Modulation of FGF receptor signaling as an intervention and potential therapy for myelin breakdown in Alzheimer’s disease

Li, Jia-Su; Yao, Zhong-Xiang

doi:10.1016/j.mehy.2012.12.008

Cited by 15 publications

(13 citation statements)

References 47 publications

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“…Not surprisingly, aging (GO: 0016280; overlap 140 genes; q-value = 2.28E−84) is the top one enriched gene group. In addition, 11 pathways/groups were related to the neuronal system (619 unique genes) (Gong and Lippa, 2010, Marcello et al, 2012), 10 to cell growth and proliferation (441 unique genes) (Hohman et al, 2015, Li and Yao, 2013), 8 to cell apoptosis (452 unique genes) (Behl, 2000), 5 to protein phosphorylation (246 unique genes) (Shapiro et al, 1991), 2 to protein kinase (209 unique genes) (Martin et al, 2013), 3 to brain function/development (99 unique genes) (Llorente-Vizcaíno and Cejudo-Bolívar, 2001), and 2 to immune system (268 unique genes) (Heneka et al, 2001). Due to lack of space, we only present the top 10 pathways enriched in Table 1 (p-value ≤ 2.6e−55, including 755 out of 1699 genes).…”

Section: Resultsmentioning

confidence: 99%

Integrative computational evaluation of genetic markers for Alzheimer’s disease

Xiong

Manor

et al. 2018

Saudi Journal of Biological Sciences

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Recent studies have reported hundreds of genes linked to Alzheimer's Disease (AD). However, many of these candidate genes may be not identified in different studies when analyses were replicated. Moreover, results could be controversial. Here, we proposed a computational workflow to curate and evaluate AD related genes. The method integrates large scale literature knowledge data and gene expression data that were acquired from postmortem human brain regions (AD case/control: 31/32 and 22/8). Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analysis were conducted to study the pathogenic profile of the candidate genes, with 4 metrics proposed and validated for each gene. By using our approach, a scalable AD genetic database was developed, including AD related genes, pathways, diseases and info of supporting references. The AD case/control classification supported the effectiveness of the 4 proposed metrics, which successfully identified 21 well-studied AD genes (i.g. TGFB1, CTNNB1, APP, IL1B, PSEN1, PTGS2, IL6, VEGFA, SOD1, AKT1, CDK5, TNF, GSK3B, TP53, CCL2, BDNF, NGF, IGF1, SIRT1, AGER and TLR) and highlighted one recently reported AD gene (i.g. ITGB1). The computational biology approach and the AD database developed in this study provide a valuable resource which may facilitate the understanding of the AD genetic profile.

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Section: Resultsmentioning

confidence: 99%

Integrative computational evaluation of genetic markers for Alzheimer’s disease

Xiong

Manor

et al. 2018

Saudi Journal of Biological Sciences

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“…Fibroblast growth factors (FGFs), in a family of 23 different members, along with four tyrosine kinase FGF receptors, have been implicated in diverse cellular processes including cell apoptosis, adhesion and migration [25,26,27]. Recent studies showed that AZD4547 is a small-molecule FGFRs tyrosine kinase inhibitor [24].…”

Section: Resultsmentioning

confidence: 99%

Reductions in Myeloid-Derived Suppressor Cells and Lung Metastases using AZD4547 Treatment of a Metastatic Murine Breast Tumor Model

Liu

Han

et al. 2014

Cell Physiol Biochem

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Background: AZD4547, a small-molecule inhibitor targeting the tyrosine kinase of Fibroblast Growth Factor Receptors (FGFRs), is currently under phase II clinical study for human subjects having breast cancer, while the underlying mechanism remains elusive. The aim of this study is to explore the potential mechanism by which AZD4547 inhibits breast tumor lung metastases at the level of the tumor microenvironment. Methods: First, through in vitro experiments, we investigated the efficacy of the FGFRs inhibitor AZD4547 on 4T1 tumor cells for their proliferation, apoptosis, migration, and invasion. Second, by in vivo animal experiments, we evaluated the effects of AZD4547 on tumor growth and lung metastases in 4T1 tumor-bearing mice. Finally, we examined the impact of AZD4547 on the infiltration of myeloid-derived suppressor cells (MDSCs) in lung, spleens, peripheral blood and tumor. Results: Through this study we found that AZD4547 could efficiently suppress tumor 4T1 cells through restraining their proliferation, blocking migration and invasion, and inducing apoptosis in vitro. In animal model we also demonstrated that AZD4547 was able to inhibit tumor growth and lung metastases, consistent with the decreased MDSCs accumulation in the tumor and lung tissues, respectively. Moreover, the reduced number of MDSCs in peripheral blood and spleens were also observed in the AZD4547-treated mice. Importantly, through the AZD4547 treatment, the CD4⁺ and CD8⁺ T-cells were significantly increased in tumor and spleens. Conclusion: Our studies showed that AZD4547 can inhibit breast cancer cell proliferation, induce its apoptosis and block migration and invasion in vitro and suppress tumor growth and lung metastases by modulating the tumor immunologic microenvironment in vivo.

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“…In the CUX1 PCST subtree, EGFR1 and FGF signaling pathway were two most significant enriched pathways. The modulation of EGFR [23] and FGF [36] have been reported to be potential therapy for the AD. The most significant pathways in KHDRBS1 PCST subtree were related to mRNA processing and splicing.…”

Section: Resultsmentioning

confidence: 99%

VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in genome-wide association studies: Applied to Alzheimer's disease

Chen

Gao

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding variations. Increasing evidences suggest that non-coding variants play critical roles in the etiology of complex diseases. However, few computational tools are available to study the effect of rare non-coding variants on phenotypes. Here we have developed a multiscale modeling variant-to-function-to-network framework VariFunNet to address these challenges. VariFunNet first predict the functional variations of molecular interactions, which result from the non-coding variants. Then we incorporate the genes associated with the functional variation into a tissue-specific gene network, and identify subnetworks that transmit the functional variation to molecular phenotypes. Finally, we quantify the functional implication of the subnetwork, and prioritize the association of the non-coding variants with the phenotype. We have applied VariFunNet to investigating the causal effect of rare non-coding variants on Alzheimer’s disease (AD). Among top 21 ranked causal non-coding variants, 16 of them are directly supported by existing evidences. The remaining 5 novel variants dysregulate multiple downstream biological processes, all of which are associated with the pathology of AD. Furthermore, we propose potential new drug targets that may modulate diverse pathways responsible for AD. These findings may shed new light on discovering new biomarkers and therapies for the prevention, diagnosis, and treatment of AD. Our results suggest that multiscale modeling is a potentially powerful approach to studying causal genotype-phenotype associations.

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Modulation of FGF receptor signaling as an intervention and potential therapy for myelin breakdown in Alzheimer’s disease

Cited by 15 publications

References 47 publications

Integrative computational evaluation of genetic markers for Alzheimer’s disease

Integrative computational evaluation of genetic markers for Alzheimer’s disease

Reductions in Myeloid-Derived Suppressor Cells and Lung Metastases using AZD4547 Treatment of a Metastatic Murine Breast Tumor Model

VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in genome-wide association studies: Applied to Alzheimer's disease

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