The rise of systems biology, seeking to comprehend biological processes as a whole, highlighted the need to not only develop corresponding quantitative models but also to create standards allowing their exchange and integration. This concern drove the community to design common data formats, such as  SBML  and  CellML . SBML is now largely accepted and used in the field.  However, as important as the definition of a common syntax is, it is also necessary to make clear the semantics of models. SBO tries to give us a way to label models with words that describe how they should be used in a large group of models that are commonly used in computational systems biology. [ 1 ] [ 2 ]  The development of SBO was first discussed at the 9th SBML Forum Meeting in Heidelberg on October 14–15, 2004. During the forum,  Pedro Mendes  mentioned that modellers possessed a lot of knowledge that was necessary to understand the model and, more importantly, to simulate it,  but this knowledge was not encoded in SBML.  Nicolas Le Novère  proposed to create a controlled vocabulary to store the content of Pedro Mendes' mind before he wandered out of the community. [ 3 ]  The development of the ontology was announced more officially in a message from Le Novère to Michael Hucka and Andrew Finney on October 19.


SBO is currently made up of seven different vocabularies:


To curate and maintain SBO, a dedicated resource has been developed and the public interface of the SBO browser can be accessed at  http://www.ebi.ac.uk/sbo .
A relational database management system ( MySQL ) at the back-end is
accessed through a web interface based on  Java Server Pages  (JSP) and  JavaBeans .  Its
content is encoded in  UTF-8 , therefore supporting a large set of
characters in the definitions of terms.  Distributed curation is made possible
by using a custom-tailored locking system allowing concurrent access.
This system allows a continuous update of the ontology with immediate
availability and suppress merging problems.


Several exports formats ( OBO  flat file,  SBO-XML  and  OWL ) are generated daily or on request and can be downloaded from the web interface.


To allow programmatic access to the resource,  Web Services  have been implemented based on  Apache Axis  for the communication layer and  Castor  for the validation. [ 4 ]  The libraries, full documentation, samples and tutorial are available  online .


The SourceForge project can be accessed at  http://sourceforge.net/projects/sbo/ .


Since  Level 2 Version 2   SBML  provides a mechanism to annotate model components with SBO terms, therefore increasing the semantics of the
model beyond the sole topology of interaction and mathematical expression.  Modelling tools such as  SBMLsqueezer [ 5 ]  interpret SBO terms to augment the mathematics in the SBML file. Simulation tools can check the consistency of a rate law, convert reaction from one modelling framework to another (e.g., continuous to discrete), or distinguish between identical mathematical expressions based on different assumptions (e.g., Michaelis–Menten vs. Briggs–Haldane).  To add missing SBO terms to models, software such as  SBOannotator [ 6 ]  can be used.  Other tools such as  semanticSBML [ 7 ]  can use the SBO annotation to integrate individual models into a larger one.  The use of SBO is not restricted to the development of models. Resources providing quantitative experimental information such as  SABIO Reaction Kinetics  will be able to annotate the parameters (what do they mean exactly, how were they calculated) and determine relationships between them.


All the graphical symbols used in the  SBGN  languages are associated with an SBO term. This permits, for instance, to help generate  SBGN  maps from  SBML  models.


The Systems Biology Pathway Exchange (SBPAX) allows SBO terms to be added to  Biological Pathway Exchange (BioPAX) . This links BioPAX to information useful for modelling, especially by adding quantitative descriptions described by SBO.


SBO is built in collaboration by the  Computational Neurobiology Group  (Nicolas Le Novère,  EMBL - EBI , United-Kingdom) and the  SBML Team (Michael Hucka,  Caltech , USA).


SBO has benefited from the funds of the  European Molecular Biology Laboratory  and the  National Institute of General Medical Sciences .


In  genetics , a  three-point cross  is used to determine the  loci  of three  genes  in an organism's  genome .


An individual  heterozygous  for three mutations is crossed with a  homozygous   recessive  individual, and the  phenotypes  of the  progeny  are scored. The two most common phenotypes that result are the parental  gametes ; the two least common phenotypes that result come from a double  crossover  in  gamete formation . By comparing the parental and double-crossover phenotypes, the geneticist can determine which gene is located between the others on the chromosome.


The  recombinant frequency  is the ratio of non-parental phenotypes to total individuals. It is expressed as a  percentage , which is equivalent to the number of map units (or  centiMorgans ) between two genes. For example, if 100 out of 1000 individuals display the phenotype resulting from a crossover between genes  a  and  b , then the recombination frequency is 10 percent and genes  a  and  b  are 10 map-units apart on the chromosome.


If the recombination frequency is greater than 50 percent, it means that the genes are unlinked - they are either located on different  chromosomes  or are sufficiently distant from each other on the same chromosome. Any recombination frequency greater than 50 percent is expressed as exactly 50 percent because, being unlinked, they are equally as likely as not to be separated during gamete formation.  [ 1 ]


A  transcription bubble  is a molecular structure formed during DNA  transcription  when a limited portion of the  DNA double helix  is unwound. The size of a transcription bubble ranges from 12 to 14 base pairs. A transcription bubble is formed when the  RNA polymerase  enzyme binds to a  promoter  and causes two DNA strands to detach. [ 1 ]  It presents a region of unpaired DNA, where a short stretch of  nucleotides  are exposed on each strand of the  double helix . [ 1 ] [ 2 ]


The bacterial  RNA polymerase , a leading  enzyme  involved in formation of a transcription bubble, uses DNA template to guide RNA synthesis. [ 2 ]  It is present in two main forms: as a core enzyme, when it is inactive, and as a holoenzyme, when it is activated. A  sigma (σ) factor  is a subunit that assists the process of transcription and it stabilizes the transcription bubble when it binds to unpaired bases. [ 1 ]  These two components,  RNA polymerase  and sigma factor, when paired together, build RNA polymerase holoenzyme which is then in its active form and ready to bind to a promoter and initiate  DNA transcription . [ 3 ]  Once it binds to the DNA, RNA polymerase turns from a closed to an open complex, forming the transcription bubble. RNA polymerase synthesizes the new RNA in the  5' to 3'  direction by adding complementary bases to the 3' end of a new strand. [ 3 ]  The holoenzyme composition dissociates after transcription initiation, where the σ factor disengages the complex and the RNA polymerase, in its core form, slides along the DNA molecule. [ 1 ]


The RNA polymerase holoenzyme binds to a  promoter  of an exposed  DNA  strand and begins to synthesize the new strand of  RNA . The double helix DNA is unwound and a short nucleotide sequence is accessible on each strand. [ 1 ]  The transcription bubble is a region of unpaired  bases  on one of the exposed  DNA strands . The starting transcription point is determined by the place where the  holoenzyme  binds to a promoter. The DNA is unwound and single-stranded at the start site. The DNA promoter interaction is interrupted as the  RNA polymerase  moves down the template DNA strand and the sigma factor is released. [ 1 ]  The σ factor is required for the initiation but not for the remaining steps of the DNA transcription. Once the σ factor dissociates from the RNA polymerase, the transcription continues. About 10 synthesized  nucleotides  of a new RNA strand are required for this to proceed to the elongation step. The process of transcribing during elongation is very fast. Elongation takes place until the RNA polymerase comes across a termination signal (terminator) which arrests the process and causes the release of both the DNA template and the new RNA molecule. The DNA usually encodes the termination signal. [ 1 ] [ 2 ]


The majority of eukaryotic genes are transcribed by  RNA polymerase II , proceeding in the  5' to 3'  direction. [ 4 ]  In  eukaryotes , specific subunits within the RNA polymerase II complex allow it to carry out multiple functions.  General transcription factors  help binding RNA polymerase II to DNA.  Promoters  are sites where RNA polymerase II binds to start transcription and, in eukaryotes, transcription starting point is positioned at +1 nucleotide. [ 2 ]  Like all  RNA polymerases , it travels along the template DNA, in the 3' to 5' direction and synthesizes a new RNA strand in the 5' to 3' direction, by adding new bases to the  3' end  of the new RNA. [ 4 ]  A transcription bubble occurs as a result of the double stranded DNA unwinding. After about 25 base pairs of the DNA double strand are unwound, RNA synthesis takes place within the transcription bubble region. [ 4 ]   Supercoiling  is also part of this process since DNA regions in front of the RNA polymerase II are unwinding, while DNA regions behind it are rewinding, forming a double helix again. [ 1 ]


The  RNA polymerase  carries out the majority of the steps during the  transcription  cycle, especially in maintaining the transcription bubble open for the complementary  base pairing . [ 2 ]  There are some steps of the  transcription  cycle that require more  proteins , such as the Rpb4/7 complex and the  RNA polymerase  attached to the elongation factor transcription factor IIS (TFIIS). [ 4 ]


In  genetics ,  transcriptional amplification  is the process in which the total amount of  messenger RNA  (mRNA) molecules from expressed genes is increased during disease,  development , or in response to stimuli.


In eukaryotic cells, the  transcribing  activity of  RNA Polymerase II  results in mRNA production. Transcriptional amplification is specifically defined as the increase in per-cell abundance of this set of expressed mRNAs. Transcriptional amplification is caused by changes in the amount or activity of transcription-regulating proteins.


Gene expression is  regulated  by numerous types of proteins that directly or indirectly influence transcription by RNA Polymerase II. As opposed to transcriptional  activators  or  repressors  that selectively activate or repress specific genes, amplifiers of transcription act globally on expressed genes.


Several known regulators of transcriptional amplification have been characterized including the oncogene  Myc , [ 1 ] [ 2 ]  the  Rett syndrome  protein  MECP2 , [ 3 ]  and the BET bromodomain protein  BRD4 . [ 4 ]  In particular, the Myc protein amplifies transcription by binding to  promoters  and  enhancers  of active genes where it directly recruits the transcription elongation factor  P-TEFb . Furthermore, the BRD4 protein is a regulator of Myc activity.


Commonly used gene expression experiments interrogate the expression of one gene ( qPCR ) or many genes ( microarray , 	 RNA-Seq ). These techniques generally measure relative mRNA levels and employ  normalization methods  that assume only a small number of genes show altered expression. [ 5 ]  In contrast, single cell or cell-count normalized absolute measurements of mRNA abundance are required to reveal transcriptional amplification. [ 6 ]  Additionally, global measurements of mRNA or total mRNA per cell can also uncover evidence for transcriptional amplification. [ 7 ] [ 8 ]


Cells in which transcription has been amplified have additional hallmarks suggesting that amplification has occurred. Cells with increased mRNA levels may be larger, consistent with an increased abundance of gene products. This increase in the amount of gene products may result in a decreased doubling time.


