrounded by the tapetum. Thus, the transcriptome data of a whole anther tissue is a mixed gene expression data from diverse cell types with different functions in the anther development process. It is necessary to obtain transcriptional dynamics from different cell layers separately to investigate anther development and the underlying molecular mechanism at a cell type-specific level. Several studies have identified cell layer-specifically expressed genes (e.g., tapetum cells or microgametes). Ma et al. identified 104 MS-related and non-pollen expressed genes most specifically expressed in tapetum by comparative transcriptomics analysis on four diverse MS lines in *Brassica oleracea* [37]. The other way to obtain cell layer-specific transcriptome in anther is firstly separating the investigated cell layer by laser capture microdissection (LCM) technology and then performing RNA-seq or GEM experiment on the separated samples. This strategy has been successfully used in rice, maize, and woodland strawberry to identify the tapetum- or microgamete-specifically expressed genes and their expression dynamics [34, 38, 39]. A recent published research has investigated maize male meiosis using singlecell RNA sequencing (scRNA-seq) technology on pre-meiotic and meiotic cells from maize anthers, which greatly promoted studies on plant anther scRNA-seq [40]. The comparative studies on transcriptomic dynamics between different types of cells facilitate the deeper understanding of functions of specific cell layers on

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

in plant comparative transcriptomics studies.

#### **Table 1.**

*Published studies on anther transcriptome data between WT and MS lines.*

#### *Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

Furthermore, it is a developmental process for most types of plant organs from the organ identity (e.g., meristematic cells) to the final mature organ. Thus, how to reveal the dynamic changes of gene transcription levels and how to explain the morphological alterations regulated by gene expression changes are important tasks in plant comparative transcriptomics studies.

Meiosis is an important step in gametophyte generation process and sexual plant reproduction. Morphologic changes during cell meiosis process have been well described by cellular level investigations, while the molecular level alterations and their corresponding gene regulatory networks are not well understood. Plant transcriptomes are a powerful dataset to estimate the gene expression changes and infer the regulatory roles of key genes. Based on GEM technology, Ma et al. investigated maize anther transcriptomes during seven developmental stages and found that transcriptomes during meiosis stages exhibited the lowest complexity [33]. Hollender et al. surveyed the gene transcription profiles of anther of woodland strawberry (*Fragaria vesca*) from developmental stages 7–12 and identified numerous F-Box genes induced in transcription levels at meiosis stage [34]. Besides, tapetum is the inner cell layer of anther with important functions in anther development and gametocyte maturation. The generation, development, and degradation of tapetum are fine regulated during the anther development, while the regulatory framework and the details are far from complete. Yue et al. identified 243 DEG and 108 stage-specific genes during four anther developmental stages in *Hamelia patens* [35]. Chen et al. investigated the expression of genes involving in tapetum development of male floral bud during eight developmental stages in *Populus tomentosa* [36]. Thus, anther transcriptome data during different developmental stages provide valuable data sources for anther development studies. By the combination of comparative transcriptomics and bioinformatics analyses, more key functional genes and the underlying regulatory mechanisms for anther development will be further revealed.

#### **3.3 Different types of anther cells**

The cytological structure of anther consists of four cell layers, including the epidermis, endothecium, middle layer, and tapetum, and the archesporial cells are directly surrounded by the tapetum. Thus, the transcriptome data of a whole anther tissue is a mixed gene expression data from diverse cell types with different functions in the anther development process. It is necessary to obtain transcriptional dynamics from different cell layers separately to investigate anther development and the underlying molecular mechanism at a cell type-specific level. Several studies have identified cell layer-specifically expressed genes (e.g., tapetum cells or microgametes). Ma et al. identified 104 MS-related and non-pollen expressed genes most specifically expressed in tapetum by comparative transcriptomics analysis on four diverse MS lines in *Brassica oleracea* [37]. The other way to obtain cell layer-specific transcriptome in anther is firstly separating the investigated cell layer by laser capture microdissection (LCM) technology and then performing RNA-seq or GEM experiment on the separated samples. This strategy has been successfully used in rice, maize, and woodland strawberry to identify the tapetum- or microgamete-specifically expressed genes and their expression dynamics [34, 38, 39]. A recent published research has investigated maize male meiosis using singlecell RNA sequencing (scRNA-seq) technology on pre-meiotic and meiotic cells from maize anthers, which greatly promoted studies on plant anther scRNA-seq [40]. The comparative studies on transcriptomic dynamics between different types of cells facilitate the deeper understanding of functions of specific cell layers on anther development.

*Transcriptome Analysis*

*Arabidopsis thaliana*

**Plants MS gene or** 

**MS line**

*ROXY1* and *ROXY2*

*bHLH010, bHLH08, bHLH091*

MS line *WSLA*

MS line *DAH3615-MS* **Method Tissue Data sourcea Reference**

SD [7]

SD [12]

GSE55799 [15]

GSE56497 [16]

[17]

SRS838173

SRR2192464, SRR2192489

GSE69638 [30]

SRP068170 [31]

GSE69073 [32]

[29]

inflorescences

*AMS* Microarray Anthers GSE18225 [8] *AMS* Microarray Anthers SD [9] *AMS* Microarray Floral buds SD [10] *EMS1* Microarray Anthers SD [9] *EMS1* Microarray Anthers SD [11]

buds

buds

buds

*UDT1* Microarray Anthers GSE2619 [19] *OsGAMYB* Microarray Anthers SD [20] *TDR* Microarray Spikelets SD [21] *MADS3* Microarray Anthers SD [22]

*Ms32* Microarray Anthers GSE90968 [23] *MAC1* Microarray Anthers SD [24]

*TaMs1* RNA-seq Anthers SRP113349 [25] *TaMs2* RNA-seq Anthers SRP092366 [26]

*ms1035* RNA-seq Floral buds SD [27] MS line *7B-1* RNA-seq Anthers GSE85859 [28]

buds

buds

RNA-seq Floral buds and flowers

*"SD" indicates the raw data is unavailable, while the up- and downregulated genes are listed in the supplemental* 

RNA-seq Young flower buds

MS line *SP2S* RNA-seq Young flower

MS line *TE5A* RNA-seq Young flower

*Published studies on anther transcriptome data between WT and MS lines.*

*Oryza sativa PTC1* Microarray Anthers SD [18]

*Zea mays Ms23* RNA-seq Anthers GSE90849 [23]

RNA-seq Anthers SRS838170,

*Ms1* Microarray Floral buds GSE8864 [13] *ICE1* RNA-seq Anthers GSE107260 [14] *DYT1* Microarray Anthers GSE18225 [8]

Microarray Young

*Ms1* Microarray Young closed

*CDM1* Microarray Young floral

*TEK* Microarray Closed floral

**44**

**Table 1.**

*a*

*Triticum aestivum*

*Solanum lycopersicum*

*Brassica napus*

*Citrullus lanatus*

*data (SD) in references cited.*

#### **3.4 Different treatments**

At the reproductive stage, plant is more sensitive to external environment conditions. The abiotic stresses, such as high temperature, drought, and cold and freezing stresses, will critically affect the developmental process of anther and pollen in flowing plants. Though there have been numerous studies on stress resistance and response in plant, the regulatory pathways of stress response and their cross talk at molecular level should be further investigated for anther development. Additionally, more effective stress-resistant genes should be identified for the purpose of crop improvement. Plant comparative transcriptomics between normal and stress-treated plants provide a wide insight into the stress response mechanisms of plant during sexual reproductive stage. Zhang et al. investigated the genomewide transcriptional changes of rice panicle under heat treatment (40°C) and found thousands of DEGs participating in transcriptional regulation, transport, cellular homeostasis, and stress response [41]. Studies on photosensitive or thermosensitive GMS lines can also reveal a lot of genes responding to environmental changes.

#### **4. A case study: revealing the molecular functions of a MS gene,**  *ZmMs33***, by comparative transcriptomics**

The discoveries of genes that play key roles in the development of maize anther provide important genetic resources for the utilization of heterosis in maize. Analysis of functional mechanism of GMS genes can effectively promote researches on anther development biology and deepen our understanding of molecular mechanism controlling sexual plant reproduction [42]. There are several published case studies containing comparative transcriptomics analysis on maize GMS genes in our laboratory, including *ZmMs7* [43], *ZmMs20* [44], *ZmMs30* [45], and *ZmMs33* [46, 47]. We used comparative transcriptomics analysis based on developmental anthers of *ZmMs33* wild type and *ms33–6038* mutant to analyze the transcription changes corresponding to male sterility phenotype and to further investigate the underlying molecular mechanisms of GMS regulated by *ZmMs33* gene.

This *ms33–6038* mutant is complete male sterility and displays small and paleyellow anthers (**Figure 3A**). Transmission electron microscope (TEM) observation and dynamic scanning electron microscopy (SEM) analysis were performed to analyze the phenotypic alteration of anther wall layers, microspores, Ubisch bodies, and exine between wild type and *ms33–6038* mutant during anther developmental stages (**Figure 3A**–**C**).

Maize *Zm00001d007714* was identified as *ZmMs33* via a map-based cloning approach (**Figure 3D**). *ZmMs33* encodes an esterase that belongs to gene family of glycerol-3-phosphate acyltransferase (GPAT) in maize. To further confirm gene function of *Zm00001d007714*, a CRISPR/Cas9 system was used to generate targeted knockout lines. Three types of T0-generation maize plants homozygous for null alleles of *Zm00001d007714* were observed to be complete male sterility (**Figure 3E**), suggesting that function loss of *Zm00001d007714* is the causal mutation for male sterile phenotype of the *ms33* mutant.

Subsequently, RNA-seq was performed using anther tissues during developmental stages 5–9 to obtain a comprehensive transcriptional profile of WT and *ms33-6038*. Three biological samples were collected at each developmental stage for sequencing. After data preparation and transcription level estimation, we compared similarities of transcriptional profiles of protein-coding genes by principal component analysis (PCA) (**Figure 3F**) and found good repeatability among three biological repeats.

**47**

**Figure 3.**

*Figure 3D and E was cited from [47].*

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

Finally, we identified DEGs between WT and mutant and between adjacent developmental stages, separately. We found that the amount of DEGs between WT and mutant at stages 5–7 was significantly smaller in magnitude than that at

*Reveal ZmMs33 gene functions for anther development by comparative transcriptomics analysis. (A) Phenotype of whole plants (A1), anthers (A2), pollen grains (A3), and outer surface of anther wall (A4) of WT and ms33–6038 mutant. (B) TEM analysis of anther wall layers, microspores, Ubisch bodies, and exine in WT and ms33–6038 mutant. (C) SEM analysis of microspores and pollen grains in WT and ms33–6038 mutant. (D) Map-based cloning of ZmMs33 gene. (E) Phenotypes of tassels, anthers, and pollen grains in three ms33 knockout lines generated by a CRISPR/Cas9 system. (F) PCA analysis of RNA-seq data from WT and ms33–6038 mutant. (G) Venn plot of DEGs at each developmental stage. Figure 3A–C was cited from [46].* 

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

#### **Figure 3.**

*Transcriptome Analysis*

**3.4 Different treatments**

At the reproductive stage, plant is more sensitive to external environment conditions. The abiotic stresses, such as high temperature, drought, and cold and freezing stresses, will critically affect the developmental process of anther and pollen in flowing plants. Though there have been numerous studies on stress resistance and response in plant, the regulatory pathways of stress response and their cross talk at molecular level should be further investigated for anther development. Additionally, more effective stress-resistant genes should be identified for the purpose of crop improvement. Plant comparative transcriptomics between normal and stress-treated plants provide a wide insight into the stress response mechanisms of plant during sexual reproductive stage. Zhang et al. investigated the genomewide transcriptional changes of rice panicle under heat treatment (40°C) and found thousands of DEGs participating in transcriptional regulation, transport, cellular homeostasis, and stress response [41]. Studies on photosensitive or thermosensitive GMS lines can also reveal a lot of genes responding to environmental changes.

**4. A case study: revealing the molecular functions of a MS gene,** 

provide important genetic resources for the utilization of heterosis in maize.

on anther development biology and deepen our understanding of molecular mechanism controlling sexual plant reproduction [42]. There are several published case studies containing comparative transcriptomics analysis on maize GMS genes in our laboratory, including *ZmMs7* [43], *ZmMs20* [44], *ZmMs30* [45], and *ZmMs33* [46, 47]. We used comparative transcriptomics analysis based on developmental anthers of *ZmMs33* wild type and *ms33–6038* mutant to analyze the transcription changes corresponding to male sterility phenotype and to further investigate the

underlying molecular mechanisms of GMS regulated by *ZmMs33* gene.

The discoveries of genes that play key roles in the development of maize anther

Analysis of functional mechanism of GMS genes can effectively promote researches

This *ms33–6038* mutant is complete male sterility and displays small and paleyellow anthers (**Figure 3A**). Transmission electron microscope (TEM) observation and dynamic scanning electron microscopy (SEM) analysis were performed to analyze the phenotypic alteration of anther wall layers, microspores, Ubisch bodies, and exine between wild type and *ms33–6038* mutant during anther developmental

Maize *Zm00001d007714* was identified as *ZmMs33* via a map-based cloning approach (**Figure 3D**). *ZmMs33* encodes an esterase that belongs to gene family of glycerol-3-phosphate acyltransferase (GPAT) in maize. To further confirm gene function of *Zm00001d007714*, a CRISPR/Cas9 system was used to generate targeted knockout lines. Three types of T0-generation maize plants homozygous for null alleles of *Zm00001d007714* were observed to be complete male sterility (**Figure 3E**), suggesting that function loss of *Zm00001d007714* is the causal mutation for male

Subsequently, RNA-seq was performed using anther tissues during developmental stages 5–9 to obtain a comprehensive transcriptional profile of WT and *ms33-6038*. Three biological samples were collected at each developmental stage for sequencing. After data preparation and transcription level estimation, we compared similarities of transcriptional profiles of protein-coding genes by principal component analysis (PCA) (**Figure 3F**) and found good repeatability among three

*ZmMs33***, by comparative transcriptomics**

**46**

biological repeats.

stages (**Figure 3A**–**C**).

sterile phenotype of the *ms33* mutant.

*Reveal ZmMs33 gene functions for anther development by comparative transcriptomics analysis. (A) Phenotype of whole plants (A1), anthers (A2), pollen grains (A3), and outer surface of anther wall (A4) of WT and ms33–6038 mutant. (B) TEM analysis of anther wall layers, microspores, Ubisch bodies, and exine in WT and ms33–6038 mutant. (C) SEM analysis of microspores and pollen grains in WT and ms33–6038 mutant. (D) Map-based cloning of ZmMs33 gene. (E) Phenotypes of tassels, anthers, and pollen grains in three ms33 knockout lines generated by a CRISPR/Cas9 system. (F) PCA analysis of RNA-seq data from WT and ms33–6038 mutant. (G) Venn plot of DEGs at each developmental stage. Figure 3A–C was cited from [46]. Figure 3D and E was cited from [47].*

Finally, we identified DEGs between WT and mutant and between adjacent developmental stages, separately. We found that the amount of DEGs between WT and mutant at stages 5–7 was significantly smaller in magnitude than that at stages 8a–9 (**Figure 3G**), indicating that *ms33* mutant transcriptomes are significantly divergent from WT starting from stage 8a. The transcriptome landscapes of WT were similar to those of *ms33* mutant at stages 5–7. Besides, DEG amounts were various between adjacent developmental stages. It is worth noting that the DEG amount between WT and mutant exceeded that between adjacent stages from stage 8a–9. This result implied that the transcriptomes were significantly changed at the later three stages. Therefore, we compared the transcriptomes between genotypes at the former three and the later three stages, separately. In contrast to a limited number of DEGs (only two genes) shared by the former three stages, there were thousands of shared DEGs at the later three stages. GSE analysis based on KEGG database suggested that the upregulated gene set was firstly enriched in the function of biosynthesis of secondary metabolites, while the downregulated genes were significantly related to the photosynthesis process. This pathway enrichment analysis partly represents the alterations in metabolisms and physiological activities closely associated with the transcriptional changes caused by function defect of *ms33*.

#### **5. Gene co-expression and regulatory networks reconstructed by comparative transcriptomics method**

Though DEGs are mainly identified by pairwise comparisons between transcriptomes of tissues, stages, or treatment conditions and can reflect most of the transcriptional changes between two sets of samples, these transcriptional alterations are not sufficient to explain the detailed molecular mechanism underlying tissue-specific development processes and stress-resistant pathways. Moreover, the molecular functions of genes act under GRNs. All the biological processes of growth, development, stress response, and reproduction are regulated by GRNs. The prediction of gene regulatory relationships and the reconstruction of the GRNs by using the transcriptome data are also the major aims in transcriptomics studies, except for the DGEP and DEG analyses.

#### **5.1 Gene co-expression analysis**

Function-related genes tend to co-express in a cell, either to form a complex or to involve in the same biological pathway. Thus, the similar pattern of gene expressions can be used as an indicator to predict gene functions. Gene co-expression (GCE) analysis is a powerful tool to discover important functional genes in biological processes including anther development. A relatively early study identified two functional GMS genes, *POLYKETIDE SYNTHASE A* (*PKSA*) and *PKSB*, through detecting co-expressed genes with *ACOS5*, a GMS gene belonging to fatty acyl-CoA synthetase gene family, based on microarray data in *A. thaliana* [48]. Similarly, *ABORTED MICROSPORES* (*AMS*) gene was reported participating in the pollen wall formation in rice by the analyses of 98 co-expressed genes with *AMS* in flower development [49]. GCE analysis can be also used to investigate the biological functions and the regulatory targets of a gene. This genome-wide analysis on GCE networks has been performed based on microarray data from *A. thaliana* anther tissues, and 254 complete GCE groups containing 10,513 anthertranscribed genes were revealed [50]. Another microarray-based GCE network was reconstructed in *A. thaliana* anther by using 10,797 genes expressed in anther/ flora, and transcriptional landscape of GMS mutant was included in the stable examination of this newly constructed network [51]. In rice, microarrays from WT

**49**

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

anther tissue across stages 2–14 and nine GMS lines were integrated to reconstruct a big GCE network containing more than 9000 genes and 0.4 million pairs of co-

RNA-seq data-based GCE network analysis was performed in anther when high-throughput sequencing technology was developed. In woodland strawberry, stages 1–12 floral samples dissected by LCM or hand, including stages 6–12 anther tissues, were sequenced by RNA-seq. Gene co-expression network analysis was used to reconstruct GCE networks in strawberry's flower development, and 23 modules were discovered from the GCE networks including 4584 pollen-specific genes [34]. These genome-wide GCE networks are useful for characterization of genes associ-

Genes with their products forming one protein complex, genes encoding transcription factor (TF) and TF target genes, and genes functioning in the same metabolic pathway or stress-resistant process often tend to be co-expressed in a cell. Therefore, the expression-associated genes in GCE network may be not directly functionally linked. A more accurate and robust gene regulatory network is needed for both the biological function and network researches at molecular and genome levels. One way to improve the gene regulatory network is to introduce gene regulatory types into the network. Several TF gene regulatory networks (TF-GRN), also called as transcriptional regulatory network (TRN), were reconstructed based on expression patterns of TF-encoding genes and TF target genes from transcriptome data. One TF-GRN comprised 19 TFs and their 101 target genes involving in *A. thaliana* pollen development [53]. Another GRN of early anther development was constructed by interactively analyzing transcriptome data from three GMS lines of TF-encoding gene knockout mutants [9]. In the maize genome, there are 2298 TF-encoding genes identified which belonged to 56 diverse families [54]. A total of 3078 TF-encoding genes belonging to 59 families are predicted in silico analysis in rice genome [55]. These TF databases, combining with increased amount of transcriptome data from mutants of TF-encoding genes and other omics data (e.g., Chip-seq, DAP-seq), provide abundant data for the reconstruction of TF-GRN

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

ated with anther development and floral reproduction.

with increased credibility, applicability, and completeness.

Both transcriptional and posttranscriptional regulations are crucial in controlling the normal development and stress-resistant process in cellular life. The miRNA-mediated regulation model on target genes is a well-studied posttranscriptional gene regulation pathway that plays important roles in floral identification and the following development of flower organs [56–58] as well as male fertility [59, 60]. Beyond numerous case studies on functional miRNAs in anther development and GMS genes [61–64], the expression profile of miRNAs and the regulatory networks were investigated to elevate our understanding on the transcriptional regulatory mechanism of miRNAs. GRNs between miRNA and their target genes have been constructed via flower/anther transcriptomics in the model plant species, *A. thaliana*, and some other plants [65–68]. Furthermore, comparative transcriptomics analysis on small miRNAs has been commonly used as a research method to reveal the transcriptional alterations between fertility and sterility lines in economic and food plant species, such as maize [45], tomato [69], cotton [70, 71], wheat [72,

73], pine [74], lycium [75], watermelon [32], and *Brassica campestris* [76].

**5.3 miRNA target gene regulatory network**

**5.2 TF-encoding gene regulatory network**

expression relationships [52].

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

anther tissue across stages 2–14 and nine GMS lines were integrated to reconstruct a big GCE network containing more than 9000 genes and 0.4 million pairs of coexpression relationships [52].

RNA-seq data-based GCE network analysis was performed in anther when high-throughput sequencing technology was developed. In woodland strawberry, stages 1–12 floral samples dissected by LCM or hand, including stages 6–12 anther tissues, were sequenced by RNA-seq. Gene co-expression network analysis was used to reconstruct GCE networks in strawberry's flower development, and 23 modules were discovered from the GCE networks including 4584 pollen-specific genes [34]. These genome-wide GCE networks are useful for characterization of genes associated with anther development and floral reproduction.

#### **5.2 TF-encoding gene regulatory network**

*Transcriptome Analysis*

changes caused by function defect of *ms33*.

**comparative transcriptomics method**

except for the DGEP and DEG analyses.

**5.1 Gene co-expression analysis**

stages 8a–9 (**Figure 3G**), indicating that *ms33* mutant transcriptomes are significantly divergent from WT starting from stage 8a. The transcriptome landscapes of WT were similar to those of *ms33* mutant at stages 5–7. Besides, DEG amounts were various between adjacent developmental stages. It is worth noting that the DEG amount between WT and mutant exceeded that between adjacent stages from stage 8a–9. This result implied that the transcriptomes were significantly changed at the later three stages. Therefore, we compared the transcriptomes between genotypes at the former three and the later three stages, separately. In contrast to a limited number of DEGs (only two genes) shared by the former three stages, there were thousands of shared DEGs at the later three stages. GSE analysis based on KEGG database suggested that the upregulated gene set was firstly enriched in the function of biosynthesis of secondary metabolites, while the downregulated genes were significantly related to the photosynthesis process. This pathway enrichment analysis partly represents the alterations in metabolisms and physiological activities closely associated with the transcriptional

**5. Gene co-expression and regulatory networks reconstructed by** 

Though DEGs are mainly identified by pairwise comparisons between transcriptomes of tissues, stages, or treatment conditions and can reflect most of the transcriptional changes between two sets of samples, these transcriptional alterations are not sufficient to explain the detailed molecular mechanism underlying tissue-specific development processes and stress-resistant pathways. Moreover, the molecular functions of genes act under GRNs. All the biological processes of growth, development, stress response, and reproduction are regulated by GRNs. The prediction of gene regulatory relationships and the reconstruction of the GRNs by using the transcriptome data are also the major aims in transcriptomics studies,

Function-related genes tend to co-express in a cell, either to form a complex or to involve in the same biological pathway. Thus, the similar pattern of gene expressions can be used as an indicator to predict gene functions. Gene co-expression (GCE) analysis is a powerful tool to discover important functional genes in biological processes including anther development. A relatively early study identified two functional GMS genes, *POLYKETIDE SYNTHASE A* (*PKSA*) and *PKSB*, through detecting co-expressed genes with *ACOS5*, a GMS gene belonging to fatty acyl-CoA synthetase gene family, based on microarray data in *A. thaliana* [48]. Similarly, *ABORTED MICROSPORES* (*AMS*) gene was reported participating in the pollen wall formation in rice by the analyses of 98 co-expressed genes with *AMS* in flower development [49]. GCE analysis can be also used to investigate the biological functions and the regulatory targets of a gene. This genome-wide analysis on GCE networks has been performed based on microarray data from *A. thaliana* anther tissues, and 254 complete GCE groups containing 10,513 anthertranscribed genes were revealed [50]. Another microarray-based GCE network was reconstructed in *A. thaliana* anther by using 10,797 genes expressed in anther/ flora, and transcriptional landscape of GMS mutant was included in the stable examination of this newly constructed network [51]. In rice, microarrays from WT

**48**

Genes with their products forming one protein complex, genes encoding transcription factor (TF) and TF target genes, and genes functioning in the same metabolic pathway or stress-resistant process often tend to be co-expressed in a cell. Therefore, the expression-associated genes in GCE network may be not directly functionally linked. A more accurate and robust gene regulatory network is needed for both the biological function and network researches at molecular and genome levels. One way to improve the gene regulatory network is to introduce gene regulatory types into the network. Several TF gene regulatory networks (TF-GRN), also called as transcriptional regulatory network (TRN), were reconstructed based on expression patterns of TF-encoding genes and TF target genes from transcriptome data. One TF-GRN comprised 19 TFs and their 101 target genes involving in *A. thaliana* pollen development [53]. Another GRN of early anther development was constructed by interactively analyzing transcriptome data from three GMS lines of TF-encoding gene knockout mutants [9]. In the maize genome, there are 2298 TF-encoding genes identified which belonged to 56 diverse families [54]. A total of 3078 TF-encoding genes belonging to 59 families are predicted in silico analysis in rice genome [55]. These TF databases, combining with increased amount of transcriptome data from mutants of TF-encoding genes and other omics data (e.g., Chip-seq, DAP-seq), provide abundant data for the reconstruction of TF-GRN with increased credibility, applicability, and completeness.

#### **5.3 miRNA target gene regulatory network**

Both transcriptional and posttranscriptional regulations are crucial in controlling the normal development and stress-resistant process in cellular life. The miRNA-mediated regulation model on target genes is a well-studied posttranscriptional gene regulation pathway that plays important roles in floral identification and the following development of flower organs [56–58] as well as male fertility [59, 60]. Beyond numerous case studies on functional miRNAs in anther development and GMS genes [61–64], the expression profile of miRNAs and the regulatory networks were investigated to elevate our understanding on the transcriptional regulatory mechanism of miRNAs. GRNs between miRNA and their target genes have been constructed via flower/anther transcriptomics in the model plant species, *A. thaliana*, and some other plants [65–68]. Furthermore, comparative transcriptomics analysis on small miRNAs has been commonly used as a research method to reveal the transcriptional alterations between fertility and sterility lines in economic and food plant species, such as maize [45], tomato [69], cotton [70, 71], wheat [72, 73], pine [74], lycium [75], watermelon [32], and *Brassica campestris* [76].

#### **5.4 ceRNA-miRNA regulatory network**

It is well known that miRNAs are crucial regulators on gene expressions that control key biological functions including anther development, since miRNA was firstly found in nematodes in 1993 [77]. It is noteworthy that a novel type of gene regulatory model, the competing endogenous RNA (ceRNA) hypothesis, was recently proposed [78]. According to the ceRNA hypothesis, some endogenous transcripts have abilities to adsorb miRNA molecules; subsequently, the expression levels of miRNA target genes can be derepressed [78, 79]. A typical ceRNA in plant, a long noncoding RNA, *IPSI*, was found in *A. thaliana*. It could completely sponge miRNA *ath-miR399* and indirectly increase the transcription levels of an important gene involved in phosphate homeostasis [80]. The following studies revealed that transcripts of protein-coding genes, pseudogene, transposable elements, simple sequence repeat, and circular RNAs have molecular functions as ceRNAs [79, 81, 82], indicating that the ceRNA-miRNA relationship is an essential gene regulatory mechanism in the growth and development of plants and animals. Consequently, it is necessary to introduce ceRNA regulators into GRN construction. Here, we present our recent study on reconstructing ceRNA regulatory network mainly based on RNA-seq and small RNA-seq transcriptomes from developmental maize anther.

#### **6. A case study: reconstructing ceRNA-miRNA target gene regulatory networks using transcriptome data of maize anther**

Here we summarized the research progress of one recently completed research related to the ceRNA-mediated GRN in our laboratory. Generally speaking, this is the first study introducing ceRNA regulation into miRNA target gene regulatory pathway for deeply dissecting the mechanism of anther development and sexual plant reproduction at a network level. This provides a fresh example for GRN research by plant comparative transcriptomics and has dual significance in both theoretical and practical senses. It may also provide new thoughts and strategies for further transcriptome-based GRN studies.

It is well known that gene expressions are controlled by the GRN in cellular life. Newly found regulatory patterns (e.g., miRNA pathway and epigenetic modification) have enhanced our understanding on the GRN. Recently, "ceRNA hypothesis" was proposed as a novel type of gene regulatory relationship and was found to participate in different development and stress response processes of organisms by a number of case studies. However, the network level study on ceRNA regulatory functions is still rare, which limited our deep understanding on the GRN. In addition, studies on the GRN of sexual plant reproduction and male sterility are crucial for both fundamental biological significance and applications in plant hybrid breeding and seed production. We investigated ceRNA-miRNA target gene regulatory network in maize anther developmental process by plant comparative transcriptomics method. Six steps were performed from raw sequencing data preparation to the finally constructed GRN (**Figure 4**). *Firstly*, we performed RNA- and small RNA-seq using anther tissues at three developmental stages from two maize lines to obtain a relative broad transcriptional landscape in anther development and transcribed loci that are stably expressed in maize species. *Secondly*, we identified stably transcribed loci based on the maize reference genome and estimated their transcription levels. In this step, we only used shared transcription loci identified from RNA-seq data between two maize lines (**Figure 4A**). Notably, these transcribed loci were divided into five groups such as protein-coding genes, lncRNAs, transposable elements, and unassigned loci. *Thirdly*, we identified known miRNAs

**51**

**Figure 4.**

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

and predicted potential novel miRNAs that may be involved in maize anther development. Sequenced small RNA data were obtained from the same samples that were used in RNA-seq. A matched dataset (e.g., matched RNA and small RNA sequenced dataset here) is important in experimental design and more powerful to reveal the investigated biological questions. Though the analysis workflow of small RNA-seq data is similar to that of RNA-seq data in general (**Figure 1**), there are some differences between them. In our analysis, we reanalyzed two sets of published small RNA data to compare with their results from our own sequenced data for credible known and potential novel miRNAs involved in maize anther development [23, 83] (**Figure 4B**). This is an important check method to confirm the stability of research

*of ceRNA-miRNA target gene regulatory networks. (E) GSE analysis of target genes in the networks.*

*A flowchart of reconstructing the ceRNA-miRNA target gene regulatory network in developmental maize anther. (A) Identification and classification of stably transcribed loci in maize anther. (B) Identification of known miRNA in maize anther. (C) Prediction of ceRNA-miRNA and miRNA target gene interaction pairs. (D) Reconstruction* 

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

#### **Figure 4.**

*Transcriptome Analysis*

**5.4 ceRNA-miRNA regulatory network**

It is well known that miRNAs are crucial regulators on gene expressions that control key biological functions including anther development, since miRNA was firstly found in nematodes in 1993 [77]. It is noteworthy that a novel type of gene regulatory model, the competing endogenous RNA (ceRNA) hypothesis, was recently proposed [78]. According to the ceRNA hypothesis, some endogenous transcripts have abilities to adsorb miRNA molecules; subsequently, the expression levels of miRNA target genes can be derepressed [78, 79]. A typical ceRNA in plant, a long noncoding RNA, *IPSI*, was found in *A. thaliana*. It could completely sponge miRNA *ath-miR399* and indirectly increase the transcription levels of an important gene involved in phosphate homeostasis [80]. The following studies revealed that transcripts of protein-coding genes, pseudogene, transposable elements, simple sequence repeat, and circular RNAs have molecular functions as ceRNAs [79, 81, 82], indicating that the ceRNA-miRNA relationship is an essential gene regulatory mechanism in the growth and development of plants and animals. Consequently, it is necessary to introduce ceRNA regulators into GRN construction. Here, we present our recent study on reconstructing ceRNA regulatory network mainly based on RNA-seq and small RNA-seq transcriptomes from developmental maize anther.

**6. A case study: reconstructing ceRNA-miRNA target gene regulatory** 

Here we summarized the research progress of one recently completed research related to the ceRNA-mediated GRN in our laboratory. Generally speaking, this is the first study introducing ceRNA regulation into miRNA target gene regulatory pathway for deeply dissecting the mechanism of anther development and sexual plant reproduction at a network level. This provides a fresh example for GRN research by plant comparative transcriptomics and has dual significance in both theoretical and practical senses. It may also provide new thoughts and strategies for

It is well known that gene expressions are controlled by the GRN in cellular life. Newly found regulatory patterns (e.g., miRNA pathway and epigenetic modification) have enhanced our understanding on the GRN. Recently, "ceRNA hypothesis" was proposed as a novel type of gene regulatory relationship and was found to participate in different development and stress response processes of organisms by a number of case studies. However, the network level study on ceRNA regulatory functions is still rare, which limited our deep understanding on the GRN. In addition, studies on the GRN of sexual plant reproduction and male sterility are crucial for both fundamental biological significance and applications in plant hybrid breeding and seed production. We investigated ceRNA-miRNA target gene regulatory network in maize anther developmental process by plant comparative transcriptomics method. Six steps were performed from raw sequencing data preparation to the finally constructed GRN (**Figure 4**). *Firstly*, we performed RNA- and small RNA-seq using anther tissues at three developmental stages from two maize lines to obtain a relative broad transcriptional landscape in anther development and transcribed loci that are stably expressed in maize species. *Secondly*, we identified stably transcribed loci based on the maize reference genome and estimated their transcription levels. In this step, we only used shared transcription loci identified from RNA-seq data between two maize lines (**Figure 4A**). Notably, these transcribed loci were divided into five groups such as protein-coding genes, lncRNAs, transposable elements, and unassigned loci. *Thirdly*, we identified known miRNAs

**networks using transcriptome data of maize anther**

further transcriptome-based GRN studies.

**50**

*A flowchart of reconstructing the ceRNA-miRNA target gene regulatory network in developmental maize anther. (A) Identification and classification of stably transcribed loci in maize anther. (B) Identification of known miRNA in maize anther. (C) Prediction of ceRNA-miRNA and miRNA target gene interaction pairs. (D) Reconstruction of ceRNA-miRNA target gene regulatory networks. (E) GSE analysis of target genes in the networks.*

and predicted potential novel miRNAs that may be involved in maize anther development. Sequenced small RNA data were obtained from the same samples that were used in RNA-seq. A matched dataset (e.g., matched RNA and small RNA sequenced dataset here) is important in experimental design and more powerful to reveal the investigated biological questions. Though the analysis workflow of small RNA-seq data is similar to that of RNA-seq data in general (**Figure 1**), there are some differences between them. In our analysis, we reanalyzed two sets of published small RNA data to compare with their results from our own sequenced data for credible known and potential novel miRNAs involved in maize anther development [23, 83] (**Figure 4B**). This is an important check method to confirm the stability of research

results and conclusions. *Fourthly*, we predicted ceRNA-miRNA interaction pairs and miRNA target gene regulatory pairs by computational approach (**Figure 4C**). Bioinformatics analysis in this step is mainly based on genome sequence but not the transcriptomes. *Fifthly*, we reconstructed ceRNA-miRNA target gene regulatory networks by predicted interaction pairs and transcription correlation patterns from transcriptomics data (**Figure 4D**). It is well known that miRNAs could repress the transcription levels of their target genes. Additionally, ceRNA was demonstrated to negatively regulate the transcription levels of matched miRNAs. The negatively associated gene pairs in transcription levels may be more credible in mutual interactions. By integrating ceRNA-miRNA and miRNA target gene interactions, we reconstructed ceRNA-miRNA target gene regulatory networks in maize anther. *Finally*, we generally investigated the functional significance of genes in the regulatory network by GO enrichment analysis. In these networks, we found a number of well-studied genes and miRNA target gene pairs involved in maize anther development and male sterility, suggesting that the ceRNA-miRNA target gene regulatory networks contribute to anther development in maize. Besides, GO analysis of target genes in the network revealed that they are functionally enriched in flower development process (**Figure 4E**) [84].

#### **7. Conclusions**

Here, we summarized major points in comparative transcriptomics analysis from the commonly utilized workflow to the closely related research cases and from the single gene-based function analysis to GRN-based gene function investigation. In GMS gene studies, the research experiments using comparative transcriptomics method to investigate key functional genes and the genome-wide GRNs in developmental anther will facilitate our systematical understanding on the biological processes and molecular regulatory networks for anther development and sexual plant reproduction. More importantly, case studies illustrated here have a general meaning on technologies and methodologies for functional researches of other biological pathways and processes. With the fast advancement of sequencing technology, plant comparative transcriptomics has achieved considerable development. However, our understanding on the transcriptional dynamics and gene regulatory relationships of biological processes are far from being completed. Consequently, more efforts are needed for the further improvement of comparative transcriptomics in plant biological studies.

#### **Acknowledgements**

The research in our lab was supported by the National Key Research and Development Program of China (2018YFD0100806, 2017YFD0102001, 2017YFD0101201), the National Transgenic Major Program of China (2018ZX0800922B, 2018ZX0801006B), the National Natural Science Foundation of China (31,771,875, 31,871,702), the Fundamental Research Funds for the Central Universities of China (06500060), and the "Ten Thousand Plan" of National Highlevel Talents Special Support Plan (For Xiangyuan Wan).

**53**

**Author details**

Beijing, Beijing, China

Xiangyuan Wan1,2\* and Ziwen Li1,2\*

Beijing Solidwill Sci-Tech Co. Ltd., Beijing, China

provided the original work is properly cited.

1 Biology and Agriculture Research Center, University of Science and Technology

\*Address all correspondence to: wanxiangyuan@ustb.edu.cn and liziwen@ustb.edu.cn

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

2 Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding,

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

GCE gene co-expression

GMS genic male sterility GO gene ontology

GRN gene regulatory network GSE gene set enrichment

LncRNA long noncoding RNA

miRNA microRNA

MS male sterility

RNA-seq RNA sequencing

TF transcription factor

WT wild type

TF-GRN TF gene regulatory network TRN transcriptional regulatory network

LCM laser capture microdissection

NGS the next-generation sequencing

SAGE serial analysis of gene expression scRNA-seq single-cell RNA sequencing SEM scanning electron microscopy TEM transmission electron microscope

KEGG Kyoto encyclopedia of genes and genomes

MPSS massively parallel signature sequencing

ceRNA competing endogenous RNA DE differentially expressed DGEP digital gene expression profile

GEG differentially expressed gene GEM gene expression microarray

**Abbreviations**

#### **Conflict of interest**

The authors declare that they have no conflict of interest.

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

### **Abbreviations**

*Transcriptome Analysis*

ment process (**Figure 4E**) [84].

**7. Conclusions**

biological studies.

**Acknowledgements**

**Conflict of interest**

results and conclusions. *Fourthly*, we predicted ceRNA-miRNA interaction pairs and miRNA target gene regulatory pairs by computational approach (**Figure 4C**). Bioinformatics analysis in this step is mainly based on genome sequence but not the transcriptomes. *Fifthly*, we reconstructed ceRNA-miRNA target gene regulatory networks by predicted interaction pairs and transcription correlation patterns from transcriptomics data (**Figure 4D**). It is well known that miRNAs could repress the transcription levels of their target genes. Additionally, ceRNA was demonstrated to negatively regulate the transcription levels of matched miRNAs. The negatively associated gene pairs in transcription levels may be more credible in mutual interactions. By integrating ceRNA-miRNA and miRNA target gene interactions, we reconstructed ceRNA-miRNA target gene regulatory networks in maize anther. *Finally*, we generally investigated the functional significance of genes in the regulatory network by GO enrichment analysis. In these networks, we found a number of well-studied genes and miRNA target gene pairs involved in maize anther development and male sterility, suggesting that the ceRNA-miRNA target gene regulatory networks contribute to anther development in maize. Besides, GO analysis of target genes in the network revealed that they are functionally enriched in flower develop-

Here, we summarized major points in comparative transcriptomics analysis from the commonly utilized workflow to the closely related research cases and from the single gene-based function analysis to GRN-based gene function investigation. In GMS gene studies, the research experiments using comparative transcriptomics method to investigate key functional genes and the genome-wide GRNs in developmental anther will facilitate our systematical understanding on the biological processes and molecular regulatory networks for anther development and sexual plant reproduction. More importantly, case studies illustrated here have a general meaning on technologies and methodologies for functional researches of other biological pathways and processes. With the fast advancement of sequencing technology, plant comparative transcriptomics has achieved considerable development. However, our understanding on the transcriptional dynamics and gene regulatory relationships of biological processes are far from being completed. Consequently, more efforts are needed for the further improvement of comparative transcriptomics in plant

The research in our lab was supported by the National Key Research and Development Program of China (2018YFD0100806, 2017YFD0102001, 2017YFD0101201), the National Transgenic Major Program of China

(2018ZX0800922B, 2018ZX0801006B), the National Natural Science Foundation of China (31,771,875, 31,871,702), the Fundamental Research Funds for the Central Universities of China (06500060), and the "Ten Thousand Plan" of National High-

level Talents Special Support Plan (For Xiangyuan Wan).

The authors declare that they have no conflict of interest.

**52**


### **Author details**

Xiangyuan Wan1,2\* and Ziwen Li1,2\*

1 Biology and Agriculture Research Center, University of Science and Technology Beijing, Beijing, China

2 Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd., Beijing, China

\*Address all correspondence to: wanxiangyuan@ustb.edu.cn and liziwen@ustb.edu.cn

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **References**

[1] Crick F. Central dogma of molecular biology. Nature. 1970;**227**:561-563

[2] Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;**270**:467-470

[3] Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: Opportunities and challenges. Nature Reviews. Genetics. 2016;**17**:257-271

[4] Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science. 1995;**270**:484-487

[5] Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nature Biotechnology. 2000;**18**:630-634

[6] Stelpflug SC, Sekhon RS, Vaillancourt B, Hirsch CN, Buell CR, de Leon N, et al. An expanded maize gene expression atlas based on RNA sequencing and its use to explore root development. Plant Genome. 2016;**9**:1-15

[7] Xing S, Zachgo S. ROXY1 and ROXY2, two Arabidopsis glutaredoxin genes, are required for anther development. The Plant Journal. 2008;**53**:790-801

[8] Feng B, Lu D, Ma X, Peng Y, Sun Y, Ning G, et al. Regulation of the Arabidopsis anther transcriptome by DYT1 for pollen development. The Plant Journal. 2012;**72**:612-624

[9] Ma X, Feng B, Ma H. AMSdependent and independent regulation of anther transcriptome and comparison with those affected by other Arabidopsis anther genes. BMC Plant Biology. 2012;**12**:23

[10] Xu J, Yang C, Yuan Z, Zhang D, Gondwe MY, Ding Z, et al. The aborted microspores regulatory network is required for postmeiotic male reproductive development in *Arabidopsis thaliana*. Plant Cell. 2010;**22**:91-107

[11] Wijeratne AJ, Zhang W, Sun Y, Liu W, Albert R, Zheng Z, et al. Differential gene expression in Arabidopsis wild-type and mutant anthers: Insights into anther cell differentiation and regulatory networks. The Plant Journal. 2007;**52**:14-29

[12] Yang C, Vizcay-Barrena G, Conner K, Wilson ZA. Male sterility1 is required for tapetal development and pollen wall biosynthesis. Plant Cell. 2007;**19**:3530-3548

[13] Alves-Ferreira M, Wellmer F, Banhara A, Kumar V, Riechmann JL, et al. Global expression profiling applied to the analysis of Arabidopsis stamen development. Plant Physiology. 2007;**145**:747-762

[14] Wei D, Liu M, Chen H, Zheng Y, Liu Y, Wang X, et al. Inducer of CBF expression 1 is a male fertility regulator impacting anther dehydration in Arabidopsis. PLoS Genetics. 2018;**14**:e1007695

[15] Lu P, Chai M, Yang J, Ning G, Wang G, Ma H. The Arabidopsis callose defective microspore1 gene is required for male fertility through regulating callose metabolism during microsporogenesis. Plant Physiology. 2014;**164**:1893-1904

[16] Lou Y, Xu XF, Zhu J, Gu JN, Blackmore S, Yang ZN. The tapetal AHL family protein TEK determines nexine formation in the pollen wall. Nature Communications. 2014;**5**:3855

**55**

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

in pre-meiotic maize anthers. G3 (Bethesda). 2014;**4**:993-1010

[25] Wang Z, Li J, Chen S, Heng Y, Chen Z, Yang J, et al. Poaceae-specific MS1 encodes a phospholipid-binding protein for male fertility in bread wheat. Proceedings of the National Academy of Sciences of the United States of America. 2017;**114**:12614-12,619

[26] Ni F, Qi J, Hao Q, Lyu B, Luo MC, Wang Y, et al. Wheat Ms2 encodes for an orphan protein that confers male sterility in grass species. Nature Communications. 2017;**8**:15121

[27] Jeong HJ, Kang JH, Zhao M,

Zheng Y, Fei Z, Pucci A, et al. Transcriptional regulation of malesterility in 7B-1 male-sterile tomato mutant. PLoS One. 2017;**12**:e0170715

[30] Liu XQ, Liu ZQ, Yu CY, Dong JG, Hu SW, Xu AX. TGMS in rapeseed (*Brassica napus*) resulted in aberrant transcriptional regulation, asynchronous microsporocyte meiosis, defective tapetum, and fused sexine. Frontiers in Plant Science. 2017;**8**:1268

[31] Yan X, Zeng X, Wang S, Li K, Yuan R, Gao H, et al. Aberrant meiotic prophase I leads to genic male sterility in the novel TE5A mutant of *Brassica napus*. Scientific Reports. 2016;**6**:33955

[32] Rhee SJ, Seo M, Jang YJ, Cho S, Lee GP. Transcriptome profiling of differentially expressed genes in floral

2014;**65**:6693-6709

Kwon JK, Choi HS, Bae JH, et al. Tomato male sterile 1035 is essential for pollen development and meiosis in anthers. Journal of Experimental Botany.

