U.S. patent application number 11/729924 was filed with the patent office on 2008-01-17 for sirna targeting connective tissue growth factor (ctgf).
This patent application is currently assigned to DHARMACON, INC.. Invention is credited to Anastasia Khvorova, Devin Leake, William Marshall, Steven Read, Angela Reynolds, Stephen Scaringe.
Application Number | 20080015114 11/729924 |
Document ID | / |
Family ID | 38119221 |
Filed Date | 2008-01-17 |
United States Patent
Application |
20080015114 |
Kind Code |
A1 |
Khvorova; Anastasia ; et
al. |
January 17, 2008 |
siRNA targeting connective tissue growth factor (CTGF)
Abstract
Efficient sequence specific gene silencing is possible through
the use of siRNA technology. By selecting particular siRNAs by
rational design, one can maximize the generation of an effective
gene silencing reagent, as well as methods for silencing genes.
Methods, compositions, and kits generated through rational design
of siRNAs are disclosed including those directed to CTGF.
Inventors: |
Khvorova; Anastasia;
(Boulder, CO) ; Reynolds; Angela; (Conifer,
CO) ; Leake; Devin; (Denver, CO) ; Marshall;
William; (Boulder, CO) ; Read; Steven;
(Denver, CO) ; Scaringe; Stephen; (Lafayette,
CO) |
Correspondence
Address: |
KALOW & SPRINGUT LLP
488 MADISON AVENUE
19TH FLOOR
NEW YORK
NY
10022
US
|
Assignee: |
DHARMACON, INC.
Lafayette
CO
|
Family ID: |
38119221 |
Appl. No.: |
11/729924 |
Filed: |
March 29, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10940892 |
Sep 14, 2004 |
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11729924 |
Mar 29, 2007 |
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PCT/US04/14885 |
May 12, 2004 |
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10940892 |
Sep 14, 2004 |
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10714333 |
Nov 14, 2003 |
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11729924 |
Mar 29, 2007 |
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60502050 |
Sep 10, 2003 |
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60426137 |
Nov 14, 2002 |
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Current U.S.
Class: |
506/17 ;
536/24.5 |
Current CPC
Class: |
C12N 15/1135 20130101;
A61P 43/00 20180101; C12N 15/113 20130101; C12N 2310/14 20130101;
C12N 2320/11 20130101; C12N 15/111 20130101; C12N 2310/11 20130101;
G16B 20/00 20190201 |
Class at
Publication: |
506/017 ;
536/024.5 |
International
Class: |
C40B 40/08 20060101
C40B040/08; C07H 21/04 20060101 C07H021/04 |
Claims
1. An siRNA comprising a sense region and an antisense region,
wherein said sense region and said antisense region are at least
90% complementary, said sense region and said antisense region
together form a duplex region comprising 18-30 base pairs, and said
sense region comprises a sequence that is at least 90% similar to a
sequence selected from the group consisting of SEQ. ID NOs.
438-559.
2. An siRNA comprising a sense region and an antisense region,
wherein said sense region and said antisense region are at least
90% complementary, said sense region and said antisense region
together form a duplex region comprising 18-30 base pairs, and said
sense region comprises a sequence that is identical to a contiguous
stretch of at least 18 bases of a sequence selected from the group
consisting of SEQ. ID NOs. 438-559.
3. The siRNA of claim 2, wherein the duplex region is 19-30 base
pairs, and said sense region comprises a sequence that is identical
to a sequence selected from the group consisting of: SEQ. ID NOs.
438-559.
4. A pool of at least two siRNAs, wherein said pool comprises a
first siRNA and a second siRNA, said first siRNA comprises a duplex
region of length 18-30 base pairs that has a first sense region
that is at least 90% similar to 18 bases of a first sequence
selected from the group consisting of: SEQ. ID NOs. 438-559 and
said second siRNA comprises a duplex region of length 18-30 base
pairs that has a second sense region that is at least 90% similar
to 18 bases of a second sequence selected from the group consisting
of: SEQ. ID NOs. 438-559 wherein said first sense region and said
second sense region are not identical.
5. The pool of claim 4, wherein said first sense region comprises a
sequence that is identical to at least 18 bases of a sequence
selected from the group consisting of: SEQ. ID NOs. 438-559, and
said second sense region comprises a sequence that is identical to
at least 18 bases of a sequence selected from the group consisting
of: SEQ. ID NOs. 438-559.
6. The pool of claim 4, wherein said duplex of said first siRNA is
19-30 base pairs and said first sense region comprises a sequence
that is at least 90% similar to a sequence selected from the group
consisting of: SEQ. ID NOs. 438-559, and said duplex of said second
siRNA is 19-30 base pairs and comprises a sequence that is at least
90% similar to a sequence selected from the group consisting of:
SEQ. ID NOs. 438-559.
