An online bioinformatics tool predicts zinc finger and TALE nuclease off-target cleavage

Article (PDF Available)inNucleic Acids Research 42(6) · December 2013with37 Reads
DOI: 10.1093/nar/gkt1326 · Source: PubMed
Abstract
Although engineered nucleases can efficiently cleave intracellular DNA at desired target sites, major concerns remain on potential ‘off-target’ cleavage that may occur throughout the genome. We developed an online tool: predicted report of genome-wide nuclease off-target sites (PROGNOS) that effectively identifies off-target sites. The initial bioinformatics algorithms in PROGNOS were validated by predicting 44 of 65 previously confirmed off-target sites, and by uncovering a new off-target site for the extensively studied zinc finger nucleases (ZFNs) targeting C-C chemokine receptor type 5. Using PROGNOS, we rapidly interrogated 128 potential off-target sites for newly designed transcription activator-like effector nucleases containing either Asn-Asn (NN) or Asn-Lys (NK) repeat variable di-residues (RVDs) and 3- and 4-finger ZFNs, and validated 13 bona fide off-target sites for these nucleases by DNA sequencing. The PROGNOS algorithms were further refined by incorporating additional features of nuclease–DNA interactions and the newly confirmed off-target sites into the training set, which increased the percentage of bona fide off-target sites found within the top PROGNOS rankings. By identifying potential off-target sites in silico, PROGNOS allows the selection of more specific target sites and aids the identification of bona fide off-target sites, significantly facilitating the design of engineered nucleases for genome editing applications.
An online bioinformatics tool predicts zinc finger
and TALE nuclease off-target cleavage
Eli J. Fine, Thomas J. Cradick, Charles L. Zhao, Yanni Lin and Gang Bao*
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta,
GA 30332, USA
Received July 18, 2013; Revised November 27, 2013; Accepted November 29, 2013
ABSTRACT
Although engineered nucleases can efficiently
cleave intracellular DNA at desired target sites,
major concerns remain on potential ‘off-target’
cleavage that may occur throughout the genome.
We developed an online tool: predicted report of
genome-wide nuclease off-target sites (PROGNOS)
that effectively identifies off-target sites. The initial
bioinformatics algorithms in PROGNOS were
validated by predicting 44 of 65 previously con-
firmed off-target sites, and by uncovering a new
off-target site for the extensively studied zinc
finger nucleases (ZFNs) targeting C-C chemokine
receptor type 5. Using PROGNOS, we rapidly
interrogated 128 potential off-target sites for newly
designed transcription activator-like effector nucle-
ases containing either Asn-Asn (NN) or Asn-Lys (NK)
repeat variable di-residues (RVDs) and 3- and
4-finger ZFNs, and validated 13 bona fide off-
target sites for these nucleases by DNA sequencing.
The PROGNOS algorithms were further refined by
incorporating additional features of nuclease–DNA
interactions and the newly confirmed off-target
sites into the training set, which increased the per-
centage of bona fide off-target sites found within
the top PROGNOS rankings. By identifying potential
off-target sites in silico, PROGNOS allows the selec-
tion of more specific target sites and aids the iden-
tification of bona fide off-target sites, significantly
facilitating the design of engineered nucleases for
genome editing applications.
INTRODUCTION
The efficiency of genome editing in cells is greatly
increased by specific DNA cleavage with zinc finger nucle-
ases (ZFNs) or transcription activator-like (TAL) effector
nucleases (TALENs), which have been used to create new
model organisms (1–6), correct disease-causing mutations
(7) and genetically engineer stem cells (8). However, both
ZFNs (6,9–11) and TALENs (5,8) have off-target cleavage
that can lead to genomic instability, chromosomal re-
arrangement and disruption of the function of other
genes. It is vitally important to identify the locations
and frequency of off-target cleavage to reduce these
adverse events, and ensure the specificity and safety of
nuclease-based genome editing. Although the emerging
systems utilizing clustered regularly interspaced short pal-
indromic repeats (CRISPR) and CRISPR associated (Cas)
proteins are highly active at their intended target sites,
recent publications indicate that they likely have much
greater levels of off-target cleavage than ZFNs or
TALENs (12–14).
Experimental identification of ZFN and TALEN off-
target sites is a daunting task because of the size of the
genome and the large number of potential cleavage sites to
assay. Previous attempts to identify new off-target sites
based entirely on bioinformatics search methods have all
failed to locate any off-target cleavage sites (1–4,7,15),
which has led to the belief that identifying off-target
activity based on sequence homology alone would not
be fruitful (10). In contrast, efforts using experimental
methods to characterize the specificity of nucleases have
successfully identified several off-target cleavage sites for
ZFNs (6,9–11,16) and TALENs (5,8). While most of these
characterization methods incorporate a bioinformatics
component to search through the genome, the final
decision of what sites to investigate is dictated by the
experimental data; for example, Perez et al. applied a clas-
sifier based on their characterization of the nucleases to
narrow the full list of 136 genomic sites with two or fewer
mismatches in each ZFN down to the top 15 sites they
chose to interrogate (16). However, these experimental
characterization methods, including SELEX (5,8,16),
bacterial one-hybrid (6), in vitro cleavage (9) or IDLV
trapping (10), can be very time consuming, costly and
technically challenging (Supplementary Note 2). This has
severely limited the number of laboratories undertaking
these experiments and the number of nucleases
characterized for off-target effects. There is a clear
*To whom correspondence should be addressed. Tel: +1 404 385 0373; Fax: +1 404 385 3856; Email: gang.bao@bme.gatech.edu
Published online 30 December 2013 Nucleic Acids Research, 2014, Vol. 42, No. 6 e42
doi:10.1093/nar/gkt1326
ß The Author(s) 2013. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial
re-use, please contact journals.permissions@oup.com
unmet need for a rapid and scalable online method that
can predict nuclease off-target sites with reasonable
accuracy without requiring the user to have specialized
computational skills, especially for application of nucle-
ases in disease treatment.
MATERIALS AND METHODS
Major features of PROGNOS ranking algorithms
All PROGNOS algorithms only require the DNA target
sequence as input; prior construction and experimental
characterization of the specific nucleases are not neces-
sary. Based on the differences between the sequence of a
potential off-target site in the genome and the intended
target sequence, each algorithm generates a score that is
used to rank potential off-target sites. If two (or more)
potential off-target sites have equal scores, they are
further ranked by the type of genomic region annotated
for each site with the following order: Exon >
Promoter > Intron > Intergenic. A final ranking by
chromosomal location is employed as a tie-breaker to
ensure consistency in the ranking order. Full descriptions
and formulae of each PROGNOS algorithm are provided
in Supplementary Method M1.
The average 5
0
-base and RVD-nucleotide frequencies
for engineered TALEs were calculated by compiling
previously published SELEX results of nine engineered
TALEs (5,8,17) and calculating frequency matrices
(Supplementary Table S16).
PROGNOS Homology, RVDs and Conserved
G’s Algorithms
The ‘Homology’, ‘RVDs’ and ‘Conserved G’s’ algorithms
in PROGNOS all apply the ‘energy compensation’ model
of dimeric nuclease cleavage (9) to account for the inter-
actions between the two half-sites, but the scores for each
half-site are calculated in different ways. The Homology
algorithm can be applied to both ZFNs and TALENs and
is based largely on the number of mismatches relative to
the intended target sequence. The RVDs algorithm is
designed for use with TALENs and utilizes the RVD-
nucleotide binding frequencies of natural TAL Effectors
(18); alternate ‘5T’ and ‘5TC’ versions require either a
thymidine or a pyrimidine to be in the 5
0
position of
each half-site. The Conserved G’s algorithm is designed
for use with ZFNs and applies a weighting factor to the
Homology algorithm that biases the rankings towards
sites where intended guanosine contacts are maintained.
More details for these algorithms can be found in
Supplementary Method M1.
PROGNOS Algorithms ‘ZFN v2.0’ and ‘TALEN v2.0’
The weightings of the parameters for the refined
PROGNOS algorithms ‘ZFN v2.0’ and ‘TALEN v2.0’
were developed by training the algorithms to maximize
recovery of previously confirmed off-target sites, as well
as the novel off-target sites found using the initial algo-
rithms developed in this study. For each algorithm, 10
5
randomly assigned parameter sets (within a constrained
range) were analyzed for their performance using the
Perl off-target-ranking script. The top performing param-
eter sets were further optimized by running further
analyses allowing each parameter to vary slightly from
the original value.
ZFN v2.0 Algorithm
The ZFN v2.0 algorithm was constructed based on the
binding of individual zinc finger subunits rather than
treating all mismatches equally. Specifically, the scoring
algorithm in ZFN v2.0 for each finger is based on: (i) an
initial score of 100 is given as a starting point, (ii) if there
is at least one mismatch, a ‘First_Penalty’ is sub-
tracted, (iii) if there are additional mismatches, an
‘Additional_Penalty’ is subtracted for each additional
mismatch, (iv) if a guanosine is the intended base at pos-
itions 2 or 3 and it matches the target sequence, a
‘G_Bonus’ is added, (v) if a guanosine is the intended
base at position 1 and it matches the target sequence, a
double ‘G_Bonus’ is added, (vi) if the resulting score is <0,
it is set to zero. We further introduced parameters to
model polarity effects by weighting the impact of each
of the 2nd–4th nucleotide triplets away from the FokI
domain. The score for each zinc finger subunit is
multiplied by the corresponding polarity parameter and
all scores for the half-site are summed together. The sum
is then divided by the score of a subunit that has a perfect
match to the intended target sequence of that half-site.
