Bcl-xL deamidation in oncogenic tyrosine kinase signalling

I have been interested in the molecular mechanisms of Haematopoietic malignant diseases such as leukaemia and lymphoma, especially those involving oncogenic tyrosine kinases. About 30 of the 90 tyrosine kinases in the human genome have been implicated in cancer (Blume-Jensen P, 2001). The oncogenic tyrosine kinases (OTKs), such as Bcr-Abl (product of chromosomal translocations of two genes bcr and abl) in Chronic Myelogenous Leukaemia, and Erythroblastic leukaemia viral oncogene homolog 2(Erb-B2) in mammary and other cancers, mediate their transforming effects via a diverse array of signalling pathways involved in DNA damage, cell survival and cell cycle regulation (Deutsch E, 2001; Skorski T, 2002; Kumar R, 1996). My work has been centred around the analysis of a mouse cancer model that is driven by an oncogenic tyrosine kinase – p56 Lck-F505 expressed on CD45 knock- out background (Baker M, 2000). The investigation of this mouse model has revealed that oncogenic inhibition of deamidation of the Bcl-xL survival protein plays a critical role in protecting thymocytes from DNA-damage induced apoptosis. Cells that would normally be eliminated due to accumulating DNA damage are instead preserved with an increasing load of double-stranded breaks, leading to genomic instability, chromosomal abnormalities and transformation. This work was published in Cancer Cell (An oncogenic tyrosine kinase inhibits DNA repair and DNA-damage-induced BclxL deamidation in T cell transformation. Zhao R, 2004). Following that I have tried to elucidate the different roles of the two deamidated species of Bcl-xL in apoptosis, and also the molecular mechanisms of DNA damage- induced Bcl-xL deamidation in order to understand the inhibition of Bcl-xL deamidation by oncogenic tyrosine kinases. Recently I have shown that Bcl-xL deamidation, whereby two critical Asn residues are converted to iso-Asp, cripples the ability of the protein to sequester pro-apoptotic BH3-only proteins such as Bim and p53- upregulated modulator of apoptosis (PUMA), thereby explaining its loss of pro-survival functionality. In vivo, DNA damage causes intracellular alkalinisation that is both necessary and sufficient to deamidate Bcl-xL, promoting apoptosis: no enzyme is necessary for this process. In pre-tumourigenic thymocytes alkalinisation is blocked, so preserving Bcl-xL in its pro-survival mode. Furthermore murine tumours are protected from genotoxic attack by native Bcl-xL, but enforced alkalinisation and consequent Bcl-xL deamidation promotes apoptosis. This part of work was published in Plos Biology (DNA damage-induced Bcl-xL deamidation is mediated by NHE-1 antiport regulated intracellular pH. Zhao R, 2007). Through collaboration with Prof AR Green’s research group at the Department of Haematology of the University of Cambridge, I have also analysed the Bcl-xL deamidation pathway in human myeloproliferative disorders, e.g. Polycythemia vera(PV) and Chronic Myelogenous Leukaemia (CML). We found that the oncogenic tyrosine kinases involved in these disorders, i.e. Jak2V617F and Bcr-Abl also inhibit the Bcl-xL deamidation pathway in DNA damage responses. These findings shed light on potential therapeutic application of the Bcl-xL deamidation pathway in human malignancies. This piece of work was recently published in the New England Journal of Medicine (Inhibition of the Bcl-xL deamidation pathway in myeloproliferative disorders. Zhao R, 2008). Overall the cited work has led to several important new insights into the molecular mechanisms involved in oncogenesis: first, that Bcl-xL deamidation is important in the cascade of events leading from DNA damage to apoptosis; second, that oncogenic tyrosine kinases inhibit these events in both the murine and human context; third, that up-regulation of the NHE-1 antiport and consequent intracellular alkalinisation are critical events in this DNA damage-induced cascade leading to apoptosis. In the process I have demonstrated the first in vivo mechanism for the deamidation of an internal protein Asn. Essentially, a completely new and unexpected signalling pathway has been uncovered that seems to pertain to all murine and human haematopoietic cell lineages that have been investigated so far

The two mutation model.