Transcriptional amplification has been implicated in cancer, [ 9 ] [ 10 ]  Rett syndrome, [ 11 ]  heart disease, [ 12 ]  Down syndrome, [ 13 ]  and cellular aging. [ 14 ]  In cancer, Myc-driven transcriptional amplification is posited to help tumor cells overcome rate-limiting constraints in growth and proliferation. [ 15 ]  Drugs that target the transcription or mRNA processing machinery are known to be particularly effective against Myc-driven tumor models, [ 16 ] [ 17 ]  suggesting that dampening of transcriptional amplification can have anti-tumor effects. Similarly, small molecules targeting the BET bromodomain protein BRD4, which is up-regulated during heart failure, can block cardiac hypertrophy in mouse models. [ 18 ] [ 19 ]  In Rett syndrome, which is caused by loss of function of the transcriptional regulator MeCP2, MeCP2 was shown to specifically amplify transcription in neurons and not neuronal precursors. [ 20 ]  Restoration of MeCP2 reverses disease symptoms associated with Rett syndrome [ 21 ] [ 22 ]


Transcriptome-wide association study  (TWAS) is a genetic methodology that can be used to compare the genetic components of gene expression and the genetic components of a trait to determine if an association is present between the two components. [ 1 ] [ 2 ]  TWAS are useful for the identification and prioritization of candidate causal genes in candidate gene analysis following genome-wide association studies. [ 3 ]  TWAS looks at the RNA products of a specific tissue and gives researchers the abilities to look at the genes being expressed as well as gene expression levels, which varies by tissue type. TWAS are valuable and flexible bioinformatics tools that looks at the associations between the expressions of genes and complex traits and diseases. [ 4 ]  By looking at the association between gene expression and the trait expressed, genetic regulatory mechanisms can be investigated for the role that they play in the development of specific traits and diseases.


A transcriptome is the sum of all RNA transcripts that are present in a given cell, tissue, or organ within an organism. Transcriptomes include both mRNA, which functions as an intermediate to the central dogma; as well as noncoding RNAs that may play other roles in protein synthesis. [ 5 ]  In the central dogma, it describes how DNA is able to make proteins through transcription and translation. RNAs are present in a cell in varied concentrations, and play various roles outside of the central dogma and are able to be identified based on length and function. It is through functional elements that the transcriptional and translational activities of genes is able to be regulated. [ 4 ]  Transcriptome analysis is beneficial for obtaining information about all RNAs present and can provide valuable insight into the genetic mechanisms that are tissue specific. [ 6 ]  The transcriptome was first investigated in the 1990s in an experiment performed to identify a partial transcriptome of the human brain. Researchers were able to identify 609 mRNA sequences. [ 5 ]  Since then, many advances in Next Generation Sequencing methods have been made. [ 6 ]  Transcriptomes are now able to be routinely developed due to advances in these methods and new technologies such as microarrays and RNA-Seq. Both methods require computed imaging as well as high reads and statistical analysis. [ 5 ]  By obtaining information about gene expression through mRNAs, many applications have been discovered. Transcriptome analysis has proven to be beneficial in identifying disease processes as well as regulatory elements in disease progressions, has aided drug development through identification of disease processes, offers insight into therapeutic strategies, and has improved identification of genes that are able to respond to both biotic and abiotic environmental factors as well as how environmental conditions play a role in gene expression. [ 5 ]


A  genome-wide association study , or GWAS, is a genetic tool that uses single nucleotide polymorphisms, or SNPs, to identify if a trait or disease is linked to a specific genetic variant. By observing if frequencies of a specific variant are more commonly associated, or higher than expected, with the given trait; an association is developed between the trait and the variant. However, many of these associations can be developed throughout an individual due to  linkage disequilibrium  and the large size of the genome. Although GWAS provide valuable insight into identifying markers throughout the genome, a large portion of the SNPs are present in non-coding mRNA regions, and many have unknown functions that are difficult to determine through standard methods, as no product is manufactured by these regions of the genome. [ 4 ]


Transcription-wide association studies are able to take the information from GWAS results and are able to utilize these results as reference data, and can then help to identify and prioritize genes. [ 4 ]  In order to perform this analysis, a reference panel for gene expression should be obtained, such as an expression quantitative trait loci (eQTL), which helps to show gene expression regulation. [ 4 ]  Using the reference panel, a predictive model can be generated to impute the expression variation of genes.  Imputation  is the process by which you can predict the expression levels of genes in other organisms through the variation that exists in their genome based on a reference panel. By predicting the levels of gene expression within a tissue, other variables such as environment and epigenetic effects are eliminated as this prediction is solely based on the variant present and the expected level of gene expression. However, this can lead to inaccuracies with gene expression predictions as both the environment and epigenetic modifications are able to alter the level of gene expression. [ 7 ]  Cis-genetic components are the primary focus of the eQTLs as these are elements that are within 1 Megabase of a gene. [ 2 ]  The predictive model can then be applied to the GWAS samples to predict gene expression of the significant SNPs from that study. After expression levels are imputated, gene-trait association tests are performed by associating levels of predicted expression and genotypes with the phenotypes of the individual. [ 8 ]  Essentially, a TWAS is able to take GWAS results and are able to predict the effects of each variant on expression levels of genes that are associated with the trait, and it is able to do this for every loci that is associated with the trait throughout the genome. [ 7 ]


Results are able to be interpreted on a Manhattan plot, similarly to GWAS results. Any loci that are considered to have statistically significant results will have a higher P value, and this indicates that the loci is likely associated with the trait or disease being investigated. Any statistically significant P-values have a higher log P-value and show above the  Bonferroni correction line . The Manhattan plot is named as such as the statistically significant genes appear to show up as "skyscrapers" on the plot, and when there are many genes that are associated with the trait, the plot resembles the Manhattan skyline. Although the Manhattan plot image is for a GWAS study, TWAS results are shown the same way. Statistically significant loci are genes that have significantly associated SNPs whose expression correlates with the trait or disease of interest.


PrediXcan [ 1 ]  and FUSION [ 2 ]  are both TWAS software that have been utilized in genetic studies to investigate the gene-trait associations. PrediXcan is a well-developed TWAS software that has the ability to estimate genetically regulated expression and determine associations with the phenotype being investigated. It uses a penalized regression model to give weight to levels of observed gene expression and cis-SNPs derived from the reference dataset. [ 8 ]  The software then uses  individual genotype data to perform gene-trait association tests. FUSION is another TWAS software that utilizes a different statistical analysis to create the association tests. In this model, imputation methods from the predictive model are calculated based on a Bayesian sparse linear mixed model. [ 8 ]  The advantage of this software is that it is able to perform association tests on individual genotype data, but this software can also take information from large scale data sets using imputation.


The advantages of this methodology are through the insight it gives researchers into the function of genes and the association between gene functions and gene expression. TWAS has the potential to take results from GWAS and extend the results to aid in the understanding of disease mechanisms. [ 4 ]  Additionally, as this method uses loci that were previously identified by GWAS analysis, there is a lower testing burden associated with a TWAS as less sites are analyzed. By lowering the number of loci being analyzed, this allows more in-depth analysis of the sites analyzed and can give further insight to the functions and associations of the significant loci.


TWAS also have the advantage of reducing the effects of confounding factors. When building a predictive model, it only looks at genetic expression, not total expression. Total expression includes factors like the environment and epigenetic modifications to levels of expression, and are not accounted for in the predictive model. By not accounting for these factors, it can reduce the accuracy of predicted levels of gene expression; however, it also reduces the effects of confounding variables in the results. [ 2 ]


Another advantage of TWAS is that the results are tissue specific. The level of gene expression differs by the tissue that the genes are in, as each tissue has specific splicing patterns and patterns of regulation. By having tissue specific results, this furthers the information that can be derived through these studies as results have the ability to show how gene regulation differs by tissue types as well as how functions are regulated and if there are common regulatory mechanisms between tissues or if regulatory mechanisms have different functions in different tissues. TWAS cross tissue methods also have the possibility to identify potential causal genes for diseases and traits on a larger scale, however, single tissue methods have the ability to determine associations on a case specific basis. [ 8 ]


Many of the disadvantages of TWAS are implications of the prediction capabilities of the model used to predict gene expression levels based on genotypes. One disadvantage of TWAS is that it mainly looks at cis-genetic components for imputation and for in most studies, does not identify any trans-genetic component variants. This acts as a disadvantage for TWAS as trans-genetic component variants are any regulatory mechanisms that are outside of a 1 Megabase range of the gene, and even though they are a significant distance away from the gene of interest, many regulatory mechanisms have the potential to act long range and can still impact expression. By not taking these components into account, it lowers the accuracy of predicted genetic expression levels and can cause deviation between expected and observed expression levels. As mentioned above, another disadvantage of these studies are that environmental and epigenetic mechanisms for regulation of gene expression is not taken into account with the predictive model for gene expression, which also has the potential to lead to inaccuracies with the predicted gene expression levels and observed expression levels. Another challenge for TWAS is that it can be hard to predict accurate gene expression levels when genes have low heritability levels. eQTLs rely on a level of heritability, and when low heritability is observed, it can affect the observance of false positives and can negatively impact the prediction capabilities of the model used for TWAS. [ 4 ]


Additionally, another challenge for TWAS, very similar to GWAS results, is that these studies can only demonstrate associations from results. Even though a statistically significant association can be seen between  the gene or loci of interest and the trait or disease, no causal relationship can be derived. In order to establish a causal relationship, further studies utilizing a  reverse genetics  approach for  knock-outs  of genes or  site-directed mutagenesis  would need to be performed to identify causal relationships.


Another issue with TWAS results are the implications of tissue bias and coregulation. Due to the specificity of genetic regulation mechanisms within each tissue, many experiments would need to be performed to determine the tissue specific nature of each loci association and how these associations differ between tissue types. [ 3 ]  Co-regulation results from a regulatory mechanism controlling the expression of more than one loci at a time. By controlling more than one loci, associations may be drawn between the loci of interest along with other genes or loci that are solely controlled by the same mechanism and may not have any association with the trait or disease of interest, leading to false positive results. [ 3 ]


A TWAS study was performed following a GWAS investigating loci associated with schizophrenia. From the GWAS results, over 100 risk loci were located. A TWAS was then used to identify 157 significant loci using expression data, and 35 of the identified loci from the TWAS did not align with the GWAS loci. Results were then further narrowed using regulatory target investigations. 42 of these genes were found to have a statistically significant association with chromatin phenotypes, which is a regulatory mechanism that could further be investigated.  MAPK3  was one association that was observed to have a large impact on neurodevelopmental phenotypes, and was further prioritized as a candidate causal gene. [ 9 ]


In 2018, a TWAS was used to identify candidate causal genes for breast cancer. Data was collected from The Cancer Genome Atlas to establish genetic models as well as 229,000 women of European ancestry. In this study, 8,597 genes were evaluated. Through GWAS studies, around 170 loci were associated with at least one variant of breast cancer. In this study, 179 genes were found to have an association with a variant of breast cancer. Of the 179 genes with associations, 48 were identified to be statistically significant using a Bonferroni-correction threshold (as seen on the Manhattan plot above). [ 10 ]  14 of these had never been reported to be associated with a risk of breast cancer previously. The other 34 genes at known risk loci had 23 that do not have any associated risk SNPs. [ 10 ]  Using gene knock-downs, 13 genes with high predicted levels of expression were found to be associated with an increased risk. When knocked-down, studies showed that 11 of the genes investigated had an effect in a cell line of breast cancer, especially in 184A1 normal breast cells. [ 10 ]  These genes include the following:  PIDD1, NRBF2,  and  ABHD8 . [ 10 ]  All of the genes identified in the study, both up- and down-regulated had relatively high  cis -heritability.


