Relevance of CD40/CD40L downstream pathways to Alzheimer's disease

The amyloid cascade hypothesis places amyloid-β at the origin of Alzheimer's disease (AD). Amyloid-β (Aβ) is the product of the sequential cleavage of the amyloid precursor protein (APP) by the enzymes β- and γ-secretases. An inflammatory component to AD has been suggested in association with CD40 (a member of the tumor necrosis factor receptor superfamily (TNFRS) and its cognate ligand CD40L. In this study, I hypothesized that the neutralization of pro-inflammatory cytokines produced downstream of CD40/CD40L interaction would reduce APP processing. I also hypothesized that blocking the binding of different adaptor proteins to CD40 by mutating its cytoplasmic tail would result in significant reduction of the APP metabolites: Aβ, sAPPβ, sAPPα, CTFβ and CTFα. ^ Treatment with CD40L of human embryonic kidney cells over-expressing both APP and CD40 (HEK/APPsw/CD40) significantly increased levels of the cytokine granulocyte macrophage colony stimulating factor (GM-CSF). Neutralizing antibodies against GM-CSF mitigated the CD40L-induced production of Aβ in these cells. Treatment of the HEK/APPsw/CD40 cells with recombinant GM-CSF significantly increased Aβ levels. GM-CSF receptor gene silencing with shRNA significantly reduced Aβ levels to below base line in non-stimulated HEK/APPsw/CD40 cells. Silencing of the GM-CSF receptor also decreased APP endocytosis (therefore reducing the availability of APP to be cleaved in the endosomes). ^ Using CD40 mutants, I show that CD40L can increase levels of Aβ(1-40), Aβ(1-42), sAPPβ, sAPPα and CTFβ independently of TRAF signaling. TRAFs had been shown to be necessary for most CD40/CD40L-dependent signaling. An increase in mature/immature APP ratio after CD40L treatment of CD40wt and CD40-mutant cells was observed, reflecting alterations in APP trafficking. CD4OL treatment of a neuroblastoma cell line over-expressing CTFβ suggested that CD40L affected γ-secretase activity. Inhibition of γ-secretase activity significantly reduced sAPPβ levels in the CD40L treated HEK/APPsw CD40wt and the CD40-mutant cells. The latter suggests CD40/CD40L interaction primarily acts on γ-secretase and affects β-secretase via a positive feedback mechanism. ^ Taken together, the results of this dissertation suggest that GM-CSF operates downstream of CD40/CD40L interaction and that GM-CSF modulates Aβ production by influencing APP trafficking. Moreover, the data presented suggest that CD40/CD40L interaction can modulate APP processing via a mechanism independent of TRAF signaling. ^

Positron emission tomography (PET) tumor segmentation and quantification: Development of new algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera's point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ∼10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera's PSF. The algorithm can also improve dose estimation and treatment planning.^

Positron Emission Tomography (PET) Tumor Segmentation and Quantification: Development of New Algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.