<p>A: Schematic representation of the mutations leading to inactivation of both <i>APC</i> alleles, at rates <i>u</i>₀ and <i>u₁</i>, respectively. B: A representative example of the dynamics of somatic evolution in the linear process. Each color-coded curve represents the proportion of each cell type in the crypt column, with colors corresponding to those in panel A. The gray shaded region represents the time interval during which the stem cell is in the <i>APC</i>^+/− state. The yellow shaded region represents the time interval during which the stem cell is in the <i>APC</i>^−/− state. C–F: Conditional probability for losing the second <i>APC</i> allele before an <i>APC</i>^+/− cell at a particular position along the crypt column is “flushed” out of the crypt in the absence of cell death. At each position between 2 and 80, 1,000 simulation runs are generated in the absence of cell death to determine the probabilities of <i>APC</i>^+/− cells gaining new mutations before being “flushed” out of the crypt column. Four different rates of inactivating the second <i>APC</i> allele, <i>u</i>₁, were investigated and are shown on the top of the four sub panels. The bottom two panels provide zoomed-in views.</p

The rate of cellular movement in the crypt column.

<p>A: The left panel shows the distribution of the number of mitoses needed to push a cell at position <i>i</i> out of the crypt column for each proliferation curve. Colors match the corresponding proliferation curves in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003082#pcbi-1003082-g001" target="_blank">Fig. 1C</a>. The horizontal axis represents the position of the cell in the crypt. Box plots indicate the distribution of the number of mitoses, assuming no cell death. The size of each bar indicates the amount of variation in the number of cell divisions. The right panel provides a zoomed-in view focusing on curves 1, 2 and 3. B: The effects of cell death on cellular movement in the crypt for three selected representative positions (2, 12, and 22) in the crypt column under the assumption of a uniform death selection function. As the death rate, <i>λ</i>, increases, the rate of cell movement increases, as shown by the decreasing number of mitoses needed to push a cell out of the crypt column. C: The panel shows the effects of cell death on the mitotic stress of the stem cell assuming a uniform death selection function. The average number of times the stem cell is selected for divisions is displayed as a function of cell death for the proliferation kinetic curves. Without cell death, the number of times the stem cell is selected for cell division is identical for all curves. As <i>λ</i> increases, the mitotic stress on the stem cell increases. The magnitude of increase depends on the shape of proliferation kinetic curve. Dots represent results from simulations, whereas the lines are exact results based on the terms inside the parentheses in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003082#pcbi.1003082.e050" target="_blank">Eq. 8</a>. All graphs are generated based on 1,000 simulations for each kinetic curve under each scenario. All cells are assumed to have identical relative fitness values.</p

The effects of chromosomal instability and tunneling.

<p>A: Schematic representation of the mutations leading to the inactivation of both <i>APC</i> alleles incorporating chromosomal instability. B: The upper panel displays a representative example of linear somatic evolution dynamics. Each color-coded curves represent the proportion of each cell type in the crypt column, with colors corresponding to those in panel A. Dashed and solid lines correspond to cells with and within CIN respectively. The gray and yellow shaded regions represent the time interval during which the stem cell is in the <i>APC</i>^+/− state and <i>APC</i>^−/− state respectively, regardless of CIN status. The lower panel provides a zoomed-in view. Notice that the dashed red curve does not reach 1, which signifies tunneling. This representative simulation run is performed using the uniform proliferation kinetics in the absence of cell death and fitness differences. The mutation rate is inflated to <i>u</i>₀ = <i>u₁</i> = <i>u₂</i> = 10⁻³ and <i>u₃</i> = 0.01 for computational speed. C: The tunneling probability as a function of <i>u₃</i> and the cell death rate, <i>λ</i>, for the five proliferation curves under uniform death selection. To reduce the extent of complexity, tunneling rate is simulated using a three-state system consisting of <i>APC</i>^+/−, <i>APC</i>^+/− CIN and <i>APC</i>^−/− CIN cells instead of the six-state system as illustrated in Panel A. Each simulation run starts with a crypt column seeded with an <i>APC</i>^+/− cell at the stem position. The number of simulation runs is set at 1,000. Stem cell death is allowed. D: Concordance between simulated tunneling rates and analytical rates for linear systems of length <i>N</i> = 10, 20, …100 with equal proliferation probability at each position, using mutation rate <i>u</i>₃ = 0.001, 0.01, 0.1 and 1.0. All simulations were performed for 1,000 runs. E: Analytical tunneling rate for the five proliferation curves at different mutation rate.</p

Schematics of the linear process and proliferation kinetic curves.

