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Joel L. Bargul - One of the best experts on this subject based on the ideXlab platform.
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Gene Co-Expression Network analysis of Trypanosoma brucei in tsetse fly vector.
Parasites & vectors, 2021Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:BACKGROUND Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. METHODS In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using the weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite's development in tsetse fly. Furthermore, potential 3' untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool. RESULTS A fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3' untranslated regions of Genes within the same module were predicted. CONCLUSIONS The constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite's development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.
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Gene Co-Expression Network analysis of Trypanosoma brucei in tsetse fly vector.
Parasites & vectors, 2021Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using the weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite's development in tsetse fly. Furthermore, potential 3' untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool. A fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3' untranslated regions of Genes within the same module were predicted. The constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite's development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.
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Gene Co-Expression Network Analysis of Trypanosoma brucei in Tsetse Fly Vector
2020Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:Abstract BackgroundTrypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging due to the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. MethodsIn this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite’s development in tsetse fly. Further, potential 3’ untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using Finding Informative Regulatory Elements (FIRE) tool.ResultsA fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), Arginine kinase 1 (AK1), brucei alanine rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3’ untranslated regions of Genes within the same module were predicted. ConclusionsThe constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.
Steve Horvath - One of the best experts on this subject based on the ideXlab platform.
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signed weighted Gene co expression Network analysis of transcriptional regulation in murine embryonic stem cells
BMC Genomics, 2009Co-Authors: Mike J Mason, Kathrin Plath, Qing Zhou, Steve HorvathAbstract:Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying Genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these Genes. To that end, we investigated the use of unsigned and signed Network analysis to identify pluripotency and differentiation related Genes. We show that signed Networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned Networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted Gene Co-Expression Network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned Networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with Genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other Genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epiGenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling Gene expression during ES cell self-renewal and differentiation. Our systems biologic re-analysis of Gene expression, transcription factor binding, epiGenetic and Gene ontology data provides a novel integrative view of ES cell biology.
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Signed weighted Gene Co-Expression Network analysis of transcriptional regulation in murine embryonic stem cells
BMC Genomics, 2009Co-Authors: Mike J Mason, Kathrin Plath, Qing Zhou, Steve HorvathAbstract:Background Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying Genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these Genes. To that end, we investigated the use of unsigned and signed Network analysis to identify pluripotency and differentiation related Genes. Results We show that signed Networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned Networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted Gene Co-Expression Network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned Networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with Genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other Genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epiGenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling Gene expression during ES cell self-renewal and differentiation. Conclusion Our systems biologic re-analysis of Gene expression, transcription factor binding, epiGenetic and Gene ontology data provides a novel integrative view of ES cell biology.
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integrated weighted Gene co expression Network analysis with an application to chronic fatigue syndrome
BMC Systems Biology, 2008Co-Authors: Angela P Presson, Eric M Sobel, Jeanette C Papp, Charlyn J Suarez, Toni Whistler, Mangalathu S Rajeevan, Suzanne D Vernon, Steve HorvathAbstract:Systems biologic approaches such as Weighted Gene Co-Expression Network Analysis (WGCNA) can effectively integrate Gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of Genetic marker data allows one to characterize Network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.
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Integrated Weighted Gene Co-Expression Network Analysis with an Application to Chronic Fatigue Syndrome
BMC Systems Biology, 2008Co-Authors: Angela P Presson, Eric M Sobel, Jeanette C Papp, Charlyn J Suarez, Toni Whistler, Mangalathu S Rajeevan, Suzanne D Vernon, Steve HorvathAbstract:Background Systems biologic approaches such as Weighted Gene Co-Expression Network Analysis (WGCNA) can effectively integrate Gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of Genetic marker data allows one to characterize Network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set. Results We combine WGCNA with Genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a Co-Expression Network, 2) identify trait-related modules within the Network, 3) use a trait-related Genetic marker to prioritize Genes within the module, 4) apply an integrated Gene screening strategy to identify candidate Genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated Genes that is associated with CFS severity. Our integrated Gene screening strategy results in 20 candidate Genes. We show that our approach yields biologically interesting Genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate Gene pathways. Conclusion We show how WGCNA can be combined with Genetic marker data to identify disease-related pathways and the causal drivers within them. The systems Genetics approach described here can easily be used to Generate testable Genetic hypotheses in other complex disease studies.
