Revision as of 13:22, 15 October 2018

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 10¹⁰⁴ 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.

1. Approach

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.

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.

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 Ratko et al. 2003.

**Table 1:** Gene doping vectors advantages and disadvantages.
Vector	Advantages	Disadvantages
Plasmid	Relatively safe Generally low immune response Low cost and easy large quantity production Variable transgene insertion upto ±20 kb (Lodish et al. 2000) Long storage (Munier et al. 2005, Kircheis et al. 2001, Li and Huang 2000)	Very low transfection efficiency (Bergen et al. 2008) No targeting Transient expression
Adenovirus	High transduction efficiency Transduces proliferating and nonproliferating cells Transduces many cell types Easy Production Very high titers (1012 pfu/mL)	No targeting Transient expression Limited insert size: 4–5 kb Immune-related toxicity with repeated administration Potential replication competence
Adeno-associated virus	Continued expression No viral genes	No targeting Difficult production Not characterized well Potential Insertional Mutagenesis Limited insert size: 5kb
Lentivirus	Transduces proliferating and nonproliferating cells Prolonged expression Relatively high titers (106–107 pfu/mL)	Integrating virus Clinical experience limited Difficult to manufacture and store Limited insert size: 8 kb
Retrovirus	Relatively high titers (106–107 pfu/mL) Prolonged stable expression Larger insert size: 9–12 kb	Inefficient transduction Integrating virus Insertional mutagenesis Broad cell tropism No targeting Potential replication competence

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 (Templeton et al. 2002, Nagasaki and Shinkai, 2007) and safety of viral transfection remains a concern after several deaths (McCormack et al. 2004, Hacein-Bey-Abina et al. 2008). However, plasmid vectors have a very low transfection efficiency (Murakami et al. 2011), especially compared to adenoviruses that have been shown to have a 95% transfection efficiency in hepatocytes (Huard et al. 1995, Sullivan et al 1997). Transfection efficiency of plasmids can be increased through methods such as in vivo electroporation (Ataka et al. 2003). 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.

2. Model Design

2.1 gRNA Array Model

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:

Test

2.2 Gene Doping Model

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.

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.

Pharmacological compartment models are developed to understand the distribution of drugs administered to the human body by oral or, intramuscular, or intravenous routes (M.A. Khanday et al. 2017). 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 4. Here it is assumed that the mixing of the vectors with blood is instantaneous, based on an article by Tarr et al. 1933). Prof. Beltman, Assistant Professor Biomedical Modelling at Leiden University, later agreed with this assumption.

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 (Weidemann & Johnson 2009).

From the compartment models in Figure 4, equations 1 and 2 can be derived for the intravenous administration.

$$\frac{d[c_{blood}]}{dt} = -(k_{blood}+kel_{blood})[c_{blood}]+k_{tissue}[c_{tissue}] \tag{1}$$

$$\frac{d[c_{tissue}]}{dt} = k_{blood}[c_{blood}]-(k_{bind\,uptake}+k_{tissue})[c_{tissue}] \tag{2}$$

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.

$$\frac{d[c_{muscle}]}{dt} = -(k_{muscle}+kel_{muscle})[c_{muscle}]\tag{3}$$

$$\frac{d[c_{blood}]}{dt} = -(k_{blood}+kel_{blood})[c_{blood}]+k_{tissue}[c_{tissue}]+k_{muscle}[c_{muscle}] \tag{4}$$

$$\frac{d[c_{tissue}]}{dt} = k_{blood}[c_{blood}]-(k_{bind\,uptake}+k_{tissue})[c_{tissue}] \tag{5}$$

The initial values and constantsused are as specified in Table 2 and Table 3 respectively.

