scholarly journals Mechanisms of Functional Pleiotropy of p73 in Cancer and Beyond

2021 ◽  
Vol 9 ◽  
Author(s):  
Stella Logotheti ◽  
Christin Richter ◽  
Nico Murr ◽  
Alf Spitschak ◽  
Stephan Marquardt ◽  
...  
Keyword(s):  
Functional Diversity ◽  
Protein Interactions ◽  
Suppressor Gene ◽  
Gene Promoters ◽  
Protein Protein Interactions ◽  
Cytoplasmic Proteins ◽  
Functional Homolog ◽  
Interaction Partners ◽  
Unique Identity ◽  
High Degree

The transcription factor p73 is a structural and functional homolog of TP53, the most famous and frequently mutated tumor-suppressor gene. The TP73 gene can synthesize an overwhelming number of isoforms via splicing events in 5′ and 3′ ends and alternative promoter usage. Although it originally came into the spotlight due to the potential of several of these isoforms to mimic p53 functions, it is now clear that TP73 has its own unique identity as a master regulator of multifaceted processes in embryonic development, tissue homeostasis, and cancer. This remarkable functional pleiotropy is supported by a high degree of mechanistic heterogeneity, which extends far-beyond the typical mode of action by transactivation and largely relies on the ability of p73 isoforms to form protein–protein interactions (PPIs) with a variety of nuclear and cytoplasmic proteins. Importantly, each p73 isoform carries a unique combination of functional domains and residues that facilitates the establishment of PPIs in a highly selective manner. Herein, we summarize the expanding functional repertoire of TP73 in physiological and oncogenic processes. We emphasize how TP73’s ability to control neurodevelopment and neurodifferentiation is co-opted in cancer cells toward neoneurogenesis, an emerging cancer hallmark, whereby tumors promote their own innervation. By further exploring the canonical and non-canonical mechanistic patterns of p73, we apprehend its functional diversity as the result of a sophisticated and coordinated interplay of: (a) the type of p73 isoforms (b) the presence of p73 interaction partners in the cell milieu, and (c) the architecture of target gene promoters. We suppose that dysregulation of one or more of these parameters in tumors may lead to cancer initiation and progression by reactivating p73 isoforms and/or p73-regulated differentiation programs thereof in a spatiotemporally inappropriate manner. A thorough understanding of the mechanisms supporting p73 functional diversity is of paramount importance for the efficient and precise p73 targeting not only in cancer, but also in other pathological conditions where TP73 dysregulation is causally involved.

scholarly journals TC2N: A Novel Vital Oncogene or Tumor Suppressor Gene In Cancers

2021 ◽  
Vol 12 ◽  
Author(s):  
Hanyang Li ◽  
He Fang ◽  
Li Chang ◽  
Shuang Qiu ◽  
Xiaojun Ren ◽  
...  
Keyword(s):  
Tumor Suppressor ◽  
Tumor Suppressor Gene ◽  
Protein Interactions ◽  
Malignant Tumors ◽  
Suppressor Gene ◽  
C2 Domain ◽  
Protein Protein Interactions ◽  
C2 Domains ◽  
Different Types ◽  
Shock Proteins

Several C2 domain-containing proteins play key roles in tumorigenesis, signal transduction, and mediating protein–protein interactions. Tandem C2 domains nuclear protein (TC2N) is a tandem C2 domain-containing protein that is differentially expressed in several types of cancers and is closely associated with tumorigenesis and tumor progression. Notably, TC2N has been identified as an oncogene in lung and gastric cancer but as a tumor suppressor gene in breast cancer. Recently, a large number of tumor-associated antigens (TAAs), such as heat shock proteins, alpha-fetoprotein, and carcinoembryonic antigen, have been identified in a variety of malignant tumors. Differences in the expression levels of TAAs between cancer cells and normal cells have led to these antigens being investigated as diagnostic and prognostic biomarkers and as novel targets in cancer treatment. In this review, we summarize the clinical characteristics of TC2N-positive cancers and potential mechanisms of action of TC2N in the occurrence and development of specific cancers. This article provides an exploration of TC2N as a potential target for the diagnosis and treatment of different types of cancers.

Prediction of Protein-Protein Interactions Between Human Host and Two Mycobacterial Organisms

2013 ◽  
pp. 175-187
Author(s):  
Oruganty Krishnadev ◽  
Shveta Bisht ◽  
Narayanaswamy Srinivasan
Keyword(s):  
Protein Interactions ◽  
Serine Proteases ◽  
Cellular Membrane ◽  
Human Pathogens ◽  
Protein Protein Interactions ◽  
Human Host ◽  
Interaction Partners ◽  
Human Proteins ◽  
Mycobacterial Antigens ◽  
Ras Signalling