A TWAS study was completed in 2021 that utilized the most recent Parkinson's Disease (PD) GWAS that utilized 480.000 individuals. From those results, 18 genes were found to have a statistically significant association with PD. The most significant of these was  LRRC37A2,  which was found to be associated in all 13 brain tissues. [ 11 ]


TWAS Atlas  is a site that has been curated to integrate the findings of many TWAS studies. This atlas exists virtually and is accessible to the public. Results and findings that are published in the TWAS Atlas are able to be integrated and combined to aid future studies and the understanding of genetic regulation mechanisms. Results are presented in a visual manner to improve the integration of results. Currently, 401,226 TWAS associations have been published from 200 publications, spanning 257 traits and 22,247 genes as of April 25, 2022. [ 12 ]


A  transfer DNA  ( T-DNA )  binary system  is a pair of  plasmids  consisting of a  T-DNA binary vector  and a  vir   helper plasmid . [ 1 ] [ 2 ]  The two plasmids are used together (thus  binary [ 2 ] [ 3 ] ) to produce  genetically modified plants . They are artificial  vectors  that have been derived from the naturally occurring  Ti plasmid  found in bacterial species of the genus  Agrobacterium , such as  A. tumefaciens . The binary vector is a  shuttle vector , so-called because it is able to  replicate  in multiple hosts (e.g.  Escherichia coli  and  Agrobacterium ).


Systems in which  T-DNA  and  vir  genes are located on separate replicons are called T-DNA binary systems. T-DNA is located on the binary vector (the non-T-DNA region of this vector containing origin(s) of replication that could function both in  E. coli  and  Agrobacterium , and  antibiotic resistance  genes used to select for the presence of the binary vector in bacteria, became known as vector backbone sequences). The replicon containing the  vir  genes became known as the  vir  helper plasmid. The  vir  helper plasmid is considered disarmed if it does not contain oncogenes that could be transferred to a plant.


There are several binary vectors that replicate in  Agrobacterium  and can be used for delivery of T-DNA from  Agrobacterium  into plant cells. The T-DNA portion of the binary vector is flanked by left and right border sequences and may include a  transgene  as well as a plant  selectable marker . Outside of the T-DNA, the binary vector also contains a bacterial selectable marker and an  origin of replication  (ori) for bacteria. [ 4 ]


Representative series of binary vectors are listed below.


The  vir  helper plasmid contains the  vir  genes that originated from the  Ti plasmid  of  Agrobacterium . These genes code for a series of proteins that cut the binary vector at the left and right border sequences, and facilitate transfer and integration of T-DNA to the plant's cells and genomes, respectively. [ 4 ]


Several  vir  helper plasmids have been reported, [ 12 ]  and common  Agrobacterium  strains that include  vir  helper plasmids are:


The pBIN19 vector was developed in the 1980s and is one of the first and most widely used binary vectors. The pGreen vector, which was developed in 2000, is a newer version of the binary vector that allows for a choice of promoters, selectable markers and reporter genes. Another distinguishing feature of pGreen is its large reduction in size (from about 11,7kbp to 4,6kbp) from pBIN19, therefore increasing its  transformation efficiency . [ 13 ]


Along with higher transformation efficiency, pGreen has been engineered to ensure transformation integrity. Both pBIN19 and pGreen usually use the same selectable marker  nptII , but pBIN19 has the selectable marker next to the right border, while pGreen has it close to the left border. Due to a polarity difference in the left and right borders, the right border of the T-DNA enters the host plant first. If the selectable marker is near the right border (as is the case with pBIN19) and the transformation process is interrupted, the resulting plant may have expression of a selectable marker but contain no T-DNA giving a false positive. The pGreen vector has the selectable marker entering the host last (due to its location next to the left border) so any expression of the marker will result in full transgene integration. [ 4 ]


The pGreen-based vectors are not autonomous and they will not replicate in  Agrobacterium  if  pSoup  is not present. Series of small binary vectors that autonomously replicate in  E. coli  and  Agrobacterium  include:


Transformation efficiency  refers to the ability of a cell to take up and incorporate exogenous DNA, such as  plasmids , during a process called  transformation . The efficiency of transformation is typically measured as the number of transformants (cells that have taken up the  exogenous DNA ) per microgram of DNA added to the cells. A higher transformation efficiency means that more cells are able to take up the DNA, and a lower efficiency means that fewer cells are able to do so.


In  molecular biology , transformation efficiency is a crucial parameter, it is used to evaluate the ability of different methods to introduce plasmid DNA into cells and to compare the efficiency of different  plasmid ,  vectors  and host cells. This efficiency can be affected by a number of factors, including the method used for introducing the DNA, the type of cell and plasmid used, and the conditions under which the transformation is performed. Therefore, measuring and optimizing transformation efficiency is an important step in many molecular biology applications, including  genetic engineering ,  gene therapy  and  biotechnology .


By measuring the transformation efficiency, we can utilize the information from our experiment to evaluate how effectively our transformation went. This is a quantification of how many cells were altered by 1 μg of plasmid DNA. In essence, it is a sign that the  transformation  experiment was successful. [ 1 ]  It should be determined under conditions of cell excess. [ 2 ]


Transformation efficiency is typically measured as the number of transformed cells per total number of cells. It can be represented as a percentage or as  colony  forming units (CFUs) per microgram of DNA.


One of the most common ways to measure transformation efficiency is by performing a  colony  forming assay. Here is an example of how to calculate transformation efficiency using colony forming units (CFUs): [ 3 ]


For example, if you plate 1x 10 7  cells and count 1000 colonies, the transformation efficiency is: (1000/1x 10 7 ) x 100 = 0.1%


Alternatively, CFUs can be reported per microgram of DNA used for the transformation. This can be calculated by multiplying the number of colonies by the volume of the culture plated and dividing by the amount of DNA used.


Quantitative PCR  (qPCR) - This method utilizes the fact that the  plasmid DNA  will have a specific gene or sequence that is not present in the host cell genome, and therefore can be used as a target for qPCR. By quantifying the number of copies of this specific gene or sequence in the transformed cells, it is possible to determine the amount of plasmid DNA present in the cell, and thus the transformation efficiency. [ 4 ]


Fluorescent assay  - This method relies on the use of a plasmid that contains a fluorescent protein or reporter gene. The transformed cells are then analyzed by  flow cytometry  or  fluorescence microscopy  to determine the number of cells that express the  fluorescent protein . The transformation efficiency is then calculated as the percentage of cells that express the fluorescent protein. [ 5 ]


The number of viable cells in a preparation for a transformation reaction may range from 2×10 8  to 10 11 ; most common methods of  E. coli  preparation yield around 10 10  viable cells per reaction. The standard  plasmids  used for determination of transformation efficiency in  Escherichia coli  are  pBR322  or other similarly sized or smaller vectors, such as the pUC series of vectors.  Different vectors however may be used to determine their transformation efficiency. 10–100 pg of DNA may be used for transformation, more DNA may be necessary for low-efficiency transformation (generally saturation level is reached at over 10 ng). [ 6 ]


After transformation, 1% and 10% of the cells are plated separately, the cells may be diluted in media as necessary for ease of plating.  Further dilution may be used for high efficiency transformation.


A transformation efficiency of 1×10 8  cfu/μg for a small plasmid like  pUC19  is roughly equivalent to 1 in 2000 molecules of the plasmid used being introduced into cells.  In  E. coli , the theoretical limit of transformation efficiency for most commonly used plasmids would be over 1×10 11  cfu/μg. In practice the best achievable result may be around 2–4×10 10  cfu/μg for a small plasmid like pUC19, and considerably lower for large plasmids.


Individual cells are capable of taking up many DNA molecules, but the presence of multiple plasmids does not significantly affect the occurrence of successful transformation events. [ 7 ]  A number of factors may affect the transformation efficiency: [ 2 ]


Plasmid size  – A study done in  E. coli  found that transformation efficiency declines linearly with increasing  plasmid  size, i.e. larger plasmids transform less well than smaller plasmids. [ 7 ] [ 8 ] [ 9 ]


Forms of DNA  –  Supercoiled  plasmid have a slightly better transformation efficiency than relaxed plasmids – relaxed plasmids are transformed at around 75% efficiency of supercoiled ones. [ 7 ]   Linear and single-stranded DNA however have much lower transformation efficiency.  Single-stranded DNAs are transformed at 10 4  lower efficiency than double-stranded ones.