**Table 2:** Overview of the nitial values used in the human body model for an adenoviral vector.
Constants	Values	Meaning
$$[c_{blood} (t=0)]$$	94 billion [#/mL] for IV Single Dose 64 billion [#/mL] followed by smaller doses of 18 billion vectors every 20 days for IV Microdosing 0 [#/mL] for IM	Initial injection of vectors intravenously
$$[c_{tissue} (t=0)]$$	0 [#/mL]	Initial vector concentration in in tissue
$$[c_{muscle} (t=0)]$$	141 billion [#/mL] for IM 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days for IM Microdosing 0 [#/mL] for IV	Initial injection of vectors intramuscularly

**Table 3:** Overview of the constants used in the human body model for an adenoviral vector.
Rate Constants	Values (days^-1)	Meaning	Source
$$k_{tissue}$$	1440	Vector displacement from tissue to blood	Estimate based on mean blood circulation time
$$k_{muscle}$$	1440	Vector displacement from muscle to blood in IM injections	Estimate based on mean blood circulation time
$$k_{blood}$$	1440	Vector displacement from blood to tissue	Estimate based on mean blood circulation time
$$kel_{blood}$$	720	Elimination of viral vectors from the blood	Ganesan et al. 2011
$$kel_{muscle}$$	720	Elimination of viral vectors from the muscle	Ganesan et al. 2011
$$k_{bind\,uptake}$$	8.64	Endosomal uptake	Varga et al. 2005

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.

Cellular uptake

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 5 according to a model first developed by Varga et al. 2005. The cellular uptake of gene doping vectors as depicted in Figure 5 can be dissected into multiple steps described by a set of coupled differential equations, for which the constants are given in Table 3.

First, the complex is taken up by endocytosis after which it is either degraded or taken up, as represented by equations 6 and 7.

$$\frac{d[vesicle]}{dt} = k_{bind\,uptake}[c_{tissue}]-(k_{Escape}+k_{deg\,vesicle})[vesicle] \tag{6}$$

$$\frac{d[complex\,intracell]}{dt} = k_{escape}[vesicle]-k_{unpack}[complex\,intracell]-k_{bind\,vector}[complex\,intracell]\tag{7}$$

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.

$$\frac{d[plasmid]}{dt} = k_{unpack}[complex\,intracell]-k_{bind \,plasmid}[plasmid]-k_{deg}[plasmid]\tag{8}$$

$$\frac{d[plasmid\,bound]}{dt} = k_{bind \,plasmid}[plasmid]-k_{NPC}[plasmid\,bound] \tag{9}$$

$$\frac{d[complex\,bound]}{dt} = k_{bind \,vector}[complex\,intracell]-k_{NPC}[complex\,bound] \tag{10}$$

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.

$$\frac{d[complex\,boundNPC]}{dt} = k_{NPC}[complex\,bound]-k{in}[complex\,boundNPC] \tag{11}$$

$$\frac{d[complex\,bound\,nucleus]}{dt} = k{in}[complex\,boundNPC]-k_{dissociation}[complex\,bound\,nucleus] \tag{12}$$

$$\frac{d[complex\,nucleus]}{dt} = k_{dissociation}[complex\,bound\,nucleus] - k_{unpack2}[complex\,nucleus] \tag{13}$$

$$\frac{d[plasmid\,boundNPC]}{dt} = k_{NPC}[plasmid\,bound] -k_{in2}[plasmid\,boundNPC] \tag{14}$$

$$\frac{d[plasmid\,bound\,nucleus]}{dt} =k_{in2}[plasmid\,boundNPC]-k{kissociation2}[plasmid\,bound\,nucleus] \tag{15}$$

$$\frac{d[plasmid\,nucleus]}{dt} =k{kissociation2}[plasmid\,bound\,nucleus] + k_{unpack2}[complex\,nucleus]$$

$$- k_{cell\,death}[plasmid\,nucleus] \tag{16}$$

Detection of cell free doping DNA

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.

$$\frac{d[Doping\,DNA]}{dt} =k_{cell\,death}[plasmid\,nucleus]-kel_{cfDNA}[Doping\,DNA] \tag{17}$$

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 Weissmanshomer et al. 1975.

According to Haller et al. 2018, 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.

