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| + | <center> <h5 class="center">We thank our sponsors:</h5> |
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| − | < li class=" active"><a href=" #section1" class="jnnbl">Overview</ a></ li> | + | <a href=" http:// tudelft.nl/en"> |
| − | <li><a href="#section2" class="jnnbl">1. Approach</a></li>
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| − | <a class="dropdown-toggle jnnbl" data-toggle="dropdown" href="#">2. Model Design<span class="caret"></span></a>
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| − | <li><a href=" #gRNA Model" class="jnnbl">2. 1 gRNA Model</ a></ li> | + | </a> |
| − | <li><a href="#Gene Doping Model" class="jnnbl">2.2 Gene Doping Model</a></li>
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| − | <a class="dropdown-toggle jnnbl" data-toggle="dropdown" href="#">3. Results<span class="caret"></span></a>
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| − | <li><a href=" #gRNA Model Results" class="jnnbl">3. 1 gRNA Model</ a></ li> | + | |
| − | <li><a href="#Gene Doping Model Results" class="jnnbl">3.2 Gene Doping Model</a></li>
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| − | </ul>
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| − | <li><a href="#References" class="jnnbl">4. References</a></li>
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| − | <div id="section1">
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| − | <div class="spcmkr"></div>
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| − | <p>Gene doping is the administration of exogenous genetic material for performance enhancement. Detection of gene doping requires identifying target sequences and detection windows. Our targeted sequencing method includes our novel dxCas9-linker-Tn5 fusion protein and requires a minimal set of guide RNAs aligning with gene doping sequences. As there are 10<sup>104</sup> possible codon variations in our proof of concept target, the EPO gene, it’s not feasible to target all possible sequences in practice. We therefore implemented a search function to identify areas with minimal variation within the sequence, which reduced the testing set to twelve gRNAs. Second, we modeled the process of infection and degradation of gene doping DNA in blood. From this model, we provided the laboratory with the time dependent concentration of the target DNA. Based on our wetlab sensitivity analyses, the model predicts that with microdosing, our detection method could effectively catch gene dopers.</p>
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| − | </div>
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| − | <div id="section2">
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| − | <div class="spcmkr"></div>
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| − | <h1 class="jnnbl">1. Approach</h1>
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| − | <p>We identified a threat in detecting gene doping that lays in the possibility of modifying the genetic sequence of a gene without changing the protein sequence produced. This allows gene dopers to creatively modify their DNA sequence in several possible combinations and complicates the design of detection methods. To combat this, the exon-exon junctions with the smallest possible variation need to be identified and gRNA sequences generated cover all possible combinations of one gene.</p>
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| − | < figure>< center> <img src="https://static.igem.org/mediawiki/2018/ 8/ 8f/T--TUDelft-- ModelFigure1.png" width=" 100%" height=" auto%" alt=" Model Figure 1"> </ center>< br> | + | <a href="https://www.tudelft.nl/bioengineering/"> |
| − | <figcapture class="figjnnbl"> <b>Figure 1:</b> Modified doping gene with introns removed. Exon-exon junctions are the target sites for the gRNAs in our fusion protein. </figcapture></figure><br>
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| − | <p>Once the testing set of gRNAs is generated for our fusion protein, we identified time during which the detection of the gene doping DNA in an athlete’s blood sample is possible. This was accomplished by modelling gene doping administration. We considered the entire process of gene doping to fully understand the underlying mechanisms of gene doping and its effect on the athlete. With our chosen model gene being the erythropoietin (EPO) gene, we included the EPO dependent production of red blood cells in our model.</p>
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| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/3/37/T--TUDelft--Human_Model_Figure2_Gif.gif" width="50%" height="auto" alt="Model Figure 2"></center> <br>
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| − | <figcapture class="figjnnbl"> <b>Figure 2:</b> A representation of EPO based gene doping. Viral vectors infect kidney cells, increasing their production of EPO. The increased concentration of EPO in the blood leads to an increased production of red blood cells. The red blood cell count acts as a feedback loop that determines the endogenous production of EPO. </figcapture></figure><br>
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| − | <button class="collapsible cjnnbl">Would you like to know more? Vectors in Gene Doping</button>
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| − | <div class="content">
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| − | <p>A vector is a DNA molecule used as a vehicle to artificially carry foreign genetic material into another cell. Once there it can be replicated and/or expressed. The effect and detection of gene doping is highly dependent on the vectors that are being used. In the early stages of our project we talked to prof. Hidde Haisma from Groningen University, a gene doping expert, who told us the main vectors he would expect athletes to use now are plasmids and adenoviruses. This is because their relative safety compared to vectors that integrate the DNA into the cells genome. Integrating vectors, such as the retrovirus, present the threat of insertional mutagenesis which can lead to the development of cancer. In Table 1 we made an analysis of possible gene doping vectors and some of their properties based on the work of <a class="jnnbl" href="#References">Ratko et al. 2003</a>.</p>
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| − | <!-- TABEL WITH VECTOR OVERVIEW -->
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| − | <center><table>
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| − | <caption class="figjnnbl"><b>Table 1:</b> Gene doping vectors advantages and disadvantages. </caption>
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| − | <tr class="tableheaderadpbl">
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| − | <th class="tableheaderjnnbl">Vector</th>
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| − | <th class="tableheaderjnnbl">Advantages</th>
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| − | <th class="tableheaderjnnbl">Disadvantages</th>
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| − | </tr>
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| − | <tr class="tableunevenjnnbl">
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| − | <td>Plasmid</td>
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| − | <td>Relatively safe <br>
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| − | Generally low immune response <br>
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| − | Low cost and easy large quantity production <br>
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| − | Variable transgene insertion upto ±20 kb (<a class="jnnbl" href="#References">Lodish et al. 2000</a>) <br>
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| − | Long storage (<a class="jnnbl" href="#References">Munier et al. 2005</a>, <a class="jnnbl" href="#References">Kircheis et al. 2001</a>, <a class="jnnbl" href="#References">Li and Huang 2000</a>)</td>
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| − | <td>Very low transfection efficiency (<a class="jnnbl" href="#References">Bergen et al. 2008</a>) <br>
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| − | No targeting <br>
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| − | Transient expression</td>
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| | | | |
| − | </tr>
| + | </div> |
| − | <tr class="tableevenjnnbl">
| + | </center> |
| − | <td>Adenovirus</td>
| + | </div> |
| − | <td>High transduction efficiency <br>
| + | </footer> |
| − | Transduces proliferating and nonproliferating cells <br>
| + | </body> |
| − | Transduces many cell types <br>
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| − | Easy Production <br>
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| − | Very high titers (1012 pfu/mL)</td>
| + | |
| − | <td>No targeting <br>
| + | |
| − | Transient expression <br>
| + | |
| − | Limited insert size: 4–5 kb <br>
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| − | Immune-related toxicity with repeated administration <br>
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| − | Potential replication competence</td>
