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Immunology and Infection

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published: July 18th, 2013



1Unité de Génétique, Papillomavirus et Cancer Humain (GPCH), Institut Pasteur , 2Cellule Pasteur, Université Sorbonne Paris Cité, 3Center for Cancer Systems Biology (CCSB), Harvard Medical School, Department of Cancer Biology, Dana Farber Cancer Institute
* These authors contributed equally

This article focuses on the identification of high-confident interaction datasets between host and pathogen proteins using a combination of two orthogonal methods: yeast two-hybrid followed by a high-throughput interaction assay in mammalian cells called HT-GPCA.

Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.

This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells.

A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.

The increasing amount of data collected to generate protein-protein interaction maps opens perspectives to further understand pathogen infections. As global understanding of pathogen infections begins to emerge, it provides access to the range of perturbations induced by pathogens proteins when connecting the human proteome 1. It thereby offers a way to comprehend how pathogens manipulate the host cell machinery. In particular, the mapping of several viral-host interaction networks revealed that viral proteins preferentially target host proteins that are highly connected in the cellular network (hubs), or that are central to many paths in a network (bottlen....

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1. Yeast Two-hybrid Screenings

  1. Make the following required solutions
    1. Complete Synthetic Drop Out (SD): dissolve 26.7 g of minimal SD base with glucose in 1 L of water. Add 2 g of amino acid mixture prepared as follows: 2 g Adenine hemisulfate, 2 g Arginine HCl, 2 g Histidine HCl, 2 g Isoleucine, 4 g Leucine, 2 g Lysine HCl, 2 g Methionine, 3 g Phenylalanine, 2 g L-Serine, 2 g L-Threonine, 3 g Tryptophan, 2 g Tyrosine, 1.2 g Uracil, 9 g Valine.

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A major strength of HT-GPCA lies in its high sensitivity, as illustrated by the assessment of false positive and false negative rates for the HPV E2 protein in Figure 2 (adapted from reference 13). To determine the false negative rate, known interactions of E2 from HPV16 were assessed by HT-GPCA (Figure 2A). Four out of 18 interactions were not recovered (corresponding to a 22% false negative rate). The false positive interactions were measured to be 5.8% using 12 HPV E2 proteins against.......

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Independently, yeast-two hybrid and mammalian interaction assays, such as GST pull-down, LUMIER or MAPPIT, have proven to be effective tools to detect protein-protein interactions, but are limited by the high rate of false-positive and false-negative interactions associated with these techniques 15. Moreover, evidence is growing that combining orthogonal methods increases the reliability of the obtained interaction dataset 7. The recent development of the HT-GPCA technique described here has not onl.......

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This work was supported in part by funding from the Institut Pasteur and by grants from the Ligue nationale contre le Cancer (grants R05/75-129 and RS07/75-75), the Association pour la Recherche sur le Cancer (grants ARC A09/1/5031 and 4867), and the Agence Nationale de la Recherche (ANR07 MIME 009 02 and ANR09 MIEN 026 01). M.M was a recipient of a M.E.N.R.T fellowship.


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Name Company Catalog Number Comments
Name of Reagent/Material Company Catalog Number Comments
Yeast strains Clontech    
Minimal SD base US biological D9500  
Amino acids Sigma    
3-amino-1,2,4-Triazole (3-AT) Acros organics 264571000  
Zymolase Seikagaku 120491  
DMEM Gibco-Life Technologies 31966  
Fetal bovine serum BioWest S1810  
Phosphate buffer Saline (PBS) Gibco-Life Technologies 14190  
Penicillin-Streptomycin Gibco-Life Technologies 15140  
Trypsin-EDTA Gibco-Life Technologies 25300  
Renilla luciferase assay Promega E2820  
White culture plate Greiner Bio-One 655083  
96-wellPCR plates 4titude 4t-i0730/C  
Incubator (30 °C) Memmert    
Incubator (37 °C) Heraeus    
Luminometer Berthold Centro XS-LB 960  

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