Background Estrogen receptor β (ERβ) has been reported to play an anti-cancer role in breast cancer, but the regulatory mechanism by which ERβ exerts this effect is not clear. Claudin-6 (CLDN6), a tight junction protein, acts as a tumor suppressor gene in breast cancer. Our previous studies have found that 17β-estradiol (E2) induces CLDN6 expression and inhibits MCF-7 cell migration and invasion, but the underlying molecular mechanisms are still unclear. In this study, we aimed to investigate the role of ERβ in this process and the regulatory mechanisms involved. Methods Polymerase chain reaction (PCR) and western blot were used to characterize the effect of E2 on the expression of CLDN6 in breast cancer cells. Chromatin immunoprecipitation (ChIP) assays were carried out to confirm the interaction between ERβ and CLDN6. Dual luciferase reporter assays were used to detect the regulatory role of ERβ on the promoter activity of CLDN6. Wound healing and Transwell assays were used to examine the migration and invasion of breast cancer cells. Western blot, immunofluorescence and transmission electron microscopy (TEM) were performed to detect autophagy. Xenograft mouse models were used to explore the regulatory effect of the CLDN6-beclin1 axis on breast cancer metastasis. Immunohistochemistry (IHC) was used to detect ERβ/CLDN6/beclin1 expression in breast cancer patient samples. Results Here, E2 upregulated the expression of CLDN6, which was mediated by ERβ. ERβ regulated CLDN6 expression at the transcriptional level. ERβ inhibited the migration and invasion of breast cancer cells through CLDN6. Interestingly, this effect was associated with CLDN6-induced autophagy. CLDN6 positively regulated the expression of beclin1, which is a key regulator of autophagy. Beclin1 knockdown reversed CLDN6-induced autophagy and the inhibitory effect of CLDN6 on breast cancer metastasis. Moreover, ERβ and CLDN6 were positively correlated, and the expression of CLDN6 was positively correlated with beclin1 in breast cancer tissues. Conclusion Overall, this is the first study to demonstrate that the inhibitory effect of ERβ on the migration and invasion of breast cancer cells was mediated by CLDN6, which induced the beclin1-dependent autophagic cascade. Electronic supplementary material The online version of this article (10.1186/s13046-019-1359-9) contains supplementary material, which is available to authorized users.
The range of heterogeneous approaches available for quantifying protein abundance via mass spectrometry (MS)1 leads to considerable challenges in modeling, archiving, exchanging, or submitting experimental data sets as supplemental material to journals. To date, there has been no widely accepted format for capturing the evidence trail of how quantitative analysis has been performed by software, for transferring data between software packages, or for submitting to public databases. In the context of the Proteomics Standards Initiative, we have developed the mzQuantML data standard. The standard can represent quantitative data about regions in two-dimensional retention time versus mass/charge space (called features), peptides, and proteins and protein groups (where there is ambiguity regarding peptide-to-protein inference), and it offers limited support for small molecule (metabolomic) data. The format has structures for representing replicate MS runs, grouping of replicates (for example, as study variables), and capturing the parameters used by software packages to arrive at these values. The format has the capability to reference other standards such as mzML and mzIdentML, and thus the evidence trail for the MS workflow as a whole can now be described. Several software implementations are available, and we encourage other bioinformatics groups to use mzQuantML as an input, internal, or output format for quantitative software and for structuring local repositories. All project resources are available in the public domain from the HUPO Proteomics Standards Initiative http://www.psidev.info/mzquantml.
The ProteomeXchange (PX) consortium has been established to standardize and facilitate submission and dissemination of MS-based proteomics data in the public domain. In the consortium, the PRIDE database at the European Bioinformatics Institute, acts as the initial submission point of MS/MS data sets. In this manuscript, we explain step by step the submission process of MS/MS data sets to PX via PRIDE. We describe in detail the two available workflows: 'complete' and 'partial' submissions, together with the available tools to streamline the process. Throughout the manuscript, we will use one example data set containing identification and quantification data, which has been deposited in PRIDE/ProteomeXchange with the accession number PXD000764 (http://proteomecentral.proteomexchange.org/dataset/PXD000764).
Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.