This work was supported by Canadian Institute of Health Research (CIHR) Grants 191597, 299432 and 324265, a Natural Sciences and Engineering Research Council of Canada Discovery grant and a Human Frontier Science Program grant to CRL. CRL is a CIHR New Investigator. Guillaume Diss is supported by a PROTEO fellowship. Samuel Rochette is supported by&#160;NSERC and FRQNT fellowships.

1. Construction/verification of Bait Strains
<ol>
	<li>If the bait strain of interest is available in the MATa DHFR F[1,2] collection, retrieve it from the collection as described in step 1.1.1, otherwise construct the strain as described in step 1.1.2. 
	NOTE: The protocol described here uses a DHFR F[1,2] strain as a bait and the DHFR F[3] collection as preys, as this collection contains more strains than the DHFR F[1,2] collection. However, it is possible to perform the screen the other way around if the bait strain is only available in the DHFR F[3] collection or in both orientations if one requires higher coverage of the interactome.
	<ol>
		<li>Thaw the glycerol stock plate that contains the bait strain on ice for one hour. Sterilize the aluminum foil covering the plate using 95% ethanol. Pierce the foil with a sterile tip, pipette up and down to resuspend the cells and streak 2-3 &#956;l of glycerol stock on selective yeast extract peptone dextrose (YPD) + 100 &#956;g/ml Nourseothricin (Nat) in order to isolate single colonies. Incubate for two days at 30 &#176;C.</li>
		<li>Construction of a bait PCA strain (MATa DHFR F[1,2]).
		<ol>
			<li>Using high-fidelity polymerase and a standard PCR protocol, amplify the DHFR F[1,2] cassette from plasmid pAG25-linker-DHFR F[1,2]-ADHterm using oligonucleotides with overhanging ends homologous to the last 40 bp of the ORF&#8217;s 3&#8217;-end excluding the stop codon (Forward primer) and to the first 40 bp of the gene&#8217;s 3&#8217;-UTR (Reverse primer) (Figure 1A).</li>
			<li>Transform the PCR product into competent yeast cells (usually in a BY4741 strain) using standard LiOAc/PEG yeast transformation protocol as in29 (Figure 1A).</li>
			<li>Plate on selective YPD + Nat medium to isolate positive transformants.</li>
			<li>Perform a diagnostic colony PCR on isolated colonies to confirm the proper DHFR F[1,2] fusion. Use primers annealing 1) in the gene coding sequence (Forward oligo) about 100 bp upstream of the DHFR fusion and 2) in the ADH terminator of the cassette (Reverse oligo) (Figure 1B).</li>
			<li>Sequence the PCR product by Sanger sequencing to confirm proper gene fusion.</li>
			<li>Archive the confirmed bait strain in 25% glycerol at -80 &#176;C. 
			NOTE: The protocol can be paused at this step.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Pin-tool Sterilization and Printing Procedures
NOTE: The sterilization procedure described below was optimized for the pin-tools manipulated by the BM3-BC (S&#38;P Robotics) robotic platform, but can be adapted to other platforms as well. This section describes the Pin-tool sterilization and printing procedures that are used to transfer cells from one medium to another for the rest of the protocol. In-house scripts used to perform these routines can be obtained upon request. Note that all steps can be performed without the need of a robotic platform using a manual pin-tool30.
<ol>
	<li>Mount the appropriate pin-tool on the robotic platform.</li>
	<li>Prepare cleaning and wet stations as follows:
	<ol>
		<li>Add 500 ml of sterile water in the water bath station.</li>
		<li>Add 320 ml of sterile water in the brush station.</li>
		<li>Add 380 ml of 70% ethanol in the sonicator when replicating from agar plate (86 x 128 mm omnitray, containing 35 ml of solidified medium) to agar plate, or 400 ml when replicating from a microtiter plate containing liquid cultures to an agar plate.</li>
		<li>Add 35 ml of sterile water in the wet station (consisting of a sterile empty omnitray).</li>
	</ol>
	</li>
	<li>At the beginning of each day that requires the robotic platform, sterilize the pin-tools five times for one min in the sonicator bath. Meanwhile, turn on the UV lamp for five min to sterilize the robot enclosure. 
	NOTE: This is not required if the robot is housed under a sterile hood.</li>
	<li>Between each pin-tool replication round, sterilize the pin-tool as follows:
	<ol>
		<li>Soak the pin-tool five times for 10 sec in the water bath station to remove cell clumps.</li>
		<li>Soak the pin-tool twice back and forth in the brush station. 
		NOTE: The rotating brush will remove residual cells.</li>
		<li>Soak the pin-tool twice for 20 sec in the sonicator station. 
		NOTE: Remaining cells on the pins will be removed by sonication or killed by ethanol.</li>
		<li>Make sure that the dipping depth of the pins increases at each successive bath to ensure proper sterilization.</li>
	</ol>
	</li>
	<li>Dry the pin-tool in the air-dryer station for 25 sec.</li>
	<li>Before taking cells on the source plates, wet the pins in the wet station, which contains 35 ml of sterile water in an omnitray.</li>
	<li>Dip the pins twice in the colonies from the source plate.</li>
	<li>Print on a freshly poured destination plate by touching the agar surface twice (hereafter referred to as the action of &#8220;printing&#8221; an array).</li>
</ol>
3. Condensation of the DHFR F[3] Collection in 1,536 Arrays Using an Automated Robotic Platform
<ol>
	<li>Thaw on ice the DHFR F[3] collection (60 96-well plates) and an additional 96-well plate filled with the L-DHFR F[3] control strain (Figure 1C), which contains the DHFR F[3] fragment and the upstream linker expressed alone as a negative control. 
	NOTE: In principle, this fragment should not interact with any DHFR-fragment fusion protein (see discussion for details).</li>
	<li>Centrifuge plates (quick spin) before removing the aluminum foil to avoid risks of cross-contamination among wells.</li>
	<li>Condense the collection onto 16 arrays of 384 strains (Figure 2A). To do this, for each 384 array, print four glycerol plates on the four quadrants of a selective YPD + 250 &#956;g/ml Hygromycin B (HygB) omnitray using the 96 pin-tool (here, a 384 array can be subdivided in four equally interspaced quadrants, each consisting of 96 positions in a 2 x 2 matrix layout). Insert four 96-well plates containing the L-DHFR F[3] negative control in between the 60 other plates in order to have a final set of 64 plates that exactly fill four 1,536 arrays. Sterilize the pin-tool between each replication cycle as described in step 2. 
	NOTE: Insert the L-DHFR F[3] plates in order to have one such plate on each of the four final 1,536 arrays.</li>
	<li>Incubate plates for two days at 30 &#176;C. NOTE: At this stage, the DHFR collection can be stored in a 384 format at 4 &#730;C for up to one month on agar plates.</li>
	<li>Condense the collection into four arrays of 1,536 strains (Figure 2A). To do this, for each 1,536 array, print four arrays of 384 strains on the four quadrants of a plate of the same medium as in step 3.2 using the 384 pin-tool (here, a 1,536 array can be subdivided in four equally interspaced quadrants each consisting of 384 positions in a 2 x 2 matrix layout).</li>
	<li>Incubate plates for two days at 30 &#176;C. &#160;NOTE: At this stage, the DHFR collection can be stored in a 1,536 format at 4 &#730;C for up to one month on agar plates.</li>
	<li>Replicate the four arrays on the same medium to standardize colony size using a 1,536 pin-tool.</li>
	<li>Incubate plates for two days at 30 &#730;C.</li>
</ol>
4. High-throughput DHFR-PCA Procedure
<ol>
	<li>Inoculate a culture of the bait strain (DHFR F[1,2] fusion) obtained from step 1.1.1 or 1.1.2 in 20 ml of liquid YPD + Nat in a 50 ml tube (Figure 2B).</li>
	<li>Incubate for two days at 30 &#176;C with shaking at 250 rpm to allow the culture to reach saturation.</li>
	<li>After two days of incubation, plate 5 ml of the culture on a YPD + Nat omnitray. Let cells adsorb on the surface for 5-10 min and remove the excess liquid (Figure 2B). Repeat twice to make three replicates.</li>
	<li>Incubate for two days at 30 &#176;C.</li>