Media composition  – The composition of the media used in the transformation process can affect the efficiency. For example, certain media supplements can increase the natural competence of cells. [ 10 ]


Genotype of cells  – Cloning strains may contain mutations that improve the transformation efficiency of the cells.  For example,  E. coli  K12 strains with the  deoR  mutation, originally found to confer an ability of cell to grow in minimum media using inosine as the sole carbon source, have 4-5 times the transformation efficiency of similar strains without.  For linear DNA, which is poorly transformed in  E. coli , the  recBC  or  recD  mutation can significantly improve the efficiency of its transformation. [ 11 ]


Culture conditions  –  E. coli  cells are more susceptible to be made competent when it is growing rapidly, cells are therefore normally harvested in the early log phase of cell growth when preparing competent cells.  The optimal optical density for harvesting cells normally lies around 0.4, although it may vary with different cell strains.  A higher value of 0.94-0.95 has also been found to produce good yield of competent cells, but this can be impractical when cell growth is rapid. [ 12 ]


Presence of antibiotics  – The presence of antibiotics can increase the efficiency of transformation by inhibiting the growth of non-transformed cells and selecting for transformed cells that are resistant to the antibiotic. For instance, the use of β-lactam antibiotics  has been shown for glutamate-producing bacteria to increase its transformation efficiencies. [ 13 ] [ 14 ] [ 15 ]


Plasmid origin of replication  – The  origin of replication  of the plasmid used in the transformation process can affect the efficiency in several ways. The copy number of the plasmid in the cell, the activity of the origin of replication in the host cells, and the expression of the genes on the plasmid can all affect the efficiency. The plasmid with a high copy number origin of replication will generally have a higher transfection efficiency than one with a low copy number origin, using a plasmid with an origin of replication that is active in the host cell can lead to a higher transfection efficiency. [ 16 ]


Transformation   conditions  – The method of preparation of competent cells, the length of time of  heat shock , temperature of heat shock, incubation time after heat shock, growth medium used, pH and various additives, all can affect the transformation efficiency of the cells.  The presence of contaminants as well as ligase in a ligation mixture can reduce the transformation efficiency in  electroporation , [ 17 ]  and inactivation of ligase or  chloroform  extraction of DNA may be necessary for electroporation, alternatively only use a tenth of the ligation mixture to reduce the amount of contaminants.  Normal preparation of competent cells can yield transformation efficiency ranging from 10 6  to 10 8  cfu/μg DNA. Protocols for chemical method however exist for making super competent cells that may yield a transformation efficiency of over 1 x 10 9 . [ 18 ]


Damage to DNA  – Exposure of DNA to UV radiation in standard preparative  agarose gel electrophoresis  procedure for as little as 45 seconds can damage the DNA, and this can significantly reduce the transformation efficiency. [ 19 ]   Adding  cytidine  or  guanosine  to the electrophoresis buffer at 1 mM concentration however may protect the DNA from damage. A higher-wavelength UV radiation (365 nm) which cause less damage to DNA should be used if it is necessary work for work on the DNA on a UV transilluminator for an extended period of time.  This longer wavelength UV produces weaker fluorescence with the  ethidium bromide  intercalated into the DNA, therefore if it is necessary to capture images of the DNA bands, a shorter wavelength (302 or 312 nm) UV radiations may be used. Such exposure however should be limited to a very short time if the DNA is to be recovered later for  ligation  and transformation.


The method used for introducing the DNA have a significant impact on the transformation efficiency. [ 20 ]


Electroporation  tends to be more efficient than chemical methods and can be applied to a wide range of species and to strains that were previously resistant and recalcitrant to transformation techniques. [ 21 ] [ 22 ]


Electroporation has been found to have an average yield typically between 10 4  -  10 8  CFU/ug . However, a transformation efficiencies as high as 0.5-5 x 10 10  colony forming units (CFU) per microgram of DNA for  E. coli . For samples that are hard to handle, like  cDNA libraries ,  gDNA , and  plasmids  larger than 30 kb, it is suggested to use electrocompetent cells that have transformation efficiencies of over 1 x 10 10  CFU/μg. This will ensure a high success rate in introducing the DNA and forming a large number of colonies. [ 23 ]  It is important to adjust and optimize the electroporation buffer (Increasing the concentration of the electroporation buffer can result in increased transformation efficiencies ) and the shape, strength, number, and number of pulses these electrical parameters play a key role in transformation efficiency. [ 24 ]


Chemical transformation or  heat shock  can be performed in a simple laboratory setup, typically yielding transformation efficiencies that are adequate for cloning and subcloning applications, approximately  10 6  CFU/μg. One of the early methods used was a combination of CaCl 2  and MgCl 2  to treat the cells. However, these methods resulted in transformation efficiencies, with a maximum of  10 5  - 10 6  colony forming units (CFU) per microgram of plasmid DNA. [ 23 ]  Later research found that certain cations, such as Mn 2+ , Ca 2+ , Ba 2+ , Sr 2+  and Mg 2+  could have a positive effect on transformation efficiencies, with Mn 2+  showing the greatest effect. [ 25 ]


Some bacterial cells have restriction-modification systems that can degrade exogenous plasmids that are foreign to the host cell. This can greatly reduce the efficiency of transformation. [ 26 ] [ 20 ]  This is due to restriction systems in the recipient cells that target and destroy exogenous DNA. These systems recognize exogenous DNA based on differences in methylation patterns. To address this problem, strategies such as altering the methylation of the exogenous DNA using commercial methylases or reducing the restriction activity in the recipient cells have been applied. [ 27 ] [ 28 ]  For example, using methylation-negative mutants or temporarily inactivating the restriction system with heat can reduce the recipient cell's ability to impose restrictions on the exogenous DNA. [ 29 ]


In genetics,  transgressive segregation  is the formation of extreme  phenotypes , or transgressive phenotypes, observed in segregated  hybrid  populations compared to phenotypes observed in the parental lines. [ 1 ]  The appearance of these transgressive (extreme) phenotypes can be either positive or negative in terms of  fitness . If both parents' favorable  alleles  come together, it will result in a hybrid having a higher fitness than the two parents. The hybrid species will show more  genetic variation  and variation in  gene expression  than their parents. As a result, the hybrid species will have some traits that are transgressive (extreme) in nature. Transgressive segregation can allow a hybrid species to populate different environments/niches in which the parent species do not reside, or compete in the existing environment with the parental species.


Genetic


There are many causes for transgressive segregation in hybrids. One cause can be due to recombination of additive alleles.  Recombination  results in new pairs of alleles at two or more loci. These different pairs of alleles can give rise to new phenotypes if gene expression has been changed at these loci. Another cause can be elevated  mutation rate . When mutation rates are high, it is more probable that a mutation will occur and cause an extreme phenotypic change. Reduced  developmental stability  is another cause for transgressive segregation. Developmental stability refers to the capability of a genotype to go through a constant development of a phenotype in a certain environmental setting. If there is a disturbance due to genetic or environmental factors, the genotype will be more sensitive to phenotypic changes. Another cause arises from the interaction between two alleles of two different genes, also known as the  epistatic effect . Epistasis is the event when one allele at a locus prevents an allele at another locus to express its product as if it is masking its effect. Therefore, epistasis can be related to gene over dominance caused by heterozygosity at specific loci.[2] What this means is that the heterozygote (hybrid) when compared to the homozygote (parent) is better adapted and therefore shows more transgressive, extreme phenotypes. All of these causes lead to the appearance of these extreme phenotypes and creates a hybrid species that will deviate away from the parent species niche and eventually create an individual "hybrid" species.


Environmental


Other than the genetic factors solely causing transgressive segregation, environmental factors can cause genetic factors to take place. Environmental factors that cause transgressive segregation can be influenced by human activity and  climate change . Both human activity and climate change have the capability to force species of a specific  genome  to interact with other species with different genomes.


For example, if a bridge is built that connects two isolated areas to one another, a  gene flow  door would open. This open door will increase the interactions between different species with different genomes can create hybrid species that can potentially show transgressive phenotypes. Human activity can open the gene flow door by pursuing harmful actions such as cutting down forests and pollution. Climate change on the other hand can open the gene flow door by breaking climate and environmental barriers that were present before. This convergence between species can give rise to a hybrid species that will have more phenotypic variation when compared to the parent species. This increase in phenotypic variation has the potential for transgressive segregation to occur. [ 2 ]


In Kenya, there is a fungus called  septoria tritici blotch  (STB) that diminishes yield in wheat crop. The parent species of wheat had little resistance toward STB, but the hybrid species due to transgressive segregation showed a higher resistance toward STB and therefore a higher fitness. You can create a higher resistance to STB by crossing genes together that are efficient. In result, out of 36 crosses there were 31 that showed a higher mean fitness than the control, parent value. These 31 crosses indicate a higher resistance to STB. The crosses used were from other commercial wheat's that were high yielding which is advantageous because there is a lower chance of deleterious (unwanted traits) appearing and therefore an increase in beneficial traits. Transgressive segregation has been found to be useful to create a resistance toward this organism in order to increase the yield of wheat crop. [ 3 ]


Rieseberg used sunflowers to show the transgressive segregation of parental traits.  Helianthus annuus  and  Helianthus petiolaris  are the two parent groups for the hybrids. Ultimately there were three hybrid sunflower species. When compared to the fitness of the parents, the hybrids showed a higher tolerance in areas which the parent species would not be able to survive i.e. salt marsh, sand dunes, and deserts. Transgressive segregation allowed these hybrids to survive in areas that the parent would not be able to. Therefore, the hybrids were populated in areas where the parent species were not. This is due to hybrid species showing more  gene expression  (phenotypes) than their parents and also having some genes that are transgressive (extreme) in nature. [ 4 ]


There are many ways to test if transgressive segregation occurred within a population. One common way to test for transgressive segregation is to use a  Dunnett's test . This test looks at whether the hybrid species' performance was different from the control group by looking whether or not the mean of the control group (parent species) differs significantly from mean of the other groups. If there is a difference, that is an indication of transgressive segregation. [ 5 ]  Another commonly used test is the use of  quantitative trait loci (QTL)  to assess transgressive segregation. Alleles with QTL that were opposed (either by overdomiance or underdominance) of the parental parent QTL indicate that transgressive segregation occurred. Alleles with QTL that was the same as the predicted parent QTL showed that there was no transgressive segregation. [ 6 ]


Transgressive segregation creates an opportunity for new hybrid species to arise that are more fit than their ancestors. As seen with the STB in Kenya and Rieseberg's sunflowers, transgressive segregation can be used to create a species that is more adaptable and resistant in areas where there is environmental stress. Transgressive segregation can be seen as  genetic engineering  in the way that the goal for each of these events is to create an organism that is more fit than the last.


In  genetics , the  transmission disequilibrium test  ( TDT ) was proposed by Spielman, McGinnis and  Ewens  (1993) [ 1 ]  as a family-based association test for the presence of  genetic linkage  between a  genetic marker  and a trait. It is an application of  McNemar's test .


A specificity of the TDT is that it will detect genetic linkage only in the presence of  genetic association .
While genetic association can be caused by population structure, genetic linkage will not be affected, which makes the TDT robust to the presence of  population structure .


We first describe the TDT in the case where families consist of trios (two parents and one affected child). Our description follows the notations used in Spielman, McGinnis & Ewens (1993). [ 1 ]


The TDT measures the over-transmission of an allele from heterozygous parents to affected offsprings.
The  n  affected offsprings have  2 n  parents. These can be represented by the transmitted and the non-transmitted alleles  M 1  and  M 2  at some genetic locus. Summarizing the data in a 2 by 2 table gives:


The derivation of the TDT shows that one should only use the heterozygous parents (total number  b  +  c ).
The TDT tests whether the proportions   b / b  +  c   and   c / b  +  c   are compatible with probabilities  (0.5, 0.5) .
This hypothesis can be tested using a  binomial  (asymptotically chi-square) test with one degree of freedom:


χ

 2

 =

 [
 b
 −
 (
 b
 +
 c
 )

 /

 2

 ]

 2

 (
 b
 +
 c
 )

 /

 2

 +

 [
 c
 −
 (
 b
 +
 c
 )

 /

 2

 ]

 2

 (
 b
 +
 c
 )

 /

 2

 =

 (
 b
 −
 c

 )

 2

 b
 +
 c

 {\displaystyle \chi ^{2}={\frac {[b-(b+c)/2]^{2}}{(b+c)/2}}+{\frac {[c-(b+c)/2]^{2}}{(b+c)/2}}={\frac {(b-c)^{2}}{b+c}}}


A derivation of the test consists of using a population genetics model to obtain the expected proportions for the quantities  a ,  b ,  c ,  d  in the table above. In particular, one can show that under nearly all disease models the expected proportion of  b  and  c  are identical. This result motivates the use of a binomial (asymptotically  χ 2 ) test to test whether these proportions are equal.