7. The pool of claim 4, wherein said duplex of said first siRNA is
19-30 base pairs and said first sense region comprises a sequence
that is identical to at least 18 bases of a sequence selected the
group consisting of: SEQ. ID NOs. 438-559, and said duplex of said
second siRNA is 19-30 base pairs and said second sense region
comprises a sequence that is identical to a sequence selected from
the group consisting of: SEQ. ID NOs. 438-559.
8. The siRNA of claim 1, wherein said duplex region comprises 19-25
base pairs.
9. The siRNA of claim 4, wherein said duplex region of the first
siRNA comprises 19-25 bases pairs and said duplex region of the
second siRNA comprises 19-25 base pairs.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part of U.S. Ser. No.
10/714,333, filed Nov. 14, 2003, which claims the benefit of U.S.
Provisional Application No. 60/426,137, filed Nov. 14, 2002, and
also claims the benefit of U.S. Provisional Application No.
60/502,050, filed Sep. 10, 2003; this application is also a
continuation-in-part of U.S. Ser. No. 10/940,892, filed Sep. 14,
2004, which is a continuation of PCT Application No. PCT/US
04/14885, international filing date May 12, 2004. The disclosures
of the priority applications, including the sequence listings and
tables submitted in electronic form in lieu of paper, are
incorporated by reference into the instant specification.
SEQUENCE LISTING
[0002] The sequence listing for this application has been submitted
in accordance with 37 CFR .sctn. 1.52(e) and 37 CFR .sctn. 1.821 on
CD-ROM in lieu of paper on a disk containing the sequence listing
file entitled "DHARMA.sub.--2100-US10_CRF.txt" created Mar. 12,
2007, 100 kb. Applicants hereby incorporate by reference the
sequence listing provided on CD-ROM in lieu of paper into the
instant specification.
FIELD OF INVENTION
[0003] The present invention relates to RNA interference
("RNAi").
BACKGROUND OF THE INVENTION
[0004] Relatively recently, researchers observed that double
stranded RNA ("dsRNA") could be used to inhibit protein expression.
This ability to silence a gene has broad potential for treating
human diseases, and many researchers and commercial entities are
currently investing considerable resources in developing therapies
based on this technology.
[0005] Double stranded RNA induced gene silencing can occur on at
least three different levels: (i) transcription inactivation, which
refers to RNA guided DNA or histone methylation; (ii) siRNA induced
mRNA degradation; and (iii) mRNA induced transcriptional
attenuation.
[0006] It is generally considered that the major mechanism of RNA
induced silencing (RNA interference, or RNAi) in mammalian cells is
mRNA degradation. Initial attempts to use RNAi in mammalian cells
focused on the use of long strands of dsRNA. However, these
attempts to induce RNAi met with limited success, due in part to
the induction of the interferon response, which results in a
general, as opposed to a target-specific, inhibition of protein
synthesis. Thus, long dsRNA is not a viable option for RNAi in
mammalian systems.
[0007] More recently it has been shown that when short (18-30 bp)
RNA duplexes are introduced into mammalian cells in culture,
sequence-specific inhibition of target mRNA can be realized without
inducing an interferon response. Certain of these short dsRNAs,
referred to as small inhibitory RNAs ("siRNAs"), can act
catalytically at sub-molar concentrations to cleave greater than
95% of the target mRNA in the cell. A description of the mechanisms
for siRNA activity, as well as some of its applications are
described in Provost et al. (2002) Ribonuclease Activity and RNA
Binding of Recombinant Human Dicer, EMBO J. 21(21): 5864-5874;
Tabara et al. (2002) The dsRNA Binding Protein RDE-4 Interacts with
RDE-1, DCR-1 and a DexH-box Helicase to Direct RNAi in C. elegans,
Cell 109(7):861-71; Ketting et al. (2002) Dicer Functions in RNA
Interference and in Synthesis of Small RNA Involved in
Developmental Timing in C. elegans; Martinez et al.,
Single-Stranded Antisense siRNAs Guide Target RNA Cleavage in RNAi,
Cell 110(5):563; Hutvagner & Zamore (2002) A microRNA in a
multiple-turnover RNAi enzyme complex, Science 297:2056.