To allow for compensation between the two ZFN
dimers, the score for each half-site is raised to the power
of ‘Dimer_Exponent’ before being summed together,
divided by two, and multiplied by 100 to generate a
score from 0 to 100 (100 being a perfect match). More
details for the construction of the ZFN v2.0 algorithm
can be found in Supplementary Method M1.
TALEN v2.0 Algorithm
In constructing the TALEN v2.0 algorithm, a score
for each RVD-nucleotide interaction is calculated using
the same formula as in TALE-NT (18) (as in the
original RVDs algorithm) except that the RVD-nucleotide
frequencies used were derived from engineered TAL
domains instead of naturally occurring TAL Effectors. If
no RVDs are specified by the user in the PROGNOS
online input form, RVDs are assumed to follow the
standard code based on the intended target sequence:
NI!A, HD!C, NN!G, NG!T. Based on the
finding that the presence of the ‘strong’ RVDs NN and
HD are key to TAL binding (19), we hypothesized that
these RVDs may impart excess binding energy that could
compensate for local effects of adjacent RVD-nucleotide
mismatches. Accordingly, we developed two parameters,
‘Single_Strong’ and ‘Double_Strong’ that were applied to
the score of RVDs that were flanked on one or both sides
by NNs or HDs correctly bound to their respective
intended bases (guanosines or cytidines). If these criteria
are met, a fraction (defined by the parameter) of the dif-
ference between the mismatched RVD binding to its
intended base and the base at the potential off-target site
is subtracted from the score for that RVD-nucleotide
e42 Nucleic Acids Research, 2014, Vol. 42, No. 6 P
AGE 2 OF 13
interaction. Since a polarity effect exists in TAL–DNA
binding where mismatches further from the N-terminus
have a less disruptive effect (20), the scores for the 14th
RVD and any RVDs further towards the C-terminus are
all multiplied by the ‘Polarity’ parameter.
The scores of all positions in each half-site are summed
together to create the ‘Off_Target’ score for that half-site
and the full score for the potential off-target sites is
computed using the ‘Dimer_Exponent’ parameter and
the score for a complete match between the RVDs and
their intended target bases to yield a score from 0 to 100
(a perfect match). More details of the TALEN v2.0
algorithm can be found in Supplementary Method M1.
Nuclease construction
Four novel TALEN pairs and two novel ZFN pairs were
designed to target sequences near the A to T mutation that
causes sickle-cell anemia in the human beta-globin gene.
TALENs were assembled using the Golden Gate method
(21) and cloned into a mammalian expression destination
vector containing the wild-type FokI domain (available
through AddGene #40788). ZFNs were rationally
designed to target overlapping sites. As these ZFNs
target the same site, the activity and specificity of the 3-
finger (3F) and 4-finger (4F) ZFNs can be directly
compared. ZFN1-4F contains an additional finger added
to ZFN1-3F, extending the target site from 9 to 12 bp.
ZFN2-4F shares two proximal fingers with ZFN2-3F,
and uses a long linker between fingers two and three,
extending the target site from 9 to 13 bp (Supplementary
Figure S5). The coding sequences for the ZFNs were
ordered (IDT) and cloned into a wild-type FokI expres-
sion vector (Supplementary Data D2 and D3). The
PROGNOS search settings that were used for
investigation of the novel nucleases are available in
Supplementary Table S14.
Cellular transfection of nucleases
HEK-293T cells were cultured under standard conditions
(37
C, 5% CO
2
) in Dulbecco’s Modified Eagle’s Medium
(Sigma Aldrich), supplemented with 10% FBS. Plates
were coated with 0.1% gelatin. Passaging was performed
with 0.25% Trypsin-EDTA. For TALENs, 2 10
5
cells/
well were seeded in 6-well plates 24 h prior to transfection
with FuGene HD (Promega). Along with 80 ng of an
eGFP plasmid, 3.3 mg of each nuclease plasmid were trans-
fected with 19.8 ml of FuGene reagent. Media was changed
24 and 48 h after transfection. Seventy-two hours after
transfection, cells were trypsinized and the genomic
DNA extracted using the DNeasy Kit (Qiagen). A small
fraction of the cells were analyzed with the Accuri C6 flow
cytometer to determine transfection efficiency by GFP
fluorescence. For ZFNs, 8 10
4
cells/well were seeded in
24-well plates and 100 ng of each ZFN was transfected
using 3.4 ml of FuGene HD along with 10 ng of eGFP
and 340 ng of a Mock vector containing FokI but no
DNA-binding domain. Seventy-two hours after transfec-
tion, cells were harvested and the genomic DNA extracted
using 100 ml of QuickExtract (EpiCentre). Mock transfec-
tions were performed similarly to the TALEN
transfections, except that 6.6 mg of the mock FokI vector
was transfected instead of TALEN plasmid.
PCR amplification of regions of interest
The primers designed by PROGNOS (ordered from
Eurofins-MWG-Operon, Supplementary Table S18) were
used in a high-throughput manner to amplify genomic
regions of interest in a single-plate PCR reaction. Each
25 ml reaction contained 0.5 units of AccuPrime Taq
DNA Polymerase High Fidelity (Invitrogen) in
AccuPrime Buffer 2 along with 150 ng of genomic DNA
or 0.5 ml of QuickExtract, 0.2 mM of each primer and 5%
DMSO vol/vol. Touchdown PCR reactions were found to
yield the highest rate of specific amplification. Following
an initial 2-minute denaturing at 94
C, 15 cycles of touch-
down were performed by lowering the annealing tempera-
ture 0.5
C per cycle from 63.5
Cto56
C (94
C for 30 s,
anneal for 30 s, extend at 68
C for 90 s). After the touch-
down, an additional 29 cycles of amplification were per-
formed with the annealing temperature at 56
C before a
final extension at 68
C for 10 min. Reactions were purified
using MagBind EZ-Pure (Omega), quantified using a
Take3 Plate and SynergyH4 Reader (Biotek) and
normalized to 10 ng/ml.
High-throughput sequencing
Amplicons from each transfection were pooled in roughly
equimolar ratios and SMRT sequenced using the C2/C2
Chemistry and Consensus Sequencing options, according
to the manufacturer’s protocol (Pacific Biosciences).
Sequencing reads were aligned and processed using a
pipeline of custom Perl scripts, BLAST and Needle
(Supplementary Method M4).
Statistical analysis
P-values for off-target cleavage in Table 1 and
Supplementary Tables S6–S10 were calculated exactly as
previously described (9). Briefly, the t-statistic was
calculated based on the fraction of mutated reads in the
nuclease-treated sample compared to the fraction of
mutated reads in the mock-treated sample and the
number of sequencing reads was given as the degrees of
freedom. In a similar manner, 90% confidence intervals
were calculated by determining the upper and lower
bounds of the fractions of mutated sequences that would
yield P-values of 0.05.
Source code for PROGNOS search algorithm
PROGNOS exhaustively searches for matches by moving
the query mask iteratively across the entire genomic
sequence, base by base. PROGNOS was implemented
in Strawberry Perl 5.12 on a Windows machine
(Supplementary Method M3). Source code and user
manual are available at http://baolab.bme.gatech.edu/bao/
Research/BioinformaticTools/prognos.html or http://bit.ly/
PROGNOS. The probabilistic estimate of the number of
expected off-target sites in a genome with a given level of
homology is described in Supplementary Figure S1 and
Supplementary Method M5. Details of the online server
P
AGE 3 OF 13 Nucleic Acids Research, 2014, Vol. 42, No. 6 e42
Table 1. SMRT Sequencing confirms on- and off-target activity at sites ranked by PROGNOS
Novel TALENs
Nucleases
Closest
gene
Match
type
Mutations per half-site
(+) half-site (–) half-site
PROGNOS Rankings
293T Cell Line Modification Frequency
RVD targeting guanine
(+) (–) H RK RN TK TN NK NN
S2/S5 TALENs HBB L-16-R 0 1 TCACCTTGCCCCACAGGGCAGT tCAGGAGTCAGGTGCA 1111123.0%* 48.7%*
,
**
FAM3D R-17-R 3 3 TGCcCCTGACTCCTta AaAtGAGgCAGGTGCA 41525560.1%* 0.05%
HBD L-16-R 2 2 TCACtTTGCCCCACAGGGCAtT tCAGGAGTCAGaTGCA 222220% 5.0%*
,
**
GPR6 R-30-R 2 5 TcCACCTGgCTCCTGT gCAGGAGTtAaGgGtA 21 241 16 11 5 0% 0.09%*
,
**
.................. ............................. ....................................................................... ............................. ....................................................................... ................................................................................................................... ....................................
Total sites interrogated: 21 20
S1/S7 TALENs HBB L-15-R 0 0 TCACCTTGCCCCACAGGGCAGTAAC AGGAGTCAGGTGCACCA 111110.3% * 42.8%*
,
**
LINC00299 R-23-R 3 5 TGGaGCACCTGACcCCa AGGAGaaAaGgGCACCt 17 8 60 5 10 0.2%* 0.1%
HBD L-15-R 3 1 TCACtTTGCCCCACAGGGCAtTgAC AGGAGTCAGaTGCACCA 223220% 4.9%*
,
**
FAM3D R-21-R 3 5 ctGTGCcCCTGACTCCT AtGAGgCAGGTGCAttt 8421360% 0.1%*
,
**
.................. ............................. ....................................................................... ............................. ....................................................................... ................................................................................................................... ....................................
Total sites interrogated: 24 25
Novel ZFNs
H C Z ZFN Activity
4F ZFNs HBB L-5-R 0 0 TCACCTTGCCCC GCAGTAACGGCA 1116.3%*
PLG R-5-R 3 1 TGCCaTTgaTGC GCAGTAACtGCA 24 41 28 0.2%*
.................. ............................. ....................................................................... ............................. ....................................................................... ................................................................................................................... .
Total sites interrogated: 23
3F ZFNs HBB L-5-R 0 0 CCTTGCCCC GCAGTAACG 1111.9%*
ATG7 L-6-L 1 0 CCTTGgCCC GGGGCAAGG 37110.5%*
TMEM132C R-6-L 1 0 aGTTACTGC GGGGCAAGG 43590.2%*
PARD3B L-5-L 0 1 CCTTGCCCC GGGGCAAGc 5871.3%*
GLIS2 L-6-L 1 0 CCTgGCCCC GGGGCAAGG 9640.6%*
AFF3 L-6-L 2 0 CCTaGgCCC GGGGCAAGG 16 37 20 2.9%*
RGS10 L-6-L 0 2 CCTTGCCCC GGGGCAgaG 22 39 15 5.2%*
.................. ............................. ....................................................................... ............................. ....................................................................... ................................................................................................................... .
Total sites interrogated: 23
CCR5 ZFNs
PROGNOS
Investigation
CCR5 L-5-R 0 0 GTCATCCTCATC AAACTGCAAAAG 11131%*
CSNK1G3 L-5-R 3 1 GcCtTCCcCATC AAAgTGCAAAAG 33 13 18 0.09%*
.................. ............................. ....................................................................... ............................. ....................................................................... ................................................................................................................... .
Total sites interrogated: 16
Known Off-
Target Sites
CCR2 L-5-R 1 1 GTCgTCCTCATC AAACTGCAAAAa 25211%*
KDM2A R-5-L 2 5 CTaTTaCAGTTT GATGAGGtctca N/A N/A N/A 11%*
BTBD10 L-5-R 2 1 GTttTCCTCATC AAACTGCAAAAt 34562.6%*
KCNB2 L-5-R 3 1 aTgtTCCTCATC AAACTGCAAAtG 29 33 8 1.3%*
WBSCR17 R-6-L 2 2 CTgTTcCAGTTT GcTGAGGATaAC 60 51 95 1.4%*
TACR3 L-5-R 1 3 GTCATCtTCATC AAACTGtAAAgt 17 197 26 8.6%*
We interrogated 138 highly ranked genomic loci for the novel TALENs and ZFNs using SMRT, and observed off-target activity in 13 cases, nine of which were outside the globin gene family.
The ‘match type’ indicates the orientation of the left (L) and right (R) nucleases at the site and the length of the spacer sequence. In sequences, lower-case red letters indicate mutations compared
to the target site. Site sequences are listed as 5
0
—(+) half-site—spacer—(–) half-site—3
0
. Therefore, the (–) half-site for TALENs and the (+) half-site for ZFNs are listed in the reverse anti-sense
orientation compared to the DNA sequence that the nuclease binds. Rankings by the initial PROGNOS algorithms Homology (column H), RVDs for NK (RK), RVDs for NN (RN) and
Conserved G’s (C) are displayed as well as the rankings by the refined ‘TALEN v2.0’ algorithm for NK (TK) and for NN (TN) and the ‘ZFN v2.0’ algorithm (Z). 293T modification frequency is
the percentage of observed sequences showing evidence of non-homologous end-joining repair. For the CCR5 ZFNs, 15 off-target sites ranked by PROGNOS that had not been previously
investigated were interrogated using SMRT, validating a novel off-target site. Additionally, six known highly active off-target sites were sequenced as positive controls. The PROGNOS rankings
for the site near KDM2A are listed as ‘N/A’ because the site was not found by PRGONOS due to the high number of mismatches. *P < 0.05 in cells expressing active nuclease compared to cells
expressing empty vector. **P < 0.05 for the difference in activity between NK and NN at that site.
e42 Nucleic Acids Research, 2014, Vol. 42, No. 6 PAGE 4 OF 13
implementation are available in Supplementary Methods
M6. The current list of genomes available on the online
server is available in Supplementary Table S15.
RESULTS
Construction of initial bioinformatics ranking algorithms
The initial PROGNOS algorithms codified several estab-
lished factors influencing nuclease specificity, including
sequence homology, zinc fingers’ preference for binding
guanine residues (6) and RVD-nucleotide binding
frequencies of natural TAL effectors (22). To improve
upon simple ‘mismatch counting’, we incorporated the
recently proposed ‘energy compensation’ model of
dimeric nuclease interactions (9). Using these factors,
three different algorithms were initially developed. The
‘Homology’ algorithm, which could be used for both
ZFNs and TALENs, generates a score based primarily
on sequence divergence from the intended target site,
including the number of mismatches in the left and
right nuclease half-sites, and the maximum number of
mismatches allowed per half-site. The ‘Conserved G’s’ al-
gorithm (for ZFNs only) ranks potential ZFN off-target
sites by counting the number of guanine bases and adding
a weighting factor to the homology score accordingly. The
‘RVDs’ algorithm (for TALENs only) weighs mismatches
based on RVD nucleotide preferences observed in natural
TAL effectors and then applies the energy compensation
model. Since all three of the TALEN off-target sites dis-
covered previously using experiment-based off-target pre-
diction methods contained a pyrimidine at the 5
0
position,
a ‘5TC’ version of the ‘Homology’ and ‘RVDs’ algorithms
was also applied to TALEN rankings that required a thy-
midine or cytidine in the preceding 5
0
position of each
half-site. For any given potential off-target site, these
algorithms generate a score that allows ranking of all
potential off-target sites in a genome for a specific
nuclease target site. Search parameters, such as target
sites, maximum mismatches per half-site and allowed
spacer lengths are entered as inputs using the online inter-
face (Figure 1A and Supplementary Note 4) and ranked
lists of potential cleavage sites in the selected genome are
given as PROGNOS outputs for further analysis.
Although two online tools—ZFN Site (23) and TALE-
NT (18)—exist to help search genomes for cleavage sites
with homology to intended nuclease on-target sites,
neither automatically ranks the potential off-target sites,
nor has led to a report of any new experimentally verified
off-target cleavage sites. In a direct comparison, we found
that TALE-NT was only able to predict two of the seven
bona fide TALEN off-target sites in unrelated gene
families—three sites from previous work (5,8) and four
from this work—while PROGNOS could predict six
(Supplementary Note 3). Recently, a new tool for identify-
ing TALEN off-target sites, TALENoffer, was published
(24). Although it performs better than TALE-NT and
does provide a rank-order for the potential off-target
sites, it is outperformed by the refined TALEN v2.0 algo-
rithm (Supplementary Note 3).
Validation of PROGNOS algorithms with previously
confirmed off-target sites
To validate the initial PROGNOS ranking algorithms, we
compared PROGNOS predictions with the off-target sites
of ZFN and TALEN pairs identified by others using ex-
perimental characterization methods. If the same number
of sites (1X) were interrogated as in the original studies, but
the sites were chosen by taking the top-ranked PROGNOS
predictions, (33 ± 21)% (mean ± SD) of the off-target
sites previously found in studies of ZFNs targeting CCR5
(9), VEGF (9) and kdrl (6) could be located. Since off-target
searches using the in silico PROGNOS predictions can be
scaled up readily, we tripled (3X) the number of sites
interrogated from PROGNOS top-ranked lists, and
found that PROGNOS could identify (65 ± 24)% of the
off-target sites previously confirmed experimentally
(Figure 1B and Supplementary Tables S1–S3). Excluding
sites in highly homologous gene pairs such as CCR5/
CCR2, only three bona fide TALEN off-target sites had
previously been experimentally identified to date (5,8)
(Supplementary Note 5), making a rigorous analysis of
the predictive power of PROGNOS for ranking TALEN
off-target sites more difficult (25). Nevertheless, we found
that the ‘Homology-5TC’ and ‘RVD-5TC’ algorithms in
PROGNOS could predict several off-target sites confirmed
previously for TALEN pairs targeting the AAVS1 (8) and
IgM (5) loci (Figure 1C and Supplementary Tables S4–S5).
Since no single off-target analysis method has yet been
able to provide a comprehensive list of all off-target sites
of a nuclease (Figure 1D) (9,10), the comparison of
PROGNOS predictions with previously published results
may underestimate the power of PROGNOS. Specifically,
these comparisons are limited by the small number of
off-target sites experimentally validated previously, and
do not reflect the ability of PROGNOS to predict new
off-target sites.
Validation of novel CCR5 ZFN off-target site predicted
by PROGNOS
To date, the only nuclease pair to have its off-target sites
experimentally interrogated using two independent
methods is a ZFN-pair targeting CCR5 [analyzed using
in vitro cleavage (9) and IDLV (10)]. These two studies
located a total of 12 hetero-dimeric bona fide off-target
sites, verified by sequencing the resulting mutations.
A comparison between PROGNOS predictions using the
‘Homology’ and ‘Conserved G’s’ algorithms and those 12
sites identified experimentally shows that PROGNOS
[analyzing the top 3X number of sites interrogated
by Pattanayak et al. (9)] was able to predict 10 out of
the 12 off-target sites (Figure 1D and Supplementary
Table S1). Additionally, through investigating 16 potential
off-target sites predicted by PROGNOS, but not identified
by any other existing methods (9,10,16), a novel CCR5
ZFN off-target site was experimentally validated (Table 1
and Supplementary Tables S10–S11).
