The fibroblast growth factor-2 arrests Mycobacterium avium sp. paratuberculosis growth and immunomodulates host response in macrophages

Wang, Jianjun; Wang, Zeyou; Yao, Yongliang; Wu, Jianhong; Tang, Xin; Gu, Tao; Li, Guangxin

doi:10.1016/j.tube.2015.04.006

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…The FoxP3+Treg cell activation which will assist to set up a new target for the involvement of TB immunotherapy molecules as part of the immune-escape mechanism to provide a theoretical basis is inhibited by M. tuberculosis (Scott-Browne et al, 2007). On comparing between latent infection and TB disease, few additional pathways are involved, like regulation of actin cytoskeleton during MTB infection; to maintain the stability of the cytoskeleton, macrophages cells themselves are also trying to regulate cytoskeletal-associated proteins (Wang et al, 2015b). Thyroid hormones (hormones, T4 and T3) are produced by thyroid gland, which is essential for the regulation of metabolic processes throughout the body.…”

Section: Discussionmentioning

confidence: 99%

Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis

et al. 2019

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Tuberculosis (TB) is one of deadly transmissible disease that causes death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease, indicating that host genetic factors may play key role in determining susceptibility to TB disease. In this way, the analysis of gene expression profiling of TB infected individuals can give us a snapshot of actively expressed genes and transcripts under various conditions. In the present study, we have analyzed microarray data set and compared the gene expression profiles of patients with different datasets of healthy control, latent infection, and active TB. We observed the transition of genes from normal condition to different stages of the TB and identified and annotated those genes/pathways/processes that have important roles in TB disease during its cyclic interventions in the human body. We identified 488 genes that were differentially expressed at various stages of TB and allocated to pathways and gene set enrichment analysis. These pathways as well as GSEA’s importance were evaluated according to the number of DEGs presents in both. In addition, we studied the gene regulatory networks that may help to further understand the molecular mechanism of immune response against the TB infection and provide us a new angle for future biomarker and therapeutic targets. In this study, we identified 26 leading hubs which are deeply rooted from top to bottom in the gene regulatory network and work as the backbone of the network. These leading hubs contains 31 key regulator genes, of which 14 genes were up-regulated and 17 genes were down-regulated. The proposed approach is based on gene-expression profiling, and network analysis approaches predict some unknown TB-associated genes, which can be considered (or can be tested) as reliable candidates for further (in vivo/in vitro) studies.

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Section: Discussionmentioning

confidence: 99%

Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis

et al. 2019

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“…The FoxP3+Treg cells activation which will assist to set up a new target for the involvement of TB immunotherapy molecules as part of the immune-escape mechanism to provide a theoretical basis is inhibited by M.tuberculosis [20]. In the case of latent infection to TB disease few additional pathways are involved like Regulation of Actin cytoskeleton during MTB infection, to maintain the stability of the cytoskeleton, macrophages cells themselves are also trying to regulate cytoskeletal associated proteins [21]. Thyroid hormones (hormones, T4 and T3) are made by thyroid gland which is essential for the regulation of metabolic processes throughout the body.…”

Section: Some Beneficial Events With Respect To Host Cell Reverted Bymentioning

confidence: 99%

Stage specific classification of DEGs via statistical profiling and network analysis reveals potential biomarker associated with various stages of TB

Alam

Imam

Ahmed

et al. 2018

Preprint

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Background: Tuberculosis (TB) is a deadly transmissible disease that can infect almost any body-part of the host but is mostly infect the lungs. It is one of the top 10 causes of death worldwide. In the 30 high TB burden countries, 87% of new TB cases occurred in 2016. Seven countries: India, Indonesia, China, Philippines, Pakistan, Nigeria, and South Africa accounted for 64% of the new TB cases. To stop the infection and progression of the disease, early detection of TB is important. In our study, we used microarray data set and compared the gene expression profiles obtained from blood samples of patients with different datasets of Healthy control, Latent infection, Active TB and performed network-based analysis of DEGs to identify potential biomarker. Objectives: We want to observe the transition of genes from normal condition to different stages of the TB and identify, annotate those genes/pathways/processes that play key role in the progression of TB disease during its cyclic interventions in human body. Results: We identified 319 genes that are differentially expressed in various stages of TB (Normal to LTTB, Normal to Active TB and LTTB to active TB) and allocated to pathways from multiple databases which comprised of curated class of associated genes. These pathway's importance was then evaluated according to the no. of DEGs present in the pathway and these genes show the broad spectrum of processes that take part in every state. In addition, we studied the regulatory networks of these classified genes, network analysis does consider the interactions between genes (specific for TB) or proteins provide us new facts about TB disease, which in turn can be used for potential biomarkers identification. We identified total 29 biomarkers from various comparison groups of TB stages in which 14 genes are over expressed as host responses against pathogen, but 15 genes are down regulated that means these genes has allowed the process of host defense to cease and give time to pathogen for its progression. Conclusions:This study revealed that gene-expression profiles can be used to identify and classified the genes on stage specific pattern among normal, LTTB and active TB and network modules associated with various stages of TB were elucidated, which in turn provided a basis for the identification of potential pathways and key regulatory genes that may be involved in progression of TB disease.association' presumption, that means the physically and functionally linked genes are possibly participated in the same biological-pathways having comparable effects on the phenotypes[3]. The concept of network theory is an imperative method to know the topological properties and the complex-systems dynamics correspond to their functional modules. The complex networks may be classified into four types of networks: (a) scale-free network, (b) small-world network, (c) random network and (d) hierarchical network. For the biologist,hierarchical network has special interest because it incorporates the mien of modules and distributed h...

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In situ expression of M2 macrophage subpopulation in leprosy skin lesions

Sousa

Aarão

et al. 2016

Acta Tropica

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The fibroblast growth factor-2 arrests Mycobacterium avium sp. paratuberculosis growth and immunomodulates host response in macrophages

Cited by 5 publications

References 44 publications

Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis

Identification and Classification of Differentially Expressed Genes and Network Meta-Analysis Reveals Potential Molecular Signatures Associated With Tuberculosis

Stage specific classification of DEGs via statistical profiling and network analysis reveals potential biomarker associated with various stages of TB

In situ expression of M2 macrophage subpopulation in leprosy skin lesions

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