This work was supported by National Institutes of Health: NIH R21 EB021870-01A1 and by NSF Research Project Grant: 1517110.

1. Mapping Fastq Files
NOTE: DEEPN software as well as many bioinformatics programs use DNA sequence data wherein each sequence read has been mapped for its position in reference DNA. A variety of mapping programs can be used for this including the MAPster interface here that uses the HISTAT2 program to produce .sam files used in subsequent steps.
<ol>
	<li>Map the sequence data to the correct version of the genome. For Y2H libraries of mouse origin, use the UCSC mm10 genome; for those using human genes, use the UCSC hg38 reference genome, for Saccharomyces cerevisiae genes, use the UCSC SacCer3 reference genome.</li>
	<li>Install MAPster.
	<ol>
		<li>Download MAPster software and install. The software can be found using a web browser at the following: https://github.com/emptyewer/MAPster/releases. HISAT2 runs on Unix-based systems such as an Apple Macintosh. Because of this, the MAPster program will only run on compatible systems such as Apple Macintosh and linux. 
		NOTE: System requirements for an Apple Mac are: OSX 10.10+, &#62;4 Gb RAM, &#62;500 Gb disk space, and internet access for downloading reference genomes. Users may need to consult with an institutional IT person if their enterprise has security protocols restricting administrator rights and permissions.</li>
	</ol>
	</li>
	<li>Enter required files and parameters through the &#34;Main&#34; tab (Figure 1). Select the appropriate &#34;Pairwise&#34; button to enter files either as pairs or unpaired with FASTQ as the default file format.
	<ol>
		<li>For DEEPN analysis, turn the &#34;Pairwise&#34; option to &#34;Off&#34; to run in single read format.</li>
		<li>Load files into MAPster simply by drag-and-drop into the appropriate window.</li>
		<li>Select a reference DNA/genome source that corresponds to the source of the Y2H prey library inserts. Indexed genomes from several model organisms are listed in the &#34;Genome&#34; box and can be automatically downloaded from the Johns Hopkins University Center for Computational Biology. Reference genomes will be stored locally for later use.</li>
		<li>Indicate the number of computer processes to be devoted to the mapping program under the &#34;Threads&#34; box, since HISAT2 supports multi-threading. MAPster will search the computer and suggest the maximum number of processors available as a default.</li>
		<li>Specify an output file name. This file name will be used throughout the DEEPN process so a short yet descriptive name without space or special characters is recommended. Specify a folder to output the mapped files using the &#34;Open Output Directory&#34; button.</li>
		<li>Once the appropriate files and parameters have been selected, add the mapping job to the jobs queue using the &#34;Add to Queue&#34; button. The file names in the main window can be deleted and replaced with files corresponding to a new sample and they can be added to the queue after providing a corresponding output filename.</li>
		<li>Click the &#34;Run Queue&#34; button once all the jobs are entered into the job queue. 
		NOTE: Once a mapping job has been placed in the queue, selecting that job causes the parameter settings to be displayed in the &#34;Job Parameters&#34; window and the command line statement with all arguments to be displayed in the &#34;Job Command&#34; window. The output options include directing whether to keep reads that fail to align and specifying the number of primary alignments allowed for each read. The default output file from MAPster is in SAM format (e.g. a &#39;.sam&#39; file). It will contain all the sequence reads from the fastq files specified for that sample including those that were (mapped) and were not (unmapped) successfully mapped to the specified geome.</li>
	</ol>
	</li>
</ol>
2. Bioinformatic Processing Using DEEPN Software
NOTE: DEEPN software is currently compiled for use with prey libraries containing mouse cDNA sequences, human cDNA sequences, or S. cerevisiae genomic DNA sequences. DEEPN accepts the standard .sam file format and can accept a SAM (.sam) file containing both mapped and unmapped reads or separate files for each of the unmapped and mapped reads.
<ol>
	<li>Download DEEPN software and install. The software can be found using a web browser at the following: https://github.com/emptyewer/DEEPN/releases. Select which version matches the computing platform and download. To install, open the downloaded install package. 
	NOTE: Versions of DEEPN are available for PC, Mac, and Linux sysrems. Mac and PC systems should have &#62;500 Gb hard disk space and &#62;4 Gb RAM.</li>
	<li>Open the DEEPN software. From the main window (Figure 2) select the corresponding prey library information from the top selection box. Select a folder where the processed files can go by clicking the &#34;Work Folder&#34; button and navigating to the folder/directory. One can create a new folder/directory if needed. Once a &#34;Work Folder&#34; is selected, DEEPN will create three subfolders entitled unmapped_sam_files, mapped_sam_files, and sam_files.
	<ol>
		<li>If using .sam files containing both mapped and unmapped reads such as those produced with default settings of the MAPster program, place them in the &#39;sam_files&#39; folder. Otherwise place .sam files into the unmapped_sam_files and mapped_sam_files accordingly.</li>
	</ol>
	</li>
	<li>Initiate processing by clicking the &#34;Gene Count+Junction Make&#34; button. 
	NOTE: Processing will begin with the Gene Count module that will use mapping positions to count how many reads correspond to each gene. Junction Make will then extract junction sequences (the sequences fused directly downstream from the Gal4-activation domain) from the reads and identify them using the Blast algorithm. This will create a full set of folders pictured in Figure 3. Processing time depends on the size and number of sequence data files and processing speed of the computer used. Typical times range from 12&#8211;30 h for an experimental dataset of ~250 million reads. The Gene Count procedure and the Junction_Make procedure can be individually started by clicking the &#34;Gene Count&#34; button or the &#34;Junction Make&#34; button.</li>
	<li>Download and install Stat_Maker (https://github.com/emptyewer/DEEPN/releases). This is a statistical analysis package designed for DEEPN datasets that currently works only on Unix Mac systems.
	<ol>
		<li>Open Stat_Maker and click the button &#34;Verify Installation&#34; (Figure 4). If running for the first time, Stat_Maker will automatically install R, JAGS, and Bioconductor by pulling these resources from the internet. Once R, JAGS, and Bioconductor are detected, Stat_Maker will become active and allow further user input.</li>
		<li>Click the &#34;Choose Folder&#34; button to navigate to the working folder that DEEPN processed. Stat_Maker will automatically find and list the files for statistical analysis in the window.</li>
		<li>Drag and drop the appropriate files from the file list window above into the file windows below for each vector and bait dataset and for each growth conditions: non-selected (His+ media) and selected (His- media). Importantly, Stat_Maker requires duplicate datasets for empty vector alone, two samples of non-selected populations and two samples of selected. This gives an estimate of variability within the experiment.</li>