[0008] From a mechanistic perspective, introduction of long double
stranded RNA into plants and invertebrate cells is broken down into
siRNA by a Type III endonuclease known as Dicer. Sharp, RNA
interference--2001, Genes Dev. 2001, 15:485. Dicer, a
ribonuclease-III-like enzyme, processes the dsRNA into 19-23 base
pair short interfering RNAs with characteristic two base 3'
overhangs. Bernstein, Caudy, Hammond, & Hannon (2001) Role for
a bidentate ribonuclease in the initiation step of RNA
interference, Nature 409:363. The siRNAs are then incorporated into
an RNA-induced silencing complex (RISC) where one or more helicases
unwind the siRNA duplex, enabling the complementary antisense
strand to guide target recognition. Nykanen, Haley, & Zamore
(2001) ATP requirements and small interfering RNA structure in the
RNA interference pathway, Cell 107:309. Upon binding to the
appropriate target mRNA, one or more endonucleases within the RISC
cleaves the target to induce silencing. Elbashir, Lendeckel, &
Tuschl (2001) RNA interference is mediated by 21- and 22-nucleotide
RNAs, Genes Dev. 15:188, FIG. 1.
[0009] The interference effect can be long lasting and may be
detectable after many cell divisions. Moreover, RNAi exhibits
sequence specificity. Kisielow, M. et al. (2002) Isoform-specific
knockdown and expression of adaptor protein ShcA using small
interfering RNA, J. Biochem. 363:1-5. Thus, the RNAi machinery can
specifically knock down one type of transcript, while not affecting
closely related mRNA. These properties make siRNA a potentially
valuable tool for inhibiting gene expression and studying gene
function and drug target validation. Moreover, siRNAs are
potentially useful as therapeutic agents against: (1) diseases that
are caused by over-expression or misexpression of genes; and (2)
diseases brought about by expression of genes that contain
mutations.
[0010] Successful siRNA-dependent gene silencing depends on a
number of factors. One of the most contentious issues in RNAi is
the question of the necessity of siRNA design, i.e., considering
the sequence of the siRNA used. Early work in C. elegans and plants
circumvented the issue of design by introducing long dsRNA (see,
for instance, Fire, A. et al. (1998) Nature 391:806-811). In this
primitive organism, long dsRNA molecules are cleaved into siRNA by
Dicer, thus generating a diverse population of duplexes that can
potentially cover the entire transcript. While some fraction of
these molecules are non-functional (i.e., induce little or no
silencing) one or more have the potential to be highly functional,
thereby silencing the gene of interest and alleviating the need for
siRNA design. Unfortunately, due to the interferon response, this
same approach is unavailable for mammalian systems. While this
effect can be circumvented by passing the Dicer cleavage step and
directly introducing siRNA, this tactic carries with it the risk
that the chosen siRNA sequence may be non-functional or
semi-functional.
[0011] A number of researches have expressed the view that siRNA
design is not a crucial element of RNAi. On the other hand, others
in the field have begun to explore the possibility that RNAi can be
made more efficient by paying attention to the design of the siRNA.
Unfortunately, none of the reported methods have provided a
satisfactory scheme for reliably selecting siRNA with acceptable
levels of functionality. Accordingly, there is a need to develop
rational criteria by which to select siRNA with an acceptable level
of functionality, and to identify siRNA that have this improved
level of functionality, as well as to identify siRNAs that are
hyperfunctional.
SUMMARY OF THE INVENTION
[0012] The present invention is directed to increasing the
efficiency of RNAi, particularly in mammalian systems. Accordingly,
the present invention provides kits, siRNAs and methods for
increasing siRNA efficacy.
[0013] According to a first embodiment, the present invention
provides a kit for gene silencing, wherein said kit is comprised of
a pool of at least two siRNA duplexes, each of which is comprised
of a sequence that is complementary to a portion of the sequence of
one or more target messenger RNA, and each of which is selected
using non-target specific criteria.
[0014] According to a second embodiment, the present invention
provides a method for selecting an siRNA, said method comprising
applying selection criteria to a set of potential siRNA that
comprise 18-30 base pairs, wherein said selection criteria are
non-target specific criteria, and said set comprises at least two
siRNAs and each of said at least two siRNAs contains a sequence
that is at least substantially complementary to a target gene; and
determining the relative functionality of the at least two
siRNAs.
