scholarly journals An Integrative Systems Biology and Experimental Approach Identifies Convergence of Epithelial Plasticity, Metabolism, and Autophagy to Promote Chemoresistance

2019 ◽  
Vol 8 (2) ◽  
pp. 205 ◽  
Author(s):  
Shengnan Xu ◽  
Kathryn Ware ◽  
Yuantong Ding ◽  
So Kim ◽  
Maya Sheth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Transforming Growth Factor ◽  
Epithelial Mesenchymal Transition ◽  
Phylogenetic Analyses ◽  
Host Immune System ◽  
Expression Data ◽  
Mesenchymal Transition

The evolution of therapeutic resistance is a major cause of death for cancer patients. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure of the host immune system. These selective forces often lead to evolutionary convergence on pathways or hallmarks that drive progression. Thus, a deeper understanding of the evolutionary convergences that occur could reveal vulnerabilities to treat therapy-resistant cancer. To this end, we combined phylogenetic clustering, systems biology analyses, and molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during transforming growth factor-β (TGF-β)-mediated epithelial–mesenchymal transition in lung cancer. Phylogenetic analyses of gene expression data from TGF-β-treated cells revealed convergence of cells toward amine metabolic pathways and autophagy during TGF-β treatment. Knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. In addition, high ATG16L expression was found to be a poor prognostic marker in multiple cancer types. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities for cancer.

scholarly journals An integrative systems biology and experimental approach identifies convergence of epithelial plasticity, metabolism, and autophagy to promote chemoresistance

2018 ◽  
Author(s):  
Shengnan Xu ◽  
Kathryn E. Ware ◽  
Yuantong Ding ◽  
So Young Kim ◽  
Maya Sheth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Epithelial Mesenchymal Transition ◽  
Host Immune System ◽  
Mechanism Analysis ◽  
Expression Data ◽  
Mesenchymal Transition ◽  
Evolutionary Convergence

AbstractThe evolution of therapeutic resistance is a major cause of death for patients with solid tumors. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure induced by the host immune system. These ecological and selective forces often lead to evolutionary convergence on one or more pathways or hallmarks that drive progression. These hallmarks are, in turn, intimately linked to each other through gene expression networks. Thus, a deeper understanding of the evolutionary convergences that occur at the gene expression level could reveal vulnerabilities that could be targeted to treat therapy-resistant cancer. To this end, we used a combination of phylogenetic clustering, systems biology analyses, and wet-bench molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during TGF-β-mediated epithelial-mesenchymal transition in a lung cancer model system. Phylogenetics analyses of gene expression data from TGF-β treated cells revealed evolutionary convergence of cells toward amine-metabolic pathways and autophagy during TGF-β treatment. Using high-throughput drug screens, we found that knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. Analysis of publicly-available clinical data sets validated the adverse prognostic importance of ATG16L expression in multiple cancer types including kidney, lung, and colon cancer patients. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities.

scholarly journals Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

2020 ◽  
Author(s):  
Xanthoula Atsalaki ◽  
Lefteris Koumakis ◽  
George Potamias ◽  
Manolis Tsiknakis
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Integrative Approach ◽  
Expression Data ◽  
Gene Regulatory

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

scholarly journals Testing the Gene Expression Classification of the EMT Spectrum

2018 ◽  
Author(s):  
Dongya Jia ◽  
Jason T. George ◽  
Satyendra C. Tripathi ◽  
Deepali L. Kundnani ◽  
Mingyang Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Cancer Metastasis ◽  
Epithelial Mesenchymal Transition ◽  
Expression Profiles ◽  
Expression Patterns ◽  
M Cells ◽  
Expression Data ◽  
Mesenchymal Transition

