dsCheck: highly sensitive off-target search software for double-stranded RNA-mediated RNA interference.
ABSTRACT Off-target effects are one of the most serious problems in RNA interference (RNAi). Here, we present dsCheck (http://dsCheck.RNAi.jp/), web-based online software for estimating off-target effects caused by the long double-stranded RNA (dsRNA) used in RNAi studies. In the biochemical process of RNAi, the long dsRNA is cleaved by Dicer into short-interfering RNA (siRNA) cocktails. The software simulates this process and investigates individual 19 nt substrings of the long dsRNA. Subsequently, the software promptly enumerates a list of potential off-target gene candidates based on the order of off-target effects using its novel algorithm, which significantly improves both the efficiency and the sensitivity of the homology search. The website not only provides a rigorous off-target search to verify previously designed dsRNA sequences but also presents 'off-target minimized' dsRNA design, which is essential for reliable experiments in RNAi-based functional genomics.
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ABSTRACT: Neural stem cells in the adult brain exist primarily in a quiescent state but are reactivated in response to changing physiological conditions. How do stem cells sense and respond to metabolic changes? In the Drosophila CNS, quiescent neural stem cells are reactivated synchronously in response to a nutritional stimulus. Feeding triggers insulin production by blood-brain barrier glial cells, activating the insulin/insulin-like growth factor pathway in underlying neural stem cells and stimulating their growth and proliferation. Here we show that gap junctions in the blood-brain barrier glia mediate the influence of metabolic changes on stem cell behavior, enabling glia to respond to nutritional signals and reactivate quiescent stem cells. We propose that gap junctions in the blood-brain barrier are required to translate metabolic signals into synchronized calcium pulses and insulin secretion.Developmental cell. 07/2014;
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ABSTRACT: Since the identification of RNA-mediated interference (RNAi) in 1998, RNAi has become an effective tool to inhibit gene expression. The inhibition mechanism is triggered by introducing a short interference double-stranded RNA (siRNA,19~27 bp) into the cytoplasm, where the guide strand of siRNA (usually antisense strand) binds to its target messenger RNA and the expression of the target gene is blocked. RNAi has been widely applied in gene functional analysis, and as a potential therapeutic strategy in viral diseases, drug target discovery, and cancer therapy. Among the factors which may compromise inhibition efficiency, how to design siRNAs with high efficiency and high specificity to its target gene is critical. Although many algorithms have been developed for this purpose, it is still difficult to design such siRNAs. In this review, we will briefly discuss prediction methods for siRNA efficiency and the problems of present approaches.Cellular and Molecular Life Sciences CMLS 01/2007; 64(14). · 5.86 Impact Factor
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ABSTRACT: In molting animals, a cuticular extracellular matrix forms the first barrier to infection and other environmental insults. In the nematode Caenorhabditis elegans there are two types of cuticle: a well-studied collagenous cuticle lines the body, and a poorly-understood chitinous cuticle lines the pharynx. In the posterior end of the pharynx is the grinder, a tooth-like cuticular specialization that crushes food prior to transport to the intestine for digestion. We here show that the grinder increases in size only during the molt. To gain molecular insight into the structure of the grinder and pharyngeal cuticle, we performed a microarray analysis to identify mRNAs increased during the molt. We found strong transcriptional induction during the molt of 12 of 15 previously identified abu genes encoding Prion-like (P) glutamine (Q) and asparagine (N) rich PQN proteins, as well as 15 additional genes encoding closely related PQN proteins. abu/pqn genes, which we name the abu/pqn paralog group (APPG) genes, were expressed in pharyngeal cells and the proteins encoded by two APPG genes we tested localized to the pharyngeal cuticle. Deleting the APPG gene we abu-14 caused abnormal pharyngeal cuticular structures and knocking down other APPG genes resulted in abnormal cuticular function. We propose that APPG proteins promote the assembly and function of a unique cuticular structure. The strong developmental regulation of the APPG genes raises the possibility that such genes would be identified in transcriptional profiling experiments in which the animals' developmental stage is not precisely staged.Biology open. 10/2014;
