This page was published for Genetics 564 at the University of Wisconsin- Madison
Determining a research model
In order to determine a usable research model, it is important to explore all of the homologs of a particular gene of interest. A model must be able to provide a similar phenotype to the disease of focus, and be consistently viable. There are multiple ways to go about finding this, including the use of RNAi, chemical genetics and microarrays.
RNAi
RNA interference, or RNAi, is a natural process turned experimental technique used to silence specific genes. This process is commonly used in C. elegans as a cellular defense mechanism. RNAi uses double stranded RNA (dsRNA) to block the translation of messenger RNAs (mRNAs) of particular genes of interest. The entire mechanism of how RNAi works can be seen in the video below. The process begins by the entrance of dsRNA into the organism. The dsRNA is either produced in the cell itself or delivered experimentally. An enzyme called Dicer cuts the dsRNA into small fragments. These short dsRNA fragments then bind an argonaute protein, which selects one strand of the RNA to be used as the guide strand. The other strand is released and degraded. The argonaute protein and now single strand of RNA then bind other proteins to form the RNA induced silencing complex (RISC). RISC then seeks out the complementary mRNA strand and binds almost perfectly. Once bound, the target mRNA is sliced and degraded [1].
RNAi Analysis
After searching a variety of RNAi databases including WormBase, FlyBase, MGI and others, I determined that the most usable model organism for research on Sotos syndrome would be Drosophila Melaongaster and the mouse model. These organisms both showed the propensity to produce a viable phenotype while also still being able to express aspects of Sotos syndrome, including learning disabilities and overgrowth. The fly model might be easier to work with given the availability off stocks and the low cost whereas the mouse model is not immediately available and might require extensive recombination techniques to create the mutant mice [2][3].
After searching a variety of RNAi databases including WormBase, FlyBase, MGI and others, I determined that the most usable model organism for research on Sotos syndrome would be Drosophila Melaongaster and the mouse model. These organisms both showed the propensity to produce a viable phenotype while also still being able to express aspects of Sotos syndrome, including learning disabilities and overgrowth. The fly model might be easier to work with given the availability off stocks and the low cost whereas the mouse model is not immediately available and might require extensive recombination techniques to create the mutant mice [2][3].
Chemical Genetics
Chemical genetics is a common approach used when researchers wish to identify the purpose or function of a gene. This process is commonly used in larger animals where techniques like RNAi won't work. Small molecules are introduced into an organism and can bind to particular proteins to either inhibit or activate their function. The result can then be used for further analysis. This technique is also used to identify potential drug candidates.
Chemical genetics utilizes two types of libraries to target proteins of interest: focused libraries and diversity-oriented libraries (Figure 1). Focused libraries are designed around a specific chemical structure called a scaffold. The scaffold is already known to interact with a particular protein in some way. Different variations of the scaffold are then used to target the protein or class of proteins of interest. Diversity-oriented libraries, on the other hand, are not targeted to a particular protein. These libraries are used in broad screens to see if any one molecule can interact with the protein of interest. The goal of diversity-oriented libraries is to develop a diverse collection of molecules that has the possibility to cause an effect [4].
Chemical genetics utilizes two types of libraries to target proteins of interest: focused libraries and diversity-oriented libraries (Figure 1). Focused libraries are designed around a specific chemical structure called a scaffold. The scaffold is already known to interact with a particular protein in some way. Different variations of the scaffold are then used to target the protein or class of proteins of interest. Diversity-oriented libraries, on the other hand, are not targeted to a particular protein. These libraries are used in broad screens to see if any one molecule can interact with the protein of interest. The goal of diversity-oriented libraries is to develop a diverse collection of molecules that has the possibility to cause an effect [4].
Chemical Genetics Analysis
Unfortunately, no small molecules were found that were capable of interacting with NSD1. After searching databases like PubChem and ChemBank, no results appeared that could aid in future research for Sotos syndrome.
Unfortunately, no small molecules were found that were capable of interacting with NSD1. After searching databases like PubChem and ChemBank, no results appeared that could aid in future research for Sotos syndrome.
Microarray
Another way of determining a solid research model is with DNA microarrays. Researchers use this tool to analyze the levels of expression of various mRNA transcripts in certain cell types under various circumstances. DNA microarrays work by utilizing the mRNA's ability to bind, or hybridize, to the DNA from which it was transcribed. Expression levels for many genes can be determined from a single microarray, which allows researchers to generate profiles quickly and easily.
Arrays are a glass chip containing hundreds of different oligos corresponding to different genes of interest annealed to the chip. Researchers then isolate mRNA from two different cell types, usually diseased and healthy models, and create complementary DNA (cDNA) with an attached fluorescent tag. Each cell type corresponds to a different color tag. The samples are then washed over the chip and incubated so hybridization can occur between the cDNA and the oligos on the chip. Computer programs can be used to analyze the fluorescence with laser technology. An example of a microarray slide can be seen in Figure 2. The different colors and intensities correspond to the type and levels of mRNA expression. If, say, the healthy cell type was tagged green and the diseased cell type was tagged red, any location on the chip that expressed red and green would indicate that that particular gene was expressed in both cell types [5].
