Dynamic rewiring of the human interactome by interferon signalling

ABSTRACTBackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.ResultsWe identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.ConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

Download Full-text

Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

Download Full-text

PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data

BioMed Research International ◽

10.1155/2016/2891918 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13

Author(s):

Stefan Kalkhof ◽

Stefan Schildbach ◽

Conny Blumert ◽

Friedemann Horn ◽

Martin von Bergen ◽

...

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Isotope Labeling ◽

Affinity Purification ◽

Interaction Network ◽

Mass Spectrometry Data ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Binding Behavior ◽

Bona Fide

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

Download Full-text

mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

Nucleic Acids Research ◽

10.1093/nar/gkz383 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W338-W344 ◽

Cited By ~ 51

Author(s):

Carlos H M Rodrigues ◽

Yoochan Myung ◽

Douglas E V Pires ◽

David B Ascher

Keyword(s):

Protein Interactions ◽

Interaction Network ◽

Evolutionary Information ◽

Biological Processes ◽

Missense Mutations ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Residue Interaction ◽

User Friendly ◽

Network Metrics

AbstractProtein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

Download Full-text

Interactome Data and Databases: Different Types of Protein Interaction

Comparative and Functional Genomics ◽

10.1002/cfg.377 ◽

2004 ◽

Vol 5 (2) ◽

pp. 173-178 ◽

Cited By ~ 25

Author(s):

Javier De Las Rivas ◽

Alberto de Luis

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Interaction Network ◽

Short Review ◽

Experimental Methods ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Genome Wide ◽

Bioinformatic Tool ◽

Different Types

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.

Download Full-text

De NovoAssembly and Characterization ofSophora japonicaTranscriptome Using RNA-seq

BioMed Research International ◽

10.1155/2014/750961 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Liucun Zhu ◽

Ying Zhang ◽

Wenna Guo ◽

Xin-Jian Xu ◽

Qiang Wang

Keyword(s):

Protein Interactions ◽

De Novo ◽

Average Length ◽

Genomic Analysis ◽

Interaction Network ◽

Protein Protein Interactions ◽

Gene Expressions ◽

Sophora Japonica ◽

Protein Protein Interaction ◽

Functional Genomic Analysis

Sophora japonicaLinn (Chinese Scholar Tree) is a shrub species belonging to the subfamily Faboideae of the pea family Fabaceae. In this study, RNA sequencing ofS. japonicatranscriptome was performed to produce large expression datasets for functional genomic analysis. Approximate 86.1 million high-quality clean reads were generated and assembledde novointo 143010 unique transcripts and 57614 unigenes. The average length of unigenes was 901 bps with an N50 of 545 bps. Four public databases, including the NCBI nonredundant protein (NR), Swiss-Prot, Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Cluster of Orthologous Groups (COG), were used to annotate unigenes through NCBI BLAST procedure. A total of 27541 of 57614 unigenes (47.8%) were annotated for gene descriptions, conserved protein domains, or gene ontology. Moreover, an interaction network of unigenes inS. japonicawas predicted based on known protein-protein interactions of putative orthologs of well-studied plant genomes. The transcriptome data ofS. japonicareported here represents first genome-scale investigation of gene expressions in Faboideae plants. We expect that our study will provide a useful resource for further studies on gene expression, genomics, functional genomics, and protein-protein interaction inS. japonica.

Download Full-text

Regulation of the exocytotic machinery by cAMP-dependent protein kinase: implications for presynaptic plasticity

Biochemical Society Transactions ◽

10.1042/bst0310824 ◽

2003 ◽

Vol 31 (4) ◽

pp. 824-827 ◽

Cited By ~ 56

Author(s):

G.J.O. Evans ◽

A. Morgan

Keyword(s):

Protein Kinase ◽

Protein Interactions ◽

Cell Types ◽

Protein Protein Interactions ◽

Dependent Protein Kinase ◽

Camp Dependent Protein Kinase ◽

Cysteine String Protein ◽

Presynaptic Plasticity ◽

Dependent Protein

For over a decade, the enhancement of regulated exocytosis by cAMP-dependent protein kinase (PKA) has remained unexplained at the molecular level. The fact that this phenomenon has been observed in such a wide variety of secretory cell types, from pancreatic β-cells to neurons, suggests that it is an important and fundamental mechanism. Extensive analysis of the phosphorylation of exocytotic proteins has yielded few substrates of PKA in vitro, and fewer still have had physiological effects attributed to their phosphorylation. Here we review two proteins that do fulfil these criteria: the synaptic vesicle proteins cysteine string protein (CSP) and Snapin. Phosphorylation of these proteins by PKA produces changes in their respective protein–protein interactions, and has been attributed to modulation of the vesicle priming (Snapin) and vesicle fusion (CSP) stages of exocytosis. We also discuss how the function of CSP and Snapin phosphorylation might fit into an interesting aspect of the PKA-dependent enhancement of exocytosis: presynaptic plasticity in the brain.

