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:
In the electrophoretogram of zymogram, if the electrophoresis pipes are the vertical ones, different electrophoresis pipe contains different isozyme family for comparison. Each isozyme family is labeled as 1, 2, 3..., and E in each electrophoresis pipe; It is hypothesized that the electrophoresis bands at the same horizontal line across different isozyme families are the enzyme species at the same locus (due to the same status of relative molecular weight), named as enzyme ‘species i’ (i = 1, 2, ..., I), and each isozyme family has the same amount (I) of enzyme species. The underlying theory making the bands comparable across different isozyme families is presented below: a locus in DNA / gene molecule should be defined as the proportion of a specific gene sequence segment to the total information of the whole genome, rather than as a specific physical position in the whole genome, which is more accurate. Gene mutation at a certain locus is defined as the gene sequence alteration at a specific physical position in the genome, which is not accurate. PCR electrophoresis is a reflection of the relative molecule mass among various gene sequences. Therefore, the relative position of a specific PCR electrophoresis band is not only a reflection of its quantitative gene among all the gene sequences tested in this experiment, but also a reflection of the proportion of the specific gene sequence information to the total information of all gene sequences tested in this experiment. Correspondingly, the electrophoretic bands in the electrophoretogram also reflect the relative molecular weight of the enzyme molecules expressed by different quantitative genes tested in this experiment. The electrophoretic bands at the same locus are also comparable among different isozyme species of an individual. The electrophoretic bands on the same locus among different isozyme species are just the reflection of different gene sequences with the same quantitative gene status (or the same proportional gene information to the total gene information tested in a experiment).译文：对应的矩阵算式所蕴含的理论观点如下：DNA/基因分子结构中的某个位点（locus）应当定义为特定基因序列片段所蕴含的基因信息在整个基因组序列中信息总量的比例，不应当定义为在整个基因组上的特定物理位置，这样定义更为准确一些。基因在某个位点上的突变，定义为发生在基因组的某个特定物理位置上，这样不是很准确，仅仅是便于理解而已。PCR电泳条段是各种基因序列片段之间一种相对质量的反映。因此特定PCR电泳条段的相对位置是其自身在该实验中所有测定基因组序列中数量型基因的反映，同时也反映该特定基因序列在该实验中所有测定基因序列信息总量中的比例。对应的，酶化学电泳图谱中的电泳条段也反应各种不同数量型基因所表达出酶分子的相对分子质量。对于一个个体的多种类同工酶酶谱，在同一位点上的电泳条段，也具有可比性。对于不同同工酶种类之间的在同一位点上的电泳条段，正好就是该个体在数量型基因具备相等地位（基因信息比例具备相同地位）的不同基因序列之间的反映。
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.