scholarly journals A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

2021 ◽  
Author(s):  
Siwei Chen ◽  
Yuan Liu ◽  
Yingying Zhang ◽  
Shayne D. Wierbowski ◽  
Steven M. Lipkin ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Statistical Significance ◽  
Specific Protein ◽  
Human Interactome ◽  
Human Cancers ◽  
Therapy Targets ◽  
Mutational Hotspots ◽  
Cancer Types ◽  
Hotspot Mutations ◽  
Clinically Significant

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein–protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

scholarly journals A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

2019 ◽  
Author(s):  
Siwei Chen ◽  
Yuan Liu ◽  
Yingying Zhang ◽  
Shayne D. Wierbowski ◽  
Steven M. Lipkin ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Statistical Significance ◽  
Specific Protein ◽  
Human Interactome ◽  
Human Cancers ◽  
Therapy Targets ◽  
Mutational Hotspots ◽  
Cancer Types ◽  
Hotspot Mutations ◽  
Clinically Significant

AbstractRapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically-informed computational framework that characterizes the functional relevance of all 1,107 published mutational hotspots identified in ∼25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we demonstrate that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

scholarly journals Shared hotspot mutations in spontaneously arising cancers position dog as an unparalleled comparative model for precision therapeutics

2021 ◽  
Author(s):  
Lucas Campos Rodrigues ◽  
Joshua Watson ◽  
Yuan Feng ◽  
Benjamin Lewis ◽  
Garrett Harvey ◽  
...  
Keyword(s):  
Wide Spectrum ◽  
Cancer Therapeutics ◽  
Tumor Type ◽  
Human Cancers ◽  
Naturally Occurring ◽  
Mutational Hotspots ◽  
Canine Tumors ◽  
High Concordance ◽  
Hotspot Mutations ◽  
Tumor Types

Naturally occurring canine cancers have remarkable similarities to their human counterparts. In order to determine whether these similarities occur at the molecular level, we investigated hotspot mutations in a variety of spontaneously arising canine cancers and found high concordance in oncogenic drivers between cancers in both species. These findings suggest that canines may present a powerful and complementary model for preclinical investigations for targeted cancer therapeutics. Through analysis of 708 client-owned dogs from 96 breeds (plus mixed breeds) with 23 common tumor types, we discovered mutations in 50 well-established oncogenes and tumor suppressors, and compared them to those reported in human cancers. TP53 is the most commonly mutated gene, detected in 30.81% of canine tumors overall and >40% in hemangiosarcoma and osteosarcoma. Canine tumors share mutational hotspots with human tumors in oncogenes including PIK3CA, KRAS, NRAS, BRAF, KIT and EGFR. Hotspot mutations with significant (P<0.0001) association to tumor type include NRAS G61R and PIK3CA H1047R in hemangiosarcoma, ERBB2 V659E in pulmonary carcinoma, and BRAF V588E in urothelial carcinoma. This work positions canines as excellent spontaneous models of human cancers that can help to investigate a wide spectrum of targeted therapies.

scholarly journals PINA 3.0: mining cancer interactome

2020 ◽  
Vol 49 (D1) ◽  
pp. D1351-D1357
Author(s):  
Yang Du ◽  
Meng Cai ◽  
Xiaofang Xing ◽  
Jiafu Ji ◽  
Ence Yang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Type ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Specific Context ◽  
Human Interactome ◽  
Interactive Network ◽  
Cancer Types

Abstract Protein–protein interactions (PPIs) are crucial to mediate biological functions, and understanding PPIs in cancer type-specific context could help decipher the underlying molecular mechanisms of tumorigenesis and identify potential therapeutic options. Therefore, we update the Protein Interaction Network Analysis (PINA) platform to version 3.0, to integrate the unified human interactome with RNA-seq transcriptomes and mass spectrometry-based proteomes across tens of cancer types. A number of new analytical utilities were developed to help characterize the cancer context for a PPI network, which includes inferring proteins with expression specificity and identifying candidate prognosis biomarkers, putative cancer drivers, and therapeutic targets for a specific cancer type; as well as identifying pairs of co-expressing interacting proteins across cancer types. Furthermore, a brand-new web interface has been designed to integrate these new utilities within an interactive network visualization environment, which allows users to quickly and comprehensively investigate the roles of human interacting proteins in a cancer type-specific context. PINA is freely available at https://omics.bjcancer.org/pina/.

Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

2018 ◽  
Vol 25 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Ylenia Cau ◽  
Daniela Valensin ◽  
Mattia Mori ◽  
Sara Draghi ◽  
Maurizio Botta
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Physiological Role ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Partners ◽  
High Sequence Homology ◽  
Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

scholarly journals Novel Roles of SH2 and SH3 Domains in Lipid Binding

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1191
Author(s):  
Szabolcs Sipeki ◽  
Kitti Koprivanacz ◽  
Tamás Takács ◽  
Anita Kurilla ◽  
Loretta László ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Specific Protein ◽  
Lipid Binding ◽  
Sh2 Domain ◽  
Essential Property ◽  
Cellular Regulation ◽  
Sh3 Domains ◽  
Protein Partners ◽  
Interaction Domains ◽  
Central Paradigm

Signal transduction, the ability of cells to perceive information from the surroundings and alter behavior in response, is an essential property of life. Studies on tyrosine kinase action fundamentally changed our concept of cellular regulation. The induced assembly of subcellular hubs via the recognition of local protein or lipid modifications by modular protein interactions is now a central paradigm in signaling. Such molecular interactions are mediated by specific protein interaction domains. The first such domain identified was the SH2 domain, which was postulated to be a reader capable of finding and binding protein partners displaying phosphorylated tyrosine side chains. The SH3 domain was found to be involved in the formation of stable protein sub-complexes by constitutively attaching to proline-rich surfaces on its binding partners. The SH2 and SH3 domains have thus served as the prototypes for a diverse collection of interaction domains that recognize not only proteins but also lipids, nucleic acids, and small molecules. It has also been found that particular SH2 and SH3 domains themselves might also bind to and rely on lipids to modulate complex assembly. Some lipid-binding properties of SH2 and SH3 domains are reviewed here.

Genetic regulation of nitrogen metabolism in the fungi

1997 ◽  
Vol 61 (1) ◽  
pp. 17-32
Author(s):  
G A Marzluf
Keyword(s):  
Nitrogen Metabolism ◽  
Protein Interactions ◽  
Fungal Pathogens ◽  
Metabolic Regulation ◽  
Specific Binding ◽  
Genetic Regulation ◽  
Nitrogen Sources ◽  
Specific Protein ◽  
Genes Encoding ◽  
Gata Family

In the fungi, nitrogen metabolism is controlled by a complex genetic regulatory circuit which ensures the preferential use of primary nitrogen sources and also confers the ability to use many different secondary nitrogen sources when appropriate. Most structural genes encoding nitrogen catabolic enzymes are subject to nitrogen catabolite repression, mediated by positive-acting transcription factors of the GATA family of proteins. However, certain GATA family members, such as the yeast DAL80 factor, act negatively to repress gene expression. Selective expression of the genes which encode enzymes for the metabolism of secondary nitrogen sources is often achieved by induction, mediated by pathway-specific factors, many of which have a GAL4-like C6/Zn2 DNA binding domain. Regulation within the nitrogen circuit also involves specific protein-protein interactions, as exemplified by the specific binding of the negative-acting NMR protein with the positive-acting NIT2 protein of Neurospora crassa. Nitrogen metabolic regulation appears to play a significant role in the pathogenicity of certain animal and plant fungal pathogens.

scholarly journals WWOXgene and gene product: tumor suppression through specific protein interactions

2010 ◽  
Vol 6 (2) ◽  
pp. 249-259 ◽  
Author(s):  
Zaidoun Salah ◽  
Rami Aqeilan ◽  
Kay Huebner
Keyword(s):  
Gene Product ◽  
Protein Interactions ◽  
Tumor Suppression ◽  
Specific Protein
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