A large database of microarray information has been compiled in the Gene Expression Omnibus (GEO). This database also includes information from high throughput sequencing and other genomic tools, which allows researchers to gain access to thousands of other experiments on the same gene or disease. Researchers can analyze DataSets, which are collections of "biologically and statistically comparable GEO samples" that share a common set of array elements. Profiles can then be made from DataSets which "consist of expression measurements for an individual gene across all samples in a DataSet [6].
Arrays are a glass chip containing hundreds of different oligos corresponding to different genes of interest annealed to the chip. Researchers then isolate mRNA from two different cell types, usually diseased and healthy models, and create complementary DNA (cDNA) with an attached fluorescent tag. Each cell type corresponds to a different color tag. The samples are then washed over the chip and incubated so hybridization can occur between the cDNA and the oligos on the chip. Computer programs can be used to analyze the fluorescence with laser technology. An example of a microarray slide can be seen in Figure 2. The different colors and intensities correspond to the type and levels of mRNA expression. If, say, the healthy cell type was tagged green and the diseased cell type was tagged red, any location on the chip that expressed red and green would indicate that that particular gene was expressed in both cell types [5].
A large database of microarray information has been compiled in the Gene Expression Omnibus (GEO). This database also includes information from high throughput sequencing and other genomic tools, which allows researchers to gain access to thousands of other experiments on the same gene or disease. Researchers can analyze DataSets, which are collections of "biologically and statistically comparable GEO samples" that share a common set of array elements. Profiles can then be made from DataSets which "consist of expression measurements for an individual gene across all samples in a DataSet [6].
GEO Analysis
Although there were many different DataSets that came from the search of "NSD1", there were none directly specifying the origination from a study surrounding Sotos syndrome. NSD1 being a transcriptional regulator has a very wide reach to many different diseases including cancers and heart abnormalities, however directly relating to Sotos, there is no information. This is not surprising given the lack of concrete knowledge on NSD1's function in Sotos syndrome.
Although there were many different DataSets that came from the search of "NSD1", there were none directly specifying the origination from a study surrounding Sotos syndrome. NSD1 being a transcriptional regulator has a very wide reach to many different diseases including cancers and heart abnormalities, however directly relating to Sotos, there is no information. This is not surprising given the lack of concrete knowledge on NSD1's function in Sotos syndrome.
References
[1] National Institute of General Medical Sciences. 2013. RNA Interference Fact Sheet. Retrieved May 16, 2014 from http://www.nigms.nih.gov/News/Extras/RNAi/Pages/factsheet.aspx
[2] FlyBase. 2014. Gene Dmel\Mes-4. Retrieved May 16, 2014 from http://flybase.org/reports/FBgn0039559.html
[3] Mouse Genome Informatics. 2014. Targeted Allele. Retrieved May 16, 2014 from http://www.informatics.jax.org/allele/MGI:5451135
[4] Stockwell, B. R. (2004). Exploring biology with small organic molecules. Nature, 432(7019), 846-854. doi: 10.1038/nature03196
[5] NCBI. 2007. Microarrays: Chipping Away at the Mysteries of Science and Medicine. Retrieved May 16, 2014 from http://fenchurch.mc.vanderbilt.edu/bmif310/2008/Reading9-NCBI-2007.pdf
[6] NCBI. 2014. GEO Overview. Retrieved May 16, 2014 from http://www.ncbi.nlm.nih.gov/geo/info/overview.html
[1] National Institute of General Medical Sciences. 2013. RNA Interference Fact Sheet. Retrieved May 16, 2014 from http://www.nigms.nih.gov/News/Extras/RNAi/Pages/factsheet.aspx
[2] FlyBase. 2014. Gene Dmel\Mes-4. Retrieved May 16, 2014 from http://flybase.org/reports/FBgn0039559.html
[3] Mouse Genome Informatics. 2014. Targeted Allele. Retrieved May 16, 2014 from http://www.informatics.jax.org/allele/MGI:5451135
[4] Stockwell, B. R. (2004). Exploring biology with small organic molecules. Nature, 432(7019), 846-854. doi: 10.1038/nature03196
[5] NCBI. 2007. Microarrays: Chipping Away at the Mysteries of Science and Medicine. Retrieved May 16, 2014 from http://fenchurch.mc.vanderbilt.edu/bmif310/2008/Reading9-NCBI-2007.pdf
[6] NCBI. 2014. GEO Overview. Retrieved May 16, 2014 from http://www.ncbi.nlm.nih.gov/geo/info/overview.html