Article

Pooled-matrix protein interaction screens using
Barcode Fusion Genetics

Abstract

Subject Categories Methods & Resources; Network Biology
DOI 10.15252/msb.20156660 | Received 27 October 2015 | Revised 11 March

High-throughput binary protein interaction mapping is continuing
to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude awayfrom a complete interaction map for humans and other major
model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring
further efficiency gains in proteome-scale interaction mapping.
Here, we report Barcode Fusion Genetics-Yeast Two-Hybrid (BFGY2H), by which a full matrix of protein pairs can be screened in asingle multiplexed strain pool. BFG-Y2H uses Cre recombination tofuse DNA barcodes from distinct plasmids, generating chimeric
protein-pair barcodes that can be quantified via next-generation
sequencing. We applied BFG-Y2H to four different matrices ranging
in scale from ~25 K to 2.5 M protein pairs. The results show that
BFG-Y2H increases the efficiency of protein matrix screening, withquality that is on par with state-of-the-art Y2H methods.
Keywords DNA barcode; interactome; next-generation sequencing; protein
interaction; yeast two-hybrid

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2016 | Accepted 18 March 2016
Mol Syst Biol. (2016) 12: 863

Introduction
The impact of individual genotypes on disease is largely mediatedby interactions between proteins. Protein interaction network
mapping has shed light on cellular processes and disease mechanisms (Vidal et al, 2011). Some technologies (Rigaut et al, 1999;
Gillet et al, 2012; Roux et al, 2013) yield indirect associations
between proteins, while yeast two-hybrid (Y2H; Fields & Song,1989) and other technologies (Kerppola, 2006; Tarassov et al, 2008;
Cassonnet et al, 2011) tend to reveal direct physical (“binary”)
interactions (Rolland et al, 2014). Systematic high-quality Y2H has
now revealed more protein interactions than the combination of all
similar-quality interactions curated from smaller-scale studies in thescientific literature (Rolland et al, 2014). However, we remain anorder of magnitude away from a complete map of human protein

Donnelly Centre, University of Toronto, Toronto, ON, Canada
Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada
Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
Institute for Advanced Bioscience, Keio University, Tsuruoka, Yamagata, Japan
PRESTO, Japan Science and Technology Agency (JST), Tokyo, Japan
Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN, Institut Pasteur, Paris, France
Joint IRB-BSC Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain
Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
Department of Genetics, Harvard Medical School, Boston, MA, USA
Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
Department of Plant Systems Biology, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising, Germany
Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
Canadian Institute for Advanced Research, Toronto, ON, Canada
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada*Corresponding author. Tel: +81 3 5452 5242; E-mail: yachie@synbiol.rcast.u-tokyo.ac.jp**Corresponding author. Tel: +1 416 946 5130; E-mail: fritz.roth@utoronto.ca†
These authors contributed equally to this work‡
Present address: Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USAª 2016 The Authors. Published under the terms of the CC BY 4.0 license

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Nozomu Yachie1,2,3,4,5,*,†, Evangelia Petsalaki1,2,†, Joseph C Mellor1,2, Jochen Weile1,2,6, Yves Jacob7,
Marta Verby1,2, Sedide B Ozturk1,2, Siyang Li1,2, Atina G Cote1,2, Roberto Mosca8, Jennifer J Knapp1,2,
Minjeong Ko1,2, Analyn Yu1,2, Marinella Gebbia1,2, Nidhi Sahni9,10,‡, Song Yi9,10, Tanya Tyagi1,2,
Dayag Sheykhkarimli1,2,6, Jonathan F Roth1,2,6, Cassandra Wong1,2, Louai Musa1,2, Jamie Snider1,
Yi-Chun Liu1, Haiyuan Yu11, Pascal Braun9,10,12, Igor Stagljar1,6, Tong Hao9,10, Michael A
Calderwood9,10, Laurence Pelletier2,6, Patrick Aloy8,13, David E Hill9,10, Marc Vidal9,10 &Frederick P Roth1,2,6,9,14,15,**

Molecular Systems Biology

Nozomu Yachie et al(Fig 1B). Here, we apply the BFG concept for increased efficiency ofyeast two-hybrid protein interaction screening.
Barcode Fusion Genetics-Yeast Two-Hybrid (BFG-Y2H) technology
In preparation for BFG-Y2H, libraries of haploid strains are created,each carrying a plasmid that (i) expresses either a bait or prey
protein and (ii) encodes a barcode locus carrying two strain-specificbarcodes (“BC1” and “BC2”, Fig EV1A). The directional site-specificloxP and lox2272 recombination sites (Livet et al, 2007) flank thebait-BC1 and prey-BC2 barcodes (Fig EV1A). To screen for protein
interactions, the bait and prey libraries are pooled and mated en
masse to create a diploid cell population representing all protein
combinations (Fig 2). Diploid cells corresponding to protein interactions are then enriched via selection for the expression of the Y2Hreporter gene HIS3. The selected cells are then pooled and treatedwith doxycycline to induce Cre recombinase expression. Because
Cre selectively recombines loxP with loxP and lox2272 with lox2272,
Cre induces a double-crossover event that physically swaps the baitBC1 and prey-BC2 barcodes between the two plasmids. This leaves
a chimeric bait–prey “BC1-BC1” barcode fusion on the prey plasmid
and another chimeric bait–prey “BC2-BC2” barcode fusion on thebait plasmid (Appendix Note S1 and Fig EV2). After cell lysis andplasmid DNA extraction, the fused-barcode regions are amplified by
PCR, and the interacting protein pairs are identified by massively
parallel sequencing of the fused barcodes.
We derived two BFG-Y2H strains—“toolkit-a” and “toolkit-a”—
from strains used successfully in recent large-scale Y2H studies (Yuet al, 2008, 2011; Simonis et al, 2009; Rolland et al, 2014; seeMaterials and Methods) to permit expression of Cre recombinase via
the Tet-On system (Belli et al, 1998; Fig EV1). The toolkit-a strain
expresses rtTA (needed for the Tet-On system) and serves to hostbarcoded prey plasmids. The toolkit-a strain encodes Cre undercontrol of the tetO2 promoter and hosts barcoded bait plasmids. We
evaluated mixed cell populations and found that barcode swapping
between plasmids occurred specifically within but not between
cells, demonstrating in vivo barcode fusion (Appendix Note S1 andFig EV2).

Results

A BFG-Y2H screen targeting human centrosomal proteins

Principle of Barcode Fusion Genetics

As a proof-of-principle study, we screened a “CENT” matrix, including human centrosomal and centrosome-associated proteins,curated from a combination of sources (Andersen et al, 2003;
Lamesch et al, 2007; Temple et al, 2009), encompassing 143 bait
and 162 prey ORFs (Table EV1). Barcoded haploid strains weregenerated by in-yeast assembly (Ma et al, 1987; Gibson, 2009) toeach carry a uniquely barcoded plasmid expressing a specific bait orprey (see Materials and Methods). Four DNA fragments were pooled
and co-transformed into yeast with overlapping sequences to beassembled by homologous recombination (Fig 3A). These four fragments correspond to the following: (i) a bait or prey-encoding ORF;
(ii) either the DNA-binding or activation domain of Gal4; (iii) a
barcode locus; and (iv) the plasmid “backbone”, which encodes amarker enabling selection for cells with correctly assembled BFGY2H plasmids (Fig 3B). Among the 161 centrosome-related ORFs
attempted (Table EV1), 112 bait and 131 prey ORF fragments passed
stringent quality controls both by Sanger sequencing and by PCR

Modern sequencing-based phenotypic screening commonly uses
DNA barcodes to identify single specific engineered changes—for
example, gene deletions (Smith et al, 2009), or gene-targeting RNAi(Berns et al, 2004) or CRISPR reagents (Hart et al, 2015). However,
it is frequently interesting, for example, for genetic interactions, toobserve phenotypes resulting from two perturbations. The BFG
technology enables phenotypic analysis of a heterogeneous pool of
millions of yeast strains, each having two engineered loci or genes
of interest. In BFG, a doubly engineered cell pool is prepared so thateach of two engineered loci in each cell is associated with a distinct
DNA barcode flanked by site-specific recombination sequences(Fig 1A). Once a given selection has been applied to the strain pool,barcodes representing different engineered changes are fused bysite-specific recombination (Fig 1B). Strain abundances can then bequantified by amplification and deep sequencing of fused barcodes2