On the other hand, one can also show that under such models the proportions  a ,  b ,  c ,  d  are not equal to the product of the marginals probabilities

 a
 +
 b

 2
 n

 ,

 c
 +
 d

 2
 n

 {\displaystyle {\tfrac {a+b}{2n}},{\tfrac {c+d}{2n}}}

  and

 a
 +
 c

 2
 n

 ,

 b
 +
 d

 2
 n

 .

 {\displaystyle {\tfrac {a+c}{2n}},{\tfrac {b+d}{2n}}.}

  A rewording of this statement would be that the type of the transmitted allele is not, in general, independent of the type of the non-transmitted allele. A consequence is that a  χ 2  test for homogeneity/independence does not test the appropriate hypothesis, and thus, only heterozygous parents are included.


The TDT can be readily extended beyond the case of trios. We keep following the notations of Spielman, McGinnis & Ewens (1993). [ 1 ]  Consider a total of  h  heterozygous parents. We use the fact that the transmission to different children are independent. The information can be then summarized in three categories:


Using the notations of the previous paragraph we have:


b

 =
 2
 i
 +
 (
 h
 −
 i
 −
 j
 )
 =
 h
 +
 i
 −
 j

 c

 =
 2
 j
 +
 (
 h
 −
 i
 −
 j
 )
 =
 h
 −
 i
 +
 j

 {\displaystyle {\begin{aligned}b&=2i+(h-i-j)=h+i-j\\c&=2j+(h-i-j)=h-i+j\end{aligned}}}


leading to the  chi-squared test  statistic:


χ

 t
 d
 t

 2

 =

 4
 (
 i
 −
 j

 )

 2

 h

 .

 {\displaystyle \chi _{tdt}^{2}={\frac {4(i-j)^{2}}{h}}.}


The comparison with the more traditional (at least at the time when the TDT was proposed) linkage test proposed by Blackwelder and Elston 1985 [ 2 ]  is informative.
The Blackwelder and Elston approach uses the total number of haplotypes identical by descent (mean haplotype sharing). This measure ignores the allelic state of a marker and simply compares the number of times a parent transmits the same allele to both affected children with the number of times a different allele is transmitted.
The test statistic is:


χ

 h
 s

 2

 =

 (
 2
 i
 +
 2
 j
 −
 h

 )

 2

 h

 .

 {\displaystyle \chi _{hs}^{2}={\frac {(2i+2j-h)^{2}}{h}}.}


Under the  null hypothesis  of no linkage the expected proportions of  ( i ,  h  −  i  −  j ,  j )  are  (0.25, 0.5, 0.25) .  One can derive a simple chi-square statistic with 2 degrees of freedom:


χ

 t
 o
 t
 a
 l

 2

 =

 (
 i
 −
 h

 /

 4

 )

 2

 h

 /

 4

 +

 (
 h
 −
 i
 −
 j
 −
 h

 /

 2

 )

 2

 h

 /

 2

 +

 (
 j
 −
 h

 /

 4

 )

 2

 h

 /

 4

 =

 χ

 t
 d
 t

 2

 +

 χ

 h
 s

 2

 .

 {\displaystyle \chi _{total}^{2}={\frac {(i-h/4)^{2}}{h/4}}+{\frac {(h-i-j-h/2)^{2}}{h/2}}+{\frac {(j-h/4)^{2}}{h/4}}=\chi _{tdt}^{2}+\chi _{hs}^{2}.}


It clearly appears that the total statistic (with two degree of freedom) is the sum of two independent components: one is the traditional linkage measure and the other is the TDT statistic.


More recently, Wittkowski KM, Liu X. (2002/2004) [ 3 ]  proposed a modification to the TDT that can be more powerful under some alternatives, although the asymptotic properties under the null hypothesis are equivalent.


The motivating idea for this modification is the fact that, while the transmissions of both allele from parents to a child are independent, the effects of other filial genetic or environmental covariates on  penetrance  are the same for both alleles transmitted to the same child. This situation can be important if, for example, the genetic marker is linked to a disease locus with a strong selection against heterozygous individuals. This observation suggests to shift the statistical model from a set of independent transmissions to a set of independent children (see Sasieni (1997) [ 4 ]  for the corresponding problem in case-control association tests). While this observation does not affect the distribution under the null hypothesis of no linkage, it allows, for some disease models, to design a more powerful test.


In this modified TDT test the children are stratified by parental type and the modified test statistic becomes:


χ

 2

 =

 (

 [

 n

 P
 Q

 −

 n

 Q
 Q

 ]

 P
 Q
 ∼
 Q
 Q

 +
 2
 ×
 [

 n

 P
 P

 −

 n

 Q
 Q

 ]

 P
 Q
 ∼
 P
 Q

 +
 [

 n

 P
 P

 −

 n

 P
 Q

 ]

 P
 P
 ∼
 P
 Q

 )

 2

 [

 n

 P
 Q

 +

 n

 Q
 Q

 ]

 P
 Q
 ∼
 Q
 Q

 +
 4
 ×
 [

 n

 P
 P

 +

 n

 Q
 Q

 ]

 P
 Q
 ∼
 P
 Q

 +
 [

 n

 P
 Q

 +

 n

 P
 P

 ]

 P
 P
 ∼
 P
 Q

 {\displaystyle \chi ^{2}={\frac {{\bigl (}[n_{\rm {PQ}}-n_{\rm {QQ}}]_{\rm {PQ\sim QQ}}+2\times [n_{\rm {PP}}-n_{\rm {QQ}}]_{\rm {PQ\sim PQ}}+[n_{\rm {PP}}-n_{\rm {PQ}}]_{\rm {PP\sim PQ}}{\bigr )}^{2}}{[n_{\rm {PQ}}+n_{\rm {QQ}}]_{\rm {PQ\sim QQ}}+4\times [n_{\rm {PP}}+n_{\rm {QQ}}]_{\rm {PQ\sim PQ}}+[n_{\rm {PQ}}+n_{\rm {PP}}]_{\rm {PP\sim PQ}}}}}


where

 [

 n

 P
 Q

 ]

 P
 Q
 ∼
 Q
 Q

 {\displaystyle [n_{\rm {PQ}}]_{\rm {PQ\sim QQ}}}

  is the number of PQ children from parents with the PQ and QQ types.


Beagle


The  triangle of U  ( / uː /   OO ) is a theory about the evolution and relationships among the six most commonly known members of the plant genus  Brassica . The theory states that the  genomes  of three ancestral  diploid  species of  Brassica  combined to create three common  tetraploid   vegetables  and  oilseed  crop species. [ 1 ]  It has since been confirmed by studies of DNA and proteins. [ 2 ]


The theory is summarized by a triangular diagram that shows the three ancestral genomes, denoted by AA, BB, and CC, at the corners of the triangle, and the three derived ones, denoted by AABB, AACC, and BBCC, along its sides.


The theory was first published in 1935 by  Woo Jang-choon , [ 3 ]  a  Korean - Japanese   botanist  (writing under the Japanized name "U Nagaharu"). [ 4 ]  Woo made synthetic hybrids between the  diploid  and  tetraploid  species and examined how the chromosomes paired in the resulting triploids.


The six species are


The code in the "Chr.count" column specifies the total number of chromosomes in each somatic cell, and how it relates to the number  n  of  chromosomes  in each full genome set (which is also the number found in the pollen or  ovule ), and the number  x  of chromosomes in each component genome. For example, each somatic cell of the tetraploid species  Brassica napus , with letter tags AACC and count "2 n =4 x =38", contains two copies of the A genome, each with 10 chromosomes, and two copies of the C genome, each with 9 chromosomes, which is 38 chromosomes in total. That is two full genome sets (one A and one C), hence "2 n =38" which means " n =19" (the number of chromosomes in each  gamete ). It is also four component genomes (two A and two C), hence "4 x =38". [ 2 ]


The three diploid species exist in nature, but can easily interbreed because they are closely related.  This  interspecific breeding  allowed for the creation of three new species of tetraploid  Brassica . [ 3 ]  (Critics, however, consider the geological separation too large.)  These are said to be  allotetraploid  (containing four genomes from two or more different species); more specifically,  amphidiploid  (with two genomes each from two diploid species). [ 2 ]


The framework proposed by Woo, although backed by modern studies, leaves open questions about the time and place of hybridization and which species is the maternal or paternal parent.  B. napus  (AACC) is dated to have originated about 8,000 [ 5 ]  or 38,000–51,000 [ 6 ]  years ago. The homologous part of its constituent chromosomes has  crossed over  in many cultivars. [ 5 ]   B. juncea  (AABB) is estimated to have originated 39,000–55,000 years ago. [ 6 ]  As of 2020, research on organellar genomes shows that  B. nigra  (BB) is likely the "mother" of  B. carinata  (BBCC) and that  B. rapa  (AA) likely mothered  B. juncea . The situation with  B. napus  (AACC) is more complex: some specimens have a  rapa -like organellar genome, while the rest indicate an ancient, unidentified maternal plant. [ 2 ]


Data from molecular studies indicate the three diploid species are themselves  paleohexaploids . [ 7 ] [ 8 ]


In 2011 and 2018, novel  allohexaploids  (AABBCC) located at the "center" of the triangle of U were created by different means, [ 9 ] [ 10 ] [ 11 ]  for example by crossing  B. rapa  (AA) with  B. carinata  (BBCC), or  B. nigra  (BB) with  B. napus  (AACC), or  B. oleracea  (CC) with  B. juncea  (AABB), followed by chromosome duplication of the triploid (ABC) offspring to generate  doubled haploid  (AABBCC) offspring. [ 11 ]


In addition, two stable allohexaploid (AABBSS) intergeneric hybrids between Indian mustard ( B. juncea , AABB) and  white mustard  ( Sinapis alba , SS) were created in 2020 by  protoplast fusion . [ 12 ]