[0015] According to a third embodiment, the present invention also
provides a method for selecting an siRNA wherein said selection
criteria are embodied in a formula comprising:
(-14)*G.sub.13-13*A.sub.1-12*U.sub.7-11*U.sub.2-10*A.sub.11-10*U.sub.4-10-
*C.sub.3-10*C.sub.5-10*C.sub.6-9*A.sub.10-9*U.sub.9-9*C.sub.18-8*G.sub.10--
7*U.sub.1-7*U.sub.16-7*C.sub.17-7*C.sub.19+7*U.sub.17+8*A.sub.2+8*A.sub.4+-
8*A.sub.5+8*C.sub.4+9*G.sub.8+10*A.sub.7+10*U.sub.18+11*A.sub.19+11*C.sub.-
9+15*G.sub.1+18*A.sub.3+19*U.sub.10-Tm-3*(GC.sub.total)-6*(GC.sub.15-19)-3-
0*X; or Formula VIII
(-8)*A1+(-1)*A2+(12)*A3+(7)*A4+(18)*A5+(12)*A6+(19)*A7+(6)*A8+(-4)*A9+(-5-
)*A10+(-2)*A11+(-5)*A12+(17)*A13+(-3)*A14+(4)*A15+(2)*A16+(8)*A17+(11)*A18-
+(30)*A19+(-13)*U1+(-10)*U2+(2)*U3+(-2)*U4+(-5)*U5+(5)*U6+(-2)*U7+(-10)*U8-
+(-5)*U9+(15)*U10+(-1)*U11+(0)*U12+(10)*U13+(-9)*U14+(-13)*U15+(-10)*U16+(-
3)*U17+(9)*U18+(9)*U19+(7)*C1+(3)*C2+(-21)*C3+(5)*C4+(-9)*C5+(-20)*C6+(-18-
)*C7+(-5)*C8+(5)*C9+(1)*C10+(2)*C11+(-5)*C12+(-3)*C13+(-6)*C14+(-2)*C15+(--
5)*C16+(-3)*C17+(-12)*C18+(-18)*C19+(14)*G1+(8)*G2+(7)*G3+(-10)*G4+(-4)*G5-
+(2)*G6+(1)*G7+(9)*G8+(5)*G9+(-11)*G10+(1)*G11+(9)*G12+(-24)*G13+(18)*G14+-
(11)*G15+(13)*G16+(-7)*G17+(-9)*G18+(-22)*G19+6*(number of A+U in
position 15-19)-3*(number of G+C in whole siRNA), Formula X wherein
position numbering begins at the 5'-most position of a sense
strand, and [0016] A.sub.1=1 if A is the base at position 1 of the
sense strand, otherwise its value is 0; [0017] A.sub.2=1 if A is
the base at position 2 of the sense strand, otherwise its value is
0; [0018] A.sub.3=1 if A is the base at position 3 of the sense
strand, otherwise its value is 0; [0019] A.sub.4=1 if A is the base
at position 4 of the sense strand, otherwise its value is 0; [0020]
A.sub.5=1 if A is the base at position 5 of the sense strand,
otherwise its value is 0; [0021] A.sub.6=1 if A is the base at
position 6 of the sense strand, otherwise its value is 0; [0022]
A.sub.7=1 if A is the base at position 7 of the sense strand,
otherwise its value is 0; [0023] A.sub.10=1 if A is the base at
position 10 of the sense strand, otherwise its value is 0; [0024]
A.sub.11=1 if A is the base at position 11 of the sense strand,
otherwise its value is 0; [0025] A.sub.13=1 if A is the base at
position 13 of the sense strand, otherwise its value is 0; [0026]
A.sub.19=1 if A is the base at position 19 of the sense strand,
otherwise if another base is present or the sense strand is only 18
base pairs in length, its value is 0; [0027] C.sub.3=1 if C is the
base at position 3 of the sense strand, otherwise its value is 0;
[0028] C.sub.4=1 if C is the base at position 4 of the sense
strand, otherwise its value is 0; [0029] C.sub.5=1 if C is the base
at position 5 of the sense strand, otherwise its value is 0; [0030]
C.sub.6=1 if C is the base at position 6 of the sense strand,
otherwise its value is 0; [0031] C.sub.7=1 if C is the base at
position 7 of the sense strand, otherwise its value is 0; [0032]
C.sub.9=1 if C is the base at position 9 of the sense strand,
otherwise its value is 0; [0033] C.sub.17=1 if C is the base at
position 17 of the sense strand, otherwise its value is 0; [0034]
C.sub.18=1 if C is the base at position 18 of the sense strand,
otherwise its value is 0; [0035] C.sub.19=1 if C is the base at
position 19 of the sense strand, otherwise if another base is
present or the sense strand is only 18 base pairs in length, its
value is 0; [0036] G.sub.1=1 if G is the base at position 1 on the
sense strand, otherwise its value is 0; [0037] G.sub.2=1 if G is
the base at position 2 of the sense strand, otherwise its value is
0; [0038] G.sub.8=1 if G is the base at position 8 on the sense
strand, otherwise its value is 0; [0039] G.sub.10=1 if G is the
base at position 10 on the sense strand, otherwise its value is 0;
[0040] G.sub.13=1 if G is the base at position 13 on the sense
strand, otherwise its value is 0; [0041] G.sub.19=1 if G is the
base at position 19 of the sense strand, otherwise if another base
is present or the sense strand is only 18 base pairs in length, its
value is 0; [0042] U.sub.1=1 if U is the base at position 1 on the
sense strand, otherwise its value is 0; [0043] U.sub.2=1 if U is
the base at position 2 on the sense strand, otherwise its value is
0; [0044] U.sub.3=1 if U is the base at position 3 on the sense
strand, otherwise its value is 0; [0045] U.sub.4=1 if U is the base
at position 4 on the sense strand, otherwise its value is 0; [0046]
U.sub.7=1 if U is the base at position 7 on the sense strand,
otherwise its value is 0; [0047] U.sub.9=1 if U is the base at
position 9 on the sense strand, otherwise its value is 0; [0048]
U.sub.10=1 if U is the base at position 10 on the sense strand,
otherwise its value is 0; [0049] U.sub.15=1 if U is the base at
position 15 on the sense strand, otherwise its value is 0; [0050]
U.sub.16=1 if U is the base at position 16 on the sense strand,
otherwise its value is 0; [0051] U.sub.17=1 if U is the base at
position 17 on the sense strand, otherwise its value is 0; [0052]
U.sub.18=1 if U is the base at position 18 on the sense strand,
otherwise its value is 0. [0053] GC.sub.15-19=the number of G and C
bases within positions 15-19 of the sense strand, or within
positions 15-18 if the sense strand is only 18 base pairs in
length; [0054] GC.sub.total=the number of G and C bases in the
sense strand; [0055] Tm=100 if the siRNA oligo has the internal
repeat longer then 4 base pairs, otherwise its value is 0; and
[0056] X=the number of times that the same nucleotide repeats four
or more times in a row.
