scholarly journals A compendium of co-regulated protein complexes in breast cancer reveals collateral loss events

2017 ◽  
Author(s):  
Colm J. Ryan ◽  
Susan Kennedy ◽  
Ilirjana Bajrami ◽  
David Matallanas ◽  
Christopher J. Lord
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
Transcriptional Control ◽  
Protein Complexes ◽  
Adherens Junction ◽  
Interaction Network ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Highly Correlated ◽  
E Cadherin

SummaryProtein complexes are responsible for the bulk of activities within the cell, but how their behavior and composition varies across tumors remains poorly understood. By combining proteomic profiles of breast tumors with a large-scale protein-protein interaction network, we have identified a set of 258 high-confidence protein complexes whose subunits have highly correlated protein abundance across tumor samples. We used this set to identify complexes that are reproducibly under- or over-expressed in specific breast cancer subtypes. We found that mutation or deletion of one subunit of a complex was often associated with a collateral reduction in protein expression of additional complex members. This collateral loss phenomenon was evident from proteomic, but not transcriptomic, profiles suggesting post-transcriptional control. Mutation of the tumor suppressor E-cadherin (CDH1)was associated with a collateral loss of members of the adherens junction complex, an effect we validated using an engineered model of E-cadherin loss.

scholarly journals The role of 3′UTR-protein complexes in the regulation of protein multifunctionality and subcellular localization

2020 ◽  
Vol 48 (12) ◽  
pp. 6491-6502
Author(s):  
Diogo M Ribeiro ◽  
Alexis Prod’homme ◽  
Adrien Teixeira ◽  
Andreas Zanzoni ◽  
Christine Brun
Keyword(s):  
Protein Trafficking ◽  
Cellular Localization ◽  
Large Scale ◽  
Protein Complexes ◽  
Interaction Network ◽  
Multifunctional Proteins ◽  
Protein Protein Interaction ◽  
Moonlighting Proteins ◽  
Rna Interaction

Abstract Multifunctional proteins often perform their different functions when localized in different subcellular compartments. However, the mechanisms leading to their localization are largely unknown. Recently, 3′UTRs were found to regulate the cellular localization of newly synthesized proteins through the formation of 3′UTR-protein complexes. Here, we investigate the formation of 3′UTR-protein complexes involving multifunctional proteins by exploiting large-scale protein-protein and protein-RNA interaction networks. Focusing on 238 human ‘extreme multifunctional’ (EMF) proteins, we predicted 1411 3′UTR-protein complexes involving 54% of those proteins and evaluated their role in regulating protein cellular localization and multifunctionality. We find that EMF proteins lacking localization addressing signals, yet present at both the nucleus and cell surface, often form 3′UTR-protein complexes, and that the formation of these complexes could provide EMF proteins with the diversity of interaction partners necessary to their multifunctionality. Our findings are reinforced by archetypal moonlighting proteins predicted to form 3′UTR-protein complexes. Finally, the formation of 3′UTR-protein complexes that involves up to 17% of the proteins in the human protein-protein interaction network, may be a common and yet underestimated protein trafficking mechanism, particularly suited to regulate the localization of multifunctional proteins.

scholarly journals PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rachel Nadeau ◽  
Anastasiia Byvsheva ◽  
Mathieu Lavallée-Adam
Keyword(s):  
Breast Cancer ◽  
Protein Interaction ◽  
Quantitative Proteomics ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Experimental Conditions ◽  
Cancer Subtypes ◽  
Functional Annotations ◽  
Protein Protein Interaction

Abstract Background Quantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance. However, a functional annotation may be dysregulated in a given experimental condition, while none, or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches. Results Herein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of the differential expression of individual proteins, but rather maps protein differential expression levels onto a protein–protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering significance of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein–protein interaction network and differentially expressed across different breast cancer subtypes. PIGNON identified functional annotations that are dysregulated and clustered within the network between the HER2+, triple negative and hormone receptor positive subtypes. We show that PIGNON’s results are complementary to those of state-of-the-art functional enrichment analyses and that it highlights functional annotations missed by standard approaches. Furthermore, PIGNON detects functional annotations that have been previously associated with specific breast cancer subtypes. Conclusion PIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different experimental conditions.

scholarly journals Co-Phosphorylation Networks Reveal Subtype-Specific Signaling Modules in Breast Cancer

2020 ◽  
Author(s):  
Marzieh Ayati ◽  
Mark R Chance ◽  
Mehmet Koyutürk
Keyword(s):  
Breast Cancer ◽  
Protein Phosphorylation ◽  
Protein Interactions ◽  
Large Scale ◽  
Post Translational Modification ◽  
Disease Response ◽  
Cancer Subtypes ◽  
Kinase Substrate ◽  
Highly Correlated ◽  
Context Specific

AbstractMotivationProtein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as down-regulation of tumor suppressors and up-regulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers.ResultsWe develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural, and functional association of phosphosites, kinase-substrate associations, and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry based phosphorylation datasets for breast cancer, and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive breast cancer subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers.Availability and implementationCoPPNet is available at http://compbio.case.edu/coppnet/

scholarly journals PIGNON: A protein-protein interaction-guided functional enrichment analysis for quantitative proteomics

