The combination of
            <i>TPL2</i>
            knockdown and TNFα causes synthetic lethality via caspase-8 activation in human carcinoma cell lines

Serebrennikova, Oksana B.; Paraskevopoulou, Maria D.; Aguado-Fraile, Elia; Taraslia, Vasiliki; Ren, Wenying; Thapa, Geeta; Roper, Jatin; Du, Keyong; Croce, Carlo M.; Tsichlis, Philip N.

doi:10.1073/pnas.1901465116

Cited by 7 publications

(7 citation statements)

References 39 publications

(48 reference statements)

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“…In a recent paper 22 Although this result contrasted our expectations, it was confirmed by results obtained by the previously mentioned experimental study 22 , which suggested that the change in the expression of (table S3).…”

Section: Simulation #4: Testing Tnfα/sitpl2-dependent Synthetic Lethasupporting

confidence: 85%

PHENSIM: Phenotype Simulator

Alaimo

Rapicavoli

Marceca

et al. 2020

Preprint

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Motivation:Despite the unprecedented increase of our understanding of cell biology, connecting experimental data to the physiopathological status of cells and tissues under precise circumstances is still a challenge. This often results in difficulties during the design of validation experiments, which are usually labor-intensive, expensive to perform and hard to interpret.Results: Here we propose PHENSIM, a systems biology approach, which can simulate the effects of activation/inhibition of one or multiple biomolecules on cell phenotypes by exploiting signaling pathways. Possible applications of our tool include prediction of the outcome of drug administration, knockdown experiments, gene transduction and exposure to exosomal cargo. Importantly, this method allows the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. The basic assumption is that phenotypes can be described through changes in pathway activity. Results from our study show discrete prediction accuracy, highlighting the capabilities of this methodology.Availability and Implementation: PHENSIM has been developed in Java and it is available, along with all data and source codes, at https://github.com/alaimos/phensim. A web-based user interface, developed in PHP is accessible at https://phensim.atlas.dmi.unict.it/. IntroductionCells of living organisms are continuously exposed to signals originating in both the extracellular and the intracellular microenvironment. These signals regulate multiple cellular functions, including gene expression, chromatin remodeling, DNA replication and repair, protein synthesis and cellular metabolism. The proper response to signals depends on expression, activation or inhibition of sets of interrelated genes/proteins, acting in a well-defined order within the context of well-defined vectordriven biological processes, aiming to reach specific endpoints. Such sub-cellular processes are referred to as biological pathways. 1

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Section: Simulation #4: Testing Tnfα/sitpl2-dependent Synthetic Lethasupporting

confidence: 85%

PHENSIM: Phenotype Simulator

Alaimo

Rapicavoli

Marceca

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Although these results do not entirely reflect our expectations as there are discrepancies between the in vitro experiment and our predictions, it was confirmed by results obtained in Serebrennikova et al [24] that the change in the expression of such molecules was due to the activation of feedback mechanisms. Interestingly, this result was obtained only for four out of six cancer cell lines, of which three were sensitive (HeLa, HCT116, and U2-OS), and one was resistant (CaCo-2).…”

Section: Plos Computational Biologycontrasting

confidence: 69%

“…In a recent paper, Serebrennikova et al [ 24 ] showed that TPL2 (MAP3K8) is one of the TNFα-induced cell death checkpoints. Its knockdown resulted in the downregulation of miR-21 and the upregulation of its target CASP8 (caspase-8).…”

Section: Resultsmentioning

confidence: 99%

PHENSIM: Phenotype Simulator

et al. 2021

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Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool’s applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach’s reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

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“…4D–E ). Among the 13 genes upregulated by HOXA11-AS, tumor progression locus 2 (Tpl2, also known as COT or MAP3K8) is a hub gene of the TNF-α pathway [ 29 , 30 ].…”

Section: Resultsmentioning

confidence: 99%

LncRNA HOXA11-AS promotes glioma malignant phenotypes and reduces its sensitivity to ROS via Tpl2-MEK1/2-ERK1/2 pathway

et al. 2022

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Our previous studies showed that dysregulation of the long noncoding RNA (lncRNA) HOXA11-AS plays an important role in the development of glioma. However, the molecular mechanism of HOXA11-AS in glioma remains largely unknown. In this study, we explore the molecular mechanisms underlying abnormal expression and biological function of HOXA11-AS for identifying novel therapeutic targets in glioma. The expression of HOXA11-AS, and the relationship between HOXA11-AS and the prognosis of glioma patients were analyzed using databases and glioma samples. Transcriptomics, proteomics, RIP, ChIRP, luciferase, and ChIP assays were used to explore its upstream and downstream targets in glioma. The role of HOXA11-AS in regulating the sensitivity of glioma cells to reactive oxygen species (ROS) was also investigated in vitro and in vivo. We found that HOXA11-AS was significantly upregulated in glioma, and was correlated with the poor prognosis of glioma patients. Ectopic expression of HOXA11-AS promoted the proliferation, migration, and invasion of glioma cells in vitro and in vivo. Mechanistically, HOXA11-AS acted as a molecular sponge for let-7b-5p in the cytoplasm, antagonizing its ability to repress the expression of CTHRC1, which activates the β-catenin/c-Myc pathway. In addition, c-Myc was involved in HOXA11-AS dysregulation via binding to its promoter region to form a self-activating loop. HOXA11-AS, functioned as a scaffold in the nucleus, also recruited transcription factor c-Jun to the Tpl2 promoter, which activates the Tpl2-MEK1/2-ERK1/2 pathway to promote ROS resistance in glioma. Importantly, HOXA11-AS knockdown could sensitize glioma cells to ROS. Above, oncogenic HOXA11-AS upregulates CTHRC1 expression as a ceRNA by adsorbing let-7b-5p, which activates c-Myc to regulate itself transcription. HOXA11-AS knockdown promotes ROS sensitivity in glioma cells by regulating the Tpl2-MEK1/2-ERK1/2 axis, demonstrating that HOXA11-AS may be translated to increase ROS sensitivity therapeutically.

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The combination of TPL2 knockdown and TNFα causes synthetic lethality via caspase-8 activation in human carcinoma cell lines

Cited by 7 publications

References 39 publications

PHENSIM: Phenotype Simulator

PHENSIM: Phenotype Simulator

PHENSIM: Phenotype Simulator

LncRNA HOXA11-AS promotes glioma malignant phenotypes and reduces its sensitivity to ROS via Tpl2-MEK1/2-ERK1/2 pathway

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