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DNA Genetic Marker/DNA遗传标记

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发表于 2021-1-3 14:41:18 | 显示全部楼层 |阅读模式
This is the article 1 in the theme 'Environmental Physiology/环境生理学' of Journal of Environment and Health Science.

2016. Copyrights Register Information: The majority of these materials are registered as book '著作权人:刘焕;作品:《研究生文凭进展(第三版)》' 2016, which can be cataloged in National Copyright Database: http://qgzpdj.ccopyright.com.cn/

2016. 版权注册信息:本文大多数内容已经以图书形式登记注册在全国版权数据库,登记入库信息:著作权人:刘焕;作品:《研究生文凭进展(第三版)》 2016;可在全国版权登记数据库检索 http://qgzpdj.ccopyright.com.cn/

The formally published serials is the printing <Journal of Environment and Health Science (ISSN 2314-1628)>, and the serials NO. is the month/year when the materials accessible on this website, authorized by publisher;
正式发表的期刊是印刷版《环境与卫生科学杂志(ISSN 2314-1628)》,期刊期号为文章内容在本网站上网年/月,出版人许可自行正式发表。

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 楼主| 发表于 2021-1-6 10:10:10 | 显示全部楼层
Article 1. DNA Genetic Marker/DNA遗传标记

Author: Liu Huan, MSc (First Class Honours), The University of Auckland.


This article presents the new experiment methods of DNA genetic markers  
1. Classification of Virus by Genetic Marker and Its Theory
The methods of classifying and identifying virus will follow these steps:
Step 1. The molecular cytogenetic karyotype is analyzed by fluorescence in situ hybridization (FISH) technique [1] using transmission electron microscopy;

Step 2. Virus is classified by multivariate cluster analysis and genetic  distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of virus [2];

Step 3. The optima sampling units of each virus family, which can well represent the genetic diversity of each virus family, is examined and determined as pointed out by Liu et al.,(2015) [2] for further classification based on DNA (or RNA) molecular marker (SSR or AFLP). The sampling units can be adjusted by changing the concentration of virus solution;

Step 4. Classification of virus families is further conducted on the basis of DNA (or RNA) molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis [2].

Step 5. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA (or RNA) molecular marker.

There are three hypotheses examined by this research:
Hypothesis 1: the optima numbers of polymorphic SSR primers are examined and screened for each virus family, because we assume that the amount of polymorphic SSR primers, which are assessed on the basis of polymorphism information content (PIC), increases with the increase of total SSR primers selected from Gene Bank, but the increase rate is not constant. Consequently, the optimal number of polymorphic SSR primers is determined at the peak increase rate.

Hypothesis 2: the classification significantly differs between morphological markers of virus and DNA genetic markers.

Hypothesis 3: there are two kinds of multivariate cluster analysis and genetic distance analysis on the basis of SSR markers, resulting in two different classifications of virus families: firstly, the classification of virus families is conducted based on the Nei’ genetic identity[3](or Nei’ genetic similarity[4]) calculated by the total SSR primers from Gene Bank; or then the classification of virus families is conducted based on the genetic identity calculated by the polymorphic SSR primers only. This research aims to examine which classification method leads to better correlation with the incidence of pathological characters recorded.

Discussion:
1.The total SSR primers selected from Gene Bank are the pairs of SSR primers which lead to clear PCR bands for at least one virus family in amplified process;

2.The recommended three criteria of molecular cytogenetic karyotype for the preliminary classification of different virus families include: the ratio of length between the beginning of a short arm and the margin of rDNA probe to the total  length of a chromosome; relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research); and centromere index. The average value of each criterion should be calculated for each virus family.

3.The virus samples should be collected in the same area at local scale, which facilitates the differentiation of local virus families due to the unique nature of virus ecosystem.

4.Preparation of DNA samples in one test: 12 uniform samples are abstracted from the same DNA water solution which has been evenly mixed, named as sample 1, sample 2, ..., sample12; In total 12 different SSR primers are selected in one test, named as primer 1, primer 2,...., primer12, and each different SSR primer is injected into sample 1, sample 2, ..., sample 12 respectively for PCR amplified process; after PCR amplified process, each sample (12 in total) is electrophoresed separately in each pipe of electrophoresis instrument, and the PCR bands from different virus families, preliminarily drawn by FISH technique, would be clearly separated from each other in a electrophoresis pipe. Consequently, the distance between two PCR bands from two different virus families, which is measured in a pipe of electrophoretogram, represents the genetic distance between these two virus families per SSR primer (or locus). Then the multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) is conducted on the basis of the average genetic distance between any two different virus families across SSR primers (or loci) which can lead to clear PCR bands in all virus families preliminarily drawn by FISH technique. If the electrophoresis pipe is the vertical one, then the PCR bands around the same horizontal lines represent the same virus families due to the ‘similar weight of molecules’, which can be deduced by the ‘similar length of genomes’ within one virus family identified by FISH step. Please note: both the molecular weight and genome length mentioned above are the relatively weight and relative length, because the DNA molecular weight, shown in the gel electrophoretogram (such as the distance between two PCR bands in a pipe of electrophoretogram), is the relative weight of molecules, which can be consequently deduced by the relative length of genome (the ratio of the sum genome length within a virus family to the sum genome length of all the virus families examined in research).

5.The multivariate cluster analysis for virus family classification is on the basis of the mutual interaction among virus ecosystem. Consequently, there are two criteria of qualitative gene expression (or qualitative trait locus of gene expression), including the ratio of length between the beginning (or end) of a chromosome and rDNA probe to the total length of a chromosome as well as centromere index, and a criterion of quantitative gene expression (or quantitative trait locus of gene expression) in response to the competition mechanism in virus ecosystem, reflected by the relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research). Usually, the local virus ecosystem is relatively isolated, due to ‘the absence of gene communication’ among virus ecosystem and the limitations of airborne virus transmission. Consequently, the virus samples should be collected in the same area at local scale. However, the amount of virus families, which result in the impacts on human health, is increasing due to gene recombination and mutation in self-reproduction process.

There is an improved method presented for identification of virus families:
Step 1. The whole genome of a specific virus family, whose DNA (or RNA) molecular weight is examined in Lab[5], is cultivated for reproduction in Lab as standardized DNA molecule.

Step 2. After amplified process in PCR, the DNA fragment samples together with the cultivated genomes in step 1, are transferred into the electrophoretogram procedure, conducted by the discussion 4 above.

Step 3. The standardized DNA (or RNA) molecule should be the molecules of the highest weight; Then the molecular weight of DNA fragments from the other virus families can be calculated per SSR correspondingly[5]. This improved method facilitates the identification of virus families, regardless of variation in virus ecosystem.

Step 4. Identification of virus family with gene mutation: the virus family with gene mutation is firstly identified by FISH technology; then the specific locus of genome, in which gene mutation occurs, is identified by DNA (or RNA) molecular markers (the heterozygous bands of a specific locus is the gene mutation bands, as compared  to the homozygous bands of parental virus family without gene mutation). Please note: the heterozygous or homozygous bands here are just description of band morphology, rather than allelic gene.

Please note: the objects of dyeing procedure in step 1 is protein due to the protein ‘coat’ around virus DNA (or RNA) and the DNA (or RNA) molecules are the molecules with the highest weight in virus physiology, whereas the objects of dyeing procedure in step 3 is nucleic acid molecule. SDS-PAGE for protein separation requires lower voltage than nucleic acid molecules (or isozyme separation), so that the
DNA (or RNA) can hardly take off their protein 'coat.' The weaker clearness of protein ‘coat’s bands, the higher accuracy of this test, which can be adjusted by gradual change of voltage.

Discussion:
In this experiment, the gene mutation virus family is identified in the whole virus ecosystem, analyzed by both multivariate cluster method (FISH technology) and two-paired comparison (between parental virus and gene mutation virus). It is expected that the gene mutation virus family show closer genetic distance to the other virus families, rather than its parental virus family, conducted by FISH technology. However, the conclusion of virus classification is ‘corrected’ by further DNA (or RNA) molecular markers (gene mutation virus family should show closer genetic distance to their parental virus family). This finding will further support the distortive bio-signal caused by gene mutation virus family, which is hardly identified by host cells discussed in chapter 8.

Conclusion:
In this article, it is further to proposed that compared with the DNA sequences technology utilization only, the combination of both FISH technology and DNA sequences technology in this article results in different classification conclusions due to the relativity nature of statistics in Multivariate Classification Analysis, which is more reasonable for virus testing in terms of reflecting the whole DNA/genome information. This article further concludes that the fragment information of DNA sequences does not indicates the total pathological characters of virus, because the gene mutation does not only lead to the alteration of DNA sequences in a specific fragment, but also results in the significant changes in genome morphology which plays the key role in virus invasion process. Consequently, the virus testing based on the fragment DNA sequences only is not a reliable method to reveal the total gene mutation characters. This is especially important for COVID 19 testing.      









References:
[1]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8);
[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]. 陶玲与任珺, 进化生态学的数量研究方法, 2004, 中国林业出版社: 北京市;
[4]. Genuineness and Purity Verification of Potato Seed Tuber - SSR Molecular Marker (GB/T 28660-2012).
[5]. 朱广廉,杨中汉 SDS-聚丙烯酰胺凝胶电泳法测定蛋白质的分子量《植物生理学报》, 1982.

2. Classification of Bacteria by Genetic Marker and Its Theory
However, in addition to the five steps above, a supplementary metabolomics test is advised for further classification of bacteria families, as discussed in the Chapter 7 of this book, resulting in more specific classification of bacteria families related to the incidence of pathological characters. In principal, the more enzyme species variation between pathogenic bacteria families, the higher pathogenicity for the  epidemiological receptors due to the higher environmental adaptiveness of pathogen families. Consequently, this methodology is listed below:

Step 1. Each bacterium is isolated from bacteria samples, and cultivated separately in situ forming a bacterium stream. Then each bacterium stream is named as stream 1, stream 2,. , stream n.

Step 2. The cytoplasm sample is abstracted in each bacterium stream labeled for subsequent step 8, and the abstracting procedure and storage of isozyme is listed in page 47 of isozyme chapter [1]. Then the chromosome sample of each bacterium stream labeled is prepared for step 2.

Step 3. The molecular cytogenetic karyotype of each bacterium stream labeled is analyzed by fluorescence in situ hybridization (FISH) technique[2] using transmission electron microscopy;

Step 4. These bacterium streams are classified by multivariate cluster analysis and genetic distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of bacteria[3];

Step 5. The optima sampling units of each bacteria family, which can well represent the genetic diversity of each bacteria family, is examined and determined as pointed out by Liu et al.,(2015) [3] for further classification based on DNA molecular marker (SSR or AFLP);

Step 6. Classification of bacteria families is further conducted on the basis of DNA molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis[3];

Step 7. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA molecular marker[3];

Step 8. Bacteria family ‘F’ is identified by cluster analysis and genetic distance analysis based on DNA molecular markers (genotype), which results in apparent incidence of pathological characters when exposure dose to bacteria family ‘F’ increases significantly (phenotype);

Step 9. Biochemical samples are abstracted from streams of Bacteria family ‘F,’ leading to various zymogram calculated as the average similarity coefficient across different isozyme families, which is listed in the chapter 7 of this book.

Step 10. Bacteria family ‘F’ is further classified into different sub-families by UPGMA (unweighted pair group method with arithmetic averages) method on the basis of the average similarity coefficient across different isozyme families between any two streams. The UPGMA calculation is listed in page 63 of isozyme chapter [1].

Step 11. Sub-Bacteria families, named as F1, F2 .... Fn, should be more specific in terms of correlation to the incidence of pathological characters.

Note: the above hypotheses and discussion about virus are also required for bacteria ecosystem. However, the DNA preparation procedure in discussion 4 can be changed into the procedure in these case reports instead[3], due to the inconvenience of  labeling bacteria in discussion 4.

Further more, after the Step11, there are some improvements of bacteria classification. Different environmental conditions (such as temperature and PH) are simulated in  our Lab for cultivation of bacteria streams: this research hypothesizes that there is not absolutely the same enzyme species between two different bacteria sub-families. Consequently, the comparison of one bacteria sub-family between different environmental conditions reveals the total amount of enzyme species within a whole isozyme family expressing under the range of environmental conditions simulated in Lab, and the total amount of enzyme species is the basis for calculation of similarity coefficient in one isozyme family between different bacteria sub-families, as pointed out below. Please note: the comparison of one bacteria sub-family’s zymograms between different environmental cultivation conditions should be conducted in a ‘smooth’ way, which means the comparison should be conducted at two consecutive conditions without significant variation in environmental conditions for bacteria cultivation, otherwise two different zymograms between significantly different conditions are not comparable due to the relative weight of enzyme molecules revealed by the electrophoretogram. The specific environmental condition (or bio-signal), regulating the gene expression as a specific enzyme species, is determined by this ‘smooth’ comparison as well, as further discussed in the Chapter 7 of this book.

The calculation of similarity coefficient between zymogram of different bacteria families is performed within one isozyme family[1]. However, this method is performed on the basis of unweighted average. Hence this book advises the steps of analyzing the zymograms with weighted average in future research:

1.If the electrophoresis pipe is the vertical one, then the horizontal bands in a pipe represent various enzyme species in an isozyme family. The bands at the same horizontal line between different pipes represent the same enzyme species, and the clearness of bands indicates activity of enzyme species (the more clearness, the higher activity of enzyme). Please note: the reproduction rate of microbial streams varies among different environmental cultivation conditions. Consequently, the density of microbial samples should be counted, ensuring the uniform concentration of microbial samples for the enzyme activity observation.

2.The whole environmental conditions (such as temperature) are simulated in situ from T1 to Tn (T1,T2,……,Tn). Within the environmental range [T1, Tn], the range of [T2, Ta] is the environmental range triggering the gene expression of enzyme species A, and the range of [T3, Tb] is the environmental range triggering the gene expression of enzyme species B,… etc. Consequently, the weight of enzyme species A is the ratio of range [T2,Ta] to the total range [T1, Tn], and the weight of enzyme species B is the ratio of range [T2, Tb] to the total range [T1,Tn],… etc. Then the similarity coefficient in one isozyme family between zymogram of different bacteria sub-families is calculated as: similarity coefficient = 2*∑(enzyme i * weight i) /{∑(enzyme j * weight j) + ∑(enzyme k * weight k)}. In this equation, enzyme j is the enzyme species in bacteria sub-family 1 and weight j is the weight of enzyme species j; enzyme k is the enzyme species in bacteria sub-family 2 and weight k is the weight of enzyme species k; enzyme i is the common (or same) enzyme species between sub-family 1 and sub-family 2.

3.In principle, the gene expression of enzyme species A should start at the environmental condition T2 with increasing activity along the environmental gradient, and the activity should decrease after the peak value until gene expression ceases at environmental condition Ta, which can be observed by the ‘smooth’ comparison of one bacteria sub-family’ zymograms between different bacteria cultivation conditions. However, the comparison of zymograms between different sub-families should be conducted at the same environmental cultivation condition.

Hypothesis: The ‘memory’ of gene expression:
There are two kinds of bacteria cultivation methods conducted independently in Lab: Method 1: Each bacteria sample of the same genetic strain is cultivated separately in different environmental conditions for ten generations (T1, T2, …Tn); Then different bacteria samples are abstracted for metabolomics test.

Method 2: in Step 1, bacteria samples of the same genetic strain as method 1 are cultivated in environmental condition T1. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T1 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T2; in Step 2, the rest bacteria samples are cultivated in environmental condition T2. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T2 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T3; ….; Finally, the rest bacteria samples are cultivated in environmental condition Tn. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in Tn condition for ten generations.

This research aims to examine the gene expression difference between these two  kinds of bacteria cultivation methods, although the simulated environmental conditions are the same for bacteria cultivation, which reveals the ‘memory’ of gene expression. This means that the population does not only pass on the genome, the genetic resource, but also passes on the ‘memory,’ in terms of identifying the bio-signal triggering the gene expression, onto their offspring. If these two kinds of bacteria cultivation methods lead to different gene expression types, then the second bacteria cultivation method is closer to the field conditions. Definition of bio-signal in this book as environmental physiology: the signals, emitted from environmental factors (both biotic and abiotic), can be perceived or identified by living beings.

Conclusion and Implication for future Research & Development in Air quality Monitoring:

After a family of pathogenic virus (or bacteria) has been identified by the methodologies above, the unique SSR primers specifically for this family, which can not lead to PCR bands in the other microbial families but result in clear PCR bands in this pathogenic family only, are screened and synthesized into FISH probes for FISH step again. The methods of FISH probe preparation is listed [4]. Then the  concentration of this pathogen family in water solution can be tested by ultraviolet spectrophotometer, yielding a feasible and affordable method for routine air quality monitoring. The steps are listed below. Please note, the specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, is the key for this selection of FISH probe preparation. It is expected that the gene mutation virus family results in unusual and sharp increase of airborne density, as compared to its parental virus family, because the gene mutation significantly increases the genome replication rate discussed in chapter 8. In this case, this specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, may NOT be the unique one, but becomes the key to monitor the density of gene mutation virus.

Step 1. In total five different densities of a virus family (such as the parental virus family of gene mutation one) are cultivated and separated in Lab.

Step 2. Specific FISH probe is prepared for this virus family, and FISH procedure is conducted on five densities of this virus sample without the last drying process, leading to five different water solution concentrations (Sample 1, Sample 2 ..., Sample
5) of virus genomes binding FISH probe.

Step 3. The same volume of virus water solution are abstracted from Sample 1, Sample 2, ..., Sample 5, respectively, and the density of each virus water solution is counted by transmission electron microscopy after dying process.

Step 4. The regression equation for ultraviolet spectrophotometer is consequently worked out by detecting the fluorescence intensity in five different water solution concentrations (Sample 1, Sample 2 ..., Sample 5) of virus genomes binding FISH probe.

Conclusion:
In this article, it is further to proposed that compared with the DNA sequences technology utilization only, the combination of both FISH technology and DNA sequences technology in this article results in different classification conclusions due to the relativity nature of statistics in Multivariate Classification Analysis, which is more reasonable for virus testing in terms of reflecting the whole DNA/genome information. This article further concludes that the fragment information of DNA sequences does not indicates the total pathological characters of virus, because the gene mutation does not only lead to the alteration of DNA sequences in a specific fragment, but also results in the significant changes in genome morphology which plays the key role in virus invasion process. Consequently, the virus testing based on the fragment DNA sequences only is not a reliable method to reveal the total gene mutation characters. This is especially important for COVID 19 testing.      