The Protein Effect

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.

$$\frac{d[protein]}{dt} =k_{protein}[plasmid\,nucleus]-k_{deg\,protein}[protein]-k_{export}[protein] \tag{18}$$

$$\frac{d[protein\,extracellular]}{dt} =k_{export}[protein]-k_{deg\,protein\,extracellular}[protein\,extracellular]\tag{19}$$

**Table 4:** Overview of the constants used in the human body model for an adenoviral vector.
Rate Constants (Ad5)	Values (days^-1)	Meaning	Source
$$k_{bind\,uptake}$$	8.64	Endosomal uptake	Varga et al. 2005
$$k_{deg\,vesicle}$$	28.8	Degradation of complex within uptake vesicle	Varga et al. 2005
$$k_{escape}$$	23.0	Complex movement from endosome to intracellular/td>	Varga et al. 2005
$$k_{bind\,vector}$$	144	Binding of gene delivery vector to compound targeting for the nucleus	Varga et al. 2005
$$k_{unpack}, k_{unpack2}$$	144	Plasmid detaches from vector either in cytoplasm(1) or in the nucleus(2)	Varga et al. 2005
$$k_{deg}$$	7.2	Degradation of unbound plasmid in the cytoplasm	Lechardeur et al. 1999
$$k_{bind\,plasmid}$$	2.88	Binding of plasmid to compound targeting for the nucleus	Varga et al. 2001
$$k_{NPC}$$	1.44*10⁶	Binding formed complexes to Nuclear Pore Complex	Vacik et al. 1999 Wilson et al. 1999 Chan et al. 1999 Dean et al. 1997
$$k_{in}, k_{in2}$$	2.88	Uptake nucleus through Nuclear Pore Complex	Varga et al. 2001
$$k_{dissociation}, k_{dissociation2}$$	1.44*10⁶	Dissociation from the NPC targeting compound	Moroianu et al. 1996
$$k_{protein}$$	14.4	Protein production from plasmid	Schaffer et al. 1998
$$k_{degprot}$$	1.04	Cytoplasmic degradation of the protein	Fuertinger et al. 2012
$$k_{export}$$	1.44*10⁶	Export protein to extracellular environment	Estimate
$$k_{deg\,protein\,extracellular}$$	1.04	Cytoplasmic degradation of the protein	Fuertinger et al. 2012
$$k_{cell\,death}$$	0.0167	Average death rate of renal interstitial fibroblast	Estimate based on chicks; Weissmanshomer et al. 1975
$$kel_{cfDNA}$$	100	Clearance of cfDNA from the blood	Alegre et al. 2015

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.

.

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.

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.

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.

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 (Lichtman et al. 2005).

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.

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 (Fishbane et al. 1997). 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 (Jandl 1987). 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 (Dean 2005). The average red blood cell count is estimated to be 24.98 trillion by Lichtman et al. 2005.The entire process, from stem cell to red blood cell recycling by microphages, takes 141 days.

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.

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.

The process of red blood cell formation, from stem cell to red blood cell can be modelled in time through a series of partial differential equations based on the age of the cells . Each of cell stage, BFU-E, CFU-E, erythroblasts, marrow reticulocytes and red blood cells, is represented by a partial differential equation.The general form is seen below.

Starting at cell age 0 days, when stem cells become BFU-E cells, the age span up until cell age 141 days, when red blood cells are recycled by macrophages, the cells can be broken up into populations of cells at a certain age. For every cell stage, the population density of cells at a given maturity and time can modelled as a population mesh. A population mesh is a numerical estimation that breaks down the process of red blood cell production into populations at a given age. A mesh point is population of cells at a specific age. When modelled as a mesh, an assumption of the relation between a change in time and a change in maturity can be made.

Assumptions

For a population of cells u of maturity μ and at time t, a change in time Δt while result in an equal change in maturity Δμ, assuming that the maturation velocity $vs(E(t))=1$.
The maturation velocity is 1 for every stage except the marrow reticulocytes, where the time cells stay in this stage is dependent on the concentration of EPO.

The commitment of stems cells to becoming red blood cells is an irreversible event (Fuertinger et al. 2012). Cells cannot regress back to a previous cell type or switch to a different differentiation pathway. For this reason, the assumption above can be made. A change in time will lead to an equal change in the age of the cell. As a result, the finer you make the population mesh the smaller the time step is. This increases the accuracy of the model as the cells react to changes in EPO concentration in time faster. Once they reach the age at which they differentiated into another cell stage, the resulting growth and decay rates that govern them will change.