		<li>Click the &#34;Run&#34; button. Depending on the speed of the computer, computation will take between 5&#8211;15 min.</li>
	</ol>
	</li>
	<li>Review results from the Stat_Maker output, which are placed in a new subfolder within the main work folder labeled &#34;Stat_Maker Results&#34;. 
	NOTE: The results are found in a CSV (comma-separated values) file that can be opened in common spreadsheet programs. Stat_Maker will rank gene hits that are likely to be differentially enriched upon selection with the bait of interest over the empty pTEF-GBD (Figure 5). Also tabulated is the percentage of reads for each dataset where the gene insert is found upstream, downstream, or within the open reading frame and whether the gene is also found within the correct translational reading frame. Often, DEEPN will capture robust Y2H interactions of a bait with portions of a given cDNA that are out of the proper reading frame of the corresponding protein or are to a portion of the cDNA that is downstream of its corresponding open-reading frame. Scanning the combined output from Stat_Maker streamlines detection and elimination of these irrelevant hits.</li>
	<li>To review the data on each potential candidate, open the DEEPN software, select the corresponding prey library information and then the correct working folder using the &#34;Work Folder&#34;.
	<ol>
		<li>Click the &#34;Blast Query&#34; button. This loads a new window (Figure 6). In the top text box, type the gene name or GenBank NM number to select the candidate gene of interest. These gene names correspond to the names listed in the StatMaker output file. Type enter or return, which initiates retrieval of the gene of interest.</li>
		<li>Select which datasets will be used for the analysis using the &#34;Select Dataset&#34; menus. Typically, these include the Vector only and Bait samples grown under non-selective conditions and the Bait sample grown under selection conditions. Initially, the datasets will take a few moments to load, however, subsequent query of the same datasets with different genes will go rapidly. Blast_Query will display the fusion points along the sequence of interest and how abundant each fusion point is. This can be displayed both in a table format using the &#34;Results&#34; tab or a graphic format using the &#34;Plot&#34; tab. These results can be exported to a .csv file by clicking the &#34;Save .csv&#34; button in the top right.</li>
	</ol>
	</li>
</ol>
3. Verification of Candidates Identified by DEEPN
NOTE: The purpose of DEEPN and Stat_Maker is to identify candidate genes that give a positive Y2H interaction. Verifying such Y2H interactions can be done using a traditional binary Y2H format using the bait plasmid of interest paired with the empty Gal4-activation domain &#39;prey&#39; plasmid as well as paired with the prey plasmid carrying the gene/cDNA fragment of interest. It is not feasible to isolate the actual plasmid of interest within the mixture of DNA isolated from the yeast population subjected to Y2H selection. However, one can computationally reconstruct what the gene/cDNA fragment is that produces the Y2H interaction, design primers for the 5&#39; and 3&#39; ends of that fragment, and amplify that fragment from the DNA isolated from the yeast population. This section describes how to find the 5&#39; and 3&#39; end of the candidate prey fragment.
<ol>
	<li>Open the DEEPN software and choose the parameters &#34;Select Parameter&#34; and the work folder &#34;Select Work Folder&#34; corresponding to the project. Launch the Blast_Query module by clicking the &#34;Blast Query&#34; button.</li>
	<li>Type the name of the gene of interest or its GenBank &#34;NM&#34; number in the top text box. Select from the pull-down menu the dataset that corresponds to the selected yeast population for the bait of interest to retrieve the table of junction positions under the &#39;Results&#39; tab. By default, Blast_Query will order the different positions according their abundance in the dataset, quantified by the ppm of the total number of junctions found within the database.
	<ol>
		<li>Find the most abundant position that is &#34;In ORF&#34; and &#34;In Frame&#34;. The value for position corresponds to the nucleotide position of the gene with the NCBI Reference Sequence (&#39;NM&#39; number) found in the top text box. This sequence can be retrieved from GenBank (https://www.ncbi.nlm.nih.gov/nuccore/) or copied from the lower text box in the Blast_Query window. 
		NOTE: An example can be found in Figure 6, middle panel. In the center dataset, the &#39;Results&#39; show as the most abundant junction: &#39;Position&#39;: 867; &#39;#Junctions&#39;: 20033.821; &#39;Query Start&#39;, 1; CDS: In ORF; and &#39;Frame&#39;: In Frame. Nucleotide 867 of the GenBank NCBI reference sequence NM_019648 is the start of the prey fragment.</li>
	</ol>
	</li>
	<li>If the Query Start is 1, design the 5&#39; end of the primer to include the nucleotide corresponding to the position number and extend 25 nucleotides downstream from that position (Figure 7). If Query Start is more than 1, it indicates that there are extra nucleotides between the Gal4 activation domain and the prey sequence of interest and that the primer should start further downstream according to the Query Start value.</li>
	<li>From the DEEPN window click the &#34;Read Depth&#34; button under &#34;Analyze Data&#34;. Once the Read Depth window is open, type the NCBI reference sequence (NM) number or gene name into the top text box. Use the pull-down menu to select the relevant dataset that contains the enriched gene of interest. Use the Table on the left and the graphics display on the right to determine how many reads were found in the data that correspond to the gene of interest (Figure 7B).</li>
	<li>Design a 3&#39; end primer that will capture the sequence of the gene fragment computed by Read Depth. If the abundance of reads goes beyond the ORF and stop codon, design the primer so that it includes the stop codon and the region just upstream of the stop codon. If the sequences for the gene do not extend to past the stop codon, use the Results Table to find the most distant 3&#39; region that can be detected and use this position as the furthest 3&#39; position to place the primer. 
	NOTE: The Read Depth program scans in intervals to find sequences that match the specified gene/cDNA of interest. This helps predict where the 5&#39; and 3&#39; end of the most abundant prey fragment is for that gene in the sample. Fluctuations in the read depth along the length of the sequence are normal, as can be seen in Figure 7. If the read depth is clearly past the stop codon, it indicates that the prey fragment extends beyond the stop codon and thus the 3&#39; primer can simply correspond the region around the stop codon.</li>
	<li>Perform a 50 &#181;L PCR reaction per gene. Each reaction contains 25 pmol of each forward and reverse primer matching the prey-library plasmid (see Table of Materials). Reactions also contain 25 &#181;L of High-fidelity 2x PCR Master Mix, 5 &#181;g of DNA sample, and water up to 50 &#181;L.
	<ol>
		<li>Amplify reactions for 25 cycles with extension times of 3 min at 72 &#176;C, annealing temperature of 55 &#176;C for 30 s, and denaturing at 98 &#176;C for 10 s. Precede cycling by a 30 s denaturation at 98 &#176;C and follow with a 5 min incubation at 72 &#176;C.</li>
	</ol>
	</li>
</ol>