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| | | | |
| − | </tr>
| + | <html> |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>Adeno-associated virus
| + | |
| − | </td>
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| − | <td>Continued expression <br>
| + | |
| − | No viral genes</td>
| + | |
| − | <td>No targeting <br>
| + | |
| − | Difficult production <br>
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| − | Not characterized well <br>
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| − | Potential Insertional Mutagenesis <br>
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| − | Limited insert size: 5kb</td>
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| − | | + | |
| − | </tr>
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| − | <tr class="tableevenjnnbl">
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| − | <td>Lentivirus</td>
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| − | <td>Transduces proliferating and nonproliferating cells <br>
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| − | Prolonged expression <br>
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| − | Relatively high titers (106–107 pfu/mL)</td>
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| − | <td>Integrating virus <br>
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| − | Clinical experience limited <br>
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| − | Difficult to manufacture and store <br>
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| − | Limited insert size: 8 kb</td>
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| − | | + | |
| − | </tr>
| + | |
| − | <tr class="tableevenjnnbl">
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| − | <td>Retrovirus</td>
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| − | <td>Relatively high titers (106–107 pfu/mL) <br>
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| − | Prolonged stable expression <br>
| + | |
| − | Larger insert size: 9–12 kb <br></td>
| + | |
| − | <td>Inefficient transduction <br>
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| − | Integrating virus <br>
| + | |
| − | Insertional mutagenesis <br>
| + | |
| − | Broad cell tropism <br>
| + | |
| − | No targeting <br>
| + | |
| − | Potential replication competence</td>
| + | |
| − | | + | |
| − | </tr>
| + | |
| − | </table></center>
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| − | | + | |
| − | <p>Each vector has its own benefits and drawbacks. Plasmid vectors, as non-viral DNA vectors, have several advantages over viral vectors. Virus production is expensive (<a class="jnnbl" href="#References">Templeton et al. 2002</a>, <a class="jnnbl" href="#References">Nagasaki and Shinkai, 2007</a>) and safety of viral transfection remains a concern after several deaths (<a class="jnnbl" href="#References">McCormack et al. 2004</a>, <a class="jnnbl" href="#References">Hacein-Bey-Abina et al. 2008</a>). However, plasmid vectors have a very low transfection efficiency (<a class="jnnbl" href="#References">Murakami et al. 2011</a>), especially compared to adenoviruses that have been shown to have a 95% transfection efficiency in hepatocytes (<a class="jnnbl" href="#References">Huard et al. 1995</a>, <a class="jnnbl" href="#References">Sullivan et al 1997</a>). Transfection efficiency of plasmids can be increased through methods such as in vivo electroporation (<a class="jnnbl" href="#References">Ataka et al. 2003</a>). However, this method requires the insertion of electrode needles into the athlete to increase transfection efficiency. This is more invasive to the athlete than a single injection of vector particles. Therefore, adenoviruses could be seen as the most likely transfection method for gene doping at this stage and especially in the near future. Hence, we based most of our numerical values on this type of vector. </p>
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| − | </div><br>
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| − | <div class="spcmkr"></div>
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| − | <h1 class="jnnbl">2. Model Design</h1>
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| − | <div id="gRNA Model">
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| − | <div class="spcmkr"></div>
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| − | <h3 class="jnnbl">2.1 gRNA Array Model </h3>
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| − | | + | |
| − | <p>The four input variables of the model are the coding sequence of a gene, the protospacer adjacent motif (PAM) of the Cas protein, the length of the gRNA, and the Cas-dependent identity between gRNA seed and target (off target effect). A general view on the generated algorithm is shown below in Figure 3:</p>
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| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/7/7b/T--TUDelft--gRNA_model_overview.png" width="100%" height="auto" alt="Figure 3 Model"></center><br>
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| − | <figcapture class="figjnnbl"> <b>Figure 3:</b> General algorithm to generate gRNA array for detection of gene doping with targeted sequencing.
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| − | </figcapture></figure><br>
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| − | | + | |
| − | <button class="collapsible cjnnbl">Would you like to know more? Detailed Steps in the Algorithm</button>
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| − | <div class="content">
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| − | <p>The steps that the algorithm uses for the determination and creation of gRNAs are listed below, step by step:</p>
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| − | | + | |
| − | <ol type="1">
| + | |
| − | <li>Convert coding sequence into numerical sequence (A=1, T=2, C=3, G=4).</li>
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| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/d/d4/T--TUDelft--gRNA_Step1.png" width="50%" height="auto" alt="Figure 4 Model"></center><br>
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| − | <figcapture class="figjnnbl"> <b>Figure 4:</b> Algorithm to convert coding sequence into numerical sequence in an ‘for’-‘if’ loop.
| + | |
| − | </figcapture></figure><br>
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| − | | + | |
| − | <li>Translate genetic sequence into amino acid sequence.</li>
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| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/f/fc/T--TUDelft--gRNA_Step2.png" width="60%" height="auto" alt="Figure 5 Model"></center><br>
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| − | <figcapture class="figjnnbl"> <b>Figure 5:</b> Algorithm used to translate numeric DNA sequence into amino acid sequence in gene doping.
| + | |
| − | </figcapture></figure><br>
| + | |
| − | | + | |
| − | <li>Based on the amino acid sequence, determine the number of possible different codons that will code for the same amino acid.</li>
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| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/d/d0/T--TUDelft--gRNA_Step3.png" width="60%" height="auto" alt="Figure 6 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 6:</b> Algorithm used to determine the possible number of synonymous codons for each amino acid position in the gene.
| + | |
| − | </figcapture></figure><br>
| + | |
| − | | + | |
| − | <li>Determine positions of PAM sequence close to the exon-exon junctions.</li>
| + | |
| − | | + | |
| − | <ol type="a">
| + | |
| − | <li>Analyze the total number of gRNAs necessary for each PAM sequence to cover all possibilities of synonymous mutations (product of possible codons). Determined by the identity necessary between gRNA and target sequence.</li>
| + | |
| − | </ol>
| + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/4/4e/T--TUDelft--gRNA_Step4.png" width="60%" height="auto" alt="Figure 7 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 7:</b> Algorithm used to find possible PAM sequences on the gene.
| + | |
| − | </figcapture></figure><br>
| + | |
| − | | + | |
| − | <li>Gather the number of all PAM positions in the gene and determine their total number of gRNAs necessary, based on the codon variations.</li>
| + | |
| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/1/12/T--TUDelft--gRNA_Step5.png" width="60%" height="auto" alt="Figure 8 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 8:</b> Algorithm used to determine the total number of possible variations of codons behind a specific PAM sequence.
| + | |
| − | </figcapture></figure><br>
| + | |
| − | | + | |
| − | <li>Chose the PAM sequence that has the minimal number of gRNAs possible and generate gRNAs.</li>
| + | |
| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/0/03/T--TUDelft--gRNA_Step6.png" width="60%" height="auto" alt="Figure 9 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 9:</b> Algorithm used to choose the position with smallest variation possible behind the PAM sequence and generate the gRNA_array for this position.