[0057] According to a fourth embodiment, the invention provides a
method for developing an algorithm for selecting siRNA, said method
comprising: (a) selecting a set of siRNA; (b) measuring gene
silencing ability of each siRNA from said set; (c) determining
relative functionality of each siRNA; (d) determining improved
functionality by the presence or absence of at least one variable
selected from the group consisting of the presence or absence of a
particular nucleotide at a particular position, the total number of
As and Us in positions 15-19, the number of times that the same
nucleotide repeats within a given sequence, and the total number of
Gs and Cs; and (e) developing an algorithm using the information of
step (d).
[0058] According to a fifth embodiment, the present invention
provides a kit, wherein said kit is comprised of at least two
siRNAs, wherein said at least two siRNAs comprise a first optimized
siRNA and a second optimized siRNA, wherein said first optimized
siRNA and said second optimized siRNA are optimized according a
formula comprising Formula X.
[0059] The present invention also provides a method for identifying
a hyperfunctional siRNA, comprising applying selection criteria to
a set of potential siRNA that comprise 18-30 base pairs, wherein
said selection criteria are non-target specific criteria, and said
set comprises at least two siRNAs and each of said at least two
siRNAs contains a sequence that is at least substantially
complementary to a target gene; determining the relative
functionality of the at least two siRNAs and assigning each of the
at least two siRNAs a functionality score; and selecting siRNAs
from the at least two siRNAs that have a functionality score that
reflects greater than 80 percent silencing at a concentration in
the picomolar range, wherein said greater than 80 percent silencing
endures for greater than 120 hours.
[0060] According to a sixth embodiment, the present invention
provides a hyperfunctional siRNA that is capable of silencing
Bcl2.
[0061] According to a seventh embodiment, the present invention
provides a method for developing an siRNA algorithm for selecting
functional and hyperfunctional siRNAs for a given sequence. The
method comprises:
[0062] (a) selecting a set of siRNAs;
[0063] (b) measuring the gene silencing ability of each siRNA from
said set;
[0064] (c) determining the relative functionality of each
siRNA;
[0065] (d) determining the amount of improved functionality by the
presence or absence of at least one variable selected from the
group consisting of the total GC content, melting temperature of
the siRNA, GC content at positions 15-19, the presence or absence
of a particular nucleotide at a particular position, relative
thermodynamic stability at particular positions in a duplex, and
the number of times that the same nucleotide repeats within a given
sequence; and
[0066] (e) developing an algorithm using the information of step
(d).
[0067] According to this embodiment, preferably the set of siRNAs
comprises at least 90 siRNAs from at least one gene, more
preferably at least 180 siRNAs from at least two different genes,
and most preferably at least 270 and 360 siRNAs from at least three
and four different genes, respectively. Additionally, in step (d)
the determination is made with preferably at least two, more
preferably at least three, even more preferably at least four, and
most preferably all of the variables. The resulting algorithm is
not target sequence specific.
[0068] In another embodiment, the present invention provides
rationally designed siRNAs identified using the formulas above.
[0069] In yet another embodiment, the present invention is directed
to hyperfunctional siRNA.
[0070] The ability to use the above algorithms, which are not
sequence or species specific, allows for the cost-effective
selection of optimized siRNAs for specific target sequences.
Accordingly, there will be both greater efficiency and reliability
in the use of siRNA technologies.