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interactions (Rolland et al, 2014), and further still when we consider
that different “proteoforms” (Smith & Kelleher, 2013) can interact
with distinct partners (Corominas et al, 2014). Interaction maps areeven less complete for most model organisms.
In Y2H, the transcription factor Gal4 is reconstituted via interaction of a “bait” protein fused to the Gal4 DNA-binding domain witha “prey” protein fused to the Gal4 activation domain (Fields & Song,1989). For each tested pair of proteins, a strain is generated, encoding a specific combination of bait and prey. Interactions are thendetected via selection for expression of a Gal4-activated reporter
gene (Vidal & Legrain, 1999; Legrain & Selig, 2000; Uetz, 2002). A
single bait strain can be mated to a pool of hundreds of prey strains(Rual et al, 2005; Yu et al, 2008; Simonis et al, 2009; Venkatesan
et al, 2009), simplifying primary screening, but requiring later identification of Y2H-positive colonies. Costs of identification have been
reduced by the Stitch-seq method (Yu et al, 2011); however, Stitchseq is labor intensive, requiring isolation of individual Y2H-positive
colonies and three individual PCRs for each colony. Another “Y2Hseq” (Weimann et al, 2013) approach identifies prey interactors
from a pool by deep sequencing, but further retesting is required to
identify the specific bait protein which interacts with each identified
prey.
An ideal multiplexing strategy would allow efficient identificationof each bait–prey combination within a pool of strains that collectively represents the entire protein-pair matrix. To this end, two
combinatorial multiplexing strategies have been proposed in which
the open reading frames (ORFs) for bait and prey proteins are physically linked: (i) intracellular DNA recombination (Hastie & Pruitt,2007) and (ii) overlap-extension PCR within emulsion-encapsulated
single cells (Nirantar & Ghadessy, 2011). However, these methods
have never been implemented at large scale. Moreover, these methods require multiplexed PCR amplification of templates varying
widely in length and base composition, a procedure subject tosevere PCR competition effects (Shiroguchi et al, 2012). To addressthese issues and establish a practical, scalable, and economical
protein interaction mapping method, we developed the Barcode
Fusion Genetics (BFG) technology.

Pooled-matrix interaction maps via Barcode Fusion

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Pooled-matrix interaction maps via Barcode Fusion

A
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Crerecombination

Fused-barcode tags

PCR

Figure 1. Principle of Barcode Fusion Genetics, a technology to generate fused barcodes that uniquely identify the presence of a specific combination ofengineered loci.
A Each cell carries two engineered loci, such that each locus is identified by the presence of a barcode flanked by site-specific recombination sites. In the presence ofCre recombinase, a double-crossover DNA recombination is induced to form chimeric “fused” barcodes that represent the combination of loci.
B Multiple pairwise combinations of reagents can be tested in a pool. Fused barcodes can be amplified and analyzed by deep sequencing to analyze the abundance ofcells corresponding to each X-Y combination.

Figure 2. Design of the BFG-Y2H technology.
A pool of diploid cells, potentially expressing all possible pairwise combinations of bait and prey fusion proteins, is generated via en masse yeast mating, in which a haploid
pool of bait strains (MATa) is mated with a pool of prey strains (MATa). Diploid cells surviving the Y2H selection are pooled, and Cre recombinase is induced to swap the
positions of the bait-BC1 and prey-BC2 and to generate chimeric BC1-BC1 and BC2-BC2 barcode fusions that each uniquely identifies a candidate X-Y interaction. Cells arethen lysed, plasmids are extracted, and a DNA sequencing library is prepared by PCR for both BC1-BC1 and BC2-BC2 fused barcodes. Finally, protein interactions are identified
according to the enrichment of sequencing read counts for fused barcodes corresponding to particular protein pairs.

amplicon length. We also included 31 calibration set protein pairs(see Materials and Methods). For each bait or prey ORF, weprepared two uniquely barcoded strains (286 bait strains and 324ª 2016 The Authors

prey strains in total). To assess the quality of this assembly protocol,
we isolated 23 single colonies from each of six assembly reactions
containing different ORFs (LCP2, FKBP3, and BDNF for bait strain

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B

ORF fragment

Domainfragment

70-bp overlaps

Bait strains

C

Bar
TK
Bar
TK
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TK
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TK
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TK
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TK
Bar RY1030
TK controlAvariation of strain abundance within pools. Strains were pooled,pools were mated, and diploids were selected. Mated diploid cells
were spread on agar plates with: (i) non-selective control media
containing excess histidine (“+His”), (ii) Y2H-selective media(“–His”), and (iii) stringent Y2H-selective media lacking histidine
and supplemented by amino-1,2,4-triazole (“3-AT”), a competitive
inhibitor of the HIS3 gene product (see BFG-Y2H procedure in
Materials and Methods for detailed media descriptions). The
experimental scale was designed to achieve an average of ~100 DNA
molecules representing each distinct protein pair at the most restrictive population bottleneck (yeast plasmid extraction for nonselective +His condition), as supported by a computational Monte
Carlo simulation of the entire screening process (Appendix Note S3and Appendix Fig S1).
As expected given that the vast majority of protein pairs do notinteract, count distributions from selective media were sparser thanthose of non-selective condition (Figs 4A and B, and EV4) andtherefore exhibited a higher dynamic range because high counts
were observed for a relatively small number of protein pairs surviving the Y2H selection. High-background levels were observed forsome bait proteins under Y2H-selective conditions (Fig 4C), suggesting that these proteins have some ability to “auto-activate” thereporter gene by recruiting RNA polymerase II directly in theabsence of an interacting prey protein. Fused barcodes corresponding to seven of such “auto-activators”, GMNN, HAUS6, HAUS8, NIN,
PPP2R3C, YWHAE, and YWHAG, were highly abundant in the selective conditions of the initial screen. To provide biological replicationand to assess the effect of high-background baits on the screen, we

LCP2

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Figure 3. Massively parallel generation of barcoded Y2H strains.
A Library-scale in-yeast assembly to generate Y2H strains carrying barcoded ORF-expressing plasmids. In each reaction, the Gal4 DNA binding or activation domain, and
ORF, barcode and backbone DNA fragments were directly assembled in vivo in either the toolkit-a or toolkit-a strain background.
B Barcoded Y2H strains derived by in-yeast assembly. Colony growth indicates yeast cells harboring the correctly assembled plasmids. The yellow boxes denote “no ORF
fragment” negative controls.
C Quality confirmation of in-yeast assembly-based barcoded strain generation. After in-yeast assembly, single colonies were isolated and the DNA fragments wererecovered by yeast colony PCR. “TK” denotes genotyping PCR to confirm the presence of the chromosomal locus that defines the toolkit strains.

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generation and NCK1, NQO2, and NTF5 for barcoded prey) andanalyzed these by PCR (Fig 3C). Of the 138 colonies evaluated, only
two (1.4%) showed misassembled plasmids (both from LCP2, whichshowed success in the majority of its colonies), demonstrating highfidelity of the in-yeast assembly process.
To generate the barcode locus fragments used in the above
procedure, we used Gibson in vitro DNA assembly (Gibson et al,2009) of synthetic dsDNA-containing random 25-bp degenerateregions (Fig EV3). Each successful assembly joined BC1, BC2, loxP,and lox2272 sites in the appropriate order, together with a backbone
DNA fragment of a Gateway LR cloning-compatible destination plasmid (Walhout et al, 2000). Thus, each resulting “barcode carrier”
plasmid contained a randomized sequence at each of two barcode
positions. The assembled DNA sample was transformed into competent E. coli cells, such that each transformant carried a single
randomly barcoded destination plasmid. Colonies were picked andarrayed into 384-well plates. To identify the barcode in each well
position within the stack of plates, a row-column-plate-indexed PCR(RCP-PCR) generated amplicons that each contained a barcode locus
flanked by sequence indices identifying plate and well position(Appendix Note S2 and Fig EV3). RCP-PCR amplicons were pooled
and subjected to next-generation sequencing to identify barcode andindex sequences. Barcode fragments were later amplified directlyfrom re-arrayed E. coli strains. To date, we have created a reusable
Gateway destination collection of more than 2,500 bait and 2,500
prey barcode carrier plasmids.
To initiate the BFG-Y2H screen, bait and prey strains were individually grown to saturation in 96-deep-well plates to minimize