3.The Observation of DNA Molecules at Three Dimensions

In this article[1], DAPI fluorescence binding technology results in the appearance of AT rich region on chromosome, but the patterns of DAPI binding varies among different plant species. Consequently, this book presents the method to observe the structure of DNA molecules at three dimensions:
If the slide glass is the horizontal plane, and the vertical line is the eyesight line of microscope for DNA molecule observation, then the angle between the planes of AT DNA sequences and the eyesight line observed by microscope is ± α (0°≤ α ≤90°),  and α is generally uniform in the DNA molecules of a species, but varies among different species. If this angle tends to be zero, then the DAPI binding tends to be not observed; If this angle tends to be 90°, then the DAPI binding tends to be more clearly observed by florescence microscope. The structure of DNA molecules can be consequently deduced by the clearness of fluorescence binding.




This is the revised materials in book “Proceedings for Degree of Postgraduate Diploma in Environmental Science (3rd Edition).” Published in 2016. The ‘chapter’ content mentioned in this article is in previous book. Revised on 05/01/2021.



References:
[1]. 周延清, 张改娜与杨清香, 生物遗传标记与应用, 2008, 化学工业出版社.
[2]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8).
[3]. 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.
[4]. 郑成木, 植物分子标记原理与方法, 2003, 湖南科学技术出版社.
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 楼主| 发表于 2021-1-3 15:31:06 | 显示全部楼层
Article 1. DNA Genetic Marker/DNA遗传标记

Author: Liu Huan, MSc (First Class Honours), The University of Auckland.

This article presents the new experiment methods of DNA genetic markers  

1. Classification of Virus by Genetic Marker and Its Theory
The methods of classifying and identifying virus will follow these steps:
Step 1. The molecular cytogenetic karyotype is analyzed by fluorescence in situ hybridization (FISH) technique [1] using transmission electron microscopy;

Step 2. Virus is classified by multivariate cluster analysis and genetic  distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of virus [2];

Step 3. The optima sampling units of each virus family, which can well represent the genetic diversity of each virus family, is examined and determined as pointed out by Liu et al.,(2015) [2] for further classification based on DNA (or RNA) molecular marker (SSR or AFLP). The sampling units can be adjusted by changing the concentration of virus solution;

Step 4. Classification of virus families is further conducted on the basis of DNA (or RNA) molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis [2].

Step 5. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA (or RNA) molecular marker.

There are three hypotheses examined by this research:
Hypothesis 1: the optima numbers of polymorphic SSR primers are examined and screened for each virus family, because we assume that the amount of polymorphic SSR primers, which are assessed on the basis of polymorphism information content (PIC), increases with the increase of total SSR primers selected from Gene Bank, but the increase rate is not constant. Consequently, the optimal number of polymorphic SSR primers is determined at the peak increase rate.

Hypothesis 2: the classification significantly differs between morphological markers of virus and DNA genetic markers.

Hypothesis 3: there are two kinds of multivariate cluster analysis and genetic distance analysis on the basis of SSR markers, resulting in two different classifications of virus families: firstly, the classification of virus families is conducted based on the Nei’ genetic identity[3](or Nei’ genetic similarity[4]) calculated by the total SSR primers from Gene Bank; or then the classification of virus families is conducted based on the genetic identity calculated by the polymorphic SSR primers only. This research aims to examine which classification method leads to better correlation with the incidence of pathological characters recorded.

Discussion:
1.The total SSR primers selected from Gene Bank are the pairs of SSR primers which lead to clear PCR bands for at least one virus family in amplified process;

2.The recommended three criteria of molecular cytogenetic karyotype for the preliminary classification of different virus families include: the ratio of length between the beginning of a short arm and the margin of rDNA probe to the total  length of a chromosome; relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research); and centromere index. The average value of each criterion should be calculated for each virus family.

3.The virus samples should be collected in the same area at local scale, which facilitates the differentiation of local virus families due to the unique nature of virus ecosystem.

4.Preparation of DNA samples in one test: 12 uniform samples are abstracted from the same DNA water solution which has been evenly mixed, named as sample 1, sample 2, ..., sample12; In total 12 different SSR primers are selected in one test, named as primer 1, primer 2,...., primer12, and each different SSR primer is injected into sample 1, sample 2, ..., sample 12 respectively for PCR amplified process; after PCR amplified process, each sample (12 in total) is electrophoresed separately in each pipe of electrophoresis instrument, and the PCR bands from different virus families, preliminarily drawn by FISH technique, would be clearly separated from each other in a electrophoresis pipe. Consequently, the distance between two PCR bands from two different virus families, which is measured in a pipe of electrophoretogram, represents the genetic distance between these two virus families per SSR primer (or locus). Then the multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) is conducted on the basis of the average genetic distance between any two different virus families across SSR primers (or loci) which can lead to clear PCR bands in all virus families preliminarily drawn by FISH technique. If the electrophoresis pipe is the vertical one, then the PCR bands around the same horizontal lines represent the same virus families due to the ‘similar weight of molecules’, which can be deduced by the ‘similar length of genomes’ within one virus family identified by FISH step. Please note: both the molecular weight and genome length mentioned above are the relatively weight and relative length, because the DNA molecular weight, shown in the gel electrophoretogram (such as the distance between two PCR bands in a pipe of electrophoretogram), is the relative weight of molecules, which can be consequently deduced by the relative length of genome (the ratio of the sum genome length within a virus family to the sum genome length of all the virus families examined in research).

5.The multivariate cluster analysis for virus family classification is on the basis of the mutual interaction among virus ecosystem. Consequently, there are two criteria of qualitative gene expression (or qualitative trait locus of gene expression), including the ratio of length between the beginning (or end) of a chromosome and rDNA probe to the total length of a chromosome as well as centromere index, and a criterion of quantitative gene expression (or quantitative trait locus of gene expression) in response to the competition mechanism in virus ecosystem, reflected by the relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research). Usually, the local virus ecosystem is relatively isolated, due to ‘the absence of gene communication’ among virus ecosystem and the limitations of airborne virus transmission. Consequently, the virus samples should be collected in the same area at local scale. However, the amount of virus families, which result in the impacts on human health, is increasing due to gene recombination and mutation in self-reproduction process.

There is an improved method presented for identification of virus families:
Step 1. The whole genome of a specific virus family, whose DNA (or RNA) molecular weight is examined in Lab[5], is cultivated for reproduction in Lab as standardized DNA molecule.

Step 2. After amplified process in PCR, the DNA fragment samples together with the cultivated genomes in step 1, are transferred into the electrophoretogram procedure, conducted by the discussion 4 above.

Step 3. The standardized DNA (or RNA) molecule should be the molecules of the highest weight; Then the molecular weight of DNA fragments from the other virus families can be calculated per SSR correspondingly[5]. This improved method facilitates the identification of virus families, regardless of variation in virus ecosystem.

Step 4. Identification of virus family with gene mutation: the virus family with gene mutation is firstly identified by FISH technology; then the specific locus of genome, in which gene mutation occurs, is identified by DNA (or RNA) molecular markers (the heterozygous bands of a specific locus is the gene mutation bands, as compared  to the homozygous bands of parental virus family without gene mutation). Please note: the heterozygous or homozygous bands here are just description of band morphology, rather than allelic gene.

Please note: the objects of dyeing procedure in step 1 is protein due to the protein ‘coat’ around virus DNA (or RNA) and the DNA (or RNA) molecules are the molecules with the highest weight in virus physiology, whereas the objects of dyeing procedure in step 3 is nucleic acid molecule. SDS-PAGE for protein separation requires lower voltage than nucleic acid molecules (or isozyme separation), so that the
DNA (or RNA) can hardly take off their protein 'coat.' The weaker clearness of protein ‘coat’s bands, the higher accuracy of this test, which can be adjusted by gradual change of voltage.

Discussion:
In this experiment, the gene mutation virus family is identified in the whole virus ecosystem, analyzed by both multivariate cluster method (FISH technology) and two-paired comparison (between parental virus and gene mutation virus). It is expected that the gene mutation virus family show closer genetic distance to the other virus families, rather than its parental virus family, conducted by FISH technology. However, the conclusion of virus classification is ‘corrected’ by further DNA (or RNA) molecular markers (gene mutation virus family should show closer genetic distance to their parental virus family). This finding will further support the distortive bio-signal caused by gene mutation virus family, which is hardly identified by host cells discussed in chapter 8.

In this article, it is further to proposed that compared with the DNA sequences technology utilization only, the combination of both FISH technology and SSR DNA sequences technology in this article results in different classification conclusions due to the relativity nature of Multivariate Classification Analysis. The later one is more reasonable for virus testing.  








References:
[1]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8);
[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]. 陶玲与任珺, 进化生态学的数量研究方法, 2004, 中国林业出版社: 北京市;
[4]. Genuineness and Purity Verification of Potato Seed Tuber - SSR Molecular Marker (GB/T 28660-2012).
[5]. 朱广廉,杨中汉 SDS-聚丙烯酰胺凝胶电泳法测定蛋白质的分子量《植物生理学报》, 1982.