[0071] In various embodiments, siRNAs that target connective tissue
growth factor (CTGF) are provided. In various embodiments, the
siRNAs are rationally designed. In various embodiments, the siRNAs
are functional or hyperfunctional.
[0072] In various embodiments, an siRNA that targets CTGF is
provided, wherein the siRNA is selected from the group consisting
of various siRNA sequences targeting CTGF that are disclosed
herein. In various embodiments, the siRNA sequence is selected from
the group consisting of SEQ ID NO. 438 to SEQ ID NO. 559.
[0073] In various embodiments, siRNA comprising a sense region and
an antisense region are provided, wherein said sense region and
said antisense region are at least 90% complementary, said sense
region and said antisense region together form a duplex region
comprising 18-30 base pairs, and said sense region comprises a
sequence that is at least 90% similar to a sequence selected from
the group consisting of siRNA sequences targeting CTGF that are
disclosed herein. In various embodiments, the siRNA sequence is
selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO.
559.
[0074] In various embodiments, an siRNA comprising a sense region
and an antisense region is provided, wherein said sense region and
said antisense region are at least 90% complementary, said sense
region and said antisense region together form a duplex region
comprising 18-30 base pairs, and said sense region comprises a
sequence that is identical to a contiguous stretch of at least 18
bases of a sequence selected from the group consisting of SEQ ID
NO. 438 to SEQ ID NO. 559. In various embodiments, the duplex
region is 19-30 base pairs, and the sense region comprises a
sequence that is identical to a sequence selected from the group
consisting of SEQ ID NO. 438 to SEQ ID NO. 559.
[0075] In various embodiments, a pool of at least two siRNAs is
provided, wherein said pool comprises a first siRNA and a second
siRNA, said first siRNA comprising a duplex region of length 18-30
base pairs that has a first sense region that is at least 90%
similar to 18 bases of a first sequence selected from the group
consisting of SEQ ID NO. 438 to SEQ ID NO. 559, and said second
siRNA comprises a duplex region of length 18-30 base pairs that has
a second sense region that is at least 90% similar to 18 bases of a
second sequence selected from the group consisting of SEQ ID NO.
438 to SEQ ID NO. 559, wherein said first sense region and said
second sense region are not identical.
[0076] In various embodiments, the first sense region comprises a
sequence that is identical to at least 18 bases of a sequence
selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO.
559, and said second sense region comprises a sequence that is
identical to at least 18 bases of a sequence selected from the
group consisting of SEQ ID NO. 438 to SEQ ID NO. 559. In various
embodiments, the duplex of said first siRNA is 19-30 base pairs,
and said first sense region comprises a sequence that is at least
90% similar to a sequence selected from the group consisting of SEQ
ID NO. 438 to SEQ ID NO. 559, and said duplex of said second siRNA
is 19-30 base pairs and comprises a sequence that is at least 90%
similar to a sequence selected from the group consisting of SEQ ID
NO. 438 to SEQ ID NO. 559.
[0077] In various embodiments, the duplex of said first siRNA is
19-30 base pairs and said first sense region comprises a sequence
that is identical to at least 18 bases of a sequence selected from
the group consisting of SEQ ID NO. 438 to SEQ ID NO. 559, and said
duplex of said second siRNA is 19-30 base pairs and said second
region comprises a sequence that is identical to a sequence
selected from the group consisting of SEQ ID NO. 438 to SEQ ID NO.
559.
[0078] For a better understanding of the present invention together
with other and further advantages and embodiments, reference is
made to the following description taken in conjunction with the
examples, the scope of which is set forth in the appended
claims.
BRIEF DESCRIPTION OF THE FIGURES
[0079] FIG. 1 shows a model for siRNA-RISC interactions. RISC has
the ability to interact with either end of the siRNA or miRNA
molecule. Following binding, the duplex is unwound, and the
relevant target is identified, cleaved, and released.
[0080] FIG. 2 is a representation of the functionality of two
hundred and seventy siRNA duplexes that were generated to target
human cyclophilin, human diazepam-binding inhibitor (DB), and
firefly luciferase.
[0081] FIG. 3A is a representation of the silencing effect of 30
siRNAs in three different cells lines, HEK293, DU145, and Hela.
FIG. 3B shows the frequency of different functional groups (>95%
silencing (black), >80% silencing (gray), >50% silencing
(dark gray), and <50% silencing (white)) based on GC content. In
cases where a given bar is absent from a particular GC percentage,
no siRNA were identified for that particular group. FIG. 3C shows
the frequency of different functional groups based on melting
temperature (Tm).
