Use of Genetic Tools for Improving Biotic and Abiotic Plant Resistance

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Published on International Journal of Agriculture & Agribusiness
Publication Date: May 13, 2019

Haitham Mohyeldien Abdelrahman Elsayed
Genetics Dept., Faculty of Agric., Sohag Univ.
Sohag, Egypt

Journal Full Text PDF: Use of Genetic Tools for Improving Biotic and Abiotic Plant Resistance.

The integration of conventional breeding with genetic and genomic tools such as quantitative trait loci (QTL), microarrays and transgenic offer new opportunities for improving biotic and abiotic resistance in plant. Plant genetic diversity (PGD) can be stored in the form of plant genetic resources (PGR) such as gene bank, DNA library which preserve genetic material for long period to identify key genes and understand their function in plant improvement. The data included not only sequence information, but information on mutations, markers, maps, functional discoveries, etc. With the advent of new biotechnological techniques, this process of genetic tools are now being accelerated and carried out with more precision and rapid manner than the classical breeding techniques. It is also note that gene banks look into several issues in order to improve levels of germplasm distribution and its utilization, duplication of plant identity, and access to database, for prebreeding activities. So, it is possible to identify the genetic variation from phenotypic variation either by quantitative traits (traits that vary continuous and are governed by many genes, e.g., plant height) or discrete traits that fall into discrete categories and are governed by one or few major genes (e.g., white, pink, or red petal colour in certain flowers) which are referred as qualitative traits.

Keywords: Genetic and genomic tools, biotechnological techniques, germplasm.