| + | |
| − | </figcapture></figure><br>
| + | |
| − | | + | |
| − | <li>Output the position of PAM sequence in the gene and all the gRNAs’ sequence.</li>
| + | |
| − | </ol>
| + | |
| − | </div><br>
| + | |
| − | </div>
| + | |
| − | <div id="Gene Doping Model"l>
| + | |
| − | <div class="spcmkr"></div>
| + | |
| − | <h3 class="jnnbl">2.2 Gene Doping Model</h3>
| + | |
| − | | + | |
| − | <p>We first modeled the transit of the injected gene doping viral vectors from injection site to target cells. Both intramuscular (IM) and intravenous (IV) injection methods were considered. </p>
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| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/5/54/T--TUDelft--Compartment_Model_Figure.png" width="100%" height="auto" alt="Figure 10 Model"></center><br>
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| − | <figcapture class="figjnnbl"> <b>Figure 10:</b> Pharmacological compartment models displayed for both IV and IM injections.The arrows indicate viral vector transfer from one body compartment to another and the kinetic rate constant associated with the transfer.</figcapture></figure><br>
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| − | | + | |
| − | <button class="collapsible cjnnbl">Would you like to know more? Detailed Compartment Model</button>
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| − | <div class="content">
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| − | | + | |
| − | <p>Our model starts from the point of injection, which is mostly either intramuscularly (IM) or intravenously (IV). The advantage of intravenous injections is that the viral vectors immediately enter blood circulation. The means that more vectors reach the target kidney cells before they are degraded. However, intravenous injections require a qualified doctor to administer and can lead to vein damage such as phlebitis. The advantages of intramuscular injections are that they are easy to administer and do not rely on a qualified doctor for administration. On top of this, they can supply relatively large volumes of the gene doping DNA as the muscles have larger uptake capacity than the veins. Also, the gene doping vectors are not directly getting into the bloodstream, which can lead to sustained release. The big disadvantage of intramuscular injection in comparison with intravenous injections though is the poor absorption. Also, for the athlete, intramuscular injections can cause local swelling, drainage and severe pain at the site of injection. Nevertheless, we take both ways of administration into account.</p>
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| − | | + | |
| − | <p>Pharmacological compartment models are developed to understand the distribution of drugs administered to the human body by oral or, intramuscular, or intravenous routes (<a class="jnnbl" href="#References">M.A. Khanday et al. 2017</a>). They are formulated based on diffusion processes using Fick’s principle and law of mass action. The same diffusion processes affect the administered adenoviral vectors. We start with pharmacological compartment models for both injection types as displayed in Figure 10. Here it is assumed that the mixing of the vectors with blood is instantaneous, based on an article by <a class="jnnbl" href="#References">Tarr et al. 1933</a>). Prof. Beltman, Assistant Professor Biomedical Modelling at Leiden University, later agreed with this assumption.</p>
| + | |
| − | | + | |
| − | <p>There are multiple tissues producing EPO, including liver, brain and kidney tissue. The main cell population responsible for EPO production are the interstitial fibroblasts in the kidney, spanning an average population of approximately 100 million cells, which produce more than 80% of the EPO in blood (<a class="jnnbl" href="#References">Weidemann & Johnson 2009</a>).</p>
| + | |
| − | | + | |
| − | <p>From the compartment models in Figure 10, equations 1 and 2 can be derived for the intravenous administration.</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[c_{blood}]}{dt} = -(k_{blood}+kel_{blood})[c_{blood}]+k_{tissue}[c_{tissue}] \tag{1}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[c_{tissue}]}{dt} = k_{blood}[c_{blood}]-(k_{bind\,uptake}+k_{tissue})[c_{tissue}] \tag{2}$$</p>
| + | |
| − | | + | |
| − | <p>Similarly, equations 3, 4, and 5 can be derived for the intramuscular administration. In intramuscular administration, the viral vectors must first diffuse out of the muscle and enter the bloodstream. While in the muscle, the muscle macrophages break down and eliminate the viral vectors. </p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[c_{muscle}]}{dt} = -(k_{muscle}+kel_{muscle})[c_{muscle}]\tag{3}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[c_{blood}]}{dt} = -(k_{blood}+kel_{blood})[c_{blood}]+k_{tissue}[c_{tissue}]+k_{muscle}[c_{muscle}] \tag{4}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[c_{tissue}]}{dt} = k_{blood}[c_{blood}]-(k_{bind\,uptake}+k_{tissue})[c_{tissue}] \tag{5}$$</p>
| + | |
| − | | + | |
| − | <p>The initial values and constantsused are as specified in Table 2 and Table 3 respectively.</p>
| + | |
| − | </div>
| + | |
| − | | + | |
| − | <button class="collapsible cjnnbl">Would you like to know more? Initial Values and Constantsin the Compartment Model</button>
| + | |
| − | <div class="content">
| + | |
| − | <!-- TABELS WITH COMPARTMENT MODEL INITAL VALUES -->
| + | |
| − | | + | |
| − | <center><table>
| + | |
| − | <caption class="figjnnbl"><b>Table 2:</b> Overview of the nitial values used in the human body model for an adenoviral vector. </caption>
| + | |
| − | <tr class="tableheaderadpbl">
| + | |
| − | <th class="tableheaderjnnbl">Constants</th>
| + | |
| − | <th class="tableheaderjnnbl">Values</th>
| + | |
| − | <th class="tableheaderjnnbl">Meaning</th>
| + | |
| − | </tr>
| + | |
| − | <tr class="tableunevenjnnbl">
| + | |
| − | <td>$$[c_{blood} (t=0)]$$</td>
| + | |
| − | <td>94 billion [#/mL] for IV Single Dose <br>
| + | |
| − | 64 billion [#/mL] followed by smaller doses of 18 billion vectors every 20 days for IV Microdosing <br>
| + | |
| − | 0 [#/mL] for IM
| + | |
| − | </td>
| + | |
| − | <td>Initial injection of vectors intravenously</td>
| + | |
| − | | + | |
| − | </tr>
| + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$[c_{tissue} (t=0)]$$</td>
| + | |
| − | <td>0 [#/mL]</td>
| + | |
| − | <td>Initial vector concentration in in tissue</td>
| + | |
| − | | + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$[c_{muscle} (t=0)]$$</td>
| + | |
| − | <td>141 billion [#/mL] for IM<br>
| + | |
| − | 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days for IM Microdosing <br>
| + | |
| − | 0 [#/mL] for IV</td>
| + | |
| − | <td>Initial injection of vectors intramuscularly</td>
| + | |
| − | | + | |
| − | </tr>
| + | |
| − | </table></center><br>
| + | |
| − | <!-- TABELS WITH COMPARTMENT MODEL CONSTANTS -->
| + | |
| − | <center><table>
| + | |
| − | <caption class="figjnnbl"><b>Table 3:</b> Overview of the constants used in the human body model for an adenoviral vector. </caption>
| + | |
| − | <tr class="tableheaderadpbl">
| + | |
| − | <th class="tableheaderjnnbl">Rate Constants</th>
| + | |
| − | <th class="tableheaderjnnbl">Values (days<sup>-1</sup>)</th>
| + | |
| − | <th class="tableheaderjnnbl">Meaning</th>
| + | |
| − | <th class="tableheaderjnnbl">Source</th>
| + | |
| − | </tr>
| + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{tissue}$$</td>
| + | |
| − | <td>1440</td>
| + | |
| − | <td>Vector displacement from tissue to blood</td>
| + | |
| − | <td>Estimate based on mean blood circulation time</td>
| + | |
| − | | + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{muscle}$$</td>
| + | |
| − | <td>1440</td>
| + | |
| − | <td>Vector displacement from muscle to blood in IM injections</td>
| + | |
| − | <td>Estimate based on mean blood circulation time</td>
| + | |
| − | | + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{blood}$$</td>
| + | |
| − | <td>1440</td>
| + | |
| − | <td>Vector displacement from blood to tissue</td>
| + | |
| − | <td>Estimate based on mean blood circulation time</td>
| + | |
| − | | + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$kel_{blood}$$</td>
| + | |
| − | <td>720</td>
| + | |
| − | <td>Elimination of viral vectors from the blood</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Ganesan et al. 2011</a></td>
| + | |
| − | | + | |
| − | </tr>
| + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$kel_{muscle}$$</td>
| + | |
| − | <td>720</td>
| + | |
| − | <td>Elimination of viral vectors from the muscle</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Ganesan et al. 2011</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{bind\,uptake}$$</td>
| + | |
| − | <td>8.64</td>
| + | |
| − | <td>Endosomal uptake</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Varga et al. 2005</a></td>
| + | |
| − | </tr>
| + | |
| − | </table></center>
| + | |
| − | </div><br>
| + | |
| − | | + | |