dsCheck: highly sensitive off-target search software
for double-stranded RNA-mediated RNA interference
Yuki Naito1, Tomoyuki Yamada3, Takahiro Matsumiya3, Kumiko Ui-Tei1,2,
Kaoru Saigo1and Shinichi Morishita3,*
1Department of Biophysics and Biochemistry, Graduate School of Science,2Undergraduate Program for
Bioinformatics and Systems Biology, School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku,
Tokyo 113-0033, Japan and3Department of Computational Biology, Graduate School of Frontier Sciences,
University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
Received February 14, 2005; Revised and Accepted March 21, 2005
Off-target effects are one of the most serious prob-
lems in RNA interference (RNAi). Here, we present
software for estimating off-target effects caused by
the long double-stranded RNA (dsRNA) used in
RNAi studies. In the biochemical process of RNAi,
the long dsRNA is cleaved by Dicer into short-
lates this process and investigates individual 19 nt
gene candidates based on the order of off-target
effects using its novel algorithm, which significantly
improves both the efficiency and the sensitivity of
the homology search. The website not only provides
a rigorous off-target search to verify previously
designed dsRNA sequences but also presents ‘off-
target minimized’ dsRNA design, which is essential
for reliable experiments in RNAi-based functional
RNA interference (RNAi) is now widely used to knockdown
gene expression in a sequence-specific manner, making it a
powerful tool for studying gene function (1–3). The process of
RNAi is mediated by double-stranded RNA (dsRNA) that
contains a sequence homologous to the target mRNA. Long
dsRNA introducedintothe cellis cleaved by the enzyme Dicer
into short-interfering RNA (siRNA) followed by incorporation
into the RNA-induced silencing complex (RISC), which is
responsible for target mRNA degradation (4).
One of the most serious problems in RNAi is ‘off-target’
silencing effects (5). Off-target silencing effects are caused by
siRNA (introduced directlyintocells, orproducedinvivo from
long dsRNA) that has sequence similarities with unrelated
genes. In Caenorhabditis elegans, Drosophila or plants,
RNAi experiments are usually performed using long dsRNAs.
In these cases, there is a high risk of cross-suppression or
co-suppression between closely related genes that share a
highly conserved region.
To minimize the possibility of off-target effects, it is neces-
sary to perform an off-target search to design dsRNA or
siRNA that has limited sequence similarities with unrelated
genes. Recently, fast and sensitive off-target search software
for siRNA design has been reported (6,7), but commonly used
siRNA design servers are not useful in performing off-target
searches for long dsRNAs. DEQOR server uses BLAST to
perform off-target searches for endoribonuclease-prepared
siRNAs (8), although BLAST frequently fails to identify
off-targets (6). Therefore, we have developed a new web-
based online software system, dsCheck, to provide fast and
accurate off-target searches for long dsRNA sequences. The
software ‘dices’ the input sequence into an siRNA cocktail and
performs an exhaustive scan for each siRNA to find off-target
gene candidates, simulating the biochemical process of
dsRNA-mediated RNAi in vivo. dsCheck also provides effi-
cient design of ‘off-target minimized’ dsRNA by avoiding
regions that share a considerable number of diced siRNAs
with a specific off-target gene, and monitoring the total num-
ber of off-target hits. The software should be especially useful
for checking whether previously designed dsRNAs have off-
target gene candidates, as well as for designing target-specific
dsRNA when off-target effects are suspected.
*To whom correspondence should be addressed. Tel: +81 47 136 3984; Fax: +81 47 136 3977; Email: email@example.com
Correspondence may also be addressed to Kumiko Ui-Tei. Tel: +81 3 5841 3044; Fax: +81 3 5841 3044; Email: firstname.lastname@example.org
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
ª The Author 2005. Published by Oxford University Press. All rights reserved.
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access
version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press
only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact email@example.com
Nucleic Acids Research, 2005, Vol. 33, Web Server issue W589–W591
Off-target search strategies for long dsRNA
The key idea of the program follows the biochemical process
of dsRNA-mediated RNAi shown in Figure 1A. The input
dsRNA sequence is diced into 19 nt substrings of an siRNA
cocktail, and an exhaustive off-target search is performed for
all individual siRNAs using the siDirect engine, which makes
it possible to enumerate the complete set of off-targets in a
reasonable amount of time (7). In dsCheck, the in silico dicing
size is set to 19, as a complete match at the 19 nt double-
stranded region of an siRNA is sufficient for the target mRNA
degradation. For example, an input 500 bp dsRNA sequence is
processed into 482 substrings each 19 nt in length, which are
subjected to the off-target search individually. In the next step,
all the hits with a complete match (i.e. 19/19 matches), one
mismatch (18/19 matches) or two mismatches (17/19 matches)
are counted individually for every off-target gene candidate
and sorted in descending lexicographic order for the output.
Figure 1B shows a typical output for a 1497 bp query
sequence of the Drosophila POU domain protein, pdm2
(NM_078834, coding region). The result shows significant
hits against pdm2 (two splicing variants: NM_078834 and
NM_165017), and two unrelated genes, nub (NM_057311)
and vvl (NM_079224). These proteins share the highly
conserved POU domain shown in Figure 1C, indicating a
high risk of cross-suppression by dsRNA targeting this region.
Designing off-target minimized dsRNA sequences
To design off-target minimized dsRNA sequences, one
approach would be to suppose that the off-target effects are
siRNAs on the same gene, and to select a region that minim-
izes the maximum number of collaborative off-target hits,
which are defined as complete or partial matches of multiple
19 nt substrings against the same off-target gene. According
to this criterion, dsCheck starts by selecting a region that
minimizes the maximum number of ‘complete match’ collab-
orative off-target hits. If multiple regions are optimal, it also
examines the maximum number of ‘partial match’ collab-
orative off-target hits to select the best one. If the complete
match, collaborative hits on a sequence exceed 80% of the
total number of diced 19 nt substrings, dsCheck regards
the sequence as the intended target gene.
Some dsRNA sequences include 19 nt substrings that may
react with a large number of off-target genes, which differs
from the collaborative silencing effects acting on a single off-
target gene. An additional criterion is necessary to evaluate the
silencing effect of one siRNA sequence on many off-targets,
Query (pdm2; NM_078834)
0 mis : Total hits with a complete match (19/19 matches).
1 mis : Total hits with one mismatch (18/19 matches).
2 mis : Total hits with two mismatches (17/19 matches).
147944NM_078834.2| Drosophila melanogaster CG12287-PA (pdm2) mRNA, complete cds
0 mis 1 mis 2 mis Description:
NM_165017.1| Drosophila melanogaster CG12287-PB (pdm2) mRNA, complete cds
128 4553 NM_057311.4| Drosophila melanogaster CG6246-PA (nub) mRNA, complete cds
202872NM_079224.3| Drosophila melanogaster CG10037-PA (vvl) mRNA, complete cds
315 128NM_168571.1| Drosophila melanogaster CG32133-PA (CG32133) mRNA, complete cds
2736 NM_142597.2| Drosophila melanogaster CG12254-PA (Arc92) mRNA, complete cds
24 48NM_176557.1| Drosophila melanogaster CG33106-PB (mask) mRNA, complete cds
2448 NM_176556.1| Drosophila melanogaster CG33106-PA (mask) mRNA, complete cds
24 15 NM_132116.1| Drosophila melanogaster CG14441-PA (CG14441) mRNA, complete cds
2413NM_135957.2| Drosophila melanogaster CG31738-PB (CG31738) mRNA, complete cds
247NM_131968.2| Drosophila melanogaster CG32772-PA (CG32772) mRNA, complete cds
245NM_165168.1| Drosophila melanogaster CG31738-PA (CG31738) mRNA, complete cds