	<li>Print the bait strain from steps 4.3 and 4.4 on 12 YPD plates (enough to mating four plates of the DHFR F[3] collection &#215; three replicates) with a 1,536 pin-tool using each cell lawn no more than four times.</li>
	<li>Print the appropriate array of the DHFR F[3] collection on top of the bait cells using the 1,536 pin-tool (Figure 2C).</li>
	<li>Let the strains mate by incubating for two days at 30 &#176;C.</li>
	<li>Select diploid cells by printing colonies on omnitrays containing YPD + HygB + Nat (Figure 2C).</li>
	<li>Incubate for two days at 30 &#176;C.</li>
	<li>Repeat diploid selection as described in steps 4.8 and 4.9 (Figure 2B).</li>
	<li>Prepare plates with media containing MTX (MTX medium) a day before use following these steps21 (CAUTION: be careful when working with MTX as it is a toxic compound. Always wear gloves, protection glasses and a lab coat when handling it) (Figure 2C):
	<ol>
		<li>Prepare the 10x &#8211;lys/met/ade amino acid drop-out in deionized water. Filter-sterilize the drop-out in a sterile bottle using a 0.2 &#181;m sterile syringe filter or, if needed in large quantities, a 0.2 &#181;m bottle top filtration funnel (Table 1).</li>
		<li>Prepare a 10 mg/ml MTX stock solution in dimethyl sulfoxide (DMSO). Use immediately after preparing the solution and freeze the remainder at -20 &#176;C. Protect it from light as it is photosensitive. Do not freeze MTX again after thawing it.</li>
		<li>Prepare the media as follows (medium ingredients and quantities are the same as those used in Tarassov et al.11):
		<ol>
			<li>For one liter of medium, mix in two separate flasks: 1) 6.69 g of yeast nitrogen base without amino acids and without ammonium sulfate and 330 ml of deionized water; 2) 25 g of noble agar and 500 ml of deionized water.</li>
			<li>Autoclave flasks at 121 &#176;C for 20 min.</li>
			<li>Equilibrate temperature in a water bath at 55 &#176;C for at least one hour.</li>
			<li>Mix the two flasks together and add 50 ml of sterile 40% glucose, 100 ml of 10x sterile drop-out, and 20 ml of MTX 10 mg/ml.</li>
			<li>Pour 35 ml (see Note below) of medium in omnitrays. Let solidify for at least an hour and a half. Shield plates from light. 
			NOTE: Here, pouring 35 ml of medium per omnitray is critical to ensure equal plate thickness, which is important for all downstream steps.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Image plates from the second round of diploid selection with the robotic platform or with a regular digital camera with uniform plate illumination. Use these pictures to identify empty positions on the arrays when performing the downstream analysis. Make sure that the parameters of the camera are always the same and that the robot light is turned on.</li>
	<li>Print diploid cells on MTX media using the 1,536 pin-tool.</li>
	<li>Incubate for four days at 30 &#176;C in plastic bags to prevent drying.</li>
	<li>Prepare a second batch of omnitrays containing MTX medium as described in step 4.11.</li>
	<li>After four days of incubation, image plates using the robotic platform or regular digital camera. Make sure that the parameters of the camera are always the same and that the robot light is turned on.</li>
	<li>Perform a second round of MTX selection by replicating the cells on the second batch of MTX media. 
	NOTE: This will decrease the background growth of PCA strains and increase the quantitative resolution.</li>
	<li>Incubate for four days at 30 &#176;C in plastic bags to prevent drying.</li>
	<li>Image plates as described in step 4.16.</li>
</ol>
5. Image Analysis
<ol>
	<li>Analyze images of colony arrays with custom ImageJ31 scripts or using published softwares such as Colonizer, Ht colony grid analyzer, Cell profiler, Colony Imager, ScreenMill, YeastXtract and gitter (compiled in 32). Image analysis should output one or several spreadsheets containing colony sizes for each position of each array, use these colony sizes for all downstream analyses. 
	NOTE: In this study, we used a custom ImageJ script described in Leducq et al.33 (see discussion section for more details).</li>
</ol>
6. Data Analysis
NOTE: Results from image analysis can be processed in a tabulator such as excel or using a scripting language such as R34. The following steps describe the procedure using a custom ImageJ31 script.
<ol>
	<li>Using a custom script, concatenate output files from image analysis and annotate each row with the plate and strain information as in Supplementary Table 1.</li>
	<li>Log2 transform the colony size values (Integrated density or colony area; here, column &#8220;IntDenBackSub&#8221; from Supplementary Table 1 was used). 
	NOTE: Value distribution will look as in Figure 3A.</li>
	<li>Normalize these values by subtracting the median value of each plate. 
	NOTE: This step controls for plate bias that may result from unequal media quantity or variation in automatic image acquisition, and reduces the inter-replicate variance (Figure 3B).</li>
	<li>Verify that replicates correlate with each other (Figure 3C) to assess the reproducibility of the experiments.</li>
	<li>To differentiate interacting from non-interacting bait-prey pairs, set a high-confidence threshold corresponding to the 95th percentile of the distribution of the L-DHFR F[3] controls. 
	NOTE: In this experiment, this corresponds to 3.39 (Figure 3D). Alternatively, a threshold based on the overlap with known physical interactors such as those reported in the BioGRID35 can be used. See discussion for more details.</li>
	<li>For any bait, filter preys identified as involved in false positive interactions in DHFR-PCA screens (see discussion for more details) and listed in Supplementary Table 2 (identified as &#8220;1&#8221; in column &#8220;filtered&#8221;).</li>
	<li>Average the Log2 normalized colony sizes of the three replicates of each interaction (column &#8220;Average score&#8221; in Supplementary Table 2).</li>
</ol>
7. Validation of Physical Interactors Using Small-scale Experiments
NOTE: Any PPI of particular interest having a score above or close to the applied threshold can be validated using the DHFR-PCA assay in a small-scale experimental design using a growth assay on solid or liquid MTX medium. The steps below show the procedure to manually construct diploid PCA strains and perform spot assays on MTX medium. The experimenter should perform these steps for all necessary controls (Bait-DHFR F[1,2] x L-DHFR F[3], Zipper-linker-DHFR diploid strains and linker-DHFR diploid strain).
<ol>
	<li>Plate 2-3 &#956;l of the glycerol stock of the bait strain generated in 1.1.2.6), the L-DHFR F[3] control strain, the Zipper-linker-DHFR and linker-DHFR diploid strains on YPD+Nat, YPD+HygB and twice YPD+Nat+HygB media, respectively.</li>
	<li>Retrieve the prey of interest in the DHFR F[3] collection and follow instructions in step 1.1.1, but streak the strain on YPD+HygB medium instead of YPD+Nat medium.</li>
	<li>Perform a diagnostic PCR as in 1.1.2.4 to confirm the DHFR F[3] fusion at the prey locus and sequence the product.</li>
	<li>Inoculate 1 ml of liquid YPD medium with the haploid strains to mate (Bait x Prey, Bait x L-DHFR F[3] control) and grow at least two days at 30 &#176;C to allow the diploids to form.</li>
	<li>Select diploids by streaking 4-5 &#956;l of the culture in 7.4 on solid YPD+HygB+Nat medium. Grow two days at 30 &#176;C.</li>
	<li>Select one isolated colony and grow overnight in liquid culture (1 ml) to perform the growth assay.</li>
	<li>Prepare MTX and DMSO (same ingredients as MTX medium, but without MTX) plates a day before use. See step 4.11 for more details.</li>
	<li>Perform growth assay by spotting serial dilutions of the different cultures on the control (DMSO) and selection plates (MTX).
	<ol>
		<li>Dilute precultures to O.D.= 1.</li>
		<li>Perform five-fold dilutions (up to a dilution factor of 625) in a sterile 96-well plate.</li>
		<li>Spot 4 &#956;l of each dilution on the PCA media (DMSO and MTX).</li>
		<li>Incubate at 30 &#176;C in plastic bags to prevent drying.</li>
		<li>Image plates from days 1 to 7 of incubation using the robotic platform or a regular digital camera.</li>
	</ol>
	</li>
</ol>