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a General framework for weighted Gene co expression Network analysis
Statistical Applications in Genetics and Molecular Biology, 2005Co-Authors: Bin Zhang, Steve HorvathAbstract:Gene Co-Expression Networks are increasingly used to explore the system-level functionality of Genes. The Network construction is conceptually straightforward: nodes represent Genes and nodes are connected if the corresponding Genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such Networks. An important question is whether it is biologically meaningful to encode Gene Co-Expression using binary information (connected=1, unconnected=0). We describe a General framework for ;soft' thresholding that assigns a connection weight to each Gene pair. This leads us to define the notion of a weighted Gene Co-Expression Network. For soft thresholding we propose several adjacency functions that convert the Co-Expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We Generalize the following important Network concepts to the case of weighted Networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a Gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define Gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we Generalize the clustering coefficient to weighted Networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.
Kennedy W. Mwangi - One of the best experts on this subject based on the ideXlab platform.
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Gene Co-Expression Network analysis of Trypanosoma brucei in tsetse fly vector.
Parasites & vectors, 2021Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:BACKGROUND Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. METHODS In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using the weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite's development in tsetse fly. Furthermore, potential 3' untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool. RESULTS A fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3' untranslated regions of Genes within the same module were predicted. CONCLUSIONS The constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite's development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.
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Gene Co-Expression Network analysis of Trypanosoma brucei in tsetse fly vector.
Parasites & vectors, 2021Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using the weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite's development in tsetse fly. Furthermore, potential 3' untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool. A fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3' untranslated regions of Genes within the same module were predicted. The constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite's development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.
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Gene Co-Expression Network Analysis of Trypanosoma brucei in Tsetse Fly Vector
2020Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:Abstract BackgroundTrypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging due to the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. MethodsIn this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite’s development in tsetse fly. Further, potential 3’ untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using Finding Informative Regulatory Elements (FIRE) tool.ResultsA fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), Arginine kinase 1 (AK1), brucei alanine rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3’ untranslated regions of Genes within the same module were predicted. ConclusionsThe constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.
Kun Huang - One of the best experts on this subject based on the ideXlab platform.
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Condition-specific Gene Co-Expression Network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients
BMC, 2018Co-Authors: Shunian Xiang, Kun Huang, Zhi Huang, Tianfu Wang, Zhi Han, Jie ZhangAbstract:Abstract Background Gene Co-Expression Network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel Gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer’s disease (AD) patients and looked for their specific functions. Methods In this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified Gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules. Results We obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the Network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known Gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver Genes for AD etiology and pathology. Conclusions Through our state-of-the-art Network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets
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Predicting glioblastoma prognosis Networks using weighted Gene Co-Expression Network analysis on TCGA data
BMC Bioinformatics, 2012Co-Authors: Yang Xiang, Cun-quan Zhang, Kun HuangAbstract:Background Using Gene Co-Expression analysis, researchers were able to predict clusters of Genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted Gene Co-Expression Network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of Gene Co-Expression Networks which are related to GBM prognosis. Methods We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-Network in WGCN. Each sub-Network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of Genes are carried out to determine the thresholds for log-rank test p-values for Network selection. Sub-Networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using Gene ontology enrichment analysis. Results Using the eQCM algorithm, we identified 8,124 sub-Networks in the WGCN, out of which 170 sub-Networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters. Conclusions We identified 16 Gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epiGenetic events in GBM development and prognosis.
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Predicting glioblastoma prognosis Networks using weighted Gene Co-Expression Network analysis on TCGA data.
BMC bioinformatics, 2012Co-Authors: Yang Xiang, Cun-quan Zhang, Kun HuangAbstract:Using Gene Co-Expression analysis, researchers were able to predict clusters of Genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted Gene Co-Expression Network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of Gene Co-Expression Networks which are related to GBM prognosis. We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-Network in WGCN. Each sub-Network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of Genes are carried out to determine the thresholds for log-rank test p-values for Network selection. Sub-Networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using Gene ontology enrichment analysis. Using the eQCM algorithm, we identified 8,124 sub-Networks in the WGCN, out of which 170 sub-Networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters. We identified 16 Gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epiGenetic events in GBM development and prognosis.
