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.

1. Yeast Two-hybrid Screenings
<ol>
	<li>Make the following required solutions
	<ol>
		<li>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.</li>
		<li>Selective Drop Out SD-L/-W/-H: SD containing every essential amino acids except those specified (-L: -Leucine ; -W: -Tryptophan; -H: -Histidine).</li>
		<li>YPGLU: For 1 L, weight 10 g of yeast extract, 10 g of Bacto Peptone and 20 g of glucose. Complete with 25 g Bacto agar for solid media.</li>
		<li>YCM: Same as YPGLU, but pH adjusted to 4.5.</li>
	</ol>
	</li>
	<li>Mating and spreading
	<ol>
		<li>Use frozen aliquots of pre-transformed Y187 yeast strain (mating type &alpha;) containing a cDNA library cloned in pACT2. Check cell viability by plating serial dilutions on SD-L plates.</li>
		<li>Clone the pathogen ORF into pGBKT7 vector, and transform the recombinant plasmid using a standard procedure into AH109 yeast strain (mating type a).</li>
		<li>Spread pGBKT7-transformed AH109 yeast on SD-W plates. Incubate at least two days 30 &deg;C in a humidified incubator. Plates can be stored at 4 &deg;C until the two-hybrid screening.</li>
		<li>Use 3-aminotriazole (3-AT), a competitive inhibitor of the HIS3 enzyme, to counteract the potential self-transactivation of the HIS3 reporter gene by the pathogens' proteins. Perform an auto-activation test by determining the concentration of 3-AT, which prevents growth of pGBKT7-transformed yeast on SD-W/-H plates.</li>
		<li>Seed 30 ml of SD-W with few colonies of pGBKT7-transformed AH109. Incubate 30 hr at 30 &deg;C with rotation.</li>
		<li>Seed 200 ml of SD-W with the 30 ml of the AH109 cultures. Incubate with rotation around 20 hr at 30 &deg;C.</li>
		<li>Incubate thawed cDNA library-transformed Y187 in 20 ml YPGLU, incubate 10 min at 30 &deg;C with rotation.</li>
		<li>Centrifuge a volume of pGBKT7-transformed AH109 culture corresponding to an equivalent amount of viable Y187 yeast cells for 5 min at 3,500 rpm at 20 &deg;C. Carefully withdraw the supernatant and resuspend pellet in 10 ml YPGLU.</li>
		<li>Mix the resuspended AH109 yeast with Y187 containing library. Transfer in a 50 ml tube. Centrifuge for 5 min at 3,500 rpm at 20 &deg;C, carefully withdraw supernatant (fragile pellet).</li>
		<li>Resuspend pellet in YPGLU to get a total of 1.5 ml.</li>
		<li>Spread yeast on to 3 YCM-Agar 150 mm plates, 0.5 ml/plate. Incubate 4.5 hr at 30 &deg;C.</li>
		<li>Collect mated yeast from YCM plates by scraping with a rake glass in 10 ml of SD-L/-W/-H. Rinse the plates two times with 5 ml SD-L/-W/-H medium and pool.</li>
		<li>Centrifuge 5 min at 3,500 rpm, 20 &deg;C. Discard supernatant and resuspend the yeast pellet in 5 ml SD-L/-W/-H medium.</li>
		<li>Spread mated yeast on to 10 plates (0.5 ml/plate) of SD-L/-W/-H Agar supplemented with the appropriate concentration of 3-aminotriazole (3-AT) determined in the auto-activation test. Incubate at 30 &deg;C in a humidified atmosphere for 6-10 days.</li>
		<li>Determine the diploid (2n) rate in order to evaluate mating efficiency by spreading 250 &mu;l of serial dilutions (10-4 to 10-6) of the mated yeast suspension on SD-L/-W plates. Incubate plates at 30 &deg;C for two days. Count colonies grown on SD-L/-W, and deduce the total diploid number present in the initial volume of mated yeast. Report this diploid number to viable library-containing yeast number to calculate the diploid rate. It should range around 10-25%.</li>
		<li>6-10 days after incubation at 30 &deg;C, pick mated yeast colonies in fresh SD-L/-W/-H + 3-AT plates. [Tip: order the yeast in a scheme of 8 lanes of 12 wells reproducing a 96 well plate so that it will be easier afterward for sequencing]. Let yeast colonies grow for 4-5 days at 30 &deg;C in a humidified atmosphere.</li>
	</ol>
	</li>
	<li>Screening of his3 positive clones by PCR and sequencing 
	The goal is to PCR amplify the ORF contained in the pACT2 vectors of yeast colonies grown on selective SD-L/-H/-W plates.