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R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=qPCA:+a+scalable+assay+to+measure+the+perturbation+of+protein-protein+interactions+in+living+cells.">qPCA: a scalable assay to measure the perturbation of protein-protein interactions in living cells.</a> Mol Biosyst. 9, 36-43 (2013).</li><li>Pelletier, J. N., Campbell-Valois, F. X., Michnick, S. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oligomerization+domain-directed+reassembly+of+active+dihydrofolate+reductase+from+rationally+designed+fragments.">Oligomerization domain-directed reassembly of active dihydrofolate reductase from rationally designed fragments.</a> Proceedings of the National Academy of Sciences of the United States of America. 95, 12141-12146 (1998).</li><li>Gietz, R. D., Woods, 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=Transformation+of+yeast+by+lithium+acetate/single-stranded+carrier+DNA/polyethylene+glycol+method.">Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method.</a> Methods Enzymol. 350, 87-96 (2002).</li><li>Schuldiner, M., Collins, S. R., Weissman, J. S., Krogan, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+genetic+analysis+in+Saccharomyces+cerevisiae+using+epistatic+miniarray+profiles+(E-MAPs)+and+its+application+to+chromatin+functions.">Quantitative genetic analysis in Saccharomyces cerevisiae using epistatic miniarray profiles (E-MAPs) and its application to chromatin functions.</a> Methods. 40, 344-352 (2006).</li><li>Schneider, C. A., Rasband, W. S., Eliceiri, K. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NIH+Image+to+ImageJ:+25+years+of+image+analysis.">NIH Image to ImageJ: 25 years of image analysis.</a> Nature methods. 9, 671-675 (2012).</li><li>Wagih, O., Parts, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=gitter:+A+Robust+and+Accurate+Method+for+Quantification+of+Colony+Sizes+From+Plate+Images.">gitter: A Robust and Accurate Method for Quantification of Colony Sizes From Plate Images.</a> G3 (Bethesda). 4 (3), 547-552 (2014).</li><li>Leducq, J. 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=Evidence+for+the+robustness+of+protein+complexes+to+inter-species+hybridization.">Evidence for the robustness of protein complexes to inter-species hybridization.</a> PLoS Genet. 8, e1003161 (2012).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Development Core Team: A language and environment for statistical computing. , (2008).</li><li>Stark, C., 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=BioGRID:+a+general+repository+for+interaction+datasets.">BioGRID: a general repository for interaction datasets.</a> Nucleic Acids Res. 34, D535-D539 (2006).</li><li>Vinayagam, A., 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=Protein+complex-based+analysis+framework+for+high-throughput+data+sets.">Protein complex-based analysis framework for high-throughput data sets.</a> Science signaling. 6, rs5 (2013).</li><li>Jansen, R., Gerstein, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+protein+function+on+a+genomic+scale:+the+importance+of+gold-standard+positives+and+negatives+for+network+prediction.">Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction.</a> Current opinion in microbiology. 7, 535-545 (2004).</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> Proceedings of the National Academy of Sciences of the United States of America. 98, 4569-4574 (2001).</li><li>Costanzo, 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=The+genetic+landscape+of+a+cell.">The genetic landscape of a cell.</a> Science. 327, 425-431 (2010).</li></ol>

Part of the open access publication fees for this article were paid by S&#38;P Robotics.