2021 ◽  
Author(s):  
Rachel Nadeau ◽  
Anastasiia Byvsheva ◽  
Mathieu Lavallée-Adam
Keyword(s):  
Breast Cancer ◽  
Protein Interaction ◽  
Quantitative Proteomics ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cancer Subtypes ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Cancer Pathways

AbstractBackgroundQuantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline heavily relies on arbitrary thresholds of significance. Indeed, a functional annotation may be dysregulated in a given experimental condition, while none or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches.ResultsHerein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of individual genes, but rather maps protein differential expression levels onto a protein-protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein-protein interaction network and differentially expressed across two breast cancer subtypes. PIGNON identified 168 breast cancer pathways dysregulated and clustered within the network between the HER2+ and triple negative subtypes, 203 breast cancer pathways shared by HER2+ and hormone receptor positive subtypes, 19 breast cancer pathways shared by hormone receptor positive and triple negative breast that are not detected by standard approaches. PIGNON identifies functional annotations that have been previously associated with specific breast cancer subtypes as well as novel annotations that may be implicated in the diseases.ConclusionPIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different conditions.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals Estrogens Determine Adherens Junction Organization and E-Cadherin Clustering in Breast Cancer Cells via Amphiregulin

iScience ◽  
2020 ◽  
Vol 23 (11) ◽  
pp. 101683
Author(s):  
Philip Bischoff ◽  
Marja Kornhuber ◽  
Sebastian Dunst ◽  
Jakob Zell ◽  
Beatrix Fauler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Adherens Junction ◽  
E Cadherin

scholarly journals Synchronization of human retinal pigment epithelial-1 cells in mitosis

2020 ◽  
Vol 133 (18) ◽  
pp. jcs247940
Author(s):  
Stacey J. Scott ◽  
Kethan S. Suvarna ◽  
Pier Paolo D'Avino
Keyword(s):  
Cell Lines ◽  
Large Scale ◽  
Retinal Pigment Epithelial ◽  
Retinal Pigment ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Pigment Epithelial ◽  
Adenocarcinoma Cells ◽  
Sequential Treatments

ABSTRACTHuman retinal pigment epithelial-1 (RPE-1) cells are increasingly being used as a model to study mitosis because they represent a non-transformed alternative to cancer cell lines, such as HeLa cervical adenocarcinoma cells. However, the lack of an efficient method to synchronize RPE-1 cells in mitosis precludes their application for large-scale biochemical and proteomics assays. Here, we report a protocol to synchronize RPE-1 cells based on sequential treatments with the Cdk4 and Cdk6 inhibitor PD 0332991 (palbociclib) and the microtubule-depolymerizing drug nocodazole. With this method, the vast majority (80–90%) of RPE-1 cells arrested at prometaphase and exited mitosis synchronously after release from nocodazole. Moreover, the cells fully recovered and re-entered the cell cycle after the palbociclib–nocodazole block. Finally, we show that this protocol could be successfully employed for the characterization of the protein–protein interaction network of the kinetochore protein Ndc80 by immunoprecipitation coupled with mass spectrometry. This synchronization method significantly expands the versatility and applicability of RPE-1 cells to the study of cell division and might be applied to other cell lines that do not respond to treatments with DNA synthesis inhibitors.

scholarly journals Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

2020 ◽  
Author(s):  
Rong Jia ◽  
Zhongxian Li ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Yujie Weng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Survival Data ◽  
Breast Cancer Subtypes ◽  
Differentially Expressed ◽  
Expression Data ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Basal Like Breast Cancer

Abstract Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7 , KIF18A , STIL , and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

scholarly journals Synchronization of human retinal pigment ephitilial-1 (RPE-1) cells in mitosis

2020 ◽  
Author(s):  
Stacey J. Scott ◽  
Kethan Suvarna ◽  
Pier Paolo D’Avino
Keyword(s):  
Cell Lines ◽  
Large Scale ◽  
Retinal Pigment ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Adenocarcinoma Cells ◽  
Sequential Treatments ◽  
Dna Synthesis Inhibitors ◽  
Protein Protein Interaction Network

ABSTRACTHuman retinal pigment ephitilial-1 (RPE-1) cells are increasingly being used as a model to study mitosis because they represent a non-transformed alternative to cancer cell lines, such as HeLa cervical adenocarcinoma cells. However, the lack of an efficient method to synchronize RPE-1 cells in mitosis precludes their application for large-scale biochemical and proteomics assays. Here we report a protocol to synchronize RPE-1 cells based on sequential treatments with the Cdk4/6 inhibitor PD 0332991 (palbociclib) and the microtubule depolymerizing drug nocodazole. With this method, the vast majority (80-90%) of RPE-1 cells arrested at prometaphase and exited mitosis synchronously after release from nocodazole. Furthermore, we show that this protocol could be successfully employed for the characterization of the protein-protein interaction network of the kinetochore protein Ndc80 by immunoprecipitation coupled with mass spectrometry. This synchronization method significantly expands the versatility and applicability of RPE-1 cells to the study of cell division and might be applied to other cell lines that do not respond to treatments with DNA synthesis inhibitors.

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