PROGNOS search output
PROGNOS provides ranked lists of potential nuclease
cleavage sites that can be used to guide experimental
P
AGE 5 OF 13 Nucleic Acids Research, 2014, Vol. 42, No. 6 e42
evaluation of ZFN and TALEN off-target activities
(Figure 2A). Specifically, for each pair of ZFNs or
TALENs, the user-friendly online interface of
PROGNOS (http://bit.ly/PROGNOS (13 December
2013, date last accessed) or http://baolab.bme.gatech.edu/
bao/Research/BioinformaticTools/prognos.html) allows
entry of the nuclease search parameters (the guidelines
for de novo investigation of nucleases are given in
Supplementary Note 4) and returns lists of the top-
ranked off-target sites according to the PROGNOS
algorithms, as well as a full list of un-ranked potential
off-target sites meeting the search parameters (Figure
2B). While the top-ranked sites provide a list of likely
locations in a genome where off-target cleavage may
occur, neither the PROGNOS rankings nor any published
method can yet directly correlate the ranking with the
precise level of observed off-target mutagenesis at a given
site (Supplementary Figure S3). Furthermore, to aid
experimental analysis, PROGNOS also provides PCR
primer sequences that can be used to amplify the potential
nuclease cleavage sites in a high-throughput manner
(Supplementary Method M2), a unique feature not
present in other online search tools. Automated design of
PCR primers significantly facilitates the analysis of off-
target sites, since an initial experimental study of off-
target cleavage by a single pair of nucleases typically
requires at least 40 primers (1,8), and an in-depth investi-
gation of nuclease off-target effects may require >250
primers (6,9). Although tools such as Primer3 (26) can
assist in primer design, they require a large amount of
effort to generate primers optimal for off-target analysis
due to specific requirements of where the nuclease site
must be positioned within the amplicon. Although PCR
amplification is an essential step in examining a potential
off-target site, in previous investigations the success rates
of amplifying off-target loci varied from 31% (1) to 95%
0
10
20
30
40
50
60
70
80
90
100
AAVS1 IgM
% of Sites From Previous Work
In Top PROGNOS Rankings
RVD-5TC 1X
Homology-5TC 1X
RVD-5TC 3X
Homology-5TC 3X
PROGNOS Online Interface
Nuclease Target:
Genome to search:
Maximum mismatches per half-site (1-10):
Allowed spacer lengths:
Include homodimeric nuclease sites:
Separaon between Cel1 cleavage products (bp):
Human (hg19)
Mouse (mm9)
Rat (rn4)
O. sativa (MSU6)
D. Melanogaster (dm3)
Zebrafish (danRer7)
5’- -NNN…NNN- -3’
-
-
3’- -NNN…NNN- -5’
-
-
5’- -NNN…NNN- -3’
-
-
3’- -NNN…NNN- -5’
-
-
ZFN:
TALEN:
0
10
20
30
40
50
60
70
80
90
100
CCR5 VEGF kdrl
% of Sites From Previous Work
In Top PROGNOS Rankings
ZFNs
Conserved Gs 1X
Homology 1X
Conserved Gs 3X
Homology 3X
9 / 9
20 / 31
12 / 19
In vitro cleavage
Bacterial-1-Hybrid
1 / 2
1 / 1
1
2
2
6
PROGNOS Algorithms
Experimental Methods
CCR5 Off-
Target Sites
1
1
SELEX
TALENs
A
CD
B
Figure 1. PROGNOS search interface and comparison to previous prediction methods. (A) The PROGNOS online interface allows users to enter the
target site of their nuclease pair and specify search parameters and primer design considerations. (B) A comparison of PROGNOS predictions to
previously reported methods identifying off-target sites for different ZFNs (6,9). The Homology and Conserved G’s algorithms were used to
determine what percentage of the sites with previously identified off-target activity fell within the top fractions of PROGNOS rankings. The ‘1X’
top fraction corresponds to searching the same number of top PROGNOS sites as were investigated in the original paper and ‘3X’ corresponds to
searching three times as many PROGNOS sites as were investigated in the original manuscript. (C) A comparison of the PROGNOS search
algorithms to previously reported methods identifying off-target sites for TALENs (5,8). The top PROGNOS rankings using the Homology-5TC
and RVD-5TC algorithms were searched to determine what percentage of off-target sites found to have activity fell within the top fractions of
PROGNOS rankings. (D) Venn diagram displaying the 13 known off-target sites identified for the heterodimeric CCR5 ZFNs during development
and testing of the original PROGNOS algorithms (9,10). The sites ranked at the top of the PROGNOS Homology and Conserved G’s in silico
algorithms [allowing 3X the number of sites searched by Pattanayak et al. (9)] are compared to the 12 sites identified previously and one site
uncovered in this study.
e42 Nucleic Acids Research, 2014, Vol. 42, No. 6 PAGE 6 OF 13
(8). In contrast, the primers automatically designed by
PROGNOS had a robust 95% success rate across the 116
potential off-target loci interrogated in this study (Figure
2C and Supplementary Methods M1). PROGNOS also
provides the sequences, the sizes of expected cleavage
products of the amplicons, and site of expected cleavage.
This information is used when testing for nuclease-induced
mutations—typically short insertions and deletions (indels)
resulting from error-prone resolution of the DNA double-
strand break through the non-homologous end-joining
(NHEJ) repair pathway—using methods such as the
Surveyor Nuclease assay, high-throughput sequencing
or Sanger sequencing of TOPO-cloned fragments
(Figure 2D).
Determination of NHEJ-mediated indels using
high-throughput SMRT sequencing
To experimentally measure nuclease activity at on-target
and potential off-target sites identified by PROGNOS, we
used single molecule real-time (SMRT) sequencing of the
PCR amplicons. The consensus sequencing mode of the
SMRT platform provides highly accurate long length
reads (27) that allowed determination of nuclease
activity and specificity with reasonable sensitivity, and at
a lower cost per run than other deep sequencing platforms
(other advantages of SMRT sequencing for smaller
laboratories are described in Supplementary Note 7).
The good agreement between SMRT sequencing results
and Sanger sequencing of TOPO-cloned samples further
confirmed the accuracy of the SMRT-based analysis of
nuclease cleavage (Figure 3A). Further, the high quality
of the SMRT consensus sequence reads allowed us to
achieve a much better signal to noise ratio for the
mutation analysis than other sequencing methods (1).
We found that only three sequencing reads from mock
treated control cells (0.003% of the total) contained
indels flagged by the analysis and all three were from the
same genomic site, which in retrospect should have been
excluded from sequencing analysis due to several long
adjacent homopolymer stretches known to be error-
prone during the sequencing process (Supplementary
Tables S6–S9 and Supplementary Data D1).
Although the spectrums of indels induced by ZFNs (6)
or TALENs (1,28) have been investigated previously, the
long SMRT read lengths provided a more comprehensive
analysis (Figure 3B). We found that ZFNs induced pre-
dominately 3-, 4- and 5-bp insertions or deletions,
with just a small number of large deletions. In contrast,
TALENs induced indels over a much broader range,
centered at 5–20 bp deletions, possibly due to the flexibility
of the +63 C-terminal TAL domain (29).
Prediction and validation of off-target sites for
novel nucleases
To demonstrate the application of PROGNOS in
analyzing newly designed nucleases, we investigated the
off-target cleavage of four pairs of TALENs and two
pairs of ZFNs (Table 1). TALENs containing the Asn-
Asn (NN) RVD have been shown to be less specific than
corresponding TALENs containing the Asn-Lys (NK)
RVD (29); however the difference in off-target activity
of NN-TALENs and NK-TALENs has not been
demonstrated in a genome-wide context. For ZFNs,
although both 3F and 4F ZFNs have been shown to
have off-target cleavage (6,9,10), there has been no
direct comparison of off-target cleavage induced by
3F- and 4F-ZFNs that target the same DNA sequence.
We expressed the TALENs and ZFNs in HEK-293T
High-throughput PCR from
transfected cells with primers
designed automatically by
PROGNOS
Analyze amplicons for nuclease
induced mutations and indels
Identify potential off-sites with
PROGNOS online software
110 / 116
19 / 20
122 / 132
9 / 10
9 / 29
% Success of Off-target Site PCR
PROGNOS Automated
Primer Design
Hockemeyer et al.
Pattanayak et al.
Tesson et al.
Huang et al.
Full Plate
High-Throughput
PCR
Sample Output
d
100%
0%
ZFN-3F Off-Target Site in ATG7
Mutations in 7 of 1334 sequences 0.5% o
TGGAGTTGACTCCGCCCTTGgCCCATGGTTGGGGCAAGGTAGTTGGGA WT
TGGAGTTGACTCCGCCCTTGGCCCAtggtTGGTTGGGGCAAGGTAGTT +4 x3
TGGAGTTGACTCCGCCCTTGGCCCATGGTtggTGGGGCAAGGTAGTTG +3 x2
TGGAGTTGACTCCGCCCTTGGCCCAtggTGGTTGGGGCAAGGTAGTTG +3 x1
TGGAGTTGACTCCGCCCTTGGCCCAtggttTGGTTGGGGCAAGGTAGT +5 x1
23016 Potenal Off-target Sites Rankings Mismatches PCR Product Size
Exons: Promoters: 1—HBB 0 271 bp
1450 529 2—CANX 1 469 bp
Introns: Intergenic: 3—ATG7 1 298 bp
9671 11366
A
B
C
D
Figure 2. Using PROGNOS to identify nuclease off-target sites. (A) Outline of the procedure to identify nuclease off-target activity. (B) Sample
outputs of the PROGNOS online software showing all sites found and what types of genomic regions they are located in as well as rankings of the
top potential off-target sites. The rankings include the closest gene, the number of mismatches, the size of PCR product from the automatically
designed primers, and other helpful information. (C) Comparison of the success of the automatically designed PROGNOS primers used in high-
throughput full-plate PCR of off-target sites to primers designed in other off-target publications. (D) Sequencing reads of an off-target location for
the 3F ZFN pair that show evidence of NHEJ. In the wild-type (WT) sequence, the ZFN binding sites are highlighted in yellow and mismatches to
the intended target sequence are lowercase red. In the sequencing reads, inserted bases are lowercase and highlighted in blue. The size of the indel is
displayed to the right of the sequence, along with the number of times that mutation was observed.
