3. Genotype and Genetic Diversity Conservation
The feasibility of large-scale application of DNA markers on biodiversity assessment has been discussed by Liu et al.,(2014)[1]. However, the DNA markers suit not only for the classification of plant sub-populations for biodiversity assessment, but also provide the faster and convenient tool to identify the suitable plant varieties (genotype) from wild ecosystem for ecological restoration. The suitable environmental conditions for each variety growth (phenotype) can be identified by the analysis of both community and species interactions with environment as discussed by Liu et al.,(2015)[2]. According to the Environmental Standard on Classifying the Categories of Genetic Resources (HJ 626-2011) in Mainland China, three kinds of DNA molecular methods have been listed to rank genetic resources (or endangered species) between categoryⅠand categoryⅡ, including assessment of genetic diversity, evolutionarily significant unit (ESU), or genetic contribution rate, which have been substantially discussed by Liu et al.,(2015)[2]. However, it is advised that assessment of genetic diversity would be the first choice in ranking genetic resources (or endangered species), when the total SSR primers are calculated [3]; assessment of genetic variation would be the best method to select the suitable varieties for restoration of endangered species (or other important constructive species as well), when only polymorphic SSR primers are calculated [3]. The optimalization of both sampling units and polymorphic SSR primers, which allows to well present the genetic diversity for each variety at reasonable cost, has been pointed out as well [3].
4. Metabolomics and Environmental Adaptivity
However, the supplementary test of biochemical variation in enzyme species among different varieties collected in field, as the indicator for different varieties to adjust metabolism pathways in different environmental conditions, is advised for the conclusion of environmental adaptability between genotype and phenotype (metabolomics analysis). To be more comparable, the biochemical variation in enzyme species within one isozyme family, which catalyze the same metabolism substances, is analyzed according to the similarity coefficient. The function of each enzyme family in plant resistance to different environmental stress is summarized in table 1 below, which can be used for the development of isozyme primers initiating the isozyme test. The experiment procedure of biochemical test is listed in isozyme chapter [4]. To minimize the inaccurate conclusion between genotype and phenotype, the comparison between different varieties should be conducted on the basis of bio-samples collected in the same tissue and development phase of a plant species during the same season. In principle, the higher variation in enzyme species among varieties, the better environmental adaptability for restoration. This can be attributed into two reasons: firstly, the activity of an enzyme species only responds to the specific environmental conditions, and consequently the higher enzyme species variation of an isozyme family would result in the broader environmental conditions triggering the activity of the whole isozyme family; secondly, the gene expression of an enzyme species would be regulated by the specific environmental conditions only, which also explains the higher environmental adaptivity caused by the higher enzyme species variation of an isozyme family due to the broader environmental conditions for the regulation of gene expression as the whole isozyme family. Both reasons can result in the variation in isozyme electrophoretogram. The enzyme function in plant resistance to environmental stress is summarized below, and the chemical functional group of these enzyme molecules become the indicator synthesizing the isozyme primers for metabolomics test.
Table 1. The Enzyme Function in Plant Resistance to Environmental Stress.
Isozyme Families | Function in Plant Resistance to Environmental Stress |
| |
| Drought Stress; Temperature Stress (both cold and hot); Salinity Stress; Disease Infection; Ozone; Radiation Stress.[6] |
Malate dehydrogenase (MDH) | Acid soil; Aluminum toxicity[7] |
Alcohol dehydrogenase (ADH) | Waterlogging Stress; Salinity Stress; Cold Stress; Drought Stress; Anaerobic Stress.[8] |
NAD-dependent isocitrate dehydrogenase （ICDH） | Drought Stress; Salinity Stress; Heavy Metal Stress; Anti-Oxidation;[9] |
Lactate dehyderogenase (LDH) | |
Glucose-6-phosphate dehydrogenase (G6PDH) | Anti-Oxidation; Drought Stress; Salinity Stress; Cold Stress;[11] |
Glutamate dehydrogenase (GDH) | Drought Stress; Salinity Stress; Cold Stress; Disease Infection[12] |
| Drought Stress; Salinity Stress; Cold Stress; UV-B Radiation Stress; Physical Injury;[13] |
| Drought Stress; Temperature Stress (both cold and hot); Salinity Stress;[14] |
The calculation of similarity coefficient between zymogram of different varieties is performed in one isozyme family[4]. However, the overall similarity coefficient among different isozyme families is calculated, on the basis of matrix for PCA analysis designed below, to reveal the systematics of environmental adaptability (taking different moisture conditions as an example), as metabolomics analysis. The comparison of enzyme species variation between different seasons is required to reveal some resistance characteristics during specific environmental stress (such as cold stress). Compared with other article of this journal, the simulated environmental conditions of microbial cultivation are not suitable for botany. There are two reasons: firstly, the metabolic enzymes of botanical species is usually less sensitive to environmental conditions in comparison to microbes; secondly, the life cycle of constructive species for ecological restoration of botany communities can be hardly simulated in the controlled Lab. A novel matrix is designed below to conduct PCA analysis on the basis of comparisons between different isozyme families:
Then there is a 3-dimension (I× E × N) matrix presented in this research. I is the total amount of enzyme species within a isozyme family; E is the total amount of isozyme families; N is the total amount of zymograms among different simulated moisture conditions:
X= │Xien │( i = 1, 2, ....I; e = 1, 2, .... E; n= 1, 2, ... N)
Xien is the occurrence of enzyme ‘species i’ in the isozyme ‘family e’ during simulated moisture condition Tn. The value of Xien is one or zero. If the electrophoresis band occurs at this locus, the value is one;otherwise it is zero. The matrix X is below:
(See PDF Article)
Matrix Se = Xe × (Xe)T , where Xe = │Xin│( i = 1, 2, ....I; n= 1, 2, ... N); (Xe)T is the transpose of the matrix Xe. The matrix Xe is below:
(See PDF Article)
The Principal Components Analysis (PCA) method of matrix X is specified [1]. However, the overall matrix X can be divided by sub-factors: PCA is firstly conducted on the basis of matrix Se, revealing the biochemical dynamics of a isozyme ‘family e’ among different simulated moisture conditions. In matrix Se, it is hypothesized that the variable in PCA represents the biochemistry dynamics of each enzyme ‘species i’.