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condition screens and the correlation between two screening variants (with and without auto-activators) was also high (R = 0.85;
P < 10 15; Fig 4D).
To assess the efficiency of Cre-mediated “swapping” of barcodes
between plasmids, we examined the pool of plasmids isolated after
Cre induction and cell lysis of the CENT screen, but prior to PCR
amplification of the barcode loci. This DNA was sequenced en
masse using the Illumina Nextera method. By assessing the relative
abundance of original and chimeric barcode loci, we estimated 16–
27% of cells from each Y2H-positive colony to yield fused barcodes
after overnight Cre induction (Figs 4E and EV5).
Accurately estimating the effects of Y2H selection on each protein
pair must account for several factors: (i) uneven strain abundancein the initial bait and prey; (ii) potential competitive growth effectsof bait and prey expression; and (iii) slight barcode-dependent100

101 102 103 104
Interaction score

105

Figure 4. Screening coverage, reproducibility, and other features of BFG-Y2H CENT screen.
Normalized fused-barcode abundance is shown for 1) non-selective conditions, based on observed fused-barcode abundance at a sequencing depth that is only
sufficient for accurately determining barcode marginal abundance (“+His observed at low saturation”); 2) non-selective conditions, as inferred from marginal
abundance of single-barcode frequencies (“+His inferred”), and 3) selective conditions based on observed fused-barcode abundance (“–His” and “3-AT”).
B, C Average of normalized fused-barcode count for each ORF pair (faverage) in (B) the non-selective (+His) condition and (C) in the selective (–His) condition.
CS: calibration set space spiked in the screen.
D
Correlation of faverage between different pairs of replicate types in the selective conditions (scatter plots are log-scale).
E
Analysis of barcode fusion efficiency. Frequencies of 7-bp flanking motif combinations located upstream and downstream of loxP (yellow arrow) or lox2272 (green
arrow) sites were analyzed by Illumina Nextera sequencing for the –His condition.
F
Interaction score profile for the CENT screen with parameters optimized according to the Matthews correlation coefficient (MCC) to recapture previously reported
Y2H interactions.
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removed the auto-activator strains and repeated the screen(Fig EV4).
There was a high correlation between the average BC1-BC1
and BC2-BC2 fused-barcode counts corresponding to a given
protein pair under selective ( His) conditions (R = 0.90;
P < 10 15; Fig 4D). This indicates that read counts of fused
barcodes primarily reflect the makeup of the cell populations
without major barcode-specific effects arising from differences in
efficiency of PCR or sequencing. There was also a high correlationof average fused-barcode counts between strain replicates that
express the same bait and prey proteins, but which did not sharesame barcodes (R = 0.70; P < 10 15; Fig 4D), suggesting not onlythat the screening method is robust, but also that the screen wasclose to saturation and the sampling sensitivity was high. The
seven auto-activator bait strains did not dominate the selective

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A BFG-Y2H screen targeting cancer and cell cyclerelated proteinsTo demonstrate BFG-Y2H at larger scale and broaden discovery of
new interactions, we next carried out BFG-Y2H screening on a
“CCC” matrix, which includes proteins implicated in cancer according to the COSMIC database (Forbes et al, 2015), as well as cyclins,
cell cycle kinases, and cell cycle kinase substrates (Hornbeck et al,2012), encompassing 392 bait and 377 prey doubly barcoded ORFs
(Table EV1). CCC contained sixfold more protein pairs than the
CENT matrix. Screening and scoring of CCC was carried out inessentially the same way as for CENT, except for small differencesin the in-yeast assembly procedure (Materials and Methods andAppendix Note S4).
Using the CENT and CCC screens to evaluate
BFG-Y2H performance
Like CENT (Fig 5A), protein pairs in the CCC screen with high interaction scores were enriched for previously known interactions(Appendix Fig S3). The best performance of recapturing known
interactions (in terms of the MCC performance measure) was
achieved by taking the top 55 and 54 protein pairs for CENT and
CCC screens, respectively. Pairwise retests of the top 100 protein
pairs for the CENT and CCC screens and arbitrarily chosen lowscoring pairs were performed using an independent set of nonbarcoded Y2H strains. For each protein pair, ORFs were transferredfrom Gateway entry plasmids to the non-barcoded bait and preydestination plasmids by Gateway LR cloning. Retests were performed
both in the BFG-Y2H toolkit strains and in the parental “Y-strain”
backgrounds described previously (James et al, 1996). Both retestpositive pairs and previously known interactions were enriched at
higher interaction scores. To assess the precision of interactions
emerging from the primary screen, we first examined interactions
above the MCC-optimal rank (55 and 54 pairs for CENT and CCC,

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respectively). After excluding auto-activators and untested pairs,
pairwise retests verified 23/47 (49%) of primary “hits” from CENT
and 25/46 (54%) from the CCC screen including 4/11 (36%) and 5/
14 (36%) novel pairs, respectively. Within the top 100 protein pairs,
32/82 (39%) and 36/66 (55%) pairs were verified, with verificationfor 10/42 (24%) and 13/30 (43%) novel pairs (Fig 5A, Table EV2
and Appendix Fig S3). By contrast, only one pair (1.2%) of the 84
tested BFG-Y2H negatives was verified by pairwise Y2H retesting(Table EV2). A retest-positive rate of ~50% from the primary BFGY2H screen is within the normal range for the current Y2H pipeline
at CCSB (Rual et al, 2005; Yu et al, 2008; Simonis et al, 2009).
Despite the fact that many of the pairs we tested (87% and 78%
in CENT and CCC, respectively) had been previously screened(Rolland et al, 2014), BFG-Y2H uncovered several interesting newinteractions. For example, the CCNDBP1 protein, which is known tonegatively regulate cell cycle progression and to have tumor
suppressor functions (Ma et al, 2007), was found to physically interact with TFPT, which is involved in DNA repair and promotes apoptosis in a p53-independent manner (Franchini et al, 2006). This
interaction might play a role in the regulation of cell death during
cell cycle progression. BFG-Y2H also identified an interaction
between RBPMS, a transcriptional co-activator with a role in TGF
beta signaling (Sun et al, 2006) and SMAD3, a transcription factorof the TGF beta pathway. This interaction was only recently
reported to be responsible for cell growth and migration inhibitionin breast cancer cell lines (Fu et al, 2015). Other RBPMS partners
we identified included PICALM, a clathrin adaptor with a role in
Alzheimer’s disease (Moreau et al, 2014), TCF7L2, a Wnt signalingtranscription factor (Korinek et al, 1997) and PATZ1, a transcriptional regulator with a role in cell death and proliferation (Valentino
et al, 2013) and differentiation (Ow et al, 2014). There is a very wellestablished role of the TGF beta pathway in cell proliferation,
cancer, and Alzheimer’s disease (Kajdaniuk et al, 2013) and theseinteractions can contribute to improved understanding of the TGF
beta pathway and its roles.
Validation of BFG-Y2H interactions by calibrated
orthogonal assays
The CENT and CCC screen results were validated using an
orthogonal Gaussia princeps luciferase protein complementation
assay (GPCA) in human cells (Remy & Michnick, 2006; Cassonnet
et al, 2011). Among the top 100 hits of each of the CENT and
CCC screens, GPCA vectors were successfully generated for 83
and 64 protein pairs, respectively (Fig 5A and Appendix Fig S3).
Among the top 55 BFG-Y2H hits in the CENT screen, 74% of the
46 tested were validated by GPCA. In the top 100 of CENT, 48% of
83 tested were GPCA positive. By contrast, of 54 BFG-Y2H-negative
pairs from CENT that were tested by GPCA, only 2 (4%) were GPCA
positive. From the CCC screen, 46% of 41 GPCA-tested pairs in the
top 54 pairs and 39% of 72 GPCA-tested pairs among the top
100 pairs were GPCA positive. By contrast, only 1 (3%) of 36
BFG-Y2H-negative pairs were GPCA positive. The validation rate of
BFG-Y2H hits by GPCA compared well with that of a state-of-the-art
Y2H study (Sahni et al, 2015), in which GPCA validated 59% of 165
Y2H-positive pairs and 41% of the 17 Y2H negatives. In anotherstate-of-the-art Y2H study (Hill et al, 2014), GPCA validated ~35%
of Y2H hits and 0% of Y2H-negative pairs that were examined.

ª 2016 The Authors

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differences in amplification and sequencing. We therefore rescaled
read counts in the –His and 3-AT conditions using the total row orcolumn counts of each bait and prey strain in the +His condition
matrix. The normalized barcode count s was further rescaled toaccount for differences in background auto-activation activity toyield an interaction signal s’ for each barcode pair (Appendix Note
S4). For each protein pair, the two BFG-Y2H screens produced atotal of 32 s’ signal measurements: two fused-barcode variants(BC1-BC1 and BC2-BC2) × four diploid replicates × two selective
conditions × two library variants (with and without the seven autoactivators). The best method for deriving a single interaction scorefrom replicates was optimized according to the ability to recapture
previously reported Y2H-positive protein pairs with the best balanceof precision and recall according to the Matthews correlation coefficient (MCC; Guda et al, 2004). Although 3-AT is commonly used in
Y2H screening to increase the dependence of growth on HIS3
expression and thus to screen for interactions with higher stringency(Dreze et al, 2010), using only the “–His” screen here yielded
increased sensitivity and the best overall performance (MCC = 0.52
as opposed to MCC = 0.39 for the 3-AT screen; Appendix Fig S2).
Protein pairs achieving a high interaction score showed a clear
enrichment for known interactions (Fig 5A and B).

Pooled-matrix interaction maps via Barcode Fusion

Molecular Systems Biology

Pooled-matrix interaction maps via Barcode Fusion

D
MCC-optimal (55 pairs)

GPCA luciferase(log scaled)

103

Lit-BM-13
HI-II-14
BioGRIDUnion
Y2H retest
GPCA

**

105
104
103
Positive controls

GPCA

Luciferase (log10)
6.0
0.0
Not tested
Positive
Novel GPCA positive

P = 0.112

E%pairs100

500GPCA ++
Y2H retest

+

F

Preys
Others(average)C

106

100 10000
Rank

CEP55
CEP70
CETN3
CLASP1
DCTN2
HAUS4
HOOK2
PIBF1
POC5
TUBGCP4

1

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cal

Re

Positive
Auto-activator
Negative
Not tested
Novel retest positive*** ***

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CMC

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on
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104

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105

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residue countInteraction
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Nozomu Yachie et al

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10102 $!!!
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104 $!!!!!
105
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Interaction score threshold

Figure 5. BFG-Y2H efficiently captures protein interactions.
A Top 100 protein pairs scored by BFG-Y2H, and their presence in a high-quality literature-curated protein interaction set (Lit-BM-13), a recent systematic high-quality
human interactome dataset (HI-II-14), or the curated BioGRID protein interaction dataset (see Materials and Methods). “Union” represents the union of interacting
protein pairs in Lit-BM-13, HI-II-14, and BioGRID.
B Performance in recovering previously reported interactions (“Union”).
C Recovery rate by GPCA for BFG-Y2H-positive (+) versus BFG-Y2H-negative ( ) hits and pairwise retest-positive (+) versus retest-negative ( ) hits.
D Distribution of GPCA luciferase intensities (quadruplicates) for protein pairs in the positive control (defined as the overlap of the GPCA-tested space with the union of
the HI-II-14 and Lit-BM-13 datasets, Rolland et al, 2014; Table EV2), rank 1–55, 56–100, pairwise Y2H retested positives, auto-activators in the pairwise Y2H pipeline
and BFG-Y2H negative pairs. *P < 0.05, **P < 10 5, and ***P < 10 15 (Mann-Whitney U-test).
E HAUS1 hits captured by BFG-Y2H.
F Fold enrichment of residue contacts at protein interfaces for different interaction score thresholds. Fold-change is calculated as the ratio of the average number ofresidue contacts for the two groups of protein pairs separated by each interaction score threshold. P-value was calculated using the Mann–Whitney U-test.

Interestingly, the GPCA validation rate was not significantly different for the subset of BFG-Y2H-positive hits that showed positive
Y2H retests (Fig 5C). This suggests that filtering by pairwise Y2Hdiminished the output of our BFG-Y2H screens without substantially
improving the quality of the results. We also found that our interaction score correlated with GPCA signal intensity (Figs 5D and EV4),
highlighting the reproducibility of our interaction pairs in an orthogonal assay. The GPCA luciferase signal has been reported to correlate with protein interaction affinity (Szklarczyk et al, 2015), so thata quantitative correlation with GPCA suggests BFG-Y2H’s potential
for measuring interaction strength.
BFG-Y2H can detect interactions for high-background “autoactivator” baits
Some bait proteins are “auto-activators” that can, to varying
degrees, independently recruit RNA polymerase II and thus

ª 2016 The Authors

activate the reporter gene. Current Y2H pipelines typically remove
auto-activators prior to large-scale screening. By contrast, BFGY2H can identify interactions between protein pairs involving
auto-activators. Specifically, protein pairs that were within the top
100 and scored as auto-activators by Y2H pairwise retest yieldedsignificantly higher GPCA signal intensities than protein pairs
scored negatively by BFG-Y2H (P < 10 9; Fig 5D). For example,
the TACC3 homodimer interaction, which ranked 63rd in the CCC
screen, was successfully validated by GPCA (Table EV2) despite
behaving as a Y2H auto-activator in pairwise retesting. Other
examples included human Augmin complex (HAUS) subunit
proteins of the CENT screen. HAUS6 and 8 showed strong autoactivation (Fig 4C). HAUS1, 3, 4, and 7 showed weak autoactivation with HAUS1 and 7 also being detected as auto-activators
in pairwise retesting (Table EV2). Nevertheless, several HAUS1
connections to centrosome-related proteins emerged with BFG-Y2Hscores above background (Fig 5E). The Augmin complex regulates

Molecular Systems Biology 12: 863 | 2016

7

Molecular Systems Biology

High-scoring BFG-Y2H interactions show increased interfacial
contacts in co-crystal structures
To investigate whether the quantitative interaction score resulting
from BFG-Y2H correlates with interaction strength, we calculated
the number of residue–residue contacts in the three-dimensional
protein interface where co-crystal structures or high-quality homology models were available. We found that protein pairs above the
MCC-optimal interaction score threshold have significantly moreinterfacial residue–residue contacts (Fig 5F and Table EV3). These
results support the idea that quantitative scores from BFG-Y2H havepotential value in estimating protein interaction strength, although
this remains to be confirmed.

Nozomu Yachie et al

Application of the en masse recombinational cloning strategy toefficiently generate barcoded plasmid sets
We applied the en masse recombinational cloning approach to twotarget ORF spaces—“CV” and “A” (Materials and Methods andTable EV1). The first space CV corresponded to the union of 218
COSMIC ORFs, 272 human “virhostome” proteins targeted by DNA
tumor viral proteins (Rozenblatt-Rosen et al, 2012), and a set of 381
arbitrary picked ORFs including calibration ORF pairs, for a totalof 767 unique ORFs. Two A pools, each of which was composed of
1,896 arbitrarily picked ORFs, were, respectively, used to generatebarcoded bait and prey libraries. The successfully barcoded bait andprey libraries were used for two interactome screens: CV describedabove, and “CVA”, a larger screening space combining both CV and
A libraries, to demonstrate scalability of BFG-Y2H.
From each of the two en masse Gateway reactions, bacterialtransformant colonies were picked and arrayed to 384-well
LB+ampicillin plates. Among high-quality clones identified bysequencing, the number of different barcodes assigned to each ORF
varied (Fig 6C). From each set, we selected and re-arrayed highquality bacterial clones, so that each ORF was assigned a maximumof four different barcodes. Out of the 767 CV ORFs subjected to theen masse Gateway reaction, 623 (81%) and 619 (81%) were, respectively, recovered as barcoded bait clones and prey clones. Out of the
1,896 A ORFs, 1,169 (62%) and 1,208 (64%) were obtained as
barcoded bait clones and prey clones, respectively (Fig 6D). Notethat the probability of obtaining a barcoded clone for any given ORF
depends on the number of bacterial colonies obtained after theen masse Gateway reaction relative to the number of input ORFs.
After generating barcoded haploid yeast strain pools for each target
ORF set, we created bait and prey strain pools corresponding to both
CV and CVA screens.
En masse Gateway-based BFG-Y2H screening

A more efficient en masse strategy for producing
barcoded plasmids
To facilitate larger-scale BFG-Y2H screens, we extended a previously described “pooled ORF expression technology” (Waybrightet al, 2008) to more efficiently produce pools of barcoded bait
and prey plasmids. In this strategy, here termed en masse recombinational cloning reaction, two pools of vectors are combined:(i) A pool of entry clones corresponding to a collection of ORFs;(ii) a highly complex pool of randomly barcoded bait or preydestination plasmids. The Gateway LR Clonase recombinational
cloning reaction is then carried out en masse to produce a pool ofbarcoded bait and prey expression plasmids (Fig 6A). For eachreaction pool, bacterial cells are then transformed to obtain clonalcolonies of barcoded bait or prey expression plasmids. Bacterialcolonies are robotically picked, arrayed in 384-well plates, andsequenced to identify the barcode sequence and ORF of eachclone. A subset of sequence-identified bacterial strains is chosen
to obtain a similar number of barcoded plasmid versions for each
ORF. The chosen strains are robotically re-arrayed, grown overnight, then pooled for a single plasmid DNA purification (Fig 6B).
Barcoded bait or prey pools are used to transform toolkit-a or
toolkit-a strains, respectively, to generate BFG-Y2H-ready strain
pools.