[28] Omidvar V, Mohorianu I, Dalmay T,

[29] Qu C, Fu F, Liu M, Zhao H, Liu C, Li J, et al. Comparative transcriptome analysis of recessive male sterility (RGMS) in sterile and fertile *Brassica napus* lines. PLoS One. 2015;**10**:e0144118

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

[17] Zhu E, You C, Wang S, Cui J, Niu B, Wang Y, et al. The DYT1-interacting proteins bHLH010, bHLH089 and bHLH091 are redundantly required for Arabidopsis anther development and transcriptome. The Plant Journal.

[18] Li H, Yuan Z, Vizcay-Barrena G, Yang C, Liang W, Zong J, et al.

Persistent tapetal cell1 encodes a PHDfinger protein that is required for tapetal cell death and pollen development in rice. Plant Physiology. 2011;**156**:615-630

[19] Jung KH, Han MJ, Lee YS, Kim YW, Hwang I, Kim MJ, et al. Rice undeveloped tapetum1 is a major regulator of early tapetum development.

Plant Cell. 2005;**17**:2705-2722

[20] Aya K, Ueguchi-Tanaka M, Kondo M, Hamada K, Yano K, Nishimura M, et al. Gibberellin modulates anther development in rice via the transcriptional regulation of GAMYB. Plant Cell. 2009;**21**:1453-1472

[21] Zhang DS, Liang WQ, Yuan Z, Li N, Shi J, Wang J, et al. Tapetum degeneration retardation is critical for aliphatic metabolism and gene regulation during rice pollen development. Molecular Plant.

[22] Hu L, Liang W, Yin C, Cui X, Zong J, Wang X, et al. Rice MADS3 regulates ROS homeostasis during late anther development. Plant Cell.

[23] Nan GL, Zhai J, Arikit S, Morrow D, Fernandes J, Mai L, et al. MS23, a master basic helix-loop-helix factor, regulates the specification and development of the tapetum in maize. Development.

[24] Zhang H, Egger RL, Kelliher T, Morrow D, Fernandes J, Nan GL, et al. Transcriptomes and proteomes define gene expression progression

2008;**1**:599-610

2011;**23**:515-533

2017;**144**:163-172

2015;**83**:976-990

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

[17] Zhu E, You C, Wang S, Cui J, Niu B, Wang Y, et al. The DYT1-interacting proteins bHLH010, bHLH089 and bHLH091 are redundantly required for Arabidopsis anther development and transcriptome. The Plant Journal. 2015;**83**:976-990

[18] Li H, Yuan Z, Vizcay-Barrena G, Yang C, Liang W, Zong J, et al. Persistent tapetal cell1 encodes a PHDfinger protein that is required for tapetal cell death and pollen development in rice. Plant Physiology. 2011;**156**:615-630

[19] Jung KH, Han MJ, Lee YS, Kim YW, Hwang I, Kim MJ, et al. Rice undeveloped tapetum1 is a major regulator of early tapetum development. Plant Cell. 2005;**17**:2705-2722

[20] Aya K, Ueguchi-Tanaka M, Kondo M, Hamada K, Yano K, Nishimura M, et al. Gibberellin modulates anther development in rice via the transcriptional regulation of GAMYB. Plant Cell. 2009;**21**:1453-1472

[21] Zhang DS, Liang WQ, Yuan Z, Li N, Shi J, Wang J, et al. Tapetum degeneration retardation is critical for aliphatic metabolism and gene regulation during rice pollen development. Molecular Plant. 2008;**1**:599-610

[22] Hu L, Liang W, Yin C, Cui X, Zong J, Wang X, et al. Rice MADS3 regulates ROS homeostasis during late anther development. Plant Cell. 2011;**23**:515-533

[23] Nan GL, Zhai J, Arikit S, Morrow D, Fernandes J, Mai L, et al. MS23, a master basic helix-loop-helix factor, regulates the specification and development of the tapetum in maize. Development. 2017;**144**:163-172

[24] Zhang H, Egger RL, Kelliher T, Morrow D, Fernandes J, Nan GL, et al. Transcriptomes and proteomes define gene expression progression

in pre-meiotic maize anthers. G3 (Bethesda). 2014;**4**:993-1010

[25] Wang Z, Li J, Chen S, Heng Y, Chen Z, Yang J, et al. Poaceae-specific MS1 encodes a phospholipid-binding protein for male fertility in bread wheat. Proceedings of the National Academy of Sciences of the United States of America. 2017;**114**:12614-12,619

[26] Ni F, Qi J, Hao Q, Lyu B, Luo MC, Wang Y, et al. Wheat Ms2 encodes for an orphan protein that confers male sterility in grass species. Nature Communications. 2017;**8**:15121

[27] Jeong HJ, Kang JH, Zhao M, Kwon JK, Choi HS, Bae JH, et al. Tomato male sterile 1035 is essential for pollen development and meiosis in anthers. Journal of Experimental Botany. 2014;**65**:6693-6709

[28] Omidvar V, Mohorianu I, Dalmay T, Zheng Y, Fei Z, Pucci A, et al. Transcriptional regulation of malesterility in 7B-1 male-sterile tomato mutant. PLoS One. 2017;**12**:e0170715

[29] Qu C, Fu F, Liu M, Zhao H, Liu C, Li J, et al. Comparative transcriptome analysis of recessive male sterility (RGMS) in sterile and fertile *Brassica napus* lines. PLoS One. 2015;**10**:e0144118

[30] Liu XQ, Liu ZQ, Yu CY, Dong JG, Hu SW, Xu AX. TGMS in rapeseed (*Brassica napus*) resulted in aberrant transcriptional regulation, asynchronous microsporocyte meiosis, defective tapetum, and fused sexine. Frontiers in Plant Science. 2017;**8**:1268

[31] Yan X, Zeng X, Wang S, Li K, Yuan R, Gao H, et al. Aberrant meiotic prophase I leads to genic male sterility in the novel TE5A mutant of *Brassica napus*. Scientific Reports. 2016;**6**:33955

[32] Rhee SJ, Seo M, Jang YJ, Cho S, Lee GP. Transcriptome profiling of differentially expressed genes in floral

**54**

*Transcriptome Analysis*

[1] Crick F. Central dogma of molecular biology. Nature. 1970;**227**:561-563

anther genes. BMC Plant Biology.

[10] Xu J, Yang C, Yuan Z, Zhang D, Gondwe MY, Ding Z, et al. The aborted microspores regulatory network is required for postmeiotic male

[11] Wijeratne AJ, Zhang W, Sun Y, Liu W, Albert R, Zheng Z, et al. Differential gene expression in Arabidopsis wild-type and mutant anthers: Insights into anther cell

The Plant Journal. 2007;**52**:14-29

[12] Yang C, Vizcay-Barrena G,

[13] Alves-Ferreira M, Wellmer F, Banhara A, Kumar V, Riechmann JL, et al. Global expression profiling applied to the analysis of Arabidopsis stamen development. Plant Physiology.

[14] Wei D, Liu M, Chen H, Zheng Y, Liu Y, Wang X, et al. Inducer of CBF expression 1 is a male fertility regulator impacting anther dehydration

in Arabidopsis. PLoS Genetics.

[15] Lu P, Chai M, Yang J, Ning G, Wang G, Ma H. The Arabidopsis callose defective microspore1 gene is required for male fertility through regulating callose metabolism during microsporogenesis. Plant Physiology.

[16] Lou Y, Xu XF, Zhu J, Gu JN,

Blackmore S, Yang ZN. The tapetal AHL family protein TEK determines nexine formation in the pollen wall. Nature Communications. 2014;**5**:3855

2007;**19**:3530-3548

2007;**145**:747-762

2018;**14**:e1007695

2014;**164**:1893-1904

reproductive development in *Arabidopsis thaliana*. Plant Cell. 2010;**22**:91-107

differentiation and regulatory networks.

Conner K, Wilson ZA. Male sterility1 is required for tapetal development and pollen wall biosynthesis. Plant Cell.

2012;**12**:23

[2] Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

[3] Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: Opportunities and challenges. Nature Reviews. Genetics.

[4] Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science. 1995;**270**:484-487

[5] Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nature Biotechnology.

Science. 1995;**270**:467-470

2016;**17**:257-271

2000;**18**:630-634

2016;**9**:1-15

[6] Stelpflug SC, Sekhon RS,

Vaillancourt B, Hirsch CN, Buell CR, de Leon N, et al. An expanded maize gene expression atlas based on RNA sequencing and its use to explore root development. Plant Genome.

[7] Xing S, Zachgo S. ROXY1 and ROXY2, two Arabidopsis glutaredoxin genes, are required for anther development. The Plant Journal. 2008;**53**:790-801

[8] Feng B, Lu D, Ma X, Peng Y, Sun Y, Ning G, et al. Regulation of the Arabidopsis anther transcriptome by DYT1 for pollen development. The Plant

Journal. 2012;**72**:612-624

[9] Ma X, Feng B, Ma H. AMS-

dependent and independent regulation of anther transcriptome and comparison with those affected by other Arabidopsis

**References**

buds and flowers of male sterile and fertile lines in watermelon. BMC Genomics. 2015;**16**:914

[33] Ma J, Skibbe DS, Fernandes J, Walbot V. Male reproductive development: Gene expression profiling of maize anther and pollen ontogeny. Genome Biology. 2008;**9**:R181

[34] Hollender CA, Kang C, Darwish O, Geretz A, Matthews BF, Slovin J, et al. Floral transcriptomes in woodland strawberry uncover developing receptacle and anther gene networks. Plant Physiology. 2014;**165**:1062-1075

[35] Yue L, Twell D, Kuang Y, Liao J, Zhou X. Transcriptome analysis of *Hamelia patens* (Rubiaceae) anthers reveals candidate genes for tapetum and pollen wall development. Frontiers in Plant Science. 2016;**7**:1991

[36] Chen Z, Rao P, Yang X, Su X, Zhao T, Gao K, et al. A global view of transcriptome dynamics during male floral bud development in *Populus tomentosa*. Scientific Reports. 2018;**8**:722

[37] Ma Y, Kang J, Wu J, Zhu Y, Wang X. Identification of tapetum-specific genes by comparing global gene expression of four different male sterile lines in *Brassica oleracea*. Plant Molecular Biology. 2015;**87**:541-554

[38] Hirano K, Aya K, Hobo T, Sakakibara H, Kojima M, Shim RA, et al. Comprehensive transcriptome analysis of phytohormone biosynthesis and signaling genes in microspore/pollen and tapetum of rice. Plant and Cell Physiology. 2008;**49**:1429-1450

[39] Yuan TL, Huang WJ, He J, Zhang D, Tang WH. Stage-specific gene profiling of germinal cells helps delineate the mitosis/meiosis transition. Plant Physiology. 2018;**176**:1610-1626

[40] Nelms B, Walbot V. Defining the developmental program leading to meiosis in maize. Science. 2019;**364**:52-56

[41] Zhang X, Li J, Liu A, Zou J, Zhou X, Xiang J, et al. Expression profile in rice panicle: Insights into heat response mechanism at reproductive stage. PLoS One. 2012;**7**:e49652

[42] Wan X, Wu S, Li Z, Dong Z, An X, Ma B, et al. Maize genic male-sterility genes and their applications in hybrid breeding: Progress and perspectives. Molecular Plant. 2019;**12**:321-342

[43] Zhang D, Wu S, An X, Xie K, Dong Z, Zhou Y, et al. Construction of a multicontrol sterility system for a maize male-sterile line and hybrid seed production based on the ZmMs7 gene encoding a PHD-finger transcription factor. Plant Biotechnology Journal. 2018;**16**:459-471

[44] Wang Y, Liu D, Tian Y, Wu S, An X, Dong Z, et al. Map-based cloning, phylogenetic, and microsynteny analyses of ZmMs20 gene regulating male fertility in maize. International Journal of Molecular Sciences. 2019;**20**:1411

[45] An X, Dong Z, Tian Y, Xie K, Wu S, Zhu T, et al. ZmMs30 encoding a novel GDSL lipase is essential for male fertility and valuable for hybrid breeding in maize. Molecular Plant. 2019;**12**:343-359

[46] Zhu T, Wu S, Zhang D, Li Z, Xie K, An X, et al. Genome-wide analysis of maize GPAT gene family and cytological characterization and breeding application of ZmMs33/ZmGPAT6 gene. Theoretical and Applied Genetics. 2019;**132**:2137-2154

[47] Xie K, Wu S, Li Z, Zhou Y, Zhang D, Dong Z, et al. Map-based cloning and characterization of Zea mays male sterility33 (ZmMs33) gene, encoding a

**57**

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

genome-wide identification of tissuespecific transcription factors in rice.

[56] Luo Y, Guo Z, Li L. Evolutionary

regulatory programs in plant flower development. Developmental Biology.

[57] Li X. Next-generation sequencing sheds new light on small RNAs in plant reproductive development. Current Issues in Molecular Biology.

[59] Ru P, Xu L, Ma H, Huang H. Plant fertility defects induced by the

enhanced expression of microRNA167.

Cell Research. 2006;**16**:457-465

[60] Chuck G, Meeley R, Irish E, Sakai H, Hake S. The maize tasselseed4 microRNA controls sex determination and meristem cell fate by targeting Tasselseed6/indeterminate spikelet1. Nature Genetics. 2007;**39**:1517-1521

[61] Millar AA, Gubler F. The

2005;**17**:705-721

Journal. 2017;**91**:977-994

2016;**11**:e0146534

Arabidopsis GAMYB-like genes, MYB33 and MYB65, are microRNA-regulated genes that redundantly facilitate anther development. Plant Cell.

[62] Ding Y, Ma Y, Liu N, Xu J, Hu Q, Li Y, et al. MicroRNAs involved in auxin signaling modulate male sterility under high-temperature stress in cotton (*Gossypium hirsutum*). The Plant

[63] Field S, Thompson B. Analysis of the maize dicer-like1 mutant, fuzzy tassel, implicates microRNAs in anther maturation and dehiscence. PLoS One.

[58] Li ZF, Zhang YC, Chen YQ. miRNAs and lncRNAs in reproductive

development. Plant Science.

Plant Genome. 2019;**12**:1-11

conservation of microRNA

2013;**380**:133-144

2018;**27**:143-170

2015;**238**:46-52

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

glycerol-3-phosphate acyltransferase. Theoretical and Applied Genetics.

[48] Kim SS, Grienenberger E, Lallemand B, Colpitts CC, Kim SY, Souza Cde A, et al. LAP6/polyketide synthase A and LAP5/polyketide synthase B encode hydroxyalkyl alpha-pyrone synthases required for pollen development and sporopollenin biosynthesis in *Arabidopsis thaliana*. Plant Cell. 2010;**22**:4045-4066

[49] Xu J, Ding Z, Vizcay-Barrena G, Shi J, Liang W, Yuan Z, et al. Aborted microspores acts as a master regulator of pollen wall formation in Arabidopsis.

[50] Jiao QJ, Huang Y, Shen HB. Largescale mining co-expressed genes in Arabidopsis anther: From pair to group. Computational Biology and Chemistry.

[51] Pearce S, Ferguson A, King J, Wilson ZA. FlowerNet: A gene expression correlation network for anther and pollen development. Plant Physiology. 2015;**167**:1717-1730

[52] Lin H, Yu J, Pearce SP, Zhang D, Wilson ZA. RiceAntherNet: A gene co-expression network for identifying anther and pollen development genes. The Plant Journal. 2017;**92**:1076-1091

[53] Wang J, Qiu X, Li Y, Deng Y, Shi T. A transcriptional dynamic network during *Arabidopsis thaliana* pollen development. BMC Systems Biology. 2011;**5**(Suppl 3):S8

[54] Jiang Y, Zeng B, Zhao H, Zhang M, Xie S, Lai J. Genome-wide transcription

factor gene prediction and their expressional tissue-specificities in maize. Journal of Integrative Plant

[55] Chen W, Chen Z, Luo F, Liao M, Wei S, Yang Z, et al. RicetissueTFDB: A

Biology. 2012;**54**:616-630

Plant Cell. 2014;**26**:1544-1556

2011;**35**:62-68

2018;**131**:1363-1378

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

glycerol-3-phosphate acyltransferase. Theoretical and Applied Genetics. 2018;**131**:1363-1378

*Transcriptome Analysis*

Genomics. 2015;**16**:914

buds and flowers of male sterile and fertile lines in watermelon. BMC

[40] Nelms B, Walbot V. Defining the developmental program leading to meiosis in maize. Science.

[41] Zhang X, Li J, Liu A, Zou J, Zhou X, Xiang J, et al. Expression profile in rice panicle: Insights into heat response mechanism at reproductive stage. PLoS

[42] Wan X, Wu S, Li Z, Dong Z, An X, Ma B, et al. Maize genic male-sterility genes and their applications in hybrid breeding: Progress and perspectives. Molecular Plant. 2019;**12**:321-342

[43] Zhang D, Wu S, An X, Xie K, Dong Z, Zhou Y, et al. Construction of a multicontrol sterility system for a maize male-sterile line and hybrid seed production based on the ZmMs7 gene encoding a PHD-finger transcription factor. Plant Biotechnology Journal.

[44] Wang Y, Liu D, Tian Y, Wu S, An X, Dong Z, et al. Map-based cloning, phylogenetic, and microsynteny analyses of ZmMs20 gene regulating male fertility in maize. International Journal of Molecular Sciences.

[45] An X, Dong Z, Tian Y, Xie K, Wu S, Zhu T, et al. ZmMs30 encoding a novel GDSL lipase is essential for male fertility and valuable for hybrid breeding in maize. Molecular Plant. 2019;**12**:343-359

[46] Zhu T, Wu S, Zhang D, Li Z, Xie K, An X, et al. Genome-wide analysis of maize GPAT gene family and cytological

[47] Xie K, Wu S, Li Z, Zhou Y, Zhang D, Dong Z, et al. Map-based cloning and characterization of Zea mays male sterility33 (ZmMs33) gene, encoding a

characterization and breeding application of ZmMs33/ZmGPAT6 gene. Theoretical and Applied Genetics.

2019;**132**:2137-2154

2019;**364**:52-56

One. 2012;**7**:e49652

2018;**16**:459-471

2019;**20**:1411

[33] Ma J, Skibbe DS, Fernandes J, Walbot V. Male reproductive

Genome Biology. 2008;**9**:R181

[34] Hollender CA, Kang C,

2014;**165**:1062-1075

Plant Science. 2016;**7**:1991

Biology. 2015;**87**:541-554

[38] Hirano K, Aya K, Hobo T, Sakakibara H, Kojima M, Shim RA, et al. Comprehensive transcriptome

analysis of phytohormone biosynthesis and signaling genes in microspore/pollen and tapetum of rice. Plant and Cell Physiology.

2008;**49**:1429-1450

[36] Chen Z, Rao P, Yang X, Su X, Zhao T, Gao K, et al. A global view of transcriptome dynamics during male floral bud development in *Populus tomentosa*. Scientific Reports. 2018;**8**:722

[37] Ma Y, Kang J, Wu J, Zhu Y, Wang X. Identification of tapetum-specific genes by comparing global gene expression of four different male sterile lines in *Brassica oleracea*. Plant Molecular

[39] Yuan TL, Huang WJ, He J, Zhang D, Tang WH. Stage-specific gene profiling of germinal cells helps delineate the mitosis/meiosis transition. Plant Physiology. 2018;**176**:1610-1626

development: Gene expression profiling of maize anther and pollen ontogeny.

Darwish O, Geretz A, Matthews BF, Slovin J, et al. Floral transcriptomes in woodland strawberry uncover developing receptacle and anther gene networks. Plant Physiology.