<ol><li>Fields, S., Song, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+novel+genetic+system+to+detect+protein-protein+interactions%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">A novel genetic system to detect protein-protein interactions.</a> Nature. 340 (6230), 245-246 (1989).</li>
<li>Rajagopala, S. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mapping+the+Protein-Protein+Interactome+Networks+Using+Yeast+Two-Hybrid+Screens%5BTitle%5D)+AND+%22Advances+in+Experimental+Medicine+and+Biology%22%5BJournal%5D)">Mapping the Protein-Protein Interactome Networks Using Yeast Two-Hybrid Screens.</a> Advances in Experimental Medicine and Biology. 883, 187-214 (2015).</li>
<li>Weimann, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Y2H-seq+approach+defines+the+human+protein+methyltransferase+interactome%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">A Y2H-seq approach defines the human protein methyltransferase interactome.</a> Nature Methods. 10 (4), 339-342 (2013).</li>
<li>Yachie, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Pooled-matrix+protein+interaction+screens+using+Barcode+Fusion+Genetics%5BTitle%5D)+AND+%22Molecular+Systems+Biology%22%5BJournal%5D)">Pooled-matrix protein interaction screens using Barcode Fusion Genetics.</a> Molecular Systems Biology. 12 (4), 863(2016).</li>
<li>Trigg, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CrY2H-seq%3A+a+massively+multiplexed+assay+for+deep-coverage+interactome+mapping%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping.</a> Nature Methods. , (2017).</li>
<li>Pashkova, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DEEPN+as+an+Approach+for+Batch+Processing+of+Yeast+2-Hybrid+Interactions%5BTitle%5D)+AND+%22Cell+Reports%22%5BJournal%5D)">DEEPN as an Approach for Batch Processing of Yeast 2-Hybrid Interactions.</a> Cell Reports. 17 (1), 303-315 (2016).</li>
<li>Kim, D., Langmead, B., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((HISAT%3A+a+fast+spliced+aligner+with+low+memory+requirements%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">HISAT: a fast spliced aligner with low memory requirements.</a> Nature Methods. 12 (4), 357-360 (2015).</li>
<li>Reinert, K., Langmead, B., Weese, D., Evers, D. J. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/pubmed/25939052">Alignment of Next-Generation Sequencing Reads.</a> Annual Review of Genomics and Human Genetics. 16, 133-151 (2015).</li>
<li>Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Transcript-level+expression+analysis+of+RNA-seq+experiments+with+HISAT%2C+StringTie+and+Ballgown%5BTitle%5D)+AND+%22Nature+Protocols%22%5BJournal%5D)">Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown.</a> Nature Protocols. 11 (9), 1650-1667 (2016).</li>
<li>Conesa, A., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/pubmed/26813401">A survey of best practices for RNA-seq data analysis.</a> Genome Biology. 17, 13(2016).</li>
</ol>