Download Full-text

Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

BioMed Research International ◽

10.1155/2015/259157 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Peng Liu ◽

Lei Yang ◽

Daming Shi ◽

Xianglong Tang

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Complexes ◽

Statistical Tests ◽

Interaction Network ◽

Biological Data ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Small Parts

A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptivek-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

Download Full-text

A structural network associated with the kallikrein-kinin and renin-angiotensin systems

Biological Chemistry ◽

10.1515/bc.2010.046 ◽

2010 ◽

Vol 391 (4) ◽

Cited By ~ 7

Author(s):

Veronika Stoka ◽

Vito Turk

Keyword(s):

Protein Interactions ◽

Three Dimensional ◽

Type I ◽

Protein Protein Interactions ◽

Renin Angiotensin ◽

Protein Protein Interaction ◽

Protein Interfaces ◽

Structural Network ◽

Domain Interactions ◽

Fibronectin Type

Abstract The kallikrein-kinin and renin-angiotensin (KKS-RAS) systems represent two highly regulated proteolytic systems that are involved in several physiological and pathological processes. Although their protein-protein interactions can be studied using experimental approaches, it is difficult to differentiate between direct physical interactions and functional associations, which do not involve direct atomic contacts between macromolecules. This information can be obtained from an atomic-resolution characterization of the protein interfaces. As a result of this, various three-dimensional-based protein-protein interaction databases have become available. To gain insight into the multilayered interaction of the KKS-RAS systems, we present a protein network that is built up on three-dimensional domain-domain interactions. The essential domains that link these systems are as follows: Cystatin, Peptidase_C1, Thyroglobulin_1, Insulin, CIMR (Cation-independent mannose-6-phosphate receptor repeat), fn2 (Fibronectin type II domain), fn1 (Fibronectin type I domain), EGF, Trypsin, and Serpin. We found that the CIMR domain is located at the core of the network, thus connecting both systems. From the latter, all domain interactors up to level 4 were retrieved, thus displaying a more comprehensive representation of the KKS-RAS structural network.

Download Full-text

A split protease-E. coli ClpXP system quantifies protein–protein interactions in Escherichia coli cells

Communications Biology ◽

10.1038/s42003-021-02374-w ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Shengchen Wang ◽

Faying Zhang ◽

Meng Mei ◽

Ting Wang ◽

Yueli Yun ◽

...

Keyword(s):

Protein Interactions ◽

Protein Function ◽

High Efficiency ◽

Cellular Protein ◽

Type I ◽

Protein Protein Interactions ◽

E Coli ◽

Protein Protein Interaction ◽

Quantitative Fluorescence ◽

Temperature Adaptability

AbstractCharacterizing protein–protein interactions (PPIs) is an effective method to help explore protein function. Here, through integrating a newly identified split human Rhinovirus 3 C (HRV 3 C) protease, super-folder GFP (sfGFP), and ClpXP-SsrA protein degradation machinery, we developed a fluorescence-assisted single-cell methodology (split protease-E. coli ClpXP (SPEC)) to explore protein–protein interactions for both eukaryotic and prokaryotic species in E. coli cells. We firstly identified a highly efficient split HRV 3 C protease with high re-assembly ability and then incorporated it into the SPEC method. The SPEC method could convert the cellular protein-protein interaction to quantitative fluorescence signals through a split HRV 3 C protease-mediated proteolytic reaction with high efficiency and broad temperature adaptability. Using SPEC method, we explored the interactions among effectors of representative type I-E and I-F CRISPR/Cas complexes, which combining with subsequent studies of Cas3 mutations conferred further understanding of the functions and structures of CRISPR/Cas complexes.

Download Full-text

PINA 3.0: mining cancer interactome

Nucleic Acids Research ◽

10.1093/nar/gkaa1075 ◽

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/.

Download Full-text