[35] Yue L, Twell D, Kuang Y, Liao J, Zhou X. Transcriptome analysis of *Hamelia patens* (Rubiaceae) anthers reveals candidate genes for tapetum and pollen wall development. Frontiers in

**56**

[48] Kim SS, Grienenberger E, Lallemand B, Colpitts CC, Kim SY, Souza Cde A, et al. LAP6/polyketide synthase A and LAP5/polyketide synthase B encode hydroxyalkyl alpha-pyrone synthases required for pollen development and sporopollenin biosynthesis in *Arabidopsis thaliana*. Plant Cell. 2010;**22**:4045-4066

[49] Xu J, Ding Z, Vizcay-Barrena G, Shi J, Liang W, Yuan Z, et al. Aborted microspores acts as a master regulator of pollen wall formation in Arabidopsis. Plant Cell. 2014;**26**:1544-1556

[50] Jiao QJ, Huang Y, Shen HB. Largescale mining co-expressed genes in Arabidopsis anther: From pair to group. Computational Biology and Chemistry. 2011;**35**:62-68

[51] Pearce S, Ferguson A, King J, Wilson ZA. FlowerNet: A gene expression correlation network for anther and pollen development. Plant Physiology. 2015;**167**:1717-1730

[52] Lin H, Yu J, Pearce SP, Zhang D, Wilson ZA. RiceAntherNet: A gene co-expression network for identifying anther and pollen development genes. The Plant Journal. 2017;**92**:1076-1091

[53] Wang J, Qiu X, Li Y, Deng Y, Shi T. A transcriptional dynamic network during *Arabidopsis thaliana* pollen development. BMC Systems Biology. 2011;**5**(Suppl 3):S8

[54] Jiang Y, Zeng B, Zhao H, Zhang M, Xie S, Lai J. Genome-wide transcription factor gene prediction and their expressional tissue-specificities in maize. Journal of Integrative Plant Biology. 2012;**54**:616-630

[55] Chen W, Chen Z, Luo F, Liao M, Wei S, Yang Z, et al. RicetissueTFDB: A genome-wide identification of tissuespecific transcription factors in rice. Plant Genome. 2019;**12**:1-11

[56] Luo Y, Guo Z, Li L. Evolutionary conservation of microRNA regulatory programs in plant flower development. Developmental Biology. 2013;**380**:133-144

[57] Li X. Next-generation sequencing sheds new light on small RNAs in plant reproductive development. Current Issues in Molecular Biology. 2018;**27**:143-170

[58] Li ZF, Zhang YC, Chen YQ. miRNAs and lncRNAs in reproductive development. Plant Science. 2015;**238**:46-52

[59] Ru P, Xu L, Ma H, Huang H. Plant fertility defects induced by the enhanced expression of microRNA167. Cell Research. 2006;**16**:457-465

[60] Chuck G, Meeley R, Irish E, Sakai H, Hake S. The maize tasselseed4 microRNA controls sex determination and meristem cell fate by targeting Tasselseed6/indeterminate spikelet1. Nature Genetics. 2007;**39**:1517-1521

[61] Millar AA, Gubler F. The Arabidopsis GAMYB-like genes, MYB33 and MYB65, are microRNA-regulated genes that redundantly facilitate anther development. Plant Cell. 2005;**17**:705-721

[62] Ding Y, Ma Y, Liu N, Xu J, Hu Q, Li Y, et al. MicroRNAs involved in auxin signaling modulate male sterility under high-temperature stress in cotton (*Gossypium hirsutum*). The Plant Journal. 2017;**91**:977-994

[63] Field S, Thompson B. Analysis of the maize dicer-like1 mutant, fuzzy tassel, implicates microRNAs in anther maturation and dehiscence. PLoS One. 2016;**11**:e0146534

[64] Xing S, Salinas M, Hohmann S, Berndtgen R, Huijser P. miR156-targeted and nontargeted SBP-box transcription factors act in concert to secure male fertility in Arabidopsis. Plant Cell. 2010;**22**:3935-3950

[65] Feng N, Song G, Guan J, Chen K, Jia M, Huang D, et al. Transcriptome profiling of wheat inflorescence development from spikelet initiation to floral patterning identified stagespecific regulatory genes. Plant Physiology. 2017;**174**:1779-1794

[66] Chen J, Su P, Chen P, Li Q, Yuan X, Liu Z. Insights into the cotton anther development through association analysis of transcriptomic and small RNA sequencing. BMC Plant Biology. 2018;**18**:154

[67] Srivastava S, Zheng Y, Kudapa H, Jagadeeswaran G, Hivrale V, Varshney RK, et al. High throughput sequencing of small RNA component of leaves and inflorescence revealed conserved and novel miRNAs as well as phasiRNA loci in chickpea. Plant Science. 2015;**235**:46-57

[68] Wei LQ, Yan LF, Wang T. Deep sequencing on genome-wide scale reveals the unique composition and expression patterns of microRNAs in developing pollen of *Oryza sativa*. Genome Biology. 2011;**12**:R53

[69] Omidvar V, Mohorianu I, Dalmay T, Fellner M. Identification of miRNAs with potential roles in regulation of anther development and male-sterility in 7B-1 male-sterile tomato mutant. BMC Genomics. 2015;**16**:878

[70] Yang X, Zhao Y, Xie D, Sun Y, Zhu X, Esmaeili N, et al. Identification and functional analysis of microRNAs involved in the anther development in cotton genic male sterile line Yu98-8A. International Journal of Molecular Sciences. 2016;**17**:1677

[71] Wei M, Wei H, Wu M, Song M, Zhang J, Yu J, et al. Comparative expression profiling of miRNA during anther development in genetic male sterile and wild type cotton. BMC Plant Biology. 2013;**13**:66

[72] Sun L, Sun G, Shi C, Sun D. Transcriptome analysis reveals new microRNAs-mediated pathway involved in anther development in male sterile wheat. BMC Genomics. 2018;**19**:333

[73] Tang Z, Zhang L, Xu C, Yuan S, Zhang F, Zheng Y, et al. Uncovering small RNA-mediated responses to cold stress in a wheat thermosensitive genic male-sterile line by deep sequencing. Plant Physiology. 2012;**159**:721-738

[74] Niu SH, Liu C, Yuan HW, Li P, Li Y, Li W. Identification and expression profiles of sRNAs and their biogenesis and action-related genes in male and female cones of *Pinus tabuliformis*. BMC Genomics. 2015;**16**:693

[75] Shi J, Chen L, Zheng R, Guan C, Wang Y, Liang W, et al. Comparative phenotype and microRNAome in developing anthers of wild-type and male-sterile *Lycium barbarum* L. Plant Science. 2018;**274**:349-359

[76] Jiang J, Lv M, Liang Y, Ma Z, Cao J. Identification of novel and conserved miRNAs involved in pollen development in *Brassica campestris* ssp. chinensis by high-throughput sequencing and degradome analysis. BMC Genomics. 2014;**15**:146

[77] Lee RC, Feinbaum RL, Ambros V. The *C. elegans* heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;**75**:843-854

[78] Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: The rosetta stone of a hidden RNA language? Cell. 2011;**146**:353-358

**59**

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory…*

*DOI: http://dx.doi.org/10.5772/intechopen.88318*

[79] Thomson DW, Dinger ME. Endogenous microRNA sponges: Evidence and controversy. Nature Reviews. Genetics. 2016;**17**:272-283

[80] Franco-Zorrilla JM, Valli A, Todesco M, Mateos I, Puga MI, Rubio-Somoza I, et al. Target mimicry provides a new mechanism for regulation of microRNA activity. Nature Genetics.

[81] Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature.

[82] Paschoal AR, Lozada-Chavez I, Domingues DS, Stadler PF. ceRNAs in plants: Computational approaches and associated challenges for target mimic research. Briefings in Bioinformatics.

[83] Zhai J, Zhang H, Arikit S, Huang K, Nan GL, Walbot V, et al. Spatiotemporally dynamic, celltype-dependent premeiotic and meiotic phasiRNAs in maize anthers. Proceedings of the National Academy of Sciences of the United States of America. 2015;**112**:3146-3151

[84] Li Z, An X, Zhu T, Yan T, Wu S, Tian Y, et al. Discovering and constructing ceRNA-miRNA-target gene regulatory networks during anther development in maize. International Journal of Molecular Sciences. 2019;**20**:3480

2007;**39**:1033-1037

2014;**505**:344-352

2018;**19**:1273-1289

*Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory… DOI: http://dx.doi.org/10.5772/intechopen.88318*

[79] Thomson DW, Dinger ME. Endogenous microRNA sponges: Evidence and controversy. Nature Reviews. Genetics. 2016;**17**:272-283

*Transcriptome Analysis*

2010;**22**:3935-3950

2018;**18**:154

2015;**235**:46-57

2015;**16**:878

[64] Xing S, Salinas M, Hohmann S, Berndtgen R, Huijser P. miR156-targeted and nontargeted SBP-box transcription factors act in concert to secure male fertility in Arabidopsis. Plant Cell.

[71] Wei M, Wei H, Wu M, Song M, Zhang J, Yu J, et al. Comparative expression profiling of miRNA during anther development in genetic male sterile and wild type cotton. BMC Plant

[72] Sun L, Sun G, Shi C, Sun D. Transcriptome analysis reveals new microRNAs-mediated pathway involved in anther development in male sterile wheat. BMC Genomics. 2018;**19**:333

[73] Tang Z, Zhang L, Xu C, Yuan S, Zhang F, Zheng Y, et al. Uncovering small RNA-mediated responses to cold stress in a wheat thermosensitive genic male-sterile line by deep sequencing. Plant Physiology. 2012;**159**:721-738

[74] Niu SH, Liu C, Yuan HW, Li P, Li Y, Li W. Identification and expression profiles of sRNAs and their biogenesis and action-related genes in male and female cones of *Pinus tabuliformis*. BMC Genomics.

[75] Shi J, Chen L, Zheng R, Guan C, Wang Y, Liang W, et al. Comparative phenotype and microRNAome in developing anthers of wild-type and male-sterile *Lycium barbarum* L. Plant

[76] Jiang J, Lv M, Liang Y, Ma Z, Cao J. Identification of novel and conserved miRNAs involved in pollen development in *Brassica campestris* ssp. chinensis by high-throughput sequencing and degradome analysis. BMC Genomics.

[77] Lee RC, Feinbaum RL, Ambros V. The *C. elegans* heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell.

[78] Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: The rosetta stone of a hidden RNA language?

Science. 2018;**274**:349-359

2015;**16**:693

2014;**15**:146

1993;**75**:843-854

Cell. 2011;**146**:353-358

Biology. 2013;**13**:66

[65] Feng N, Song G, Guan J, Chen K, Jia M, Huang D, et al. Transcriptome profiling of wheat inflorescence development from spikelet initiation to floral patterning identified stagespecific regulatory genes. Plant Physiology. 2017;**174**:1779-1794

[66] Chen J, Su P, Chen P, Li Q, Yuan X, Liu Z. Insights into the cotton anther development through association analysis of transcriptomic and small RNA sequencing. BMC Plant Biology.

[67] Srivastava S, Zheng Y, Kudapa H, Jagadeeswaran G, Hivrale V, Varshney RK, et al. High throughput sequencing of small RNA component of leaves and inflorescence revealed conserved and novel miRNAs as well as phasiRNA loci in chickpea. Plant Science.

[68] Wei LQ, Yan LF, Wang T. Deep sequencing on genome-wide scale reveals the unique composition and expression patterns of microRNAs in developing pollen of *Oryza sativa*. Genome Biology. 2011;**12**:R53

[69] Omidvar V, Mohorianu I, Dalmay T, Fellner M. Identification of miRNAs with potential roles in regulation of anther development and male-sterility in 7B-1 male-sterile tomato mutant. BMC Genomics.

[70] Yang X, Zhao Y, Xie D, Sun Y, Zhu X, Esmaeili N, et al. Identification and functional analysis of microRNAs involved in the anther development in cotton genic male sterile line Yu98-8A. International Journal of Molecular Sciences. 2016;**17**:1677

**58**

[80] Franco-Zorrilla JM, Valli A, Todesco M, Mateos I, Puga MI, Rubio-Somoza I, et al. Target mimicry provides a new mechanism for regulation of microRNA activity. Nature Genetics. 2007;**39**:1033-1037

[81] Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature. 2014;**505**:344-352

[82] Paschoal AR, Lozada-Chavez I, Domingues DS, Stadler PF. ceRNAs in plants: Computational approaches and associated challenges for target mimic research. Briefings in Bioinformatics. 2018;**19**:1273-1289

[83] Zhai J, Zhang H, Arikit S, Huang K, Nan GL, Walbot V, et al. Spatiotemporally dynamic, celltype-dependent premeiotic and meiotic phasiRNAs in maize anthers. Proceedings of the National Academy of Sciences of the United States of America. 2015;**112**:3146-3151

[84] Li Z, An X, Zhu T, Yan T, Wu S, Tian Y, et al. Discovering and constructing ceRNA-miRNA-target gene regulatory networks during anther development in maize. International Journal of Molecular Sciences. 2019;**20**:3480

**61**

**1. Introduction**

**Chapter 5**

**Abstract**

Transcriptome Analysis for

Rice, a model monocot system, belongs to the family Poaceae and genus *Oryza*. Rice is the second largest produced cereal and staple food crop fulfilling the demand of half the world's population. Though rice demand is growing exponentially, its production is severely affected by variable environmental changes. The various abiotic factors drastically reduce the rice plant growth and yield by affecting its different growth stages. To fulfill the growing demand of rice, it is imperative to understand its molecular responses during stresses and to develop new varieties to overcome the stresses. Earlier, the microarray experiments have been used for the identification of coexpressive gene networks during various conditions in crop plants. Though the microarray experiments provided very useful information, the unviability of genomewide information did not provide complete information about the regulatory gene networks involved in the stress response. The advancement of molecular techniques provided breakthrough to understanding the complex regulatory gene networks and their signaling pathways during stresses. The high-throughput RNA sequencing data have opened the floodgate of transcriptome data in rice. Here we have summarized some of the transcriptome data for abiotic molecular responses in rice, which further

Abiotic Stresses in Rice

*Ashutosh Kumar and Prasanta K. Dash*

help to understand their complex regulatory mechanism.

**Keywords:** abiotic stresses, cold stress, drought, micronutrients, rice, RNA-Seq, salt stress, submergence, trace element stress, transcriptome

Rice is the most important staple food crop across the globe and is a model monocot system [1]. It is the second largest produced cereal fulfilling the demand of half world's population. Rice belongs to family Poaceae and genus *Oryza.* Two species *Oryza sativa* (Asian rice) and *Oryza glaberrima* (African rice) out of 23 species have been cultivated worldwide [2]. The *O. sativa* is native to tropical and subtropical southern and southeastern Asia, while *O. glaberrima* is grown only in South Africa. A third species, *O. rufipogon*, has also been grown in South Asian, Chinese, New Guinean, Australian, and American farms. In Asia, *O. sativa* is separated into three subspecies according to its geographical environment: indica, japonica, and javanica. The variety indica refers to the tropical and subtropical varieties grown throughout South and Southeast Asia and Southern China. The variety japonica is grown in temperate areas of Japan, China, and Korea, while javanica varieties are

(*Oryza sativa* L.)

#### **Chapter 5**

## Transcriptome Analysis for Abiotic Stresses in Rice (*Oryza sativa* L.)

*Ashutosh Kumar and Prasanta K. Dash*

#### **Abstract**

Rice, a model monocot system, belongs to the family Poaceae and genus *Oryza*. Rice is the second largest produced cereal and staple food crop fulfilling the demand of half the world's population. Though rice demand is growing exponentially, its production is severely affected by variable environmental changes. The various abiotic factors drastically reduce the rice plant growth and yield by affecting its different growth stages. To fulfill the growing demand of rice, it is imperative to understand its molecular responses during stresses and to develop new varieties to overcome the stresses. Earlier, the microarray experiments have been used for the identification of coexpressive gene networks during various conditions in crop plants. Though the microarray experiments provided very useful information, the unviability of genomewide information did not provide complete information about the regulatory gene networks involved in the stress response. The advancement of molecular techniques provided breakthrough to understanding the complex regulatory gene networks and their signaling pathways during stresses. The high-throughput RNA sequencing data have opened the floodgate of transcriptome data in rice. Here we have summarized some of the transcriptome data for abiotic molecular responses in rice, which further help to understand their complex regulatory mechanism.

**Keywords:** abiotic stresses, cold stress, drought, micronutrients, rice, RNA-Seq, salt stress, submergence, trace element stress, transcriptome

#### **1. Introduction**

Rice is the most important staple food crop across the globe and is a model monocot system [1]. It is the second largest produced cereal fulfilling the demand of half world's population. Rice belongs to family Poaceae and genus *Oryza.* Two species *Oryza sativa* (Asian rice) and *Oryza glaberrima* (African rice) out of 23 species have been cultivated worldwide [2]. The *O. sativa* is native to tropical and subtropical southern and southeastern Asia, while *O. glaberrima* is grown only in South Africa. A third species, *O. rufipogon*, has also been grown in South Asian, Chinese, New Guinean, Australian, and American farms. In Asia, *O. sativa* is separated into three subspecies according to its geographical environment: indica, japonica, and javanica. The variety indica refers to the tropical and subtropical varieties grown throughout South and Southeast Asia and Southern China. The variety japonica is grown in temperate areas of Japan, China, and Korea, while javanica varieties are

grown alongside of indica in Indonesia (http://agropedia.iitk.ac.in/?q=content/ botanical-classification-rice).

Rice is an annual plant, even though in tropical areas, it is cultivated perennially. It is self-pollinated (wind pollination) tropical C3 grass that evolved in a semiaquatic, low-radiation habitat having arenchymatic tissues [3]. Rice is cultivated in more than 100 countries, with a total harvested area till 2017 is of approximately 165 million hectares, and produced ~700 million tons (503.9 million tons of milled rice) (http://www.fao.org/3/I9243EN/i9243en.pdf). About 91% of the rice in the world is grown in Asia (nearly 640 million tons) where 60% of the world's population lives. Rice is also cultivated in Sub-Saharan Africa and Latin Americas, and evenly poised in the Eastern and Western Asia. China and India, which account for more than onethird of global population, supply over half of the world's rice. The China produces ~30% of total world rice production followed by India (21%), Indonesia (9%), and Bangladesh (6%). On the other hand, rest of Asia, Americas, and Africa produce 37, 5, and 3%, respectively, of the total world rice production [4]. However, demand of the rice is still growing day by day, as the world population is mounting exponentially. To fulfill the demand of growing population, yield needs to be increased by the application of agricultural as well as biotechnological approaches.

Rice production is severely affected by changing environment including extreme variability in temperature and rainfall pattern along with other factors [5]. The abiotic stresses including drought, high salinity, high or low temperatures, flooding, high light, ozone, low nutrient availability, mineral deficiency, heavy metals, pollutants, wind and mechanical injury, drastically reduce the rice plant growth and yield by affecting it during different growth stages [6]. However, rice has very antagonistic character about tolerances and susceptibilities to abiotic stresses, as compared to other crops. It is very well known that rice paddy grows in standing water containing soil and can tolerate submergence at levels that would kill other crops. However, it is moderately tolerant to salinity and soil acidity but highly susceptible to drought and cold. Drought influences all physiological processes involved in plant growth and development [5]. Drought at vegetative stage can moderately reduce yield, but entire yield is lost if it occurs during pollen meiosis or fertilization [7]. The high salt concentration disrupts the ability of roots for efficient water uptake, leading to perturbation of crucial metabolic reactions inside the cell restricting plant growth and yield potential [8]. Low temperature reduces germination, causes poor establishment, delays phenological development, and increases spikelet sterility [9], and other physiological and metabolite changes causing low yield [10]. Furthermore, rice can tolerate partial submergence as paddy rice or deepwater rice because it is very well adapted to waterlogged conditions as it has well-developed aerenchyma that facilitates oxygen diffusion and prevents anoxia in roots [11–13]. However, it was damaged when submerged partially or completely for a relatively longer period [14] due to the shortage of oxygen during submergence. The response of plants to low oxygen stress comprises complex biochemical and genetic programs that include the differential expressions of a large number of genes. Importantly, abiotic stress conditions not only harm the crop but also influence the manifestation and extent the pathogen infection, attack of insects, and growth of weeds [6]. Though rice has superior response to abiotic stresses, development of their improved tolerant germplasm is indispensable [11]. Besides abiotic stress, the deficiency of micronutrients also affects the crop production.

The crop plants are very sensitive and respond to environmental stimuli through signal perception. The plant responds accordingly for a specific environmental stimulus instigating specific physiochemical changes. These physiochemical changes or adaptations are administered by complex molecular regulatory mechanism of involving various sensors regulated by transcriptional factors/regulators. Various studies have been carried out for understanding the regulatory mechanism of plants during stress

**63**

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.)

**2. Transcriptome data for submergence/flooding**

Flooding is considered as a major threat to the rice crops, as irregular flash floods

are very common in the Southeast Asia (major rice producing region), severely affecting the rice productivity [29]. Rice produces high yields, when it is grown in water-logged rice paddies. It can tolerate partial submergence as paddy rice or deepwater rice. However, it is damaged when submerged for a relatively longer

conditions. Earlier, *CIPK* genes (*OsCIPK01–OsCIPK30*) in the rice genome were studied for their transcriptional responses to various abiotic stresses [15]. The results showed that 20 *OsCIPK* genes were differentially induced by at least one of the stresses, including drought, salinity, cold, polyethylene glycol, and abscisic acid treatment. Most of the genes induced by drought or salt stress were also induced by abscisic acid treatment but not by cold. A few *CIPK* genes containing none of the reported stress-responsive *cis*elements in their promoter regions were also induced by multiple stresses [15]. The proteins possessing A20/AN1 zinc-finger, named *SAP* gene family in rice and *Arabidopsis*, were inducible by one or the other abiotic stresses indicating that the *OsSAP* gene family is an important component of stress response in rice [16]. In addition, the role of *SAP* gene family in abiotic stress conditions was established by expression profiling under abiotic stress conditions. Seven Expansin A (*ExpA*) mRNAs were accumulated in leaves of deepwater rice, and their abundance was upregulated by submergence [17]. Similarly, the drought response in rice incites a signaling cascade through osmolyte synthesis that involves perception and translation of drought signal [18, 19].