We describe a protocol based on the DHFR-PCA assay enabling the systematic identification of physical interactors for any given bait protein at high-throughput. This protocol can be adapted by screening for more baits, and this at any desired level of replication. We demonstrate the reliability of this protocol by the identification of physical interaction partners for a bait protein involved in the Nuclear Pore Complex: Nup82. Our analysis enabled to find five previously reported interactors and one previously unreported interactor (Figures 3F &#38; 3G), highlighting the ability of the method to study the yeast protein interactome.
The protocol described here includes several critical steps to which the experimenter should pay attention. We recommend to 1) Make sure that the bait DHFR F[1,2] fusion is correct (Figure 1B); this can be achieved by sequencing the construction and measuring proper protein expression using an anti-DHFR F[1,2] or anti-DHFR F[3] antibody; 2) Prior to beginning the screen, it is recommended to verify if any bait of interest exhibits promiscuous interactions in PCA screens. This can be done by performing control screens with baits crossed with the appropriate L-DHFR control or by manually mating the bait with the appropriate L-DHFR control and performing a growth assay in MTX medium. 3) Plates should be poured the day before they are used so that moisture is optimal for cell adherence on the agar surface during the printing process; 4) Source plates should not be used more than four times to transfer enough cells on the destination plate. Increasing the number of copies of the destination plate can be done by successive steps of expansion (e.g. 4 copies -&#62; 16 copies -&#62; 64 copies). Alternatively, cells can be picked at different positions on the lawns or in the colony between different rounds of replication; 5) If several positions are missing after the diploid selection(s), make sure that the source plates were not used too many times in the mating step (steps 4.5 to 4.7); 6) Ensure that the MTX medium contains all essential ingredients at the right concentrations. Indeed, if no growth at all is observed on the MTX medium, it can be either because no interaction is detectable by PCA for the proteins of interest or because the MTX medium was not prepared properly. To ensure that the medium allows growth of strains showing DHFR fragments complementation, a constitutive interaction can be added at empty positions of the collection and used as a positive control such as DHFR-fragments fused to leucine zipper moieties33 (Figure 1C). Parallel tests using the linker-DHFR fragments or the zipper-linker-DHFR fragments will allow to discriminate between conditions that allow all cells to grow (low MTX concentration or bait protein that tend to make false positive interactions, as described below) and conditions that prevent growth of all strains (MTX concentration too high or essential ingredient missing in the medium); 7) Given that PCA is performed through successive rounds of replications from one medium to another, cross-contamination among strains between different plates may occur if, for instance, the pin-tool is not sterilized properly between replication rounds and/or the last water bath (i.e. wet station) in the sterilization procedure is contaminated by colonies of previous replication rounds. Several positions on the arrays are empty and can thus be used as control positions where no growth should be observed to detect cross-contaminations.
Image analysis can be performed using several published softwares (see section 5 of the protocol) or any custom script. In this study, the custom script executes the following steps: 1) The script subtracts pixel values of an empty plate to pixel values of each plate in order to correct for illumination biases. 2) The script converts each background-corrected picture to binary using a pixel value threshold of 10. 3) For each 1,536 positions of each plate, determined by overlaying a rectangle on the edge colonies, the script runs ImageJ &#8220;Analyze particles ...&#8221; function in a circular selection. The circular selection is set with a radius equal to the interval between two positions minus 10 pixels. 4) The script selects the closest particle from the center of the selection and confirms it as a colony if its location is not more than half the interval between two colonies away from the center of the selection. 5) The script measures pixel values of the selected particle on the background-corrected picture. 6) To further correct any remaining background illumination biases, the script subtracts the mean value of all the pixels from the circular selection that were not part of a particle to pixel values of the colony. The sum of these corrected pixel values, stored in the column &#8220;IntDenBackSub&#8221; of Supplementary Table 1, are used as a measure of colony size.
A critical step within the analysis part is the choice of the significance threshold. Here, we chose a threshold based on the distribution of the negative L-DHFR F[3] controls, but depending on the objective of the screen, such threshold may be too stringent. Indeed, L-DHFR F[3] controls are overexpressed (strong TEF promoter) such that the complementary fragments may spontaneously complement each other and these are thus not representative of the expression of most proteins. This is highlighted by the fact that the distribution of the L-DHFR F[3] controls is higher than the average of the background growth (Figure 3D). Thus, some interactions having scores below this stringent threshold but that are clearly outside of the background growth distribution can be considered as putative hits that may represent, for instance, transient or weak interactions. These can be further studied and cross-validated if, for instance, the two proteins are not expressed at levels that can allow the spontaneous complementation of the DHFR fragments like the L-DHFR controls. As an alternative, one could set a significance threshold based on the proportion of overlap with reported physical interactors in databases like BioGRID35 in order to maximize the proportion of true positives over false positives. However, unlike the use of the L-DHFR distribution, this alternative may not always be feasible if, for instance, the number of known physical interactors is not sufficiently high. Moreover, the choice of the significance threshold has an impact on the proportion of false positives and false negatives in the final data set. Indeed, like any other PPI detection assay, false positives can result from unspecific interaction of a protein with the DHFR-fusion protein if, for example, the protein is highly abundant as mentioned earlier. This is exemplified by the fact that some preys systematically interact with all bait proteins in PCA screens and, thus, need to be removed from the analysis11 (e.g. Tef2 and Ade17 and Supplementary Table 2). To circumvent this problem, a control PCA screen of the two collections against the appropriate L-DHFR control (F[1,2] or F[3]) to identify baits and preys exhibiting spontaneous DHFR fragments complementation can be performed in the specific conditions of each screen. Moreover, performing a Gene Ontology enrichment analysis can increase the confidence in the data if the function of a given bait is known. On the other hand, DHFR-PCA can give rise to false negatives for several reasons: 1) not all proteins can be fused to the DHFR fragments as these may destabilize the proteins or modify their localization if, for instance, the DHFR fusion to the C-terminus interferes with a localization signal; 2) DHFR reconstitution in some cellular compartments may not produce folate if, for instance, an essential precursor for folate synthesis is not available; 3) C-termini need to be within a distance of 8 nm for DHFR complementation to occur11. Thus, a well-known interaction may not be detected if their C-termini are not close enough in space. This is exemplified here by the fact that a large fraction of Nup82 physical interactions reported in databases, most of which are indirect, were not detected in our assay. Similarly, interactions between membrane proteins for which the C-termini are in trans relative to the membrane will not lead to DHFR fragments complementation and will not be detected11. Limitations 1) and 3) can be circumvented relatively simply by fusing the DHFR fragment to the N-termini of the protein. Doing so may prevent to interfere with a localization signal near the C-termini and may allow to detect an interaction between membrane proteins whose N and C-terminus are in cis relative to the membrane.
Several challenges remain in the study of PINs (reviewed in2,3). The maps of PINs produced so far have largely been described in a single experimental condition for each species and thus offer a single snapshot of how protein networks might be organized. There is therefore a need for the exploration of other experimental conditions to see how PINs may be reorganized in response to environmental changes, specific stimuli, across development or following mutations. These challenges will be overcome by the development of new technologies for interrogating PPIs in real-time, in living cells and by adapting current techniques so that they can be used by a larger community of laboratories. As a quantitative technique that can detect changes in the amount of DHFR complementation complexes27, DHFR-PCA can be adapted to overcome these challenges and has been used to study how PPIs are affected by a DNA damaging agent22, chemical agents25, gene deletions23,26 or in other yeast species and their hybrids33. Exploring these new dimensions will become more and more important to reveal the dynamic of the PIN.

Protein interaction networks (PINs) offer a low resolution map of how proteins are functionally organized in the cell1. Each physical connection between two proteins, or protein-protein interaction (PPI), may represent an association that is stable in time, such as those found within protein complexes and that contribute to the structural organization of the cell. These connections may also represent transient associations that regulate the activity, stability, localization and interactions of the two partners. Identifying the physical interaction partners of a given protein therefore provides rich information on the function and regulation of that protein2,3. For these reasons, large efforts have been put towards the mapping of PINs in model organisms, including Escherichia coli4-6, Arabidopsis thaliana7, Saccharomyces cerevisiae8-12, Drosophila melanogaster13,Caenorhabditis elegans14 and Homo sapiens15. These studies have provided important insights into how proteins are organized in the cell and thus key information on proteins with previously unknown functions.
Several strategies have been developed over the years to study PINs. These technologies can be broadly grouped in three categories based on the kind of information they provide on PPIs (reviewed in16-18) . The first one is based on yeast two-hybrid and its derivatives19. These technologies provide information on the direct association between pairs of proteins, which allows constructing binary networks. The second family is based on the affinity purification of bait proteins and the identification of their&#160;associated partners, such as affinity purification followed by mass spectrometry20. These approaches identify groups of proteins that are directly or indirectly associated, generally in a stable manner, and are extremely powerful to identify protein complexes. The third approach is based on protein-fragment complementation assays (PCAs)11,21. This approach provides an intermediate level of resolution between the two former approaches, as it allows detecting direct and proximal associations between proteins. Each technique has its own strengths and weaknesses, as recently reviewed18.
The best-described eukaryotic PIN is by far the one of the budding yeast Saccharomyces cerevisiae, in part because its proteome is relatively less complex than those of other model eukaryotes and because high-throughput assays to detect PPIs have first been assayed and are more efficiently implemented in this model organism9-12. A particularly powerful method for the yeast system is the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA), an assay that has been used in different contexts to study the yeast PIN in standard and perturbed conditions11,22-26. This method relies on a survival assay that enables the detection of direct and near-direct PPIs for a given bait protein at both endogenous expression levels and native subcellular localizations of the interaction partners11,21 in a quantitative manner27. The signal obtained using this assay (i.e. colony size on high-density colony arrays) thus reflects the amount of protein complexes formed between the bait and prey in a cellular environment almost equivalent to the one of wild-type cells. The assay is based on the reconstitution of a reporter enzyme involved in folate metabolism, the dihydrofolate reductase (DHFR), whereby two complementary fragments of the DHFR that are fused to the two proteins of interest are brought into proximity when the two proteins interact, which in turn leads to the reversible reconstitution of the enzyme activity11 and growth of the strain on a medium containing methotrexate (MTX; Figure 1). This compound inhibits the endogenous DHFR enzyme, but not the mutated one used in the assay28. Two collections of PCA strains, one containing ~4,300 MATa strains with an ORF fused to the DHFR F[1,2] fragment and one containing ~4,800 MAT&#945; strains with an ORF fused to the DHFR [3] fragment, can be purchased to implement DHFR-PCA at small or large scale in any laboratory. Here, we describe a general but detailed protocol to screen for PPIs between one bait protein and ~4,800 prey proteins using this assay.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Name of Material/Equipement</td>
			<td>&#160;Company&#160;</td>
			<td>Catalog Number</td>
			<td>&#160;Comments/Description</td>
		</tr>
		<tr>
			<td>BioMatrix Robot, Bench-top Configuration</td>
			<td>S&#38;P Robotics Inc.</td>
			<td>BM5-BC</td>
			<td></td>
		</tr>
		<tr>
			<td>96-format Pin-tool</td>
			<td>S&#38;P Robotics Inc.</td>
			<td>PH-96-10</td>
			<td>Standard 96-format Pin-tool with 96 high-precision floating pins</td>
		</tr>
		<tr>
			<td>384-format Pin-tool</td>
			<td>S&#38;P Robotics Inc.</td>
			<td>PH-384-10</td>
			<td>Standard 384-format Pin-tool with 384 high-precision floating pins</td>
		</tr>
		<tr>
			<td>1536-format Pin-tool</td>
			<td>S&#38;P Robotics Inc.</td>
			<td>PH-1536-05</td>
			<td>Custom 1536-format Pin-tool with 0.5mm high-precision floating pins</td>
		</tr>
		<tr>
			<td>Automated imaging module&#160;</td>
			<td>S&#38;P Robotics Inc.</td>
			<td>IMG-02</td>
			<td></td>
		</tr>
		<tr>
			<td>Methotrexate&#160;</td>
			<td>Bioshop Canada Inc.</td>
			<td>MTX440&#160;</td>
			<td>CAUTION: toxic compound</td>
		</tr>
		<tr>
			<td>Hygromycin B&#160;</td>
			<td>Bioshop Canada Inc.</td>
			<td>HYG003</td>
			<td></td>
		</tr>
		<tr>
			<td>Nourseothricin dihydrogen sulfate</td>
			<td>Werner BioAgents</td>
			<td>5010000</td>
			<td></td>
		</tr>
		<tr>
			<td>Yeast-Interactome Collection&#160;</td>
			<td>Thermo Scientific</td>
			<td>YSC5849</td>
			<td></td>
		</tr>
		<tr>
			<td>Omni Tray w/lid sterile&#160;</td>
			<td>Thermo Scientific</td>
			<td>242811</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-DHFR F[1,2] antibody&#160;</td>
			<td>Sigma-Aldrich</td>
			<td>D1067</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-DHFR F[3] antibody&#160;&#160;</td>
			<td>Sigma-Aldrich</td>
			<td>D0942</td>
			<td></td>
		</tr>
	</tbody>
</table>

genome-wide protein-protein interaction screening by protein-fragment complementation assay (pca) in living cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