2. Classification of Bacteria by Genetic Marker and Its Theory
However, in addition to the five steps above, a supplementary metabolomics test is advised for further classification of bacteria families, as discussed in the Chapter 7 of this book, resulting in more specific classification of bacteria families related to the incidence of pathological characters. In principal, the more enzyme species variation between pathogenic bacteria families, the higher pathogenicity for the  epidemiological receptors due to the higher environmental adaptiveness of pathogen families. Consequently, this methodology is listed below:

Step 1. Each bacterium is isolated from bacteria samples, and cultivated separately in situ forming a bacterium stream. Then each bacterium stream is named as stream 1, stream 2,. , stream n.

Step 2. The cytoplasm sample is abstracted in each bacterium stream labeled for subsequent step 8, and the abstracting procedure and storage of isozyme is listed in page 47 of isozyme chapter [1]. Then the chromosome sample of each bacterium stream labeled is prepared for step 2.

Step 3. The molecular cytogenetic karyotype of each bacterium stream labeled is analyzed by fluorescence in situ hybridization (FISH) technique[2] using transmission electron microscopy;

Step 4. These bacterium streams are classified by multivariate cluster analysis and genetic distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of bacteria[3];

Step 5. The optima sampling units of each bacteria family, which can well represent the genetic diversity of each bacteria family, is examined and determined as pointed out by Liu et al.,(2015) [3] for further classification based on DNA molecular marker (SSR or AFLP);

Step 6. Classification of bacteria families is further conducted on the basis of DNA molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis[3];

Step 7. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA molecular marker[3];

Step 8. Bacteria family ‘F’ is identified by cluster analysis and genetic distance analysis based on DNA molecular markers (genotype), which results in apparent incidence of pathological characters when exposure dose to bacteria family ‘F’ increases significantly (phenotype);

Step 9. Biochemical samples are abstracted from streams of Bacteria family ‘F,’ leading to various zymogram calculated as the average similarity coefficient across different isozyme families, which is listed in the chapter 7 of this book.

Step 10. Bacteria family ‘F’ is further classified into different sub-families by UPGMA (unweighted pair group method with arithmetic averages) method on the basis of the average similarity coefficient across different isozyme families between any two streams. The UPGMA calculation is listed in page 63 of isozyme chapter [1].

Step 11. Sub-Bacteria families, named as F1, F2 .... Fn, should be more specific in terms of correlation to the incidence of pathological characters.

Note: the above hypotheses and discussion about virus are also required for bacteria ecosystem. However, the DNA preparation procedure in discussion 4 can be changed into the procedure in these case reports instead[3], due to the inconvenience of  labeling bacteria in discussion 4.

Further more, after the Step11, there are some improvements of bacteria classification. Different environmental conditions (such as temperature  and PH) are simulated in  our Lab for cultivation of bacteria streams: this research hypothesizes that there is not absolutely the same enzyme species between two different bacteria sub-families. Consequently, the comparison of one bacteria sub-family between different environmental conditions reveals the total amount of enzyme species within a whole isozyme family expressing under the range of environmental conditions simulated in Lab, and the total amount of enzyme species is the basis for calculation of similarity coefficient in one isozyme family between different bacteria sub-families, as pointed out below. Please note: the comparison of one bacteria sub-family’s zymograms between different environmental cultivation conditions should be conducted in a ‘smooth’ way, which means the comparison should be conducted at two consecutive conditions without significant variation in environmental conditions for bacteria cultivation, otherwise two different zymograms between significantly different conditions are not comparable due to the relative weight of enzyme molecules revealed by the electrophoretogram. The specific environmental condition (or bio-signal), regulating the gene expression as a specific enzyme species, is determined by this ‘smooth’ comparison as well, as further discussed in the Chapter 7 of this book.

The calculation of similarity coefficient between zymogram of different bacteria families is performed within one isozyme family[1]. However, this method is performed on the basis of unweighted average. Hence this book advises the steps of analyzing the zymograms with weighted average in future research:

1.If the electrophoresis pipe is the vertical one, then the horizontal bands in a pipe represent various enzyme species in an isozyme family. The bands at the same horizontal line between different pipes represent the same enzyme species, and the clearness of bands indicates activity of enzyme species (the more clearness, the higher activity of enzyme). Please note: the reproduction rate of microbial streams varies among different environmental cultivation conditions. Consequently, the density of microbial samples should be counted, ensuring the uniform concentration of microbial samples for the enzyme activity observation.

2.The whole environmental conditions (such as temperature) are simulated in situ from T1 to Tn (T1,T2,……,Tn). Within the environmental range [T1, Tn], the range of [T2, Ta] is the environmental range triggering the gene expression of enzyme species A, and the range of [T3, Tb] is the environmental range triggering the gene expression of enzyme species B,… etc. Consequently, the weight of enzyme species A is the ratio of range [T2,Ta] to the total range [T1, Tn], and the weight of enzyme species B is the ratio of range [T2, Tb] to the total range [T1,Tn],… etc. Then the similarity coefficient in one isozyme family between zymogram of different bacteria sub-families is calculated as: similarity coefficient = 2*∑(enzyme i * weight i) /
{∑(enzyme j * weight j) + ∑(enzyme k * weight k)}. In this equation, enzyme j is the enzyme species in bacteria sub-family 1 and weight j is the weight of enzyme species j; enzyme k is the enzyme species in bacteria sub-family 2 and weight k is the weight of enzyme species k; enzyme i is the common (or same) enzyme species between sub-family 1 and sub-family 2.

3.In principle, the gene expression of enzyme species A should start at the environmental condition T2 with increasing activity along the environmental gradient, and the activity should decrease after the peak value until gene expression ceases at environmental condition Ta, which can be observed by the ‘smooth’ comparison of one bacteria sub-family’ zymograms between different bacteria cultivation conditions. However, the comparison of zymograms between different sub-families should be conducted at the same environmental cultivation condition.

Hypothesis: The ‘memory’ of gene expression:
There are two kinds of bacteria cultivation methods conducted independently in Lab: Method 1: Each bacteria sample of the same genetic strain is cultivated separately in different environmental conditions for ten generations (T1, T2, …Tn); Then different bacteria samples are abstracted for metabolomics test.

Method 2: in Step 1, bacteria samples of the same genetic strain as method 1 are cultivated in environmental condition T1. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T1 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T2; in Step 2, the rest bacteria samples are cultivated in environmental condition T2. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T2 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T3; ….; Finally, the rest bacteria samples are cultivated in environmental condition Tn. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in Tn condition for ten generations.

This research aims to examine the gene expression difference between these two  kinds of bacteria cultivation methods, although the simulated environmental conditions are the same for bacteria cultivation, which reveals the ‘memory’ of gene expression. This means that the population does not only pass on the genome, the genetic resource, but also passes on the ‘memory,’ in terms of identifying the bio-signal triggering the gene expression, onto their offspring. If these two kinds of bacteria cultivation methods lead to different gene expression types, then the second bacteria cultivation method is closer to the field conditions. Definition of bio-signal in this book as environmental physiology: the signals, emitted from environmental factors (both biotic and abiotic), can be perceived or identified by living beings.

Conclusion and Implication for future Research & Development in Air quality Monitoring:

After a family of pathogenic virus (or bacteria) has been identified by the methodologies above, the unique SSR primers specifically for this family, which can not lead to PCR bands in the other microbial families but result in clear PCR bands in this pathogenic family only, are screened and synthesized into FISH probes for FISH step again. The methods of FISH probe preparation is listed [4]. Then the  concentration of this pathogen family in water solution can be tested by ultraviolet spectrophotometer, yielding a feasible and affordable method for routine air quality monitoring. The steps are listed below. Please note, the specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, is the key for this selection of FISH probe preparation. It is expected that the gene mutation virus family results in unusual and sharp increase of airborne density, as compared to its parental virus family, because the gene mutation significantly increases the genome replication rate discussed in chapter 8. In this case, this specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, may NOT be the unique one, but becomes the key to monitor the density of gene mutation virus.

Step 1. In total five different densities of a virus family (such as the parental virus family of gene mutation one) are cultivated and separated in Lab.

Step 2. Specific FISH probe is prepared for this virus family, and FISH procedure is conducted on five densities of this virus sample without the last drying process, leading to five different water solution concentrations (Sample 1, Sample 2 ..., Sample
5) of virus genomes binding FISH probe.

Step 3. The same volume of virus water solution are abstracted from Sample 1, Sample 2, ..., Sample 5, respectively, and the density of each virus water solution is counted by transmission electron microscopy after dying process.

Step 4. The regression equation for ultraviolet spectrophotometer is consequently worked out by detecting the fluorescence intensity in five different water solution concentrations (Sample 1, Sample 2 ..., Sample 5) of virus genomes binding FISH probe.


In this article, it is further to proposed that compared with the DNA sequences technology utilization only, the combination of both FISH technology and SSR DNA sequences technology in this article results in different classification conclusions due to the relativity nature of Multivariate Classification Analysis. The later one is more reasonable for virus testing.  



