[0082] FIG. 4 is a representation of a statistical analysis that
revealed correlations between silencing and five sequence-related
properties of siRNA: (A) an A at position 19 of the sense strand,
(B) an A at position 3 of the sense strand, (C) a U at position 10
of the sense strand, (D) a base other than G at position 13 of the
sense strand, and (E) a base other than C at position 19 of the
sense strand. All variables were correlated with siRNA silencing of
firefly luciferase and human cyclophilin. siRNAs satisfying the
criterion are grouped on the left (Selected) while those that do
not, are grouped on the right (Eliminated). Y-axis is "% Silencing
of Control." Each position on the X-axis represents a unique
siRNA.
[0083] FIGS. 5A and 5B are representations of firefly luciferase
and cyclophilin siRNA panels sorted according to functionality and
predicted values using Formula VIII. The siRNA found within the
circle represent those that have Formula VIII values
(SMARTSCORES.TM., or siRNA rank) above zero. siRNA outside the
indicated area have calculated Formula VIII values that are below
zero. Y-axis is "Expression (% Control)." Each position on the
X-axis represents a unique siRNA.
[0084] FIG. 6a is a representation of the average internal
stability profile (AISP) derived from 270 siRNAs taken from three
separate genes (cyclophilin B, DBI and firefly luciferase). Graphs
represent AISP values of highly functional, functional, and
non-functional siRNA. FIG. 6b is a comparison between the AISP of
naturally derived GFP siRNA (filled squares) and the AISP of siRNA
from cyclophilin B, DBI, and luciferase having >90% silencing
properties (no fill) for the antisense strand. "DG" is the symbol
for .DELTA.G, free energy.
[0085] FIG. 7 is a histogram showing the differences in duplex
functionality upon introduction of base pair mismatches. The X-axis
shows the mismatch introduced in the siRNA and the position it is
introduced (e.g., 8C>A reveals that position 8 (which normally
has a C) has been changed to an A). The Y-axis is "% Silencing
(Normalized to Control)." The samples on the X-axis represent
siRNAs at 100 nM and are, reading from left to right: 1A to C, 1A
to G, 1A to U; 2A to C, 2A to G, 2A to U; 3A to C, 3A to G, 3A to
U; 4G to A, 4G to C; 4G to U; 5U to A, 5U to C, 5U to G; 6U to A,
6U to C, 6U to G; 7G to A, 7G to C, 7G to U; 8C to A, 8C to G, 8C
to U; 9G to A, 9G to C, 9G to U; 10C to A, 10C to G, 10C to U; 11G
to A, 11G to C, 11G to U; 12G to A, 12G to C, 12G to U; 13A to C,
13A to G, 13A to U; 14G to A, 14G to C, 14G to U; 15G to A, 15G to
C, 15G to U; 16A to C, 16A to G, 16A to U; 17G to A, 17G to C, 17G
to U; 18U to A, 18U to C, 18U to G; 19U to A, 19U to C, 19U to G;
20 wt; Control.
[0086] FIG. 8A is histogram that shows the effects of 5' sense and
antisense strand modification with 2'-O-methylation on
functionality. FIG. 8B is an expression profile showing a
comparison of sense strand off-target effects for IGF1R-3 and
2'-O-methyl IGF1R-3. Sense strand off-targets (lower box) are not
induced when the 5' end of the sense strand is modified with
2'-O-methyl groups (top box).
[0087] FIG. 9 shows a graph of SMARTSCORES.TM., or siRNA rank,
versus RNAi silencing values for more than 360 siRNA directed
against 30 different genes. SiRNA to the right of the vertical bar
represent those siRNA that have desirable SMARTSCORES.TM., or siRNA
rank.
[0088] FIGS. 10A-E compare the RNAi of five different genes (SEAP,
DBI, PLK, Firefly Luciferase, and Renilla Luciferase) by varying
numbers of randomly selected siRNA and four rationally designed
(SMART-selected) siRNA chosen using the algorithm described in
Formula VIII. In addition, RNAi induced by a pool of the four
SMART-selected siRNA is reported at two different concentrations
(100 and 400 nM). 10F is a comparison between a pool of randomly
selected EGFR siRNA (Pool 1) and a pool of SMART-selected EGFR
siRNA (Pool 2). Pool 1, S1-S4 and Pool 2 S1-S4 represent the
individual members that made up each respective pool. Note that
numbers for random siRNAs represent the position of the 5' end of
the sense strand of the duplex. The Y-axis represents the %
expression of the control(s). The X-axis is the percent expression
of the control.
[0089] FIG. 11 shows the Western blot results from cells treated
with siRNA directed against twelve different genes involved in the
clathrin-dependent endocytosis pathway (CHC, DynII, CALM, CLCa,
CLCb, Eps15, Eps15R, Rab5a, Rab5b, Rab5c, .beta.2 subunit of AP-2
and EEA.1). siRNA were selected using Formula VIII. "Pool"
represents a mixture of duplexes 1-4. Total concentration of each
siRNA in the pool is 25 nM. Total concentration=4.times.25=100
nM.