Supplementary Table 2 is an example of representative results obtained using the yeast protein Nup82 fused to the DHFR F[1,2] fragment as a bait. The threshold defined with the L-DHFR F[3] controls can be used as an empirical threshold to determine high confidence hits (Figures 3D &#38; 3E). Alternatively, the score ranking can be used to perform Gene Ontology enrichments or other functional analyses36 based on gold standards37. The known physical interactors of the bait can be retrieved from databases like BioGRID35 and overlaid on the data (Figures 3E &#38; 3F). In this example, five out of eight high confidence hits have been previously reported as Nup82 interactors and two are part of the Nup82 subcomplex, Nup116 and Nup159 (Figure 3F &#38; 3G). The other member of the complex, Nsp1, does not show any interaction in our experiment. Two preys, Ade17 and Tef2 (not shown in Figure 3F), had scores above the hard threshold applied, but these are likely to be false positives as they interact with almost any bait protein in PCA screens we have performed (unpublished results). On the other hand, Pex30 may represent a novel physical interactor of Nup82 and we were able to confirm this interaction using DHFR-PCA at low-throughput (Figure 3G). Pex30 is a peroxisomal membrane protein and a few direct interactions have been reported between the nuclear pore complex (NPC) and this organelle. A two-hybrid screen identified two other NPC protein, Nup53 and Asm4 (Nup59), as physical interactors of Pex3038, and a genetic interaction between Pex30 and Nup170 has been reported39. Two other interaction partners detected, Nup120 and Nup85 (Figure 3F &#38; 3G), are not part of the Nup82 sub-complex, illustrating the ability of the DHFR-PCA to detect interactions within and between subcomplexes in larger complexes11.
<img alt="Figure 1" src="/files/ftp_upload/52255/52255fig1highres.jpg" /> 
Figure 1: Engineering yeast strains for high-throughput DHFR-PCA (figure adapted from Leducq et al. 201233). (A) Construction of haploid MATa and MAT&#945; strains to fuse Gene1 (G1) and Gene2 (G2) with the DHFR F[1,2]-NatMX and the DHFR F[3]-HPH cassettes, respectively. Cassettes are amplified from plasmids pAG25-DHFR F[1,2] and pAG32-DHFR F[3] with forward primers G1-5&#8217; and G2-5&#8217;, and reverse primers G1-3&#8217; and G2-3&#8217;, and then inserted in the genome at the 3&#8217; end of the target gene by homologous recombination. The resulting proteins, P1 and P2, are respectively fused to the DHFR F[1,2] fragment (MATa) and the DHFR F[3] fragment (MAT&#945;) via a flexible linker. (B) Verification of the construction in (A) is performed by sequencing the junctions between Gene1&#8217;s and Gene2&#8217;s ORFs and the DHFR cassettes. The constructed PCA strains of opposing mating types are then mated to form a diploid. Diploid strains grow on MTX medium if the two complementary DHFR fragments are brought into proximity by an interaction between P1 and P2, which reconstitutes the activity of the DHFR enzyme. (C) Construction of control diploid PCA strains for DHFR-PCA screens. The negative controls (L-DHFR) are constructed by transforming separately haploid MATa and MAT&#945; strains with plasmids p41-linker-DHFR F[1,2] and p41-linker-DHFR F[3]11, respectively. The two strains are mated resulting in a negative control diploid strain in which the DHFR fragments are unable to complement each other (top). Positive controls are constructed using the same approach as for the negative controls, but the plasmids transformed in haploid strains (p41-zipper-linker-DHFR F[1,2] (p41-ZL-DHFR F[1,2]) and p41-zipper-linker-DHFR F[3] (p41-ZL-DHFR F[3])) contain two GCN4 parallel leucine zipper fragments fused to the complementary DHFR fragments, which leads to a strong and constitutive interaction that reconstitutes DHFR activity (bottom). <a href="https://www.jove.com/files/ftp_upload/52255/52255fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52255/52255fig2highres.jpg" /> 
Figure 2: High-throughput DHFR-PCA procedure (figure adapted from Leducq et al.33). (A) The MAT&#945; DHFR F[3] collection is condensed in a 1536 format through two successive rounds of condensation. First, glycerol stock plates are combined by groups of four on selective YPD + HygB medium in a 384 format using the 96 pin-tool. Second, 384 arrays are combined by groups of four on selective YPD + HygB medium in a 1536 format using the 384 pin-tool. (B) Cell lawns of the MATa PCA bait strain are prepared by growing a saturated culture of the bait strain in selective YPD + Nat medium and plating the culture on a YPD + Nat omnitray. (C) These lawns are used to mate the bait strain with the DHFR F[3] collection on YPD medium. Cells are successively transferred twice on YPD + HygB + Nat medium to select for diploids and twice on MTX medium to perform PCA. Growth will only be observed on MTX medium if the DHFR fragments complement each other following an interaction between the bait and the prey proteins. <a href="https://www.jove.com/files/ftp_upload/52255/52255fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52255/52255fig3highres.jpg" /> 
Figure 3: Data analyses through steps of normalization, determination of a significance threshold and identification of positive interactions. (A) Density distribution of colony size on each plate (log2). (B) Normalization by the median of each image corrects for biases associated with plate-to-plate effects. (C) Heatmap showing the Spearman correlation coefficient among the plates, confirming the reproducibility of the procedure. (D) Distribution of scores for the tested PPIs and the L-DHFR F[3] controls. A hard threshold can be set to the 95th percentile of the L-DHFR F[3] distribution to identify high-confidence PPIs (represented by a dotted vertical line). (E) Rank order distribution of the average score of each prey. Previously reported physical interaction partners of Nup82p in the BioGRID35 are identified by circles and those reported in this study are identified by red dots. The threshold defined in (D) is shown as a dotted line. (F) Network showing the high-confidence PPIs identified in this study. Blue edges show previously reported physical interactors (BioGRID35) and yellow edges show a previously unreported interaction with Pex30. (G) Spot-dilution assay of PCA diploid strains involving Nup82-DHFR F[1,2] and prey-DHFR F[3] pairs identified as physical interactors of Nup82p in the present study. Growth assay was performed in DMSO medium (MTX solvent, left panel) and MTX medium (right panel). Negative controls consisting of Nup82-DHFR F[1,2] &#8211; Linker-DHFR F[3] and Linker-DHFR F[1,2] &#8211; Linker-DHFR F[3] and a positive control consisting of the strong interaction between two leucine zipper moieties (zipper-DHFR F[1,2] &#8211; zipper-DHFR F[3]) were included. Cell growth superior to the negative controls in MTX medium should be interpreted as a physical interaction. <a href="https://www.jove.com/files/ftp_upload/52255/52255fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Amino acid</td>
			<td>Quantity (g)</td>
		</tr>
		<tr>
			<td>Adenine sulfate*</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>L-Tryptophane</td>
			<td>0.4</td>
		</tr>
		<tr>
			<td>L-Tyrosine</td>
			<td>0.3</td>
		</tr>
		<tr>
			<td>L-Phenylalanine</td>
			<td>0.5</td>
		</tr>
		<tr>
			<td>L-Glutamic acid (monosodium salt)</td>
			<td>1.0</td>
		</tr>
		<tr>
			<td>L-Asparagine</td>
			<td>1.0</td>
		</tr>
		<tr>
			<td>L-Valine</td>
			<td>1.5</td>
		</tr>
		<tr>
			<td>L-Threonine</td>
			<td>2.0</td>
		</tr>
		<tr>
			<td>L-Serine</td>
			<td>3.75</td>
		</tr>
		<tr>
			<td>Uracile</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>L-Histidine HCl</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>L-Arginine HCl</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>L-Methionine*</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>L-Lysine*</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>L-Leucine</td>
			<td>0.6</td>
		</tr>
		<tr>
			<td colspan="2">* Withdrawn when performing standard PCA (as in this protocol) but can be added for other purposes.</td>
		</tr>
	</tbody>
</table>
Table 1: Composition of the 10X -lys/met/ade drop-out in the MTX medium. Quantities are for one liter of 10x drop-out. Asterisks denote the compounds that should be withdrawn for the standard MTX medium, but that can be added for other purposes.
Supplementary Table 1: Combined data from the 12 test plates. Table 1 contains the concatenated data from the ImageJ analysis performed using a custom ImageJ script with each row corresponding to a single position on each 1,536 array. In addition, each row has been annotated with the information about the image file, DHFR collection plate, replicate, ORF and protein name.
Supplementary Table 2: Average normalized growth per strain. Table 2 contains the average Log2 normalized growth for each strain of the collection along with the standard deviation. Filtered preys, hits and known physical interactors are identified in separate columns.