References:
[1]. 周延清, 张改娜与杨清香, 生物遗传标记与应用, 2008, 化学工业出版社.
[2]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8).
[3]. 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.
[4]. 郑成木, 植物分子标记原理与方法, 2003, 湖南科学技术出版社.
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 楼主| 发表于 2021-1-3 16:40:39 | 显示全部楼层
Article 1. DNA Genetic Marker/DNA遗传标记

Author: Liu Huan, MSc (First Class Honours), The University of Auckland.

This article presents the new experiment methods of DNA genetic markers  

1. Classification of Virus by Genetic Marker and Its Theory
The methods of classifying and identifying virus will follow these steps:
Step 1. The molecular cytogenetic karyotype is analyzed by fluorescence in situ hybridization (FISH) technique [1] using transmission electron microscopy;

Step 2. Virus is classified by multivariate cluster analysis and genetic  distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of virus [2];

Step 3. The optima sampling units of each virus family, which can well represent the genetic diversity of each virus family, is examined and determined as pointed out by Liu et al.,(2015) [2] for further classification based on DNA (or RNA) molecular marker (SSR or AFLP). The sampling units can be adjusted by changing the concentration of virus solution;

Step 4. Classification of virus families is further conducted on the basis of DNA (or RNA) molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis [2].

Step 5. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA (or RNA) molecular marker.

There are three hypotheses examined by this research:
Hypothesis 1: the optima numbers of polymorphic SSR primers are examined and screened for each virus family, because we assume that the amount of polymorphic SSR primers, which are assessed on the basis of polymorphism information content (PIC), increases with the increase of total SSR primers selected from Gene Bank, but the increase rate is not constant. Consequently, the optimal number of polymorphic SSR primers is determined at the peak increase rate.

Hypothesis 2: the classification significantly differs between morphological markers of virus and DNA genetic markers.

Hypothesis 3: there are two kinds of multivariate cluster analysis and genetic distance analysis on the basis of SSR markers, resulting in two different classifications of virus families: firstly, the classification of virus families is conducted based on the Nei’ genetic identity[3](or Nei’ genetic similarity[4]) calculated by the total SSR primers from Gene Bank; or then the classification of virus families is conducted based on the genetic identity calculated by the polymorphic SSR primers only. This research aims to examine which classification method leads to better correlation with the incidence of pathological characters recorded.

Discussion:
1.The total SSR primers selected from Gene Bank are the pairs of SSR primers which lead to clear PCR bands for at least one virus family in amplified process;

2.The recommended three criteria of molecular cytogenetic karyotype for the preliminary classification of different virus families include: the ratio of length between the beginning of a short arm and the margin of rDNA probe to the total  length of a chromosome; relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research); and centromere index. The average value of each criterion should be calculated for each virus family.

3.The virus samples should be collected in the same area at local scale, which facilitates the differentiation of local virus families due to the unique nature of virus ecosystem.

4.Preparation of DNA samples in one test: 12 uniform samples are abstracted from the same DNA water solution which has been evenly mixed, named as sample 1, sample 2, ..., sample12; In total 12 different SSR primers are selected in one test, named as primer 1, primer 2,...., primer12, and each different SSR primer is injected into sample 1, sample 2, ..., sample 12 respectively for PCR amplified process; after PCR amplified process, each sample (12 in total) is electrophoresed separately in each pipe of electrophoresis instrument, and the PCR bands from different virus families, preliminarily drawn by FISH technique, would be clearly separated from each other in a electrophoresis pipe. Consequently, the distance between two PCR bands from two different virus families, which is measured in a pipe of electrophoretogram, represents the genetic distance between these two virus families per SSR primer (or locus). Then the multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) is conducted on the basis of the average genetic distance between any two different virus families across SSR primers (or loci) which can lead to clear PCR bands in all virus families preliminarily drawn by FISH technique. If the electrophoresis pipe is the vertical one, then the PCR bands around the same horizontal lines represent the same virus families due to the ‘similar weight of molecules’, which can be deduced by the ‘similar length of genomes’ within one virus family identified by FISH step. Please note: both the molecular weight and genome length mentioned above are the relatively weight and relative length, because the DNA molecular weight, shown in the gel electrophoretogram (such as the distance between two PCR bands in a pipe of electrophoretogram), is the relative weight of molecules, which can be consequently deduced by the relative length of genome (the ratio of the sum genome length within a virus family to the sum genome length of all the virus families examined in research).

5.The multivariate cluster analysis for virus family classification is on the basis of the mutual interaction among virus ecosystem. Consequently, there are two criteria of qualitative gene expression (or qualitative trait locus of gene expression), including the ratio of length between the beginning (or end) of a chromosome and rDNA probe to the total length of a chromosome as well as centromere index, and a criterion of quantitative gene expression (or quantitative trait locus of gene expression) in response to the competition mechanism in virus ecosystem, reflected by the relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research). Usually, the local virus ecosystem is relatively isolated, due to ‘the absence of gene communication’ among virus ecosystem and the limitations of airborne virus transmission. Consequently, the virus samples should be collected in the same area at local scale. However, the amount of virus families, which result in the impacts on human health, is increasing due to gene recombination and mutation in self-reproduction process.

There is an improved method presented for identification of virus families:
Step 1. The whole genome of a specific virus family, whose DNA (or RNA) molecular weight is examined in Lab[5], is cultivated for reproduction in Lab as standardized DNA molecule.

Step 2. After amplified process in PCR, the DNA fragment samples together with the cultivated genomes in step 1, are transferred into the electrophoretogram procedure, conducted by the discussion 4 above.

Step 3. The standardized DNA (or RNA) molecule should be the molecules of the highest weight; Then the molecular weight of DNA fragments from the other virus families can be calculated per SSR correspondingly[5]. This improved method facilitates the identification of virus families, regardless of variation in virus ecosystem.

Step 4. Identification of virus family with gene mutation: the virus family with gene mutation is firstly identified by FISH technology; then the specific locus of genome, in which gene mutation occurs, is identified by DNA (or RNA) molecular markers (the heterozygous bands of a specific locus is the gene mutation bands, as compared  to the homozygous bands of parental virus family without gene mutation). Please note: the heterozygous or homozygous bands here are just description of band morphology, rather than allelic gene.

Please note: the objects of dyeing procedure in step 1 is protein due to the protein ‘coat’ around virus DNA (or RNA) and the DNA (or RNA) molecules are the molecules with the highest weight in virus physiology, whereas the objects of dyeing procedure in step 3 is nucleic acid molecule. SDS-PAGE for protein separation requires lower voltage than nucleic acid molecules (or isozyme separation), so that the
DNA (or RNA) can hardly take off their protein 'coat.' The weaker clearness of protein ‘coat’s bands, the higher accuracy of this test, which can be adjusted by gradual change of voltage.

Discussion:
In this experiment, the gene mutation virus family is identified in the whole virus ecosystem, analyzed by both multivariate cluster method (FISH technology) and two-paired comparison (between parental virus and gene mutation virus). It is expected that the gene mutation virus family show closer genetic distance to the other virus families, rather than its parental virus family, conducted by FISH technology. However, the conclusion of virus classification is ‘corrected’ by further DNA (or RNA) molecular markers (gene mutation virus family should show closer genetic distance to their parental virus family). This finding will further support the distortive bio-signal caused by gene mutation virus family, which is hardly identified by host cells discussed in chapter 8.

Conclusion:
In this article, it is further to proposed that compared with the DNA sequences technology utilization only, the combination of both FISH technology and DNA sequences technology in this article results in different classification conclusions due to the relativity nature of statistics in Multivariate Classification Analysis, which is more reasonable for virus testing in terms of reflecting the whole DNA/genome information. This article further concludes that the fragment information of DNA sequences does not indicates the total pathological characters of virus, because the gene mutation does not only lead to the alteration of DNA sequences in a specific fragment, but also results in the significant changes in genome morphology which plays the key role in virus invasion process. Consequently, the virus testing based on the fragment DNA sequences only is not a reliable method to reveal the total gene mutation characters. This is especially important for COVID 19 testing.      










References:
[1]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8);
[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]. 陶玲与任珺, 进化生态学的数量研究方法, 2004, 中国林业出版社: 北京市;
[4]. Genuineness and Purity Verification of Potato Seed Tuber - SSR Molecular Marker (GB/T 28660-2012).
[5]. 朱广廉,杨中汉 SDS-聚丙烯酰胺凝胶电泳法测定蛋白质的分子量《植物生理学报》, 1982.

2. Classification of Bacteria by Genetic Marker and Its Theory
However, in addition to the five steps above, a supplementary metabolomics test is advised for further classification of bacteria families, as discussed in the Chapter 7 of this book, resulting in more specific classification of bacteria families related to the incidence of pathological characters. In principal, the more enzyme species variation between pathogenic bacteria families, the higher pathogenicity for the  epidemiological receptors due to the higher environmental adaptiveness of pathogen families. Consequently, this methodology is listed below:

Step 1. Each bacterium is isolated from bacteria samples, and cultivated separately in situ forming a bacterium stream. Then each bacterium stream is named as stream 1, stream 2,. , stream n.

Step 2. The cytoplasm sample is abstracted in each bacterium stream labeled for subsequent step 8, and the abstracting procedure and storage of isozyme is listed in page 47 of isozyme chapter [1]. Then the chromosome sample of each bacterium stream labeled is prepared for step 2.

