The pain interactome: connecting pain-specific protein interactions.
Ontology highlight
ABSTRACT:
SUBMITTER: Jamieson DG
PROVIDER: S-EPMC4247380 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein-protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities.
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Project description:Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an 'MPM interactome' with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities.
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Project description:Interactions between naturally occurring proteins are highly specific, with protein-network imbalances associated with numerous diseases. For designed protein-protein interactions (PPIs), required specificity can be notoriously difficult to engineer. To accelerate this process, we have derived peptides that form heterospecific PPIs when combined. This is achieved using software that generates large virtual libraries of peptide sequences and searches within the resulting interactome for preferentially interacting peptides. To demonstrate feasibility, we have (i) generated 1536 peptide sequences based on the parallel dimeric coiled-coil motif and varied residues known to be important for stability and specificity, (ii) screened the 1,180,416 member interactome for predicted Tm values and (iii) used predicted Tm cutoff points to isolate eight peptides that form four heterospecific PPIs when combined. This required that all 32 hypothetical off-target interactions within the eight-peptide interactome be disfavoured and that the four desired interactions pair correctly. Lastly, we have verified the approach by characterising all 36 pairs within the interactome. In analysing the output, we hypothesised that several sequences are capable of adopting antiparallel orientations. We subsequently improved the software by removing sequences where doing so led to fully complementary electrostatic pairings. Our approach can be used to derive increasingly large and therefore complex sets of heterospecific PPIs with a wide range of potential downstream applications from disease modulation to the design of biomaterials and peptides in synthetic biology.
| S-EPMC4751974 | biostudies-literature
Project description:Protein-protein interactions (PPIs) play an essential role in cellular regulatory processes. Despite, in-depth studies to uncover the mystery of PPI-mediated regulations are still lacking. Here, an integrative interactome network (MePPI-Ux) was obtained by incorporating expression data into the improved genome-scale interactome network of cassava (MePPI-U). The MePPI-U, constructed by both interolog- and domain-based approaches, contained 3,638,916 interactions and 24,590 proteins (59% of proteins in the cassava AM560 genome version 6). After incorporating expression data as information of state, the MePPI-U rewired to represent condition-dependent PPIs (MePPI-Ux), enabling us to envisage dynamic PPIs (DPINs) that occur at specific conditions. The MePPI-Ux was exploited to demonstrate timely PPIs of cassava under various conditions, namely drought stress, brown streak virus (CBSV) infection, and starch biosynthesis in leaf/root tissues. MePPI-Uxdrought and MePPI-UxCBSV suggested involved PPIs in response to stress. MePPI-UxSB,leaf and MePPI-UxSB,root suggested the involvement of interactions among transcription factor proteins in modulating how leaf or root starch is synthesized. These findings deepened our knowledge of the regulatory roles of PPIs in cassava and would undeniably assist targeted breeding efforts to improve starch quality and quantity.
| S-EPMC7162878 | biostudies-literature
Project description: Not available
| S-EPMC2891586 | biostudies-literature
Project description:The six mammalian glycosaminoglycans (GAGs), chondroitin sulfate, dermatan sulfate, heparin, heparan sulfate, hyaluronan, and keratan sulfate, are linear polysaccharides. Except for hyaluronan, they are sulfated to various extent, and covalently attached to proteins to form proteoglycans. GAGs interact with growth factors, morphogens, chemokines, extracellular matrix proteins and their bioactive fragments, receptors, lipoproteins, and pathogens. These interactions mediate their functions, from embryonic development to extracellular matrix assembly and regulation of cell signaling in various physiological and pathological contexts such as angiogenesis, cancer, neurodegenerative diseases, and infections. We give an overview of GAG-protein interactions (i.e., specificity and chemical features of GAG- and protein-binding sequences), and review the available GAG-protein interaction networks. We also provide the first comprehensive draft of the GAG interactome composed of 832 biomolecules (827 proteins and five GAGs) and 932 protein-GAG interactions. This network is a scaffold, which in the future should integrate structures of GAG-protein complexes, quantitative data of the abundance of GAGs in tissues to build tissue-specific interactomes, and GAG interactions with metal ions such as calcium, which plays a major role in the assembly of the extracellular matrix and its interactions with cells. This contextualized interactome will be useful to identify druggable GAG-protein interactions for therapeutic purpose.
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Project description:BackgroundElevated Anoctamin 7 (ANO7) expression is associated with poor survival in prostate cancer patients.ObjectiveThe aim was to discover proteins that interact with ANO7 to understand its functions and regulatory mechanisms.MethodsThe 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).ResultsAfter 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).ConclusionsThis 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.
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Project description:Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition, and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ∼380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands and binding partners for "orphan" receptors. New PPIs include proteins expressed on multiple cell types and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance and may offer new therapeutic drug targets.
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Project description:Splicing generates many mRNA strands from a single precursor mRNA, expanding the proteome and enhancing intracellular diversity. Both initial assembly and activation of the spliceosome require an essential family of splicing factors called serine-arginine (SR) proteins. Protein phosphatase 1 (PP1) regulates the SR proteins by controlling phosphorylation of a C-terminal arginine-serine-rich (RS) domain. These modifications are vital for the subcellular localization and mRNA splicing function of the SR protein. Although PP1 has been shown to dephosphorylate the prototype SR protein splicing factor 1 (SRSF1), the molecular nature of this interaction is not understood. Here, using NMR spectroscopy, we identified two electrostatic residues in helix ?2 and a hydrophobic residue in helix ?1 in the RNA recognition motif 1 (RRM1) of SRSF1 that constitute a binding surface for PP1. Substitution of these residues dissociated SRSF1 from PP1 and enhanced phosphatase activity, reducing phosphorylation in the RS domain. These effects lead to shifts in alternative splicing patterns that parallel increases in SRSF1 diffusion from speckles to the nucleoplasm brought on by regiospecific decreases in RS domain phosphorylation. Overall, these findings establish a molecular and biological connection between PP1-targeted amino acids in an RRM with the phosphorylation state and mRNA-processing function of an SR protein.
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Project description:Protein-protein interactions play a pivotal role in normal cellular functions as well as in carcinogenesis. The protein-protein interactions form functional clusters during signal transduction. To elucidate the fine calibration of the protein-protein interactions of protein tyrosine phosphatase interacting protein 51 (PTPIP51) a small molecule drug, namely LDC-3, directly targeting PTPIP51 is now available. Therefore, LDC-3 allows for the studying of the regulation of the endogenous interactome by modulating PTPIP51 binding capacity. Small interfering ribonucleic acid (siRNA) experiments show that the modification in PTPIP51 binding capacity is induced by LDC-3. Application of LDC-3 annuls the known regulatory phosphorylation mechanisms for PTPIP51 and consequently, significantly alters the assembly of the PTPIP51 associated protein complexes. The treatment of human keratinocytes (HaCaT cells) with LDC-3 induces an altered protein-protein interaction profile of the endogenous interactome of PTPIP51. In addition, LDC-3 stabilizes PTPIP51 within a mitogen activated protein kinase (MAPK) complex composed of Raf-1 and the scaffold protein 14-3-3, independent of the phosphorylation status of PTPIP51. Of note, under LDC-3 treatment the regulatory function of the PTP1B on PTPIP51 fails to impact the PTPIP51 interaction characteristics, as reported for the HaCaT cell line. In summary, LDC-3 gives the unique opportunity to directly modulate PTPIP51 in malignant cells, thus targeting potential dysregulated signal transduction pathways such as the MAPK cascade. The provided data give critical insights in the therapeutic potential of PTPIP51 protein interactions and thus are basic for possible targeted therapy regimens.
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