Dataset Information

Emerging connectivity patterns of protein-protein interaction networks in melanoma secretome: insights into the cancer interactome

ABSTRACT: Cellular communication strongly relies on the secretion of soluble mediators that, in general, interact with cell surface receptors and enable biological responses in a proper timescale for signaling processes to occur. Therefore, the study of secreted proteins (the secretome) from normal cells and tumoral counterparts allows a comprehensive portrait of the so-called ‘effectors’ in many biological circuits, including those related to tumor development. Cell homeostasis is disrupted in many ways during oncogenesis; the main biological implications are derived from a wide range of factors, including somatic mutations, epigenetic modifications and post-transcriptional/translational modifications, which ultimately results in the modulation of the gene expression. Collectively, these factors often result in rewiring connections in signaling networks in malignant cells. Since protein-protein interactions may eventually enable transformed cells to grow, proliferate and invade neighboring tissues, a comprehensive view of the imbalance caused by the oncogenic processes requires the understanding of the interplay of interactors in altered biological circuits. Pathway and network analysis are analytic approaches that reduce the data involving thousands of altered genes/proteins in complex biological systems to smaller datasets which, in turn, enable the detection and interpretation of specific biological questions such as cancer-related processes, clinically distinct outcomes and drug targets, for example. In this respect, protein-protein interaction networks (PPINs) are static representations of protein connections in biological systems. Although this simplification lacks the dynamic nature of the real protein world, it allows the identification of specific topologies related to the patterns of connectivity of a given PPIN. Moreover, some additional network features may be observed, such as the identification of subgraphs (communities), which contain proteins that may be functionally related, revealing an additional layer of complexity among protein-protein interactions within the network. Kaushik and coworkers used network analysis and to study melanoma stage progression and found that melanoma-related genes can modulate their activities by rewiring network connections, independent of altered gene expression. A recent work with DNA methylation, gene expression and corresponding clinical data from breast invasive carcinoma, skin cutaneous melanoma and uterine corpus endometrial carcinoma underscored gene co-methylation networks, resulting in the identification of core gene modules which allowed the molecular typing of cancer and the prognosis of patients. Such network-based approach revealed that gene modules were more reliable in cancer prognosis than gene expression profiles. Systems-level analysis of signaling networks, gene expression profiles and cell phenotyping, in combination with mathematical modeling, helped to understand the interface between growth factor and DNA signaling pathways in breast cancer9. According to the authors, the identification of rewiring patterns in apoptotic signaling pathways may have main implications for combined therapies in the treatment of breast cancer. In this context, the main goal of this work was to profile the secretome composition of a murine cellular melanoma model composed by a normal melanocyte cell line, Melan-a, and its tumoral phenotype, Tm1, using the proteomic data to build interactomes for both secretomes. Our data revealed an expressive rewiring in the tumoral interactome of secreted proteins, with important biological roles in malignant transformation including the transforming growth factor beta (TGF-beta) signaling pathway.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Melanocyte, Cell Culture

DISEASE(S): Skin Melanoma

SUBMITTER: Andre Zelanis

LAB HEAD: André Zelanis

PROVIDER: PXD015114 | Pride | 2020-12-07

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	OB_AZP_MELAN_A1_20180430_04.raw	Raw
	OB_AZP_MELAN_A3_20180430_06.raw	Raw
	OB_AZP_TM1_20180430_01.raw	Raw
	OB_AZP_TM3_20180430_03.raw	Raw
	Supplementarytables.xlsx	Xlsx

Items per page:

1 - 5 of 5

Publications

Community-based network analyses reveal emerging connectivity patterns of protein-protein interactions in murine melanoma secretome.

Francisquini Rodrigo R Berton Rafael R Soares Sandro Gomes SG Pessotti Dayelle S DS Camacho Maurício F MF Andrade-Silva Débora D Barcick Uilla U Serrano Solange M T SMT Chammas Roger R Nascimento Mariá C V MCV Zelanis André A

Journal of proteomics 20201201

Protein-protein interaction networks (PPINs) are static representations of protein connections in which topological features such as subgraphs (communities) may contain proteins functionally related, revealing an additional layer of interactome complexity. We created two PPINs from the secretomes of a paired set of murine melanocytes (a normal melanocyte and its transformed phenotype). Community structures, identified by a graph clustering algorithm, resulted in the identification of subgraphs i ...[more]

