Caries is the most well-known disease and relates to the oral health of billions of people around... more Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous stateof-the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.
Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any c... more Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.
Breast cancer is currently the foremost cause in women_s mortality worldwide. In Sudan, the incre... more Breast cancer is currently the foremost cause in women_s mortality worldwide. In Sudan, the increasing incidence, detection at late stages, and the early onset of the disease make early detection and diagnosis of breast cancer an overbearing task. The objective of this study is to create an automated computer interfacing system for the localization, detection, and classification of breast mass. This study is implemented using MATLAB software. A Graphical User Interface is designed for the implemented algorithm. Results demonstrated that the proposed methodologies have high potential localizing, detecting, and classifying the breast tumor. The system was able to achieve an accuracy of 88% sensitivity, 72% specificity, an Az value of 0.84, and an overall classification accuracy of 80%. The created systems have therefore proved to effectively discriminate between malignant and benign tumors with an effective level of precision.
Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the ... more Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN model...
Proceedings of the 2019 4th International Conference on Biomedical Imaging, Signal Processing, 2019
Mediastinal lymph nodes are one of the most critical factors to identify the clinical stages of l... more Mediastinal lymph nodes are one of the most critical factors to identify the clinical stages of lung cancer. As the lymph nodes are low in attenuation and cluttering with various shapes and sizes, manual detection is usually error-prone and effort-intensive. This paper introduces a method for automatic detection of mediastinal lymph nodes by proposing three significant contributions. First, we constraint the detection area, mediastinal region, using grey-level thresholding. Next, we apply the watershed method and hessian eigenvalues to separate a cluster of lymph nodes. Finally, we build a three-dimensional convolutional neural network (3D CNN) to distinguish the actual lymph nodes from other false lesions. Our experiment is conducted using 70 CT exams containing 314 lymph nodes and achieved a favorable result with 94 % detection rate.
This research concerns the design and development of an assistive system based on eye tracking, w... more This research concerns the design and development of an assistive system based on eye tracking, which can be used to improve the quality of life of disabled patients. With the use of their eye movement, whose function is not affected by their illness, patients are capable of communicating with and sending notifications to caretakers, controlling various appliances, including wheelchairs. The designed system is divided into two subsystems: stationary and mobile assistive systems. Both systems provide a graphic user interface (GUI) that is used to link the eye tracker with the appliance control. There are six GUI pages for the stationary assistive system and seven for the mobile assistive system. GUI pages for the stationary assistive system include the home page, smart appliance page, eye-controlled television page, eye-controlled air conditional page, i-speak page and entertainment page. GUI pages for the mobile assistive system are similar to the GUI pages for the stationary assist...
Proceedings of the 2019 4th International Conference on Biomedical Imaging, Signal Processing, 2019
In this paper, we propose a framework for detection and classification of cervical cancer from pa... more In this paper, we propose a framework for detection and classification of cervical cancer from pap smear images. Early detection and accurate diagnosis of cervical cancer can reduce the death rate of cervical cancer patients. Pap smear or pap test is the most popular technique for early detection of cervical cancer. However, the manual analysis is labor intensive and time consuming process which relies on expert cytologist. Hence, it is needed to develop a computer aided diagnosis system to make the pap smear test more accurate and reliable. The objective of this paper is to present an innovative idea of applying random forest algorithm (RF) as a feature selection method using proposed bagging ensemble classifier for improving the predictive performance. The four basic steps of cervical cancer detection and classification system, image enhancement, segmentation, feature extraction and classification were used. K-means clustering combining with morphology operations obtained good segmentation for cell nuclei and cytoplasm. The most important features, shape, color and texture of nuclei and cytoplasm were applied to detect cervical cancer. To improve the accuracy of prediction results, random forest (RF) algorithm was used as a feature selection method. In classification stage, bagging ensemble classifier was applied which aggregated the results of five classifiers, linear discriminant (LD), support vector machine (SVM), weighted k-nearest neighbor (KNN), boosted trees and bagged trees. Herlev data set was used to prove the effectiveness of our proposed method. According to the experimental results, the high classification accuracy was achieved with top10 features using our proposed combined classifier. The accuracy was 97.83% in two class problem and 81.54% in seven class problem. When the results were compared with five classifiers, our proposed method was significantly better in two class and seven class problems.