8

Molecular Systems Biology 12: 863 | 2016

The BFG-Y2H process for CV and CVA was similar to that of the
CENT and CCC screens. Because heterogeneity was higher inthe haploid pool of the en masse Gateway reaction-based screen thanin the in-yeast assembly-based screen, we considered only strains
exhibiting sufficient barcode counts in the unselected (+His) pool
(Appendix Note S4). Two replicate screens were performed for CVA
in parallel and s’ signals were combined to calculate interaction
scores. Of the pool of barcoded expression plasmids transformed into
BFG-Y2H toolkit strains for the CV screen, 578 (93%) of bait ORFs
and 579 (94%) of prey ORFs (334,662 pairs) were scored. For the
CVA screen, 1,571 (88%) of bait ORFs and 1,639 (90%) of prey ORFs
(totaling 2.6 million pairs) were scored (Figs 6D and 7A and
Table EV1). ORF attrition occurred primarily at the en masse Gateway
reaction stage for overall slightly longer ORFs (Fig 6D; no marked
bias throughout the pooled yeast transformation), suggesting an iterative strategy to map a given space: (i) carry out a first round en
masse Gateway reaction as described above; (ii) carry out a second
iteration for bait and prey ORFs lost at any prior stage; (iii) poolbarcoded bait and prey clones and carry out BFG-Y2H. With single
rounds of en masse Gateway reaction and BFG-Y2H, we obtained 389
and 591 interacting protein pairs in the CV and CVA screens, respectively, at the MCC-optimal interaction score thresholds recapturing
previously known Y2H hits (Fig 7B and Table EV2).

ª 2016 The Authors

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centrosome and spindle assembly, co-localizing with the centrosome during interphase but with microtubules during mitosis(Lawo et al, 2009). In the CENT screen, HAUS1 had a markedly
higher interaction score with proteins localizing with centrioles or
pericentriolar material (CEP55, CEP70, and POC5) and proteinspromoting microtubule dynamics (CEP70, CETN3, and CLASP1).
The mitotic co-localization of HAUS1 with microtubules might be
explained by its interactions with dynactin (DCTN2), whichanchors microtubules to the centrosome (Echeverri et al, 1996)
and interacts with microtubule regulator MAPRE3 (Berrueta et al,1999). The BFG-Y2H screens also captured the interaction between
DCTN2 and MAPRE3. HAUS1 had a markedly higher interaction
score with the gamma-tubulin ring complex (TuRC) protein
TUBGCP4 than its other partners, which is consistent with previous observations that the Augmin complex is required for localization of gamma-TuRC (and chromosome segregation andcytokinesis) in human cells (Uehara et al, 2009). We also foundthat HAUS1 interacted with HOOK2, which is known to interact
with microtubules (Szebenyi et al, 2007; Fig 5E).

Pooled-matrix interaction maps via Barcode Fusion

A

AD/DB

Molecular Systems Biology

Pooled-matrix interaction maps via Barcode Fusion

Random 25-bp
cassettes

C

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Cumulative %ORFs
P (n)Nozomu Yachie et al

100

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Scored in BFG-Y2H

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its

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n.s. n.s.

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Cherry pick and array clones

3000

Queried to Gateway

Barcoded ORF
pool

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ORF size (bp)D
Bacterial transformation

Prey
CVA

Figure 6. Scalable generation of barcoded bait and prey strains based on a pooled recombinational cloning reaction.
A Schematic representation of the en masse recombinational cloning process. Randomly barcoded bait or prey destination plasmid pool was combined with a pool ofentry ORF plasmids and subjected to a Gateway LR reaction. Randomly barcoded ORF expression clones were isolated by bacterial transformation and colony picking
and identified by sequencing.
B Generation of BFG-Y2H-ready bait and prey haploid pools by en masse transformation of purified barcoded bait and prey expression plasmid pools to the appropriate
mating type yeast cells.
C Fraction of ORFs assigned to at least n barcodes indicated on the horizontal axis.
D Attrition of ORFs and their lengths at steps of the en masse recombinational cloning-based BFG-Y2H procedure. **P < 10 4 and ***P < 10 7.

To enable comparison across screens, we included ORFs corresponding to the common set of 31 protein pairs described above,of which 18 yielded a score in all of the four BFG-Y2H screens.
Of the 18 pairs in this “calibration set”, we found 13, 11, 13, and
11 interactions in CENT, CCC, CV, and CVA screens, respectively(Fig 7C), indicating consistent sensitivity at increasing scales.
Furthermore, within the sub-matrix of protein pairs examined inboth the CV and CVA screens (312 thousand pairs), the resulting
interactions overlapped significantly (P < 10 15; Fig 7D). Thus,
BFG-Y2H is scalable to matrices of at least ~2.5 M protein pairs
with no increase in hands-on time during the screen. The costs ofsequencing scale linearly with matrix size, while costs relating tobarcoded strain construction scale as the square root of matrix
size.

ª 2016 The Authors

Performance comparison between BFG-Y2H and state-of-theart Y2H
To compare the practical performance of BFG-Y2H with state-of-theart Y2H methods, the four BFG-Y2H screen results and a recent
high-quality Y2H-based human interactome dataset HI-II-14(Rolland et al, 2014) were each evaluated using Lit-BM-13, a highquality set of literature-curated physical interactions (Rolland et al,2014), as a benchmark standard. Each screen was assessed
according to its ability to recover the subset of Lit-BM-13 fallingwithin its tested space (Fig 7E). Generally, BFG-Y2H screening
outperformed state-of-the-art Y2H in terms of precision albeit with
reduced recall (Fig 7F). According to the MCC measure, whichbalances precision and recall, BFG-Y2H performed on par with

Molecular Systems Biology 12: 863 | 20169

Molecular Systems Biology

162

CENT
377

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CCC

CV

CVA

18 prey ORFs

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Nozomu Yachie et al

Pooled-matrix interaction maps via Barcode Fusion

CCC

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)

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Figure 7. Scalability and performance of BFG-Y2H.
A
B

Schematic representation of the increasing size of the four protein pair spaces tested (CENT, CCC, CV, and CVA).
Protein interaction networks identified by each BFG-Y2H screen. Red lines indicate novel interactions, blue lines indicate previously known interactions (those in
the “Union” set) captured by BFG-Y2H, and gray lines denote known interactions among proteins in the hit list that were not captured by BFG-Y2H.
C
Sub-matrices for the 18 calibration pairs that were commonly tested in all of the four screens. The X and Y ORFs were ordered to present calibration pairs on the
diagonal.
D
Overlap between CV and CVA interactions.
E, F The performance of each BFG-Y2H screen was measured using Lit-BM-13 and compared with that of HI-II-14 after restricting both screening spaces to theircommon ORFs.
G
Numbers of protein interactions among virhostome proteins (V-V) and among COSMIC cancer proteins (C-C) and number of virhostome interactions targeted bysame viral proteins. Gray bars demonstrate expectations from the randomly generated networks by a random edge rewiring.

current Y2H. Specifically, the larger en masse recombinationalcloning-based BFG-Y2H screens showed MCC = 0.20 and 0.14 for
CV and CVA, respectively, as compared with 0.16 and 0.13 for the
HI-II-14 dataset within the corresponding BFG-Y2H search spaces.
The smaller in-yeast assembly-based BFG-Y2H screens CENT and
CCC yielded MCC = 0.40 and 0.24, respectively, as compared with
0.50 and 0.31 for the HI-II-14 dataset in the corresponding search
spaces. It should be noted that HI-II-14 was based on the union of
two primary screens and was filtered by pairwise retesting, while
the performance of BFG-Y2H was based on single primary screensthat were not filtered by secondary Y2H retesting.
The CV protein network
The CV protein interaction network was composed of 389 interactions including 247 novel interactions (Fig 7B). After excluding

10

Molecular Systems Biology 12: 863 | 2016

homodimeric interactions, there was significant enrichment forprotein interactions between virhostome proteins, between
COSMIC cancer proteins, and between virhostome and COSMIC
cancer proteins, relative to networks generated by a random edge
rewiring (Yachie et al, 2011). This supports the biological relevance of BFG-Y2H interactions.
We similarly found enrichment for interactions between pairs ofhuman proteins where each is known to interact with a common
viral protein (Fig 7G). As one example, Epstein–Barr virus (EBV)targeting proteins DVL2 and KLHL12 were found to interact witheach other. Wnt signaling activation and b-catenin accumulation are
observed in EBV-infected B cells with type III latency expression
and in the epithelial cells of the EBV-associated malignancy carcinoma (Hayward et al, 2006). DVL2 participates in Wnt signaling bybinding to the cytoplasmic C-terminus of frizzled family membranes
and transduces the Wnt signal downstream (Katoh, 2005). KLHL12ª 2016 The Authors

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18 bait ORFs

CVA

18 bait ORFs

1,571

C

18 bait ORFs

1,639

Nozomu Yachie et al

is a substrate-specific adapter of a BCR E3 ubiquitin ligase, which
negatively regulates Wnt signaling by mediating ubiquitination andsubsequent proteolysis of DVL3 (Angers et al, 2006). The newly
discovered DVL2-KLHL12 interaction suggests the possibility that,like DVL3, DVL2 is regulated by KLHL12.

