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Metabolomics (1) --- The Systematic Chemistry Fingerprints Between Genotype a...

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This journal article is previously published as: Liu Huan. (2021). Metabolomics (1) --- The Systematic Chemistry Fingerprints Between Genotype and Phenotype and its Application on the Conservation Genetics. Journal of Environment and Health Science (ISSN 2314-1628), 2021(02).https://doi.org/10.58473/JBS0005, which is converted into Journal of Biological Sciences (ISSN 2958-4035). Both Journals belong to the same publisher, Liu Huan. The previous journal article is closed to the public, but the previous reference is still valid.

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Article 3: Metabolomics (1) --- The Systematic Chemistry Fingerprints Between Genotype and Phenotype and its Application on the Conservation Genetics/г´úл×éѧ---Á¬½Ó»ùÒòÐͺͱíÏÖÐ͵ÄÒ»Ïîϵͳ»¯Ñ§Ö¸ÎÆʶ±ð¼¼ÊõÓëÔÚ±£»¤ÒÅ´«Ñ§ÖеÄÓ¦ÓÃ

Author: Liu Huan (1983-), Master of Science (First Class Honours), The University of Auckland

1. Introduction
DNA should be defined as the main carrier of material genetic information. Because cells are intelligent creatures, in addition to material genetic information, there are also memory of spirit passed on to the offspring cells in the genetic process. This has been discussed in my previous paper. After genetics, complex metabolic processes begins in cells, which can be divided into three categories in this article: primary metabolic molecules (such as tRNA), secondary metabolic biochemistry molecules (such as glutamic acid discussed in plant physiology paper), and the final metabolites (such as polysaccharide compounds). However, enzyme is the indispensable biochemicals in this metabolic process. Enzyme is the principal biochemical regulator to initiate the biochemical process of metabolism in cell biology. The primary and secondary metabolites can be significantly influenced by the cellular physiological environment and genetics, whereas the cell functions constantly relies on the regulator of isozyme family (there are various enzyme species within one isozyme family) initiating the pathways of various metabolite process regardless of environmental changes and genetics. For example, in my previous article, it is concluded that the isozyme spectrum correspondingly to the valid antibiotics of specific pathogens must be relatively specific and unique, regardless of DNA genetics and environmental changes between different host cells. The thinking ability of multi-cellular individuals of higher intelligent is limited to three-dimension, as the thinking ability of single-cell organisms should only stay in two dimension: the recognition of bio-signals and utilization of biological enzymes. The enzyme utilization capacity can be ¡®learned¡¯ by cells. The previous paper has discussed the intelligence of cells.
2. Key features of metabolomics in this paper:
1. Succession of study focus on the metabolic biochemistry pathways from primary metabolites to final metabolites, but choose enzymes as the indicator of metabolic reaction pathways;
2.Compared with previous enzyme spectroscopy, this article designed the novel matrix calculation methods to systematically analyze the cell function regulation pathway, because the environment adaptability trait of cell (such as a specific pathogen resistance) is functioned by multiple metabolic pathways to regulate a number of sub-functions, and the multiple sub-functions and pathways are not independent of each other;
3. It connects genotypes with phenotypes to distinguish which functions of cells are inherited and which need to be cultivated.
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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 category¢ñand category¢ò, 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
Peroxidase (POD)
Disease Infection[5]
Catalase (CAT)
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)
Heavy Metal Stress;[10]
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]
Malic Enzyme (ME)
Drought Stress; Salinity Stress; Cold Stress; UV-B Radiation Stress; Physical
Injury;[13]
Beta-
Amylase(BAM£©
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, 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).ÒëÎÄ£º¶ÔÓ¦µÄ¾ØÕóËãʽËùÔ̺¬µÄÀíÂÛ¹ÛµãÈçÏ£ºDNA/»ùÒò·Ö×ӽṹÖеÄij¸öλµã£¨locus£©Ó¦µ±¶¨ÒåΪÌض¨»ùÒòÐòÁÐƬ¶ÎËùÔ̺¬µÄ»ùÒòÐÅÏ¢ÔÚÕû¸ö»ùÒò×éÐòÁÐÖÐÐÅÏ¢×ÜÁ¿µÄ±ÈÀý£¬²»Ó¦µ±¶¨ÒåΪÔÚÕû¸ö»ùÒò×éÉϵÄÌض¨ÎïÀíλÖã¬ÕâÑù¶¨Òå¸üΪ׼ȷһЩ¡£»ùÒòÔÚij¸öλµãÉϵÄÍ»±ä£¬¶¨ÒåΪ·¢ÉúÔÚ»ùÒò×éµÄij¸öÌض¨ÎïÀíλÖÃÉÏ£¬ÕâÑù²»ÊǺÜ׼ȷ£¬½ö½öÊDZãÓÚÀí½â¶øÒÑ¡£PCRµçÓ¾Ìõ¶ÎÊǸ÷ÖÖ»ùÒòÐòÁÐƬ¶ÎÖ®¼äÒ»ÖÖÏà¶ÔÖÊÁ¿µÄ·´Ó³¡£Òò´ËÌض¨PCRµçÓ¾Ìõ¶ÎµÄÏà¶ÔλÖÃÊÇÆä×ÔÉíÔÚ¸ÃʵÑéÖÐËùÓвⶨ»ùÒò×éÐòÁÐÖÐÊýÁ¿ÐÍ»ùÒòµÄ·´Ó³£¬Í¬Ê±Ò²·´Ó³¸ÃÌض¨»ùÒòÐòÁÐÔÚ¸ÃʵÑéÖÐËùÓвⶨ»ùÒòÐòÁÐÐÅÏ¢×ÜÁ¿ÖеıÈÀý¡£¶ÔÓ¦µÄ£¬Ã¸»¯Ñ§µçӾͼÆ×ÖеĵçÓ¾Ìõ¶ÎÒ²·´Ó¦¸÷ÖÖ²»Í¬ÊýÁ¿ÐÍ»ùÒòËù±í´ï³öø·Ö×ÓµÄÏà¶Ô·Ö×ÓÖÊÁ¿¡£¶ÔÓÚÒ»¸ö¸öÌåµÄ¶àÖÖÀàͬ¹¤Ã¸Ã¸Æ×£¬ÔÚͬһλµãÉϵĵçÓ¾Ìõ¶Î£¬Ò²¾ßÓпɱÈÐÔ¡£¶ÔÓÚ²»Í¬Í¬¹¤Ã¸ÖÖÀàÖ®¼äµÄÔÚͬһλµãÉϵĵçÓ¾Ìõ¶Î£¬ÕýºÃ¾ÍÊǸøöÌåÔÚÊýÁ¿ÐÍ»ùÒò¾ß±¸ÏàµÈµØ루»ùÒòÐÅÏ¢±ÈÀý¾ß±¸ÏàͬµØ룩µÄ²»Í¬»ùÒòÐòÁÐÖ®¼äµÄ·´³¡£
Then there is a 3-dimension (I¡Á E ¡Á 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:
file:///C:/Users/ADMINI~1/AppData/Local/Temp/ksohtml14308/wps1.png
Matrix Se = Xe ¡Á (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:
file:///C:/Users/ADMINI~1/AppData/Local/Temp/ksohtml14308/wps2.png
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 = file:///C:/Users/ADMINI~1/AppData/Local/Temp/ksohtml14308/wps3.png(e = 1, 2, ..., E)
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.  
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.