The authors have nothing to disclose

The software suite described here allows one to completely process and analyze high throughput DNA sequencing data from a DEEPN experiment. The first program used is MAPster, which takes the DNA sequence reads in standard fastq files and maps their position onto a reference DNA for downstream processing by a whole host of informatics programs including the DEEPN software. The utility of the MAPster interface and its ability to queue multiple jobs, combine input files, coveniently name output files, coupled with the speed...

One approach to discover protein interactions is the yeast 2-hybrid (Y2H) assay, which exploits engineered yeast cells that grow only when a protein of interest binds to a fragment of an interacting partner1. Detection of multiple Y2H interactions can now be done with the help of massive parallel high-throughput sequencing. Several formats have been described2,3,4,5 including one that we developed where populations are grown in batch under conditions that select for yeast containing plasmids that produce a positive Y2H interaction6. The workflow we developed, termed DEEPN (Dynamic Enrichment for Evaluation of Protein Networks), identifies differential interactomes from the same prey libraries to identify proteins that interact with one protein (or domain) vs. another protein or a conformationally distinct mutant domain. One of the major steps in this workflow is proper processing and analysis of the DNA sequencing data. Some information can be gleaned by just counting the number of reads for each gene both before and after selection of Y2H interactions in a fashion analogous to an RNA-seq experiment. However, much more in-depth information can be extracted from these datasets including information on the subdomain of a given protein that is capable of producing a Y2H interaction. In addition, whereas the DEEPN approach is valuable, analyzing many sample replicates can be cumbersome and expensive. This problem is alleviated by using a statistical model that was developed specifically for DEEPN datasets where the number of replicates is limited6. To make processing and analysis of DNA sequencing datasets reliable, complete, robust, and accessible for investigators without bioinformatics expertise, we developed a suite of software programs that cover all steps of analysis.
This suite of stand alone software programs that run on desktop computers includes MAPster, DEEPN, and Stat_Maker. MAPster is a graphic user interface that allows each fastq file queued for mapping to the genome using the HISAT2 program7, producing a standard .sam file for use in downstream applications. DEEPN has several modules. It assigns and counts reads corresponding to particular gene similar to an RNA-seq type quantification using the module &#39;Gene Count&#39;. It also extracts the sequences corresponding to the junction between the Gal4 transcriptional domain and the prey sequence and collates the position of those junctions to allow their inspection by comparative tables and graphs (using the module &#39;Junction_Make&#39;) The module &#39;Blast_Query&#39; allows easy inspection, quantitation, and comparison of the junction Gal4 junction sequences. Stat_Maker evaluates the reads per gene enrichment data statistically as a way of prioritizing likely Y2H hits. Here, we describe how to use these software programs and to fully analyze the DNA sequence data from a DEEPN Y2H experiment. Versions of DEEPN are available to run on PC, Mac, and Linux systems. Other programs, such as the mapping program MAPster and the DEEPN statistics module Stat_Maker rely on subroutines that run under Unix and are available only on Mac and linux systems.