PAGE 7 OF 13 Nucleic Acids Research, 2014, Vol. 42, No. 6 e42
cells, and analyzed the PROGNOS top-ranked off-target
sites (Table 1 and Supplementary Tables S6–S9).
We found that TALENs exclusively using the NN RVD
to target all of the guanosine nucleotides in the target
sequence imparted higher activity level than TALENs
exclusively using the NK RVD at corresponding pos-
itions, in agreement with previous reports (1,29).
However, the NN-TALENs tested in this study had
higher off-target cleavage activity than the corresponding
NK-TALENs. For the first time, off-target cleavage by
NK-TALENs was uncovered, as well as bona fide
TALEN off-target sites with substantial (>5%) sequence
divergence from the intended target that lacked a 5
0
pyr-
imidine and a site with a spacer >24 bp (Table 1). For
ZFNs, we found that the 4F-ZFNs had higher on-target
activity [consistent with previous reports that additional
fingers increased activity (31)] and much lower off-target
activity compared with the corresponding 3F-ZFNs tar-
geting the same DNA site. Specifically, all six of the off-
target sites found for the 3F-ZFNs had equal or greater
activity than the off-target site of the 4F-ZFNs (a single
site with 0.2% activity), with three sites having activity
>1% (Table 1).
Refinement of PROGNOS ranking algorithms
Although the set of initial PROGNOS algorithms (two for
ZFNs and four for TALENs) performed well in locating
bona fide off-target sites for newly designed nucleases
based solely on in silico prediction, a user would still
need to choose a specific algorithm or use all the available
algorithms without knowing a priori which one would be
most predictive for their nuclease. Using the expanded set
of bona fide off-target sites including those found in this
study (Table 1) as well as new insights into TALEN-DNA
binding (19,20), we refined the PROGNOS algorithms so
that they are more sensitive, efficient and user friendly
compared with the initial algorithms. Although the
‘Homology’, ‘Conserved G’s’ and ‘RVDs’ algorithms
(including the ‘5TC’ version for TALENs) all located
bona fide off-target sites, no algorithm was consistently
superior across all ZFNs or all TALENs studied
(Figure 1B and C and Table 1). In developing the
refined algorithms, we were able to unify the different al-
gorithms for each type of nuclease into a single algorithm
(ZFN v2.0 for ZFNs, TALEN v2.0 for TALENs).
Compared with the original PROGNOS algorithms,
Relative Frequency
Indel length (bp)
ZFNs
TALENs
0
10
20
30
40
50
60
70
80
90
100
S2/S5 NK S116/S120 S2/S5 NN J7/J8
% Mutated Sequences
TOPO Sequencing
SMRT Sequencing
11/59
333/1699
10/41
252/803
14/35
760/1692
32/41
460/599
90% Confidence Intervals
A
B
Figure 3. Using SMRT Sequencing to analyze nuclease activity. (A) SMRT sequencing produced very similar results to standard TOPO sequencing
over a range of mutation rates from 20% to 76%. Error bars are 90% confidence intervals. S2/S5 NK and S2/S5 NN are the TALENs targeting
beta-globin compared in this study. S116/S120 and J7/J8 are NK-TALENs targeting beta-globin and CDH1, respectively (30). (B) Comparison of the
range and frequency of different sizes of indels observed in cells treated with TALENs or ZFNs. The observed frequencies of the different sizes are
normalized to the frequency of the most common indel size for each nuclease type.
e42 Nucleic Acids Research, 2014, Vol. 42, No. 6 PAGE 8 OF 13
ZFN v2.0 and TALEN v2.0 predicted a larger total
number of bona fide off-target sites within the top 16
rankings (representing the minimum recommended size
of a small-scale off-target analysis), located higher mean
percentages of known off-target sites per nuclease across
all nucleases tested (within the top 3X rankings for previ-
ously investigated nucleases and within the same number
of sites as in the PROGNOS-based investigations,
Supplementary Table S17), and had lower standard devi-
ations of the mean percentages, demonstrating that the
refined algorithms performed more consistently across
all nucleases tested.
In developing the refined and unified ZFN algorithm,
we added factors weighing a model of the binding energy
of each zinc finger subunit (9) and polarity effects reflect-
ing the distance of a mismatch from the FokI domain and
allowed more flexible models of the previous concepts
of energy compensation between the two half-sites of a
nuclease pair and a stronger affinity for guanosine
residues (Supplementary Method M1). This new ‘ZFN
v2.0’ algorithm outperforms the initial ‘Homology’ and
‘Conserved G’s’ algorithms for ZFNs in terms of both
identifying a larger set of bona fide off-target sites for
the nucleases tested and having a superior true discovery
rate in the Top 16 rankings (Figure 4A). The Top 16
ranked sites were chosen as a cutoff (instead of the
Top 24, as recommended in Supplementary Note 4)
because by necessity nearly all of the novel off-target
sites found were within the Top 24 rankings of one of
the original algorithms since that was their initial criteria
for being selected for investigation. Therefore, a stricter
cutoff was required in order to observe differential per-
formances between the algorithms for these new sites.
Recently, Sander et al. (11) used Bayesian machine
learning to re-analyze the original results of the in vitro
cleavage experiments for CCR5 and VEGF ZFNs (9) and
subsequently developed two separate classifiers that
ranked all sequences in the human genome for their
potential as off-target sites of either the CCR5 or VEGF
ZFNs, respectively. Their work validated 25 new bona fide
off-target sites for the CCR5 ZFNs and 26 new sites for
the VEGF ZFNs, but did not locate—among any of the
15 882 possible off-target sites predicted for the CCR5
ZFNs by their classifier system—the novel off-target site
for the CCR5 ZFNs predicted by the PROGNOS algo-
rithms near CSNK1G3 that was validated in this study.
ChIP-Seq
**
In vitro Cleavage
IDLV
Bayesian Machine Learning
Abstraction of
In vitro Cleavage Profile
PROGNOS ZFN v2.0 Algorithm
Extended Predictions
Expanded Landscape of CCR5 Off-Target Sites
1
2
8
1
11
5
8
1
1
Mean % of Known Off-Target
Sites Located per Nuclease
Total # of Off-Target
Sites in Top 16 Rankings
Mean % of Known Off-Target
Sites Located per Nuclease
Total # of Off-Target
Sites in Top 16 Rankings
M
ean
%
o
f
K
no
w
n
O
f
f
-
T
a
S
i
t
e
s
Lo
c
a
t
e
d
pe
r
Nu
c
l
ea
T
o
t
a
l
#
o
f
O
ff
-
T
a
r
ge
t
S
i
t
e
s
i
n
T
op
1
6
R
a
n
k
i
ng
s
M
ean
%
o
f
K
no
w
n
O
f
f
-
T
a
r
ge
S
i
t
e
s
Lo
c
a
t
e
d
pe
r
Nu
c
l
ea
s
e
T
o
t
a
l
#
o
f
O
f
f
-
T
a
r
ge
t
Si
t
e
s
i
n
T
o
p
1
6
R
a
n
k
i
n
g
s
A
C
B
Figure 4. Improved performance of the refined PROGNOS algorithms. (A) The performance of the two initial ZFN algorithms and the refined
‘ZFN v2.0’ algorithm are compared for their ability to predict off-target sites for all the ZFNs in the training and validation sets. Percentages of off-
target sites located were calculated according to 3X limits for previous studies and within the number of sites interrogated for PROGNOS-based
studies (typically the top 24 ranked sites). Error bars represent SD. (B) The expanded landscape of 38 total heterodimeric off-target sites for the
CCR5 ZFNs found by four different experiment-based prediction methods and the refined ‘ZFN v2.0’ PROGNOS algorithm. The PROGNOS sites
are drawn from the top rankings spanning 3X the number of predictions by the Bayesian abstraction of the in vitro cleavage profile. (
**
) Note that
only six of the sites found using ChIP-Seq were provided by Sander et al. (11), so the full degree of overlap of all ChIP-Seq sites with sites found by
other methods is unclear. (C) The performance of the four original TALEN algorithms and the refined ‘TALEN v2.0’ algorithm are compared for
their ability to predict off-target sites for all TALENs in the training and validation sets.
PAGE 9 OF 13 Nucleic Acids Research, 2014, Vol. 42, No. 6 e42
Although the analysis by Sander et al. combined machine
learning and in vitro cleavage experiments, it was unable to
locate all the known off-target sites for the CCR5 ZFNs.
Details of the comparison to the Sander et al. analysis (11)
can be found in Supplementary Note 6.