	<ol>
		<li>Put a 96 well PCR plate on ice.</li>
		<li>To lyse yeast cells, add 50 &mu;l per well of a Zymolase 20T solution (2.5 mg/ml in 10 mM HEPES Buffer pH 7.7).</li>
		<li>Gently resuspend one colony per well.</li>
		<li>Incubate 15 min at 37 &deg;C and 15 min at 95 &deg;C.</li>
		<li>Put the plate back on ice. Add 50 &mu;l of distilled water per well. Mix well by pipetting up and down.</li>
		<li>Centrifuge for 5 min at 3,500 rpm and 4 &deg;C.</li>
		<li>PCR amplify 10 &mu;l to detect an insert using primers annealing either sides of the ORF derived from cDNA in pACT2 vectors. The protocol is given for amplification of 96 lysed yeast colonies with the ExTaq polymerase. Prepare the following mix: 525 &mu;l 10X ExTaq buffer, 420 &mu;l dNTPs mix (2.5 mM), 10.5 &mu;l Forward Primer (1 &mu;g/&mu;l), 10.5 &mu;l Reverse Primer (1 &mu;g/&mu;l), 31.5 &mu;l ExTaq (5 U/&mu;l) and 3202.5 &mu;l H20. Distribute 40 &mu;l of the mix per well in a 96 well PCR plate. Add 10 &mu;l of lysed yeast per well. Perform amplification as follows: 94 &deg;C for 3 min, then 35 cycles at 94 &deg;C for 30 sec, 60 &deg;C for 1 min, 72 &deg;C for 4 min, and end with 72 &deg;C for 7 min. Run 3 &mu;l of the PCR products on a 1.2% agarose gel to detect PCR-positive clones. Note that using pre-cast 96 well agarose gels is recommended. 
		[Tip: plate can be stored at -20 &deg;C for a few months and re-used for new PCR]</li>
		<li>Sequence the PCR amplified fragments isolated from HIS3 positive clones. Perform a BLAST analysis to identify cellular ORF. Low-confidence alignments (E value &gt;10-10, identity &lt;80%, peptide length &lt;20 amino acids) are discarded as well as frameshifted and premature STOP codon containing sequences.</li>
	</ol>
	</li>
</ol>
2. Matrix building for HT-GPCA
HT-GPCA is a mammalian cell-based assay where both pathogen and host proteins are transiently expressed fused to complementary fragments of the split Gaussia princeps luciferase. Upon interaction of the partners, this enzyme is reconstituted allowing the measure of its enzymatic activity. The interaction intensity is thus deduced from a Normalized Luminescence Ratio (see below and Figure 1)
<ol start="4">
	<li>Selection of host cell partners
	<ol>
		<li>Select proteins that are identified more than three times in the Y2H screenings for further validation in mammalian cells to increase the dataset confidence 9.</li>
		<li>Add known interacting partners of the pathogen proteins as positive controls.</li>
	</ol>
	</li>
	<li>Transfer into HT-GPCA compatible vectors
	<ol>
		<li>Clone each pathogen protein ORF in a pSPICA-N2 vector to generate pathogen proteins with amino acids 110 to 185 of the humanized Gaussia princeps luciferase fused at their N-terminus (GL2-pathogen protein fusions).</li>
		<li>PCR amplify cellular protein ORF directly from the Y2H positive clones or from other ORF resources (Human ORFeome <a href="http://horfdb.dfci.harvard.edu/" target="_blank">http://horfdb.dfci.harvard.edu/</a>) and transfer them into pSPICA-N1 vectors to generate proteins fused at the N-terminus with the amino acids 18 to 109 of humanized Gaussia princeps luciferase (GL1-host protein fusions). 
		[Tip: For large-scale cloning, the Gateway cloning system is recommended. In that case, for PCR amplification, use primers designed to contain Gateway compatible sites].</li>
		<li>Sequence all expression vectors. Sequences of the pSPICA-N1 and pSPICA-N2 can be found in reference 8.</li>
	</ol>
	</li>
</ol>
Note that plasmid constructs can be inversed, i.e. pathogens proteins in pSPICA-N1 and host protein into pSPICA-N2.
3. High-Throughput Gaussia princeps Luciferase-Based Protein Complementation Assay (HT-GPCA)
<ol start="6">
	<li>Cell Transfection
	<ol>
		<li>Seed 293T cells at 350,000 cells/ml of DMEM with 10% Fetal Bovine Serum (FBS) without antibiotic. Distribute 100 &mu;l/well in sterile white culture plates. Do not exceed 10 plates in a single experiment. Let cells grow for 24 hr at 37 &deg;C with 5% CO2. 