| − | <p>Upon reaching the target kidney cells, the gene doping viral vectors infect the cells. The infection process and production of gene doping EPO was modeled through a series of kinetic equation. Kidney cells have a lifespan of around 57 days. Upon their death, the infected cells release the gene doping DNA back into the bloodstream as cell free DNA (cfDNA). This increases the detection window of the gene doping DNA. </p>
| + | |
| − | | + | |
| − | <figure><center><img src="https://static.igem.org/mediawiki/2018/9/97/T--TUDelft--2018_cellularuptake.png" width="100%" height="auto" alt="Figure 11 Model"></center> <br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 11:</b> Cellular uptake of gene doping vectors. Figure inspired by <a class="jnnbl" href="#References">Varga et al. 2005</a>.</figcapture></figure><br>
| + | |
| − | | + | |
| − | <button class="collapsible cjnnbl">Would you like to know more? Detailed Intracellular Gene Delivery Model</button>
| + | |
| − | <div class="content">
| + | |
| − | <p class="jnnbl"><u><b>Cellular uptake</b></u></p>
| + | |
| − | | + | |
| − | <p>After the uptake of the vectors in the tissue, the stage of cellular uptake ensues. In this process we modelled multiple steps as indicated in Figure 11 according to a model first developed by <a class="jnnbl" href="#References">Varga et al. 2005</a>. The cellular uptake of gene doping vectors as depicted in Figure 11 can be dissected into multiple steps described by a set of coupled differential equations, for which the constants are given in Table 3.</p>
| + | |
| − | | + | |
| − | <p>First, the complex is taken up by endocytosis after which it is either degraded or taken up, as represented by equations 6 and 7.</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[vesicle]}{dt} = k_{bind\,uptake}[c_{tissue}]-(k_{Escape}+k_{deg\,vesicle})[vesicle] \tag{6}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[complex\,intracell]}{dt} = k_{escape}[vesicle]-k_{unpack}[complex\,intracell]-k_{bind\,vector}[complex\,intracell]\tag{7}$$</p>
| + | |
| − | | + | |
| − | <p>Second, vector dissociation and either degradation or nuclear target complex binding takes place in either dissociated or complexed form, as given by equations 8, 9 and 10.</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[plasmid]}{dt} = k_{unpack}[complex\,intracell]-k_{bind \,plasmid}[plasmid]-k_{deg}[plasmid]\tag{8}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[plasmid\,bound]}{dt} = k_{bind \,plasmid}[plasmid]-k_{NPC}[plasmid\,bound] \tag{9}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[complex\,bound]}{dt} = k_{bind \,vector}[complex\,intracell]-k_{NPC}[complex\,bound] \tag{10}$$</p>
| + | |
| − | | + | |
| − | <p>Subsequently, transport to the inner part of the nucleus is believed to take place through first binding to a nuclear pore complex (NPC) and finally inside the nucleus dissociation of the nuclear target complex takes place. This is represented by equations 11 till 16.</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[complex\,boundNPC]}{dt} = k_{NPC}[complex\,bound]-k{in}[complex\,boundNPC] \tag{11}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[complex\,bound\,nucleus]}{dt} = k{in}[complex\,boundNPC]-k_{dissociation}[complex\,bound\,nucleus] \tag{12}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[complex\,nucleus]}{dt} = k_{dissociation}[complex\,bound\,nucleus] - k_{unpack2}[complex\,nucleus] \tag{13}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[plasmid\,boundNPC]}{dt} = k_{NPC}[plasmid\,bound] -k_{in2}[plasmid\,boundNPC] \tag{14}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[plasmid\,bound\,nucleus]}{dt} =k_{in2}[plasmid\,boundNPC]-k{kissociation2}[plasmid\,bound\,nucleus] \tag{15}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[plasmid\,nucleus]}{dt} =k{kissociation2}[plasmid\,bound\,nucleus] + k_{unpack2}[complex\,nucleus]$$</p>
| + | |
| − | <p>$$- k_{cell\,death}[plasmid\,nucleus] \tag{16}$$</p>
| + | |
| − | | + | |
| − | <p class="jnnbl"><u><b>Detection of cell free doping DNA</b></u></p>
| + | |
| − | | + | |
| − | <p>Apart from the effect of the gene doping EPO on the production of red blood cells, the purpose of the model is to determine the dynamics of the detectable cfDNA concentration in the blood. Cell free doping DNA is released from dying infected cells and circulates in the blood where it is assumed to degrade at the same rate as natural cfDNA.</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[Doping\,DNA]}{dt} =k_{cell\,death}[plasmid\,nucleus]-kel_{cfDNA}[Doping\,DNA] \tag{17}$$</p>
| + | |
| − | | + | |
| − | <p>Equation 17 provides us with a detection window for which we assume that we can detect both, DNA left in the tissue and bloodstream after injection, and DNA released after cell death (kcelldeath). Any other degradation terms or transient expression we incorporated in the constants used. Based on the above model we obtained the concentration developments of cfDNA in time for both intramuscular and intravenous injections and the estimated detection windows where we assumed an estimated detection limit of 100 copies DNA. The concentrations of DNA over time were used in the laboratory for our sample preparation to mimic real life detection potential. The cell death rate constant is directly linked to the average lifetime of renal interstitial fibroblasts, which we estimated to be around 57 days based upon measurements in chicks by <a class="jnnbl" href="#References">Weissmanshomer et al. 1975</a>.</p>
| + | |
| − | | + | |
| − | <p>According to <a class="jnnbl" href="#References">Haller et al. 2018</a>, the expected concentration of doping cell free DNA may be higher for athletes in endurance and intermittent sports. Haller et al. found a 22.7 fold increase in venous cfDNA concentrations in footballers after a professional football match. Given the high amount of training top level athletes endure, this finding leads us to believe that we might be able to have even longer detection windows than our model predicts.</p>
| + | |
| − | | + | |
| − | <p class="jnnbl"><u><b>The Protein Effect</b></u></p>
| + | |
| − | | + | |
| − | <p>Lastly, the uptaken DNA can be translated into protein according to equation 18, after which it can be exported to the extracellular environment according to equation 19.</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[protein]}{dt} =k_{protein}[plasmid\,nucleus]-k_{deg\,protein}[protein]-k_{export}[protein] \tag{18}$$</p>
| + | |
| − | | + | |
| − | <p>$$\frac{d[protein\,extracellular]}{dt} =k_{export}[protein]-k_{deg\,protein\,extracellular}[protein\,extracellular]\tag{19}$$</p>
| + | |
| − | </div>
| + | |
| − | | + | |
| − | <button class="collapsible cjnnbl">Would you like to know more? Constants for Intracellular Gene Delivery Model</button>
| + | |
| − | <div class="content">
| + | |
| − | <!-- TABELS WITH INFECTION MODEL CONSTANTS -->
| + | |
| − | <center><table>
| + | |
| − | <caption class="figjnnbl"><b>Table 4:</b> Overview of the constants used in the human body model for an adenoviral vector. </caption>
| + | |
| − | <tr class="tableheaderadpbl">
| + | |
| − | <th class="tableheaderjnnbl">Rate Constants (Ad5)</th>
| + | |
| − | <th class="tableheaderjnnbl">Values (days<sup>-1</sup>)</th>
| + | |
| − | <th class="tableheaderjnnbl">Meaning</th>
| + | |
| − | <th class="tableheaderjnnbl">Source</th>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{bind\,uptake}$$</td>
| + | |
| − | <td>8.64</td>
| + | |
| − | <td>Endosomal uptake</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Varga et al. 2005</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{deg\,vesicle}$$</td>
| + | |
| − | <td>28.8</td>
| + | |
| − | <td>Degradation of complex within uptake vesicle</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Varga et al. 2005</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{escape}$$</td>
| + | |
| − | <td>23.0</td>
| + | |
| − | <td>Complex movement from endosome to intracellular</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Varga et al. 2005</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{bind\,vector}$$</td>
| + | |
| − | <td>144</td>
| + | |
| − | <td>Binding of gene delivery vector to compound targeting for the nucleus</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Varga et al. 2005</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{unpack}, k_{unpack2}$$</td>
| + | |
| − | <td>144</td>
| + | |
| − | <td>Plasmid detaches from vector either in cytoplasm(1) or in the nucleus(2)</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Varga et al. 2005</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{deg}$$</td>
| + | |
| − | <td>7.2</td>
| + | |
| − | <td>Degradation of unbound plasmid in the cytoplasm</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Lechardeur et al. 1999</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{bind\,plasmid}$$</td>
| + | |
| − | <td>2.88</td>
| + | |
| − | <td>Binding of plasmid to compound targeting for the nucleus</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Varga et al. 2001</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{NPC}$$</td>
| + | |
| − | <td>1.44*10<sup>6</sup></td>
| + | |
| − | <td>Binding formed complexes to Nuclear Pore Complex</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Vacik et al. 1999</a><br>
