Dataset Information

Assessing methods to quantitatively validate TGF?-dependent autophagy.

ABSTRACT: Transforming growth factor beta (TGF?) promotes tumorigenesis by suppressing immune surveillance and inducing epithelial to mesenchymal transition (EMT). TGF? may augment tumorigenesis by activating autophagy, which protects cancer cells from chemotherapy and promotes invasive and anti-apoptotic properties. Here, we assess how TGF?1 modulates autophagy related (ATG) gene expression and ATG protein levels. We also assessed microtubule-associated protein light chain 3 (LC3) lipidation, LC3 puncta formation and autophagosome-lysosome co-localization in non-small cell lung cancer (NSCLC) cell lines. These experimental approaches were validated using pharmacological autophagy inhibitors (chloroquine and spautin-1) and an autophagy activator (MG132). We found that TGF?1, chloroquine and MG132 had little effect on ATG protein levels but increased LC3 lipidation, LC3 puncta formation and autophagosome-lysosome co-localization. Since similar outcomes were observed using chloroquine and MG132, we concluded that several techniques employed to assess TGF?-dependent autophagy may not differentiate between the activation of autophagy versus lysosomal inhibition. Thus, NSCLC cell lines stably expressing a GFP-LC3-RFP-LC3?G autophagic flux probe were used to assess TGF?-mediated autophagy. Using this approach, we observed that TGF?, MG132 and serum starvation increased autophagic flux, whereas chloroquine and spautin-1 decreased autophagic flux. Finally, we demonstrated that ATG5 and ATG7 are critical for TGF?-dependent autophagy in NSCLC cells. The application of this model will fuel future experiments to characterize TGF?-dependent autophagy, which is necessary to understand the molecular processes that link, TGF?, autophagy and tumorigenesis.

SUBMITTER: Trelford CB

PROVIDER: S-EPMC7710024 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing methods to quantitatively validate TGFβ-dependent autophagy.

Trelford Charles B CB Di Guglielmo Gianni M GM

Biology open 20201123 11

Transforming growth factor beta (TGFβ) promotes tumorigenesis by suppressing immune surveillance and inducing epithelial to mesenchymal transition (EMT). TGFβ may augment tumorigenesis by activating autophagy, which protects cancer cells from chemotherapy and promotes invasive and anti-apoptotic properties. Here, we assess how TGFβ1 modulates autophagy related (<i>ATG</i>) gene expression and ATG protein levels. We also assessed microtubule-associated protein light chain 3 (LC3) lipidation, LC3 ...[more]

PMID: 33168592

Similar Datasets

Project description:During development, the interconnected generation of various neural cell types within the cerebellar primordium is essential. Over embryonic (E) days E9-E13, Purkinje cells (PCs), and cerebellar nuclei (CN) neurons are among the created primordial neurons. The molecular and cellular mechanisms fundamental for the early cerebellar neurogenesis, migration/differentiation, and connectivity are not clear yet. Autophagy has a vital role in controlling cellular phenotypes, such as epithelial-to-mesenchymal transition (EMT) and endothelial to mesenchymal transition (EndMT). Transforming growth factor-beta 1 (TGF-β1) is the main player in pre-and postnatal development and controlling cellular morphological type via various mechanisms, such as autophagy. Thus, we hypothesized that TGF-β1 may regulate early cerebellar development by modifying the levels of cell adhesion molecules (CAMs) and consequently autophagy pathway in the mouse cerebellar primordium. We demonstrated the stimulation of the canonical TGF-β1 signaling pathway at the point that concurs with the generation of the nuclear transitory zone and PC plate in mice. Furthermore, our data show that the stimulated TGF-β1 signaling pathway progressively and chronologically could upregulate the expression of β-catenin (CTNNB1) and N-cadherin (CDH2) with the most expression at E11 and E12, leading to upregulation of chromodomain helicase DNA binding protein 8 (CDH8) and neural cell adhesion molecule 1 (NCAM1) expression, at E12 and E13. Finally, we demonstrated that the stimulated TGF-β signaling pathway may impede the autophagic flux at E11/E12. Nevertheless, basal autophagy flux happens at earlier developmental phases from E9-E10. Our study determined potential role of the TGF-β signaling and its regulatory impacts on autophagic flux during cerebellar development and cadherin expression, which can facilitate the proliferation, migration/differentiation, and placement of PCs and the CN neurons in their designated areas.

Project description:Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied "R4" resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ϵ. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions.

Project description:Multiple statistical approaches have been proposed to validate reference genes in qPCR assays. However, conflicting results from these statistical methods pose a major hurdle in the choice of the best reference genes. Recent studies have proposed the use of at least three different methods but there is no consensus on how to interpret conflicting results. Researchers resort to averaging the stability ranks assessed by different approaches or attributing a weighted rank to candidate genes. However, we report here that the suitability of these validation methods can be influenced by the experimental setting. Therefore, averaging the ranks can lead to suboptimal assessment of stable reference genes if the method used is not suitable for analysis. As the respective approaches of these statistical methods are different, a clear understanding of the fundamental assumptions and the parameters that influence the calculation of reference gene stability is necessary. In this study, the stability of 10 candidate reference genes (Actb, Gapdh, Tbp, Sdha, Pgk1, Ppia, Rpl13a, Hsp60, Mrpl10, Rps26) was assessed using four common statistical approaches (GeNorm, NormFinder, Coefficient of Variation or CV analysis and Pairwise ΔCt method) in a longitudinal experimental setting. We used the development of the cerebellum and the spinal cord of mice as a model to assess the suitability of these statistical methods for reference gene validation. GeNorm and the Pairwise ΔCt were found to be ill suited due to a fundamental assumption in their stability calculations. Highly correlated genes were given better stability ranks despite significant overall variation. NormFinder fares better but the presence of highly variable genes influences the ranking of all genes because of the algorithm's construct. CV analysis estimates overall variation, but it fails to consider variation across groups. We thus highlight the assumptions and potential pitfalls of each method using our longitudinal data. Based on our results, we have devised a workflow combining NormFinder, CV analysis along with visual representation of mRNA fold changes and one-way ANOVA for validating reference genes in longitudinal studies. This workflow proves to be more robust than any of these methods used individually.

Dataset Information

Assessing methods to quantitatively validate TGF?-dependent autophagy.

Publications

Assessing methods to quantitatively validate TGFβ-dependent autophagy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets