Mammalian Target of Rapamycin (mTOR) Regulates Transforming Growth Factor-β1 (TGF-β1)-Induced Epithelial-Mesenchymal Transition via Decreased Pyruvate Kinase M2 (PKM2) Expression in Cervical Cancer Cells

Cheng, Kun; Hao, Min

doi:10.12659/msm.901542

Cited by 38 publications

(34 citation statements)

References 32 publications

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“…This was likely due to CNF1-induced cytoskeletal alterations that also underlined the augmented capacity of movement of HT-29 cells and conferred an EMT-like morphology from cobblestone, in control cells, to isolated CNF1-treated cells, leading to more rapid closure of the wound and to invasion. The highly conserved and ubiquitously expressed mTOR is regarded as an important regulator of EMT (Cheng & Hao, 2017). We herein demonstrate that CNF1 activates mTOR in HT-29 cells, leading to changes in key molecules related to mTOR signalling.…”

Section: Discussionmentioning

confidence: 62%

“…It enables epithelial cells to go through numerous biochemical changes that permit them to assume a mesenchymal phenotype. The highly conserved and ubiquitously expressed mTOR is regarded as an important regulator of EMT (Cheng & Hao, 2017). Although the possibility to link virulence factors and EMT is not completely new to the bacterial world, this is the first report on CNF1 as a cause of EMT.…”

Section: Discussionmentioning

confidence: 87%

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The Escherichia coli protein toxin cytotoxic necrotizing factor 1 induces epithelial mesenchymal transition

Fabbri

Travaglione

Rosadi

et al. 2019

Cellular Microbiology

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Some toxigenic bacteria produce protein toxins with carcinogenic signatures, which either directly damage DNA or stimulate signalling pathways related to cancer. So far, however, only a few of them have been proved to favour the induction or progression of cancer. In this work, we report that the Rho-activating Escherichia coli protein toxin, cytotoxic necrotising factor 1 (CNF1), induces epithelial to mesenchymal transition (EMT) in intestinal epithelial cells. EMT is a crucial step in malignant tumour conversion and invasiveness. In the case of CNF1, it occurs by up-regulation of the transcription factors ZEB1 and Snail1, delocalisation of E-cadherin and β-catenin, activation of the serine/threonine kinase mTOR, accelerated wound healing, and invasion. However, our results highlight that nontransformed epithelial cells entail the presence of inflammatory factors, in addition to CNF1, to acquire a mesenchymal-like behaviour. All this suggests that the surrounding microenvironment, as well as the cell type, dramatically influences the CNF1 ability to promote carcinogenic traits.

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Section: Discussionmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 87%

The Escherichia coli protein toxin cytotoxic necrotizing factor 1 induces epithelial mesenchymal transition

Fabbri

Travaglione

Rosadi

et al. 2019

Cellular Microbiology

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“…However, E-Cadherin's contribution to the vector 330 linking S6 with mTor, and the vector linking gamma-H2AX with PCNA, are less 331 expected. The former may reflect the effect of mTor on the Epithelial-mesenchymal 332 transition (EMT) [37], and the latter may reflect the involvement of S phase (as scored 333 by PCNA) and gamma-H2AX in DNA repair. These biological details will require 334 further analysis but the key point is that single cell microscopy (CyCIF) data resembles 335 FACs data with respect to sparsity, integer-ratio entries, and the appearance of sensible 336 connections between sets of proteins.…”