Matrix S = (See PDF Article)
PCA is further conducted on the basis of matrix S, revealing the biochemical dynamics among different isozyme families over the whole simulated moisture conditions. In matrix S, it is hypothesized that the variable in PCA represents the biochemistry dynamics of each enzyme ‘species i’ across all the isozyme families. Further application has been discussed in later articles of this journal.
In my next article [22], the Matrix Xsum is designed as an i×n dimension matrix, to classify and differentiate different bio-samples based on the comprehensive isozyme zymograms. To better conduct PCA statistics, Matrix Xsum can be transformed into (Matrix Xsum)T × Matrix Xsum , where (Matrix Xsum)T is the transpose of the Matrix Xsum , and this transformed matrix is n×n dimension; my next article also designs Matrix Sn sum as the I× E dimension matrix, to conduct PCA among different isozyme families explaining quantitatively ‘which isozyme families contribute to the most variations in this statistic matrix.’ To better fit the statistics model, Matrix Sn sum can be also transformed into (Matrix Sn sum)T × Matrix Sn sum , where (Matrix Sn sum)T is the transpose of (Matrix Sn sum)T , and the transformed matrix is e×e dimension.
5.Phenotype and Gene Mapping for Genetic Breeding
Environmental adaptivity is definitely one of the main considerations for plant genetic breeding in restoration work. Nevertheless, as discussed in other article of this journal, gene expression traits as higher environmental adaptivity are usually associated with the gene traits of lower biomass productivity (or carbon sink), which means that both gene traits would be located in the same linkage group of genome. However, the gene trait of plant drought tolerance would increase the capacity of water & soil conservation due to the advantageous partitioning for root system, which results in higher ratio of root biomass to the total biomass. For the conservation of endangered birds, the gene traits as the partitioning of more branches for habitats or suitable fruits would become the major consideration in variety selection as well.
According to the results sourcing from the ‘Crop Science’ course instructed by Lincoln University NZ in 2007, yield components were also significantly affected by genotypes. The highest values of pods/plant, seeds/pod, and mean seed weight were achieved from genotype Aragorn, genotype PRO, and genotype Midichi, respectively. However, the total seed yield was not affected by pea genotypes. This result indicated the interdependent compensation mechanism among yield components. Wilson (1987) and Taweekul (1999) also suggested that large variation in one yield component might not lead to changes in total seed yield, due to the ‘plasticity’ of yield components [16-21]. However, my article here further points out that the experiments above are conducted under the ‘comfortable environment’ with sufficient growth conditions, which does not reveal the environmental adaptiveness under environmental stress in the field. This is firstly explained as the inter-dependent compensation mechanism among these yield components. However, my article would also explain this inter-dependent compensation mechanism by the theory that the sets of gene, underlying the expression as these yield component traits above, should locate in the same linkage group of genome. This plastic inter-dependent compensation mechanism leads some agriculture scientists to announce that the gene traits of yield components are not useful in breeding selection. However, this article hypothesizes that the gene expressed as partitioning more branches would locate in the same linkage group as some gene traits of environmental adaptivity (such as drought tolerance and higher capacity of nitrogen fixation in root system), which becomes the objectives of my future study. The infection between microbes of biological nitrogen fixation and botanical roots must be quite specific[15], so the thinner root skin, usually associated with the partitioning of more root branches, would benefit the parasitic infection of microbes, enhancing the biological nitrogen fixation in root system. Additionally, the gene trait of partitioning more branches should result in higher radiation use efficience (RUE) as well, an environmental adaptivity trait in shading side of hills. This gene trait provides not only more suitable shelters for endangered birds, but also higher sustainability of habitats for food.