A  trinucleotide repeat expansion , also known as a  triplet repeat expansion , is the  DNA   mutation  responsible for causing any type of  disorder  categorized as a  trinucleotide repeat disorder . These are labelled in  dynamical genetics  as  dynamic mutations . [ 1 ]  Triplet expansion is caused by  slippage  during DNA replication, also known as "copy choice" DNA replication. [ 2 ]  Due to the repetitive nature of the DNA sequence in these regions, 'loop out' structures may form during DNA replication while maintaining complementary base pairing between the parent strand and daughter strand being synthesized. If the loop out structure is formed from the sequence on the daughter strand this will result in an increase in the number of repeats. However, if the loop out structure is formed on the parent strand, a decrease in the number of repeats occurs. It appears that expansion of these repeats is more common than reduction. Generally, the larger the expansion the more likely they are to cause disease or increase the severity of disease. Other proposed mechanisms for expansion and reduction involve the interaction of RNA and DNA molecules. [ 3 ]


In addition to occurring during  DNA replication , trinucleotide repeat expansion can also occur during  DNA repair . [ 4 ]  When a DNA trinucleotide repeat sequence is  damaged , it may be repaired by processes such as  homologous recombination ,  non-homologous end joining ,  mismatch repair  or  base excision repair . Each of these processes involves a DNA synthesis step in which  strand slippage  might occur leading to trinucleotide repeat expansion. [ 4 ]


The number of trinucleotide repeats appears to predict the progression, severity, and age of onset of  Huntington's disease  and similar trinucleotide repeat disorders. [ 5 ]  Other human diseases in which triplet repeat expansion occurs are  fragile X syndrome , several  spinocerebellar ataxias ,  myotonic dystrophy  and  Friedreich's ataxia . [ 4 ]


The first documentation of  anticipation  in genetic disorders was in the 1800s. However, from the eyes of geneticists, this relationship was disregarded and attributed to  ascertainment bias ; because of this, it took almost 200 years for a link between onset of disease and trinucleotide repeats (TNR) to be acknowledged. [ 6 ]


The following findings of served as support for TNR's link to onset of disease; the detection of various repeats within these diseases demonstrated this relationship.


Because of these discoveries, ideas involving anticipation in disease began to develop, and curiosity formed about how the causes could be related to TNRs. [ 6 ]  After the breakthroughs, the four mechanisms for TNRs were determined, and more types of repeats were identified as well. [ 9 ]  Repeat composition and location are used to determine the mechanism of a given expansion. [ 9 ]  Onwards from 1995, it was also possible to observe the formation of  hairpins  in triplet repeats, which consisted of repeating CG pairs and a mismatch. [ 11 ]


During the decade after evidence that linked TNR to onset of disease was found, focus was placed on studying repeat length and dynamics on diseases, as well as investigating the mechanism behind parent-child disease inheritance. [ 6 ]  Research has shown that there is a clear inverse relationship between the length of the repeats in parents and the age of disease onset in children; therefore, the lengths of TNRs are used to predict age of disease onset as well as outcome in  clinical diagnosis . [ 12 ] [ 13 ]  In addition to this finding, another aspect of the diseases, the high variability of onset, was revealed. [ 6 ]  Although the onset of HD could be predicted by examining TNR length inheritance, the onset could vary up to fourfold depending on the patient, leading to the possibility of existence of age-modifying factors for disease onset; there were notable efforts in this search. [ 14 ] [ 15 ]  Currently, CAG repeat length is considered the biggest onset age modifier for TNR diseases. [ 16 ] [ 17 ]


Detection of TNRs was made difficult by limited technology and methods early on, and years passed before the development of sufficient ways to measure the repeats. [ 6 ]  When  PCR  was first attempted in the detection of TNRs, multiple band artifacts were prevalent in the results, and this made recognition of TNRs troublesome; at the time, debate centered around whether disease was brought on by smaller amounts of short expansions or a small amount of long expansions. [ 6 ] [ 18 ]  Since then, accurate methods have been established over the years. Together, the following clinically necessary protocols have 99% accuracy in measuring TNRs. [ 6 ]


These repetitive sequences lead to instability amongst the DNA strands after reaching a certain threshold number of repeats, which can result in DNA slippage during replication. [ 23 ]  The most common and well-known triplet repeats are CAG, GCG, CTG, CGG, and GAA. During DNA replication, the strand being synthesized can misalign with its template strand due to the dynamic nature and flexibility of these triplet repeats. [ 24 ]  This slippage allows for the strand to find a stable intermediate amongst itself through base pairing, forming a secondary structure other than a duplex. [ 24 ]


In terms of location, these triplet repeats can be found in both coding and non-coding regions. CAG and GCN repeats, which lead to polyglutamine and polyalanine tracts respectively, are normally found in the coding regions. [ 25 ]  At the 5' untranslated region, CGG and CAG repeats are found and responsible for fragile X syndrome and spinocerebellar ataxia 12. [ 25 ]  At the 3' untranslated region, CTG repeats are found, while GAA repeats are located in the intron region. Other disease-causing repeats, but not triplet repeats, have been located in the promoter region. [ 25 ]  Once the number of repeats exceeds normal levels, Triplet Repeat Expansions (TRE) become more likely and the number of triplet repeats can typically increase to around 100 in coding regions and up to thousands in non-coding regions. [ 25 ]  This difference is due to overexpression of glutamine and alanine, which is selected against due to cell toxicity. [ 26 ]


Depending on the sequence of the repeat, at least three intermediates with different secondary structures are known to form. [ 27 ]  A CGG repeat will form a G-quadruplex due to Hoogsteen base pairing, while a GAA repeat forms a triplex due to negative supercoiling. [ 25 ] [ 27 ]  CAG, CTG, and CGG repeats form a hairpin. After the hairpin forms, the primer realigns with the 3' end of the newly synthesized strand and continues the synthesis, leading to triplet repeat expansion. [ 23 ]  The structure of the hairpin is based on a stem and a loop that contains both Watson-Crick base pairs and mismatched pairs. [ 25 ]  In CTG and CAG repeats, the number of nucleotides present in the loop depends on if the number of triplet repeats is odd or even. [ 28 ]  An even number of repeats forms a tetraloop structure, while an odd number leads to the formation of a triloop. [ 28 ] [ 29 ]


In trinucleotide repeat expansion there is a certain threshold or maximum amount of repeats that can occur before a sequence becomes unstable. Once this threshold is reached the repeats will start to rapidly expand causing longer and longer expansions in future generations. [ 30 ]  Once it hits this minimum allele size which is normally around 30-40 repeats, diseases and instability can be contracted, but if the number of repeats found within a sequence are below the threshold it will remain relatively stable. [ 30 ]  There is still not enough research found to understand the molecular nature that causes thresholds but researchers are continuing to study that the possibility could lie with the formation of the secondary structure when these repeats occur. It was found that diseases associated with trinucleotide repeat expansions contained secondary structures with hairpins, triplexes, and slipped-strand duplexes. [ 30 ]  These observations have led to the hypothesis that the threshold is determined by the number of repeats that must occur to stabilize the formation of these unwanted secondary structures, due to the fact that when these structures form there is an increased number of mutations [ 31 ]  that will form in the sequence resulting in more trinucleotide expansion.


Research suggests that there is a direct, important correlation between the sex of the parent that transmits the mutation and the degree and phenotype of disorder in the child., [ 32 ] [ 33 ]  The degree of repeat expansion and whether or not an expansion will occur has been directly linked to the sex of the transmitting parent in both non-coding and coding trinucleotide repeat disorders. [ 32 ]  For example, research regarding the correlation between Huntington's Disease CAG trinucleotide repeat and parental transmission has found that there is a strong correlation between the two with differences in maternal and paternal transmission. [ 32 ]  Maternal transmission has been observed to only consist of an increase in repeat units of 1 while the paternal transmission is typically anywhere from 3 to 9 extra repeats. [ 32 ]  Paternal transmission is almost always responsible for large repeat transmission resulting in the early onset of Huntington's Disease while maternal transmission results in affected individuals experiencing symptom onset mirroring that of their mother., [ 32 ] [ 34 ]  While this transmission of a trinucleotide repeat expansion is regarded to be a result of "meiotic instability", the degree to which meiosis plays a role in this process and the mechanism is not clear and numerous other processes are predicted to simultaneously play a role in this process. [ 32 ]


One proposed but highly unlikely mechanism that plays a role in trinucleotide expansion transmission occurs during meiotic or mitotic recombination. [ 32 ]  It is suggested that during these processes it is possible for a homologous repeat misalignment, commonly known for causing alpha-globin locus deletions, causes the meiotic instability of a trinucleotide repeat expansion. [ 32 ]  This process is unlikely to contribute to the transmission and presence of trinucleotide repeat expansions due to differences in expansion mechanisms. [ 32 ]  Trinucleotide repeat expansions typically favor expansions of the CAG region but, in order for the unequal homologous exchange to be a plausible suggestion, these repeats would have to go through expansion and contraction events at the same time. [ 32 ]  In addition, numerous diseases that result from transmitted trinucleotide repeat expansions, such as Fragile X syndrome, involve unstable trinucleotide repeats on the X chromosome that cannot be explained by meiotic recombination. [ 32 ]  Research has shown that although unequal homologous recombination is unlikely to be the sole cause of transmitted trinucleotide repeat expansions, this homologous recombination likely plays a minor role in the length of some trinucleotide repeat expansions. [ 32 ]


DNA replication  errors are predicted to be the main perpetrator of trinucleotide repeat expansion transmission in many predicted models due to the difficulty of Trinucleotide Repeat Expansion (TRE). [ 32 ]  TREs have been shown to occur during DNA replication in both in vitro and in vivo studies, allowing for these long tracts of triplet repeats to assemble rapidly in different mechanisms that can result in either small scale or large scale expansions. [ 25 ]


These expansions can occur through either strand slippage or flap ligation. [ 25 ]  Okazaki fragments are a key element of the proposed error in DNA replication. [ 32 ]  It is suggested that the small size of Okazaki fragments, typically between 150 and 200 nucleotides long, makes them more likely to fall off or "slip" off the lagging strand, which creates room for trinucleotide repeats to attach to the lagging strand copy. [ 32 ] 	In addition to this possibility of trinucleotide repeat expansion changes occurring due to slippage of Okazaki fragments, the ability of CG-rich trinucleotide repeat expansion sequences to form a special hairpin, toroid, and triplex DNA structures contributes to this model, suggesting error occurs during DNA replication. [ 32 ]   Hairpin structures  can form as a result of the freedom of the lagging strand during DNA replication and are typically observed to form in extremely long trinucleotide repeat sequences. [ 32 ]  Research has found that this hairpin formation depends on the orientation of the trinucleotide repeats within each CAG/CTG trinucleotide strand. [ 32 ]  Strands that have duplex formation by CTG repeats in the leading strand are observed to result in extra repeats, while those without CTG repeats in the leading strand result in repeat deletions. [ 32 ]  These intermediates can pause activity of the replication fork based on their interaction with DNA polymerases through strand slippage. [ 25 ]  Contractions occur when the replication fork skips over the intermediate on the Okazaki fragment. Expansions occur when the fork reverses and restarts, which forms a chicken-foot structure. [ 25 ]  This structure results in the unstable intermediate forming on the nascent leading strand, leading to further TRE. [ 25 ]  Furthermore, this intermediate can avoid mismatch repair due to its affinity for the MSH-2-MSH3 complex, which stabilizes the hairpin instead of repairing it. [ 25 ] [ 27 ]   In non-dividing cells, a process called flap-ligation can be responsible for TRE. [ 25 ] [ 27 ]  8-oxo-guanine DNA glycosylase removes a guanine and forms a nick in the sequence. [ 25 ]  The coding strand then forms a flap due to displacement, which prevents removal by an endonuclease. When the repair process finishes for either mechanism, the length of the expansion is equivalent to the number of triplet repeats involved in the formation of the hairpin intermediate. [ 25 ]