The genomes of many human pathogens have been sequenced but the protein-protein interactions across a pathogen and human are still poorly understood. The authors apply a simple homology-based method to predict protein-protein interactions between human host and two mycobacterial organisms viz., M.tuberculosis and M.leprae. They focused on secreted proteins of pathogens and cellular membrane proteins to restrict to uncovering biologically significant and feasible interactions. Predicted interactions include five mycobacterial proteins of yet unknown function, thus suggesting a role for these proteins in pathogenesis. The authors predict interaction partners for secreted mycobacterial antigens such as MPT70, serine proteases and other proteins interacting with human proteins, such as toll-like receptors, ras signalling proteins and immune maintenance proteins, that are implicated in pathogenesis. These results suggest that the list of predicted interactions is suitable for further analysis and forms a useful step in the understanding of pathogenesis of these mycobacterial organisms.

scholarly journals Peptide-based Interaction Proteomics

2020 ◽  
Vol 19 (7) ◽  
pp. 1070-1075 ◽  
Author(s):  
Katrina Meyer ◽  
Matthias Selbach
Keyword(s):  
Protein Interactions ◽  
Synthetic Peptides ◽  
Protein Protein Interactions ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Interaction Partners ◽  
Linear Motifs ◽  
Cellulose Membranes ◽  
Interaction Proteomics ◽  
Proteins Interactions

Protein-protein interactions are often mediated by short linear motifs (SLiMs) that are located in intrinsically disordered regions (IDRs) of proteins. Interactions mediated by SLiMs are notoriously difficult to study, and many functionally relevant interactions likely remain to be uncovered. Recently, pull-downs with synthetic peptides in combination with quantitative mass spectrometry emerged as a powerful screening approach to study protein-protein interactions mediated by SLiMs. Specifically, arrays of synthetic peptides immobilized on cellulose membranes provide a scalable means to identify the interaction partners of many peptides in parallel. In this minireview we briefly highlight the relevance of SLiMs for protein-protein interactions, outline existing screening technologies, discuss unique advantages of peptide-based interaction screens and provide practical suggestions for setting up such peptide-based screens.

scholarly journals NMR Spectroscopy of supramolecular chemistry on protein surfaces

2020 ◽  
Vol 16 ◽  
pp. 2505-2522
Author(s):  
Peter Bayer ◽  
Anja Matena ◽  
Christine Beuck
Keyword(s):  
Nmr Spectroscopy ◽  
Supramolecular Chemistry ◽  
Molecular Interactions ◽  
Protein Interactions ◽  
Atomic Resolution ◽  
Protein Protein Interactions ◽  
Protein Surfaces ◽  
Interaction Partners ◽  
Binding Pockets ◽  
Nmr Methods

As one of the few analytical methods that offer atomic resolution, NMR spectroscopy is a valuable tool to study the interaction of proteins with their interaction partners, both biomolecules and synthetic ligands. In recent years, the focus in chemistry has kept expanding from targeting small binding pockets in proteins to recognizing patches on protein surfaces, mostly via supramolecular chemistry, with the goal to modulate protein–protein interactions. Here we present NMR methods that have been applied to characterize these molecular interactions and discuss the challenges of this endeavor.

scholarly journals Subcellular Targeting of Plant Sucrose Transporters Is Affected by Their Oligomeric State

Plants ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 158 ◽  
Author(s):  
Varsha Garg ◽  
Aleksandra Hackel ◽  
Christina Kühn
Keyword(s):  
Protein Interactions ◽  
Subcellular Distribution ◽  
Sucrose Transporter ◽  
Membrane Microdomains ◽  
Protein Protein Interactions ◽  
Subcellular Targeting ◽  
Oligomeric State ◽  
Sucrose Transporters ◽  
Interaction Partners ◽  
The Impact

Post-translational regulation of sucrose transporters represents one possibility to adapt transporter activity in a very short time frame. This can occur either via phosphorylation/dephosphorylation, oligomerization, protein–protein interactions, endocytosis/exocytosis, or degradation. It is also known that StSUT1 can change its compartmentalization at the plasma membrane and concentrate in membrane microdomains in response to changing redox conditions. A systematic screen for protein–protein-interactions of plant sucrose transporters revealed that the interactome of all three known sucrose transporters from the Solanaceous species Solanum tuberosum and Solanum lycopersicum represents a specific subset of interaction partners, suggesting different functions for the three different sucrose transporters. Here, we focus on factors that affect the subcellular distribution of the transporters. It was already known that sucrose transporters are able to form homo- as well as heterodimers. Here, we reveal the consequences of homo- and heterodimer formation and the fact that the responses of individual sucrose transporters will respond differently. Sucrose transporter SlSUT2 is mainly found in intracellular vesicles and several of its interaction partners are involved in vesicle traffic and subcellular targeting. The impact of interaction partners such as SNARE/VAMP proteins on the localization of SlSUT2 protein will be investigated, as well as the impact of inhibitors, excess of substrate, or divalent cations which are known to inhibit SUT1-mediated sucrose transport in yeast cells. Thereby we are able to identify factors regulating sucrose transporter activity via a change of their subcellular distribution.

scholarly journals Gene phylogenies and protein–protein interactions: possible artifacts resulting from shared protein interaction partners

