Dataset Information

The interactome of the prostate-specific protein Anoctamin 7.

ABSTRACT: BACKGROUND:Elevated Anoctamin 7 (ANO7) expression is associated with poor survival in prostate cancer patients. OBJECTIVE:The aim was to discover proteins that interact with ANO7 to understand its functions and regulatory mechanisms. METHODS:The proximity-dependent biotin identification (BioID) method was utilized. ANO7 fused to biotin ligase was transiently transfected into LNCaP cells, and the biotinylated proteins were collected and analysed by mass spectrometry. Four identified proteins were stained with dual fluorescent immunostaining and visualized using Stimulated emission depletion microscopy (STED). RESULTS:After bioinformatic filtering steps, 64 potentially ANO7-interacting proteins were identified and analysed with the GO enrichment analysis tool. One of the most prominently enriched cellular components was cellular vesicle. Co-localization was showed for staphylococcal nuclease and tudor domain containing 1 (SND1), heat shock protein family A (Hsp70) member 1A (HSPA1A), adaptor related protein complex 2 subunit beta 1 (AP2B1) and coatomer protein complex subunit gamma 2 (COPG2). CONCLUSIONS:This is the first study in which ANO7 interacting proteins have been identified. Although further studies are needed, the findings reported here expand our understanding of the role and regulation of ANO7 in prostate cancer cells. Furthermore, these results are likely to introduce new targets for the novel cancer therapies.

SUBMITTER: Kaikkonen E

PROVIDER: S-EPMC7306890 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The interactome of the prostate-specific protein Anoctamin 7.

Kaikkonen Elina E Takala Aliisa A Pursiheimo Juha-Pekka JP Wahlström Gudrun G Schleutker Johanna J

Cancer biomarkers : section A of Disease markers 20200101 1

<h4>Background</h4>Elevated Anoctamin 7 (ANO7) expression is associated with poor survival in prostate cancer patients.<h4>Objective</h4>The aim was to discover proteins that interact with ANO7 to understand its functions and regulatory mechanisms.<h4>Methods</h4>The proximity-dependent biotin identification (BioID) method was utilized. ANO7 fused to biotin ligase was transiently transfected into LNCaP cells, and the biotinylated proteins were collected and analysed by mass spectrometry. Four id ...[more]

PMID: 32176628

Similar Datasets

Project description:The purpose of our study was to construct prostate cancer cell-specific interactome by aggregating microarray samples available in public, and identify novel transcriptional regulators that control prostate cancer cell cycle progression. We collected 121 samples of PC-3 cell microarray data from 11 prior studies and our own study data, GSE45567, and performed pre-processing steps, such as quality control test, normalization, and batch effect adjusting. In quality control test, 10 low-quality samples were removed. The matrix data we deposited in GEO has normalized log2 signal intensity of 11,877 genes common in 110 samples, as the 110 samples came from different Affymetrix. The Gene IDs were identified by Entrez Gene IDs that overlapped across different Affymetrix platforms (File: GSE67157_Matrix_PC-3cell_Interactome.txt). In GSE45567 dataset, which is one of the 12 data sets, we selected microarray samples of vehicle-, cineol-, linalool-, and geraniol-treated PC-3 cells, and performed unsupervised clustering analysis to establish dependable clusters of a clear phenotypic transition. Then, the gene set specifically enriched or depleted in the geraniol cluster against vehicle cluster was used for gene set enrichment analysis (GSEA). We found cell cycle-related gene signatures were specifically down-regulated in the geraniol cluster. PC-3 cell-specific interactome was assembled from 110 microarray samples by Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe, PMID: 16723010). We used Master Regulator Analysis-Fisher’s Extact Test (MRA-FET, PMID: 20531406) to infer transcription factors which are geraniol target molecules and control cell cycle gene signatures. We discovered E2F8 is as a master regulator to modulate geraniol-specific genes toward G2/M cell cycle arrest. Our findings will provide biological insights into the role of E2F8 in prostate cancer for transcriptional regulation leading to the development of cell cycle-targeting chemotherapeutic reagent. In addition, our approach serves as an example to decide the most potent anti-cancer regime in other cancer types and identify the target molecules of cancer-preventive reagents through computational analyses.

Project description:UnlabelledProtein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels.Availabilityhttp://services.bio.ifi.lmu.de/coin-db/.

Project description:Nipah virus is an emerging, highly pathogenic, zoonotic virus of the Paramyxoviridae family. Human transmission occurs by close contact with infected animals, the consumption of contaminated food, or, occasionally, via other infected individuals. Currently, we lack therapeutic or prophylactic treatments for Nipah virus. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. This aim led us to perform the present work, in which we identified 101 human-Nipah virus protein-protein interactions (PPIs), most of which (88) are novel. This data set provides a comprehensive view of the host complexes that are manipulated by viral proteins. Host targets include the PRP19 complex and the microRNA (miRNA) processing machinery. Furthermore, we explored the biologic consequences of the interaction with the PRP19 complex and found that the Nipah virus W protein is capable of altering p53 control and gene expression. We anticipate that these data will help in guiding the development of novel interventional strategies to counter this emerging viral threat.IMPORTANCE Nipah virus is a recently discovered virus that infects a wide range of mammals, including humans. Since its discovery there have been yearly outbreaks, and in some of them the mortality rate has reached 100% of the confirmed cases. However, the study of Nipah virus has been largely neglected, and currently we lack treatments for this infection. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. In the present work, we identified 101 human-Nipah virus protein-protein interactions using an affinity purification approach coupled with mass spectrometry. Additionally, we explored the cellular consequences of some of these interactions. Globally, this data set offers a comprehensive and detailed view of the host machinery's contribution to the Nipah virus's life cycle. Furthermore, our data present a large number of putative drug targets that could be exploited for the treatment of this infection.

Dataset Information

The interactome of the prostate-specific protein Anoctamin 7.

Publications

The interactome of the prostate-specific protein Anoctamin 7.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets