scholarly journals Copy number networks to guide combinatorial therapy for cancer and other disorders

2014 ◽  
Author(s):  
Andy Lin ◽  
Desmond James Smith
Keyword(s):  
Drug Treatment ◽  
Protein Interactions ◽  
Copy Number ◽  
Genetic Interaction ◽  
Single Gene ◽  
Growth Factor Receptor ◽  
Molecular Networks ◽  
Protein Protein Interactions ◽  
Gene Pairs ◽  
Cancer Networks

The dwindling drug pipeline is driving increased interest in the use of genome datasets to inform drug treatment. In particular, networks based on transcript data and protein-protein interactions have been used to design therapies that employ drug combinations. But there has been less focus on employing human genetic interaction networks constructed from copy number alterations (CNAs). These networks can be charted with sensitivity and precision by seeking gene pairs that tend to be amplified and/or deleted in tandem, even when they are located at a distance on the genome. Our experience with radiation hybrid (RH) panels, a library of cell clones that have been used for genetic mapping, have shown this tool can pinpoint statistically significant patterns of co-inherited gene pairs. In fact, we were able to identify gene pairs specifically associated with the mechanism of cell survival at single gene resolution. The strategy of seeking correlated CNAs can also be used to map survival networks for cancer. Although the cancer networks have lower resolution, the RH network can be leveraged to provide single gene specificity in the tumor networks. In a survival network for glioblastoma possessing single gene resolution, we found that the epidermal growth factor receptor (EGFR) oncogene interacted with 46 genes. Of these genes, ten (22%) happened to be targets for existing drugs. Here, we briefly review the previous use of molecular networks to design novel therapies. We then highlight the potential of using correlated CNAs to guide combinatorial drug treatment in common medical conditions. We focus on therapeutic opportunities in cancer, but also offer examples from autoimmune disorders and atherosclerosis.

scholarly journals Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482097667
Author(s):  
Ju-Yueh Li ◽  
Chia-Jung Li ◽  
Li-Te Lin ◽  
Kuan-Hao Tsui
Keyword(s):  
Ovarian Cancer ◽  
Protein Interactions ◽  
Malignant Tumors ◽  
Single Gene ◽  
Protein Protein Interactions ◽  
Normal Tissues ◽  
Human Protein Atlas ◽  
Copy Number Changes ◽  
And Function ◽  
Cancer Tissues

Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein–protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the “Human Protein Atlas” database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.

scholarly journals Identification of Protein Complex Associated with LYT1 ofTrypanosoma cruzi

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
C. Lugo-Caballero ◽  
G. Ballesteros-Rodea ◽  
S. Martínez-Calvillo ◽  
Rebeca Manning-Cela
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Single Gene ◽  
Protein Protein Interactions ◽  
Protein Traffic ◽  
Parasite Stage ◽  
Transgenic Parasites ◽  
Stage Development ◽  
Acidic Conditions ◽  
Interaction Map

To carry out the intracellular phase of its life cycle,Trypanosoma cruzimust infect a host cell. Although a few molecules have been reported to participate in this process, one known protein is LYT1, which promotes lysis under acidic conditions and is involved in parasite infection and development. Alternative transcripts from a singleLYT1gene generate two proteins with differential functions and compartmentalization. Single-gene products targeted to more than one location can interact with disparate proteins that might affect their function and targeting properties. The aim of this work was to study the LYT1 interaction map using coimmunoprecipitation assays with transgenic parasites expressing LYT1 products fused to GFP. We detected several proteins of sizes from 8 to 150 kDa that bind to LYT1 with different binding strengths. By MS-MS analysis, we identified proteins involved in parasite infectivity (trans-sialidase), development (kDSPs and histones H2A and H2B), and motility and protein traffic (dynein andα- andβ-tubulin), as well as protein-protein interactions (TPR-protein and kDSPs) and several hypothetical proteins. Our approach led us to identify the LYT1 interaction profile, thereby providing insights into the molecular mechanisms that contribute to parasite stage development and pathogenesis ofT. cruziinfection.

scholarly journals Subcellular micropatterning for visual immunoprecipitation reveals differences in cytosolic protein complexes downstream the EGFR

2021 ◽  
Author(s):  
Roland Hager ◽  
Ulrike Mueller ◽  
Nicole Ollinger ◽  
Julian Weghuber ◽  
Peter Lanzerstorfer
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Growth Factor Receptor ◽  
Spatial Arrangement ◽  
Spatiotemporal Analysis ◽  
Protein Domain ◽  
Protein Protein Interactions ◽  
Cytosolic Protein ◽  
Bait Construct ◽  
Biochemical Analyses

Analysis of protein-protein interactions in living cells by protein micropatterning is currently limited to the spatial arrangement of transmembrane proteins and their corresponding downstream molecules. Here we present a robust method for visual immunoprecipitation of cytosolic protein complexes by use of an artificial transmembrane bait construct in combination with micropatterned antibody arrays on cyclic olefin polymer (COP) substrates. The method was used to characterize Grb2-mediated signalling pathways downstream the epidermal growth factor receptor (EGFR). Ternary protein complexes (Shc1:Grb2:SOS1 and Grb2:Gab1:PI3K) were identified and we found that EGFR downstream signalling is based on constitutively bound (Grb2:SOS1 and Grb2:Gab1) as well as on agonist-dependent protein associations with transient interaction properties (Grb2:Shc1 and Grb2:PI3K). Spatiotemporal analysis further revealed significant differences in stability and exchange kinetics of protein interactions. Furthermore, we could show that this approach is well suited to study the efficacy and specificity of SH2 and SH3 protein domain inhibitors in a live cell context. Altogether, this method represents a significant enhancement of quantitative subcellular micropatterning approaches as an alternative to standard biochemical analyses.

scholarly journals Tuned polymerization of the transcription factor Yan limits off-DNA sequestration to confer context-specific repression

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
C Matthew Hope ◽  
Jemma L Webber ◽  
Sherzod A Tokamov ◽  
Ilaria Rebay
Keyword(s):  
Transcription Factor ◽  
Protein Interactions ◽  
Genetic Interaction ◽  
Protein Protein Interactions ◽  
Regulate Gene Expression ◽  
Sam Domain ◽  
Ets Domain ◽  
Context Specific ◽  
Regulatory Decisions ◽  
Type Specification

During development, transcriptional complexes at enhancers regulate gene expression in complex spatiotemporal patterns. To achieve robust expression without spurious activation, the affinity and specificity of transcription factor–DNA interactions must be precisely balanced. Protein–protein interactions among transcription factors are also critical, yet how their affinities impact enhancer output is not understood. The Drosophila transcription factor Yan provides a well-suited model to address this, as its function depends on the coordinated activities of two independent and essential domains: the DNA-binding ETS domain and the self-associating SAM domain. To explore how protein–protein affinity influences Yan function, we engineered mutants that increase SAM affinity over four orders of magnitude. This produced a dramatic subcellular redistribution of Yan into punctate structures, reduced repressive output and compromised survival. Cell-type specification and genetic interaction defects suggest distinct requirements for polymerization in different regulatory decisions. We conclude that tuned protein–protein interactions enable the dynamic spectrum of complexes that are required for proper regulation.

Protein Interaction Networks

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Large Scale ◽  
Scientific Literature ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Protein Protein Interactions ◽  
Expression Array ◽  
Biological Responses ◽  
Altered Gene

Genes and proteins interact with each other in many complicated ways. For example, proteins can interact directly with each other to form complexes or to modify each other so that their function is altered. Gene expression can be repressed or induced by transcription factor proteins. In addition there are countless other types of interactions. They constitute the key physiological steps in regulating or initiating biological responses. For example the binding of transcription factors to DNA triggers the assembly of the RNA assembly machinery that transcribes the mRNA that then is used as the template for protein production. Interactions such as these have been carefully elucidated and have been described in great detail in the scientific literature. Modern assays such as yeast-2-hybrid screens offer rapid means to ascertain many of the potential protein–protein interactions in an organism in a large-scale approach. In addition, other experimental modalities such as gene-expression array assays offer indirect clues about possible genetic interactions. One area that has been greatly explored in the bioinformatics literature is the possibility of learning genetic or protein networks, both from the scientific literature and from large-scale experimental data. Indeed, as we get to know more and more genes, it will become increasingly important to appreciate their interactions with each other. An understanding of the interactions between genes and proteins in a network allows for a meaningful global view of the organism and its physiology and is necessary to better understand biology. In this chapter we will explore methods to either (1) mine the scientific literature to identify documented genetic interactions and build networks of genes or (2) to confirm protein interactions that have been proposed experimentally. Our focus here is on direct physical protein–protein interactions, though the techniques described could be extended to any type of biological interaction between genes or proteins. There are multiple steps that must be addressed in identifying genetic interaction information contained within the text. After compiling the necessary documents and text, the first step is to identify gene and protein names in the text.