Earlier, microarray experiments have been used for expression analysis of multiple genes during various conditions in different tissues for crop plants. The microarray experiments helped to identify the coexpressive genes during a stress condition [20–23]. Though the microarray experiments provided very useful information, the unviability of genome-wide information about the transcripts did not provide the complete information about the regulatory gene networks involved in the stress response. Nowadays, the availability of high-throughput techniques, achieved through advancement of molecular techniques, provided breakthrough in the understanding of complex regulatory gene networks and their signaling pathways involved in stress responses [24]. The techniques are comprised of whole genome transcriptome analyses, small RNA sequencing analysis (RNA-Seq), proteomic analyses, epigenetic sequencing analysis, and metabolomic analyses [25]. These high-throughput techniques use sequence-based approaches instead of hybridization-based approaches (like microarray), which require known genomic sequences, rather able to determine the transcript sequences directly from new genomes, able to map and quantify them [26, 27]. The RNA-Seq has superiority among these techniques due to its in-depth coverage of genome, global expression of transcripts, and also providing detailed information about alternative splicing and allele-specific expressions [27]. The inception of RNA-Seq technique has reformed the perception of complex and dynamic nature of the genomes, further helps to comprehensively elucidate the complex regulatory gene networks pertaining to different physiological and developmental stages of plants [28]. Currently, the various transcriptome analyses of rice genome, accomplished through RNA-Seq, during various abiotic stresses have generated enormous data. Further, these data have been able to decipher the complex regulatory gene networks in rice during various abiotic stresses which helped to understand the adaptive physiological measures taken by rice at cellular level and ascertain the development of tolerant rice varieties. Here, we are describing some of the different transcriptome studies carried out to understand the molecular responses in rice genome during various abiotic stresses.

*DOI: http://dx.doi.org/10.5772/intechopen.84955*

#### *Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.) *DOI: http://dx.doi.org/10.5772/intechopen.84955*

*Transcriptome Analysis*

botanical-classification-rice).

grown alongside of indica in Indonesia (http://agropedia.iitk.ac.in/?q=content/

It is self-pollinated (wind pollination) tropical C3 grass that evolved in a semiaquatic, low-radiation habitat having arenchymatic tissues [3]. Rice is cultivated in more than 100 countries, with a total harvested area till 2017 is of approximately 165 million hectares, and produced ~700 million tons (503.9 million tons of milled rice) (http://www.fao.org/3/I9243EN/i9243en.pdf). About 91% of the rice in the world is grown in Asia (nearly 640 million tons) where 60% of the world's population lives. Rice is also cultivated in Sub-Saharan Africa and Latin Americas, and evenly poised in the Eastern and Western Asia. China and India, which account for more than onethird of global population, supply over half of the world's rice. The China produces ~30% of total world rice production followed by India (21%), Indonesia (9%), and Bangladesh (6%). On the other hand, rest of Asia, Americas, and Africa produce 37, 5, and 3%, respectively, of the total world rice production [4]. However, demand of the rice is still growing day by day, as the world population is mounting exponentially. To fulfill the demand of growing population, yield needs to be increased by the

application of agricultural as well as biotechnological approaches.

stress, the deficiency of micronutrients also affects the crop production.

The crop plants are very sensitive and respond to environmental stimuli through signal perception. The plant responds accordingly for a specific environmental stimulus instigating specific physiochemical changes. These physiochemical changes or adaptations are administered by complex molecular regulatory mechanism of involving various sensors regulated by transcriptional factors/regulators. Various studies have been carried out for understanding the regulatory mechanism of plants during stress

Rice is an annual plant, even though in tropical areas, it is cultivated perennially.

Rice production is severely affected by changing environment including extreme variability in temperature and rainfall pattern along with other factors [5]. The abiotic stresses including drought, high salinity, high or low temperatures, flooding, high light, ozone, low nutrient availability, mineral deficiency, heavy metals, pollutants, wind and mechanical injury, drastically reduce the rice plant growth and yield by affecting it during different growth stages [6]. However, rice has very antagonistic character about tolerances and susceptibilities to abiotic stresses, as compared to other crops. It is very well known that rice paddy grows in standing water containing soil and can tolerate submergence at levels that would kill other crops. However, it is moderately tolerant to salinity and soil acidity but highly susceptible to drought and cold. Drought influences all physiological processes involved in plant growth and development [5]. Drought at vegetative stage can moderately reduce yield, but entire yield is lost if it occurs during pollen meiosis or fertilization [7]. The high salt concentration disrupts the ability of roots for efficient water uptake, leading to perturbation of crucial metabolic reactions inside the cell restricting plant growth and yield potential [8]. Low temperature reduces germination, causes poor establishment, delays phenological development, and increases spikelet sterility [9], and other physiological and metabolite changes causing low yield [10]. Furthermore, rice can tolerate partial submergence as paddy rice or deepwater rice because it is very well adapted to waterlogged conditions as it has well-developed aerenchyma that facilitates oxygen diffusion and prevents anoxia in roots [11–13]. However, it was damaged when submerged partially or completely for a relatively longer period [14] due to the shortage of oxygen during submergence. The response of plants to low oxygen stress comprises complex biochemical and genetic programs that include the differential expressions of a large number of genes. Importantly, abiotic stress conditions not only harm the crop but also influence the manifestation and extent the pathogen infection, attack of insects, and growth of weeds [6]. Though rice has superior response to abiotic stresses, development of their improved tolerant germplasm is indispensable [11]. Besides abiotic

**62**

conditions. Earlier, *CIPK* genes (*OsCIPK01–OsCIPK30*) in the rice genome were studied for their transcriptional responses to various abiotic stresses [15]. The results showed that 20 *OsCIPK* genes were differentially induced by at least one of the stresses, including drought, salinity, cold, polyethylene glycol, and abscisic acid treatment. Most of the genes induced by drought or salt stress were also induced by abscisic acid treatment but not by cold. A few *CIPK* genes containing none of the reported stress-responsive *cis*elements in their promoter regions were also induced by multiple stresses [15]. The proteins possessing A20/AN1 zinc-finger, named *SAP* gene family in rice and *Arabidopsis*, were inducible by one or the other abiotic stresses indicating that the *OsSAP* gene family is an important component of stress response in rice [16]. In addition, the role of *SAP* gene family in abiotic stress conditions was established by expression profiling under abiotic stress conditions. Seven Expansin A (*ExpA*) mRNAs were accumulated in leaves of deepwater rice, and their abundance was upregulated by submergence [17]. Similarly, the drought response in rice incites a signaling cascade through osmolyte synthesis that involves perception and translation of drought signal [18, 19].

Earlier, microarray experiments have been used for expression analysis of multiple genes during various conditions in different tissues for crop plants. The microarray experiments helped to identify the coexpressive genes during a stress condition [20–23]. Though the microarray experiments provided very useful information, the unviability of genome-wide information about the transcripts did not provide the complete information about the regulatory gene networks involved in the stress response. Nowadays, the availability of high-throughput techniques, achieved through advancement of molecular techniques, provided breakthrough in the understanding of complex regulatory gene networks and their signaling pathways involved in stress responses [24]. The techniques are comprised of whole genome transcriptome analyses, small RNA sequencing analysis (RNA-Seq), proteomic analyses, epigenetic sequencing analysis, and metabolomic analyses [25]. These high-throughput techniques use sequence-based approaches instead of hybridization-based approaches (like microarray), which require known genomic sequences, rather able to determine the transcript sequences directly from new genomes, able to map and quantify them [26, 27]. The RNA-Seq has superiority among these techniques due to its in-depth coverage of genome, global expression of transcripts, and also providing detailed information about alternative splicing and allele-specific expressions [27]. The inception of RNA-Seq technique has reformed the perception of complex and dynamic nature of the genomes, further helps to comprehensively elucidate the complex regulatory gene networks pertaining to different physiological and developmental stages of plants [28]. Currently, the various transcriptome analyses of rice genome, accomplished through RNA-Seq, during various abiotic stresses have generated enormous data. Further, these data have been able to decipher the complex regulatory gene networks in rice during various abiotic stresses which helped to understand the adaptive physiological measures taken by rice at cellular level and ascertain the development of tolerant rice varieties. Here, we are describing some of the different transcriptome studies carried out to understand the molecular responses in rice genome during various abiotic stresses.

#### **2. Transcriptome data for submergence/flooding**

Flooding is considered as a major threat to the rice crops, as irregular flash floods are very common in the Southeast Asia (major rice producing region), severely affecting the rice productivity [29]. Rice produces high yields, when it is grown in water-logged rice paddies. It can tolerate partial submergence as paddy rice or deepwater rice. However, it is damaged when submerged for a relatively longer

period [14] due to the slow diffusion of oxygen in water fails to match the demands of respiration [30] resulting an anaerobic metabolism and energy crisis [12]. Also, in deepwater rice, energy generation through fermentative metabolism, aerenchyma development in parenchymal tissues that improves access to O2, activation of ethylene promoted gibberellic acid (GA)-mediated internode elongation cause foliage to shoot up above the water surface for gas exchange and restricting growth and conserving available energy until floodwater recedes [12, 13]. Similarly, floodtolerant rice varieties have developed the capacity to generate ATP without the presence of oxygen and/or to develop specific morphologies that improve the entrance of oxygen [31]. Moreover, the phytohormonal regulation revealed that gibberellin (GA) has negative effects on submergence tolerance, whereas paclobutrazol (PB), chemical inhibitor of GA, acted contrary to GA [32]. The transcriptome analysis between GA- and PB-treated samples and control identified 3936 differentially expressed genes largely associated with the stress response, phytohormone biosynthesis and signaling, photosynthesis, and nutrient metabolism. It was observed that the PB improved the rice survival during submergence through sustaining the photosynthesis capacity and by dropping nutrient metabolism [32].

Despite knowledge of adaptive mechanisms and regulation at the gene and protein level, our understanding of the mechanisms behind plant responses to submergence is still limited. Even in flood-intolerant species, such as *Arabidopsis thaliana*, many genes are triggered in response to flooding stress [33, 34]. The response of plants to low oxygen stress comprises complex biochemical and genetic programs that include the differential expressions of a large number of genes (**Table 1**). Gene expression is altered under low oxygen stress, and the existence of *anaerobic response elements* (*AREs*) along with their binding factors has already been reported [35]. Eventually, a *SUB1* locus and three ethylene response factors (ERFs) were identified within the locus in tolerant rice varieties (e.g., FR13A), whereas *SUB1* is a major determinant of tolerance [36]. Introduction of the *SUB1A* gene into submergence-intolerant rice variety significantly increased its flooding tolerance, thus demonstrating the importance of the *SUB1* locus for flooding tolerance [36]. Two different types of molecular mechanisms are adapted by rice ecotypes to survive under stress, *SUB1A*-mediated "quiescence strategy" [37, 38] and "escape strategy" induced by *SNORKEL1/2* [13]. The submergence response in rice consists of the differential expression of genes related to gibberellin biosynthesis, trehalose biosynthesis, anaerobic fermentation, cell wall modification, and transcription factors that include ethylene-responsive factor genes [39]. Though the regulatory mechanism in rice during submergence response has been comprehensively studied, the genome-wide gene expression as well as allelic variation among the cultivars for specific quantitative traits remained elusive. One of the studies was conducted in six rice genotypes to estimate the coleoptile elongation rates during submergence [39]. The result postulated that the coleoptile elongation was augmented by transcriptional regulation. Further, the reason for the variation in anaerobic germination was due to the allelic variation caused by the small-to-large deletions in the coding region of susceptible varieties [39].

Recently, a study on *SUB1A-1* genotypes is carried to understand the molecular mechanism pertaining to the physiological function upon desubmergence through transcriptomic analysis [29]. The results enumerated around 1400 genes that were differentially expressed to recover from the stress to preserve the plastid integrity, and the genes regulating the cell division, chromatin structure, and signaling associated with starch catabolism [29]. They also found that the rice plants recover shoot transcriptome significantly to the control state and return to homeostasis during the 24-h recovery period. It also regulated the GA-responsive starch metabolism

**65**

**Table 1.**

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.)

**Downstream key gene/s Physiological functions**

Quiescence strategy to stop all physiological functions

Stomatal closure, repression of cell growth, photosynthesis and activation of respiration and production of phytohormone

Imbalance in ion homeostasis of cells at plasma membrane and sequestration of vacuolar ion, and stomatal closure which causes higher leaf temperature and reserve shoot elongation

Altered chlorophyll content and fluorescence causing reduction in photosynthesis, increases content of ROS and malondialdehyde causing oxidative damage to cells

Fatal damage to rice seedlings during their development

Important for energy transfer, signal transduction, photosynthesis, and respiration

oxidative stresses

Important for catalyzing the water-splitting reaction of oxygen-evolving complex in photosystem II (PSII), acts as cofactor that activates different enzymes, such as Mn-superoxide dismutase and others, to protect against

water level

ABA

of GA-responsive starch metabolism, anaerobic fermentation, cell wall modification, JA-mediated internode elongation, and biotic

ABA-responsive genes, *LEA*, *NAC, DBP*, α-linolenic acid metabolic pathway genes, osmolyte biosynthesis genes, phospholipid metabolism genes; water channel protein, sugar and proline transporters, and detoxification enzyme-encoding genes; and signaling molecule-

Genes related to antioxidants, transcription factors, signaling, ion and metabolic homeostasis

ABA-responsive genes, *ABF*, *NAC, NACRS* containing genes, *ERF922*, *WRKY25*, and *WRKY74,* gene related to signal transduction, phytohormones, antioxidant system and biotic

ROS-scavenging enzymes, chelators, and metal transporterencoding genes and many drought stress-related genes

monitoring path genes

protein genes, catalytic protein encoding genes, *WRKY*, and potassium transporter-related genes, *Aux*/*IAA* family, and sodium transporter-related

related to hormone signal transduction and secondary metabolite biosynthesis

*SNORKEL1/2* Escape strategy to supersede

responsive

encoding genes

and transporters

stress

genes

pathways

*Regulatory role of different abiotic stress-responsive genes based on RNA-Seq analysis.*

Cadmium (Cd) Cd-responsive transporters,

Phosphorus (P) RNA transport and mRNA

Manganese (Mn) TFs, transporters, transferase

Alkaline stress Alkali-responsive genes Alkaline resistant genes, TFs

*DOI: http://dx.doi.org/10.5772/intechopen.84955*

**tolerance**

Drought *DREBs* (*DREB1A-D/*

Salt *SOS1*, *NHX, HKT2*,

*HBP1B*

Cold *CBF1*, *DREB1A*, and *DREB1B*

**Gene/s responsible for** 

*CBF1–4* and *DREB2)*

*CAX1*, *AKT1*, *KCO1*, *TPC1*, *CLC1*, *NRT1*, *CDPK7*, *MAPK5*, *CaMBP*, *GST*, *LEA*, *V-ATPase*, *OSAP1*, and

Submergence *SUB1A ERFs* regulating genes

**Abiotic stress condition**


#### *Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.) *DOI: http://dx.doi.org/10.5772/intechopen.84955*

*Transcriptome Analysis*

period [14] due to the slow diffusion of oxygen in water fails to match the demands of respiration [30] resulting an anaerobic metabolism and energy crisis [12]. Also, in deepwater rice, energy generation through fermentative metabolism, aerenchyma development in parenchymal tissues that improves access to O2, activation of ethylene promoted gibberellic acid (GA)-mediated internode elongation cause foliage to shoot up above the water surface for gas exchange and restricting growth and conserving available energy until floodwater recedes [12, 13]. Similarly, floodtolerant rice varieties have developed the capacity to generate ATP without the presence of oxygen and/or to develop specific morphologies that improve the entrance of oxygen [31]. Moreover, the phytohormonal regulation revealed that gibberellin (GA) has negative effects on submergence tolerance, whereas paclobutrazol (PB), chemical inhibitor of GA, acted contrary to GA [32]. The transcriptome analysis between GA- and PB-treated samples and control identified 3936 differentially expressed genes largely associated with the stress response, phytohormone biosynthesis and signaling, photosynthesis, and nutrient metabolism. It was observed that the PB improved the rice survival during submergence through sustaining the

photosynthesis capacity and by dropping nutrient metabolism [32].

deletions in the coding region of susceptible varieties [39].

Recently, a study on *SUB1A-1* genotypes is carried to understand the molecular mechanism pertaining to the physiological function upon desubmergence through transcriptomic analysis [29]. The results enumerated around 1400 genes that were differentially expressed to recover from the stress to preserve the plastid integrity, and the genes regulating the cell division, chromatin structure, and signaling associated with starch catabolism [29]. They also found that the rice plants recover shoot transcriptome significantly to the control state and return to homeostasis during the 24-h recovery period. It also regulated the GA-responsive starch metabolism

Despite knowledge of adaptive mechanisms and regulation at the gene and protein level, our understanding of the mechanisms behind plant responses to submergence is still limited. Even in flood-intolerant species, such as *Arabidopsis thaliana*, many genes are triggered in response to flooding stress [33, 34]. The response of plants to low oxygen stress comprises complex biochemical and genetic programs that include the differential expressions of a large number of genes (**Table 1**). Gene expression is altered under low oxygen stress, and the existence of *anaerobic response elements* (*AREs*) along with their binding factors has already been reported [35]. Eventually, a *SUB1* locus and three ethylene response factors (ERFs) were identified within the locus in tolerant rice varieties (e.g., FR13A), whereas *SUB1* is a major determinant of tolerance [36]. Introduction of the *SUB1A* gene into submergence-intolerant rice variety significantly increased its flooding tolerance, thus demonstrating the importance of the *SUB1* locus for flooding tolerance [36]. Two different types of molecular mechanisms are adapted by rice ecotypes to survive under stress, *SUB1A*-mediated "quiescence strategy" [37, 38] and "escape strategy" induced by *SNORKEL1/2* [13]. The submergence response in rice consists of the differential expression of genes related to gibberellin biosynthesis, trehalose biosynthesis, anaerobic fermentation, cell wall modification, and transcription factors that include ethylene-responsive factor genes [39]. Though the regulatory mechanism in rice during submergence response has been comprehensively studied, the genome-wide gene expression as well as allelic variation among the cultivars for specific quantitative traits remained elusive. One of the studies was conducted in six rice genotypes to estimate the coleoptile elongation rates during submergence [39]. The result postulated that the coleoptile elongation was augmented by transcriptional regulation. Further, the reason for the variation in anaerobic germination was due to the allelic variation caused by the small-to-large

**64**

#### **Table 1.**

*Regulatory role of different abiotic stress-responsive genes based on RNA-Seq analysis.*

indirectly through *SUB1A* and downstream regulatory network to resume the photosynthesis [29]. Similar studies have also been carried between two contrasting deepwater growth rice cultivars [40]. The RNA-Seq analysis was conducted from different tissues, shoot base region, including basal nodes, internodes, and shoot apices of seedlings at two developmental stages. The study elucidated the possible role of jasmonic acid-mediated internode elongation and expression of biotic stressrelated genes during submergence response [40].

#### **3. Transcriptome data for drought stress**

One of the major abiotic stresses that severely affect the rice production is drought stress. Drought stress causes a series of physiological and biochemical changes which included stomatal closure, repression of cell growth, photosynthesis, and activation of respiration along with production of the phytohormone abscisic acid (ABA) [41]. In response to the drought stress, ABA triggers stomatal closure and induces expression of stress-related genes (**Table 1**) [41]. However, some of drought-related genes were not expressed by the external ABA treatment. Therefore, the drought response is either of ABA-independent or of ABA-dependent or both inducible gene regulatory system networks [42]. These regulatory networks are the amalgamation of interaction between transcription factors and their respective promoter *cis*-elements. It was observed that the promoters of ABA-dependent genes have ABA-responsive element (*ABRE*) and, dehydrationand cold-responsive element (*C-repeat*/*DRE*) [42]. The transcription factors, which specifically bind to *ABRE* are known as DREBs, trigger the expression of ABAresponsive genes [43], which further encode AP2 domain-containing transcription factors regulating the stress-related genes in an ABA-independent manner [44]. The *DREB* gene family has two groups *DREB1*/*CBF* and *DREB2*, whereas *DREB1*/*CBF* consists of *DREB1A* (CBF3), *DREB1B* (CBF1), *DREB1C* (CBF2), and *DREB1D* (CBF4). However, five *DREB* homologs were identified in rice, *OsDREB1A*, *OsDREB1B*, *OsDREB1C*, *OsDREB1D*, and *OsDREB2A* [45, 46]. These gene-encoded proteins are classified into two: the first group belongs to the functional proteins included chaperones, late embryogenesis abundant (LEA) proteins, osmotin, antifreeze proteins, mRNA-binding proteins, enzymes for osmolyte biosynthesis, water channel proteins, sugar and proline transporters, and detoxification enzymes; the second group is of regulatory proteins (signal transduction and stress-responsive) including various transcription factors, protein kinases, protein phosphatases, enzymes involved in phospholipid metabolism, and other signaling molecules such as calmodulin-binding protein [22, 41]. Interestingly, it was found that many of these proteins, especially *DREBs*, are also involved in transcriptional regulation of stress-response mechanism during cold and salt stresses [46, 47].