Step 3. The molecular cytogenetic karyotype of each bacterium stream labeled is analyzed by fluorescence in situ hybridization (FISH) technique[2] using transmission electron microscopy;

Step 4. These bacterium streams are classified by multivariate cluster analysis and genetic distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of bacteria[3];

Step 5. The optima sampling units of each bacteria family, which can well represent the genetic diversity of each bacteria family, is examined and determined as pointed out by Liu et al.,(2015) [3] for further classification based on DNA molecular marker (SSR or AFLP);

Step 6. Classification of bacteria families is further conducted on the basis of DNA molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis[3];

Step 7. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA molecular marker[3];

Step 8. Bacteria family ‘F’ is identified by cluster analysis and genetic distance analysis based on DNA molecular markers (genotype), which results in apparent incidence of pathological characters when exposure dose to bacteria family ‘F’ increases significantly (phenotype);

Step 9. Biochemical samples are abstracted from streams of Bacteria family ‘F,’ leading to various zymogram calculated as the average similarity coefficient across different isozyme families, which is listed in the chapter 7 of this book.

Step 10. Bacteria family ‘F’ is further classified into different sub-families by UPGMA (unweighted pair group method with arithmetic averages) method on the basis of the average similarity coefficient across different isozyme families between any two streams. The UPGMA calculation is listed in page 63 of isozyme chapter [1].

Step 11. Sub-Bacteria families, named as F1, F2 .... Fn, should be more specific in terms of correlation to the incidence of pathological characters.

Note: the above hypotheses and discussion about virus are also required for bacteria ecosystem. However, the DNA preparation procedure in discussion 4 can be changed into the procedure in these case reports instead[3], due to the inconvenience of  labeling bacteria in discussion 4.

Further more, after the Step11, there are some improvements of bacteria classification. Different environmental conditions (such as temperature  and PH) are simulated in  our Lab for cultivation of bacteria streams: this research hypothesizes that there is not absolutely the same enzyme species between two different bacteria sub-families. Consequently, the comparison of one bacteria sub-family between different environmental conditions reveals the total amount of enzyme species within a whole isozyme family expressing under the range of environmental conditions simulated in Lab, and the total amount of enzyme species is the basis for calculation of similarity coefficient in one isozyme family between different bacteria sub-families, as pointed out below. Please note: the comparison of one bacteria sub-family’s zymograms between different environmental cultivation conditions should be conducted in a ‘smooth’ way, which means the comparison should be conducted at two consecutive conditions without significant variation in environmental conditions for bacteria cultivation, otherwise two different zymograms between significantly different conditions are not comparable due to the relative weight of enzyme molecules revealed by the electrophoretogram. The specific environmental condition (or bio-signal), regulating the gene expression as a specific enzyme species, is determined by this ‘smooth’ comparison as well, as further discussed in the Chapter 7 of this book.

The calculation of similarity coefficient between zymogram of different bacteria families is performed within one isozyme family[1]. However, this method is performed on the basis of unweighted average. Hence this book advises the steps of analyzing the zymograms with weighted average in future research:

1.If the electrophoresis pipe is the vertical one, then the horizontal bands in a pipe represent various enzyme species in an isozyme family. The bands at the same horizontal line between different pipes represent the same enzyme species, and the clearness of bands indicates activity of enzyme species (the more clearness, the higher activity of enzyme). Please note: the reproduction rate of microbial streams varies among different environmental cultivation conditions. Consequently, the density of microbial samples should be counted, ensuring the uniform concentration of microbial samples for the enzyme activity observation.

2.The whole environmental conditions (such as temperature) are simulated in situ from T1 to Tn (T1,T2,……,Tn). Within the environmental range [T1, Tn], the range of [T2, Ta] is the environmental range triggering the gene expression of enzyme species A, and the range of [T3, Tb] is the environmental range triggering the gene expression of enzyme species B,… etc. Consequently, the weight of enzyme species A is the ratio of range [T2,Ta] to the total range [T1, Tn], and the weight of enzyme species B is the ratio of range [T2, Tb] to the total range [T1,Tn],… etc. Then the similarity coefficient in one isozyme family between zymogram of different bacteria sub-families is calculated as: similarity coefficient = 2*∑(enzyme i * weight i) /
{∑(enzyme j * weight j) + ∑(enzyme k * weight k)}. In this equation, enzyme j is the enzyme species in bacteria sub-family 1 and weight j is the weight of enzyme species j; enzyme k is the enzyme species in bacteria sub-family 2 and weight k is the weight of enzyme species k; enzyme i is the common (or same) enzyme species between sub-family 1 and sub-family 2.

3.In principle, the gene expression of enzyme species A should start at the environmental condition T2 with increasing activity along the environmental gradient, and the activity should decrease after the peak value until gene expression ceases at environmental condition Ta, which can be observed by the ‘smooth’ comparison of one bacteria sub-family’ zymograms between different bacteria cultivation conditions. However, the comparison of zymograms between different sub-families should be conducted at the same environmental cultivation condition.

Hypothesis: The ‘memory’ of gene expression:
There are two kinds of bacteria cultivation methods conducted independently in Lab: Method 1: Each bacteria sample of the same genetic strain is cultivated separately in different environmental conditions for ten generations (T1, T2, …Tn); Then different bacteria samples are abstracted for metabolomics test.

Method 2: in Step 1, bacteria samples of the same genetic strain as method 1 are cultivated in environmental condition T1. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T1 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T2; in Step 2, the rest bacteria samples are cultivated in environmental condition T2. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T2 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T3; ….; Finally, the rest bacteria samples are cultivated in environmental condition Tn. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in Tn condition for ten generations.

This research aims to examine the gene expression difference between these two  kinds of bacteria cultivation methods, although the simulated environmental conditions are the same for bacteria cultivation, which reveals the ‘memory’ of gene expression. This means that the population does not only pass on the genome, the genetic resource, but also passes on the ‘memory,’ in terms of identifying the bio-signal triggering the gene expression, onto their offspring. If these two kinds of bacteria cultivation methods lead to different gene expression types, then the second bacteria cultivation method is closer to the field conditions. Definition of bio-signal in this book as environmental physiology: the signals, emitted from environmental factors (both biotic and abiotic), can be perceived or identified by living beings.

Conclusion and Implication for future Research & Development in Air quality Monitoring:

After a family of pathogenic virus (or bacteria) has been identified by the methodologies above, the unique SSR primers specifically for this family, which can not lead to PCR bands in the other microbial families but result in clear PCR bands in this pathogenic family only, are screened and synthesized into FISH probes for FISH step again. The methods of FISH probe preparation is listed [4]. Then the  concentration of this pathogen family in water solution can be tested by ultraviolet spectrophotometer, yielding a feasible and affordable method for routine air quality monitoring. The steps are listed below. Please note, the specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, is the key for this selection of FISH probe preparation. It is expected that the gene mutation virus family results in unusual and sharp increase of airborne density, as compared to its parental virus family, because the gene mutation significantly increases the genome replication rate discussed in chapter 8. In this case, this specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, may NOT be the unique one, but becomes the key to monitor the density of gene mutation virus.

Step 1. In total five different densities of a virus family (such as the parental virus family of gene mutation one) are cultivated and separated in Lab.

Step 2. Specific FISH probe is prepared for this virus family, and FISH procedure is conducted on five densities of this virus sample without the last drying process, leading to five different water solution concentrations (Sample 1, Sample 2 ..., Sample
5) of virus genomes binding FISH probe.

Step 3. The same volume of virus water solution are abstracted from Sample 1, Sample 2, ..., Sample 5, respectively, and the density of each virus water solution is counted by transmission electron microscopy after dying process.

Step 4. The regression equation for ultraviolet spectrophotometer is consequently worked out by detecting the fluorescence intensity in five different water solution concentrations (Sample 1, Sample 2 ..., Sample 5) of virus genomes binding FISH probe.

Conclusion:
In this article, it is further to proposed that compared with the DNA sequences technology utilization only, the combination of both FISH technology and DNA sequences technology in this article results in different classification conclusions due to the relativity nature of statistics in Multivariate Classification Analysis, which is more reasonable for virus testing in terms of reflecting the whole DNA/genome information. This article further concludes that the fragment information of DNA sequences does not indicates the total pathological characters of virus, because the gene mutation does not only lead to the alteration of DNA sequences in a specific fragment, but also results in the significant changes in genome morphology which plays the key role in virus invasion process. Consequently, the virus testing based on the fragment DNA sequences only is not a reliable method to reveal the total gene mutation characters. This is especially important for COVID 19 testing.      








This is the revised materials in book “Proceedings for Degree of Postgraduate Diploma in Environmental Science (3rd Edition).” published in 2016. Revised on 03/01/2021.  




References:
[1]. 周延清, 张改娜与杨清香, 生物遗传标记与应用, 2008, 化学工业出版社.
[2]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8).
[3]. 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.
[4]. 郑成木, 植物分子标记原理与方法, 2003, 湖南科学技术出版社.
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 楼主| 发表于 2021-1-4 10:21:05 | 显示全部楼层
Article 1. DNA Genetic Marker/DNA遗传标记

Author: Liu Huan, MSc (First Class Honours), The University of Auckland.