Despite recent advances in therapeutic approaches, patients with MLL-rearranged leukemia still ha... more Despite recent advances in therapeutic approaches, patients with MLL-rearranged leukemia still have poor outcomes. Here, we find that the RNA-binding protein IGF2BP3, which is overexpressed in MLL-translocated leukemia, strongly amplifies MLL-Af4-mediated leukemogenesis. Deletion of Igf2bp3 significantly increases the survival of mice with MLL-Af4-driven leukemia and greatly attenuates disease, with a minimal impact on baseline hematopoiesis. At the cellular level, MLL-Af4 leukemia-initiating cells require Igf2bp3 for their function in leukemogenesis. At the molecular level, IGF2BP3 regulates a complex posttranscriptional operon governing leukemia cell survival and proliferation. IGF2BP3-targeted mRNA transcripts include important MLL-Af4-induced genes, such as those in the Hoxa locus, and the Ras signaling pathway. Targeting of transcripts by IGF2BP3 regulates both steady-state mRNA levels and, unexpectedly, pre-mRNA splicing. Together, our findings show that IGF2BP3 represents an ...
B-cell development in the bone marrow is followed by specification into functional subsets in the... more B-cell development in the bone marrow is followed by specification into functional subsets in the spleen, including marginal zone (MZ) B-cells. MZ B-cells are classically characterized by T-independent antigenic responses and require the elaboration of distinct gene expression programs for development. Given their role in gene regulation, it is not surprising that microRNAs are important factors in B-cell development. Recent work demonstrated that deficiency of the NFκB feedback regulator, miR-146a, led to a range of hematopoietic phenotypes, but B-cell phenotypes have not been extensively characterized. Here, we found that miR-146a-deficient mice demonstrate a reduction in MZ B-cells, likely from a developmental block. Utilizing high-throughput sequencing and comparative analysis of developmental stage-specific transcriptomes, we determined that MZ cell differentiation was impaired due to decreases in Notch2 signaling. Our studies reveal miR-146a-dependent B-cell phenotypes and hig...
Erythrocyte invasion is an essential step in the pathogenesis of malaria. The erythrocyte binding... more Erythrocyte invasion is an essential step in the pathogenesis of malaria. The erythrocyte binding-like (EBL) family of Plasmodium falciparum proteins recognizes glycophorins (Gp) on erythrocytes and plays a critical role in attachment during invasion. However, the molecular basis for specific receptor recognition by each parasite ligand has remained elusive, as is the case with the ligand/receptor pair P. falciparum EBA-175 (PfEBA-175)/GpA. This is due largely to difficulties in producing properly glycosylated and functional receptors. Here, we developed an expression system to produce recombinant glycosylated and functional GpA, as well as mutations and truncations. We identified the essential binding region and determinants for PfEBA-175 engagement, demonstrated that these determinants are required for the inhibition of parasite growth, and identified the glycans important in mediating the PfEBA-175–GpA interaction. The results suggest that PfEBA-175 engages multiple glycans of Gp...
Apicomplexan parasites are the causative agents of diseases that include malaria, toxoplasmosis, ... more Apicomplexan parasites are the causative agents of diseases that include malaria, toxoplasmosis, and coccidiosis. These obligate intracellular parasites have evolved to use a conserved mechanism for host-cell invasion. The apicomplexan phylum is defined by the presence of micronemes and rhoptries, which are distinct organelles located at the apical end of the parasite. These organelles secrete molecules necessary for host-cell invasion [1]. Apicomplexan parasites can invade disparate cell types, including hepatocytes, erythrocytes, lymphocytes, macrophages, and cells lining the digestive tract. Unlike viruses and intracellular bacteria, apicomplexans actively invade host cells without relying on host uptake pathways. As such, host-cell sensing and subsequent invasion are driven entirely by the parasite in a dynamic and rapid process. Intracellular residence protects the parasite from immune attack and enables parasite replication prior to host-cell lysis and subsequent invasion of neighboring host cells. The repertoire of ligand-receptor complexes utilized by parasites for entry into host cells is diverse. Some interactions occur through cell-specific receptors resulting in high-affinity interactions, while others occur through multiple lower-affinity interactions via surface moieties found on several cell types. Receptor-specific and general cell binding may explain host-cell tropism of different pathogens, although additional factors are important. There is growing evidence that multimeric assembly of parasite ligands and host surface molecules strengthens the hostpathogen interactions necessary for invasion. We discuss recent work that has advanced our knowledge of the assembly of adhesive complexes from two critical apicomplexan pathogens and highlight areas of research that require further investigation.