2004 ◽  
Vol 231 (2) ◽  
pp. 197-202 ◽  
Author(s):  
Paulo R.A. Campos ◽  
Viviane M. de Oliveira ◽  
Günter P. Wagner ◽  
Peter F. Stadler
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Interaction Partners

scholarly journals Inferring interaction partners from protein sequences

2016 ◽  
Author(s):  
Anne-Florence Bitbol ◽  
Robert S. Dwyer ◽  
Lucy J. Colwell ◽  
Ned S. Wingreen
Keyword(s):  
Protein Interactions ◽  
Sequence Data ◽  
Protein Complexes ◽  
A Priori ◽  
Specific Interaction ◽  
Three Dimensional ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Entropy Model ◽  
Interaction Partners

Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners. Hence, the sequences of interacting partners are correlated. Here we exploit these correlations to accurately identify which proteins are specific interaction partners from sequence data alone. Our general approach, which employs a pairwise maximum entropy model to infer direct couplings between residues, has been successfully used to predict the three-dimensional structures of proteins from sequences. Building on this approach, we introduce an iterative algorithm to predict specific interaction partners from among the members of two protein families. We assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. The algorithm proves successful without any a priori knowledge of interaction partners, yielding a striking 0.93 true positive fraction on our complete dataset, and we uncover the origin of this surprising success. Finally, we discuss how our method could be used to predict novel protein-protein interactions.

scholarly journals Cell cycle regulation of mitochondrial protein import revealed by genome-scale pooled bimolecular fluorescence complementation screening

2019 ◽  
Author(s):  
Kim Blakely ◽  
Patricia Mero ◽  
Roland Arnold ◽  
Ayesha Saleem ◽  
Christine Misquitta ◽  
...  
Keyword(s):  
Cell Cycle ◽  
High Throughput ◽  
Protein Interactions ◽  
Protein Import ◽  
Mitochondrial Protein ◽  
Protein Protein Interactions ◽  
Mitochondrial Protein Import ◽  
Interaction Partners ◽  
Genome Scale ◽  
Bimolecular Fluorescence

ABSTRACTA central focus of systems biology is the functional mapping of protein-protein interactions under physiological conditions. Here we describe MaGiCaL-BiFC, a lentivirus-based bimolecular fluorescence protein-fragment complementation approach for the high-throughput, genome-scale identification of protein-protein interactions in mammalian cells. After developing and validating this methodology using known protein-protein interaction pairs, we constructed genome-scale pooled BiFC libraries using the human ORFeome cDNA collection. These pooled libraries, containing ∼ 12,000 unique human cDNAs, were used to screen for candidate interaction partners of the mitochondrial transmembrane protein TOMM22. Following infection of cells with the TOMM22 bait and the pooled cDNA libraries, cells harboring candidate TOMM22 interacting proteins were isolated from the cell pool via fluorescence activated cell sorting, and identified via microarray analysis. This approach identified several known interaction partners of TOMM22, as well as novel physical and functional partners that link the mitochondrial network to proteins involved in diverse cellular processes. Notably, protein kinase CK2 was identified as a novel physical interaction partner of human TOMM22. We found that this association occurs preferentially during mitosis and involves direct phosphorylation of TOMM22, an event that may lead to attenuation of mitochondrial protein import. Together, this data contributes to the growing body of evidence suggesting eloquent coordination between cell cycle progression and mitochondrial physiology. Importantly, through high-throughput screening and focused validation, our study demonstrates the power of the MaGiCaL-BiFC approach to uncover novel functional protein-protein interactions, including those involving proteins with membrane-spanning domains, or of a transient nature, all within their native cellular environment.

scholarly journals Inferring interaction partners from protein sequences using mutual information

2018 ◽  
Author(s):  
Anne-Florence Bitbol
Keyword(s):  
Mutual Information ◽  
Protein Interactions ◽  
Sequence Data ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Specific Protein ◽  
Amino Acid Residues ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Interaction Partners

AbstractSpecific protein-protein interactions are crucial in most cellular processes. They enable multiprotein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are specific interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. This stands in contrast with structure prediction of proteins and of multiprotein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins.Author summarySpecific protein-protein interactions are at the heart of most intra-cellular processes. Mapping these interactions is thus crucial to a systems-level understanding of cells, and has broad applications to areas such as drug targeting. Systematic experimental identification of protein interaction partners is still challenging. However, a large and rapidly growing amount of sequence data is now available. Recently, algorithms have been proposed to identify which proteins interact from their sequences alone, thanks to the co-variation of the sequences of interacting proteins. These algorithms build upon inference methods that have been used with success to predict the three-dimensional structures of proteins and multi-protein complexes, and their focus is on the amino-acid residues that are in direct contact. Here, we propose a simpler method to identify which proteins interact among the paralogous proteins of two families, starting from their sequences alone. Our method relies on an approximate maximization of mutual information between the sequences of the two families, without specifically emphasizing the contacting residue pairs. We demonstrate that this method slightly outperforms the earlier one. This result highlights that partner prediction does not only rely on the identities and interactions of directly contacting amino-acids.

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