		[Tip: Since the pSPICA plasmids contain an SV40 origin, the use of 293T cells allows their replication in transfected cells and thus leads to overexpression of the two luciferase fragments.]</li>
		<li>The following day, transfect cells using any suitable transfection protocol. Note that for certain transfection reagents, the number of cells seeded may have to be adapted. Use 100 ng per well of both the pathogen and host ORF plasmid constructs. Co-transfect 10 ng per well of a of CMV-Firefly luciferase plasmid to normalize for transfection efficiency. Each point is tested in triplicate. 
		[Tip: DNA mixes can be performed the day before transfection and stored at -20 &deg;C with adhesive seals.]</li>
		<li>Transfer transfection mix to the plates of cultured 293T cells. Be careful to gently deposit the DNA mix onto the cells, as 293T are not strongly adherent and easily detach from the bottom of the well. Incubate in a cell culture incubator for 24-30 hr at 37 &deg;C with 5% CO2.</li>
	</ol>
	</li>
</ol>
<ol start="7">
	<li>Cell lysis and Gaussia luciferase dosage
	<ol>
		<li>Wash cells with PBS (100 &mu;l/well). Add 40 &mu;l 1X Renilla lysis buffer. Incubate for 30 min at room temperature under agitation.</li>
		<li>Save 10 &mu;l of cell lysates in another white plate for subsequent dosage of the Firefly luciferase activity. Store at 4 &deg;C or alternatively at -20 &deg;C with sealing.</li>
		<li>Measure the Gaussia activity on the remaining 30&mu;l of cell lysates by adding 100 &mu;l of Renilla luciferase assay reagent and counting luminescence for 10 sec with a luminometer. Dosage of a 96 well plate takes about half an hour. Perform measurement with fresh cell extracts since freezing or long-term storage may affect interaction-mediated Gaussia activity.</li>
	</ol>
	</li>
</ol>
<ol start="8">
	<li>Measurement of Firefly activity
	<ol>
		<li>Measure the Firefly activity on the saved 10 &mu;l of cell lysates by adding 50 &mu;l of Firefly luciferase assay reagent and counting luminescence for 10 sec. Dosage of a 96 well plate takes about half an hour. [Tip: The dosage of Firefly luciferase can be performed the day after on frozen lysates.]</li>
	</ol>
	</li>
</ol>
<ol start="9">
	<li>NLR Calculations
	<ol>
		<li>For each interaction, calculate the ratio between Gaussia luciferase activity and Firefly luciferase activity to obtain a normalized Gaussia luminescence value taking into account transfection efficiency and cell viability. Calculate the mean of the triplicates.</li>
		<li>To monitor the interaction, estimate a Normalized Luminescence Ratio (NLR) for each pathogen-host pair. To do so, divide the normalized Gaussia luminescence activity detected in presence of both pathogen and host proteins by the sum of the activities measured in control wells (Figure 1 adapted from reference 8). The NLR cut-off value for positive interactions has been estimated to be around 3.5 8.</li>
	</ol>
	</li>
</ol>
<ol start="10">
	<li>Data analysis
	<ol>
		<li>Perform hierarchical clustering using the R statistic package. First, group the NLR data into categories, then calculate an interaction dendrogram using the complete linkage method. Visualize this dendrogram with JavaTreeView 10. Calculate the distance between interaction dendrogram and phylogenetic tree using a pearson correlation coefficient.</li>
		<li>To generate interaction networks, select the positive interactions from the HT-GPCA dataset and use the Cytoscape software 1. Extract the degrees of the host proteins and plot their distribution to access the network local dynamics. Host-pathogens networks can be superimposed to the host interactome to provide an appraisal of the global impact of pathogen proteins in the host cellular network.</li>