The sorting of signaling receptors to lysosomes is an essential regulatory process in mammalian c... more The sorting of signaling receptors to lysosomes is an essential regulatory process in mammalian cells. During degradation, receptors are modified with ubiquitin and sorted by endosomal sorting complex required for transport (ESCRT)–0, –I, –II, and –III complexes into intraluminal vesicles (ILVs) of multivesicular bodies (MVBs). However, it remains unclear whether a single universal mechanism mediates MVB sorting of all receptors. We previously showed that protease-activated receptor 1 (PAR1), a G protein–coupled receptor (GPCR) for thrombin, is internalized after activation and sorted to lysosomes independent of ubiquitination and the ubiquitin-binding ESCRT components hepatocyte growth factor–regulated tyrosine kinase substrate and Tsg101. In this paper, we report that PAR1 sorted to ILVs of MVBs through an ESCRT-III–dependent pathway independent of ubiquitination. We further demonstrate that ALIX, a charged MVB protein 4–ESCRT-III interacting protein, bound to a YPX3L motif of PAR...
Proceedings of the National Academy of Sciences, 2009
Protease-activated receptor-1 (PAR 1 ) is a G-protein–coupled receptor uniquely activated by prot... more Protease-activated receptor-1 (PAR 1 ) is a G-protein–coupled receptor uniquely activated by proteolysis. Thrombin, a coagulant protease, induces inflammatory responses and endothelial barrier permeability through the activation of PAR 1 . Activated protein C (APC), an anti-coagulant protease, also activates PAR 1 . However, unlike thrombin, APC elicits anti-inflammatory responses and protects against endothelial barrier dysfunction induced by thrombin. We found that thrombin and APC signaling were lost in PAR 1 -deficient endothelial cells, indicating that PAR 1 is the major effector of protease signaling. To delineate the mechanism responsible for protease-selective signaling by PAR 1 , we examined the effect of APC and thrombin on the activation of RhoA and Rac1, small GTPases that differentially regulate endothelial barrier permeability. Thrombin caused robust RhoA signaling but not Rac1 activation, whereas APC stimulated a marked increase in Rac1 activation but not RhoA signali...
Disrupting erythrocyte invasion by Plasmodium falciparum is an attractive approach to combat mala... more Disrupting erythrocyte invasion by Plasmodium falciparum is an attractive approach to combat malaria. P. falciparum engages the host receptor Glycophorin A (GpA) during invasion and is a leading vaccine candidate. Antibodies that recognize PfEBA-175 can prevent parasite growth, although not all antibodies are inhibitory. Here, using xray crystallography, small-angle x-ray scattering and functional studies, we report the structural basis and mechanism for inhibition by two PfEBA-175 antibodies. Structures of each antibody in complex with the PfEBA-175 receptor binding domain reveal that the most potent inhibitory antibody, R217, engages critical GpA binding residues and the proposed dimer interface of PfEBA-175. A second weakly inhibitory antibody, R218, binds to an asparagine-rich surface loop. We show that the epitopes identified by structural studies are critical for antibody binding. Together, the structural and mapping studies reveal distinct mechanisms of action, with R217 directly preventing receptor binding while R218 allows for receptor binding. Using a direct receptor binding assay we show R217 directly blocks GpA engagement while R218 does not. Our studies elaborate on the complex interaction between PfEBA-175 and GpA and highlight new approaches to targeting the molecular mechanism of P. falciparum invasion of erythrocytes. The results suggest studies aiming to improve the efficacy of blood-stage vaccines, either by selecting single or combining multiple parasite antigens, should assess the antibody response to defined inhibitory epitopes as well as the response to the whole protein antigen. Finally, this work demonstrates the importance of identifying inhibitory-epitopes and avoiding decoy-epitopes in antibody-based therapies, vaccines and diagnostics.