Watch this Scientific Journal Video about Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells at JoVE.com

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA).

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often ...

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13.2K Views.  Université Laval. Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA). 

Video: Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Bimolecular Fluorescence Complementation (BiFC) Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular environment. The Bimolecular Fluorescence Complementation (BiFC) Assay1 allows researchers to visualize protein-protein interactions in living cells and has become an essential research tool. This assay is based on the facilitated association of two fragments of a fluorescent protein (GFP) that are each fused to a potential interacting protein partner. The interaction of the two protein partners would facilitate the association of the N-terminal and C-terminal fragment of GFP, leading to fluorescence. For plant researchers, onion epidermal cells are an ideal experimental system for conducting the BiFC assay because of the ease in obtaining and preparing onion tissues and the direct visualization of fluorescence with minimal background fluorescence. The Helios Gene Gun (BioRad) is commonly used for bombarding plasmid DNA into onion cells. We demonstrate the use of Helios Gene Gun to introduce plasmid constructs for two interacting Arabidopsis thaliana transcription factors, SEUSS (SEU) and LEUNIG HOMOLOG (LUH)2 and the visualization of their interactions mediated by BiFC in onion epidermal cells.

Bimolecular Fluorescence Complementation BiFC Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

This article illustrates how to properly use the BioRad Helios Gene Gun to introduce plasmid DNA into onion epidermal cells and how to test for protein-protein interactions in onion cells based on the principle of Bimolecular Fluorescence Complementation (BiFC)

Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular ...

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene 1. While the creation of libraries of RNAi clones covering most of the C. elegans genome 2,3 opened the way for true functional genomic studies (see for example 4-7), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens 8. The approach relies on microscopic imaging and image analysis. 
	Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture.
	We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing9. When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

We describe a protocol using C. elegans and RNAi feeding libraries that allows automated measurement of multiple parameters such as fluorescence, size and opacity of individual worms in a population. We give one example of a screen to identify genes involved in anti-fungal innate immunity in C. elegans.

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. ...

A Liquid Phase Affinity Capture Assay Using Magnetic Beads to Study Protein-Protein Interaction: The Poliovirus-Nanobody Example

In this article, a simple, quantitative, liquid phase affinity capture assay is presented. Provided that one protein can be tagged and another protein labeled, this method can be implemented for the investigation of protein-protein interactions. It is based on one hand on the recognition of the tagged protein by cobalt coated magnetic beads and on the other hand on the interaction between the tagged protein and a second specific protein that is labeled. First, the labeled and tagged proteins are mixed and incubated at room temperature. The magnetic beads, that recognize the tag, are added and the bound fraction of labeled protein is separated from the unbound fraction using magnets. The amount of labeled protein that is captured can be determined in an indirect way by measuring the signal of the labeled protein remained in the unbound fraction. The described liquid phase affinity assay is extremely useful when conformational conversion sensitive proteins are assayed. The development and application of the assay is demonstrated for the interaction between poliovirus and poliovirus recognizing nanobodies1. Since poliovirus is sensitive to conformational conversion2 when attached to a solid surface (unpublished results), the use of ELISA is limited and a liquid phase based system should therefore be preferred. An example of a liquid phase based system often used in polioresearch3,4 is the micro protein A-immunoprecipitation test5. Even though this test has proven its applicability, it requires an Fc-structure, which is absent in the nanobodies6,7. However, as another opportunity, these interesting and stable single-domain antibodies8 can be easily engineered with different tags. The widely used (His)6-tag shows affinity for bivalent ions such as nickel or cobalt, which can on their turn be easily coated on magnetic beads. We therefore developed this simple quantitative affinity capture assay based on cobalt coated magnetic beads. Poliovirus was labeled with 35S to enable unhindered interaction with the nanobodies and to make a quantitative detection feasible. The method is easy to perform and can be established with a low cost, which is further supported by the possibility of effectively regenerating the magnetic beads.

In this article, a simple, quantitative, liquid phase affinity capture assay is presented. It is a reliable technique based on the interaction between magnetic beads and tagged proteins (e.g. nanobodies) on one hand and the affinity between the tagged protein and a second, labeled protein (e.g. poliovirus) on the other.

In this article, a simple, quantitative, liquid phase affinity capture assay is presented. Provided that one protein can be tagged and another protein ...

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells.
BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

Flow cytometric analysis of Bimolecular Fluorescence Complementation provides a high throughput quantitative method to study protein-protein interaction. This methodology can be applied to mapping protein binding sites and for screening factors that regulate protein-protein interaction.

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC ...

Identifying Protein-protein Interaction in Drosophila Adult Heads by Tandem Affinity Purification (TAP)

Genetic screens conducted using Drosophila melanogaster (fruit fly) have made numerous milestone discoveries in the advance of biological sciences. However, the use of biochemical screens aimed at extending the knowledge gained from genetic analysis was explored only recently. Here we describe a method to purify the protein complex that associates with any protein of interest from adult fly heads. This method takes advantage of the Drosophila GAL4/UAS system to express a bait protein fused with a Tandem Affinity Purification (TAP) tag in fly neurons in vivo, and then implements two rounds of purification using a TAP procedure similar to the one originally established in yeast1 to purify the interacting protein complex. At the end of this procedure, a mixture of multiple protein complexes is obtained whose molecular identities can be determined by mass spectrometry. Validation of the candidate proteins will benefit from the resource and ease of performing loss-of-function studies in flies. Similar approaches can be applied to other fly tissues. We believe that the combination of genetic manipulations and this proteomic approach in the fly model system holds tremendous potential for tackling fundamental problems in the field of neurobiology and beyond.

Identifying Protein-protein Interaction in Drosophila Adult Heads by Tandem Affinity Purification TAP

Drosophila is famous for its powerful genetic manipulation, but not for its suitability of in-depth biochemical analysis. Here we present a TAP-based procedure to identify interacting partners of any protein of interest from the fly brain. This procedure can potentially lead to new avenues of research.