scholarly journals Kir2.1 Interactome Mapping Uncovers PKP4 as a Modulator of the Kir2.1-Regulated Inward Rectifier Potassium Currents

2020 ◽  
Vol 19 (9) ◽  
pp. 1436-1449
Author(s):  
Sung-Soo Park ◽  
Daniela Ponce-Balbuena ◽  
Rork Kuick ◽  
Guadalupe Guerrero-Serna ◽  
Justin Yoon ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Growth Factor Receptor ◽  
Potassium Currents ◽  
Inward Rectifier ◽  
Resting Membrane Potential ◽  
Protein Protein Interactions ◽  
Channel Interactions ◽  
Labeling Approach ◽  
Functional Relevance

Kir2.1, a strong inward rectifier potassium channel encoded by the KCNJ2 gene, is a key regulator of the resting membrane potential of the cardiomyocyte and plays an important role in controlling ventricular excitation and action potential duration in the human heart. Mutations in KCNJ2 result in inheritable cardiac diseases in humans, e.g. the type-1 Andersen-Tawil syndrome (ATS1). Understanding the molecular mechanisms that govern the regulation of inward rectifier potassium currents by Kir2.1 in both normal and disease contexts should help uncover novel targets for therapeutic intervention in ATS1 and other Kir2.1-associated channelopathies. The information available to date on protein-protein interactions involving Kir2.1 channels remains limited. Additional efforts are necessary to provide a comprehensive map of the Kir2.1 interactome. Here we describe the generation of a comprehensive map of the Kir2.1 interactome using the proximity-labeling approach BioID. Most of the 218 high-confidence Kir2.1 channel interactions we identified are novel and encompass various molecular mechanisms of Kir2.1 function, ranging from intracellular trafficking to cross-talk with the insulin-like growth factor receptor signaling pathway, as well as lysosomal degradation. Our map also explores the variations in the interactome profiles of Kir2.1WTversus Kir2.1Δ314-315, a trafficking deficient ATS1 mutant, thus uncovering molecular mechanisms whose malfunctions may underlie ATS1 disease. Finally, using patch-clamp analysis, we validate the functional relevance of PKP4, one of our top BioID interactors, to the modulation of Kir2.1-controlled inward rectifier potassium currents. Our results validate the power of our BioID approach in identifying functionally relevant Kir2.1 interactors and underline the value of our Kir2.1 interactome as a repository for numerous novel biological hypotheses on Kir2.1 and Kir2.1-associated diseases.

scholarly journals Internetwork connectivity of molecular networks across species of life

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tarun Mahajan ◽  
Roy D. Dar
Keyword(s):  
Molecular Interactions ◽  
Protein Interactions ◽  
Transcriptional Regulatory Network ◽  
Building Blocks ◽  
Hierarchical Organization ◽  
Molecular Networks ◽  
Protein Protein Interactions ◽  
Interacting Protein ◽  
Protein Protein Interaction ◽  
Eukaryotic Species

AbstractMolecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein–protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene–gene and protein–protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN–PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

Influence of Amino Acid Mutations and Small Molecules on Targeted Inhibition of Proteins Involved in Cancer

2019 ◽  
Vol 19 (6) ◽  
pp. 457-466 ◽  
Author(s):  
V. Kanakaveti ◽  
P. Anoosha ◽  
R. Sakthivel ◽  
S.K. Rayala ◽  
M.M. Gromiha
Keyword(s):  
Amino Acid ◽  
Small Molecules ◽  
Protein Interactions ◽  
Chemical Space ◽  
Clinical Success ◽  
Growth Factor Receptor ◽  
Protein Protein Interactions ◽  
Functional Aspects ◽  
Amino Acid Mutations ◽  
Targeted Inhibition

Background:Protein-protein interactions (PPIs) are of crucial importance in regulating the biological processes of cells both in normal and diseased conditions. Significant progress has been made in targeting PPIs using small molecules and achieved promising results. However, PPI drug discovery should be further accelerated with better understanding of chemical space along with various functional aspects.Objective:In this review, we focus on the advancements in computational research for targeted inhibition of protein-protein interactions involved in cancer.Methods:Here, we mainly focused on two aspects: (i) understanding the key roles of amino acid mutations in epidermal growth factor receptor (EGFR) as well as mutation-specific inhibitors and (ii) design of small molecule inhibitors for Bcl-2 to disrupt PPIs.Results:The paradigm of PPI inhibition to date reflect the certainty that inclination towards novel and versatile strategies enormously dictate the success of PPI inhibition. As the chemical space highly differs from the normal drug like compounds the lead optimization process has to be given the utmost priority to ensure the clinical success. Here, we provided a broader perspective on effect of mutations in oncogene EGFR connected to Bcl-2 PPIs and focused on the potential challenges.Conclusion:Understanding and bridging mutations and altered PPIs will provide insights into the alarming signals leading to massive malfunctioning of a biological system in various diseases. Finding rational elucidations from a pharmaceutical stand point will presumably broaden the horizons in future.