<table><tbody><tr><td>Mapster</td><td></td><td></td><td>https://github.com/emptyewer/MAPster/releases</td></tr><tr><td>DEEPN software</td><td></td><td></td><td>https://github.com/emptyewer/DEEPN/releases</td></tr><tr><td>Statmaker</td><td></td><td></td><td>https://github.com/emptyewer/DEEPN/releases</td></tr><tr><td>Minimum computer system</td><td>Apple</td><td></td><td>Mac Intel Core i5 or better</td></tr><tr><td></td><td>-</td><td></td><td>4 Gb RAM or better</td></tr><tr><td></td><td>-</td><td></td><td>500 Gb Disk spce or better</td></tr><tr><td></td><td>-</td><td></td><td>OS 10.10 or higher</td></tr><tr><td></td><td>Dell</td><td></td><td>Intel i5-7400 or better</td></tr><tr><td></td><td>-</td><td></td><td>4 Gb RAM or better</td></tr><tr><td></td><td>-</td><td></td><td>500 Gb Disk spce or better</td></tr><tr><td></td><td>-</td><td></td><td>Windows 7 or higher</td></tr></tbody></table>

informatic analysis of sequence data from batch yeast 2-hybrid screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Mapping fastq data: the first step 
In practically all NGS applications including DEEPN the initial output is a file of short sequence reads that must be mapped by alignment to genomic, transcriptomic, or other reference DNA8. Recently, the HISAT2 alignment program was developed that uses state-of-the-art indexing algorithms to dramatically increase the mapping speed7,9. HISAT2 runs ef...

Watch this Scientific Journal Video about Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens at JoVE.com

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

informatic-analysis-of-sequence-data-from-batch-yeast-2-hybrid-screens

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Genetics

7.1K Views.  University of Iowa. Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

Video: Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

A Modified Yeast-one Hybrid System for Heteromeric Protein Complex-DNA Interaction Studies

Over the years, the yeast one-hybrid assay has proven to be an important technique for the identification and validation of physical interactions between proteins such as transcription factors (TFs) and their DNA target. The method presented here utilizes the underlying concept of the Y1H but is modified further to study and validate protein complexes binding to their target DNA. Hence, it is referred to as the modified yeast one-hybrid (Y1.5H) assay. This assay is cost effective and can be easily performed in a regular laboratory setting. Albeit using a heterologous system, the described method could be a valuable tool to test and validate the heteromeric protein complex binding to their DNA target(s) for functional genomics in any system of study, especially plant genomics.

The modified yeast one-hybrid assay described here is an extension of the classical yeast one-hybrid (Y1H) assay to study and validate the heteromeric protein complex-DNA interaction in a heterologous system for any functional genomics study.

Over the years, the yeast one-hybrid assay has proven to be an important technique for the identification and validation of physical interactions between ...

Biochemistry

A Yeast 2-Hybrid Screen in Batch to Compare Protein Interactions

Screening for protein-protein interactions using the yeast 2-hybrid assay has long been an effective tool, but its use has largely been limited to the discovery of high-affinity interactors that are highly enriched in the library of interacting candidates. In a traditional format, the yeast 2-hybrid assay can yield too many colonies to analyze when conducted at low stringency where low affinity interactors might be found. Moreover, without a comprehensive and complete interrogation of the same library against different bait plasmids, a comparative analysis cannot be achieved. Although some of these problems can be addressed using arrayed prey libraries, the cost and infrastructure required to operate such screens can be prohibitive. As an alternative, we have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing a strategy termed DEEPN (Dynamic Enrichment for Evaluation of Protein Networks), which incorporates high-throughput DNA sequencing and computation to follow the evolution of a population of plasmids that encode interacting partners. Here, we describe customized reagents and protocols that allow a DEEPN screen to be executed easily and cost-effectively.

Batch processing of yeast 2-hybrid screens allows for direct comparison of the interaction profiles of multiple bait proteins with a highly complex set of prey fusion proteins. Here, we describe refined methods, new reagents, and how to implement their use for such screens.

Screening for protein-protein interactions using the yeast 2-hybrid assay has long been an effective tool, but its use has largely been limited to the ...

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

A genetic screen for mutant alleles that suppress phenotypic defects caused by a mutation is a powerful approach to identify genes that belong to closely related biochemical pathways. Previous methods such as the Synthetic Genetic Array (SGA) analysis, and random mutagenesis techniques using ultraviolet (UV) or chemicals like ethyl methanesulfonate (EMS) or N-ethyl-N- nitrosourea (ENU), have been widely used but are often costly and laborious. Also, these mutagen-based screening methods are frequently associated with severe side effects on the organism, inducing multiple mutations that add to the complexity of isolating the suppressors. Here, we present a simple and effective protocol to identify suppressor mutations in mutants which confer a growth defect in Schizosaccharomyces pombe. The fitness of cells with a growth deficiency in standard rich liquid media or synthetic liquid media can be monitored for recovery using an automated 96-well plate reader over an extended period. Once a cell acquires a suppressor mutation in the culture, its descendants outcompete those of the parental cells. The recovered cells that have a competitive growth advantage over the parental cells can then be isolated and backcrossed with the parental cells. The suppressor mutations are then identified using whole-genome sequencing. Using this approach, we have successfully isolated multiple suppressors that alleviate the severe growth defects caused by loss of Elf1, an AAA+ family ATPase that is important in nuclear mRNA transport and maintenance of genomic stability. There are currently over 400 genes in S. pombe with mutants conferring a growth defect. As many of these genes are uncharacterized, we propose that our method will hasten the identification of novel functional interactions with this user-friendly, high-throughput approach.

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

We present a simple suppressor screen protocol in fission yeast. This method is efficient, mutagen-free, and&#160; selective&#160; for&#160; mutations that&#160; often&#160; occur at&#160; &#160;a&#160; single&#160; genomic&#160; locus.&#160; The protocol is suitable for isolating suppressors that alleviate growth defects in liquid culture that are caused by a mutation or a drug.

A genetic screen for mutant alleles that suppress phenotypic defects caused by a mutation is a powerful approach to identify genes that belong to closely ...

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and computational approaches. One such method is the yeast one-hybrid (Y1H) assay, in which interactions between TFs and DNA regions are tested in the milieu of the yeast nucleus using reporter genes. Y1H assays involve two components: a &#8216;DNA-bait&#8217; (e.g., promoters, enhancers, silencers, etc.) and a &#8216;TF-prey,&#8217; which can be screened for reporter gene activation. Most published protocols for performing Y1H screens are based on transforming TF-prey libraries or arrays into DNA-bait yeast strains. Here, we describe a pipeline, called enhanced Y1H (eY1H) assays, where TF-DNA interactions are interrogated by mating DNA-bait strains with an arrayed collection of TF-prey strains using a high density array (HDA) robotic platform that allows screening in a 1,536 colony format. This allows for a dramatic increase in throughput (60 DNA-bait sequences against &#62;1,000 TFs takes two weeks per researcher) and reproducibility. We illustrate the different types of expected results by testing human promoter sequences against an array of 1,086 human TFs, as well as examples of issues that can arise during screens and how to troubleshoot them.

Here, we present an enhanced yeast one-hybrid screening protocol to identify the transcription factors (TFs) that can bind to a human DNA region of interest. This method uses a high-throughput screening pipeline that can interrogate the binding of &#62;1,000 TFs in a single experiment.

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and ...

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

A central goal of modern genetics is to understand how and why organisms in the wild differ in phenotype. To date, the field has advanced largely on the strength of linkage and association mapping methods, which trace the relationship between DNA sequence variants and phenotype across recombinant progeny from matings between individuals of a species. These approaches, although powerful, are not well suited to trait differences between reproductively isolated species. Here we describe a new method for genome-wide dissection of natural trait variation that can be readily applied to incompatible species. Our strategy, RH-seq, is a genome-wide implementation of the reciprocal hemizygote test. We harnessed it to identify the genes responsible for the striking high temperature growth of the yeast Saccharomyces cerevisiae relative to its sister species S. paradoxus. RH-seq utilizes transposon mutagenesis to create a pool of reciprocal hemizygotes, which are then tracked through a high-temperature competition via high-throughput sequencing. Our RH-seq workflow as laid out here provides a rigorous, unbiased way to dissect ancient, complex traits in the budding yeast clade, with the caveat that resource-intensive deep sequencing is needed to ensure genomic coverage for genetic mapping. As sequencing costs drop, this approach holds great promise for future use across eukaryotes.