Discussion

Given pools of barcoded vectors, the current BFG-Y2H pipeline
requires only four days of hands-on time and seven days for theentire screen starting at the mating stage, with a single BFG-Y2H
screen having the capacity to screen at least ~2.5 M ORF pairs(Appendix Fig S4). In our laboratory, one researcher can perform up
to 24 parallel screens. Thus, BFG-Y2H opens the door to the exciting
possibility of efficient, high-throughput screening for conditional
protein interactions, for example, in the presence of alternative environments or third proteins, such as kinase or scaffold proteins(Grossmann et al, 2015), or in alternative genetic backgrounds.
Although the BFG technology was developed to screen protein
interactions, it is applicable to other context-dependent phenotypic
screens involving multiple reagents amenable to barcoding. For example, it might be applied to discover genetic interactions using highmultiplicity-of-infection lentiviral transduction to express or delete
combinations of genes. There are no conceptual barriers to applying
fused-barcode sequencing as a readout for multi-dimensional relationship maps in many species and cell types. Thus, BFG mapping
approaches can harness the disruptive efficiencies of next-generation
sequencing to generate diverse next-generation cellular maps.

Materials and Methods
DNA oligomers
The complete list of DNA oligonucleotides used in this study can befound in Table EV5.
BFG-Y2H toolkit strains
The toolkit-a (RY1010) and toolkit-a (RY1030) strains were generated from the “Y-strains” Y8800 and Y8930. Y-strains were generated in C. Boone’s laboratory from PJ69-4a (James et al, 1996) andused successfully in recent large-scale Y2H studies (Yu et al, 2008,2011; Simonis et al, 2009; Rolland et al, 2014). In detail, the genotype of RY1010 is MATa leu2-3,112 trp1-901 his3-200 ura3-52 gal4Δ
gal80Δ PGAL2-ADE2 LYS2::PGAL1-HIS3 MET2::PGAL7-lacZ cyh2R
can1Δ::PCMV-rtTA-KanMX4. The genotype of RY1030 is MATa leu23,112 trp1-901 his3-200 ura3-52 gal4Δ gal80Δ PGAL2-ADE2 LYS2::
PGAL1-HIS3 MET2::PGAL7-lacZ cyh2R can1Δ::TADH1-PtetO2-Cre-TCYC1KanMX4. PCMV-rtTA-KanMX4 was PCR-amplified from pYOGM019(Suzuki et al, 2011) with O001 and O002 primers and integrated
into the CAN1 locus of the strain Y8800 to make RY1010. Cre ORF
fragment was PCR-amplified from pSH47 (Euroscarf) with O003 andO004 primers and assembled into the BamHI-ApaI locus of pCM251(Euroscarf) to make pNZM1010. TADH1-PtetO2-Cre-TCYC1 fragment
was amplified from pNZM1010 with O005 and O006 primers.
KanMX4 fragment was amplified from pIS418 plasmid with O007and O008 primers. The TADH1-PtetO2-Cre-TCYC1 and KanMX4 fragments were fused by a fusion PCR with O009 and O010 primers,
and the TADH1-PtetO2-Cre-TCYC1-KanMX4 fragment was integrated into
the CAN1 locus of the strain Y8930 to make RY1030.
Preparation of “barcode carrier” destination plasmid collection
For the in-yeast assembly-based BFG-Y2H, we designed the bait
and prey destination plasmids to each carry two specific barcodes

Molecular Systems Biology 12: 863 | 201611

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Although much has been learned from large-scale protein interaction mapping, it remains substantially incomplete in humans and allother organisms. Given limitations in assay sensitivity, completion
will be asymptotic, requiring the use of multiple assay variants(Uetz et al, 2000). Screening matrices will grow as the square of thenumber of clones, with more clones needed to cover splice isoforms(Corominas et al, 2014) and other coding sequence variants (Sahni
et al, 2015). Further adding to the dimension of the search space, it
is now clear that screening with fragments can increase the assay
sensitivity (Boxem et al, 2008), for example, by eliminating repressive domains. While significant improvements have been made to
reduce the cost and effort required to screen large libraries of genes,
state-of-the-art Y2H methods still require manual colony picking
and multiple PCR steps for each Y2H-positive colony (Yu et al,
2011; Rolland et al, 2014). BFG-Y2H allows high-throughput Y2Hscreening at substantially lower cost and effort than the other Y2H
procedures (Appendix Fig S4 and Table EV4). We demonstrated
performance with a matrix of up to ~2.5 M pairs, and there are noevident barriers to screening at genome scale.
Each of our primary BFG-Y2H screens showed performance on
par with a recent large-scale Y2H dataset resulting from two primary
screens and filtering by pairwise retesting (Rolland et al, 2014). We
also demonstrated the ability of BFG-Y2H to identify interactions forhigh-background “auto-activator” baits that might be consideredunscreenable by current Y2H. Moreover, quantitative scoring
provides greater enrichment of true interactions at higher scores,
allowing user-defined trade-offs in precision versus recall.
Additionally, we show by pairwise interface modeling and co-crystal
structural analysis that quantitative BFG-Y2H scores are related tointeraction strength. Although overall performance of BFG-Y2H
rivals that of current Y2H pipelines, there are many directions forfurther optimization. For example, improved methods for generating
barcoded strains, making strain pools with uniform representation,
yeast mating and transformation, increasing the capacity of Y2H
selection plates and yeast plasmid extraction efficiency, reducing
PCR “jackpotting” effects, and increasing sequencing depth(Appendix Note S3). BFG-Y2H toolkit strains are compatible withother genetic screening reporters such as ADE2 and LacZ. ADE2
reporter screening reproduced 63.6% of HIS3 reporter screening
pairs (Fig EV2). Although no interaction was uniquely detected by
the ADE2 reporter assay, other reporters may in future prove complementary to HIS3. The Cre-mediated barcode fusion approach of BFG
could, in principle, be applied to other protein interaction methods
such as MAPPIT (Lemmens et al, 2010) or PCA (Tarassov et al,2008). The “barcode carrier” destination plasmid collection describedhere also represents a resource reusable for other studies, either for
Y2H or as a source of barcodes for other vector designs. Moreover,
we described the en masse recombinational cloning approach as anefficient general strategy to generate barcoded reagents.

ª 2016 The Authors

Molecular Systems Biology

Pooled-matrix interaction maps via Barcode Fusion

Molecular Systems Biology

In-yeast assembly-based generation of uniquely barcodedstrain collection
To generate a plasmid expressing a Gal4-domain-tagged ORF and
two specific barcodes, a collection of four DNA fragments wereassembled: (i) a fragment containing the ADH1 promoter andsegments encoding either the DNA-binding or activation domain
of Gal4; (ii) a specific ORF; (iii) a barcode locus (describedfurther below); and (iv) a vector backbone fragment. ORF