This article presents the new experiment methods of DNA genetic markers  

1. Classification of Virus by Genetic Marker and Its Theory
The methods of classifying and identifying virus will follow these steps:
Step 1. The molecular cytogenetic karyotype is analyzed by fluorescence in situ hybridization (FISH) technique [1] using transmission electron microscopy;

Step 2. Virus is classified by multivariate cluster analysis and genetic  distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of virus [2];

Step 3. The optima sampling units of each virus family, which can well represent the genetic diversity of each virus family, is examined and determined as pointed out by Liu et al.,(2015) [2] for further classification based on DNA (or RNA) molecular marker (SSR or AFLP). The sampling units can be adjusted by changing the concentration of virus solution;

Step 4. Classification of virus families is further conducted on the basis of DNA (or RNA) molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis [2].

Step 5. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA (or RNA) molecular marker.

There are three hypotheses examined by this research:
Hypothesis 1: the optima numbers of polymorphic SSR primers are examined and screened for each virus family, because we assume that the amount of polymorphic SSR primers, which are assessed on the basis of polymorphism information content (PIC), increases with the increase of total SSR primers selected from Gene Bank, but the increase rate is not constant. Consequently, the optimal number of polymorphic SSR primers is determined at the peak increase rate.

Hypothesis 2: the classification significantly differs between morphological markers of virus and DNA genetic markers.

Hypothesis 3: there are two kinds of multivariate cluster analysis and genetic distance analysis on the basis of SSR markers, resulting in two different classifications of virus families: firstly, the classification of virus families is conducted based on the Nei’ genetic identity[3](or Nei’ genetic similarity[4]) calculated by the total SSR primers from Gene Bank; or then the classification of virus families is conducted based on the genetic identity calculated by the polymorphic SSR primers only. This research aims to examine which classification method leads to better correlation with the incidence of pathological characters recorded.

Discussion:
1.The total SSR primers selected from Gene Bank are the pairs of SSR primers which lead to clear PCR bands for at least one virus family in amplified process;

2.The recommended three criteria of molecular cytogenetic karyotype for the preliminary classification of different virus families include: the ratio of length between the beginning of a short arm and the margin of rDNA probe to the total  length of a chromosome; relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research); and centromere index. The average value of each criterion should be calculated for each virus family.

3.The virus samples should be collected in the same area at local scale, which facilitates the differentiation of local virus families due to the unique nature of virus ecosystem.

4.Preparation of DNA samples in one test: 12 uniform samples are abstracted from the same DNA water solution which has been evenly mixed, named as sample 1, sample 2, ..., sample12; In total 12 different SSR primers are selected in one test, named as primer 1, primer 2,...., primer12, and each different SSR primer is injected into sample 1, sample 2, ..., sample 12 respectively for PCR amplified process; after PCR amplified process, each sample (12 in total) is electrophoresed separately in each pipe of electrophoresis instrument, and the PCR bands from different virus families, preliminarily drawn by FISH technique, would be clearly separated from each other in a electrophoresis pipe. Consequently, the distance between two PCR bands from two different virus families, which is measured in a pipe of electrophoretogram, represents the genetic distance between these two virus families per SSR primer (or locus). Then the multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) is conducted on the basis of the average genetic distance between any two different virus families across SSR primers (or loci) which can lead to clear PCR bands in all virus families preliminarily drawn by FISH technique. If the electrophoresis pipe is the vertical one, then the PCR bands around the same horizontal lines represent the same virus families due to the ‘similar weight of molecules’, which can be deduced by the ‘similar length of genomes’ within one virus family identified by FISH step. Please note: both the molecular weight and genome length mentioned above are the relatively weight and relative length, because the DNA molecular weight, shown in the gel electrophoretogram (such as the distance between two PCR bands in a pipe of electrophoretogram), is the relative weight of molecules, which can be consequently deduced by the relative length of genome (the ratio of the sum genome length within a virus family to the sum genome length of all the virus families examined in research).

5.The multivariate cluster analysis for virus family classification is on the basis of the mutual interaction among virus ecosystem. Consequently, there are two criteria of qualitative gene expression (or qualitative trait locus of gene expression), including the ratio of length between the beginning (or end) of a chromosome and rDNA probe to the total length of a chromosome as well as centromere index, and a criterion of quantitative gene expression (or quantitative trait locus of gene expression) in response to the competition mechanism in virus ecosystem, reflected by the relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research). Usually, the local virus ecosystem is relatively isolated, due to ‘the absence of gene communication’ among virus ecosystem and the limitations of airborne virus transmission. Consequently, the virus samples should be collected in the same area at local scale. However, the amount of virus families, which result in the impacts on human health, is increasing due to gene recombination and mutation in self-reproduction process.

There is an improved method presented for identification of virus families:
Step 1. The whole genome of a specific virus family, whose DNA (or RNA) molecular weight is examined in Lab[5], is cultivated for reproduction in Lab as standardized DNA molecule.

Step 2. After amplified process in PCR, the DNA fragment samples together with the cultivated genomes in step 1, are transferred into the electrophoretogram procedure, conducted by the discussion 4 above.

Step 3. The standardized DNA (or RNA) molecule should be the molecules of the highest weight; Then the molecular weight of DNA fragments from the other virus families can be calculated per SSR correspondingly[5]. This improved method facilitates the identification of virus families, regardless of variation in virus ecosystem.

Step 4. Identification of virus family with gene mutation: the virus family with gene mutation is firstly identified by FISH technology; then the specific locus of genome, in which gene mutation occurs, is identified by DNA (or RNA) molecular markers (the heterozygous bands of a specific locus is the gene mutation bands, as compared  to the homozygous bands of parental virus family without gene mutation). Please note: the heterozygous or homozygous bands here are just description of band morphology, rather than allelic gene.

Please note: the objects of dyeing procedure in step 1 is protein due to the protein ‘coat’ around virus DNA (or RNA) and the DNA (or RNA) molecules are the molecules with the highest weight in virus physiology, whereas the objects of dyeing procedure in step 3 is nucleic acid molecule. SDS-PAGE for protein separation requires lower voltage than nucleic acid molecules (or isozyme separation), so that the
DNA (or RNA) can hardly take off their protein 'coat.' The weaker clearness of protein ‘coat’s bands, the higher accuracy of this test, which can be adjusted by gradual change of voltage.

Discussion:
In this experiment, the gene mutation virus family is identified in the whole virus ecosystem, analyzed by both multivariate cluster method (FISH technology) and two-paired comparison (between parental virus and gene mutation virus). It is expected that the gene mutation virus family show closer genetic distance to the other virus families, rather than its parental virus family, conducted by FISH technology. However, the conclusion of virus classification is ‘corrected’ by further DNA (or RNA) molecular markers (gene mutation virus family should show closer genetic distance to their parental virus family). This finding will further support the distortive bio-signal caused by gene mutation virus family, which is hardly identified by host cells discussed in chapter 8.

Conclusion:
In this article, it is further to proposed that compared with the DNA sequences technology utilization only, the combination of both FISH technology and DNA sequences technology in this article results in different classification conclusions due to the relativity nature of statistics in Multivariate Classification Analysis, which is more reasonable for virus testing in terms of reflecting the whole DNA/genome information. This article further concludes that the fragment information of DNA sequences does not indicates the total pathological characters of virus, because the gene mutation does not only lead to the alteration of DNA sequences in a specific fragment, but also results in the significant changes in genome morphology which plays the key role in virus invasion process. Consequently, the virus testing based on the fragment DNA sequences only is not a reliable method to reveal the total gene mutation characters. This is especially important for COVID 19 testing.      










References:
[1]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8);
[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]. 陶玲与任珺, 进化生态学的数量研究方法, 2004, 中国林业出版社: 北京市;
[4]. Genuineness and Purity Verification of Potato Seed Tuber - SSR Molecular Marker (GB/T 28660-2012).
[5]. 朱广廉,杨中汉 SDS-聚丙烯酰胺凝胶电泳法测定蛋白质的分子量《植物生理学报》, 1982.

2. Classification of Bacteria by Genetic Marker and Its Theory
However, in addition to the five steps above, a supplementary metabolomics test is advised for further classification of bacteria families, as discussed in the Chapter 7 of this book, resulting in more specific classification of bacteria families related to the incidence of pathological characters. In principal, the more enzyme species variation between pathogenic bacteria families, the higher pathogenicity for the  epidemiological receptors due to the higher environmental adaptiveness of pathogen families. Consequently, this methodology is listed below:

Step 1. Each bacterium is isolated from bacteria samples, and cultivated separately in situ forming a bacterium stream. Then each bacterium stream is named as stream 1, stream 2,. , stream n.

Step 2. The cytoplasm sample is abstracted in each bacterium stream labeled for subsequent step 8, and the abstracting procedure and storage of isozyme is listed in page 47 of isozyme chapter [1]. Then the chromosome sample of each bacterium stream labeled is prepared for step 2.