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Reciprocal hemizygosity via sequencing (RH-seq) is a powerful new method to map the genetic basis of a trait difference between species. Pools of hemizygotes are generated by transposon mutagenesis and their fitness is tracked through competitive growth using high-throughout sequencing. Analysis of the resulting data pinpoints genes underlying the trait.

A central goal of modern genetics is to understand how and why organisms in the wild differ in phenotype. To date, the field has advanced largely on the ...

Split-Ubiquitin Based Membrane Yeast Two-Hybrid (MYTH) System: A Powerful Tool For Identifying Protein-Protein Interactions

The fundamental biological and clinical importance of integral membrane proteins prompted the development of a yeast-based system for the high-throughput identification of protein-protein interactions (PPI) for full-length transmembrane proteins. To this end, our lab developed the split-ubiquitin based Membrane Yeast Two-Hybrid (MYTH) system. This technology allows for the sensitive detection of transient and stable protein interactions using Saccharomyces cerevisiae as a host organism. MYTH takes advantage of the observation that ubiquitin can be separated into two stable moieties: the C-terminal half of yeast ubiquitin (Cub) and the N-terminal half of the ubiquitin moiety (Nub). In MYTH, this principle is adapted for use as a 'sensor' of protein-protein interactions. Briefly, the integral membrane bait protein is fused to Cub which is linked to an artificial transcription factor. Prey proteins, either in individual or library format, are fused to the Nub moiety. Protein interaction between the bait and prey leads to reconstitution of the ubiquitin moieties, forming a full-length 'pseudo-ubiquitin' molecule. This molecule is in turn recognized by cytosolic deubiquitinating enzymes, resulting in cleavage of the transcription factor, and subsequent induction of reporter gene expression. The system is highly adaptable, and is particularly well-suited to high-throughput screening. It has been successfully employed to investigate interactions using integral membrane proteins from both yeast and other organisms.

Split-Ubiquitin Based Membrane Yeast Two-Hybrid MYTH System: A Powerful Tool For Identifying Protein-Protein Interactions

MYTH allows the sensitive detection of transient and stable interactions between proteins that are expressed in the model organism Saccharomyces cerevisiae. It has been successfully applied to study exogenous and yeast integral membrane proteins in order to identify their interacting partners in a high throughput manner.

The fundamental biological and clinical importance of integral membrane proteins prompted the development of a yeast-based system for the high-throughput ...

Biology

Unravelling the Function of a Bacterial Effector from a Non-cultivable Plant Pathogen Using a Yeast Two-hybrid Screen

Unravelling the molecular mechanisms of disease manifestations is important to understand pathologies and symptom development in plant science. Bacteria have evolved different strategies to manipulate their host metabolism for their own benefit. This bacterial manipulation is often coupled with severe symptom development or the death of the affected plants. Determining the specific bacterial molecules responsible for the host manipulation has become an important field in microbiological research. After the identification of these bacterial molecules, called &#34;effectors,&#34; it is important to elucidate their function. A straightforward approach to determine the function of an effector is to identify its proteinaceous binding partner in its natural host via a yeast two-hybrid (Y2H) screen. Normally the host harbors numerous potential binding partners that cannot be predicted sufficiently by any in silico algorithm. It is thus the best choice to perform a screen with the hypothetical effector against a whole library of expressed host proteins. It is especially challenging if the causative agent is uncultivable like phytoplasma. This protocol provides step-by-step instructions for DNA purification from a phytoplasma-infected woody host plant, the amplification of the potential effector, and the subsequent identification of the plant&#39;s molecular interaction partner with a Y2H screen. Even though Y2H screens are commonly used, there is a trend to outsource this technique to biotech companies that offer the Y2H service at a cost. This protocol provides instructions on how to perform a Y2H in any decently equipped molecular biology laboratory using standard lab techniques.

Bacterial effector proteins are important for establishing successful infections. This protocol describes the experimental identification of proteinaceous binding partners of a bacterial effector protein in its natural plant host. Identifying these effector interactions via yeast two-hybrid screens has become an important tool in unravelling molecular pathogenicity strategies.

Unravelling the molecular mechanisms of disease manifestations is important to understand pathologies and symptom development in plant science. Bacteria ...