1. Overview
Plant production has considerably demands in the future. Expanding global markets and the competition of food and non-food uses require another significant progress in productivity levels. Thus, the scenario which agriculture is facing, is crop rotations with a limited number of high-yielding crops for the food or raw materials market, under aggravated climatic conditions. In sum, these developments will result in a significant increase in problems caused by biotic and abiotic stresses, which will decrease yield levels. Crops are exposed more frequently to abiotic stresses such as drought, salinity, High temperature, Flooding, and nutrient deficiencies. These stresses limit production. Recently, advances in physiology, molecular biology and genetics have greatly improved our knowledge of crops response to these stresses and the basis of varietal differences in tolerance. Stress is known as the force per unit area acting upon a material, and leading to dimensional change. Generally, it is used to describe the effect of adverse forces, and this is how it is usually applied to biological systems. In the biological sense, stress can be any factor that may produce an adverse effect in individual, populations, or communities. Stress is also defined as the overpowering pressure that affects the normal functions of individual life or the conditions in which plants are prevented from fully expressing their genetic potential for growth, development and reproduction (Levitt, 1980). Biotic stresses originate through interactions between organisms resulting from competition between organisms for resources, from predation and parasitism, and from the actions of allelopathic chemicals released by one organism and affecting another. Plants are constantly in face to both abiotic and biotic stresses that seriously reduce their productivity. Plant responses to these stresses are complex and involve numerous physiological, molecular, and cellular adaptations. Breeding programmes are currently set up to meet the new challenges.
Recently, biotechnological progress has opened new avenues for further and faster advances in crop breeding. Cultivars with better resistance to biotic and abiotic stress are becoming a real option. Forward and reverse genetics methods are used to determine the function of genes. Forward genetics, refers to the identification and characterization of the gene that is responsible for the mutant phenotype; while, the objective of reverse genetics is to examine the effect of induced mutation or altered expression of a particular gene and to understand the gene function (Ahringer, 2006)
Productive and sustainable agriculture needs growing plants in stressful environments with less input of precious resources such as fresh water. For a better understanding and rapid improvement of this case, it is important to link physiological and biochemical work to molecular studies in genetically model organisms. With the use of several technologies for the discovery of stress tolerance genes and their appropriate alleles, transgenic ways focus on improving stress tolerance in crops with breeding principles with a greatly expanded germplasm. In spite of, the importance of the environment (especially the osmotic environment (mainly quantity and quality of available water)) remains in preeminent. Under careful examination, it can be seen that any trait measured is really a quantitative trait and shows a continuous distribution. It is only when the segregating germplasm is sufficiently different genetically that a clear separation of subpopulations can be made without sophisticated statistical analyses. Physiological, biochemical, and genetic studies of environmental stress tolerance have also been slowed because of a lack of consensus of how stress tolerance can be viewed as a measurable phenotype. Without a consensus phenotype(s), results from different studies cannot be entered in comparison. The use of various species for these studies also complicates the comparison of results, as does studying of diverse developmental stages and then drawing generalizations. Sensitivity to drought at an early developmental stage in a plant’s life may not be a stressful condition at a later stage, such as during seed filling. Although, the extensive use of model systems, especially Arabidopsis, has greatly reduced these problems. Therefore, the exhaustive physiological work that has been able to define the greatest degree of contribution to physiological tolerance has finally begun to be linked to genetic differences. The genetic variation for tolerance can be organized into four major processes that contribute to physiological tolerance: (1) water homeostasis, (2) metabolic adjustment including hormone regulation, (3) growth control, and (4) injury control (Zhu, 2002). To identify the genes (genetic loci) that are involved in these four major areas of osmotic tolerance physiology, there has been intensive research effort for the last decade coincident with the availability of the major molecular genetic tools by the adoption of Arabidopsis as a versatile plant model system. The earliest genetic ways using this model plant and its formidable tools since influenced greatly by the transcription control paradigm because transcription factors (TFs) and subsequently other signal components connected to TFs have historical importance arising from the progress of understanding the gene to organism paradigm. Also because specific studies reporting that evolution in general and crop domestication in particular, have apparently been influenced more dramatically by mutations in TFs or mutations in gene promoters (controlled by TFs) than by mutations in other loci (Doebleyetal,1997). The transcriptional control is very important to phenotype manifestation but many other molecular components and processes play important roles (Maggio et al., 2003).
The crop improvement through genetic manipulations may be achieved major traces e.g. many loci involved in the trait of the environmental adaptation need to be identified, and the appropriate alleles for the major loci need to be identified as well. In general, large-scale genome projects have greatly changed the face of biology. Genomics has often been referred to as a novel field that has led to a paradigm shift in the way science. By taking full advantage of the vast amount of sequencing data, it has become possible to look at biology in a different way. However, the genomic era has undergone a transformation in modern post-genomic times. Recent technological advances and the rapid development of novel tools now permit the interrogation of a complete genome all at once and in a single experiment.
Nowadays, the mass of genome data is being converted into gene-function data, meaning that value is added to the nucleotide sequence collections. Understanding the exact sequence and location of all the genes of a given organism is only the first step towards knowing how all the parts of a biological system work together. Furthermore, the high amount of biological diversity in plant systems allow the identification of novel gene functions. Essential functions needs to be matched with agronomically important traits, leading to the crop improvements in plants.

2. Functional Genomics
Functional genomics is a general way toward identifying how the genes of an organism work together by assigning new functions to unknown genes. Information about the hypothesized function of an unknown gene may be deduced from its sequence structure using already known functions of similar genes as the basis for the comparison. Moreover, the location of given gene in the structure of the chromosome allows prediction with respective to gene function, providing the function and chromosomal location of genes with similar known sequence. Consequently, to well known the exact function of unknown genes it is necessary to understand each gene role in the complex orchestration of all the gene activities in the plant cell.
There are two ways for functional genomics called Forward Genetics and Reverse Genetics. Forward and Reverse Genetics has been used to determine the function of gene(s) and how genotypes are linked to phenotypes. In Forward Genetics (phenotype to genotype) the gene sequence is finally inferred through selecting large numbers of mutagenized individuals for phenotypic variants needs wide analysis primarily for gene coding to a particular phenotype (Alonso and Ecker, 2006). In Reverse Genetics (from genotype to phenotype), the gene sequence is known, and mutants are identified and screened with structural alterations in the gene of interest (Nagy et al., 2003).