Caries is the most well-known disease and relates to the oral health of billions of people around... more Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous stateof-the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.
Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any c... more Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.
Breast cancer is currently the foremost cause in women_s mortality worldwide. In Sudan, the incre... more Breast cancer is currently the foremost cause in women_s mortality worldwide. In Sudan, the increasing incidence, detection at late stages, and the early onset of the disease make early detection and diagnosis of breast cancer an overbearing task. The objective of this study is to create an automated computer interfacing system for the localization, detection, and classification of breast mass. This study is implemented using MATLAB software. A Graphical User Interface is designed for the implemented algorithm. Results demonstrated that the proposed methodologies have high potential localizing, detecting, and classifying the breast tumor. The system was able to achieve an accuracy of 88% sensitivity, 72% specificity, an Az value of 0.84, and an overall classification accuracy of 80%. The created systems have therefore proved to effectively discriminate between malignant and benign tumors with an effective level of precision.
Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the ... more Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN model...
Proceedings of the 2019 4th International Conference on Biomedical Imaging, Signal Processing, 2019
Mediastinal lymph nodes are one of the most critical factors to identify the clinical stages of l... more Mediastinal lymph nodes are one of the most critical factors to identify the clinical stages of lung cancer. As the lymph nodes are low in attenuation and cluttering with various shapes and sizes, manual detection is usually error-prone and effort-intensive. This paper introduces a method for automatic detection of mediastinal lymph nodes by proposing three significant contributions. First, we constraint the detection area, mediastinal region, using grey-level thresholding. Next, we apply the watershed method and hessian eigenvalues to separate a cluster of lymph nodes. Finally, we build a three-dimensional convolutional neural network (3D CNN) to distinguish the actual lymph nodes from other false lesions. Our experiment is conducted using 70 CT exams containing 314 lymph nodes and achieved a favorable result with 94 % detection rate.
This research concerns the design and development of an assistive system based on eye tracking, w... more This research concerns the design and development of an assistive system based on eye tracking, which can be used to improve the quality of life of disabled patients. With the use of their eye movement, whose function is not affected by their illness, patients are capable of communicating with and sending notifications to caretakers, controlling various appliances, including wheelchairs. The designed system is divided into two subsystems: stationary and mobile assistive systems. Both systems provide a graphic user interface (GUI) that is used to link the eye tracker with the appliance control. There are six GUI pages for the stationary assistive system and seven for the mobile assistive system. GUI pages for the stationary assistive system include the home page, smart appliance page, eye-controlled television page, eye-controlled air conditional page, i-speak page and entertainment page. GUI pages for the mobile assistive system are similar to the GUI pages for the stationary assist...
Proceedings of the 2019 4th International Conference on Biomedical Imaging, Signal Processing, 2019
In this paper, we propose a framework for detection and classification of cervical cancer from pa... more In this paper, we propose a framework for detection and classification of cervical cancer from pap smear images. Early detection and accurate diagnosis of cervical cancer can reduce the death rate of cervical cancer patients. Pap smear or pap test is the most popular technique for early detection of cervical cancer. However, the manual analysis is labor intensive and time consuming process which relies on expert cytologist. Hence, it is needed to develop a computer aided diagnosis system to make the pap smear test more accurate and reliable. The objective of this paper is to present an innovative idea of applying random forest algorithm (RF) as a feature selection method using proposed bagging ensemble classifier for improving the predictive performance. The four basic steps of cervical cancer detection and classification system, image enhancement, segmentation, feature extraction and classification were used. K-means clustering combining with morphology operations obtained good segmentation for cell nuclei and cytoplasm. The most important features, shape, color and texture of nuclei and cytoplasm were applied to detect cervical cancer. To improve the accuracy of prediction results, random forest (RF) algorithm was used as a feature selection method. In classification stage, bagging ensemble classifier was applied which aggregated the results of five classifiers, linear discriminant (LD), support vector machine (SVM), weighted k-nearest neighbor (KNN), boosted trees and bagged trees. Herlev data set was used to prove the effectiveness of our proposed method. According to the experimental results, the high classification accuracy was achieved with top10 features using our proposed combined classifier. The accuracy was 97.83% in two class problem and 81.54% in seven class problem. When the results were compared with five classifiers, our proposed method was significantly better in two class and seven class problems.