2316 NM_133124.1| Drosophila melanogaster CG7502-PA (CG7502) mRNA, complete cds
2310NM_079524.3| Drosophila melanogaster CG1030-PA (Scr) mRNA, complete cds
2310NM_206443.1| Drosophila melanogaster CG1030-PB (Scr) mRNA, complete cds
2310 NM_206442.1| Drosophila melanogaster CG1030-PC (Scr) mRNA, complete cds
237NM_057525.2| Drosophila melanogaster CG7937-PA (C15) mRNA, complete cds
234 NM_134555.1| Drosophila melanogaster CG15454-PA (CG15454) mRNA, complete cds
NM_057268.3| Drosophila melanogaster CG6944-PA (Lam) mRNA, complete cds
1 1833NM_132246.2| Drosophila melanogaster CG10555-PA (CG10555) mRNA, complete cds
1 17109 NM_167335.1| Drosophila melanogaster CG4013-PC (Smr) mRNA, complete cds
1 17 109NM_167334.1| Drosophila melanogaster CG4013-PB (Smr) mRNA, complete cds
1 17 109NM_080536.2| Drosophila melanogaster CG4013-PA (Smr) mRNA, complete cds
1 1065NM_078592.2| Drosophila melanogaster CG11172-PA (NFAT) mRNA, complete cds
174NM_132281.1| Drosophila melanogaster CG12660-PA (CG12660) mRNA, complete cds
16 48NM_143409.1| Drosophila melanogaster CG11873-PA (CG11873) mRNA, complete cds
16 45NM_206357.1| Drosophila melanogaster CG33261-PD (Trl) mRNA, complete cds
Cleavage by Dicer
Figure 1. (A)BiochemicalprocessofdsRNA-mediatedRNAi.Off-targeteffectsarecausedby‘diced’siRNAsthathavesequencesimilaritieswithunrelatedgenes.
(B) The output for the 1497 bp query sequence of the Drosophila pdm2 gene (NM_078834, coding region). Significant hits against two off-target genes, nub
(NM_057311)and vvl(NM_079224)were detected.(C) pdm,nub andvvl sharea highlyconservedPOU domain.Eachdotrepresentsa positionwith17/19or more
W590Nucleic Acids Research, 2005, Vol. 33, Web Server issue
although the effect may not be as serious as the collaborative
silencing effect, as the concentration of single siRNA is low in
diced siRNA cocktails. One reasonable measure would be the
total number of off-target hits for each 19 nt substring of
designed dsRNA. To attract attention to this risk, dsCheck
displays a warning if the total number of off-target hits
exceeds a specified threshold.
Figure 2 illustrates how dsCheck designs target-specific
dsRNA for the Drosophila pdm2 gene (NM_078834, coding
region). Given that the length of dsRNA is 100 bp, dsCheck
returns the positions424–523forthe target-specific region that
successfully avoids the collaborative silencing effects on the
Efficacy of each diced siRNA
In mammalian RNAi, the efficacy of each siRNA varies widely
depending on its sequence; hence, several groups have reported
guidelines for the selection of siRNAs (9–12). However, in
Drosophila cells, it is reported that most, if not all, siRNA
sequences may act as effective silencers (9). Incorporation of
siRNA efficacy prediction may run the risk of underestimating
off-target effects in non-mammalian RNAi. Therefore, all
siRNA sequences are treated equally in dsCheck.
Currently, off-target searches can be performed against the
mRNA sequences stored in the NCBI RefSeq database (13).
Since off-target searches demand a substantial number of
mRNA sequences that are likely to cover the entire set of
transcripts, we plan to incorporate additional species when
ample cDNA collections are available.
This work was supported in part by the Special Coordination
Fund for Promoting Science and Technology to K.S., the
Leading Project for Biosimulation to S.M. and Grants-in-
Aid for Scientific Research to K.U.-T., K.S. and S.M. from
the Ministry of Education, Culture, Sports, Science and
Technology of Japan. Funding to pay the Open Access pub-
licationcharges for this article was provided by the Ministry of
Education, Culture, Sports, Science and Technology of Japan.
Conflict of interest statement. None declared.
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Figure 2. Designing ‘off-target minimized’ dsRNA for the Drosophila pdm2 gene (NM_078834, coding region). (A) The maximum number of ‘collaborative
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Nucleic Acids Research, 2005, Vol. 33, Web Server issueW591