		<li>Extract the GO (Gene Ontology) terms associated with the targeted cellular factors with the DAVID bioinformatics database 12 to assess the functional enrichment of the dataset.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Davey, N. E., Trave, G., Gibson, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+viruses+hijack+cell+regulation.">How viruses hijack cell regulation.</a> Trends in Biochemical Sciences. 36, 159-169 (2011).</li><li>Calderwood, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epstein-Barr+virus+and+virus+human+protein+interaction+maps.">Epstein-Barr virus and virus human protein interaction maps.</a> Proc. Natl. Acad. Sci. U.S.A. 104, 7606-7611 (2007).</li><li>de Chassey, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hepatitis+C+virus+infection+protein+network.">Hepatitis C virus infection protein network.</a> Mol. Syst. Biol. 4, 230 (2008).</li><li>Simonis, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Host-pathogen+interactome+mapping+for+HTLV-1+and+-2+retroviruses.">Host-pathogen interactome mapping for HTLV-1 and -2 retroviruses.</a> Retrovirology. 9, 26 (2012).</li><li>Dyer, M. D., Murali, T. M., Sobral, B. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+landscape+of+human+proteins+interacting+with+viruses+and+other+pathogens.">The landscape of human proteins interacting with viruses and other pathogens.</a> PLoS Pathog. 4, e32 (2008).</li><li>Rual, J. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+a+proteome-scale+map+of+the+human+protein-protein+interaction+network.">Towards a proteome-scale map of the human protein-protein interaction network.</a> Nature. 437, 1173-1178 (2005).</li><li>Venkatesan, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+empirical+framework+for+binary+interactome+mapping.">An empirical framework for binary interactome mapping.</a> Nat. Methods. 6, 83-90 (2009).</li><li>Cassonnet, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Benchmarking+a+luciferase+complementation+assay+for+detecting+protein+complexes.">Benchmarking a luciferase complementation assay for detecting protein complexes.</a> Nat. Methods. 8, 990-992 (2011).</li><li>Ito, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comprehensive+two-hybrid+analysis+to+explore+the+yeast+protein+interactome.">A comprehensive two-hybrid analysis to explore the yeast protein interactome.</a> Proc. Natl. Acad. Sci. U.S.A. 98, 4569-4574 (2001).</li><li>Saldanha, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Java+Treeview--extensible+visualization+of+microarray+data.">Java Treeview--extensible visualization of microarray data.</a> Bioinformatics. 20, 3246-3248 (2004).</li><li>Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L., Ideker, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytoscape+2.8:+new+features+for+data+integration+and+network+visualization.">Cytoscape 2.8: new features for data integration and network visualization.</a> Bioinformatics. 27, 431-432 (2011).</li><li>Huang da, W., Sherman, B. T., Lempicki, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+and+integrative+analysis+of+large+gene+lists+using+DAVID+bioinformatics+resources.">Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.</a> Nat. Protoc. 4, 44-57 (2009).</li><li>Muller, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Large+scale+genotype+comparison+of+human+papillomavirus+E2-host+interaction+networks+provides+new+insights+for+e2+molecular+functions.">Large scale genotype comparison of human papillomavirus E2-host interaction networks provides new insights for e2 molecular functions.</a> PLoS Pathog. 8, e1002761 (2012).</li><li>Neveu, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+analysis+of+virus-host+interactomes+with+a+mammalian+high-throughput+protein+complementation+assay+based+on+Gaussia+princeps+luciferase.">Comparative analysis of virus-host interactomes with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase.</a> Methods. , (2012).</li><li>Braun, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+experimentally+derived+confidence+score+for+binary+protein-protein+interactions.">An experimentally derived confidence score for binary protein-protein interactions.</a> Nat. Methods. 6, 91-97 (2009).</li></ol>

The authors declare that they have no competing financial interests.

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...

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 (bottlenecks proteins) 2-4. These interactions allow viruses to manipulate important cellular processes, which is instrumental to replicate and produce infectious progeny. Comparative interactions mapping were recently conducted on related viruses with the goal to extract information relative to pathogenesis 4. Furthermore, studies of the host-pathogens interactions landscape have been extended to many pathogens 5. The targeted cell factors identification provides insights into the strategies used by pathogens to infect cells and allows detection of potential pathogenic markers.
The yeast two-hybrid system is the most popular method to identify binary interactions since genetic screening is an efficient and sensitive tool for high-throughput mapping of protein-protein interactions. The approach proposed here further improves individual Y2H screenings by assessing a protein of multiple pathogens strain variants, thus providing access to a comparative overview of pathogen-host protein-protein interactions. Moreover, a major limitation of yeast two-hybrid screening lies in a high false-negative rate, since it recovers about 20% of total interactions 6. This implies that interactions detected with only a subset of pathogens variants might have escaped detection with the others. Therefore, individual partners emerging from all the two-hybrid screenings are further challenged for interaction with the complete range of strains studied, providing comparative interaction datasets. Since it was demonstrated that combining different methodologies strongly increases the robustness of protein-protein interaction datasets 7, this validation is performed in mammalian cells by a newly developed protein-fragment complementation assay called HT-GPCA 8. This cell-based system allows the detection of protein interactions by complementation of the Gaussia princeps split luciferase and has been designed to be compatible with a high-throughput format. Thanks to its luminescence-based technology and its low background noise compared with other fluorescence-based assay, HT-GPCA shows a strikingly high sensitivity.
Overall, this approach constitutes an efficient tool to generate a comprehensive mapping of host-pathogen interplays, which represents the first step toward a global understanding of the host cell hijacking.

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

a comparative approach to characterize the landscape of host-pathogen protein-protein interactions

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.

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...

Watch this Scientific Journal Video about A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions at JoVE.com

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

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 ...

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11.1K Views.  Institut Pasteur. 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.

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

Comparative in vivo Study of gp96 Adjuvanticity in the Frog Xenopus laevis

We have developed in the amphibian Xenopus laevis a unique non-mammalian model to study the ability of certain heat shock proteins (hsps) such as gp96 to facilitate cross-presentation of chaperoned antigens and elicit innate and adaptive T cell responses. Xenopus skin graft rejection provides an excellent platform to study the ability of gp96 to elicit classical MHC class Ia (class Ia) restricted T cell responses. Additionally, the Xenopus model system also provides an attractive alternative to mice for exploring the ability of gp96 to generate responses against tumors that have down-regulated their class Ia molecules thereby escaping immune surveillance. Recently, we have developed an adoptive cell transfer assay in Xenopus clones using peritoneal leukocytes as antigen presenting cells (APCs), and shown that gp96 can prime CD8 T cell responses in vivo against minor histocompatibility skin antigens as well as against the Xenopus thymic tumor 15/0 that does not express class Ia molecules. We describe here the methodology involved to perform these assays including the elicitation, pulsing and adoptive transfer of peritoneal leukocytes, as well as the skin graft and tumor transplantation assays. Additionally we are also describing the harvesting and separation of peripheral blood leukocytes used for flow cytometry and proliferation assays which allow for further characterization of the effector populations involved in skin rejection and anti-tumor responses.

Comparative in vivo Study of gp96 Adjuvanticity in the Frog Xenopus laevis

The frog Xenopus laevis provides an attractive alternative non-mammalian model for exploring the ability of heat shock protein such as gp96 to promote antigen-specific CD8 T cell responses. We present methods to study in vivo facilitation of cross-presentation of skin and tumor antigens by gp96.

We have developed in the amphibian Xenopus laevis a unique non-mammalian model to study the ability of certain heat shock proteins (hsps) such as gp96 to ...

Use of an Optical Trap for Study of Host-Pathogen Interactions for Dynamic Live Cell Imaging

Dynamic live cell imaging allows direct visualization of real-time interactions between cells of the immune system1, 2; however, the lack of spatial and temporal control between the phagocytic cell and microbe has rendered focused observations into the initial interactions of host response to pathogens difficult. Historically, intercellular contact events such as phagocytosis3 have been imaged by mixing two cell types, and then continuously scanning the field-of-view to find serendipitous intercellular contacts at the appropriate stage of interaction. The stochastic nature of these events renders this process tedious, and it is difficult to observe early or fleeting events in cell-cell contact by this approach. This method requires finding cell pairs that are on the verge of contact, and observing them until they consummate their contact, or do not. To address these limitations, we use optical trapping as a non-invasive, non-destructive, but fast and effective method to position cells in culture.
Optical traps, or optical tweezers, are increasingly utilized in biological research to capture and physically manipulate cells and other micron-sized particles in three dimensions4. Radiation pressure was first observed and applied to optical tweezer systems in 19705, 6, and was first used to control biological specimens in 19877. Since then, optical tweezers have matured into a technology to probe a variety of biological phenomena8-13.
We describe a method14 that advances live cell imaging by integrating an optical trap with spinning disk confocal microscopy with temperature and humidity control to provide exquisite spatial and temporal control of pathogenic organisms in a physiological environment to facilitate interactions with host cells, as determined by the operator. Live, pathogenic organisms like Candida albicans and Aspergillus fumigatus, which can cause potentially lethal, invasive infections in immunocompromised individuals15, 16 (e.g. AIDS, chemotherapy, and organ transplantation patients), were optically trapped using non-destructive laser intensities and moved adjacent to macrophages, which can phagocytose the pathogen. High resolution, transmitted light and fluorescence-based movies established the ability to observe early events of phagocytosis in living cells. To demonstrate the broad applicability in immunology, primary T-cells were also trapped and manipulated to form synapses with anti-CD3 coated microspheres in vivo, and time-lapse imaging of synapse formation was also obtained. By providing a method to exert fine spatial control of live pathogens with respect to immune cells, cellular interactions can be captured by fluorescence microscopy with minimal perturbation to cells and can yield powerful insight into early responses of innate and adaptive immunity.

A method is described to individually select, manipulate, and image live pathogens using an optical trap coupled to a spinning disk microscope. The optical trap provides spatial and temporal control of organisms and places them adjacent to host cells. Fluorescence microscopy captures dynamic intercellular interactions with minimal perturbation to cells.

Dynamic live cell imaging allows direct visualization of real-time interactions between cells of the immune system1, 2; however, the lack of spatial and ...

Use of the EpiAirway Model for Characterizing Long-term Host-pathogen Interactions

Nontypeable Haemophilus influenzae (NTHi) are human-adapted Gram-negative bacteria that can cause recurrent and chronic infections of the respiratory mucosa 1; 2. To study the mechanisms by which these organisms survive on and inside respiratory tissues, a model in which successful long-term co-culture of bacteria and human cells can be performed is required. We use primary human respiratory epithelial tissues raised to the air-liquid interface, the EpiAirway model (MatTek, Ashland, MA). These are non-immortalized, well-differentiated, 3-dimensional tissues that contain tight junctions, ciliated and nonciliated cells, goblet cells that produce mucin, and retain the ability to produce cytokines in response to infection. 
This biologically relevant in vitro model of the human upper airway can be used in a number of ways; the overall goal of this method is to perform long-term co-culture of EpiAirway tissues with NTHi and quantitate cell-associated and internalized bacteria over time. As well, mucin production and the cytokine profile of the infected co-cultures can be determined. This approach improves upon existing methods in that many current protocols use submerged monolayer or Transwell cultures of human cells, which are not capable of supporting bacterial infections over extended periods3. For example, if an organism can replicate in the overlying media, this can result in unacceptable levels of cytotoxicity and loss of host cells, arresting the experiment. The EpiAirway model allows characterization of long-term host-pathogen interactions. Further, since the source for the EpiAirway is normal human tracheo-bronchial cells rather than an immortalized line, each is an excellent representation of actual human upper respiratory tract tissue, both in structure and in function4.
For this method, the EpiAirway tissues are weaned off of anti-microbial and anti-fungal compounds for 2 days prior to delivery, and all procedures are performed under antibiotic-free conditions. This necessitates special considerations, since both bacteria and primary human tissues are used in the same biosafety cabinet, and are co-cultured for extended periods.

This method allows characterization of extended bacterial co-culture with EpiAirways, primary human respiratory epithelial tissue grown at the air-liquid interface, a biologically relevant in vitro model. The approach can be used with any microbe that is amenable to long-term co-culture.

Nontypeable Haemophilus influenzae  (NTHi) are human-adapted Gram-negative bacteria that can cause recurrent and chronic infections of the respiratory ...

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysis by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.

Here, we present phenomic approaches for the functional characterization of putative phage genes. Techniques include a developed assay capable of monitoring host anabolic metabolism, the Multi-phenotype Assay Plates (MAPs), in addition to the established method of metabolomics, capable of measuring effects to catabolic metabolism.

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage ...

Biomimetic Materials to Characterize Bacteria-host Interactions

Bacterial attachment to host cells is one of the earliest events during bacterial colonization of host tissues and thus a key step during infection. The biochemical and functional characterization of adhesins mediating these initial bacteria-host interactions is often compromised by the presence of other bacterial factors, such as cell wall components or secreted molecules, which interfere with the analysis. This protocol describes the production and use of biomimetic materials, consisting of pure recombinant adhesins chemically coupled to commercially available, functionalized polystyrene beads, which have been used successfully to dissect the biochemical and functional interactions between individual bacterial adhesins and host cell receptors. Protocols for different coupling chemistries, allowing directional immobilization of recombinant adhesins on polymer scaffolds, and for assessment of the coupling efficiency of the resulting &#8220;bacteriomimetic&#8221; materials are also discussed. We further describe how these materials can be used as a tool to inhibit pathogen mediated cytotoxicity and discuss scope, limitations and further applications of this approach in studying bacterial - host interactions.