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Using Gene Co-Expression Network analysis to predict biomarkers for chronic lymphocytic leukemia.
BMC bioinformatics, 2010Co-Authors: Jie Zhang, Yang Xiang, Liya Ding, Kristin Keen-circle, Tara B Borlawsky, Hatice Gulcin Ozer, Ruoming Jin, Philip Payne, Kun HuangAbstract:Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heteroGeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. In this paper, we investigate the use of Gene Co-Expression Network analysis to identify potential biomarkers for CLL. Specifically we focused on the Co-Expression Network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent Network mining algorithm CODENSE to identify highly connected Gene Co-Expression Networks spanning the entire genome, then evaluated the Genes in the Co-Expression Network in which ZAP70 is involved. We then applied a set of feature selection methods to further select Genes which are capable of predicting IgVH mutation status from the ZAP70 Co-Expression Network. We have identified a set of Genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.
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Using Gene Co-Expression Network analysis to predict biomarkers for chronic lymphocytic leukemia
BMC Bioinformatics, 2010Co-Authors: Jie Zhang, Yang Xiang, Liya Ding, Kristin Keen-circle, Tara B Borlawsky, Hatice Gulcin Ozer, Ruoming Jin, Philip Payne, Kun HuangAbstract:Background Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heteroGeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgV_H) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgV_H status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgV_H mutational status which can accurately predict the survival outcome are yet to be discovered. Results In this paper, we investigate the use of Gene Co-Expression Network analysis to identify potential biomarkers for CLL. Specifically we focused on the Co-Expression Network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent Network mining algorithm CODENSE to identify highly connected Gene Co-Expression Networks spanning the entire genome, then evaluated the Genes in the Co-Expression Network in which ZAP70 is involved. We then applied a set of feature selection methods to further select Genes which are capable of predicting IgV_H mutation status from the ZAP70 Co-Expression Network. Conclusions We have identified a set of Genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgV_H mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.
Rosaline W. Macharia - One of the best experts on this subject based on the ideXlab platform.
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Gene Co-Expression Network analysis of Trypanosoma brucei in tsetse fly vector.
Parasites & vectors, 2021Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:BACKGROUND Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. METHODS In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using the weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite's development in tsetse fly. Furthermore, potential 3' untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool. RESULTS A fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3' untranslated regions of Genes within the same module were predicted. CONCLUSIONS The constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite's development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.
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Gene Co-Expression Network analysis of Trypanosoma brucei in tsetse fly vector.
Parasites & vectors, 2021Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using the weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite's development in tsetse fly. Furthermore, potential 3' untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool. A fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3' untranslated regions of Genes within the same module were predicted. The constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite's development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.
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Gene Co-Expression Network Analysis of Trypanosoma brucei in Tsetse Fly Vector
2020Co-Authors: Kennedy W. Mwangi, Rosaline W. Macharia, Joel L. BargulAbstract:Abstract BackgroundTrypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display diGenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging due to the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. MethodsIn this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei Gene Co-Expression Network using weighted Gene Co-Expression analysis (WGCNA) method. The study identified significant enriched modules for Genes that play key roles during the parasite’s development in tsetse fly. Further, potential 3’ untranslated region (UTR) regulatory elements for Genes that clustered in the same module were identified using Finding Informative Regulatory Elements (FIRE) tool.ResultsA fraction of Gene modules (12 out of 27 modules) in the constructed Network were found to be enriched in functional roles associated with cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub Genes encoding proteins such as RNA-binding protein 6 (RBP6), Arginine kinase 1 (AK1), brucei alanine rich protein (BARP), among others, were identified for the 12 significantly enriched Gene modules. In addition, the potential regulatory elements located in the 3’ untranslated regions of Genes within the same module were predicted. ConclusionsThe constructed Gene Co-Expression Network provides a useful resource for Network-based data mining to identify candidate Genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.