| + | |
| − | <a class="jnnbl" href="#References">Wilson et al. 1999</a><br>
| + | |
| − | <a class="jnnbl" href="#References">Chan et al. 1999</a><br>
| + | |
| − | <a class="jnnbl" href="#References">Dean et al. 1997</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{in}, k_{in2}$$</td>
| + | |
| − | <td>2.88</td>
| + | |
| − | <td>Uptake nucleus through Nuclear Pore Complex</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Varga et al. 2001</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{dissociation}, k_{dissociation2}$$</td>
| + | |
| − | <td>1.44*10<sup>6</sup></td>
| + | |
| − | <td>Dissociation from the NPC targeting compound</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Moroianu et al. 1996</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{protein}$$</td>
| + | |
| − | <td>14.4</td>
| + | |
| − | <td>Protein production from plasmid</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Schaffer et al. 1998</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{degprot}$$</td>
| + | |
| − | <td>1.04</td>
| + | |
| − | <td>Cytoplasmic degradation of the protein</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Fuertinger et al. 2012</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{export}$$</td>
| + | |
| − | <td>1.44*10<sup>6</sup></td>
| + | |
| − | <td>Export protein to extracellular environment</td>
| + | |
| − | <td>Estimate</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{deg\,protein\,extracellular}$$</td>
| + | |
| − | <td>1.04</td>
| + | |
| − | <td>Cytoplasmic degradation of the protein</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Fuertinger et al. 2012</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{cell\,death}$$</td>
| + | |
| − | <td>0.0167</td>
| + | |
| − | <td>Average death rate of renal interstitial fibroblast</td>
| + | |
| − | <td>Estimate based on chicks; <a class="jnnbl" href="#References">Weissmanshomer et al. 1975</a></td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$kel_{cfDNA}$$</td>
| + | |
| − | <td>100</td>
| + | |
| − | <td>Clearance of cfDNA from the blood</td>
| + | |
| − | <td><a class="jnnbl" href="#References">Alegre et al. 2015</a></td>
| + | |
| − | </tr>
| + | |
| − | </table></center>
| + | |
| − | </div><br>
| + | |
| − | | + | |
| − | <p>The EPO from the infected cells is released into the bloodstream. The EPO reaches the bone marrow where it stimulates red blood cell production through erythropoiesis. Red blood cells begin as stem cells and go through a series of cell differentiations before maturing into red blood cells.</p>.
| + | |
| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/0/02/T--TUDelft--ModelRBCsGif.gif" width="100%" height="auto" alt="Figure 12 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 12:</b> Cell stages in red blood cell production. Stage 1 is the BFU-E cell stage. Stage 2 is the CFU-E cell stage. The proliferation rate of CFU-E cells is dependent on the concentration of EPO. 3 is the erythroblasts stage. Stage 4 is the marrow reticulocytes stage. The length of time cells stay in this stage is dependent on the concentration of EPO. Stage 5 is the circulating red blood cell (RBC) stage. Neocytolysis of RBCs is triggered when the concentration of EPO drops below a threshold. The red blood cell count acts as a feedback loop that determines the endogenous production of EPO. </figcapture></figure><br>
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| − | | + | |
| − | <p>EPO promotes the proliferation of CFU-E cells. Increases in EPO levels led to faster proliferation rates of CFU-E cells. Increases in EPO levels reduces the marrow transit time of cells for marrow reticulocytes, releasing the cells into the blood in a shorter time frame. In the blood, the reticulocytes mature into red blood cells which increase the oxygen carrying capacity of blood. If the concentration of EPO in blood is low due to an excess of circulating red blood cells, the programmed death of young red blood cells occurs to reduce the red blood cell count. This is referred to as neocytolysis.</p>
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| − | | + | |
| − | <button class="collapsible cjnnbl">Would you like to know more? Erythropoiesis: The Production of Red Blood Cells</button>
| + | |
| − | <div class="content">
| + | |
| − | <p>With the doping DNA degradation and doping EPO formation determined, the effect of EPO on erythropoiesis, the process which produces red blood cells, is determined. We developed a model using an anemia EPO treatment model by Fuertinger et al. 2012 as reference.</p>
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| − | | + | |
| − | <p>Red blood cells begin as stem cells and progress into different cell stages as they age. As progenitor and precursor cells age, they proliferate or undergo apoptosis at a rate dependent on the cell stage they are in. The resulting growth or decay rates may be constant or dependent on the concentration of EPO in the blood. Burst-Forming Unit-Erythroid (BFU-E) cells have a very small number of EPO receptors. EPO concentration has no effect on BFU-E proliferation, their proliferation is assumed to be constant. After leaving the stem cell stage, cells stay in the BFU-E stage for 7 days, after which they enter the Colony-Forming Unit-Erythroid (CFU-E) stage. In this stage, the cells divide at a faster rate than in the BFU-E cell stage. The CFU-E cells have a large number of EPO receptors and are strongly dependent on EPO for their survival. Their rate of apoptosis is inversely related to the concentration of EPO. Under normal conditions within the human body, a large number of CFU-E generated do not survive. As the concentration of EPO in the blood increases, the number of cells which survive increases.</p>
| + | |
| − | | + | |
| − | <p>After spending 6 days as CFU-E cells, the cells enter the erythroblasts stage. Here, the number of EPO receptors decline. During this stage, there is no evidence that additional divisions occur when production of EPO increases. For this reason we assume that the proliferation of erythroblasts is constant (<a class="jnnbl" href="#References">Lichtman et al. 2005</a>).</p>
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| − | | + | |
| − | <p>The cells stay in the erythroblasts stage for 5 days until they stop dividing, extrude their nuclei and mitochondria, and become marrow reticulocytes. Marrow reticulocytes no longer proliferate and their mortality rate is inversely dependent on iron concentration in the plasma. Since we assume that athletes have a sufficient iron supply, a constant apoptosis rate for marrow reticulocytes is assumed. The time cells stay in the reticulocytes stage is between 0.75-3 days. An increase in EPO concentration shortens the marrow transit time of reticulocytes.</p>
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| − | | + | |
| − | <p>Once reticulocytes are released from the bone marrow and enter the blood, they mature into erythrocytes (red blood cells) within 1-3 days. Reticulocytes have a hemoglobin content of around 27.5 ± 2.8 pg per cell and, red blood cells have a hemoglobin content of around 26.4 ± 2.4 pg per cell (<a class="jnnbl" href="#References">Fishbane et al. 1997</a>). Due to the similar ability of blood reticulocytes and red blood cells to carry oxygen, when red blood cells are discussed, we refer to both blood reticulocytes and mature red blood cells. The lifespan of red blood cells (RBCs) in healthy human adults is about 120 days before their components are recycled by microphages (<a class="jnnbl" href="#References">Jandl 1987</a>). Over the course of this time a small number of RBCs die due to random daily breakdown or, internal or external bleeding. This is taken into account with a small apoptosis rate for RBCs. Adults have a red blood cell count ranging from about 20 to 30 trillion. Women have a blood cell count range of 3.5-5.5 trillion cells per liter, while men have a range of 4.3-5.9 trillion cells per liter (<a class="jnnbl" href="#References">Dean 2005</a>). The average red blood cell count is estimated to be 24.98 trillion by <a class="jnnbl" href="#References">Lichtman et al. 2005</a>.The entire process, from stem cell to red blood cell recycling by microphages, takes 141 days.</p>
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| − | | + | |
| − | <p>The endogenous release of EPO is inversely related to the partial pressure of oxygen in the blood. The partial pressure of oxygen in the blood is proportionally related to the number of red blood cells circulating. An increase in the red blood cell population in blood will decrease endogenous EPO production. If the concentration of EPO in the blood falls below a certain level (9.8 mU/ml in the case of this model), neocytolysisis is triggered. Neocytolysisis the selective lysis of young red blood cells by the body to allow it to decrease its red blood count at a faster rate and reach the desired partial pressure of oxygen in the blood.</p>
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| − | </div><br>
| + | |
| − | | + | |