Two mechanisms have been proposed for large scale repeats: template switching and break-induced replication. [ 27 ]


Template switching, a mechanism for large scale GAA repeats that can double the number of triplet repeats, has been proposed. [ 27 ]  GAA repeats expand when their repeat length is greater than the Okazaki fragment's length. [ 27 ]  These repeats are involved in the stalling of the replication fork as these repeats form a triplex when the 5' flap of  TTC repeats fold back. [ 27 ]   Okazaki fragment synthesis continues when the template is switched to the nascent leading strand. [ 27 ]  The Okazaki fragment eventually ligates back to the 5' flap, which results in TRE. [ 27 ]


A different mechanism, based on break-induced replication, has been proposed for large scale CAG repeats and can also occur in non-dividing cells. [ 27 ]  At first, this mechanism follows the same process as the small scale strand slippage mechanism until replication fork reversal. [ 27 ]  An endonuclease then cleaves the chicken-foot structure, which results in a one-ended double strand break. [ 27 ]  The CAG repeat of this broken daughter strand forms a hairpin and invades the CAG strand on the sister chromatid, which results in expansion of this repeat in a migrating  D-loop  DNA synthesis. [ 27 ]  This synthesis continues until it reaches the replication fork and is cleaved, which results in an expanded sister chromatid. [ 27 ]


Fragile X syndrome  is the second most common form of intellectual disability affecting 1 in 2,000-4,000 women and 1 in 4,000-8,000 men, women being twice as likely to inherit this disability due to their XX chromosomes. [ 35 ]  This disability arises from a mutation at the end of the X chromosome in the  FMR1 gene  (fragile X mental retardation gene) which produces a protein essential for brain development called FMRP. [ 35 ]  Individuals with fragile X syndrome experience a variety of symptoms at varying degrees that depend on gender and mutation degree such as attention deficit disorders, irritability, stimuli sensitivity, various anxiety disorders, depression, and/or aggressive behavior. [ 35 ]  Some treatments for these symptoms seen in individuals with Fragile X syndrome include  SSRI 's, antipsychotic medications, stimulants,  folic acid , and mood stabilizers. [ 35 ]


Fragile X syndrome is caused by expansion of CGG repeats in the FMR1 gene. In males without fragile X syndrome, the CGG repeat number ranges from 53 to 200 while those affected have greater than 200 repeats of this trinucleotide sequence located at the end of the X chromosome on band  Xq28.3.1 . [ 36 ]  Carriers that have repeats falling within the 53 to 200 repeat range are said to have "premutation alleles", as the  alleles  within this range approach 200, the likelihood of expansion to a full mutation increases, and the mRNA levels are elevated five-fold. [ 36 ]  Research has shown that individuals with premutation alleles in the range of 59-69 repeats have about a 30% risk of developing full mutation and compared to those in the high range of ≥ 90 repeats. [ 37 ]  Fragile X syndrome carriers (those that fall within the premutation range) typically have unmethylated alleles, normal phenotype, and normal levels of FMR1 mRNA and FMRP protein. [ 36 ]  Fragile X syndrome men possess alleles in the full mutation range (>200 repeats) with FMRP protein levels much lower than normal and experience hypermethylation of the promoter region of the FMR1 gene. [ 36 ]   Some men with alleles in the full mutation range experience partial or no methylation which results in only slightly abnormal phenotypes due to only slight down-regulation of FMR1 gene transcription. [ 36 ]  Unmethylated and partially methylated alleles in the mutation range experience increased and normal levels of FMR1 mRNA when compared to normal controls. [ 36 ]  In contrast, when unmethylated alleles reach a repeat number of approximately 300, the transcription levels are relatively unaffected and operate at normal levels; the transcription levels of repeats greater than 300 is currently unknown. [ 36 ]


The CGG trinucleotide repeat expansion is present within the FMR1 mRNA and its interactions are responsible for  promoter  silencing. [ 36 ]  The CGG trinucleotide expansion resides within the 5' untranslated region of the mRNA, which undergoes hybridization to form a complementary CGG repeat portion. [ 36 ]  The binding of this genomic repeat to the mRNA results in silencing of the promoter. [ 36 ]   Beyond this point, the mechanism of promoter silencing is unknown and still being further investigated. [ 36 ]


Huntington's disease  (HD) is a dominantly, paternally transmitted neurological disorder that affects 1 in 15,000-20,000 people in many Western populations. [ 38 ]  HD involves the basal ganglia and the cerebral cortex and manifests as symptoms such as cognitive, motor, and/or psychiatric impairment. [ 38 ]


This autosomal dominant disorder results from the expansions of a trinucleotide repeat which involves CAG in exon 1 of the IT15 gene. [ 39 ]  The majority of all juvenile HD cases stem from the transmission of a high CAG trinucleotide repeat number that is a result of paternal  gametogenesis . [ 40 ]  While an individual without HD has a number of CAG repeats that fall within a range between 9 and 37, an individual with HD has CAG is typically found to have repeats in a range between 37 and 102. [ 39 ]  Research has shown an inverse relationship between the number of trinucleotide repeats and age of onset, however, no relationship between trinucleotide repeat numbers and rate of HD progression and/or effected individual's body weight has been observed. [ 39 ]  Severity of functional decline has been found to be similar across a wide range of individuals with varying numbers of CAG repeats and differing ages of onset, therefore, it is suggested that the rate of disease progression is also linked to factors other than the CAG repeat such as environmental and/or genetic factors. [ 39 ]


Myotonic dystrophy  is a rare muscular disorder in which numerous bodily systems are affected. There are four forms of Myotonic Dystrophy: mild phenotype and late-onset, onset in adolescence/young adulthood, early childhood featuring only learning disabilities, and a congenital form. [ 41 ]  Individuals with Myotonic Dystrophy experience severe, debilitating physical symptoms such as muscle weakness, heartbeat issues, and difficulty breathing that can be improved through treatment to maximize patients' mobility and everyday activity to alleviate some stress of their caretakers. [ 42 ]  The muscles of individuals with Myotonic Dystrophy feature an increase of type 1 fibers as well as an increased deterioration of these type 1 fibers. [ 42 ]  In addition to these physical ailments, individuals with Myotonic Dystrophy have been found to experience varying internalized disorders such as anxiety and mood disorders as well as cognitive delays,  attention deficit disorders ,  autism spectrum disorders , lower IQ's, and visual-spatial difficulties. [ 42 ]  Research has shown that there is a direct correlation between expansion repeat number, IQ, and an individual's degree of visual-spatial impairment. [ 42 ]


Myotonic dystrophy results from a (CTG)n trinucleotide repeat expansion that resides in a 3' untranslated region of a  serine/threonine kinase  coding transcript. [ 43 ]  This (CTG)n trinucleotide repeat is located within  leukocytes ; the length of the repeat and the age of the individual have been found to be directly related to disease progression and  type 1 muscle fiber  predominance. [ 43 ]  Age and (CTG)n length only have small correlation coefficients to disease progression, research suggests that various other factors play a role in disease progression such as changes in  signal transduction pathway , somatic expression, and cell heterogeneity in (CTG)n repeats. [ 43 ]


Friedreich's ataxia  is a progressive neurological disorder.  Individuals experience gait and speech disturbances due to degeneration of the spinal cord and peripheral nerves.  Other symptoms may include cardiac complications and diabetes.  Typical age at symptom onset is 5–15, with symptoms progressively getting worse over time. [ 44 ]


Friedreich's ataxia is an autosomal recessive disorder cause by a GAA expansion in the  intron  of the  FXN  gene.  This gene codes for the protein  frataxin , a mitochondrial protein involved in iron homeostasis.  The mutation impairs  transcription  of the protein, so affected cells produce only 5-10% of the frataxin of healthy cells. [ 45 ]
This leads to iron accumulation in the mitochondria, and makes cells vulnerable to oxidative damage.
Research shows that GAA repeat length is correlated with disease severity. [ 46 ]


The precise timing of TNR occurrence varies by disease. Although the exact timing for FXS is not certain, research has suggested that the earliest CGG expansions for this disorder are seen in  primary oocytes . [ 47 ] [ 48 ]  It has been proposed that the repeat expansion happens in the maternal oocyte during  meiotic  cell cycle arrest in  prophase I , however the mechanism remains nebulous. [ 49 ] [ 50 ]  Maternally inherited premutation  alleles  may expand into full mutation alleles (greater than 200 repeats), resulting in decreased production of the FMR-1 gene product FMRP and causing fragile X mental retardation syndrome. [ 51 ]  For females, the large repeat expansions are based upon repair, while for males, the shortening of long repeat expansions is due to replication; therefore, their sperm lack these repeats, and paternal inheritance of long repeat expansions does not occur. [ 52 ] [ 53 ] [ 54 ]  Between weeks 13 and 17 of  human fetal development , the large CGG repeats are shortened. [ 54 ]


Many similarities can be drawn between DM1 and FXS involving aspects of mutation. Full maternal inheritance is present within DM1, repeat expansion length is linked to maternal age and the earliest instance of expansions is seen in the two-cell stage of preimplantation embryos. [ 55 ] [ 56 ]  There is a positive correlation between male inheritance and allele length. [ 57 ]  A study of mice found the exact timing of CTG repeat expansion to be during development of  spermatogonia . [ 58 ]  In DM1 and FXS, it is hypothesized that expansion of TNRs occurs by means of multiple missteps by  DNA polymerase  in replication. [ 59 ] [ 60 ]  An inability of DNA polymerase to properly move across the TNR may cause transactivation of translesion polymerases (TLPs), which will attempt to complete the replication process and overcome the block. It is understood that as the DNA polymerase fails in this way, the resulting single-stranded loops left behind in the template strand undergo deletion, affecting TNR length. This process leaves the potential for TNR expansions to occur. [ 15 ]


In Huntington's disease (HD), the exact timing has not been determined; however there are a number of proposed points during germ cell development at which expansion is thought to occur. [ 61 ] [ 62 ]


Spinocerebellar ataxia type 1  (SCA1) CAG repeats are most often passed down through paternal inheritance and similarities can be seen with HD. [ 15 ]  The tract size for offspring of mothers with these repeats does not display any degree of change. [ 64 ]  Because TNR instability is not present in young female mice, and female SCA1 patient age and instability are directly related, expansions must occur in inactive oocytes. [ 65 ]  A trend has seemed to emerge of larger expansions occurring in cells inactive in division and smaller expansions occurring in actively dividing or nondividing cells. [ 15 ]


Trinucleotide repeat expansion, is a DNA mutation that is responsible for causing any type of disorder classified as a trinucleotide repeat disorder. These disorders are progressive and affect the sequences of the human genome, frequently within the nervous system. So far the available therapeutics only have modest results at best [ 66 ]  with emphasis on the research and studying of genomic manipulation. The most advanced available therapies aim to target mutated gene expression by using antisense oligonucleotides (ASO) or RNA interference (RNAi) to target the messenger RNA (mRNA). [ 66 ]  While solutions for the interventions of this disease is a priority, RNAi and ASO have only reached clinical trial stages.