The rice is the only crop which is grown in the waterlogged fields and it has very low water-use efficiency [48]. Therefore, it is imperative to decipher the molecular regulatory mechanism to increase the water usage efficiency of rice or the drought tolerance. Nowadays, the drought stress is continuously affecting the rice productivity due to the harsh environmental condition. The transcriptome studies proved to be the boom for researchers due to its global genomes depth and all at once allele mining among different rice genotypes. Earlier, a transcriptome analysis between drought-tolerant and drought-sensitive cultivars was carried out for the identification of novel genetic regulatory mechanisms [48]. This study suggested that the upregulation of genes related to carbon fixation, glycolysis/gluconeogenesis, and flavonoid biosynthesis, whereas the downregulation of genes associated with starch and sucrose metabolism during drought. Further, they also found the upregulation

**67**

*OsNHX1* (Na+

Ca<sup>+</sup>

/H+

antiporter), *OsAKT1* (K<sup>+</sup>

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.)

of genes associated with α-linolenic acid metabolic pathway in tolerant genotype during the stress which supported the previous findings. Consecutively, the analysis of consensus *cis*-motif among the coexpressed drought-induced genes led to the identification of novel *cis*-motifs [48]. Similar comparative studies have been carried out between tolerant and susceptible rice cultivars and in other crops to understand the regulatory mechanisms during drought [49–51]. Their result suggested that 801 transcripts differentially expressed in tolerant cultivar including the TFs NAC and DBP, and thioredoxin involved in phenylpropanoid metabolism [49]. To sustain the drought condition, the roots have a very important role. To understand the molecular regulation in rice seedling roots (4-weeks old) during drought condition, comparative RNA-Seq analysis has been carried out between wet and dry soil conditions [52]. This analysis suggested that 68% of identified genes were novel, and also found that the one of the enzymes RING box E3 ligases from ubiquitin-proteasome pathway was induced by drought. Interestingly, it was found that the *OsPhyB* represses the activity of ascorbate peroxidase and catalase-mediating reactive oxygen species (ROS) processing machinery required for drought

tolerance of roots in soil condition, contrary to the previous results [52].

antiporters), *OsHKT2;1* (Na+

understand the molecular response to salinity stress [65].

rectifying channel), *OsTPC1* (Ca2+ permeable channel), *OsCLC1* (Cl<sup>−</sup> channel), *OsNRT1;2* (nitrate transporter), *OsCDPK7*, *OsMAPK5*, *CaMBP* (*calmodulin motif binding protein*), *GST* (*glutathione-S-transferase II*), *LEA* (*late embryogenesis abundant protein*), *V-ATPase* (*vacuolar ATP synthase 16KD proteolipid subunit*), *OSAP1* (zinc finger protein), and *HBP1B* (histone binding protein, TF) [55–63]. The salt stress response mechanism is moreover of complex physiological process pertaining to metabolic and morphological changes, which is comprehensively studied, but in rice, the molecular regulatory mechanism to salt tolerance is elusive [64]. Some of the transcriptome analyses have been completed in conjugation with the drought stress to understand the salt tolerance in rice [46, 49, 59]. Earlier, a comparative study has been carried out between salt tolerant and susceptible rice cultivars to understand the regulatory mechanisms [49]. The result suggested higher expression of bHLH and C2H2 TF family members, which might be regulating the genes associated with wax and terpenoid metabolism pathways [49]. Similarly, to understand the salinity stress, a comparative leaf transcriptome analysis at three time points on rice seedlings has been completed [65]. They identified 1375 novel genes, whereas 286 differentially expressed genes exclusively found in tolerant cultivar. They validated two genes: disease resistance response protein *206* and *TIFY10A* to

Some of the abiotic stresses are complementary to each other such as the drought and salt, drought and cold stresses, etc., affecting the rice productivity. It is evident that excessive loss of water from the soil evaporation due to drought causes salt accumulation in soil. The salinity is defined as deposition of sodium chloride from natural accumulation or irrigation in soil. It causes imbalance in ion homeostasis of cells regulated by ion influx and efflux at the plasma membrane and sequestration of vacuolar ion [8]. The salt stress affects stomatal closure causing increased leaf temperature and reserved shoot elongation [53]. Studies on the salinity tolerant in rice have shown the regulation of genes related to antioxidants, transcription factors, signaling, ion and metabolic homeostasis, and transporters (**Table 1**) [54]. The identified important class of genes regulated during a salt stress in rice are *OsSOS1*,

/K+

inward-rectifying channel), *OsKCO1* (K+

symporter), *OsCAX1* (H+

/

outward-

*DOI: http://dx.doi.org/10.5772/intechopen.84955*

**4. Transcriptome data for salt stress**

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.) *DOI: http://dx.doi.org/10.5772/intechopen.84955*

*Transcriptome Analysis*

indirectly through *SUB1A* and downstream regulatory network to resume the photosynthesis [29]. Similar studies have also been carried between two contrasting deepwater growth rice cultivars [40]. The RNA-Seq analysis was conducted from different tissues, shoot base region, including basal nodes, internodes, and shoot apices of seedlings at two developmental stages. The study elucidated the possible role of jasmonic acid-mediated internode elongation and expression of biotic stress-

One of the major abiotic stresses that severely affect the rice production is drought stress. Drought stress causes a series of physiological and biochemical changes which included stomatal closure, repression of cell growth, photosynthesis, and activation of respiration along with production of the phytohormone abscisic acid (ABA) [41]. In response to the drought stress, ABA triggers stomatal closure and induces expression of stress-related genes (**Table 1**) [41]. However, some of drought-related genes were not expressed by the external ABA treatment. Therefore, the drought response is either of ABA-independent or of ABA-dependent or both inducible gene regulatory system networks [42]. These regulatory networks are the amalgamation of interaction between transcription factors and their respective promoter *cis*-elements. It was observed that the promoters of ABA-dependent genes have ABA-responsive element (*ABRE*) and, dehydrationand cold-responsive element (*C-repeat*/*DRE*) [42]. The transcription factors, which specifically bind to *ABRE* are known as DREBs, trigger the expression of ABAresponsive genes [43], which further encode AP2 domain-containing transcription factors regulating the stress-related genes in an ABA-independent manner [44]. The *DREB* gene family has two groups *DREB1*/*CBF* and *DREB2*, whereas *DREB1*/*CBF* consists of *DREB1A* (CBF3), *DREB1B* (CBF1), *DREB1C* (CBF2), and *DREB1D* (CBF4). However, five *DREB* homologs were identified in rice, *OsDREB1A*,

*OsDREB1B*, *OsDREB1C*, *OsDREB1D*, and *OsDREB2A* [45, 46]. These gene-encoded proteins are classified into two: the first group belongs to the functional proteins included chaperones, late embryogenesis abundant (LEA) proteins, osmotin, antifreeze proteins, mRNA-binding proteins, enzymes for osmolyte biosynthesis, water channel proteins, sugar and proline transporters, and detoxification enzymes; the second group is of regulatory proteins (signal transduction and stress-responsive) including various transcription factors, protein kinases, protein phosphatases, enzymes involved in phospholipid metabolism, and other signaling molecules such as calmodulin-binding protein [22, 41]. Interestingly, it was found that many of these proteins, especially *DREBs*, are also involved in transcriptional regulation of

The rice is the only crop which is grown in the waterlogged fields and it has very low water-use efficiency [48]. Therefore, it is imperative to decipher the molecular regulatory mechanism to increase the water usage efficiency of rice or the drought tolerance. Nowadays, the drought stress is continuously affecting the rice productivity due to the harsh environmental condition. The transcriptome studies proved to be the boom for researchers due to its global genomes depth and all at once allele mining among different rice genotypes. Earlier, a transcriptome analysis between drought-tolerant and drought-sensitive cultivars was carried out for the identification of novel genetic regulatory mechanisms [48]. This study suggested that the upregulation of genes related to carbon fixation, glycolysis/gluconeogenesis, and flavonoid biosynthesis, whereas the downregulation of genes associated with starch and sucrose metabolism during drought. Further, they also found the upregulation

stress-response mechanism during cold and salt stresses [46, 47].

related genes during submergence response [40].

**3. Transcriptome data for drought stress**

**66**

of genes associated with α-linolenic acid metabolic pathway in tolerant genotype during the stress which supported the previous findings. Consecutively, the analysis of consensus *cis*-motif among the coexpressed drought-induced genes led to the identification of novel *cis*-motifs [48]. Similar comparative studies have been carried out between tolerant and susceptible rice cultivars and in other crops to understand the regulatory mechanisms during drought [49–51]. Their result suggested that 801 transcripts differentially expressed in tolerant cultivar including the TFs NAC and DBP, and thioredoxin involved in phenylpropanoid metabolism [49].

To sustain the drought condition, the roots have a very important role. To understand the molecular regulation in rice seedling roots (4-weeks old) during drought condition, comparative RNA-Seq analysis has been carried out between wet and dry soil conditions [52]. This analysis suggested that 68% of identified genes were novel, and also found that the one of the enzymes RING box E3 ligases from ubiquitin-proteasome pathway was induced by drought. Interestingly, it was found that the *OsPhyB* represses the activity of ascorbate peroxidase and catalase-mediating reactive oxygen species (ROS) processing machinery required for drought tolerance of roots in soil condition, contrary to the previous results [52].

#### **4. Transcriptome data for salt stress**

Some of the abiotic stresses are complementary to each other such as the drought and salt, drought and cold stresses, etc., affecting the rice productivity. It is evident that excessive loss of water from the soil evaporation due to drought causes salt accumulation in soil. The salinity is defined as deposition of sodium chloride from natural accumulation or irrigation in soil. It causes imbalance in ion homeostasis of cells regulated by ion influx and efflux at the plasma membrane and sequestration of vacuolar ion [8]. The salt stress affects stomatal closure causing increased leaf temperature and reserved shoot elongation [53]. Studies on the salinity tolerant in rice have shown the regulation of genes related to antioxidants, transcription factors, signaling, ion and metabolic homeostasis, and transporters (**Table 1**) [54]. The identified important class of genes regulated during a salt stress in rice are *OsSOS1*, *OsNHX1* (Na+ /H+ antiporters), *OsHKT2;1* (Na+ /K+ symporter), *OsCAX1* (H+ / Ca<sup>+</sup> antiporter), *OsAKT1* (K<sup>+</sup> inward-rectifying channel), *OsKCO1* (K+ outwardrectifying channel), *OsTPC1* (Ca2+ permeable channel), *OsCLC1* (Cl<sup>−</sup> channel), *OsNRT1;2* (nitrate transporter), *OsCDPK7*, *OsMAPK5*, *CaMBP* (*calmodulin motif binding protein*), *GST* (*glutathione-S-transferase II*), *LEA* (*late embryogenesis abundant protein*), *V-ATPase* (*vacuolar ATP synthase 16KD proteolipid subunit*), *OSAP1* (zinc finger protein), and *HBP1B* (histone binding protein, TF) [55–63]. The salt stress response mechanism is moreover of complex physiological process pertaining to metabolic and morphological changes, which is comprehensively studied, but in rice, the molecular regulatory mechanism to salt tolerance is elusive [64]. Some of the transcriptome analyses have been completed in conjugation with the drought stress to understand the salt tolerance in rice [46, 49, 59]. Earlier, a comparative study has been carried out between salt tolerant and susceptible rice cultivars to understand the regulatory mechanisms [49]. The result suggested higher expression of bHLH and C2H2 TF family members, which might be regulating the genes associated with wax and terpenoid metabolism pathways [49]. Similarly, to understand the salinity stress, a comparative leaf transcriptome analysis at three time points on rice seedlings has been completed [65]. They identified 1375 novel genes, whereas 286 differentially expressed genes exclusively found in tolerant cultivar. They validated two genes: disease resistance response protein *206* and *TIFY10A* to understand the molecular response to salinity stress [65].

#### **5. Transcriptome data for cold stress**

The cold stress is defined according to the temperature affecting the plant growth and development which ranges 0–15°C (chilling stress) and <0°C (freezing stress) [66]. The tropical origin of rice makes it more susceptible to cold, critically affecting reproductive stages and grain quality leading to yield reductions [67]. The cold stress affects chlorophyll content and fluorescence causing reduction in photosynthesis, increases content of reactive oxygen species (ROS) and malondialdehyde (MDA) causing oxidative damage to cells in rice [68]. The molecular regulation of cold stress is identified in conjugation of drought stress (**Table 1**) [45]. Many stress-inducible genes are regulated via ABA-independent pathway, characteristically having a *cis* element responsible for dehydration (*DRE*) as well as low-temperature-induced expression. The low-temperature-inducible genes possess C-repeat (*CRT*) and low-temperature-responsive element (*LTRE*). The DRE-binding proteins encoding genes *CBF1*, *DREB1A*, and *DREB1B* were induced by cold stress [46]. During cold stress, ABA also accumulates and initiates the ABA signaling cascade, which regulates the ABA-responsive genes through *ABRE* and the ABRE-binding bZIP transcription factor ABF [69]. The *OsNAC* gene transduces the ABA signal through an *ABRE* in its promoter and regulates the expression of NACRS-containing genes to control cold tolerance in rice [67]. Further, to understand comprehensively the regulation of genes during cold stress, a transcriptome study is carried out between weedy and cultivated rice [70]. The analysis suggested that some typical cold stress-related genes were of basic helix-loop-helix (bHLH) gene and leucine-rich repeat (LRR) domain genes, and several genes associated with phytohormones like abscisic acid (ABA), gibberellic acid (GA), auxin, and ethylene [70]. Similarly, the wild rice, *O. longistaminata*, tolerates nonfreezing cold temperatures, is used for the identification of molecular mechanisms in response to low temperature in its shoots and rhizomes at seedling and reproductive stages using transcriptome analysis [71]. They found photosynthesis pathway-related genes were prevalent in shoots, whereas metabolic pathways and the programmed cell death process-related genes were expressed only in rhizomes. Further, they found that the TFs *CBF*/*DREB1*, *AP2*/*EREBPs*, *MYBs*, and *WRKYs* were synergistically expressed in shoots, whereas *OsERF922*, *OsNAC9*, *OsWRKY25*, *OsWRKY74*, and eight antioxidant enzymes encoding genes were expressed in rhizomes during cold stress. The *cis*-regulatory element analysis suggested the enrichment of ICE1 binding site, GATA element, and W-box in both tissues. And the highly expressed genes in shoots were associated with photosynthesis, whereas signal transductionrelated genes were highly expressed in rhizomes [71].

Furthermore, a transcriptome analysis is performed in germination phase for contrasting cultivars of rice in cold stress [72], suggesting the higher expression of gene related to signal transduction, phytohormones, antioxidant system, and biotic stress during germination in cold stress [72].

#### **6. Transcriptome data for trace element stress**

The rice is the staple food fulfilling the dietary needs of a large population around the world. Besides dietary energy and proteins, it also contains trace elements (Li, B, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr., Mo, Cd, Ba, Pb, and Bi) in low amounts [73]. Some of these trace elements Se, Mo, Cr, Mn, Fe, Co, Cu, Zn are micronutrients that help in proper functioning of human biological systems, while nonessential heavy elements such as Pb, As, Cd, Hg are referred as toxins for consumption [73, 74]. However, the trace elements in rice are invariably increasing

**69**

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.)

stresses on rice achieved through transcriptome studies (**Table 1**).

either due to the use of agrochemicals or irrigation with contaminated water. The deficiency or accumulation of these trace elements in soil hampers plant growth and development. On the other hand, their biofortification helps to add nutrition supplement. Henceforth, the detailed study about the effects of these trace elements on the rice is indispensable. There are many reports about trace element

The higher concentration of heavy metal cadmium (Cd) severely hampers the rice growth. Therefore, to understand the molecular mechanism during Cd stress, transcriptome analysis has been completed by exposing rice to higher concentrations of Cd [75]. They found constitutively expressed genes were less affected by low Cd concentrations, whereas high Cd concentration causes fatal damage to rice seedlings during their development. They also found some novel Cd-responsive transporters encoding genes [75]. Previously, they found the upregulation of many genes related to ROS-scavenging enzymes, chelators, and metal transporters during Cd exposure along with upregulation of many drought stress-related genes [76]. Phosphorus (P) is an essential trace element required for proper plant growth and development where it plays an important role in energy transfer, signal transduction, photosynthesis, and respiration [77]. A comparative transcriptome study has been carried out in leaf and root tissues during phosphorus stress to elucidate their molecular mechanisms [78]. The transcriptome analysis suggested that many differentially expressed TFs and functional genes were uniquely involved in multiple regulatory pathways (including RNA transport and mRNA monitoring path)

Manganese (Mn) is an essential trace element which plays an important role in catalyzing the water-splitting reaction of oxygen-evolving complex in photosystem II (PSII). It also acts as a cofactor that activates different enzymes, such as Mn-superoxide dismutase and others, to protect against oxidative stresses in plants [79]. However, higher Mn affects the physiological and biochemical pathways associated with plant growth and development. Therefore, to decipher the molecular mechanisms in leaves of Mn-sensitive rice exposed to high Mn stress, transcriptome analysis has been done [79]. The analysis suggested that a large number of TFs, transporters, transferase proteins, catalytic proteins encoding genes were differentially expressed having a major role in primary and secondary metabolisms. Further, it was found that the *WRKY* family and potassium transporter-related genes were significantly upregulated, whereas *Aux/IAA* family and sodium trans-

Besides common abiotic stresses, some other stresses are also studied with the help of transcriptome analysis. A transcriptome study has been carried out for alkaline stress caused by alkaline NaHCO3 and Na2CO [80]. The study reported the identification of 926 differentially expressed important alkali-responsive genes including 28 alkaline-resistant genes and 74 transcription factor genes. These genes were related to hormone signal transduction and secondary metabolite biosynthesis pathways [80]. The RNA-Seq or transcriptome analysis has tremendous potential to divulge the complex molecular machinery of plant regulatory response during stress conditions. However, this large number of transcriptome data of abiotic stresses in rice has contributed significantly to rice researchers. It helped to understand complete molecular mechanism pertaining to their physiological and biochemical changes. Such data mining could be a high impact methodical source for identification of candidate gene through integration of functional genomics approach. This will also

*DOI: http://dx.doi.org/10.5772/intechopen.84955*

during phosphorus deficiency tolerance [78].

porter-related genes were strongly downregulated [79].

**7. Transcriptome data for other stresses**

#### *Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.) *DOI: http://dx.doi.org/10.5772/intechopen.84955*

*Transcriptome Analysis*

**5. Transcriptome data for cold stress**

related genes were highly expressed in rhizomes [71].

**6. Transcriptome data for trace element stress**

stress during germination in cold stress [72].

Furthermore, a transcriptome analysis is performed in germination phase for contrasting cultivars of rice in cold stress [72], suggesting the higher expression of gene related to signal transduction, phytohormones, antioxidant system, and biotic

The rice is the staple food fulfilling the dietary needs of a large population around the world. Besides dietary energy and proteins, it also contains trace elements (Li, B, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr., Mo, Cd, Ba, Pb, and Bi) in low amounts [73]. Some of these trace elements Se, Mo, Cr, Mn, Fe, Co, Cu, Zn are micronutrients that help in proper functioning of human biological systems, while nonessential heavy elements such as Pb, As, Cd, Hg are referred as toxins for consumption [73, 74]. However, the trace elements in rice are invariably increasing

The cold stress is defined according to the temperature affecting the plant growth and development which ranges 0–15°C (chilling stress) and <0°C (freezing stress) [66]. The tropical origin of rice makes it more susceptible to cold, critically affecting reproductive stages and grain quality leading to yield reductions [67]. The cold stress affects chlorophyll content and fluorescence causing reduction in photosynthesis, increases content of reactive oxygen species (ROS) and malondialdehyde (MDA) causing oxidative damage to cells in rice [68]. The molecular regulation of cold stress is identified in conjugation of drought stress (**Table 1**) [45]. Many stress-inducible genes are regulated via ABA-independent pathway, characteristically having a *cis* element responsible for dehydration (*DRE*) as well as low-temperature-induced expression. The low-temperature-inducible genes possess C-repeat (*CRT*) and low-temperature-responsive element (*LTRE*). The DRE-binding proteins encoding genes *CBF1*, *DREB1A*, and *DREB1B* were induced by cold stress [46]. During cold stress, ABA also accumulates and initiates the ABA signaling cascade, which regulates the ABA-responsive genes through *ABRE* and the ABRE-binding bZIP transcription factor ABF [69]. The *OsNAC* gene transduces the ABA signal through an *ABRE* in its promoter and regulates the expression of NACRS-containing genes to control cold tolerance in rice [67]. Further, to understand comprehensively the regulation of genes during cold stress, a transcriptome study is carried out between weedy and cultivated rice [70]. The analysis suggested that some typical cold stress-related genes were of basic helix-loop-helix (bHLH) gene and leucine-rich repeat (LRR) domain genes, and several genes associated with phytohormones like abscisic acid (ABA), gibberellic acid (GA), auxin, and ethylene [70]. Similarly, the wild rice, *O. longistaminata*, tolerates nonfreezing cold temperatures, is used for the identification of molecular mechanisms in response to low temperature in its shoots and rhizomes at seedling and reproductive stages using transcriptome analysis [71]. They found photosynthesis pathway-related genes were prevalent in shoots, whereas metabolic pathways and the programmed cell death process-related genes were expressed only in rhizomes. Further, they found that the TFs *CBF*/*DREB1*, *AP2*/*EREBPs*, *MYBs*, and *WRKYs* were synergistically expressed in shoots, whereas *OsERF922*, *OsNAC9*, *OsWRKY25*, *OsWRKY74*, and eight antioxidant enzymes encoding genes were expressed in rhizomes during cold stress. The *cis*-regulatory element analysis suggested the enrichment of ICE1 binding site, GATA element, and W-box in both tissues. And the highly expressed genes in shoots were associated with photosynthesis, whereas signal transduction-

**68**

either due to the use of agrochemicals or irrigation with contaminated water. The deficiency or accumulation of these trace elements in soil hampers plant growth and development. On the other hand, their biofortification helps to add nutrition supplement. Henceforth, the detailed study about the effects of these trace elements on the rice is indispensable. There are many reports about trace element stresses on rice achieved through transcriptome studies (**Table 1**).

The higher concentration of heavy metal cadmium (Cd) severely hampers the rice growth. Therefore, to understand the molecular mechanism during Cd stress, transcriptome analysis has been completed by exposing rice to higher concentrations of Cd [75]. They found constitutively expressed genes were less affected by low Cd concentrations, whereas high Cd concentration causes fatal damage to rice seedlings during their development. They also found some novel Cd-responsive transporters encoding genes [75]. Previously, they found the upregulation of many genes related to ROS-scavenging enzymes, chelators, and metal transporters during Cd exposure along with upregulation of many drought stress-related genes [76].

Phosphorus (P) is an essential trace element required for proper plant growth and development where it plays an important role in energy transfer, signal transduction, photosynthesis, and respiration [77]. A comparative transcriptome study has been carried out in leaf and root tissues during phosphorus stress to elucidate their molecular mechanisms [78]. The transcriptome analysis suggested that many differentially expressed TFs and functional genes were uniquely involved in multiple regulatory pathways (including RNA transport and mRNA monitoring path) during phosphorus deficiency tolerance [78].

Manganese (Mn) is an essential trace element which plays an important role in catalyzing the water-splitting reaction of oxygen-evolving complex in photosystem II (PSII). It also acts as a cofactor that activates different enzymes, such as Mn-superoxide dismutase and others, to protect against oxidative stresses in plants [79]. However, higher Mn affects the physiological and biochemical pathways associated with plant growth and development. Therefore, to decipher the molecular mechanisms in leaves of Mn-sensitive rice exposed to high Mn stress, transcriptome analysis has been done [79]. The analysis suggested that a large number of TFs, transporters, transferase proteins, catalytic proteins encoding genes were differentially expressed having a major role in primary and secondary metabolisms. Further, it was found that the *WRKY* family and potassium transporter-related genes were significantly upregulated, whereas *Aux/IAA* family and sodium transporter-related genes were strongly downregulated [79].