Acknowledge:
Advice on the section 5 (yield components and nitrogen fixation content) of this article: Hill, G. D. & Mckenzie, B. A.  Lincoln University, New Zealand. 2007.
This journal article is previously published as: Liu Huan. (2021). Article 7. Metabolomics (1) --- The Systematic Chemistry Fingerprints Between Genotype and Phenotype and its Application on the Conservation Genetics. Journal of Environment and Health Science (ISSN 2314-1628), 2021(02)., which is converted into Journal of Biological Sciences (ISSN 2958-4035). Both Journals belong to the same publisher, Liu Huan. The previous journal article is closed to the public, but the previous reference is still valid.  Latest revised on 29/05/2023.

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[1]. Review of Conservation Genetics and Its Application on Biodiversity Monitoring and Assessment. Áõ»À, Õźé³õÓëÌÆÇïÊ¢, ±£»¤ÒÅ´«Ñ§·½·¨ÔÚÉúÎï¶àÑùÐÔ¼à²âºÍÆÀ¼ÛÁìÓòµÄÓ¦ÓÃÑо¿. ¿Æ¼¼ÊÓ½ç, 2014(8). DOI: 10.3969/j.issn.2095-2457.2014.08.203
[2]. Liu, H, Ouyang T. L, Tian Chengqing (2015). Review of Evolutionary Ecology Study and Its Application on Biodiversity Monitoring and Assessment. Science & Technology Vision (6) 2015.
[3].  Liu Huan. 2022 (12).Epidemiology------Comparison between indoor and outdoor air quality at three representative sites in Auckland Center. Journal of Environment and Health Science (ISSN 2314-1628). https://doi.org/10.58473/JEHS0010
[4]. ÖÜÑÓÇå, ÕŸÄÄÈÓëÑîÇåÏã, ÉúÎïÒÅ´«±ê¼ÇÓëÓ¦ÓÃ, 2008, »¯Ñ§¹¤Òµ³ö°æÉç.
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[6].³Â½ð·å, Íõ¹¬ÄÏ, ³ÌËØÂú(2008).¹ýÑõ»¯ÇâøÔÚÖ²ÎïвÆÈÏìÓ¦ÖеŦÄÜÑо¿½øÕ¹.Î÷±±Ö²Îïѧ±¨, 2008, 28(1):0188-0193.
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