Since the 51 new sites found by Sander et al. (11) were
not part of the training set for the ‘ZFN v2.0’ algorithm,
this provided an opportunity to test the new algorithm for
its ability to locate additional off-target sites. By extending
the standard PROGNOS search limit recommendations
(Supplementary Note 4) for the CCR5 ZFNs to allow
for a larger number of possible off-target sites (3X the
number of possible off-target sites considered by Sander
et al.), we found that the refined ZFN algorithm success-
fully identified more than half (13 of 25 = 52%) of the
new off-target sites for those ZFNs (Figure 4B and
Supplementary Note 5). For the VEGF ZFNs, the
standard PROGNOS search provided enough potential
off-target sites to make an appropriate 3X comparison
to Sander et al. ( 11), and the refined algorithm again
located more than half (18 of 26 = 69%) of the new off-
target sites for those ZFNs (Supplementary Note 5). Three
additional pairs of ZFNs (a 3F pair, a 4F pair and a 5F
CompoZr pair from Sigma-Aldrich) which had previously
been investigated using the Homology and Conserved G’s
PROGNOS algorithms (Mussolino,C. et al. and
Abarrategui-Pontes,C. et al., manuscripts in preparation)
were also re-analyzed using the refined algorithm and all
six of the previously located bona fide off-target sites were
highly ranked by ZFN v2.0 (Supplementary Table S17).
Taken together, these results provide significant evidence
that the refined ZFN algorithm was not over trained to
existing sites during its development and is able to
robustly predict additional bona fide off-target sites. An
analysis of each of the components of the ZFN v2.0 algo-
rithm showed that while all play a part in the improved
performance, some parameters are more critical to the al-
gorithm than others (Supplementary Figure S7).
In developing the refined and unified TALEN algo-
rithm, we added new parameters based on compensatory
effects of strong RVDs (NN and HD) (19) on adjacent
mismatches and polarity effects indicating that
mismatches further from the N-terminus are less disrup-
tive (20). These new considerations were combined with a
model of dimeric nuclease interactions, as well as RVD-
nucleotide association frequencies. To improve upon the
RVD-nucleotide association frequencies derived from
natural TAL effectors (18), as were used in the initial
‘RVDs’ algorithm and the TALE-NT online tool (18),
we calculated association frequencies based on
SELEX data from engineered TAL domains (5,8,17)
(Supplementary Figure S6 and Table S16). Importantly,
this generated an association frequency for the 5
0
‘Position
0’ in the TALEN-binding site that allowed us to use this
parameter to unify the ‘5TC’ and unrestricted versions of
the ‘RVDs’ algorithm. Further, we found that while the
nucleotide frequencies for the RVDs NI, HD, NK and
NG did not appreciably vary between engineered
TALEs and natural TALEs, the results for NN were sub-
stantially different. Although the NN RVD is still the least
specific of all the standard RVDs, in engineered TALEs it
showed a stronger preference for its intended base (guano-
sine) and a reduced preference for adenosines and cyti-
dines compared with that of naturally occurring TALEs
(Supplementary Table S16). We found that the new
unified ‘TALEN v2.0’ algorithm outperforms the four
initial algorithms for TALENs in terms of both finding
a larger number of bona fide off-target sites in the Top 16
rankings and locating a higher mean percentage of known
off-target sites per nuclease across all nucleases tested
(Figure 4C). The refined TALEN algorithm was addition-
ally able to predict several bona fide TALEN off-target
sites not in its training set that were found using the
initial PROGNOS algorithms (Supplementary Table
S17, Mussolino,C. et al., manuscript in preparation),
demonstrating that the refined algorithm was not over
trained during development and retains robust predictive
capabilities. An analysis of each of the components of the
TALEN v2.0 algorithm showed that while all play a part
in the improved performance, some parameters are more
critical to the algorithm than others (Supplementary
Figure S8).
Sensitivity and specificity of PROGNOS
search algorithms
When applying the initial PROGNOS algorithms to iden-
tify off-target sites for newly constructed NN-TALENs
and 3F and 4F ZFNs, we obtained a very manageable
average false positive ratio—defined as the number of
interrogated sites with no detectable activity compared
to the number with detectable activity—of only 11:1,
which is less than 2-fold greater than current experimental
prediction methods (Figure 5A and Supplementary Table
S12). When interrogating three additional pairs of NN-
TALENs with the initial algorithms, we observed a simi-
larly low false positive ratio of 11:1 (Mussolino,C. et al., in
preparation). For NK-TALENs, the false positive ratio
was higher (21:1); however, since no previously
published method has identified any off-target sites for
NK-TALENs, we were not able to make a meaningful
comparison of the false positive ratio with experiment-
based prediction methods. As the new ‘ZFN v2.0’ and
‘TALEN v2.0’ algorithms have a higher true discovery
rate among the top 16 rankings, we would expect that
their false positive ratios would be even lower than the
initial algorithms when used as the basis for investigations
of novel nucleases.
As mentioned above, to date only a single nuclease pair
(the heterodimeric sites of the CCR5 ZFNs) has had its
off-target cleavage investigated by independent experi-
mental prediction methods (9–11), and it is therefore the
only pair for which a false negative rate analysis can be
conducted. Defining the false negative rate as the percent-
age of all known off-target sites that are not predicted
by the particular method within a top portion of the
rankings, the PROGNOS algorithms had false negative
rates equal or superior to the IDLV and in vitro
cleavage experimental prediction methods (Figure 5B
and Supplementary Table S13). An ROC-like analysis
of the different predictive methods for the CCR5 ZFNs
using the false discovery and true positive rates also
e42 Nucleic Acids Research, 2014, Vol. 42, No. 6 P
AGE 10 OF 13
demonstrates that the PROGNOS algorithms perform
comparably to experimental based prediction methods
(Supplementary Figure S4).
DISCUSSION
Engineered nucleases can readily be designed and
optimized to target specific endogenous sequences in a
genome. However, to reach their potential for generating
model research systems and treating human diseases, the
specificity of engineered nucleases must be better under-
stood. However, the analysis of the location and frequency
of TALEN and ZFN off-target effects has been beyond
the reach of most laboratories due to the limitations of the
existing methods. We created PROGNOS, an online
search tool solely based on bioinformatics and the
current understanding of nuclease–DNA interactions,
which allows users to predict potential nuclease off-
target sites by following a simple set of instructions
(Supplementary Note 4), and to evaluate the sites using
standard molecular biology techniques if so desired
(Supplementary Figure S2). The novel bioinformatics
ranking algorithms in PROGNOS predict many of the
off-target sites of the CCR5 ZFNs that were identified
previously using experimental methods and also identified
a novel off-target site that was missed in those studies.
However, there are several very active (>5% mutation
rate) off-target sites for these ZFNs that PROGNOS did
not rank highly, suggesting that there are still unknown
factors influencing ZFN off-target activity that are not
accounted for in our current models. Future unbiased
genome-wide analyses of off-target activity [such as the
IDLV method (10)] will be critical to build a larger
database of sites with low sequence homology from
which further insight into the factors affecting off-target
activity can be gained. Nevertheless, PROGNOS is able to
successfully predict many off-target sites and overcomes
the drawbacks of the current experiment-based prediction
methods that limit the number of nucleases tested, as
evidenced by the fact that no bona fide off-target sites
for new ZFNs or TALENs have been reported over the
last 2 years (5,8) (see Supplementary Note 5). The
improved performance of the refined ‘ZFN v2.0’ and
‘TALEN v2.0’ algorithms over the initial algorithms high-
lights a key advantage of bioinformatics-based predic-
tions: as more bona fide off-target sites are discovered,
increasingly better predictive models can be incorporated.
PROGNOS allowed interrogation and comparison of
the off-target activities of several novel nucleases targeting
the beta-globin gene. We directly compared 3F versus 4F
ZFNs that targeted the same site, and compared
NK-TALENs versus NN-TALENs that shared target
sites. We found that these NN-TALENs and 3F ZFNs
had more off-target activity than the corresponding NK-
TALENs and 4F ZFNs, respectively. While NN-TALENs
generally have high on-target cleavage, this may be
accompanied by decreased specificity leading to high off-
target activity. To confirm the conclusion that the
4F-ZFNs targeting this site are more specific than the
3F versions, we interrogated several of the validated
3F-ZFN off-target sites in cells expressing 4F-ZFNs and
found no statistically significant off-target activity
(Supplementary Table S9). Our comparison of the speci-
ficity of NN-TALENs versus NK-TALENs is somewhat
limited by the fact that the NN-TALENs had higher
on-target activity than the corresponding NK-TALENs,
but the dramatic difference in off-target activity at HBD
for the S2/S5 NN- and NK-TALENs (Table 1) strongly
supports the notion that NK-TALENs have improved
specificity over NN-TALENs. The nature of the new
off-target sites and their implications are discussed
further in Supplementary Note 1.
In summary, PROGNOS provides a user-friendly, web-
based tool for rapid identification of potential nuclease
off-target cleavage sites that can be evaluated using
standard molecular biology techniques. The bioinfor-
matics-based ranking algorithms in PROGNOS identify
most nuclease off-target cleavage sites found by existing
experimental methods. PROGNOS has relatively low
false positive ratios and comparable false negative rates
to experiment-based predictions, making it a robust
method that can be readily implemented by most
Averages
3-finger ZFNs 4-finger ZFNs
NN-TALENs
0
5
10
15
20
ZFN-3F
kdrl
VEGF
ZFN-4F
CCR5
S2/S5 NN
S1/S7 NN
AAVS1
IgM
False Posive Rao
65%
70%
75%
80%
85%
90%
Homology Conserved G's ZFN v2.0 In Vitro
Cleavage
IDLV Trapping Bayesian
Abstracon
False Negave Rate
PROGNOS
Experimental Predicon Methods
AB
Figure 5. Sensitivity and specificity analysis of PROGNOS algorithms. (A) Average false positive ratios are shown for the PROGNOS investigation
of novel nucleases using the initial algorithms, and for previous experimental prediction methods. Ratios are also shown for individual nucleases in
the three different categories of nuclease that have been investigated previously by experimental prediction methods. (B) The false negative rates of
the different PROGNOS algorithms and previous experimental prediction methods are shown. These were determined by each method’s ability to
identify the 38 known hetero-dimeric off-target sites of the CCR5 ZFNs in their top ranking predictions.