Bacterial attachment to host cells is a key step during host colonization and infection. This protocol describes the generation of polymer-coupled recombinant adhesins as biomimetic materials which allow analysis of the contribution of individual adhesins to these processes, independent of other bacterial factors.

Bacterial attachment to host cells is one of the earliest events during bacterial colonization of host tissues and thus a key step during infection. The ...

Retroviral Transduction of Helper T Cells as a Genetic Approach to Study Mechanisms Controlling their Differentiation and Function

Helper T cell development and function must be tightly regulated to induce an appropriate immune response that eliminates specific pathogens yet prevents autoimmunity. Many approaches involving different model organisms have been utilized to understand the mechanisms controlling helper T cell development and function. However, studies using mouse models have proven to be highly informative due to the availability of genetic, cellular, and biochemical systems. One genetic approach in mice used by many labs involves retroviral transduction of primary helper T cells. This is a powerful approach due to its relative ease, making it accessible to almost any laboratory with basic skills in molecular biology and immunology. Therefore, multiple genes in wild type or mutant forms can readily be tested for function in helper T cells to understand their importance and mechanisms of action. We have optimized this approach and describe here the protocols for production of high titer retroviruses, isolation of primary murine helper T cells, and their transduction by retroviruses and differentiation toward the different helper subsets. Finally, the use of this approach is described in uncovering mechanisms utilized by microRNAs (miRNAs) to regulate pathways controlling helper T cell development and function.

Many experimental systems have been utilized to understand the mechanisms regulating T cell development and function in an immune response. Here a genetic approach using retroviral transduction is described, which is economic, time efficient, and most importantly, highly informative in identifying regulatory pathways.

Helper T cell development and function must be tightly regulated to induce an appropriate immune response that eliminates specific pathogens yet prevents ...

A Protocol to Characterize the Morphological Changes of Clostridium difficile in Response to Antibiotic Treatment

Assessment of antibiotic action with new drug development directed towards anaerobic bacteria is difficult and technically demanding. To gain insight into possible MOA, morphologic changes associated with antibiotic exposure can be visualized using scanning electron microscopy (SEM). Integrating SEM imaging with traditional kill curves may improve our insight into drug action and advance the drug development process. To test this premise, kill curves and SEM studies were conducted using drugs with known but different MOA (vancomycin and metronidazole). C. difficile cells (R20291) were grown with or without the presence of antibiotic for up to 48 h. Throughout the 48 h interval, cells were collected at multiple time points to determine antibiotic efficacy and for imaging on the SEM. Consistent with previous reports, vancomycin and metronidazole had significant bactericidal activity following 24 h of treatment as measured by colony-forming unit (CFU) counting. Using SEM imaging we determined that metronidazole had significant effects on cell length (&#62; 50% reduction in cell length for each antibiotic; P&#60; 0.05) compared to controls and vancomycin. While the phenotypic response to drug treatment has not been documented previously in this manner, they are consistent with the drug&#39;s MOA demonstrating the versatility and reliability of the imaging and measurements and the application of this technique for other experimental compounds.

A Protocol to Characterize the Morphological Changes of Clostridium difficile in Response to Antibiotic Treatment

Antibiotic efficacy is most commonly determined by conducting killing kinetic studies and measuring colony forming units (CFUs). By integrating scanning electron microscopy (SEM) with these standard methods, we can distinguish the pharmacological effects of treatment between different antibiotics.

Assessment of antibiotic action with new drug development directed towards anaerobic bacteria is difficult and technically demanding. To gain insight into ...