| − | <p>The series of partial differential equations (PDEs) that describe red blood production was modeled with a simplified linear age population method which provided similar accuracy to more computationally intensive PDEs solvers. The red blood cell production model is then combined with the compartment and infection model to determine the effects on EPO gene doping on red blood cell count.</p>
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| − |
| + | |
| − | | + | |
| − | <button class="collapsible cjnnbl">Would you like to know more? Overview of the constants used in the red blood cell production model</button>
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| − | <div class="content">
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| − | <p>The parameters used in the model are based on the parameters derived in <a class="jnnbl" href="#References">Fuertinger et al. (2012)</a> except fora3and b3which are derived from <a class="jnnbl" href="#References">WE and WL Owen Roberts (2011)</a>.</p>
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| − | <!-- TABELS WITH RED BLOOD CELL MODEL CONSTANTS -->
| + | |
| − | <center><table>
| + | |
| − | <caption class="figjnnbl"><b>Table 4:</b> Table of the parameters used in the red blood cell production model, their values, their units, and their meaning. </caption>
| + | |
| − | <tr class="tableheaderadpbl">
| + | |
| − | <th class="tableheaderjnnbl">Parameters</th>
| + | |
| − | <th class="tableheaderjnnbl">Values</th>
| + | |
| − | <th class="tableheaderjnnbl">Units</th>
| + | |
| − | <th class="tableheaderjnnbl">Meaning</th>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\beta_{BFU-E}$$</td>
| + | |
| − | <td>0.2</td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Proliferation rate for BFU-E cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\beta_{CFU-E}$$</td>
| + | |
| − | <td>0.57</td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Proliferation rate for CFU-E cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\beta_{Erythroblasts}$$</td>
| + | |
| − | <td>1.024</td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Proliferation rate for erythroblasts</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{BFU-E, \, max}$$</td>
| + | |
| − | <td>7</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Maximal maturity for BFU-E cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{CFU-E, \, min}$$</td>
| + | |
| − | <td>7</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Minimal maturity for CFU-E cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{CFU-E, \, max}$$</td>
| + | |
| − | <td>13</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Maximal maturity for CFU-E cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{Erythroblasts, \, min}$$</td>
| + | |
| − | <td>13</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Minimal maturity for erythroblasts</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{Erythroblasts, \, max}$$</td>
| + | |
| − | <td>18</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Maximal maturity for erythroblasts</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{Reticulocytes, \, max}(EPO(t))$$</td>
| + | |
| − | <td>18.75 to 21</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Maximal maturity for marrow reticulocytes</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\alpha_{Reticulocytes}$$</td>
| + | |
| − | <td>0.089</td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Rate of ineffective erythropoiesis in the marrow reticulocytes stage</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\alpha_{RBCs}$$</td>
| + | |
| − | <td>0.005</td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Intrinsic mortality rate for erythrocytes</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$a_1, \, b_1$$</td>
| + | |
| − | <td>0.35, 0.07</td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Constants for the sigmoid apoptosis rate for CFU-E cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$c_1, \, k_1$$</td>
| + | |
| − | <td>3, 0.14</td>
| + | |
| − | <td>Dimensionless, ml/mU</td>
| + | |
| − | <td>Constants for the sigmoid apoptosis rate for CFU-E cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$a_2, \, b_2$$</td>
| + | |
| − | <td>3.225, 2.475</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Constants for the sigmoid maturation velocity/marrow transit time for marrow reticulocytes</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$c_2, \, k_2$$</td>
| + | |
| − | <td>2.3, 0.2</td>
| + | |
| − | <td>Dimensionless, ml/mU</td>
| + | |
| − | <td>Constants for the sigmoid maturation velocity/marrow transit time for marrow reticulocytes</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$a_3, \, b_3$$</td>
| + | |
| − | <td>9.1, 0.2</td>
| + | |
| − | <td>Dimensionless, ml/mU</td>
| + | |
| − | <td>Constants for the sigmoid function governing the release of EPO from the kidneys </td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{RBCs, \, neocytolysis \, min}$$</td>
| + | |
| − | <td>35</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Lower bound of erythrocytes which are possibly exposed to neocytolysis</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{RBCs, \, neocytolysis \, max}$$</td>
| + | |
| − | <td>42</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Upper bound of erythrocytes which are possibly exposed to neocytolysis</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\mu_{RBCs, \, max}$$</td>
| + | |
| − | <td>141</td>
| + | |
| − | <td>Days</td>
| + | |
| − | <td>Maximal life span for red blood cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$b_E$$</td>
| + | |
| − | <td>0.1</td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Constant in the mortality rate for red blood cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$c_E$$</td>
| + | |
| − | <td>3.5</td>
| + | |
| − | <td>mU<sup>3</sup>/(ml<sup>3</sup>\(\times\)day)</td>
| + | |
| − | <td>Constant in the mortality rate for red blood cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_E$$</td>
| + | |
| − | <td>3</td>
| + | |
| − | <td>Dimensionless</td>
| + | |
| − | <td>Exponent in the mortality rate for red blood cells</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$\tau_E$$</td>
| + | |
| − | <td>9.8</td>
| + | |
| − | <td>mU/ml</td>
| + | |
| − | <td>EPO threshold for neocytolysis</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$k_{deg\,protein\,extracellular}$$</td>
| + | |
| − | <td>1.04</td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Degradation rate of EPO in the blood</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$S_0$$</td>
| + | |
| − | <td>10<sup>8</sup></td>
| + | |
| − | <td>1/day</td>
| + | |
| − | <td>Rate at which cells are committing to the erythroid lineage</td>
| + | |
| − | </tr>
| + | |
| − | | + | |
| − | <tr class="tableevenjnnbl">
| + | |
| − | <td>$$TBV$$</td>
| + | |
| − | <td>5000</td>
| + | |
| − | <td>ml</td>
| + | |
| − | <td>Total blood volume</td>
| + | |
| − | </tr>
| + | |
| − | </table></center>
| + | |
| − | </div><br>
| + | |
| − | </div>
| + | |
| − | <div id="Results">
| + | |
| − | <div class="spcmkr"></div>
| + | |
| − | <h1 class="jnnbl">3. Results</h1>
| + | |
| − | <div id="gRNA Model Results">
| + | |
| − | <div class="spcmkr"></div>
| + | |
| − | <h3 class="jnnbl">3.1 gRNA Array Model </h3>
| + | |
| − | | + | |
| − | <p>The functionality of our algorithm lays in creating a tool to generate an array of gRNAs necessary to screen for gene doping with our novel targeted sequencing platform. The model works with any input gene . It was tested for the EPO gene, as EPO is our main model target for gene doping. We worked with the following information:</p>
| + | |
| − | | + | |
| − | <ul class="uljnnbl">
| + | |
| − | <li>Gene cds sequence: Human EPO cds (GenBank: BC143225.1)</li>
| + | |
| − | <li>Type of Cas: dxCas9</li>
| + | |
| − | <li>PAM sequence: NG</li>
| + | |
| − | <li>gRNA length: 20 bp of target gRNA</li>
| + | |
| − | <li>Off target possibility (seed to target): 10 bp (50 % adjacent to PAM should be identical)</li>
| + | |
| − | | + | |
| − | </ul>
| + | |
| − | <p>As presented in Figure 20, the algorithm detected several PAM sequences close to exon-exon junctions and found a minimal number of necessary guides for each one.</p>
| + | |
| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/6/63/T--TUDelft--Heat_Map_gRNAs.png" width="100%" height="auto" alt="Figure 20 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 20:</b> Results from the model on finding possible PAM sequences and minimal number of gRNAs needed at each position on EPO gene coding sequence.</figcapture></figure><br>
| + | |
| − | | + | |
| − | <p>Figure 20 clearly shows that in junction 3 there is the optimal PAM sequence with smallest number of gRNAs possible. This way, the algorithm generates the gRNAs and gives an output with the position of such gRNA. In this specific case, there were two PAM sequences near Junction 3 that had the same minimal number of possible gRNAs necessary (12 guides each). The algorithm doesn’t neglect one or another, but outputs both options (or more if possible). This array can be used to generate gRNAs’ libraries for targeted next generation sequencing of gene doping.