mentioning

confidence: 99%

Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

Wang

Lin

Sontag

et al. 2019

Preprint

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The goal of many single-cell studies on eukaryotic cells is to gain insight into the biochemical reactions that control cell fate and state. In this paper we introduce the concept of effective stoichiometric space (ESS) to guide the reconstruction of biochemical networks from multiplexed, fixed time-point, single-cell data. In contrast to methods based solely on statistical models of data, the ESS method leverages the power of the geometric theory of toric varieties to begin unraveling the structure of chemical reaction networks (CRN). This application of toric theory enables a data-driven mapping of covariance relationships in single cell measurements into stoichiometric information, one in which each cell subpopulation has its associated ESS interpreted in terms of CRN theory. In the development of ESS we reframe certain aspects of the theory of CRN to better match data analysis. As an application of our approach we process cytomery-and image-based single-cell datasets and identify differences in cells treated with kinase inhibitors. Our approach is directly applicable to data acquired using readily accessible experimental methods such as Fluorescence Activated Cell Sorting (FACS) and multiplex immunofluorescence. Author summaryWe introduce a new notion, which we call the effective stoichiometric space (ESS), that 1 elucidates network structure from the covariances of single-cell multiplexed data. The 2 ESS approach differs from methods that are based on purely statistical models of data: 3 it allows a completely new and data-driven translation of the theory of toric varieties in 4 geometry and specifically their role in chemical reaction networks (CRN). In the 5 process, we reframe certain aspects of the theory of CRN. As illustrations of our 6 approach, we find stoichiometry in different single-cell datasets, and pinpoint 7 dose-dependence of network perturbations in drug-treated cells. 8 Introduction 9Single-cell, multiplexed datasets have become prevalent [1,2], and include data on 10 transcript levels measured by sc-RNAseq [3], protein levels measured by flow 11 cytometry [4], or cell morphology and protein localization measured by multiplex 12 imaging [5-8]. An obvious advantage of such data is that it makes possible the detection 13 August 6, 2019 1/23 and quantification of differences among cells in a population, including those arising 14 from cyclic processes such as cell division and differentiation programs that are 15 asynchronous between cells [9, 10]. A more subtle advantage of single-cell data is that 16 they report on relationships among measured features, phosphorylation state of 17 receptors and nuclear localization of transcription factors for example. Because such 18 features are subject to natural stochastic fluctuation across a population of cells [11], 19 measuring the extent of correlation between otherwise independently fluctuating 20 features makes it possible to infer the topologies of biological networks [12, 13]. 21 A wide variety of tools have been developed for visualization of s...

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“…The procedures were performed according to the literature [6]. Multifluorescent PCR assays were performed to determine the genotypes of HR HPVs, including HPV16, 18,45,31,33,52,58, and 67, with a Real Quality RQ-HPV HR kit (Padua, Italy). The kit contained primers specific to HPV16, HPV18/45, and HPV31 and primers for HPV33/45/52/58/67.…”

Section: Dna Extraction and Hpv Detectionmentioning

confidence: 99%

Tissue-based metabolomics reveals potential biomarkers for cervical carcinoma and HPV infection

Abudula¹,

Rouzi²,

Xu³

et al. 2019

Bosn J of Basic Med Sci

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Aberrant metabolic regulation has been observed in human cancers, but the corresponding regulation in human papillomavirus (HPV) infection-associated cervical cancer is not well understood. Here, we explored potential biomarkers for the early prediction of cervical carcinoma based on the metabolic profile of uterine cervical tissue specimens that were positive for HPV16 infection. Fifty-two fresh cervical tissues were collected from women confirmed to have cervical squamous cell carcinoma (SCC; n = 21) or cervical intraepithelial neoplasia (CIN) stages II-III (n = 20). Eleven healthy women constituted the controls (negative controls [NCs]). Real-time polymerase chain reaction (PCR) was performed to detect HPV infection in the tissues. High-resolution magic angle spinning nuclear magnetic resonance was utilized for the analysis of the metabolic profile in the tissues. The expression of rate-limiting enzymes involved in key metabolic pathways was detected by reverse-transcription quantitative PCR. An independent immunohistochemical analysis was performed using 123 cases of paraffin-embedded cervical specimens. A profile of 17 small molecular metabolites that showed differential expression in HPV16-positive cervical SCC or CIN II-III compared with HPV-negative NC group was identified. According to the profile, the levels of α-and β-glucose decreased, those of lactate and low-density lipoproteins increased, and the expression of multiple amino acids was altered. Significantly increased transcript and protein levels of glycogen synthase kinase 3 beta (GSK3β) and glutamate decarboxylase 1 (GAD1) and decreased transcript and protein levels of pyruvate kinase muscle isozyme 2 (PKM2) and carnitine palmitoyltransferase 1A (CPT1A) were observed in the patient group (p < 0.05). HPV infection and cervical carcinogenesis drive metabolic modifications that might be associated with the aberrant regulation of enzymes related to metabolic pathways.

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Mammalian Target of Rapamycin (mTOR) Regulates Transforming Growth Factor-β1 (TGF-β1)-Induced Epithelial-Mesenchymal Transition via Decreased Pyruvate Kinase M2 (PKM2) Expression in Cervical Cancer Cells

Cited by 38 publications

References 32 publications

The Escherichia coli protein toxin cytotoxic necrotizing factor 1 induces epithelial mesenchymal transition

The Escherichia coli protein toxin cytotoxic necrotizing factor 1 induces epithelial mesenchymal transition

Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

Tissue-based metabolomics reveals potential biomarkers for cervical carcinoma and HPV infection

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