Evaluation of Host-Pathogen Responses and Vaccine Efficacy in Mice

Vaccines are a 20th century medical marvel. They have dramatically reduced the morbidity and mortality caused by infectious diseases and contributed to a striking increase in life expectancy around the globe. Nonetheless, determining vaccine efficacy remains a challenge. Emerging evidence suggests that the current acellular vaccine (aPV) for Bordetella pertussis (B. pertussis) induces suboptimal immunity. Therefore, a major challenge is designing a next-generation vaccine that induces protective immunity without the adverse side effects of a whole-cell vaccine (wPV). Here we describe a protocol that we used to test the efficacy of a promising, novel adjuvant that skews immune responses to a protective Th1/Th17 phenotype and promotes a better clearance of a B. pertussis challenge from the murine respiratory tract. This article describes the protocol for mouse immunization, bacterial inoculation, tissue harvesting, and analysis of immune responses. Using this method, within our model, we have successfully elucidated crucial mechanisms elicited by a promising, next-generation acellular pertussis vaccine. This method can be applied to any infectious disease model in order to determine vaccine efficacy.

Here we present an elegant protocol for in vivo evaluation of vaccine effectiveness and host immune responses. This protocol can be adapted for vaccine models that study viral, bacterial, or parasitic pathogens.

Vaccines are a 20th century medical marvel. They have dramatically reduced the morbidity and mortality caused by infectious diseases and contributed to a ...

Infection of Zebrafish Larvae with Aspergillus Spores for Analysis of Host-Pathogen Interactions

Invasive aspergillosis (IA) is one of&#160;the most common fungal infections among immunocompromised individuals. Despite the availability of antifungal drugs, IA can cause &#62;50% mortality in infected immunocompromised patients. It is crucial to determine both host and pathogen factors that contribute to infection susceptibility and low survival rates in infected patients in order to develop novel therapeutics. Innate immune responses play a pivotal role in recognition and clearance of Aspergillus spores, though little is known about the exact cellular and molecular mechanisms. Reliable models are required to investigate detailed mechanistic interactions between the host and pathogen. The optical clarity and genetic tractability of zebrafish larvae make them an intriguing model&#160;to study host-pathogen interactions of multiple human bacterial and fungal infections in a live and intact host. This protocol describes a larval zebrafish Aspergillus infection model. First, Aspergillus spores are isolated and injected into the zebrafish hindbrain ventricle via microinjection. Then, chemical inhibitors such as immunosuppressive drugs are added directly to the larval water. Two methods to monitor the infection in injected larvae are described, including the 1) homogenization of larvae for colony forming unit (CFU) enumeration and 2) a repeated, daily live imaging setup. Overall, these techniques can be used to mechanistically analyze the progression of Aspergillus infection in vivo and can be applied to different host backgrounds and Aspergillus strains to interrogate host-pathogen interactions.

Infection of Zebrafish Larvae with Aspergillus Spores for Analysis of Host-Pathogen Interactions

This protocol describes an Aspergillus infection model in zebrafish larvae. Aspergillus spores are microinjected into the hindbrain of larvae, and chemical treatment is used to induce immunosuppression. Infection progression is monitored via a daily imaging setup to monitor fungal growth and immune responses as well as enumeration of live spores by colony forming unit plating.

Invasive aspergillosis (IA) is one of&#160;the most common fungal infections among immunocompromised individuals. Despite the availability of antifungal ...

Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

The technological achievements of mass spectrometry (MS)-based quantitative proteomics opens many undiscovered avenues for analyzing an organism&#8217;s global proteome under varying conditions. This powerful strategy applied to the interactions of microbial pathogens with the desired host comprehensively characterizes both perspectives towards infection. Herein, the workflow describes label-free quantification (LFQ) of the infectome of Cryptococcus neoformans, a fungal facultative intracellular pathogen that is the causative agent of the deadly disease cryptococcosis, in the presence of immortalized macrophage cells. The protocol details the proper protein preparation techniques for both pathogen and mammalian cells within a single experiment, resulting in appropriate peptide submission for liquid-chromatography (LC)-MS/MS analysis. The high throughput generic nature of LFQ allows a wide dynamic range of protein identification and quantification, as well as transferability to any host-pathogen infection setting, maintaining extreme sensitivity. The method is optimized to catalogue extensive, unbiased protein abundance profiles of a pathogen within infection-mimicking conditions. Specifically, the method demonstrated here provides essential information on C. neoformans pathogenesis, such as protein production necessary for virulence and identifies critical host proteins responding to microbial invasion.

Here, we present a protocol to profile the interplay between host and pathogen during infection by mass spectrometry-based proteomics. This protocol uses label-free quantification to measure changes in protein abundance of both host (e.g., macrophages) and pathogen (e.g., Cryptococcus neoformans) in a single experiment.

The technological achievements of mass spectrometry (MS)-based quantitative proteomics opens many undiscovered avenues for analyzing an organism&#8217;s ...