AbstractThe epithelial-mesenchymal transition (EMT) plays a central role in cancer metastasis and drug resistance – two persistent clinical challenges. Epithelial cells can undergo a partial or full EMT, attaining either a hybrid epithelial/mesenchymal (E/M) or mesenchymal phenotype, respectively. Recent studies have emphasized that hybrid E/M cells may be more aggressive than their mesenchymal counterparts. However, mechanisms driving hybrid E/M phenotypes remain largely elusive. Here, to better characterize the hybrid E/M phenotype(s) and tumor aggressiveness, we integrate two computational methods – (a) RACIPE – to identify the robust gene expression patterns emerging from the dynamics of a given gene regulatory network, and (b) EMT scoring metric - to calculate the probability that a given gene expression profile displays a hybrid E/M phenotype. We apply the EMT scoring metric to RACIPE-generated gene expression data generated from a core EMT regulatory network and classify the gene expression profiles into relevant categories (epithelial, hybrid E/M, mesenchymal). This categorization is broadly consistent with hierarchical clustering readouts of RACIPE-generated gene expression data. We show that the EMT scoring metric can be used to distinguish between samples composed of exclusively hybrid E/M cells and those containing mixtures of epithelial and mesenchymal subpopulations using the RACIPE-generated gene expression data.

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

2015 ◽  
Vol 11 (11) ◽  
pp. 3137-3148
Author(s):  
Nazanin Hosseinkhan ◽  
Peyman Zarrineh ◽  
Hassan Rokni-Zadeh ◽  
Mohammad Reza Ashouri ◽  
Ali Masoudi-Nejad
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
P Gene ◽  
Stress Related Genes ◽  
High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

scholarly journals FERMT3 mediates cigarette smoke-induced epithelial–mesenchymal transition through Wnt/β-catenin signaling

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiaoshan Su ◽  
Junjie Chen ◽  
Xiaoping Lin ◽  
Xiaoyang Chen ◽  
Zhixing Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cell Cycle ◽  
Cell Migration ◽  
Cigarette Smoke ◽  
Epithelial Mesenchymal Transition ◽  
Control Group ◽  
Data Set ◽  
Mesenchymal Transition

Abstract Background Cigarette smoking is a major risk factor for chronic obstructive pulmonary disease (COPD) and lung cancer. Epithelial–mesenchymal transition (EMT) is an essential pathophysiological process in COPD and plays an important role in airway remodeling, fibrosis, and malignant transformation of COPD. Previous studies have indicated FERMT3 is downregulated and plays a tumor-suppressive role in lung cancer. However, the role of FERMT3 in COPD, including EMT, has not yet been investigated. Methods The present study aimed to explore the potential role of FERMT3 in COPD and its underlying molecular mechanisms. Three GEO datasets were utilized to analyse FERMT3 gene expression profiles in COPD. We then established EMT animal models and cell models through cigarette smoke (CS) or cigarette smoke extract (CSE) exposure to detect the expression of FERMT3 and EMT markers. RT-PCR, western blot, immunohistochemical, cell migration, and cell cycle were employed to investigate the potential regulatory effect of FERMT3 in CSE-induced EMT. Results Based on Gene Expression Omnibus (GEO) data set analysis, FERMT3 expression in bronchoalveolar lavage fluid was lower in COPD smokers than in non-smokers or smokers. Moreover, FERMT3 expression was significantly down-regulated in lung tissues of COPD GOLD 4 patients compared with the control group. Cigarette smoke exposure reduced the FERMT3 expression and induces EMT both in vivo and in vitro. The results showed that overexpression of FERMT3 could inhibit EMT induced by CSE in A549 cells. Furthermore, the CSE-induced cell migration and cell cycle progression were reversed by FERMT3 overexpression. Mechanistically, our study showed that overexpression of FERMT3 inhibited CSE-induced EMT through the Wnt/β-catenin signaling. Conclusions In summary, these data suggest FERMT3 regulates cigarette smoke-induced epithelial–mesenchymal transition through Wnt/β-catenin signaling. These findings indicated that FERMT3 was correlated with the development of COPD and may serve as a potential target for both COPD and lung cancer.

scholarly journals Hyperglycemia promotes Snail-induced epithelial–mesenchymal transition of gastric cancer via activating ENO1 expression