3. Genetic Map
Genetic mapping can be generally classified into family-based mapping when mapping is performed in progenies of biparental or multiparent crosses and natural population-based mapping when mapping is conducted in natural populations in which relationships are unknown. So, the objective of genetic mapping is to identify QTL responsible for natural phenotypic variation. Two strategies have been widely applied to genetic mapping in plants: (1) linkage mapping and (2) association or linkage disequilibrium (LD) mapping. Linkage mapping, a conventional mapping method, depends upon genetic recombination during the construction of mapping populations. (Yang et al., 2016). Therefore, The overall goal of genetic map is to identify the locus of the gene that are responsible for the trait of interest. The first step in all mapping studies is to find markers that are linked with the trait. Physical linkage will lead to co-inheritance of markers, while recombination events will break these associations. Then, to develop appropriate mapping populations screening, parents for marker polymorphism and genotype mapping population are required. After that, a linkage analysis is performed to find out recombination frequencies between markers which in turn lead to the fine mapping of the location of the gene of interest. If the genome of the plant of interest is not fully sequenced, the system between physical and genetic maps of closely related plants with sequenced genome enables the assessment of the gene content at the fine mapped locus. The follows databases and their online genome browsers and blast search capabilities are essential for these system studies:

4. Candidate Gene
This method is appropriate for plants where mutant collections, represented by multiple independent mutant alleles. The major difficulty with this method is that to choose a potential candidate gene for the mutation, researchers must already have an understanding of the mechanisms underlying the phenotypic disorder. Very good “educated guesses” can be done if a study of similar mutants has been performed in another related plant and the corresponding orthologous gene has been identified as well. Next, this gene can be a potential candidate for the mutation in the investigated plant and the principle proof that this candidate gene is responsible for the observed phenotype is coming from comparative sequence analysis of all available mutant alleles in the particular locus (Zakhrabekova et al., 2012).
Exome sequencing is a powerful method to selectively sequence of the coding regions of the genome as a less costly alternative to whole genome sequencing (Ng et al., 2009). This method can be combined with target-enrichment strategies, which give possibility to selectively capture genomic regions of interest from a DNA sample prior to sequencing (Basiardes et al., 2005). Identification of mutations by this method requires a number of different mutant alleles to more clearly a trust answer.

5. Bioinformatics
Many software packages are available for assessing phenotypic and molecular diversity parameters that increased the efficiency of germplasm curators and, plant breeders to speed up the crop improvement. Bioinformatics is a discipline that manages wide biological data that is generated from comparative, functional and structural genomics and other related tools. It has sophisticated over the years due to emergence of powerful algorithms that can be used to manage and manipulate the existing data in order to be biologically meaningful. Bioinformatics data is stored in public and private databases. Databases that are of research interest include DNA Databank of Japan, NCBI, Gramene database among others. Data needs to be modelled to help in creating desired varieties of crop cultivars.

6. Conclusion
Plant genomics research has inserted the phase of functional characterization of all plant genes. For efficient gene function analysis, researchers can choose from a multitude of different methods from the toolbox of plant functional genomics. The strength of functional genomics enables us to bring together complementary approaches in parallel. If one wants to take full benefit of the available genomic information on plant genes, only the multidisciplinary integrated method will permit the functional characterization of plant genes. In addition, the wide amount of data from various ways has to be connected and organized into central databases in parallel in order to allow easy extraction and comparison of data for meaningful analysis. Such resources need to link information as to the sequence, genomic context, expression, and mutant phenotype of a gene.
Genomics and its related omics technologies has, to a fair extent, revolutionalized the science of plant breeding and it is expected to contribute more due to rapid advances in sequencing of plant genomes using high throughput methods. Development of end user softwares to handle and analyze vast amounts of data from these experiments is one area that much effort should be put on so as to make sense out of such information and apply it in breeding programs. Marker technology has rapidly exerted influence in plant breeding due to advances in genomics. Functional markers have contributed to diagnostics technology that enable identification of molecules or sequences that contribute or participate during plant response to various stresses. This has enabled designing of better crop/plant varieties. The future of food security depends on availability of funding to improve agricultural practises for millions of people in developing countries who depend on it as a source of income and to ensure the world’s poor are foods secure. This can only be realized with the application of the above stated techniques in plant breeding.