Despite recent advances in therapeutic approaches, patients with MLL-rearranged leukemia still ha... more Despite recent advances in therapeutic approaches, patients with MLL-rearranged leukemia still have poor outcomes. Here, we find that the RNA-binding protein IGF2BP3, which is overexpressed in MLL-translocated leukemia, strongly amplifies MLL-Af4-mediated leukemogenesis. Deletion of Igf2bp3 significantly increases the survival of mice with MLL-Af4-driven leukemia and greatly attenuates disease, with a minimal impact on baseline hematopoiesis. At the cellular level, MLL-Af4 leukemia-initiating cells require Igf2bp3 for their function in leukemogenesis. At the molecular level, IGF2BP3 regulates a complex posttranscriptional operon governing leukemia cell survival and proliferation. IGF2BP3-targeted mRNA transcripts include important MLL-Af4-induced genes, such as those in the Hoxa locus, and the Ras signaling pathway. Targeting of transcripts by IGF2BP3 regulates both steady-state mRNA levels and, unexpectedly, pre-mRNA splicing. Together, our findings show that IGF2BP3 represents an ...
B-cell development in the bone marrow is followed by specification into functional subsets in the... more B-cell development in the bone marrow is followed by specification into functional subsets in the spleen, including marginal zone (MZ) B-cells. MZ B-cells are classically characterized by T-independent antigenic responses and require the elaboration of distinct gene expression programs for development. Given their role in gene regulation, it is not surprising that microRNAs are important factors in B-cell development. Recent work demonstrated that deficiency of the NFκB feedback regulator, miR-146a, led to a range of hematopoietic phenotypes, but B-cell phenotypes have not been extensively characterized. Here, we found that miR-146a-deficient mice demonstrate a reduction in MZ B-cells, likely from a developmental block. Utilizing high-throughput sequencing and comparative analysis of developmental stage-specific transcriptomes, we determined that MZ cell differentiation was impaired due to decreases in Notch2 signaling. Our studies reveal miR-146a-dependent B-cell phenotypes and hig...
Erythrocyte invasion is an essential step in the pathogenesis of malaria. The erythrocyte binding... more Erythrocyte invasion is an essential step in the pathogenesis of malaria. The erythrocyte binding-like (EBL) family of Plasmodium falciparum proteins recognizes glycophorins (Gp) on erythrocytes and plays a critical role in attachment during invasion. However, the molecular basis for specific receptor recognition by each parasite ligand has remained elusive, as is the case with the ligand/receptor pair P. falciparum EBA-175 (PfEBA-175)/GpA. This is due largely to difficulties in producing properly glycosylated and functional receptors. Here, we developed an expression system to produce recombinant glycosylated and functional GpA, as well as mutations and truncations. We identified the essential binding region and determinants for PfEBA-175 engagement, demonstrated that these determinants are required for the inhibition of parasite growth, and identified the glycans important in mediating the PfEBA-175–GpA interaction. The results suggest that PfEBA-175 engages multiple glycans of Gp...
Apicomplexan parasites are the causative agents of diseases that include malaria, toxoplasmosis, ... more Apicomplexan parasites are the causative agents of diseases that include malaria, toxoplasmosis, and coccidiosis. These obligate intracellular parasites have evolved to use a conserved mechanism for host-cell invasion. The apicomplexan phylum is defined by the presence of micronemes and rhoptries, which are distinct organelles located at the apical end of the parasite. These organelles secrete molecules necessary for host-cell invasion [1]. Apicomplexan parasites can invade disparate cell types, including hepatocytes, erythrocytes, lymphocytes, macrophages, and cells lining the digestive tract. Unlike viruses and intracellular bacteria, apicomplexans actively invade host cells without relying on host uptake pathways. As such, host-cell sensing and subsequent invasion are driven entirely by the parasite in a dynamic and rapid process. Intracellular residence protects the parasite from immune attack and enables parasite replication prior to host-cell lysis and subsequent invasion of neighboring host cells. The repertoire of ligand-receptor complexes utilized by parasites for entry into host cells is diverse. Some interactions occur through cell-specific receptors resulting in high-affinity interactions, while others occur through multiple lower-affinity interactions via surface moieties found on several cell types. Receptor-specific and general cell binding may explain host-cell tropism of different pathogens, although additional factors are important. There is growing evidence that multimeric assembly of parasite ligands and host surface molecules strengthens the hostpathogen interactions necessary for invasion. We discuss recent work that has advanced our knowledge of the assembly of adhesive complexes from two critical apicomplexan pathogens and highlight areas of research that require further investigation.