12

Molecular Systems Biology 12: 863 | 2016

Nozomu Yachie et alfragments were amplified from an ORFeome-level collection of
bait and prey expression vectors generated in our previous study(Rolland et al, 2014). Each of the amplicons was generated using
PCR primers that introduce 70-bp overlaps between adjacentamplicons. The DNA fragments were co-transformed into theappropriate toolkit-a or toolkit-a strain, relying on endogenous
yeast DNA repair proteins to assemble the DNA fragments via
overlaps between specific fragment ends. Bait ORF fragments
were amplified with O023 and O024 primers, and prey ORF fragments were amplified with O025 and O026 primers. The ORF PCR
products were analyzed with E-Gel 48 1% Agarose Gels (Invitrogen), and the gel band images were automatically sorted according to the expected product sizes by a Perl script developed basedon ImageMagick (ImageMagick Studio) to check the quality of
ORF fragments by size. We used the ORF fragments passing theSanger sequencing and the ORF size quality control. The barcode
fragments were amplified from the clonal barcode carrier destination plasmid collection (O027 and O028 primers for bait, andO029 and O030 primers for prey). The Gal4 bait and prey domain
fragments were amplified by O031 and O032 primers and O033
and O034 primers, respectively. The common bait and prey backbone fragments were amplified from the destination plasmids by
O035 and O036 primers and O037 and O038 primers, respectively,
to generate yeast strains for the CENT space. The backbone fragments contain E. coli and yeast replication origins, AmpR marker,
and either LEU2 (bait) or TRP1 (prey) markers. In addition, theprey backbone fragment contains CYH2 allowing counter-selectionof the prey plasmid. For the CCC space, the backbone fragment
was split into two for better PCR amplification without reduced
in-yeast assembly efficiency (two fragments were produced by
PCRs using O035 and O039 primer pair and O040 and O036
primer pair for bait; O037 and O041 primer pair and O042 andO038 primer pair for prey). The backbone and domain fragments
were purified to adjust their molar concentrations.
Frozen-EZ Yeast Transformation II Kit (Zymo Research) was used
for the library-scale in-yeast assembly reaction. In each well of 96well plates, 10 ll of competent cells of the toolkit strain (toolkit-a
bait and toolkit-a for prey), 50 fmole of the purified backbone
fragment(s), 50 fmole of the purified domain fragment, 2 ll of ORF
fragment PCR product, and 2 ll of barcode fragment PCR productwere mixed and incubated with 100 ll of EZ3 solution for 105 min
at 30 °C and 900 rpm. The plates were centrifuged at 500 g for
5 min, supernatants were removed, and each sample was resuspended in 100 ll of YPAD and incubated for another 105 min at
30°C and 900 rpm. The samples were then washed with sterilized
water, serial dilutions of the samples were spotted on appropriate
3% agar OmniTrays (SC–Leu+Ade for bait strains and SC–Trp+Adefor prey strains), and the barcoded cells were selected for a few days
at 30°C. Negative control reactions were performed at multiple
random wells of each plate by replacing ORF PCR product with 2 llof sterilized water.
Calibration protein pair set
For BFG-Y2H screens, the 31 protein pairs were arbitrarily pickedfrom the positive reference set (hsPRSRRSv2) of the CCSB Y2H
screening pipeline with the condition that they were also includedin the HI-II-14 or Lit-BM-13 sets.

ª 2016 The Authors

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(BC1 and BC2) at the PspOMI restriction site with loxP andlox2272 sites (Fig EV1, Data EV1 and EV2). For each of the fourbarcode regions, single-stranded DNA (ssDNA) oligomer poolcontaining random 25-bp degenerate region flanked by universal
PCR priming sites was synthesized; O011 and O012 ssDNA pools
were prepared for BC1 and BC2 of bait plasmids; and O013 andO014 ssDNA pools were prepared for BC1 and BC2 of prey plasmids. The universal PCR priming sequences were designed to be
unique and distinct from yeast and human chromosomes and the
backbone Y2H destination vectors. Random bait-BC1, bait-BC2,
prey-BC1, and prey-BC2 fragments were amplified by PCR eachfrom ~15 × 1018 molecules of the corresponding ssDNA with theappropriate pair of primers (O015 and O016 primers for bait-BC1,
O017 and O018 for bait-BC2, O019 and O020 for prey-BC1, andO021 and O022 for prey-BC2). After the PCR, bait-BC1 and preyBC2 fragments were flanked by loxP and lox2272 sequences, and30-bp appropriate “sealing” sequences were attached to both endsof the fragments for in vitro DNA assembly (Gibson et al, 2009).
The PCR-based fragment pool preparations were designed to selectively enrich barcode sequences having high PCR efficiencies fromthe random ssDNA pool. The destination vectors were digested by
PspOMI endonuclease and purified by a standard DNA purification. We assembled 1 ll of the bait-BC1 PCR product, 1 ll of thebait-BC2 PCR product, and 25 fmole of the linearized bait destination plasmid backbone by in vitro DNA assembly to make arandomly barcoded bait destination plasmid pool each having
loxP’-BC1-lox2272-linker-BC2 fragment at the PspOMI locus (loxP’denotes the reverse complement of loxP). Similarly, a barcodedprey destination plasmid pool was made to have BC1-linker-loxP’BC2-lox2272 fragment.
To isolate clonal barcoded plasmids, we transformed thebarcoded bait or prey destination plasmid pool into One Shot ccdB
Survival 2 T1R Competent Cells (Invitrogen). The transformation
samples were spread on 245 mm × 245 mm square LB–ampicillin
plates and incubated overnight at 37°C so that each plate confers
500–1,500 isolated single colonies. Single colonies were picked by
QPix robot (Genetix) and arrayed into 384-well cell culture plates
containing 80 ll of LB–ampicillin media in each well. The cellsamples were incubated overnight at 37°C in a damp box with wet
paper towels to prevent sample evaporation. Glycerol stocks weremade and stored in a 80°C freezer. Template DNA samples wereprepared by ~20-fold dilution of the cell samples. Two RCP-PCR(Appendix Note S2) was performed to identify clonal samples with
their barcode sequences and to check the integrity of loxP andlox2272 sequences. The clonal samples were re-arrayed from theglycerol stocks using Biomatrix robot (S&P Robotics). Quality
control of a subset of the re-arrayed samples was done by Sanger
sequencing.

Pooled-matrix interaction maps via Barcode Fusion

Nozomu Yachie et al

Pooled-matrix interaction maps via Barcode Fusion

Quality control by Sanger sequencing

BFG-Y2H procedure
Barcoded bait and prey strains were pre-cultured individually in
96-deep-well plates. One milliliter of SC–Leu+Ade media (1 mM
excess of adenine sulfate) was used to culture each bait strain, and
1 ml of SC–Trp+Ade media was used to culture each prey strain.
The deep-well plates were incubated for 2 days at 30°C and
900 rpm to saturation. The culture samples of equal volume werepooled to make bait and prey pools. The bait and prey pools were
washed twice with sterilized water, resuspended in sterilized water,
combined 1:1 at same OD600 nm unit, and incubated for 3 h at room
temperature to increase mating efficiency (Bickle et al, 2006). The
cells were pelleted by centrifugation at 500 g for 4 min, and thesupernatant was removed. The cell pellets were spread on YPAD
plates and incubated overnight at room temperature for mating.
Post-mating, cells were scraped with sterilized water, collected in asample tube, washed twice with sterilized water, resuspended in
500 ml of SC–Leu–Trp+His+Ade media (8 mM excess of histidine
and 1 mM excess of Adenine sulfate) in a 2-liter flask at 1.0
OD600 nm, and incubated 2 d at 30°C and 200 rpm to enrich diploidcell population until its OD600 nm reaches 10. The diploid sample
was washed twice with sterilized water, resuspended in sterilized
water, and queried to en masse Y2H selection. We spread 200 ll ofwhich OD600 nm of 50-fold dilution was 1.0 (~1 × 108 diploid cells),
each to 150-mm agar plates of SC–Leu–Trp+His+Ade (+His),
SC–Leu–Trp–His+Ade (–His), and SC–Leu–Trp–His+Ade containing
1 mM amino-1,2,4-triazole (3-AT) and serial dilutions of the diploidsample on +His condition plates to confirm the sample complexity.
For the larger CV and CVA screens, 20 150-mm plates were used foreach selection to ensure sufficient coverage of the complexity. The
selection plates were incubated for 2–3 days at 30°C. For eachcondition, selected cell samples were scraped with sterilized water,collected in a sample tube, washed twice with sterilized water,
resuspended in 5 ml of SC–Leu–Trp+His+Ade containing 10 lg ml 1
doxycycline at 1.0 OD600 nm, and incubated overnight at 30°C until
its OD600 nm exceeds 5.0 to allow the in vivo Cre-mediated barcode
fusion within each cell. Plasmid DNA was extracted from yeast cellsª 2016 The Authors