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Xin Xu ◽  
Bang Chen ◽  
Shaopu Zhu ◽  
Jiawei Zhang ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Proliferation ◽  
Signaling Pathway ◽  
Transforming Growth Factor ◽  
Epithelial Mesenchymal Transition ◽  
The Cancer Genome Atlas ◽  
Migration And Invasion ◽  
Gastrointestinal Malignancies ◽  
Mesenchymal Transition

Abstract Background Gastric cancer (GC) is one of the most common gastrointestinal malignancies worldwide. Emerging evidence indicates that hyperglycemia promotes tumor progression, especially the processes of migration, invasion and epithelial–mesenchymal transition (EMT). However, the underlying mechanisms of GC remain unclear. Method Data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to detect the expression of glycolysis-related enzymes and EMT-related transcription factors. Small interfering RNA (siRNA) transfection was performed to decrease ENO1 expression. Immunohistochemistry (IHC), Western blot and qRT-PCR analyses were used to measure gene expression at the protein or mRNA level. CCK-8, wound-healing and Transwell assays were used to assess cell proliferation, migration and invasion. Results Among the glycolysis-related genes, ENO1 was the most significantly upregulated in GC, and its overexpression was correlated with poor prognosis. Hyperglycemia enhanced GC cell proliferation, migration and invasion. ENO1 expression was also upregulated with increasing glucose concentrations. Moreover, decreased ENO1 expression partially reversed the effect of high glucose on the GC malignant phenotype. Snail-induced EMT was promoted by hyperglycemia, and suppressed by ENO1 silencing. Moreover, ENO1 knockdown inhibited the activation of transforming growth factor β (TGF-β) signaling pathway in GC. Conclusions Our results indicated that hyperglycemia induced ENO1 expression to trigger Snail-induced EMT via the TGF-β/Smad signaling pathway in GC.

scholarly journals PDLIM5 inhibits STUB1-mediated degradation of SMAD3 and promotes the migration and invasion of lung cancer cells

2020 ◽  
Vol 295 (40) ◽  
pp. 13798-13811 ◽  
Author(s):  
Yueli Shi ◽  
Xinyu Wang ◽  
Zhiyong Xu ◽  
Ying He ◽  
Chunyi Guo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Transforming Growth Factor ◽  
Epithelial Mesenchymal Transition ◽  
Effector Proteins ◽  
Scaffolding Protein ◽  
Migration And Invasion ◽  
Tgfβ Signaling ◽  
Mesenchymal Transition ◽  
Protein Levels ◽  
Smad3 Protein

Transforming growth factor β (TGFβ) signaling plays an important role in regulating tumor malignancy, including in non–small cell lung cancer (NSCLC). The major biological responses of TGFβ signaling are determined by the effector proteins SMAD2 and SMAD3. However, the regulators of TGFβ–SMAD signaling are not completely revealed yet. Here, we showed that the scaffolding protein PDLIM5 (PDZ and LIM domain protein 5, ENH) critically promotes TGFβ signaling by maintaining SMAD3 stability in NSCLC. First, PDLIM5 was highly expressed in NSCLC compared with that in adjacent normal tissues, and high PDLIM5 expression was associated with poor outcome. Knockdown of PDLIM5 in NSCLC cells decreased migration and invasion in vitro and lung metastasis in vivo. In addition, TGFβ signaling and TGFβ-induced epithelial–mesenchymal transition was repressed by PDLIM5 knockdown. Mechanistically, PDLIM5 knockdown resulted in a reduction of SMAD3 protein levels. Overexpression of SMAD3 reversed the TGFβ-signaling-repressing and anti-migration effects induced by PDLIM5 knockdown. Notably, PDLIM5 interacted with SMAD3 but not SMAD2 and competitively suppressed the interaction between SMAD3 and its E3 ubiquitin ligase STUB1. Therefore, PDLIM5 protected SMAD3 from STUB1-mediated proteasome degradation. STUB1 knockdown restored SMAD3 protein levels, cell migration, and invasion in PDLIM5-knockdown cells. Collectively, our findings indicate that PDLIM5 is a novel regulator of basal SMAD3 stability, with implications for controlling TGFβ signaling and NSCLC progression.

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