PAGE 11 OF 13 Nucleic Acids Research, 2014, Vol. 42, No. 6 e42
laboratories. Screening potential target sites using
PROGNOS can facilitate the selection of superior
nuclease target sites that minimize the number of likely
genomic off-target sites. PROGNOS allows nuclease off-
target analysis to become a routine component of nuclease
design and testing, facilitating the discovery of new off-
target sites for ZFNs and TALENs, which expand the off-
target database and may improve future versions of the
PROGNOS algorithms. These capabilities give
PROGNOS the potential to help expand and expedite
the application of engineered nucleases for a wide range
of biological and medical applications.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online,
including [32–40].
ACKNOWLEDGEMENTS
We thank Mike Tschannen at the Medical College of
Wisconsin Sequencing Core for his help and Dr Ayal
Hendel at Stanford University for his suggestions on
SMRT sequencing. We thank Dr Claudio Mussolino
and Dr Toni Cathoman at the University of Freiburg
for the genomic DNA from HEK-293T cells transfected
with the CCR5 ZFNs.
FUNDING
National Institutes of Health (NIH Nanomedicine
Development Center Award; grant number
PN2EY018244 to G.B.); National Science Foundation
Graduate Research Fellowship (DGE-1148903 to
E.J.F.). Funding for open access charge: National
Institutes of Health.
Conflict of interest statement. None declared.
REFERENCES
1. Huang,P., Xiao,A., Zhou,M., Zhu,Z., Lin,S. and Zhang,B. (2011)
Heritable gene targeting in zebrafish using customized TALENs.
Nat. Biotech., 29, 699–700.
2. Lei,Y., Guo,X., Liu,Y., Cao,Y., Deng,Y., Chen,X.,
Cheng,C.H.K., Dawid,I.B., Chen,Y. and Zhao,H. (2012) Efficient
targeted gene disruption in Xenopus embryos using engineered
transcription activator-like effector nucleases (TALENs). PNAS,
109, 17484–17489.
3. Zschemisch,N.-H., Glage,S., Wedekind,D., Weinstein,E.J., Cui,X.,
Dorsch,M. and Hedrich,H.-J. (2012) Zinc-finger nuclease mediated
disruption of Rag1 in the LEW/Ztm rat. BMC Immunol., 13, 60.
4. Watanabe,T., Ochiai,H., Sakuma,T., Horch,H.W., Hamaguchi,N.,
Nakamura,T., Bando,T., Ohuchi,H., Yamamoto,T., Noji,S. et al.
(2012) Non-transgenic genome modifications in a hemimetabolous
insect using zinc-finger and TAL effector nucleases. Nat.
Commun., 3, 1017.
5. Tesson,L., Usal,C., Me
´
noret,S., Leung,E., Niles,B.J., Remy,S.,
Santiago,Y., Vincent,A.I., Meng,X., Zhang,L. et al. (2011)
Knockout rats generated by embryo microinjection of TALENs.
Nat. Biotechnol., 29, 695–696.
6. Gupta,A., Meng,X., Zhu,L.J., Lawson,N.D. and Wolfe,S.A.
(2011) Zinc finger protein-dependent and -independent
contributions to the in vivo off-target activity of zinc finger
nucleases. Nucleic Acids Res., 39, 381–392.
7. Sebastiano,V., Maeder,M.L., Angstman,J.F., Haddad,B.,
Khayter,C., Yeo,D.T., Goodwin,M.J., Hawkins,J.S.,
Ramirez,C.L., Batista,L.F.Z. et al. (2011) In Situ Genetic
Correction of the Sickle Cell Anemia Mutation in Human
Induced Pluripotent Stem Cells Using Engineered Zinc Finger
Nucleases. Stem Cells, 29, 1717–1726.
8. Hockemeyer,D., Wang,H., Kiani,S., Lai,C.S., Gao,Q.,
Cassady,J.P., Cost,G.J., Zhang,L., Santiago,Y., Miller,J.C. et al.
(2011) Genetic engineering of human pluripotent cells using
TALE nucleases. Nat. Biotechnol., 29, 731–734.
9. Pattanayak,V., Ramirez,C.L., Joung,J.K. and Liu,D.R. (2011)
Revealing off-target cleavage specificities of zinc-finger nucleases
by in vitro selection. Nat. Methods, 8, 765–770.
10. Gabriel,R., Lombardo,A., Arens,A., Miller,J.C., Genovese,P.,
Kaeppel,C., Nowrouzi,A., Bartholomae,C.C., Wang,J.,
Friedman,G. et al. (2011) An unbiased genome-wide analysis of
zinc-finger nuclease specificity. Nat. Biotech., 29, 816–823.
11. Sander,J.D., Ramirez,C.L., Linder,S.J., Pattanayak,V.,
Shoresh,N., Ku,M., Foden,J.A., Reyon,D., Bernstein,B.E.,
Liu,D.R. et al. (2013) In silico abstraction of zinc finger nuclease
cleavage profiles reveals an expanded landscape of off-target sites.
Nucleic Acids Res, 41, e181.
12. Hsu,P.D., Scott,D.A., Weinstein,J.A., Ran,F.A., Konermann,S.,
Agarwala,V., Li,Y., Fine,E.J., Wu,X., Shalem,O. et al. (2013)
DNA targeting specificity of RNA-guided Cas9 nucleases. Nat.
Biotechnol., 31
, 827–832.
13. Fu,Y., Foden,J.A., Khayter,C., Maeder,M.L., Reyon,D.,
Joung,J.K. and Sander,J.D. (2013) High-frequency off-target
mutagenesis induced by CRISPR-Cas nucleases in human cells.
Nat. Biotechnol., 31, 822–826.
14. Cradick,T.J., Fine,E.J., Antico,C.J. and Bao,G. (2013) CRISPR/
Cas9 systems targeting b-globin and CCR5 genes have substantial
off-target activity. Nucleic Acids Res, 21, 9584–9592.
15. Ding,Q., Lee,Y.-K., Schaefer,E.A.K., Peters,D.T., Veres,A.,
Kim,K., Kuperwasser,N., Motola,D.L., Meissner,T.B.,
Hendriks,W.T. et al. (2013) A TALEN Genome-Editing System
for Generating Human Stem Cell-Based Disease Models. Cell
Stem Cell, 12, 238–251.
16. Perez,E.E., Wang,J., Miller,J.C., Jouvenot,Y., Kim,K.A., Liu,O.,
Wang,N., Lee,G., Bartsevich,V.V., Lee,Y.-L. et al. (2008)
Establishment of HIV-1 resistance in CD4+ T cells by genome
editing using zinc-finger nucleases. Nat. Biotech., 26, 808–816.
17. Miller,J.C., Tan,S., Qiao,G., Barlow,K.A., Wang,J., Xia,D.F.,
Meng,X., Paschon,D.E., Leung,E., Hinkley,S.J. et al. (2011)
A TALE nuclease architecture for efficient genome editing.
Nat. biotechnol., 29, 143–148.
18. Doyle,E.L., Booher,N.J., Standage,D.S., Voytas,D.F.,
Brendel,V.P., VanDyk,J.K. and Bogdanove,A.J. (2012) TAL
Effector-Nucleotide Targeter (TALE-NT) 2.0: Tools for TAL
Effector Design and Target Prediction. Nucleic Acids Res, 40,
W117–W122.
19. Streubel,J., Blu
¨
cher,C., Landgraf,A. and Boch,J. (2012) TAL
effector RVD specificities and efficiencies. Nat. Biotechnol., 30,
593–595.
20. Meckler,J.F., Bhakta,M.S., Kim,M.-S., Ovadia,R., Habrian,C.H.,
Zykovich,A., Yu,A., Lockwood,S.H., Morbitzer,R., Elsa
¨
esser,J.
et al . (2013) Quantitative analysis of TALE–DNA interactions
suggests polarity effects. Nucleic Acids Res., 41, 4118–4128.
21. Cermak,T., Doyle,E.L., Christian,M., Wang,L., Zhang,Y.,
Schmidt,C., Baller,J.A., Somia,N.V., Bogdanove,A.J. and
Voytas,D.F. (2011) Efficient design and assembly of custom
TALEN and other TAL effector-based constructs for DNA
targeting. Nucleic Acids Res., 39, e82.
22. Moscou,M.J. and Bogdanove,A.J. (2009) A simple cipher governs
DNA recognition by TAL effectors. Science, 326, 1501.
23. Cradick,T.J., Ambrosini,G., Iseli,C., Bucher,P. and
McCaffrey,A.P. (2011) ZFN-site searches genomes for zinc
finger nuclease target sites and off-target sites. BMC Bioinform.,
12, 152.
24. Grau,J., Boch,J. and Posch,S. (2013) TALENoffer: genome-
wide TALEN off-target prediction. Bioinformatics
, 29, 2931–2932.
25. Mussolino,C., Morbitzer,R., Lu
¨
tge,F., Dannemann,N., Lahaye,T.
and Cathomen,T. (2011) A novel TALE nuclease scaffold enables
e42 Nucleic Acids Research, 2014, Vol. 42, No. 6 PAGE 12 OF 13
high genome editing activity in combination with low toxicity.
Nucleic Acids Research , 39, 9283–9293.
26. Rozen,S. and Skaletsky,H. (1999) Primer3 on the WWW for
general users and for biologist programmers. In: Misener,S. and
Krawetz,S.A. (eds), Bioinformatics Methods and Protocols:
Methods in Molecular Biology. Humana Press, Totowa, NJ,
pp. 365–386.