using Charge Switch Plasmid Yeast Mini Kit (Invitrogen). The BC1BC1 and BC2-BC2 fused barcodes were amplified by Phusion HighFidelity PCR Master Mix with HF Buffer (New England Biosciences)
and appropriate sets of primers containing Illumina paired-end
sequencing adapters with index tags for multiplexed sequencing(Table EV5) with the PCR protocol: (i) 98°C for 30 s; (ii) 24 cyclesof 98°C for 10 s, 60°C for 10 s, and 72°C for 1 min; (iii) 72 °C for
5 min; and (iv) 4°C forever. The PCR volume and template DNA
amount were designed to ensure the sample complexity for eachscreen (one 40 ll reaction with 2 ng template DNA to amplify eachof BC1-BC1 and BC2-BC2 fused barcodes for CENT and CCC screensand 20 40-ll reaction each with 2 ng template DNA for CV and CVA
screens, Appendix Note S3). The PCR products were size-selectedby E-Gel SizeSelect 2% Agarose gel (Invitrogen) and quantified by
qPCR for Illumina sequencing. The reads were demultiplexed andfused barcodes were counted by aligning the reads to the primer
sequences and DNA barcodes using BLAST+ program with theshort-read option and E-value cutoff of 1e–10.
Pairwise retesting of candidate interactions
Pairwise Y2H retesting was performed based on the previously
established protocol (Rolland et al, 2014). Bait and prey expression
plasmids were prepared by Gateway LR cloning. Each protein pair
was tested in the toolkit strain background and the Y-strain background in quadruplicate. For each reaction, bait cells and prey cells
were combined 1:1 in PCR plate wells, in 120 ll of YPAD, and incubated overnight at 30°C and 200 rpm. Diploid cells were enrichedby inoculating 10 ll of each mated sample to 120 ll of SC–Leu–Trp+His+Ade in 96-well cell culture plates and incubating overnight at 30°C and 200 rpm. Using a Biomatrix robot (S&P Robotics),
we spotted the 4 replicate samples on SC–Leu–Trp–His+Ade+3-AT
agar plates (384 spots per plate) and SC–Leu–His+Ade agar plates
containing 1 mg l 1 cycloheximide (CHX) to identify auto-activators.
Additionally, bait cells were mated with opposite-mating-type cells
harboring “null ORF” prey plasmids.
Gaussia princeps luciferase protein complementation assays
Gaussia princeps luciferase protein complementation assay (GPCA)based validation assay was performed to validate the in-yeast assembly-based BFG-Y2H hits as described previously (Remy & Michnick,
2006; Cassonnet et al, 2011). In order to define the threshold of
GPCA signal for each validation screen, positive controls wereobtained from the overlap of the GPCA-tested space with the unionof the systematic Y2H human interactome HI-II-14 dataset (Rolland
et al, 2014) and the high-quality literature-curated protein interaction dataset Lit-BM-13 (Rolland et al, 2014). Fifty-four and 36 arbitrarily protein pairs that scored poorly in CENT and CCC screens,
respectively, were selected as negative controls (Table EV2). The
luciferase signal threshold for each validation assay was defined by
the best balance of recall and precision according to the MCC.
En masse recombinational cloning-based creation of barcodedstrain pools
To scale up the BFG-Y2H method, we generated pools of barcode
carrier destination plasmids encoding random barcodes and lox sites

Molecular Systems Biology 12: 863 | 201613

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To check the integrity of barcodes and lox sites of random clones
after RCP-PCR and re-arraying of clonal samples, we amplified eachbait barcode region by direct colony PCR with O043 and O044
primers and prey barcode region with O045 and O046 primers.
Twenty-five microliters of each PCR product was then assembled
with 10 ll of an enzyme cocktail containing Antarctic phosphatasebuffer (New England Biolabs), 0.5 unit of Antarctic phosphatase(New England Biolabs), and 0.5 unit of Exo I endonuclease (NewEngland Biolabs), incubated at 37°C for 30 min, and heat-inactivated
at 95°C for 5 min to remove PCR primers and phosphates of dNTP.
The cleaned-up samples were sequenced by Sanger sequencing withprimers O043, O044, O047, and O048 (for bait); O045, O046, O049,
and O050 (for prey); and O051 and O052 (for both). To check ORFs
on the entry plasmids (pDONR223), purified DNA samples weresequenced with M13 forward and reverse primers. To check ORFs
on bait and prey expression plasmids, purified DNA samples weresequenced with sequencing primers O053 and O054 (for bait); O055
and O056 (for prey); and O057 and O058 (for both).

Molecular Systems Biology

Molecular Systems Biology

Nozomu Yachie et al

Data availability
The protein interactions from this publication have been submitted
to the IMEx (http://imexconsortium.org) consortium through IntAct(Orchard et al, 2014) and assigned the identifier IM-25015. The data
used for the method evaluation is found under the IMEx identifier
IM-23318. Raw read counts can be found in Data EV3.
Expanded View for this article is available online.

Acknowledgements
This work was supported by the Krembil Foundation (F.P.R. and L.P,), theAvon Foundation (F.P.R.), the Ontario Research Fund (F.P.R. and L.P.), theCanada Excellence Research Chairs Program (F.P.R.), and by US National
Institutes of Health (NIH) grants HG001715 (M.Vidal, D.E.H and F.P.R.) and
HG004233 (a “Centers of Excellence in Genomic Science” award; M.Vidal
and F.P.R.). N.Y. was supported by a JSPS Fellowship (Research Abroad),
Japan Society for the Promotion of Science, a Banting Postdoctoral Fellowship, National Sciences and Engineering Research Council of Canada, Japan
Science and Technology Agency (JST) PRESTO grant, JSPS grant 15K18466,
Astellas Foundation for Research on Metabolic Disorders, Shimazu Foundation, Life Science Foundation of Japan, and by Nestlé Nutrition Council,
Japan. L.P. was supported by Canadian Institutes of Health Research (CIHR)
grants MOP-123468 and MOP-130507. I.S. was supported by grants fromthe CQDM/OCE Explore Program, Ontario Genomics Institute, Canadian
Cystic Fibrosis Foundation, Canadian Cancer Society, Pancreatic Cancer
Canada, University Health Network and Genome Canada DIG1 and DIG2
programs. Sequencing was performed at the Donnelly Sequencing Centre,
University of Toronto. We are grateful for early discussions with J. Weissman, for Y-strains from C. Boone (University of Toronto), and for advice andassistance from B. Andrews, M. Chee, G. Church, A.-C. Gingras, L. Heisler, J.
Liu, N. Mohammad, L. Ng, C. Nislow, A. Rosebrock, K. Salehi-Ashtiani, B.
Steen, and D. Torti; members of the Roth Lab, especially C. Cheung, A.
Karkhanina, S. Sun, Y. Suzuki, M. Tasan and J. Weile; members of the DFCI
Center for Cancer Systems Biology (CCSB); and members of the Pelletier
laboratory, especially M. Bashkurov, D. Comartin, J. Goncalves, S. Lawo, B.
Mojarad, and C. Yeh.

Author contributions
NY, JCM, SBO, and FPR developed the BFG-Y2H technology; NY, EP, and FPR

Reported protein interaction dataset

prepared the manuscript; NY, EP, and FPR designed the experiments; NY, EP,
MVe, TT, and MG made the barcode carrier plasmid collection; NY, EP, MVe,

Reported Y2H-positive human protein interaction dataset HI-II-14
and the Lit-BM-13 dataset were downloaded from the CCSB Human
Interactome Database (http://interactome.dfci.harvard.edu), andreported interactions by orthogonal assays were obtained from the
BioGRID database (Chatr-Aryamontri et al, 2013) Release 3.1.91.

and DS performed the BFG-Y2H screens; NY, EP, AY, MVe, SL, JFR, CW, and JJK
performed pairwise retesting; AGC performed sequencing; RM and PA carriedout computational structural analysis; NY, EP, and JW performed other computational analyses; YJ performed GPCA analysis; MK, NS, SY, LM, JS, Y-CL, HY, PB,
IS, TH, MAC, LP, DEH, and MVi provided reagents, technical support, andadvice.

Statistical analysis

Conflict of interest
Sample correlations were evaluated using Pearson’s correlation coefficient. Sample enrichments were evaluated by Fisher’s exact test orPearson’s chi-squared test. Comparison of distributions of GPCA orinteraction scores was performed using the Mann–Whitney U-test.

The authors declare that they have no conflict of interest.

Plasmids

Andersen JS, Wilkinson CJ, Mayor T, Mortensen P, Nigg EA, Mann M (2003)

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Plasmids used in this study can be found in Data EV1 and EV2.

14

Molecular Systems Biology 12: 863 | 2016

correlation profiling. Nature 426: 570 – 574ª 2016 The Authors

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2–3 days at 30°C. Selected colonies were pooled and subjected to the
BFG-Y2H procedure.

Pooled-matrix interaction maps via Barcode Fusion

Nozomu Yachie et al

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