#### **7. Transcriptome data for other stresses**

Besides common abiotic stresses, some other stresses are also studied with the help of transcriptome analysis. A transcriptome study has been carried out for alkaline stress caused by alkaline NaHCO3 and Na2CO [80]. The study reported the identification of 926 differentially expressed important alkali-responsive genes including 28 alkaline-resistant genes and 74 transcription factor genes. These genes were related to hormone signal transduction and secondary metabolite biosynthesis pathways [80].

The RNA-Seq or transcriptome analysis has tremendous potential to divulge the complex molecular machinery of plant regulatory response during stress conditions. However, this large number of transcriptome data of abiotic stresses in rice has contributed significantly to rice researchers. It helped to understand complete molecular mechanism pertaining to their physiological and biochemical changes. Such data mining could be a high impact methodical source for identification of candidate gene through integration of functional genomics approach. This will also help to establish the hierarchical relationships between specific signaling components and downstream effector genes to cope up the stress conditions.

### **Acknowledgements**

PKD acknowledges ICAR-NASF and ICAR-NPTC for funding and support of research work at NRC on Plant Biotechnology.

### **Author details**

Ashutosh Kumar\* and Prasanta K. Dash ICAR-National Institute for Plant Biotechnology, PUSA, New Delhi, India

\*Address all correspondence to: kr.ashutosh@yahoo.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**71**

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.)

Combining mapping of physiological quantitative trait loci and transcriptome for cold tolerance for counteracting male sterility induced by low temperatures during reproductive stage in rice. Physiologia Plantarum.

[10] Liu CT, Wang W, Mao BG, Chu CC. Cold stress tolerance in rice: Physiological changes, molecular mechanism, and future prospects. Yi chuan = Hereditas. 2018;**40**(3):171-185

[11] Lafitte HR, Ismail A, Bennett J. Abiotic stress tolerance in rice for Asia: Progress and the future. New directions for a diverse planet. In: 4th International Crop Science Congress; Brisbane,

[12] Bailey-Serres J, Voesenek LA. Flooding stress: Acclimations and genetic diversity. Annual Review of Plant Biology. 2008;**59**:313-339

[13] Hattori Y, Nagai K, Furukawa S, Song XJ, Kawano R, Sakakibara H, et al. The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water. Nature.

[14] Agarwal S, Grover A. Isolation and transcription profiling of low-O2 stress-associated cDNA clones from the flooding-stress-tolerant FR13A rice genotype. Annals of Botany.

[15] Xiang Y, Huang Y, Xiong L. Characterization of stress-responsive CIPK genes in rice for stress tolerance improvement. Plant Physiology.

[16] Vij S, Tyagi AK. Genome-wide analysis of the stress associated protein (SAP) gene family containing A20/AN1 zinc-finger(s) in rice and their phylogenetic relationship with

2009;**460**(7258):1026-1030

2005;**96**(5):831-844

2007;**144**(3):1416-1428

2016;**157**(2):175-192

Australia. 2004

*DOI: http://dx.doi.org/10.5772/intechopen.84955*

[1] Cantrell RP, Reeves TG. The rice genome. The cereal of the world's poor takes center stage. Science.

[2] Khush GS. Origin, dispersal, cultivation and variation of rice. Plant Molecular Biology. 1997;**35**(1-2):25-34

[3] Gao J, Chao D, Lin H. Toward understanding molecular mechanisms of abiotic stress responses in rice. Rice.

[4] Rosell CM, Marco C. Rice. In: Gluten-Free Cereal Products and Beverages. Amsterdam, Netherlands: Academic

[5] Wassmann R, Jagadish SVK, Heuer S, Ismail A, Redona E, Serraj R, et al. Production: The physiological and agronomic basis for possible adaptation

strategies. In: Sparks DL, editor. Advances in Agronomy. Burlington:

Bagavathiannan MV, Senthil-Kumar M. Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Frontiers in Plant Science. 2017;**8**:537

[7] Alqudah AM, Samarah NH, Mullen RE. Drought stress effect on crop

pollination, seed set, yield and quality. In: Lichtfouse E, editor. Alternative Farming Systems, Biotechnology, Drought Stress and Ecological Fertilisation. Dordrecht:

[8] Hasegawa PM, Bressan RA, Zhu JK, Bohnert HJ. Plant cellular and molecular responses to high salinity. Annual Review of Plant Physiology and Plant Molecular Biology. 2000;**51**:463-499

[9] Shimono H, Abe A, Aoki N, Koumoto T, Sato M, Yokoi S, et al.

Academic Press; 2009. p. 63

[6] Pandey P, Irulappan V,

Springer; 2011. p. 20

**References**

2008;**1**:15

Press; 2008. p. 20

2002;**296**(5565):53

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.) *DOI: http://dx.doi.org/10.5772/intechopen.84955*

#### **References**

*Transcriptome Analysis*

**Acknowledgements**

research work at NRC on Plant Biotechnology.

**70**

**Author details**

provided the original work is properly cited.

Ashutosh Kumar\* and Prasanta K. Dash

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

ICAR-National Institute for Plant Biotechnology, PUSA, New Delhi, India

help to establish the hierarchical relationships between specific signaling compo-

PKD acknowledges ICAR-NASF and ICAR-NPTC for funding and support of

nents and downstream effector genes to cope up the stress conditions.

\*Address all correspondence to: kr.ashutosh@yahoo.com

[1] Cantrell RP, Reeves TG. The rice genome. The cereal of the world's poor takes center stage. Science. 2002;**296**(5565):53

[2] Khush GS. Origin, dispersal, cultivation and variation of rice. Plant Molecular Biology. 1997;**35**(1-2):25-34

[3] Gao J, Chao D, Lin H. Toward understanding molecular mechanisms of abiotic stress responses in rice. Rice. 2008;**1**:15

[4] Rosell CM, Marco C. Rice. In: Gluten-Free Cereal Products and Beverages. Amsterdam, Netherlands: Academic Press; 2008. p. 20

[5] Wassmann R, Jagadish SVK, Heuer S, Ismail A, Redona E, Serraj R, et al. Production: The physiological and agronomic basis for possible adaptation strategies. In: Sparks DL, editor. Advances in Agronomy. Burlington: Academic Press; 2009. p. 63

[6] Pandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M. Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Frontiers in Plant Science. 2017;**8**:537

[7] Alqudah AM, Samarah NH, Mullen RE. Drought stress effect on crop pollination, seed set, yield and quality. In: Lichtfouse E, editor. Alternative Farming Systems, Biotechnology, Drought Stress and Ecological Fertilisation. Dordrecht: Springer; 2011. p. 20

[8] Hasegawa PM, Bressan RA, Zhu JK, Bohnert HJ. Plant cellular and molecular responses to high salinity. Annual Review of Plant Physiology and Plant Molecular Biology. 2000;**51**:463-499

[9] Shimono H, Abe A, Aoki N, Koumoto T, Sato M, Yokoi S, et al. Combining mapping of physiological quantitative trait loci and transcriptome for cold tolerance for counteracting male sterility induced by low temperatures during reproductive stage in rice. Physiologia Plantarum. 2016;**157**(2):175-192

[10] Liu CT, Wang W, Mao BG, Chu CC. Cold stress tolerance in rice: Physiological changes, molecular mechanism, and future prospects. Yi chuan = Hereditas. 2018;**40**(3):171-185

[11] Lafitte HR, Ismail A, Bennett J. Abiotic stress tolerance in rice for Asia: Progress and the future. New directions for a diverse planet. In: 4th International Crop Science Congress; Brisbane, Australia. 2004

[12] Bailey-Serres J, Voesenek LA. Flooding stress: Acclimations and genetic diversity. Annual Review of Plant Biology. 2008;**59**:313-339

[13] Hattori Y, Nagai K, Furukawa S, Song XJ, Kawano R, Sakakibara H, et al. The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water. Nature. 2009;**460**(7258):1026-1030

[14] Agarwal S, Grover A. Isolation and transcription profiling of low-O2 stress-associated cDNA clones from the flooding-stress-tolerant FR13A rice genotype. Annals of Botany. 2005;**96**(5):831-844

[15] Xiang Y, Huang Y, Xiong L. Characterization of stress-responsive CIPK genes in rice for stress tolerance improvement. Plant Physiology. 2007;**144**(3):1416-1428

[16] Vij S, Tyagi AK. Genome-wide analysis of the stress associated protein (SAP) gene family containing A20/AN1 zinc-finger(s) in rice and their phylogenetic relationship with

*Arabidopsis*. Molecular Genetics and Genomics. 2006;**276**(6):565-575

[17] Lee Y, Kende H. Expression of alpha-expansin and expansin-like genes in Deepwater rice. Plant Physiology. 2002;**130**(3):1396-1405

[18] Dash PK, Rai R, Rai V, Pasupalak S. Drought induced signaling in rice: Delineating canonical and noncanonical pathways. Frontiers in Chemistry. 2018;**6**:264

[19] Shivaraj SM, Deshmukh RK, Rai R, Belanger R, Agrawal PK, Dash PK. Genome-wide identification, characterization, and expression profile of aquaporin gene family in flax (*Linum usitatissimum*). Scientific Reports. 2017;**7**:46137

[20] Lasanthi-Kudahettige R, Magneschi L, Loreti E, Gonzali S, Licausi F, Novi G, et al. Transcript profiling of the anoxic rice coleoptile. Plant Physiology. 2007;**144**(1):218-231

[21] Lee WP, Tzou WS. Computational methods for discovering gene networks from expression data. Briefings in Bioinformatics. 2009;**10**(4):408-423

[22] Rabbani MA, Maruyama K, Abe H, Khan MA, Katsura K, Ito Y, et al. Monitoring expression profiles of rice genes under cold, drought, and high-salinity stresses and abscisic acid application using cDNA microarray and RNA gel-blot analyses. Plant Physiology. 2003;**133**(4):1755-1767

[23] Dash PK, Cao Y, Jailani AK, Gupta P, Venglat P, Xiang D, et al. Genomewide analysis of drought induced gene expression changes in flax (*Linum usitatissimum*). GM Crops & Food. 2014;**5**(2):106-119

[24] Grennan AK. Abiotic stress in rice. An "omic" approach. Plant Physiology. 2006;**140**(4):1139-1141

[25] Sana TR, Fischer S, Wohlgemuth G, Katrekar A, Jung KH, Ronald PC, et al. Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen *Xanthomonas oryzae* pv. oryzae. Metabolomics: Official Journal of the Metabolomic Society. 2010;**6**(3):451-465

[26] Wang Z, Gerstein M, Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics. 2009;**10**(1):57-63

[27] Kukurba KR, Montgomery SB. RNA sequencing and analysis. Cold Spring Harbor Protocols. 2015;**2015**(11):951-969

[28] Dash PK, Rai R, Mahato AK, Gaikwad K, Singh NK. Transcriptome landscape at different developmental stages of a drought tolerant cultivar of flax (*Linum usitatissimum*). Frontiers in Chemistry. 2017;**5**:82

[29] Locke AM, Barding GA Jr, Sathnur S, Larive CK, Bailey-Serres J. Rice SUB1A constrains remodelling of the transcriptome and metabolome during submergence to facilitate postsubmergence recovery. Plant, Cell & Environment. 2018;**41**(4):721-736

[30] Mohanty B, Krishnan SP, Swarup S, Bajic VB. Detection and preliminary analysis of motifs in promoters of anaerobically induced genes of different plant species. Annals of Botany. 2005;**96**(4):669-681

[31] Voesenek LA, Colmer TD, Pierik R, Millenaar FF, Peeters AJ. How plants cope with complete submergence. The New Phytologist. 2006;**170**(2):213-226

[32] Xiang J, Wu H, Zhang Y, Zhang Y, Wang Y, Li Z, et al. Transcriptomic analysis of gibberellin- and paclobutrazol-treated rice seedlings under submergence. International Journal of Molecular Sciences. 2017;**18**(10):1-16

**73**

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.)

stress response and tolerance. Journal of Experimental Botany.

Science. 2005;**10**(2):88-94

[43] Stockinger EJ, Gilmour SJ, Thomashow MF. *Arabidopsis thaliana* CBF1 encodes an AP2 domaincontaining transcriptional activator that binds to the C-repeat/DRE, a cis-acting DNA regulatory element that stimulates transcription in response to low temperature and water deficit. Proceedings of the National Academy of Sciences of the United States of America. 1997;**94**(3):1035-1040

[42] Yamaguchi-Shinozaki K, Shinozaki K. Organization of cis-acting regulatory elements in osmotic- and cold-stressresponsive promoters. Trends in Plant

[44] Shinozaki K, Yamaguchi-Shinozaki K. Molecular responses to dehydration and low temperature: Differences and cross-talk between two stress signaling pathways. Current Opinion in Plant

Biology. 2000;**3**(3):217-223

2002;**290**(3):998-1009

[45] Sakuma Y, Liu Q, Dubouzet JG, Abe H, Shinozaki K, Yamaguchi-Shinozaki K. DNA-binding specificity of the ERF/AP2 domain of *Arabidopsis* DREBs, transcription factors involved in dehydration- and cold-inducible gene expression. Biochemical and Biophysical Research Communications.

[46] Dubouzet JG, Sakuma Y, Ito Y, Kasuga M, Dubouzet EG, Miura S, et al. OsDREB genes in rice, *Oryza sativa* L., encode transcription activators that function in drought-, high-salt- and cold-responsive gene expression. The Plant Journal: For Cell and Molecular

Biology. 2003;**33**(4):751-763

2008;**30**(12):2191-2198

[47] Chen JQ, Meng XP, Zhang Y, Xia M, Wang XP. Over-expression of OsDREB genes lead to enhanced drought

tolerance in rice. Biotechnology Letters.

2007;**58**(2):221-227

*DOI: http://dx.doi.org/10.5772/intechopen.84955*

[33] Branco-Price C, Kawaguchi R, Ferreira RB, Bailey-Serres J. Genomewide analysis of transcript abundance and translation in *Arabidopsis* seedlings subjected to oxygen deprivation. Annals

of Botany. 2005;**96**(4):647-660

[34] Gonzali S, Loreti E, Novi G, Poggi A, Alpi A, Perata P. The use of microarrays to study the anaerobic response in *Arabidopsis*. Annals of Botany. 2005;**96**(4):661-668

[35] Klok EJ, Wilson IW, Wilson D, Chapman SC, Ewing RM, Somerville SC, et al. Expression profile analysis of the low-oxygen response in

[36] Xu K, Xu X, Fukao T, Canlas P, Maghirang-Rodriguez R, Heuer S, et al. Sub1A is an ethylene-

[37] Voesenek LA, Bailey-Serres J. Flood adaptive traits and processes: An overview. The New Phytologist.

response-factor-like gene that confers submergence tolerance to rice. Nature.

[38] Bailey-Serres J, Voesenek LA. Life in the balance: A signaling network controlling survival of flooding. Current Opinion in Plant Biology.

[39] Hsu SK, Tung CW. RNA-Seq analysis of diverse Rice genotypes to identify the genes controlling coleoptile growth during submerged germination. Frontiers in Plant Science. 2017;**8**:762

[40] Minami A, Yano K, Gamuyao R, Nagai K, Kuroha T, Ayano M, et al. Time-course transcriptomics analysis reveals key responses of submerged deepwater rice to flooding. Plant Physiology. 2018;**176**(4):3081-3102

[41] Shinozaki K, Yamaguchi-Shinozaki K. Gene networks involved in drought

2002;**14**(10):2481-2494

2006;**442**(7103):705-708

2015;**206**(1):57-73

2010;**13**(5):489-494

*Arabidopsis* root cultures. The Plant Cell.

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.) *DOI: http://dx.doi.org/10.5772/intechopen.84955*

[33] Branco-Price C, Kawaguchi R, Ferreira RB, Bailey-Serres J. Genomewide analysis of transcript abundance and translation in *Arabidopsis* seedlings subjected to oxygen deprivation. Annals of Botany. 2005;**96**(4):647-660

*Transcriptome Analysis*

2002;**130**(3):1396-1405

Chemistry. 2018;**6**:264

2017;**7**:46137

2007;**144**(1):218-231

2003;**133**(4):1755-1767

2014;**5**(2):106-119

2006;**140**(4):1139-1141

*Arabidopsis*. Molecular Genetics and Genomics. 2006;**276**(6):565-575

[25] Sana TR, Fischer S, Wohlgemuth G, Katrekar A, Jung KH, Ronald PC, et al. Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen *Xanthomonas oryzae* pv. oryzae. Metabolomics: Official Journal of the Metabolomic

Society. 2010;**6**(3):451-465

Genetics. 2009;**10**(1):57-63

2015;**2015**(11):951-969

Chemistry. 2017;**5**:82

[27] Kukurba KR, Montgomery SB. RNA sequencing and analysis. Cold Spring Harbor Protocols.

[28] Dash PK, Rai R, Mahato AK, Gaikwad K, Singh NK. Transcriptome landscape at different developmental stages of a drought tolerant cultivar of flax (*Linum usitatissimum*). Frontiers in

[29] Locke AM, Barding GA Jr, Sathnur S, Larive CK, Bailey-Serres J. Rice SUB1A constrains remodelling of the transcriptome and metabolome during submergence to facilitate postsubmergence recovery. Plant, Cell & Environment. 2018;**41**(4):721-736

[30] Mohanty B, Krishnan SP, Swarup S, Bajic VB. Detection and preliminary analysis of motifs in promoters of anaerobically induced genes of different

plant species. Annals of Botany.

[31] Voesenek LA, Colmer TD, Pierik R, Millenaar FF, Peeters AJ. How plants cope with complete submergence. The New Phytologist. 2006;**170**(2):213-226

[32] Xiang J, Wu H, Zhang Y, Zhang Y, Wang Y, Li Z, et al. Transcriptomic

paclobutrazol-treated rice seedlings under submergence. International Journal of Molecular Sciences.

analysis of gibberellin- and

2017;**18**(10):1-16

2005;**96**(4):669-681

[26] Wang Z, Gerstein M, Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews

[17] Lee Y, Kende H. Expression of alpha-expansin and expansin-like genes in Deepwater rice. Plant Physiology.

[18] Dash PK, Rai R, Rai V, Pasupalak S. Drought induced signaling in rice: Delineating canonical and noncanonical pathways. Frontiers in

[19] Shivaraj SM, Deshmukh RK, Rai R, Belanger R, Agrawal PK, Dash PK. Genome-wide identification, characterization, and expression profile of aquaporin gene family in flax (*Linum usitatissimum*). Scientific Reports.

[20] Lasanthi-Kudahettige R, Magneschi L, Loreti E, Gonzali S, Licausi F, Novi G, et al. Transcript profiling of the anoxic rice coleoptile. Plant Physiology.

[21] Lee WP, Tzou WS. Computational methods for discovering gene networks from expression data. Briefings in Bioinformatics. 2009;**10**(4):408-423

[22] Rabbani MA, Maruyama K, Abe H, Khan MA, Katsura K, Ito Y, et al. Monitoring expression profiles of rice genes under cold, drought, and high-salinity stresses and abscisic acid application using cDNA microarray and RNA gel-blot analyses. Plant Physiology.

[23] Dash PK, Cao Y, Jailani AK, Gupta P, Venglat P, Xiang D, et al. Genomewide analysis of drought induced gene expression changes in flax (*Linum usitatissimum*). GM Crops & Food.

[24] Grennan AK. Abiotic stress in rice. An "omic" approach. Plant Physiology.

