Computational Analysis of Rice Transcriptomic and Genomic Datasets in Search for SNPs Involved in Flavonoid Biosynthesis

This chapter describes the computational approach used in analyzing rice transcriptomics and genomics data to identify and annotate potential single nucleotide polymorphism (SNPs) as potential biomarker in the production of flavonoid. SNPs play a role in the accumulation of nutritional components (e.g. antioxidants), and flavonoid is one of them. However, the number of identified SNPs associated with flavonoid nutritional trait is still limited. We develop a knowledge-based bioinformatic workflow to search for specific SNPs and integration analysis on the SNPs and their co-expressed genes to investigate their influence on the gain/loss of functional genes that are involved in the production of flavonoids. Raw files obtained from the functional genomics studies can be analyzed in details to obtain a useful biological insight. Different tools, algorithms and databases are available to analyze the ontology, metabolic and pathway at the molecular level in order to observe the effects of gene and protein expression. The usage of different tools, algorithms and databases allows the integration, interpretation and the inference of analysis to provide better understanding of the biological meaning of the resutls. This chapter illustrates how to select and bring together several software to develop a specific bioinformatic workflow that processes and analyses omics data. The implementation of this bioinformatic workflow revealed the identification of potential flavonoid biosynthetic genes that can be used as guided-gene to screen the single nucleotide polymorphisms (SNPs) in the flavonoid biosynthetic genes from genome and transcriptomics data

Potential of plant's Bowman-Birk protease inhibitor in combating abiotic stresses: a mini review

Bowman-Birk Inhibitor (BBI) is one of the subfamilies of serine protease inhibitors. Numerous studies have shown that in plants, BBI functions as part of their defense mechanism against pathogens and microorganisms. The BBI is also known to have anti-carcinogenic properties. Furthermore, the BBI has been reported to function in controlling abiotic stresses such as salinity and drought stresses. Abiotic stresses are the major problems in agricultural industry. Therefore, numerous researches have been carried out to characterize the BBI and to determine its roles during biotic and abiotic stresses. This paper presents a review regarding the relationship between Bowman-Birk inhibitor and the plant defensive mechanism against abiotic stresses

RNA-Seq and validation analysis on the important genes involved in early responses to salinity stress of Malaysian rice seedlings (Oryza sativa ssp. Indica)

Salinization of rice cultivation land has progressively enlarged, thus negatively impaired the world's rice bowl. Due to the polygenic nature and complexity of salinity tolerance mechanisms in plants, the development of new rice varieties with better adaptation to salinity has become a great challenge. Regarding this, transcriptomic profiling has been seen as a promising approach for a holistic understanding of salinity tolerance mechanisms in rice. Using Illumina RNA-Seq method, transcriptomes of two contrasting Malaysian rice varieties named as salt-tolerant MR211 and salt-sensitive MR220 were analysed within a short-term exposure (9 h) to salt-shock treatment (12 dS m-1) at early seedling stage. Transcriptomic analysis using Tuxedo package enabled the identification of 252 differentially expressed genes (DEGs). Interestingly, 93.3% of the DEGs (n=235) were identified as higher and specifically expressed in salt-tolerant MR211 compared to the sensitive variety (MR220's FPKM ≤ 0). This group of DEGs was assigned in 33 KEGG pathways, with the highest number of transcripts accounted in purine and thiamine metabolism pathways. Meanwhile, functional annotation analyses revealed the presence of regulatory genes, annotated functional and unknown genes involved in various salt adaptation mechanisms in the salt-tolerant variety MR211. The expression accuracy and reproducibility of the 252 DEGs identified from the RNA-Seq experiment were further verified through semi-quantitative RT-PCR followed by real time PCR analysis

A Review of Omics Technologies and Bioinformatics to Accelerate Improvement of Papaya Traits