| + | |
| − | </p>
| + | |
| − | </div>
| + | |
| − | <div id="Gene Doping Model Results">
| + | |
| − | <div class="spcmkr"></div>
| + | |
| − | <h3 class="jnnbl">3.2 Gene Doping Model </h3>
| + | |
| − | <p>The concentration of EPO gene doping DNA in blood increased rapidly in the first 1.5 days after injection before decreasing exponentially. Due to the rapid clearance of the adenoviral vectors from the blood and muscle, the majority of the infection events occurs right after injection. As more infected cells die, the number of infected cells decreases. This decreases the amount of doping DNA released into the blood over time. </p>
| + | |
| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/9/99/T--TUDelft--Doping_DNA_in_Blood.png" width="50%" height="auto" alt="Figure 21 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 21:</b> Concentration of doping DNA in the blood over time after a single IM injection of 141 billion viral vectors. The detection limit of 1000 copies per mL of blood is estimated based on the loss of DNA that occurs during sample preparation and targeted sequencing preparation.</figcapture></figure><br>
| + | |
| − | | + | |
| − | <p>Regression was performed to determine that the half life of EPO gene doping DNA in the blood is around 41 days. While the half-life of cfDNA in blood is around 10 minutes, the slow release of the gene doping DNA from the dying infected cells increases the detection window significantly.</p>
| + | |
| − | | + | |
| − | <p>Microdosing was determined to be the best doping method for doping athletes. The benefit of this method is that it avoids detection through the biological passport. Assuming that the athlete begins the treatment prior to becoming a professional athlete and continued microdosing after, their red blood cell count would appear to be constant and naturally high.</p>
| + | |
| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/8/88/T--TUDelft--IM_MicrodosingResults.png" width="60%" height="auto" alt="Figure 22 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 22:</b> Effect of EPO gene doping using IM microdosing on red blood cell count. The initial dose is 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days. The black horizontal lines are the range of EPO concentrations found in healthy adults. The red line indicates the red blood cell count at which the risk of stroke to due blood clot becomes significant.
| + | |
| − | </figcapture></figure><br>
| + | |
| − | | + | |
| − | <button class="collapsible cjnnbl">Would you like to know more? Comparison of a Large Single Dose and Microdosing for IM and IV Administration</button>
| + | |
| − | <div class="content">
| + | |
| − | <p> Test </p>
| + | |
| − | </div><br>
| + | |
| − | | + | |
| − | <p>The downside of this method is the requirement for constant microdosing. Repeated injections would cause noticeable damage to the veins. The athlete would then be required to disguise the injection sites or perform the IV microdosing in parts of the body normally covered. For this reason athletes would likely favor IM injection to IV injection as IM is less invasive. </p>
| + | |
| − | | + | |
| − | <figure><center> <img src="https://static.igem.org/mediawiki/2018/e/e7/T--TUDelft--Doping_DNA.png" width="60%" height="auto" alt="Figure 23 Model"></center><br>
| + | |
| − | <figcapture class="figjnnbl"> <b>Figure 23:</b> Concentration of EPO gene doping DNA in the blood during IM microdosing with an initial dose is 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days.
| + | |
| − | </figcapture></figure><br>
| + | |
| − | | + | |
| − | | + | |
| − | <p>Though the microdoses are smaller, the copies of DNA stay above our detection limit (40,000 to 50,000 fragments per mL) due to IM injections occuring every 20 days. While the athlete would bypass detection through the biological passport, they would be detected by our gene doping detection method.</p>
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| − | </div>
| + | |
| − | </div>
| + | |
| − | <div id="References">
| + | |
| − | <div class="spcmkr"></div>
| + | |
| − | <h1 class="jnnbl">4. References</h3>
| + | |
| − | <ol class="jnnbl" type="1">
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S0065242314000419" target="_blank"> Alegre, E., Sammamed, M., Fernández-Landázuri, S., Zubiri, L., & González, Á. (2015). Circulating Biomarkers in Malignant Melanoma. Advances in Clinical Chemistry, 47-89. doi:10.1016/bs.acc.2014.12.002</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.karger.com/article/FullText/72913" target="_blank"> Ataka, K., Maruyama, H., Neichi, T., Miyazaki, J., & Gejyo, F. (2003). Effects of Erythropoietin-Gene Electrotransfer in Rats with Adenine-Induced Renal Failure. American Journal of Nephrology, 23(5), 315-323. doi:10.1159/000072913</a></li>
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| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2292496/" target="_blank"> J.M. Bergen, I.-K. Park, P.J. Horner, S.H.Pun (2008). Nonviral approaches for neuronal delivery of nucleic acids. Pharm. Res., 25, pp. 983-998.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.liebertpub.com/doi/10.1089/10430349950017699" target="_blank"> C.K. Chan, D.A. Jans (1999). Enhancement of polylysine-mediated transferrin-fection by nuclear localization sequences: polylysine does not function as a nuclear localization sequence. Hum. Gene Ther, 10, pp. 1695-1702.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S0014482796934278?via%3Dihub" target="_blank"> D. A. Dean et al. (1997). Import of plasmid DNA into the nucleus is sequence specific. Exp. Cell Res, 230, pp. 293-302.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/books/NBK2267/" target="_blank"> Dean, L. (2005). Blood groups and red cell antigens. Bethesda, MD: NCBI.</a></li>
| + | |
| − | <li><a class="jnnbl" href="http://www.jbc.org/content/273/43/28004.full" target="_blank"> D.V. Schaffer, D.A. Lauffenburger (1998). Optimization of cell surface binding enhances efficiency and specificity of molecular conjugate gene delivery. J. Biol. Chem, 273, pp. 28004-28009..</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402043/" target="_blank"> Elliott S, Sinclair AM (2012). The effect of erythropoietin on normal and neoplastic cells. Biologics. 6: 163–89. doi:10.2147/BTT.S32281.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.nejm.org/doi/full/10.1056/NEJM198701083160203" target="_blank"> Eschbach JW, Egrie JC, Downing MR, Browne JK, Adamson JW (1987). Correction of the anemia of end-stage renal disease with recombinant human erythropoietin. Results of a combined phase I and II clinical trial. The New England Journal of Medicine. 316 (2): 73–8. doi:10.1056/NEJM198701083160203.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S0085253815601636?via%3Dihub" target="_blank"> Fishbane, S., Galgano, C., Langley, R. C., Canfield, W., & Maesaka, J. K. (1997). Reticulocyte hemoglobin content in the evaluation of iron status of hemodialysis patients. Kidney International, 52(1), 217-222. doi:10.1038/ki.1997.323</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://link.springer.com/article/10.1007/s00285-012-0530-0" target="_blank"> Fuertinger, D. H., Kappel, F., Thijssen, S., Levin, N. W., & Kotanko, P. (2012). A model of erythropoiesis in adults with sufficient iron availability. Journal of Mathematical Biology, 66(6), 1209-1240. doi:10.1007/s00285-012-0530-0.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3182912/" target="_blank"> Ganesan, L. P., Mohanty, S., Kim, J., Clark, K. R., Robinson, J. M., & Anderson, C. L. (2011). Rapid and Efficient Clearance of Blood-borne Virus by Liver Sinusoidal Endothelium. PLoS Pathogens, 7(9). doi:10.1371/journal.ppat.1002281</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4397984/" target="_blank"> G.L. Wilson, B.S. Dean, G. Wang, D.A. Dean (1999). Nuclear import of plasmid DNA in digitonin-permeabilized cells requires both cytoplasmic factors and specific DNA sequences. J. Biol. Chem, 274 (1999), pp. 22025-22032.