**72**

[34] Gonzali S, Loreti E, Novi G, Poggi A, Alpi A, Perata P. The use of microarrays to study the anaerobic response in *Arabidopsis*. Annals of Botany. 2005;**96**(4):661-668

[35] Klok EJ, Wilson IW, Wilson D, Chapman SC, Ewing RM, Somerville SC, et al. Expression profile analysis of the low-oxygen response in *Arabidopsis* root cultures. The Plant Cell. 2002;**14**(10):2481-2494

[36] Xu K, Xu X, Fukao T, Canlas P, Maghirang-Rodriguez R, Heuer S, et al. Sub1A is an ethyleneresponse-factor-like gene that confers submergence tolerance to rice. Nature. 2006;**442**(7103):705-708

[37] Voesenek LA, Bailey-Serres J. Flood adaptive traits and processes: An overview. The New Phytologist. 2015;**206**(1):57-73

[38] Bailey-Serres J, Voesenek LA. Life in the balance: A signaling network controlling survival of flooding. Current Opinion in Plant Biology. 2010;**13**(5):489-494

[39] Hsu SK, Tung CW. RNA-Seq analysis of diverse Rice genotypes to identify the genes controlling coleoptile growth during submerged germination. Frontiers in Plant Science. 2017;**8**:762

[40] Minami A, Yano K, Gamuyao R, Nagai K, Kuroha T, Ayano M, et al. Time-course transcriptomics analysis reveals key responses of submerged deepwater rice to flooding. Plant Physiology. 2018;**176**(4):3081-3102

[41] Shinozaki K, Yamaguchi-Shinozaki K. Gene networks involved in drought

stress response and tolerance. Journal of Experimental Botany. 2007;**58**(2):221-227

[42] Yamaguchi-Shinozaki K, Shinozaki K. Organization of cis-acting regulatory elements in osmotic- and cold-stressresponsive promoters. Trends in Plant Science. 2005;**10**(2):88-94

[43] Stockinger EJ, Gilmour SJ, Thomashow MF. *Arabidopsis thaliana* CBF1 encodes an AP2 domaincontaining transcriptional activator that binds to the C-repeat/DRE, a cis-acting DNA regulatory element that stimulates transcription in response to low temperature and water deficit. Proceedings of the National Academy of Sciences of the United States of America. 1997;**94**(3):1035-1040

[44] Shinozaki K, Yamaguchi-Shinozaki K. Molecular responses to dehydration and low temperature: Differences and cross-talk between two stress signaling pathways. Current Opinion in Plant Biology. 2000;**3**(3):217-223

[45] Sakuma Y, Liu Q, Dubouzet JG, Abe H, Shinozaki K, Yamaguchi-Shinozaki K. DNA-binding specificity of the ERF/AP2 domain of *Arabidopsis* DREBs, transcription factors involved in dehydration- and cold-inducible gene expression. Biochemical and Biophysical Research Communications. 2002;**290**(3):998-1009

[46] Dubouzet JG, Sakuma Y, Ito Y, Kasuga M, Dubouzet EG, Miura S, et al. OsDREB genes in rice, *Oryza sativa* L., encode transcription activators that function in drought-, high-salt- and cold-responsive gene expression. The Plant Journal: For Cell and Molecular Biology. 2003;**33**(4):751-763

[47] Chen JQ, Meng XP, Zhang Y, Xia M, Wang XP. Over-expression of OsDREB genes lead to enhanced drought tolerance in rice. Biotechnology Letters. 2008;**30**(12):2191-2198

[48] Lenka SK, Katiyar A, Chinnusamy V, Bansal KC. Comparative analysis of drought-responsive transcriptome in Indica rice genotypes with contrasting drought tolerance. Plant Biotechnology Journal. 2011;**9**(3):315-327

[49] Shankar R, Bhattacharjee A, Jain M. Transcriptome analysis in different rice cultivars provides novel insights into desiccation and salinity stress responses. Scientific Reports. 2016;**6**:23719

[50] Gupta P, Dash PK. Molecular details of secretory phospholipase A2 from flax (*Linum usitatissimum* L.) provide insight into its structure and function. Scientific Reports. 2017;**7**(1):11080

[51] Gupta P, Saini R, Dash PK. Origin and evolution of group XI secretory phospholipase A2 from flax (*Linum usitatissimum*) based on phylogenetic analysis of conserved domains. 3 Biotech. 2017;**7**(3):216

[52] Yoo YH, Nalini Chandran AK, Park JC, Gho YS, Lee SW, An G, et al. OsPhyB-mediating novel regulatory pathway for drought tolerance in Rice root identified by a global RNA-Seq transcriptome analysis of rice genes in response to water deficiencies. Frontiers in Plant Science. 2017;**8**:580

[53] Rajendran K, Tester M, Roy SJ. Quantifying the three main components of salinity tolerance in cereals. Plant, Cell & Environment. 2009;**32**(3):237-249

[54] Das P, Nutan KK, Singla-Pareek SL, Pareek A. Understanding salinity responses and adopting 'omics-based' approaches to generate salinity tolerant cultivars of rice. Frontiers in Plant Science. 2015;**6**:712

[55] Kumar K, Kumar M, Kim SR, Ryu H, Cho YG. Insights into genomics of salt stress response in rice. Rice (NY). 2013;**6**(1):27

[56] Mishra S, Singh B, Panda K, Singh BP, Singh N, Misra P, et al. Association of SNP haplotypes of HKT family genes with salt tolerance in Indian wild Rice germplasm. Rice (NY). 2016;**9**(1):15

[57] Yang T, Zhang S, Hu Y, Wu F, Hu Q, Chen G, et al. The role of a potassium transporter OsHAK5 in potassium acquisition and transport from roots to shoots in rice at low potassium supply levels. Plant Physiology. 2014;**166**(2):945-959

[58] Kurusu T, Hamada H, Koyano T, Kuchitsu K. Intracellular localization and physiological function of a rice Ca(2)(+)-permeable channel OsTPC1. Plant Signaling & Behavior. 2012;**7**(11):1428-1430

[59] Golldack D, Quigley F, Michalowski CB, Kamasani UR, Bohnert HJ. Salinity stress-tolerant and -sensitive rice (*Oryza sativa* L.) regulate AKT1-type potassium channel transcripts differently. Plant Molecular Biology. 2003;**51**(1):71-81

[60] Wang H, Zhang M, Guo R, Shi D, Liu B, Lin X, et al. Effects of salt stress on ion balance and nitrogen metabolism of old and young leaves in rice (*Oryza sativa* L.). BMC Plant Biology. 2012;**12**:194

[61] Xiong L, Yang Y. Disease resistance and abiotic stress tolerance in rice are inversely modulated by an abscisic acid-inducible mitogen-activated protein kinase. The Plant Cell. 2003;**15**(3):745-759

[62] Saijo Y, Hata S, Kyozuka J, Shimamoto K, Izui K. Over-expression of a single Ca2+-dependent protein kinase confers both cold and salt/ drought tolerance on rice plants. The Plant Journal: For Cell and Molecular Biology. 2000;**23**(3):319-327

[63] Kumari S, Sabharwal VP, Kushwaha HR, Sopory SK, Singla-Pareek SL, Pareek A. Transcriptome map for seedling stage specific salinity stress

**75**

*Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.)

[71] Zhang T, Huang L, Wang Y, Wang W, Zhao X, Zhang S, et al. Differential transcriptome profiling of chilling stress response between shoots and rhizomes of *Oryza longistaminata* using RNA sequencing. PLoS One.

[72] da Maia LC, Cadore PRB, Benitez LC, Danielowski R, Braga EJB, Fagundes PRR, et al. Transcriptome profiling of rice seedlings under cold stress. Functional Plant Biology.

[73] Diyabalanage S, Navarathna T, Abeysundara HT, Rajapakse S, Chandrajith R. Trace elements in native and improved paddy rice from different climatic regions of Sri Lanka: Implications for public health.

Springerplus. 2016;**5**(1):1864

2015;**5**:30

[74] Sebastian A, Prasad MNV. Trace element management in rice. Agronomy.

[75] Oono Y, Yazawa T, Kanamori H, Sasaki H, Mori S, Handa H, et al. Genome-wide transcriptome analysis of cadmium stress in rice. BioMed Research

International. 2016;**2016**:9739505

[76] Oono Y, Yazawa T, Kawahara Y, Kanamori H, Kobayashi F, Sasaki H, et al. Genome-wide transcriptome analysis reveals that cadmium stress signaling controls the expression of genes in drought stress signal pathways in rice. PLoS One. 2014;**9**(5):e96946

[77] Abel S, Ticconi CA, Delatorre CA. Phosphate sensing in higher plants. Physiologia Plantarum. 2002;**115**(1):1-8

[78] Deng QW, Luo XD, Chen YL, Zhou Y, Zhang FT, Hu BL, et al. Transcriptome analysis of phosphorus stress responsiveness in the seedlings of Dongxiang wild rice (*Oryza rufipogon* Griff.). Biological Research.

2018;**51**(1):7

2017;**12**(11):e0188625

2016;**44**(4):419-429

*DOI: http://dx.doi.org/10.5772/intechopen.84955*

response indicates a specific set of genes as candidate for saline tolerance in *Oryza sativa* L. Functional & Integrative

Genomics. 2009;**9**(1):109-123

2016;**117**(6):1083-1097

Reports. 2018;**8**(1):2085

[64] Rahman MA, Thomson MJ, Shah EAM, de Ocampo M, Egdane J, Ismail AM. Exploring novel genetic sources of salinity tolerance in rice through molecular and physiological characterization. Annals of Botany.

[65] Wang J, Zhu J, Zhang Y, Fan F, Li W, Wang F, et al. Comparative transcriptome analysis reveals

molecular response to salinity stress of salt-tolerant and sensitive genotypes of indica rice at seedling stage. Scientific

[66] Zhu J, Dong CH, Zhu JK. Interplay

acclimation. Current Opinion in Plant

[67] Zhang Q, Chen Q, Wang S, Hong Y, Wang Z. Rice and cold stress: Methods for its evaluation and summary of cold tolerance-related quantitative trait loci.

between cold-responsive gene regulation, metabolism and RNA processing during plant cold

Biology. 2007;**10**(3):290-295

Rice (NY). 2014;**7**(1):24

2009;**583**(17):2734-2738

2010;**167**(17):1512-1520

2019;**685**:96-105

[68] Xie G, Kato H, Sasaki K, Imai R. A cold-induced thioredoxin h of rice, OsTrx23, negatively regulates kinase activities of OsMPK3 and OsMPK6 in vitro. FEBS Letters.

[69] Hossain MA, Cho JI, Han M, Ahn CH, Jeon JS, An G, et al. The ABRE-binding bZIP transcription factor OsABF2 is a positive regulator of abiotic stress and ABA signaling in rice. Journal of Plant Physiology.

[70] Guan S, Xu Q, Ma D, Zhang W, Xu Z, Zhao M, et al. Transcriptomics profiling in response to cold stress in cultivated rice and weedy rice. Gene. *Transcriptome Analysis for Abiotic Stresses in Rice* (Oryza sativa L.) *DOI: http://dx.doi.org/10.5772/intechopen.84955*

response indicates a specific set of genes as candidate for saline tolerance in *Oryza sativa* L. Functional & Integrative Genomics. 2009;**9**(1):109-123

*Transcriptome Analysis*

Journal. 2011;**9**(3):315-327

2016;**6**:23719

[49] Shankar R, Bhattacharjee A, Jain M. Transcriptome analysis in different rice cultivars provides novel insights into desiccation and salinity stress responses. Scientific Reports.

[50] Gupta P, Dash PK. Molecular details of secretory phospholipase A2 from flax (*Linum usitatissimum* L.) provide insight into its structure and function. Scientific Reports. 2017;**7**(1):11080

[51] Gupta P, Saini R, Dash PK. Origin and evolution of group XI secretory phospholipase A2 from flax (*Linum usitatissimum*) based on phylogenetic analysis of conserved domains. 3

[52] Yoo YH, Nalini Chandran AK, Park JC, Gho YS, Lee SW, An G, et al. OsPhyB-mediating novel regulatory pathway for drought tolerance in Rice root identified by a global RNA-Seq transcriptome analysis of rice genes in response to water deficiencies. Frontiers

in Plant Science. 2017;**8**:580

2009;**32**(3):237-249

Science. 2015;**6**:712

2013;**6**(1):27

[53] Rajendran K, Tester M, Roy SJ. Quantifying the three main components of salinity tolerance in cereals. Plant, Cell & Environment.

[54] Das P, Nutan KK, Singla-Pareek SL, Pareek A. Understanding salinity responses and adopting 'omics-based' approaches to generate salinity tolerant cultivars of rice. Frontiers in Plant

[55] Kumar K, Kumar M, Kim SR, Ryu H, Cho YG. Insights into genomics of salt stress response in rice. Rice (NY).

Biotech. 2017;**7**(3):216

[48] Lenka SK, Katiyar A, Chinnusamy V, Bansal KC. Comparative analysis of drought-responsive transcriptome in Indica rice genotypes with contrasting drought tolerance. Plant Biotechnology [56] Mishra S, Singh B, Panda K, Singh BP, Singh N, Misra P, et al. Association of SNP haplotypes of HKT family genes with salt tolerance in Indian wild Rice germplasm. Rice (NY). 2016;**9**(1):15

[57] Yang T, Zhang S, Hu Y, Wu F, Hu Q, Chen G, et al. The role of a potassium transporter OsHAK5 in potassium acquisition and transport from roots to shoots in rice at low potassium supply levels. Plant Physiology.

[58] Kurusu T, Hamada H, Koyano T, Kuchitsu K. Intracellular localization and physiological function of a rice Ca(2)(+)-permeable channel OsTPC1. Plant Signaling & Behavior.

[59] Golldack D, Quigley F, Michalowski CB, Kamasani UR, Bohnert HJ. Salinity stress-tolerant and -sensitive rice (*Oryza sativa* L.) regulate AKT1-type potassium channel transcripts differently. Plant Molecular Biology. 2003;**51**(1):71-81

[60] Wang H, Zhang M, Guo R, Shi D, Liu B, Lin X, et al. Effects of salt stress on ion balance and nitrogen metabolism of old and young leaves in rice (*Oryza sativa* L.). BMC Plant Biology. 2012;**12**:194

[61] Xiong L, Yang Y. Disease resistance and abiotic stress tolerance in rice are inversely modulated by an abscisic acid-inducible mitogen-activated protein kinase. The Plant Cell.

Shimamoto K, Izui K. Over-expression of a single Ca2+-dependent protein kinase confers both cold and salt/ drought tolerance on rice plants. The Plant Journal: For Cell and Molecular

[63] Kumari S, Sabharwal VP, Kushwaha HR, Sopory SK, Singla-Pareek SL, Pareek A. Transcriptome map for seedling stage specific salinity stress

2014;**166**(2):945-959

2012;**7**(11):1428-1430

2003;**15**(3):745-759

[62] Saijo Y, Hata S, Kyozuka J,

Biology. 2000;**23**(3):319-327

**74**

[64] Rahman MA, Thomson MJ, Shah EAM, de Ocampo M, Egdane J, Ismail AM. Exploring novel genetic sources of salinity tolerance in rice through molecular and physiological characterization. Annals of Botany. 2016;**117**(6):1083-1097

[65] Wang J, Zhu J, Zhang Y, Fan F, Li W, Wang F, et al. Comparative transcriptome analysis reveals molecular response to salinity stress of salt-tolerant and sensitive genotypes of indica rice at seedling stage. Scientific Reports. 2018;**8**(1):2085

[66] Zhu J, Dong CH, Zhu JK. Interplay between cold-responsive gene regulation, metabolism and RNA processing during plant cold acclimation. Current Opinion in Plant Biology. 2007;**10**(3):290-295

[67] Zhang Q, Chen Q, Wang S, Hong Y, Wang Z. Rice and cold stress: Methods for its evaluation and summary of cold tolerance-related quantitative trait loci. Rice (NY). 2014;**7**(1):24

[68] Xie G, Kato H, Sasaki K, Imai R. A cold-induced thioredoxin h of rice, OsTrx23, negatively regulates kinase activities of OsMPK3 and OsMPK6 in vitro. FEBS Letters. 2009;**583**(17):2734-2738

[69] Hossain MA, Cho JI, Han M, Ahn CH, Jeon JS, An G, et al. The ABRE-binding bZIP transcription factor OsABF2 is a positive regulator of abiotic stress and ABA signaling in rice. Journal of Plant Physiology. 2010;**167**(17):1512-1520

[70] Guan S, Xu Q, Ma D, Zhang W, Xu Z, Zhao M, et al. Transcriptomics profiling in response to cold stress in cultivated rice and weedy rice. Gene. 2019;**685**:96-105

[71] Zhang T, Huang L, Wang Y, Wang W, Zhao X, Zhang S, et al. Differential transcriptome profiling of chilling stress response between shoots and rhizomes of *Oryza longistaminata* using RNA sequencing. PLoS One. 2017;**12**(11):e0188625

[72] da Maia LC, Cadore PRB, Benitez LC, Danielowski R, Braga EJB, Fagundes PRR, et al. Transcriptome profiling of rice seedlings under cold stress. Functional Plant Biology. 2016;**44**(4):419-429

[73] Diyabalanage S, Navarathna T, Abeysundara HT, Rajapakse S, Chandrajith R. Trace elements in native and improved paddy rice from different climatic regions of Sri Lanka: Implications for public health. Springerplus. 2016;**5**(1):1864

[74] Sebastian A, Prasad MNV. Trace element management in rice. Agronomy. 2015;**5**:30

[75] Oono Y, Yazawa T, Kanamori H, Sasaki H, Mori S, Handa H, et al. Genome-wide transcriptome analysis of cadmium stress in rice. BioMed Research International. 2016;**2016**:9739505

[76] Oono Y, Yazawa T, Kawahara Y, Kanamori H, Kobayashi F, Sasaki H, et al. Genome-wide transcriptome analysis reveals that cadmium stress signaling controls the expression of genes in drought stress signal pathways in rice. PLoS One. 2014;**9**(5):e96946

[77] Abel S, Ticconi CA, Delatorre CA. Phosphate sensing in higher plants. Physiologia Plantarum. 2002;**115**(1):1-8

[78] Deng QW, Luo XD, Chen YL, Zhou Y, Zhang FT, Hu BL, et al. Transcriptome analysis of phosphorus stress responsiveness in the seedlings of Dongxiang wild rice (*Oryza rufipogon* Griff.). Biological Research. 2018;**51**(1):7

[79] Li P, Song A, Li Z, Fan F, Liang Y. Transcriptome analysis in leaves of rice (*Oryza sativa*) under high manganese stress. Biologia. 2017;**72**(4):9 Chapter 6

Abstract

Revealing the Symmetry of

Triplet Statistics

and Senashova Maria

be universal for plants.

rotational symmetry

1. Introduction

tutes T in RNAs).

genomes [1].

77

Conifer Transcriptomes through

Sadovsky Michael, Putintseva Yulia, Biryukov Vladislav

sion of transcriptome sequences. The distribution is revealed through PCA presentation and elastic map technique. The transcriptomic data of Siberian larch (Larix sibirica Ledeb.) and Siberian pine (Pinus sibirica Du Tour) were studied. The transcriptomes exhibit unusual symmetries. The octahedral structure exhibiting rotational symmetry in transcriptome contig distribution was found for L. sibirica, while mirror symmetry was found for P. sibirica. The octahedron structure seems to

Keywords: Chargaff's parity, order, structuredness, mirror symmetry,

A discovery of an order and new structures in genetic entities is an up-to-date scientific problem. Indeed, the amount of primary genomic data shows the daily growth for billions of megabases. The symbol sequences from four-letter alphabet ¼ f g A, C, G, T (with few variations in some nucleotide sequences; say, U substi-

We studied an order and structuredness over a set of sequences representing the

transcriptome of Siberian larch (Larix sibirica Ledeb.) and Siberian pine (Pinus sibirica Du Tour), also known as Siberian cedar. Transcriptome represents

structuredness or not heavily depends on the concept of a structuredness to be revealed and analyzed. One may face a huge number of patterns claimed to be structural units; a number of papers report on newly discovered structures in

biological cells or tissues. Obviously, whether a transcriptome exhibits

sequences of expressed genes and corresponds to the mRNA molecule isolated from

There are two approaches to discuss structuredness in a set of symbol sequences (transcriptome nucleotide sequences, in our case). The first implies that one seeks for inhomogeneities in the mutual distribution of the sequences form the ensemble under consideration. Of course, to do it, one must introduce a metrics to measure

The novel powerful technique is used for a study of combinatorial and statistical properties of transcriptome sequences. The main approach stands on the study of distribution of nucleotide triplet frequency dictionaries obtained from the conver-

[80] Li N, Liu H, Sun J, Zheng H, Wang J, Yang L, et al. Transcriptome analysis of two contrasting rice cultivars during alkaline stress. Scientific Reports. 2018;**8**(1):9586

#### Chapter 6

*Transcriptome Analysis*

2018;**8**(1):9586

[79] Li P, Song A, Li Z, Fan F, Liang Y. Transcriptome analysis in leaves of rice (*Oryza sativa*) under high manganese stress. Biologia. 2017;**72**(4):9

[80] Li N, Liu H, Sun J, Zheng H, Wang J, Yang L, et al. Transcriptome analysis of two contrasting rice cultivars during alkaline stress. Scientific Reports.

**76**

## Revealing the Symmetry of Conifer Transcriptomes through Triplet Statistics

Sadovsky Michael, Putintseva Yulia, Biryukov Vladislav and Senashova Maria

#### Abstract

The novel powerful technique is used for a study of combinatorial and statistical properties of transcriptome sequences. The main approach stands on the study of distribution of nucleotide triplet frequency dictionaries obtained from the conversion of transcriptome sequences. The distribution is revealed through PCA presentation and elastic map technique. The transcriptomic data of Siberian larch (Larix sibirica Ledeb.) and Siberian pine (Pinus sibirica Du Tour) were studied. The transcriptomes exhibit unusual symmetries. The octahedral structure exhibiting rotational symmetry in transcriptome contig distribution was found for L. sibirica, while mirror symmetry was found for P. sibirica. The octahedron structure seems to be universal for plants.

Keywords: Chargaff's parity, order, structuredness, mirror symmetry, rotational symmetry

#### 1. Introduction

A discovery of an order and new structures in genetic entities is an up-to-date scientific problem. Indeed, the amount of primary genomic data shows the daily growth for billions of megabases. The symbol sequences from four-letter alphabet ¼ f g A, C, G, T (with few variations in some nucleotide sequences; say, U substitutes T in RNAs).

We studied an order and structuredness over a set of sequences representing the transcriptome of Siberian larch (Larix sibirica Ledeb.) and Siberian pine (Pinus sibirica Du Tour), also known as Siberian cedar. Transcriptome represents sequences of expressed genes and corresponds to the mRNA molecule isolated from biological cells or tissues. Obviously, whether a transcriptome exhibits structuredness or not heavily depends on the concept of a structuredness to be revealed and analyzed. One may face a huge number of patterns claimed to be structural units; a number of papers report on newly discovered structures in genomes [1].

There are two approaches to discuss structuredness in a set of symbol sequences (transcriptome nucleotide sequences, in our case). The first implies that one seeks for inhomogeneities in the mutual distribution of the sequences form the ensemble under consideration. Of course, to do it, one must introduce a metrics to measure

the difference between any two sequences; there are various ways to do it [2–4]. An alignment might be such a measure [5, 6] (see also much more prominent approach presented in [7, 8]). Alternatively, the second approach implies the search for inhomogeneities within a sequence, e.g., through the comparison of the formally identified fragments of a sequence.

Regardless the specific approach to seek for structuredness, one must introduce a way to measure the difference between the objects to be analyzed. Alignment [9–11] is the most widespread approach here. An alternative idea to search a structure and order in symbol sequences is to transform them into frequency dictionary [12–15]. A frequency dictionary could be defined in various ways, but basically it is a list of all the strings of a given length accompanied with a frequency of each string (a detailed description is given below). A transformation of a symbol sequence into a frequency dictionary provides a mapping of a set of sequences into a metric space. Hence, one may apply all the tools for analysis.

As soon, as a structure in ensemble of sequences, or over a sequence is defined, the question arises toward the properties of those structures. Probably, symmetry of such structures is the most fundamental and basic one. Again, there could be various notions of the symmetry. The first concept of the symmetry aims to figure out structures that seem to remain similar, when some simple transformations in a proper space are provided. First of all, a rotational symmetry of a cluster structure [3, 4] or mirror symmetry [16, 17] must be mentioned here.

Few words should be said toward the symmetry. Here we shall consider two notions of that issue. The first is a well-known rotational, mirror, or similar symmetry observed in the distribution of the contigs converted into triplet frequency dictionary as they are distributed in the relevant Euclidean space (where the triplets are the coordinates). The second issue is measured through the proximity (or deviation) to Chargaff's parity rules, to be observed for various entities, both natural (these are contigs) and artificial (kernels or arithmetic means of the frequency of identical triplets counted over an ensemble of contigs).

have two or more CDS in them; the distribution of number of CDS in transcripts is