Papaya (Carica papaya) is an economically important fruit crop that is mostly planted in tropical and subtropical regions. Major diseases of papaya, such as the papaya dieback disease (PDD), papaya ringspot virus (PRSV) disease, and papaya sticky disease (PSD), have caused large yield and economic losses in papaya-producing countries worldwide. Postharvest losses have also contributed to the decline in papaya production. Hence, there is an urgent need to secure the production of papaya for a growing world population. Integration of omics resources in crop breeding is anticipated in order to facilitate better-designed crops in the breeding programme. In papaya research, the application of omics and bioinformatics approaches are gradually increased and are underway. Hence, this review focuses on addressing omics technologies and bioinformatics that are used in papaya research. To date, four traits of the papaya have been studied using omics and bioinformatics approaches, which include its ripening process, abiotic stress, disease resistance, and fruit quality (i.e., sweetness, fruit shape, and fruit size). This review also highlights the potential of genetics and genomics data, as well as the systems biology approach that can be applied in a papaya-breeding programme in the near future

Gene co-expression network tools and databases for crop improvement

Transcriptomics has significantly grown as a functional genomics tool for understanding the expression of biological systems. The generated transcriptomics data can be utilised to produce a gene co-expression network that is one of the essential downstream omics data analyses. To date, several gene co-expression network databases that store correlation values, expression profiles, gene names and gene descriptions have been developed. Although these resources remain scattered across the Internet, such databases complement each other and support efficient growth in the functional genomics area. This review presents the features and the most recent gene co-expression network databases in crops and summarises the present status of the tools that are widely used for constructing the gene co-expression network. The highlights of gene co-expression network databases and the tools presented here will pave the way for a robust interpretation of biologically relevant information. With this effort, the researcher would be able to explore and utilise gene co-expression network databases for crops improvement

A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing

Reconstruction of Curcuma aeruginosa secondary metabolite biosynthetic pathway using omics data

Curcuma aeruginosa or temu hitam is herbaceous plant with high therapeutic values in its rhizome that is widely used in traditional medicine. However, molecular studies on the secondary metabolite biosynthetic pathway of C. aeruginosa is still limited. Hence, the aim of this study was to explore and reconstruct the secondary metabolite biosynthetic pathway of C. aeruginosa rhizome by integrating the metabolite profiling and transcriptomic data. A total of 81 metabolites were identified in the rhizome of C. aeruginosa; amongst others are curzerene and β-Cubebene which are potent antioxidants. A total of 28,225 unigene were obtained from the transcriptomic sequencing of C. aeruginosa rhizome and analysed to identify potential genes associated with the biosynthesis of its metabolites. Of these, 43 unigenes were identified and mapped onto five sub-pathways; i.e. carotenoid biosynthetic pathway, diterpenoid biosynthetic pathway, monoterpenoid biosynthetic pathway, terpenoid and steroid biosynthetic pathway, and sesquiterpenoid and triterpenoid biosynthetic pathway. This study demonstrated a systematic bioinformatic approach to reconstruct a metabolic pathway in the rhizome of C. aeruginosa using bioinformatic approach

DNA profile of commercial pineapples in Malaysia by using SSR markers

Nine commercial varieties of pineapples in Malaysia (Josapine, Maspine, MD2, Sarawak, Gandul, N36, Moris, Crystal Honey and Yankee) were collected from various places in Peninsular Malaysia and analysed for cultivar identifications using nine simple sequence repeat (SSR) markers. A total of 27 alleles have been observed which ranged from 2 to 5 with an average of 3 alleles per locus. The polymorphic information content (PIC) value ranged from 0.3426 (Acom_82.8) to 0.6561 (Acom_67.2) with a mean of 0.4524 while the heterozygosity value ranged from 0.1097 (TsuAC021) to 0.8010 (TsuAC039) with a mean of 0.5481. The pairwise Nei’s genetic distances had also been calculated and the value ranged from 0.0562 (Gandul and Josapine) to 0.6383 (MD2 and Yankee) with an average value of 0.3169. The above data emphasised a moderate level of polymorphisms among the nine varieties. A dendrogram was constructed by using the unweighted pair group method with arithmetic mean (UPGMA) which showed all the nine successfully differentiated pineapple commercial varieties. A principal coordinate analysis (PCoA) was also had been generated which revealed an agreement with the dendrogram output. Therefore, these nine SSR markers can be used to identify the nine selected commercial varieties to ensure pure planting materials