Genetic screens conducted using Drosophila melanogaster (fruit fly) have made numerous milestone discoveries in the advance of biological sciences. ...

Protein-protein Interactions Visualized by Bimolecular Fluorescence Complementation in Tobacco Protoplasts and Leaves

Many proteins interact transiently with other proteins or are integrated into multi-protein complexes to perform their biological function. Bimolecular fluorescence complementation (BiFC) is an in vivo method to monitor such interactions in plant cells. In the presented protocol the investigated candidate proteins are fused to complementary halves of fluorescent proteins and the respective constructs are introduced into plant cells via agrobacterium-mediated transformation. Subsequently, the proteins are transiently expressed in tobacco leaves and the restored fluorescent signals can be detected with a confocal laser scanning microscope in the intact cells. This allows not only visualization of the interaction itself, but also the subcellular localization of the protein complexes can be determined. For this purpose, marker genes containing a fluorescent tag can be coexpressed along with the BiFC constructs, thus visualizing cellular structures such as the endoplasmic reticulum, mitochondria, the Golgi apparatus or the plasma membrane. The fluorescent signal can be monitored either directly in epidermal leaf cells or in single protoplasts, which can be easily isolated from the transformed tobacco leaves. BiFC is ideally suited to study protein-protein interactions in their natural surroundings within the living cell. However, it has to be considered that the expression has to be driven by strong promoters and that the interaction partners are modified due to fusion of the relatively large fluorescence tags, which might interfere with the interaction mechanism. Nevertheless, BiFC is an excellent complementary approach to other commonly applied methods investigating protein-protein interactions, such as coimmunoprecipitation, in vitro pull-down assays or yeast-two-hybrid experiments.

Formation of protein complexes in vivo can be visualized by bimolecular fluorescence complementation. Interaction partners are fused to complementary parts of fluorescent tags and transiently expressed in tobacco leaves, resulting in a reconstituted fluorescent signal upon close proximity of the two proteins.

Many proteins interact transiently with other proteins or are integrated into multi-protein complexes to perform their biological function. Bimolecular ...

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may result in disease, making protein interactions important drug targets. It is thus highly important to understand these interactions at the molecular level. Protein interactions are studied using a variety of techniques ranging from cellular and biochemical assays to quantitative biophysical assays, and these may be performed either with full-length proteins, with protein domains or with peptides. Peptides serve as excellent tools to study protein interactions since peptides can be easily synthesized and allow the focusing on specific interaction sites. Peptide arrays enable the identification of the interaction sites between two proteins as well as screening for peptides that bind the target protein for therapeutic purposes. They also allow high throughput SAR studies. For identification of binding sites, a typical peptide array usually contains partly overlapping 10-20 residues peptides derived from the full sequences of one or more partner proteins of the desired target protein. Screening the array for binding the target protein reveals the binding peptides, corresponding to the binding sites in the partner proteins, in an easy and fast method using only small amount of protein.
In this article we describe a protocol for screening peptide arrays for mapping the interaction sites between a target protein and its partners. The peptide array is designed based on the sequences of the partner proteins taking into account their secondary structures. The arrays used in this protocol were Celluspots arrays prepared by INTAVIS Bioanalytical Instruments. The array is blocked to prevent unspecific binding and then incubated with the studied protein. Detection using an antibody reveals the binding peptides corresponding to the specific interaction sites between the proteins.

Peptide array screening is a high throughput assay for identifying protein-protein interaction sites. This allows mapping multiple interactions of a target protein and can serve as a method for identifying sites for inhibitors that target a protein. Here we describe a protocol for screening and analyzing peptide arrays.

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may ...

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC (Crosslinking of Small Molecules to Isolate Chromatin)

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to dose-limiting toxicity and many adverse side effects. Targeting the genome with sequence-specific small molecules may enable molecules with increased therapeutic index and fewer off-target effects. N-methylpyrrole/N-methylimidazole polyamides are molecules that can be rationally designed to target specific DNA sequences with exquisite precision. And unlike most natural transcription factors, polyamides can bind to methylated and chromatinized DNA without a loss in affinity. The sequence specificity of polyamides has been extensively studied in vitro with cognate site identification (CSI) and with traditional biochemical and biophysical approaches, but the study of polyamide binding to genomic targets in cells remains elusive. Here we report a method, the crosslinking of small molecules to isolate chromatin (COSMIC), that identifies polyamide binding sites across the genome. COSMIC is similar to chromatin immunoprecipitation (ChIP), but differs in two important ways: (1) a photocrosslinker is employed to enable selective, temporally-controlled capture of polyamide binding events, and (2) the biotin affinity handle is used to purify polyamide&#8211;DNA conjugates under semi-denaturing conditions to decrease DNA that is non-covalently bound. COSMIC is a general strategy that can be used to reveal the genome-wide binding events of polyamides and other genome-targeting chemotherapeutic agents.

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin

Identifying the direct targets of genome-targeting molecules remains a major challenge. To understand how DNA-binding molecules engage the genome, we developed a method that relies on crosslinking of small molecules to isolate chromatin (COSMIC).

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to ...

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

The DNA adenine methyltransferase identification (DamID) assay is a powerful method to detect protein-DNA interactions both locally and genome-wide. It is an alternative approach to chromatin immunoprecipitation (ChIP). An expressed fusion protein consisting of the protein of interest and the E. coli DNA adenine methyltransferase can methylate the adenine base in GATC motifs near the sites of protein-DNA interactions. Adenine-methylated DNA fragments can then be specifically amplified and detected. The original DamID assay detects the genomic locations of methylated DNA fragments by hybridization to DNA microarrays, which is limited by the availability of microarrays and the density of predetermined probes. In this paper, we report the detailed protocol of integrating high throughput DNA sequencing into DamID (DamID-seq). The large number of short reads generated from DamID-seq enables detecting and localizing protein-DNA interactions genome-wide with high precision and sensitivity. We have used the DamID-seq assay to study genome-nuclear lamina (NL) interactions in mammalian cells, and have noticed that DamID-seq provides a high resolution and a wide dynamic range in detecting genome-NL interactions. The DamID-seq approach enables probing NL associations within gene structures and allows comparing genome-NL interaction maps with other functional genomic data, such as ChIP-seq and RNA-seq.

We describe herein an assay by coupling DNA adenine methyltransferase identification (DamID) to high throughput sequencing (DamID-seq). This improved method provides a higher resolution and a wider dynamic range, and allows analyzing DamID-seq data in conjunction with other high throughput sequencing data such as ChIP-seq, RNA-seq, etc.

The DNA adenine methyltransferase identification (DamID) assay is a powerful method to detect protein-DNA interactions both locally and genome-wide. It is ...