RNA interference  is a mechanism that can be used to silence the expression of genes, RNAi is a naturally occurring process that is leveraged using synthetic small interfering RNAs (siRNAs) that are used to change the action and duration of the natural RNAi process. [ 67 ]  Another synthetic RNA is the short hairpin RNAs (shRNA) [ 67 ]  these can also be used to monitor the action and predictability of the RNAi process.


RNAi begins with RNase Dicer cleaving a 21-25 nucleotide long stand of double stranded RNA substrates into small fragments. This process results in the creation of the siRNA duplexes that will be used by the complex RNA induced silencing complex (RISC). [ 67 ]  The RISC contains the antisense that will bind to complementary mRNA strands, once they are bound they are cleaved by the protein found within the RISC complex called Argonaute 2 (Ago2) between the bases 10 and 11 relative to the 5' end. Before the cleavage of the mRNA strand the double stranded antisense of the siRNA is also cleaved by the Ago2 complex, this leaves a single stranded guide within the RISC compound that will be used to find the desired mRNA strand resulting in this process to have specificity. [ 68 ]  Some problems that may occur is if the guide single strand siRNA within the RISC complex may become unstable when cleaved and begin to unwind, resulting in binding to an unfavorable mRNA strand. The perfect complementary guides for the targeted RNAs are easily recognized and will be cleaved within the RISC complex; if there is only partial complementary pairing between the guide strand and the targeted mRNA may cause the incorrect translation or destabilization at the target sites. [ 68 ]


Antisense oligonucleotides (ASOs) are small strand single stranded oligodeoxynucleotides approximately 15-20 nucleic acids in length that can alter the expression of a protein. [ 69 ]  The goal of using these antisense oligonucleotides are the decrease in protein expression of a specific target usually by the inhibition of the RNase H endonuclease, as well as inhibition of the 5' cap formation or alteration of the splicing process. [ 70 ]  In the native state ASOs are rapidly digested, this requires the use of phosphorylation order for the ASO to go through the cell membranes.


Despite the obvious benefits that antisense therapeutics can bring to the world with their ability to silence neural disease, there are many issues with the development of this therapy. One problem is the ASOs are highly susceptible to degradation by the nucleases [ 71 ]  within the body. This results in a high amount of chemical modification when altering the chemistry to allow for the nucleases to surpass the degradation of these synthetic nucleic acids. Native ASOs have a very short half-life even before being filtered throughout the body especially in the kidney and with the a high negative charge makes the crossing through the vascular system or membranes very difficult when trying to reach the targeted DNA or mRNA strands. With all these barriers, the chemical modifications may lead to devastating effects when being introduced into the body making each problem develop more and more side effects.


The synthetic oligonucleotides are negatively charged molecules that are chemically modified in order for the molecule to regulate the gene expression within the cell. Some issues that come about this process is the toxicity and variability that can come about with chemical modification. [ 70 ]  The goal of the ASO is to modulate the gene expression through proteins which can be done in 2 complex ways; a)the RNase H-dependent oligonucleotides, which induce the degradation of mRNA, and (b) the steric-blocker oligonucleotides, which physically prevent or inhibit the progression of splicing or the translational machinery. The majority of investigated ASOs utilize the first mechanism with the Rnase H  enzyme that hydrolyzes an RNA strand, when this enzyme is assisted using the oligonucleotides the reduction of RNA expression is efficiently reduced by 80-95% and can still inhibit expression on any region of the mRNA.


Uniparental inheritance  is a non-Mendelian form of inheritance that consists of the transmission of genotypes from one parental type to all progeny. That is, all the genes in offspring will originate from only the mother or only the father. This phenomenon is most commonly observed in eukaryotic  organelles  such as   mitochondria  and  chloroplasts . This is because such organelles contain their own DNA and are capable of independent mitotic replication that does not endure crossing over with the DNA from another parental type. Although uniparental inheritance is the most common form of inheritance in organelles, there is increased evidence of diversity. Some studies found doubly uniparental inheritance (DUI) and biparental transmission to exist in cells. Evidence suggests that even when there is biparental inheritance, crossing-over doesn't always occur. Furthermore, there is evidence that the form of organelle inheritance varied frequently over time. Uniparental inheritance can be divided into multiple subtypes based on the pathway of inheritance. [ 1 ] [ 2 ]


Although most of the eukaryotic sub-cellular parts do not have their own  DNA  nor are capable of replication independent of the nucleus, there are some exceptions such as mitochondria and chloroplasts. Not only are these organelles capable of independent DNA replication, translation, and transcription, they are commonly known to inherit genes from only one parental type. In the case of mitochondria,  maternal inheritance  is almost the exclusive form of inheritance. Although, during egg cell fertilization, mitochondria are brought into the fertilized cell both by the egg cell and the sperm, the paternal mitochondria are usually marked with  ubiquitin  and are later destroyed. [ 3 ]  Even if they are not destroyed, the DNA's of different mitochondria rarely  genetically recombine  with one another. Thus, mitochondria in most animals are inherited from the maternal type only.


Like all other genetic concepts, the discovery of uniparental inheritance stems from the days of an  Augustinian  priest known as  Gregor Johann Mendel . The soon-to-be "father of modern genetics" spent his days conducting  hybridization experiments  on pea plants( Pisum sativum ) in his monastery's garden. During a period of seven years (1856 to 1863), Mendel cultivated and tested some 29,000 pea plants which led to him deducing the two famous generalizations known as  Mendel's Laws of Heredity . The first, the law of segregation, states that "when any individual produces  gametes , the copies of a gene separate, so that each gamete receives only one copy". The second, the law of independent assortment, states that " alleles  of different genes assort independently of one another during gamete formation". Although his work was published, it lay dormant until it was rediscovered in 1900 by  Hugo de Vries  and  Carl Correns   but it was not until 1909 that non-mendelian inheritance was even suggested.  Carl Erich Correns  and  Erwin Baur , in separately conducted researches on  Pelargonium  and  Mirabilis  plants, observed a green-white variation (later found as the result of  mutations  in the  chloroplast  genome) that did not follow the Mendelian laws of inheritance. Nearly twenty years later, non-mendelian inheritance of a mitochondrial mutation was also observed and, in the sixties, it was proven that chloroplasts and mitochondria have their own  DNA  and that they are capable translation, transcription, and replication independent of the nucleus. Soon after, the discoveries of uniparental and doubly uniparental inheritance came. [ 1 ]


A well studied example of uniparental inheritance involves the  poky   mutants  of  Neurospora crassa .  The original poky mutant was isolated by Mitchell and Mitchell in 1952 as a spontaneously occurring slow growing variant. [ 4 ]   In genetic crosses, the poky phenotype was found to be maternally inherited.  The protoperithecial parent is regarded as the female (maternal) parent in  Neurospora .  When poky females were crossed to wild-type males, all progeny had the poky phenotype.  When wild-type females were crossed to poky males, all progeny had the wild-type phenotype.  That the poky determinant is only passed through the female line suggested that the determinant resides in the maternal  cytoplasm .  It was eventually shown that the primary defect in the  poky  mutants is a deletion in the  mitochondrial DNA  sequence encoding the small  ribosomal RNA  subunit. [ 5 ]


The  universal stress protein  ( USP ) domain is a  superfamily  of  conserved genes  which can be found in  bacteria ,  archaea ,  fungi ,  protozoa  and  plants . [ 2 ]  Proteins containing the domain are induced by many environmental stressors such as nutrient starvation, drought, extreme temperatures, high salinity, and the presence of uncouplers, antibiotics and metals. [ 2 ]


In the presence of these stressors,  Usp  genes are upregulated resulting in large quantities of Usp  proteins  being produced by the  cell . The over production of USP  genes  allows the  organisms  to better cope with stresses by largely unknown mechanisms. However, the USPs will alter the expression of a variety of genes that help to cope with stress. [ 3 ]


The primary function of this superfamily is to protect the organism from  environmental stress  such as exposure to  UV light , which may induce  genes  containing the USP domain in order to protect the  DNA  and more generally the cell from further damage. [ 2 ]  During  bacterial   starvation  the USP genes upregulated will often arrest  cell growth  and promote its metabolism to adapt to sparse  nutrients . [ 2 ]


Recent research also suggests proteins containing this domain have functions beyond the realms of dealing with environmental stresses. [ 5 ]  Nachin et al. demonstrated in  Escherichia coli  that USPs are involved in actions such as  adhesion  and  motility . The researchers, through means of  "knocking out"  USP genes known as  UspE  and  UspC , saw results suggesting an inability to swim and completely lack of motility, respectively. Conversely, mutants for genes  UspF  and  UspG  were shown to have enhanced swimming abilities. Therefore, mobility is affected both positively and negatively USPs within  E. coli . This demonstrates USPs influence throughout the cell could be widespread for a number of reasons.


Additionally, in  Halmonas elongate , there is a USP called TeaD has been described as a key regulator in the  transport  of  Ectoine  across the cell membrane. [ 6 ]  This demonstrates how versatile USPs can be. Their function, while primarily encompasses increasing survival during stressful conditions, is not always limited to this.


The ubiquitous nature of these proteins suggests the domain evolved in an ancestral species as well as highlighting the clear biological significance these proteins have in order to  still be present in the three domains of life. It has been suggested that the USP A domain was part of an ancient protein family. This is due to the similarity in structure between many distantly related organisms. [ 7 ]  Aravind et al. confirmed these ideas with extensive evolutionary analysis. Aravind suggested that these proteins were part of a much larger protein structural family which was present and diversified in our last universal common ancestor for all extant life. [ 7 ]
The original function has been suggested to be a nucleotide binding domain which was implicated in signal transduction  [ 8 ]