The sorting of signaling receptors to lysosomes is an essential regulatory process in mammalian c... more The sorting of signaling receptors to lysosomes is an essential regulatory process in mammalian cells. During degradation, receptors are modified with ubiquitin and sorted by endosomal sorting complex required for transport (ESCRT)–0, –I, –II, and –III complexes into intraluminal vesicles (ILVs) of multivesicular bodies (MVBs). However, it remains unclear whether a single universal mechanism mediates MVB sorting of all receptors. We previously showed that protease-activated receptor 1 (PAR1), a G protein–coupled receptor (GPCR) for thrombin, is internalized after activation and sorted to lysosomes independent of ubiquitination and the ubiquitin-binding ESCRT components hepatocyte growth factor–regulated tyrosine kinase substrate and Tsg101. In this paper, we report that PAR1 sorted to ILVs of MVBs through an ESCRT-III–dependent pathway independent of ubiquitination. We further demonstrate that ALIX, a charged MVB protein 4–ESCRT-III interacting protein, bound to a YPX3L motif of PAR...
Proceedings of the National Academy of Sciences, 2009
Protease-activated receptor-1 (PAR 1 ) is a G-protein–coupled receptor uniquely activated by prot... more Protease-activated receptor-1 (PAR 1 ) is a G-protein–coupled receptor uniquely activated by proteolysis. Thrombin, a coagulant protease, induces inflammatory responses and endothelial barrier permeability through the activation of PAR 1 . Activated protein C (APC), an anti-coagulant protease, also activates PAR 1 . However, unlike thrombin, APC elicits anti-inflammatory responses and protects against endothelial barrier dysfunction induced by thrombin. We found that thrombin and APC signaling were lost in PAR 1 -deficient endothelial cells, indicating that PAR 1 is the major effector of protease signaling. To delineate the mechanism responsible for protease-selective signaling by PAR 1 , we examined the effect of APC and thrombin on the activation of RhoA and Rac1, small GTPases that differentially regulate endothelial barrier permeability. Thrombin caused robust RhoA signaling but not Rac1 activation, whereas APC stimulated a marked increase in Rac1 activation but not RhoA signali...
Disrupting erythrocyte invasion by Plasmodium falciparum is an attractive approach to combat mala... more Disrupting erythrocyte invasion by Plasmodium falciparum is an attractive approach to combat malaria. P. falciparum engages the host receptor Glycophorin A (GpA) during invasion and is a leading vaccine candidate. Antibodies that recognize PfEBA-175 can prevent parasite growth, although not all antibodies are inhibitory. Here, using xray crystallography, small-angle x-ray scattering and functional studies, we report the structural basis and mechanism for inhibition by two PfEBA-175 antibodies. Structures of each antibody in complex with the PfEBA-175 receptor binding domain reveal that the most potent inhibitory antibody, R217, engages critical GpA binding residues and the proposed dimer interface of PfEBA-175. A second weakly inhibitory antibody, R218, binds to an asparagine-rich surface loop. We show that the epitopes identified by structural studies are critical for antibody binding. Together, the structural and mapping studies reveal distinct mechanisms of action, with R217 directly preventing receptor binding while R218 allows for receptor binding. Using a direct receptor binding assay we show R217 directly blocks GpA engagement while R218 does not. Our studies elaborate on the complex interaction between PfEBA-175 and GpA and highlight new approaches to targeting the molecular mechanism of P. falciparum invasion of erythrocytes. The results suggest studies aiming to improve the efficacy of blood-stage vaccines, either by selecting single or combining multiple parasite antigens, should assess the antibody response to defined inhibitory epitopes as well as the response to the whole protein antigen. Finally, this work demonstrates the importance of identifying inhibitory-epitopes and avoiding decoy-epitopes in antibody-based therapies, vaccines and diagnostics.
Uploads
Papers by may phyo paing