BFU-E Cells

10⁸ stem cells become BFU-E cells on day 0. BFU-E cells grow exponentially as they age. Their proliferation rate is not dependent on EPO. As result, the population of BFU-E cells grows at a constant exponential rate as they age.

$$ BFU{-}E_n = BFU{-}E_{n-1}\times exp(\beta_{BFU-E} \times \delta t) \tag{20}$$

$$ BFU{-}E_{n+1} = BFU{-}E_{n}\times exp(\beta_{BFU-E} \times \delta t) \tag{20}$$

Cells committed to become red blood cells continue to grow in this manner until they reach the age of 7 days. At this point, the BFU-E differentiate into CFU-E cells.

CFU-E Cells

CFU-E cells are strongly dependent on EPO for their survival.

$$ CFU{-}E_n = CFU{-}E_{n-1}\times exp((\beta_{CFU{-}E}-\alpha(t)_{CFU{-}E} \times \Delta t) \tag{20}$$

$$ CFU{-}E_{n+1} = CFU{-}E_{n}\times exp((\beta_{CFU{-}E}-\alpha(t)_{CFU{-}E} \times \Delta t) \tag{20}$$

The apoptosis rate of CFU-E cells is dependent on the concentration of EPO at a given time. As the concentration of EPO increases, the apoptosis rate of CFU-E cells decreases.

Test

3. Results

3.1 gRNA Array Model

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:

Gene cds sequence: Human EPO cds (GenBank: BC143225.1)
Type of Cas: dxCas9
PAM sequence: NG
gRNA length: 20 bp of target gRNA
Off target possibility (seed to target): 10 bp (50 % adjacent to PAM should be identical)

As presented in Figure 7, the algorithm detected several PAM sequences close to exon-exon junctions and found a minimal number of necessary guides for each one.

Figure 7 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.

3.2 Gene Doping Model

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.

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.

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.

Test

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.

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.

4. References

5. Download

In case we want to include any download here.

@@ Line 510: / Line 510: @@
      <p>10<sup>8</sup> stem cells become BFU-E cells on day 0. BFU-E cells grow exponentially as they age. Their proliferation rate is not dependent on EPO. As result, the population of BFU-E cells grows at a constant exponential rate as they age.</p>
-     <p>$$ BFU-E_n = BFU{-}E_{n-1}\times exp(\beta_{BFU-E} \times \delta t) \tag{20}$$</p>
+     <p>$$ BFU{-}E_n = BFU{-}E_{n-1}\times exp(\beta_{BFU-E} \times \delta t) \tag{20}$$</p>
+    <p>$$ BFU{-}E_{n+1} = BFU{-}E_{n}\times exp(\beta_{BFU-E} \times \delta t) \tag{20}$$</p>
-      </div><br>
+    <p>Cells committed to become red blood cells continue to grow in this manner until they reach the age of 7 days. At this point, the BFU-E differentiate into CFU-E cells.</p>
+    <p class="jnnbl"><u><b>CFU-E Cells</b></u></p>
+    <p>CFU-E cells are strongly dependent on EPO for their survival.</p>
+    <p>$$ CFU{-}E_n = CFU{-}E_{n-1}\times exp((\beta_{CFU{-}E}-\alpha(t)_{CFU{-}E} \times \Delta t) \tag{20}$$</p>
+    <p>$$ CFU{-}E_{n+1} = CFU{-}E_{n}\times exp((\beta_{CFU{-}E}-\alpha(t)_{CFU{-}E} \times \Delta t) \tag{20}$$</p>
+    <p>The apoptosis rate of CFU-E cells is dependent on the concentration of EPO at a given time. As the concentration of EPO increases, the apoptosis rate of CFU-E cells decreases. </p>
+      </div>
 <button class="collapsible cjnnbl">Would you like to know more? Overview of the constants used in the red blood cell production model</button>

Difference between revisions of "Team:TUDelft/Model/HBK"

Revision as of 13:22, 15 October 2018

1. Approach

2. Model Design

2.1 gRNA Array Model

2.2 Gene Doping Model

3. Results

3.1 gRNA Array Model

3.2 Gene Doping Model

4. References

5. Download