scholarly journals Medication for Acromegaly Reduces Expression of MUC16, MACC1 and GRHL2 in Pituitary Neuroendocrine Tumour Tissue

2021 ◽  
Vol 10 ◽  
Author(s):  
Rihards Saksis ◽  
Ivars Silamikelis ◽  
Pola Laksa ◽  
Kaspars Megnis ◽  
Raitis Peculis ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Drug Treatment ◽  
Protein Interactions ◽  
Neuroendocrine Tumour ◽  
Somatostatin Analogs ◽  
Tumor Development ◽  
Gh3 Cells ◽  
Protein Protein Interactions ◽  
Antiproliferative Effects ◽  
Pathway Analyses

Acromegaly is a disease mainly caused by pituitary neuroendocrine tumor (PitNET) overproducing growth hormone. First-line medication for this condition is the use of somatostatin analogs (SSAs), that decrease tumor mass and induce antiproliferative effects on PitNET cells. Dopamine agonists (DAs) can also be used if SSA treatment is not effective. This study aimed to determine differences in transcriptome signatures induced by SSA/DA therapy in PitNET tissue. We selected tumor tissue from twelve patients with somatotropinomas, with half of the patients receiving SSA/DA treatment before surgery and the other half treatment naive. Transcriptome sequencing was then carried out to identify differentially expressed genes (DEGs) and their protein–protein interactions, using pathway analyses. We found 34 upregulated and six downregulated DEGs in patients with SSA/DA treatment. Three tumor development promoting factors MUC16, MACC1, and GRHL2, were significantly downregulated in therapy administered PitNET tissue; this finding was supported by functional studies in GH3 cells. Protein–protein interactions and pathway analyses revealed extracellular matrix involvement in the antiproliferative effects of this type of the drug treatment, with pronounced alterations in collagen regulation. Here, we have demonstrated that somatotropinomas can be distinguished based on their transcriptional profiles following SSA/DA therapy, and SSA/DA treatment does indeed cause changes in gene expression. Treatment with SSA/DA significantly downregulated several factors involved in tumorigenesis, including MUC16, MACC1, and GRHL2. Genes that were upregulated, however, did not have a direct influence on antiproliferative function in the PitNET cells. These findings suggested that SSA/DA treatment acted in a tumor suppressive manner and furthermore, collagen related interactions and pathways were enriched, implicating extracellular matrix involvement in this anti-tumor effect of drug treatment.

scholarly journals Pseudorabies virus particles lacking tegument proteins pUL11 or pUL16 incorporate less full-length pUL36 than wild-type virus, but specifically accumulate a pUL36 N-terminal fragment

2006 ◽  
Vol 87 (12) ◽  
pp. 3503-3507 ◽  
Author(s):  
Kathrin Michael ◽  
Sindy Böttcher ◽  
Barbara G. Klupp ◽  
Axel Karger ◽  
Thomas C. Mettenleiter
Keyword(s):  
Protein Interactions ◽  
Pseudorabies Virus ◽  
Single Gene ◽  
Full Length ◽  
Wild Type Virus ◽  
Type Virus ◽  
Wild Type ◽  
Protein Protein Interactions ◽  
Tegument Proteins ◽  
Terminal Fragment

Proteins of the virion tegument of alphaherpesviruses are involved in protein–protein interactions, which play important roles in virus morphogenesis. Seven single-gene deletion mutants of Pseudorabies virus were analysed for alterations in the overall composition of the virion beyond the loss of the targeted protein. The UL36 protein (pUL36) was present in equal amounts in wild-type virions and mutants lacking pUL21, pUL49, pUL51, pUS3 or pUS8. Virions lacking pUL11 or pUL16 incorporated less full-length pUL36 than wild-type particles, but contained increased amounts of an N-terminal fragment of pUL36 that is present only in traces in wild-type virus and the other mutants.

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