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2496963/" target="_blank"> Hacein-Bey-Abina, S.; Garrigue, A.; Wang, G.P.; Soulier, J.; Lim, A.; Morillon, E.; Clappier, E.; Caccavelli, L.; Delabesse, E.; Beldjord, K.; et al. (2008). Insertional oncogenesis in 4 patients after retrovirus-mediated gene therapy of SCID-X1. J. Clin. Investig. 118, 3132–3142.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.elsevier.com/books/guyton-and-hall-textbook-of-medical-physiology/hall/978-0-8089-2400-5" target="_blank"> Hall, John (2011). Guyton and Hall textbook of medical physiology (12th ed.). Philadelphia, Pa.: Saunders/Elsevier. pp. 286–287. ISBN 978-1-4160-4574-8.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191915" target="_blank"> Haller, N. et al. (2018). Circulating, cell-free DNA as a marker for exercise load in intermittent sports. PLOS ONE. 13. e0191915. 10.1371/journal.pone.0191915.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/pubmed/7719927" target="_blank"> Huard, J., et al. (1995). The route of administration is a major determinant of the transduction efficiency of rat tissues by adenoviral recombinants. Gene Ther., 2, pp. 107-115.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.amazon.com/Blood-Textbook-Hematology-James-Jandl/dp/0316457310" target="_blank"> Jandl JH (1987) Blood. Textbook of Hematology. Little, Brown and Company, Boston.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S2090506816300136" target="_blank"> M.A. Khanday et al. (2017). Mathematical models for drug diffusion through the compartments of blood and tissue medium. Alexandria Journal of Medicine, Vol. 53, Iss 3, pp. 245-249.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S0169409X01002022?via%3Dihub" target="_blank"> R. Kircheis, L. Wightman, E. Wagner (2001). Design and gene delivery activity of modified polyethylenimines. Adv. Drug Deliv. Rev., 53, pp. 341-358.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.nature.com/articles/3301110" target="_blank"> S. Li, L. Huang (2000). Nonviral gene therapy: promises and challenges. Gene Ther., 7, pp. 31-34.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/books/NBK21475/" target="_blank"> Lodish H, Berk A, Zipursky SL, et al. (2000). Molecular Biology of the Cell 4th Edition. New York: W. H. Freeman.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC38935/" target="_blank"> J. Moroianu, G. Blobel, A. Radu (1996). Nuclear protein import: Ran-GTP dissociates the karyopherin ab heterodimer by displacing a from an overlapping binding site on b Proc. Natl. Acad. Sci. USA, 93, pp. 7059-7062.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S0927776505001323?via%3Dihub" target="_blank"> S. Munier, I. Messai, T. Delair, B. Verrier, Y. Ataman-Önal (2005). Cationic PLA nanoparticles for DNA delivery: comparison of three surface polycations for DNA binding, protection and transfection properties. Colloids Surf. B Biointerfaces, 43, pp. 163-173.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://link.springer.com/article/10.1007/s10847-007-9303-6" target="_blank"> Nagasaki, T., Shinkai, S. (2007). The concept of molecular machinery is useful for design of stimuli-responsive gene delivery systems in the mammalian cell. J. Incl. Phenom. Macrocycl. Chem., 58, pp. 205-219.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408932/" target="_blank"> J. Vacik, B.S. Dean, W.E. Zimmer, D.A. Dean (1999). Cell-specific nuclear import of plasmid DNA. Gene Ther, 6, pp. 1006-1014.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.nature.com/articles/3300867" target="_blank"> Lechardeur, D. et al. (1999). Metabolic instability of plasmid DNA in the cytosol: a potential barrier to gene transfer. Gene Ther, 6, pp. 482-497.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.amazon.com/Williams-Hematology-Eighth-Kenneth-Kaushansky/dp/0071621512" target="_blank"> Lichtman MA, Beutler E, Kipps TJ, Seligsohn U, Kaushansky K, Prchal JT (eds) (2005) Williams hematology, 7th edn. McGraw-Hill, New York.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.nejm.org/doi/full/10.1056/NEJMra032207" target="_blank"> McCormack, M.P.; Rabbitts, T.H. (2004). Activation of the T-cell oncogene LMO2 after gene therapy for X-linked severe combined immunodeficiency. N. Engl. J. Med. 350, 913–922.</a></li>
| + | |
| − | <li><a class="jnnbl" href="http://www.eurekaselect.com/75646/article" target="_blank"> Murakami, T. et al. (2011). Plasmid DNA Gene Therapy by Electroporation: Principles and Recent Advances. Current Gene Therapy. 11(6):447-56.</a></li>
| + | |
| − | <li><a class="jnnbl" href="http://www.bloodjournal.org/content/116/4/625.long?sso-checked=true" target="_blank"> Pillay, J. et al (2010). In vivo labelling with 2H2O reveals a human neutrophil lifespan of 5.4 days. Blood. 116 (4): 625-7. doi:10.1182/blood-2010-01-259028. PMID 20410504.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S0002934303004479?via%3Dihub" target="_blank"> Ratko,T.A., Cummings,J.P., Blebea,J., Matuszewski, K.A. (2003). Clinical gene therapy for non malignant disease. Am. J. Med., 115, pp. 560-569.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.liebertpub.com/doi/abs/10.1089/hum.1997.8.10-1195?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed" target="_blank"> Sullivan, D.E., et al. (1997). Liver-directed gene transfer into non-human primates. Hum. Gene Ther., 8, pp. 1195-1206.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S0002870333901398" target="_blank"> Tarr, L., Oppenheimer, B., & Sager, R. V. (1933). The circulation time in various clinical conditions determined by the use of sodium dehydrocholate. American Heart Journal, 8(6), 766-786. doi:10.1016/s0002-8703(33)90139-8</a></li>
| + | |
| − | <li><a class="jnnbl" href="http://www.bioscirep.org/content/22/2/283.long" target="_blank"> Templeton, N.S. et al. (2002). Cationic liposome-mediated gene delivery in vivo. Biosci. Rep., 22, pp. 283-295.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S1525001601904757?via%3Dihub" target="_blank"> Varga, CM, et al. (2001). Quantitative Analysis of Synthetic Gene Delivery Vector Design Properties. Cell, Molecular Therapy. Volume 4, Issue 5, November 2001, Pages 438-446. https://doi.org/10.1006/mthe.2001.0475.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.nature.com/articles/3302495" target="_blank"> Varga, CM, Tedford, NC, Thomas, M, Klibanov, AM, Griffith, LG and Lauffenburger, DA. Quantitative comparison of polyethylenimine formulations and adenoviral vectors in terms of intracellular gene delivery processes. (2005). Gene Ther 12: 1023-1032.</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S0009898110007059" target="_blank"> Owen, W. E., & Roberts, W. L. (2011). Performance characteristics of a new Immulite® 2000 system erythropoietin assay. Clinica Chimica Acta, 412(5-6), 480-482. doi:10.1016/j.cca.2010.11.023</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/S008525381553771X?via%3Dihub" target="_blank"> Weidemann, A., & Johnson, R. S. (2009). Nonrenal regulation of EPO synthesis. Kidney International, 75(7), 682-688. doi:10.1038/ki.2008.687</a></li>
| + | |
| − | <li><a class="jnnbl" href="https://www.sciencedirect.com/science/article/pii/0047637475900172" target="_blank"> Weissmanshomer, P. et al. (1975). Chick embryo fibroblasts senescence in vitro: Pattern of cell division and life span as a function of cell density. Mechanisms of Ageing and Development. 4 (2): 159-166. doi:10.1016/0047-6374(75)90017-2. PMID 1152547.</a></li>
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