<p>A: We designed a linear process model to describe the essential features of cell movement in a colonic crypt. During each time step, a cell at position <i>i</i> is selected to divide. During mitosis, a mutation may occur with probability <i>u</i>, giving rise to one mutated and one wild type daughter cell, with equal probability of occupying either position <i>i</i> or <i>i</i>+1. Cell division pushes all cells to the right of position <i>i</i> upwards in the crypt by one position. The last cell is shed into the lumen of the colon. B: Cell death may occur after each round of cell division. The number of dying cells follows a Poisson distribution with mean <i>λ</i>; the positions of the dying cells are selected according to a uniform distribution. Dead cells are replaced by replenishing cell divisions. Dying cells at position <i>j</i> can only be replaced by cells of a similar (<i>j</i>+1≤<i>k</i>≤<i>j</i>+δ) or less (<i>k</i><<i>j</i>) differentiated stage. Position <i>k</i> is selected according to the proliferation kinetics curve. If multiple cells die simultaneously, the replenishing cell divisions occur sequentially, in the order of <i>j₍₁₎</i>, <i>j₍₂₎ … j_(m)</i>, where <i>j₍₁₎</i>, <i>j₍₂₎ … j_(m)</i> are ordered death position. The <i>m</i> positions for the replenishing cell divisions are selected according to the reweighted kinetic curve. C: Proliferation kinetic curves as a function of cell positions. The black curve represents the measured labeling index for normal human colon using bromodeoxyuridine (BRDU) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003082#pcbi.1003082-Potten2" target="_blank">[23]</a>. The colored curves represent the five kinetic curves under investigation. See the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003082#s2" target="_blank">Methods</a> section for details. Note that curve 1 is in good agreement with the measured labeling index.</p

The probabilities of selecting the stem cell for a replenishing division per cell death event.

<p>The data is shown for all combinations of birth and death kinetics in the absence of stem cell death and of fitness differences among mutant cells. The five birth curves are the measured curve from the labeling index, the logistic curve, the uniform curve, and, mirror images of the measured curve and logistics curve with the place of reflection between cell 40 and 41, respectively. Due to the lack of quantitative measurements of the distribution of death as a function of cell position, birth curves are used as proxies for death curves.</p

The values used for individual mutation rates.

<p>The values used for individual mutation rates.</p

The single mutation model.

<p>A: Schematic representation of the single mutation model. B: An example of the dynamics of somatic evolution in the crypt column. The curves show the proportions of cells in the crypt column. The colors correspond to cell types in panel A. The gray shaded region indicates the stem cell is mutated to <i>APC</i>^+/−. For illustration purposes, u₀ = 0.01. C: The number of mitoses needed for a wild type crypt column to transition to an <i>APC</i>^+/− state for various proliferation kinetic curves in the absence of cell death. As expected, the numbers of cell division needed to reach mutant fixation are the same for all curves. The mutation rate is <i>u</i>₀ = 10⁻⁷ per cell division. Box plots are color coded, corresponding to the curves in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003082#pcbi-1003082-g001" target="_blank">Fig. 1C</a>. D: The number of mitoses needed for fixation of <i>APC</i>^+/− cells for various proliferation kinetic curves, measured from the time at which the stem cell accumulates the <i>APC</i>^+/− mutation assuming no cell death. The gray area corresponds to the gray shaded interval in panel B. E: Acceleration of mutation accumulation due to cell death. As the death rate, <i>λ</i>, increases, fewer cell divisions are required for a mutated stem cell to arise. The comparison between panels C–E highlights the importance of proliferation kinetics of non-stem cells in the presence of cell death. F: Effects of fitness differences and proliferation curves on the rate of somatic evolution. The range of relative fitness spans from 0.5 to 2.0. G: The left panel shows the effects of fitness differences and different proliferation curves on the rate of <i>APC</i>^+/− fixation, starting from an <i>APC</i>^+/− stem cell in the absence of cell death. The panel on the right provides a zoomed-in view on curves 1, 2 and 3.</p

The impact of inequity-aversion and consideration of relative kindness intention (RKI) on agents’ best individual effort compared with the selfish condition.

<p>The impact of inequity-aversion and consideration of relative kindness intention (RKI) on agents’ best individual effort compared with the selfish condition.</p

The impact of the model’s parameters on the agents’ best individual effort.

<p>The impact of the model’s parameters on the agents’ best individual effort.</p