PMID: 33276191

Similar Datasets

Project description:Abstract Background Traumatic brain injury (TBI) results in irreversible damage at the site of impact and initiates cellular and molecular processes that lead to secondary neural injury in the surrounding tissue. We used microarray analysis to determine which genes, pathways and networks were significantly altered using a rat model of TBI. Adult rats received a unilateral controlled cortical impact (CCI) and were sacrificed 24h post-injury. The ipsilateral hemi-brain tissue at the site of the injury, the corresponding contralateral hemi-brain tissue, and naM-CM-/ve (control) brain tissue were used for microarray analysis. Ingenuity Pathway Analysis (IPA) software was used to identify molecular pathways and networks that were associated with the altered gene expression in brain tissues following TBI. Results Inspection of the top fifteen biological functions in IPA associated with TBI in the ipsilateral tissues revealed that all had an inflammatory component. IPA analysis also indicated that inflammatory genes were altered on the contralateral side, but many of the genes were inversely expressed compared to the ipsilateral side. The contralateral gene expression pattern suggests a remote anti-inflammatory molecular response. We created a network of the inversely expressed common (i.e., same gene changed on both sides of the brain) inflammatory response (IR) genes and those IR genes included in pathways and networks identified by IPA that changed on only one side. We ranked the genes by the number of direct connections each had in the network, creating a gene interaction hierarchy (GIH). Two well characterized signaling pathways, toll-like receptor/NF-kappaB signaling and JAK/STAT signaling, were prominent in our GIH. Conclusions Bioinformatic analysis of microarray data following TBI identified key molecular pathways and networks associated with neural injury following TBI. The GIH created here provides a starting point for investigating therapeutic targets in a ranked order that is somewhat different than what has been presented previously. In addition to being a vehicle for identifying potential targets for post-TBI therapeutic strategies, our findings can also provide a context for evaluating the potential of therapeutic agents currently in development. The ipsilateral hemi-brain tissue at the site of the injury, the corresponding contralateral hemi-brain tissue, and naM-CM-/ve (control) brain tissue (n=3 for each) were used for RNA isolation. The TBI injured animals were Todd 1, 2 Todd, and Todd 3, each yielding an ispilateral and contralateral sample. The naM-CM-/ve animals were Xu 13 control, Xu 2 control, and Xu 6 control.

Project description:Recent data demonstrate that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways. This network model suggests why inhibition of a single component of a canonical pathway, even when targeting a mutationally activated driver of cancer, has insufficiently dramatic effects on the treatment of cancer. The biological outcome of signals propagated through a network is inherently more robust and resistant to inhibition of a single network component due to compensatory and redundant signaling events. In this study, we performed a functional chemical genetic screen analogous to synthetic lethal screening in yeast genetics to identify novel interactions between signaling inhibitors that would not be predicted based on our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. Among the most robust and surprising results was synergy between sorafenib, a multi-kinase inhibitor with activity against Raf, and diclofenac, a non-steroidal anti-inflammatory drug (NSAID). This synergy did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. The NSAIDs celecoxib and ibuprofen could qualitatively substitute for diclofenac. Similarly, the MEK inhibitor PD325901 and the Raf inhibitor RAF265 could qualitatively substitute for sorafenib. These drug substitution experiments suggest that inhibition of cyclo-oxygenase and MAP kinase signaling are components of the observed synergistic cytotoxicity. Genome-wide expression profiling demonstrates synergy-specific down-regulation of survival-related genes. This study provides proof of principle that synthetic lethal screening can uncover novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially depending on unexplored components of the cell genotype. RNA from VMM39, DM331, and SLM2 cells with/without mutations in Ras and/or Braf, treated with Sorafenib and/or Diclofenac.

Project description:Protein phosphorylation regulated by plant hormone is involved in the coordination of fundamental plant development. Brassinosteroids (BRs), a group of phytohormones, regulated phosphorylation dynamics remains to be delineated in plants. In this study, we performed a mass spectrometry (MS)-based phosphoproteomics to conduct a global and dynamic phosphoproteome profiling across five time points of BR treatment in the period between 5 minutes and 12 hours. MS coupling with phosphopeptide enrichment techniques has become the powerful tool for profiling protein phosphorylation. However, MS-based methods tend to have data consistency and coverage issues. To address these issues, bioinformatics approaches were used to complement the non-detected proteins and recover the dynamics of phosphorylation events. A total of 1,104 unique phosphorylated peptides from 739 unique phosphoproteins were identified. The time-dependent gene ontology (GO) analysis shows the transition of biological processes from signaling transduction to morphogenesis and stress response. The protein-protein interaction analysis found most of identified phosphoproteins have strongly connections with known BR signaling components. The analysis by using Motif-X was performed to identify 15 enriched motifs, 11 of which correspond to 6 known kinase families. To uncover the dynamic activities of kinases, the enriched motifs were combined with phosphorylation profiles and revealed that the substrates of casein kinase 2 and mitogen-activated protein kinase were significantly phosphorylated and dephosphorylated at initial time of BR treatment, respectively. The time-dependent kinase-substrate interaction networks were constructed and showed many substrates are the downstream of other signalings, such as auxin and ABA signalings. Comparing BR responsive phosphoproteome and gene expression data, we found most of phosphorylation changes were not led by gene expression changes. Our results suggested BR signaling not only induces gene expression, but also change protein activation by phosphorylation via various kinases. Through a large-scale dynamic profile of phosphoproteome coupling bioinformatics, a complicated kinase-centered network related to BR-regulated growth was deciphered. The phosphoproteins and phosphosites identified from our study provide a useful dataset for revealing signaling networks of BR regulation, and also expanded our knowledge of protein phosphorylation modification in plants as well as further deal to solve the plant growth problems.