Immunology and Infection

Competitive Genomic Screens of Barcoded Yeast Libraries

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily. The tempo of these advances is accelerating, promising greater depth and breadth. In light of these extraordinary advances, the need for fast, parallel methods to define gene function becomes ever more important. Collections of genome-wide deletion mutants in yeasts and E. coli have served as workhorses for functional characterization of gene function, but this approach is not scalable, current gene-deletion approaches require each of the thousands of genes that comprise a genome to be deleted and verified. Only after this work is complete can we pursue high-throughput phenotyping. Over the past decade, our laboratory has refined a portfolio of competitive, miniaturized, high-throughput genome-wide assays that can be performed in parallel. This parallelization is possible because of the inclusion of DNA 'tags', or 'barcodes,' into each mutant, with the barcode serving as a proxy for the mutation and one can measure the barcode abundance to assess mutant fitness. In this study, we seek to fill the gap between DNA sequence and barcoded mutant collections. To accomplish this we introduce a combined transposon disruption-barcoding approach that opens up parallel barcode assays to newly sequenced, but poorly characterized microbes. To illustrate this approach we present a new Candida albicans barcoded disruption collection and describe how both microarray-based and next generation sequencing-based platforms can be used to collect 10,000 - 1,000,000 gene-gene and drug-gene interactions in a single experiment.

We have developed comprehensive, unbiased genome-wide screens to understand gene-drug and gene-environment interactions. Methods for screening these mutant collections are presented.

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily.  The tempo of these advances is ...

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. Saccharomyces cerevisiae, or budding yeast, is a model eukaryote for which a complete collection of ~6,000 gene deletion mutants and hypomorphic essential gene mutants are commercially available. These collections of mutants can be used to systematically detect chemical-gene interactions, i.e. genes necessary to tolerate a chemical. This information, in turn, reports on the likely mode of action of the compound. Here we describe a protocol for the rapid identification of chemical-genetic interactions in budding yeast. We demonstrate the method using the chemotherapeutic agent 5-fluorouracil (5-FU), which has a well-defined mechanism of action. Our results show that the nuclear TRAMP RNA exosome and DNA repair enzymes are needed for proliferation in the presence of 5-FU, which is consistent with previous microarray based bar-coding chemical genetic approaches and the knowledge that 5-FU adversely affects both RNA and DNA metabolism. The required validation protocols of these high-throughput screens are also described.

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Here we present a cost-effective method for defining chemical-genetic interactions in budding yeast. The approach is built on fundamental techniques in yeast molecular biology and is well suited for the mechanistic interrogation of small to medium collections of chemicals and other media environments.

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. ...

Biomolecular Interaction Studies Using Yeast Two-Hybrid Technique 

Isolating Interaction-Null&#47;Impaired Mutants Using the Yeast Two-Hybrid Assay

Protein-protein interactions are one of the most basic processes that underlie biological phenomena. One of the simplest and best ways to understand the role(s) and function(s) of a specific protein-protein interaction is to compare the phenotype of the wild-type (with the relevant protein-protein interaction) and those of mutants that lack the relevant interaction. Therefore, if such mutants can be isolated, they will help to elucidate the related biological processes. The yeast two-hybrid (Y2H) procedure is a powerful approach not only to detect protein-protein interactions but also to isolate interaction-null/impaired mutants. In this article, a protocol is presented to isolate interaction-null/impaired mutants using Y2H technology. First, a mutation library is constructed by combining the polymerase chain reaction and efficient seamless cloning technology, which efficiently excludes the empty vector from the library. Second, interaction-null/impaired mutants are screened by the Y2H assay. Because of a trick in the Y2H vector, undesired mutants, such as those with frameshift and nonsense mutations, are efficiently eliminated from the screening process. This strategy is simple and can, therefore, be applied to any combination of proteins whose interaction can be detected by the two-hybrid system.

Author Spotlight: Enhanced Isolation of Interaction-Null Mutants in Yeast

The interactions of biomolecules, such as protein-protein interactions, are the molecular basis of biological functions. If interaction-null/impaired mutants that specifically lack the relevant interaction can be isolated, they will greatly help to understand the function(s) of this interaction. This article presents an efficient way to isolate interaction-null/impaired mutants.

Protein-protein interactions are one of the most basic processes that underlie biological phenomena. One of the simplest and best ways to understand the ...

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Summary

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Split-Ubiquitin Based Membrane Yeast Two-Hybrid (MYTH) System: A Powerful Tool For Identifying Protein-Protein Interactions

Unravelling the Function of a Bacterial Effector from a Non-cultivable Plant Pathogen Using a Yeast Two-hybrid Screen

Competitive Genomic Screens of Barcoded Yeast Libraries

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Isolating Interaction-Null/Impaired Mutants Using the Yeast Two-Hybrid Assay