Step 3. The molecular cytogenetic karyotype of each bacterium stream labeled is analyzed by fluorescence in situ hybridization (FISH) technique[2] using transmission electron microscopy;

Step 4. These bacterium streams are classified by multivariate cluster analysis and genetic distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of bacteria[3];

Step 5. The optima sampling units of each bacteria family, which can well represent the genetic diversity of each bacteria family, is examined and determined as pointed out by Liu et al.,(2015) [3] for further classification based on DNA molecular marker (SSR or AFLP);

Step 6. Classification of bacteria families is further conducted on the basis of DNA molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis[3];

Step 7. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA molecular marker[3];

Step 8. Bacteria family ‘F’ is identified by cluster analysis and genetic distance analysis based on DNA molecular markers (genotype), which results in apparent incidence of pathological characters when exposure dose to bacteria family ‘F’ increases significantly (phenotype);

Step 9. Biochemical samples are abstracted from streams of Bacteria family ‘F,’ leading to various zymogram calculated as the average similarity coefficient across different isozyme families, which is listed in the chapter 7 of this book.

Step 10. Bacteria family ‘F’ is further classified into different sub-families by UPGMA (unweighted pair group method with arithmetic averages) method on the basis of the average similarity coefficient across different isozyme families between any two streams. The UPGMA calculation is listed in page 63 of isozyme chapter [1].

Step 11. Sub-Bacteria families, named as F1, F2 .... Fn, should be more specific in terms of correlation to the incidence of pathological characters.

Note: the above hypotheses and discussion about virus are also required for bacteria ecosystem. However, the DNA preparation procedure in discussion 4 can be changed into the procedure in these case reports instead[3], due to the inconvenience of  labeling bacteria in discussion 4.

Further more, after the Step11, there are some improvements of bacteria classification. Different environmental conditions (such as temperature  and PH) are simulated in  our Lab for cultivation of bacteria streams: this research hypothesizes that there is not absolutely the same enzyme species between two different bacteria sub-families. Consequently, the comparison of one bacteria sub-family between different environmental conditions reveals the total amount of enzyme species within a whole isozyme family expressing under the range of environmental conditions simulated in Lab, and the total amount of enzyme species is the basis for calculation of similarity coefficient in one isozyme family between different bacteria sub-families, as pointed out below. Please note: the comparison of one bacteria sub-family’s zymograms between different environmental cultivation conditions should be conducted in a ‘smooth’ way, which means the comparison should be conducted at two consecutive conditions without significant variation in environmental conditions for bacteria cultivation, otherwise two different zymograms between significantly different conditions are not comparable due to the relative weight of enzyme molecules revealed by the electrophoretogram. The specific environmental condition (or bio-signal), regulating the gene expression as a specific enzyme species, is determined by this ‘smooth’ comparison as well, as further discussed in the Chapter 7 of this book.

The calculation of similarity coefficient between zymogram of different bacteria families is performed within one isozyme family[1]. However, this method is performed on the basis of unweighted average. Hence this book advises the steps of analyzing the zymograms with weighted average in future research:

1.If the electrophoresis pipe is the vertical one, then the horizontal bands in a pipe represent various enzyme species in an isozyme family. The bands at the same horizontal line between different pipes represent the same enzyme species, and the clearness of bands indicates activity of enzyme species (the more clearness, the higher activity of enzyme). Please note: the reproduction rate of microbial streams varies among different environmental cultivation conditions. Consequently, the density of microbial samples should be counted, ensuring the uniform concentration of microbial samples for the enzyme activity observation.

2.The whole environmental conditions (such as temperature) are simulated in situ from T1 to Tn (T1,T2,……,Tn). Within the environmental range [T1, Tn], the range of [T2, Ta] is the environmental range triggering the gene expression of enzyme species A, and the range of [T3, Tb] is the environmental range triggering the gene expression of enzyme species B,… etc. Consequently, the weight of enzyme species A is the ratio of range [T2,Ta] to the total range [T1, Tn], and the weight of enzyme species B is the ratio of range [T2, Tb] to the total range [T1,Tn],… etc. Then the similarity coefficient in one isozyme family between zymogram of different bacteria sub-families is calculated as: similarity coefficient = 2*∑(enzyme i * weight i) /
{∑(enzyme j * weight j) + ∑(enzyme k * weight k)}. In this equation, enzyme j is the enzyme species in bacteria sub-family 1 and weight j is the weight of enzyme species j; enzyme k is the enzyme species in bacteria sub-family 2 and weight k is the weight of enzyme species k; enzyme i is the common (or same) enzyme species between sub-family 1 and sub-family 2.

3.In principle, the gene expression of enzyme species A should start at the environmental condition T2 with increasing activity along the environmental gradient, and the activity should decrease after the peak value until gene expression ceases at environmental condition Ta, which can be observed by the ‘smooth’ comparison of one bacteria sub-family’ zymograms between different bacteria cultivation conditions. However, the comparison of zymograms between different sub-families should be conducted at the same environmental cultivation condition.

Hypothesis: The ‘memory’ of gene expression:
There are two kinds of bacteria cultivation methods conducted independently in Lab: Method 1: Each bacteria sample of the same genetic strain is cultivated separately in different environmental conditions for ten generations (T1, T2, …Tn); Then different bacteria samples are abstracted for metabolomics test.

Method 2: in Step 1, bacteria samples of the same genetic strain as method 1 are cultivated in environmental condition T1. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T1 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T2; in Step 2, the rest bacteria samples are cultivated in environmental condition T2. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T2 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T3; ….; Finally, the rest bacteria samples are cultivated in environmental condition Tn. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in Tn condition for ten generations.

This research aims to examine the gene expression difference between these two  kinds of bacteria cultivation methods, although the simulated environmental conditions are the same for bacteria cultivation, which reveals the ‘memory’ of gene expression. This means that the population does not only pass on the genome, the genetic resource, but also passes on the ‘memory,’ in terms of identifying the bio-signal triggering the gene expression, onto their offspring. If these two kinds of bacteria cultivation methods lead to different gene expression types, then the second bacteria cultivation method is closer to the field conditions. Definition of bio-signal in this book as environmental physiology: the signals, emitted from environmental factors (both biotic and abiotic), can be perceived or identified by living beings.

Conclusion and Implication for future Research & Development in Air quality Monitoring:

After a family of pathogenic virus (or bacteria) has been identified by the methodologies above, the unique SSR primers specifically for this family, which can not lead to PCR bands in the other microbial families but result in clear PCR bands in this pathogenic family only, are screened and synthesized into FISH probes for FISH step again. The methods of FISH probe preparation is listed [4]. Then the  concentration of this pathogen family in water solution can be tested by ultraviolet spectrophotometer, yielding a feasible and affordable method for routine air quality monitoring. The steps are listed below. Please note, the specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, is the key for this selection of FISH probe preparation. It is expected that the gene mutation virus family results in unusual and sharp increase of airborne density, as compared to its parental virus family, because the gene mutation significantly increases the genome replication rate discussed in chapter 8. In this case, this specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, may NOT be the unique one, but becomes the key to monitor the density of gene mutation virus.

Step 1. In total five different densities of a virus family (such as the parental virus family of gene mutation one) are cultivated and separated in Lab.

Step 2. Specific FISH probe is prepared for this virus family, and FISH procedure is conducted on five densities of this virus sample without the last drying process, leading to five different water solution concentrations (Sample 1, Sample 2 ..., Sample
5) of virus genomes binding FISH probe.

Step 3. The same volume of virus water solution are abstracted from Sample 1, Sample 2, ..., Sample 5, respectively, and the density of each virus water solution is counted by transmission electron microscopy after dying process.

Step 4. The regression equation for ultraviolet spectrophotometer is consequently worked out by detecting the fluorescence intensity in five different water solution concentrations (Sample 1, Sample 2 ..., Sample 5) of virus genomes binding FISH probe.

Conclusion:
In this article, it is further to proposed that compared with the DNA sequences technology utilization only, the combination of both FISH technology and DNA sequences technology in this article results in different classification conclusions due to the relativity nature of statistics in Multivariate Classification Analysis, which is more reasonable for virus testing in terms of reflecting the whole DNA/genome information. This article further concludes that the fragment information of DNA sequences does not indicates the total pathological characters of virus, because the gene mutation does not only lead to the alteration of DNA sequences in a specific fragment, but also results in the significant changes in genome morphology which plays the key role in virus invasion process. Consequently, the virus testing based on the fragment DNA sequences only is not a reliable method to reveal the total gene mutation characters. This is especially important for COVID 19 testing.      



3.The Observation of DNA Molecules at Three Dimensions

In this article[1], DAPI fluorescence binding technology results in the appearance of AT rich region on chromosome, but the patterns of DAPI binding varies among different plant species. Consequently, this book presents the method to observe the structure of DNA molecules at three dimensions:
If the slide glass is the horizontal plane, and the vertical line is the eyesight line of microscope for DNA molecule observation, then the angle between the planes of AT DNA sequences and the eyesight line observed by microscope is ± α (0°≤ α ≤90°),  and α is generally uniform in the DNA molecules of a species, but varies among different species. If this angle tends to be zero, then the DAPI binding tends to be not observed; If this angle tends to be 90°, then the DAPI binding tends to be more clearly observed by florescence microscope. The structure of DNA molecules can be consequently deduced by the clearness of fluorescence binding.















This is the revised materials in book “Proceedings for Degree of Postgraduate Diploma in Environmental Science (3rd Edition).” published in 2016. Revised on 03/01/2021.  



References:
[1]. 周延清, 张改娜与杨清香, 生物遗传标记与应用, 2008, 化学工业出版社.
[2]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8).
[3]. 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.
[4]. 郑成木, 植物分子标记原理与方法, 2003, 湖南科学技术出版社.
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