27. Travers,K.J., Chin,C.-S., Rank,D.R., Eid,J.S. and Turner,S.W.
(2010) A flexible and efficient template format for circular consensus
sequencing and SNP detection. Nucleic Acids Res., 38, e159.
28. Kim,Y., Kweon,J. and Kim,J.-S. (2013) TALENs and ZFNs
are associated with different mutation signatures. Nat. Methods,
10, 185.
29. Christian,M.L., Demorest,Z.L., Starker,C.G., Osborn,M.J.,
Nyquist,M.D., Zhang,Y., Carlson,D.F., Bradley,P.,
Bogdanove,A.J. and Voytas,D.F. (2012) Targeting G with TAL
effectors: a comparison of activities of TALENs constructed
with NN and NK repeat variable di-residues. PLoS ONE, 7,
e45383.
30. Lin,Y., Fine,E.J., Zheng,Z., Antico,C., Voit,R., Porteus,M.,
Cradick,T. and Bao,G. (2013) SAPTA: a new design tool for
improving TALE nuclease activity. Nucleic Acids Res., e47.
31. Bhakta,M.S., Henry,I.M., Ousterout,D.G., Das,K.T.,
Lockwood,S.H., Meckler,J.F., Wallen,M.C., Zykovich,A., Yu,Y.,
Leo,H. et al. (2013) Highly active zinc finger nucleases by
extended modular assembly. Genome Res, 23, 530–538.
32. Needleman,S.B. and Wunsch,C.D. (1970) A general method
applicable to the search for similarities in the amino acid
sequence of two proteins. J. Mol. Biol., 48, 443–453.
33. Bogdanove,A.J. and Voytas,D.F. (2011) TAL Effectors:
Customizable Proteins for DNA Targeting. Science, 333, 1843–1846.
34. Kent,W.J., Sugnet,C.W., Furey,T.S., Roskin,K.M., Pringle,T.H.,
Zahler,A.M. and Haussler,A.D. (2002) The human genome
browser at UCSC. Genome Res., 12, 996–1006.
35. Flicek,P., Amode,M.R., Barrell,D., Beal,K., Brent,S.,
Carvalho-Silva,D., Clapham,P., Coates,G., Fairley,S.,
Fitzgerald,S. et al. (2011) Ensembl 2012. Nucleic Acids Res., 40,
D84–D90.
36. Tuskan,G.A., Difazio,S., Jansson,S., Bohlmann,J., Grigoriev,I.,
Hellsten,U., Putnam,N., Ralph,S., Rombauts,S., Salamov,A. et al.
(2006) The genome of black cottonwood, Populus trichocarpa
(Torr. & Gray). Science (New York, N.Y.), 313, 1596–1604.
37. Jeong,H., Barbe,V., Lee,C.H., Vallenet,D., Yu,D.S., Choi,S.-H.,
Couloux,A., Lee,S.-W., Yoon,S.H., Cattolico,L. et al. (2009)
Genome Sequences of Escherichia coli B strains REL606 and
BL21(DE3). J. Mol. Biol., 394, 644–652.
38. Porteus,M.H. and Baltimore,D. (2003) Chimeric nucleases
stimulate gene targeting in human cells.
Science, 300, 763.
39. Doyon,Y., Vo,T.D., Mendel,M.C., Greenberg,S.G., Wang,J.,
Xia,D.F., Miller,J.C., Urnov,F.D., Gregory,P.D. and
Holmes,M.C. (2011) Enhancing zinc-finger-nuclease activity with
improved obligate heterodimeric architectures. Nat. Methods, 8,
74–79.
40. Osborn,M.J., Starker,C.G., McElroy,A.N., Webber,B.R.,
Riddle,M.J., Xia,L., Defeo,A.P., Gabriel,R., Schmidt,M.,
Von Kalle,C. et al . (2013) TALEN-based Gene Correction for
Epidermolysis Bullosa. Mol. Ther. J. Am. Soc. Gene Ther., 21,
1151–1159.
PAGE 13 OF 13 Nucleic Acids Research, 2014, Vol. 42, No. 6 e42
    • "Stage 7 of DNA repair, which is confirmation that no other mutations have been generated during the repair procedure , was completed by PROGNOS software and Sanger sequencing of the predicted off-target sites. Predicted Report Of Genome-wide Nuclease Off-target Sites (PROGNOS) online software from Gang Bao laboratory was used to evaluate potential rearrangements in off-target sequences throughout the genome after pF508Δ correction by HR [30]. Target sequences for both TALENs, variable length of the spacer from 10 to 30 bases, and precise repeat variable domains (RVDs) of the TALENs were used to interrogate PROGNOS for possible off-target sequences. "
    [Show abstract] [Hide abstract] ABSTRACT: Cystic fibrosis is one of the most frequent inherited rare diseases, caused by mutations in the cystic fibrosis transmembrane conductance regulator gene. Apart from symptomatic treatments, therapeutic protocols for curing the disease have not yet been established. The regeneration of genetically corrected, disease-free epithelia in cystic fibrosis patients is envisioned by designing a stem cell/genetic therapy in which patient-derived pluripotent stem cells are genetically corrected, from which target tissues are derived. In this framework, we present an efficient method for seamless correction of pF508del mutation in patient-specific induced pluripotent stem cells by gene edited homologous recombination. Gene edition has been performed by transcription activator-like effector nucleases and a homologous recombination donor vector which contains a PiggyBac transposon-based double selectable marker cassette. This new method has been designed to partially avoid xenobiotics from the culture system, improve cell culture efficiency and genome stability by using a robust culture system method, and optimize timings. Overall, once the pluripotent cells have been amplified for the first nucleofection, the procedure can be completed in 69 days, and can be easily adapted to edit and change any gene of interest.
    Full-text · Article · Dec 2016
    • "Activity and cytotoxicity of designer nucleases are mainly determined by the target site specificity of the DNA-binding domain [26], and nuclease-induced toxicity most likely results from off-target cleavage [25]. As compared to the ZFNs, the TALEN pair revealed considerably less cleavage activity at off-target sites predicted by bioinformatics [36] . The impact of both higher specificity and reduced nuclease-associated cytotoxicity was emphasized by the number of correctly targeted iPSC clones, which was consistently higher in TALEN than in ZFN-corrected samples. "
    [Show abstract] [Hide abstract] ABSTRACT: X-linked chronic granulomatous disease (X-CGD) is an inherited disorder of the immune system. It is characterized by a defect in the production of reactive oxygen species (ROS) in phagocytic cells due to mutations in the NOX2 locus, which encodes gp91phox. Because the success of retroviral gene therapy for X-CGD has been hampered by insertional activation of proto-oncogenes, targeting the insertion of a gp91phox transgene into potential safe harbor sites, such as AAVS1, may represent a valid alternative. To conceptually evaluate this strategy, we generated X-CGD patient-derived induced pluripotent stem cells (iPSCs), which recapitulate the cellular disease phenotype upon granulocytic differentiation. We examined AAVS1-specific zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) for their efficacy to target the insertion of a myelo-specific gp91phox cassette to AAVS1. Probably due to their lower cytotoxicity, TALENs were more efficient than ZFNs in generating correctly targeted iPSC colonies, but all corrected iPSC clones showed no signs of mutations at the top-ten predicted off-target sites of both nucleases. Upon differentiation of the corrected X-CGD iPSCs, gp91phox mRNA levels were highly up-regulated and the derived granulocytes exhibited restored ROS production that induced neutrophil extracellular trap (NET) formation. In conclusion, we demonstrate that TALEN-mediated integration of a myelo-specific gp91phox transgene into AAVS1 of patient-derived iPSCs represents a safe and efficient way to generate autologous, functionally corrected granulocytes. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Full-text · Article · Aug 2015
    • "For each off-target ChIP-seq site, normalized DNase-seq signal from IL1RN-target- ed TALE-VP64 (C), IL1RN-targeted dCas9-VP64 (D), HBG1/2-targeted TALE-VP64s (E), and HBG1/2-tar- geted dCas9-VP64 (F ) was compared to normalized DNase-seq signal from control cells transfected with empty plasmid. target TALE binding and TALEN activity (Hockemeyer et al. 2011; Osborn et al. 2013; Fine et al. 2014; Guilinger et al. 2014). Notably, both technologies showed similar exceptional levels of specificity of gene activation by RNA-seq (Fig. 2; Perez-Pinera et al. 2013a), suggesting that for some applications, these off-target events may be inconsequential, similar to the observation that offtarget binding by the Cas9 nuclease frequently does not typically lead to detectable gene editing (Mendenhall et al. 2013; Duan et al. 2014; Kuscu et al. 2014; Wu et al. 2014; O'Geen et al. 2015). "
    [Show abstract] [Hide abstract] ABSTRACT: Genome engineering technologies based on the CRISPR/Cas9 and TALE systems are enabling new approaches in science and biotechnology. However, the specificity of these tools in complex genomes and the role of chromatin structure in determining DNA-binding are not well understood. We analyzed the genome-wide effects of TALE- and CRISPR-based transcriptional activators in human cells using ChIP-seq to assess DNA-binding specificity and RNA-seq to measure the specificity of perturbing the transcriptome. Additionally, DNase-seq was used to assess genome-wide chromatin remodeling that occurs as a result of their action. Our results show that these transcription factors are highly specific in both DNA-binding and gene regulation, and are able to open targeted regions of closed chromatin independent of gene activation. Collectively, these results underscore the potential for these technologies to make precise changes to gene expression for gene and cell therapies or fundamental studies of gene function. Published by Cold Spring Harbor Laboratory Press.
    Full-text · Article · May 2015
Show more

    Recommended publications

    Discover more