[0090] FIG. 12 is a representation of the gene silencing
capabilities of rationally-selected siRNA directed against ten
different genes (human and mouse cyclophilin, C-myc, human lamin
A/C, QB (ubiquinol-cytochrome c reductase core protein 1), MEK1 and
MEK2, ATE1 (arginyl-tRNA protein transferase), GAPDH, and Eg5). The
Y-axis is the percent expression of the control. Numbers 1, 2, 3
and 4 represent individual rationally selected siRNA. "Pool"
represents a mixture of the four individual siRNA.
[0091] FIG. 13 is the sequence of the top ten Bcl2 siRNAs as
determined by Formula VIII. Sequences are listed 5' to 3'.
[0092] FIG. 14 is the knockdown by the top ten Bcl2 siRNAs at 100
nM concentrations. The Y-axis represents the amount of expression
relative to the non-specific (ns) and transfection mixture
control.
[0093] FIG. 15 represents a functional walk where siRNA beginning
on every other base pair of a region of the luciferase gene are
tested for the ability to silence the luciferase gene. The Y-axis
represents the percent expression relative to a control. The X-axis
represents the position of each individual siRNA. Reading from left
to right across the X-axis, the position designations are 1, 3, 5,
7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39,
41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73,
75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.
[0094] FIGS. 16A and 16B are histograms demonstrating the
inhibition of target gene expression by pools of 2 (16A) and 3
(16B) siRNA duplexes taken from the walk described in FIG. 15. The
Y-axis in each represents the percent expression relative to
control. The X-axis in each represents the position of the first
siRNA in paired pools, or trios of siRNAs. For instance, the first
paired pool contains siRNAs 1 and 3. The second paired pool
contains siRNAs 3 and 5. Pool 3 (of paired pools) contains siRNAs 5
and 7, and so on. For each of 16A and 16B, the X-axis from left to
right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29,
31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63,
65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and
Plasmid.
[0095] FIGS. 17A and 17B are histograms demonstrating the
inhibition of target gene expression by pools of 4 (17A) and 5
(17B) siRNA duplexes. The Y-axis in each represents the percent
expression relative to control. The X-axis in each represents the
position of the first siRNA in each pool. For each of 17A and 17B,
the X-axis from left to right reads 1, 3, 5, 7, 9, 11, 13, 15, 17,
19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51,
53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85,
87, 89, and Plasmid.
[0096] FIGS. 18A and 18B are histograms demonstrating the
inhibition of target gene expression by siRNAs that are ten (18A)
and twenty (18B) base pairs base pairs apart. The Y-axis represents
the percent expression relative to a control. The X-axis represents
the position of the first siRNA in each pool. For each of 18A and
18B, the X-axis from left to right reads 1, 3, 5, 7, 9, 11, 13, 15,
17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49,
51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83,
85, 87, 89, and Plasmid.
[0097] FIG. 19 shows that pools of siRNAs (dark gray bar) work as
well (or better) than the best siRNA in the pool (light gray bar).
The Y-axis represents the percent expression relative to a control.
The X-axis represents the position of the first siRNA in each pool.
The X-axis from left to right reads 1, 3, 5, 7, 9, 11, 13, 15, 17,
19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51,
53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85,
87, 89, and Plasmid.
[0098] FIG. 20 shows that the combination of several semifunctional
siRNAs (dark gray) result in a significant improvement of gene
expression inhibition over individual (semi-functional; light gray)
siRNA. The Y-axis represents the percent expression relative to a
control.
[0099] FIG. 21 shows both pools (Library, Lib) and individual
siRNAs in inhibition of gene expression of Beta-Galactosidase,
Renilla Luciferase and SEAP (alkaline phosphatase). Numbers on the
X-axis indicate the position of the 5'-most nucleotide of the sense
strand of the duplex. The Y-axis represents the percent expression
of each gene relative to a control. Libraries contain 19 nucleotide
long siRNAs (not including overhangs) that begin at the following
nucleotides: SEAP: Lib 1: 206, 766, 812, 923, Lib 2: 1117, 1280,
1300, 1487, Lib 3: 206, 766, 812, 923, 1117, 1280, 1300, 1487, Lib
4: 206, 812, 1117, 1300, Lib 5: 766, 923, 1280, 1487, Lib 6: 206,
1487; Bgal: Lib 1: 979, 1339, 2029, 2590, Lib 2: 1087, 1783, 2399,
3257, Lib 3: 979, 1783, 2590, 3257, Lib 4: 979, 1087, 1339, 1783,
2029, 2399, 2590, 3257, Lib 5: 979, 1087, 1339, 1783, Lib 6: 2029,
2399, 2590, 3257; Renilla: Lib 1: 174, 300, 432, 568, Lib 2: 592,
633, 729, 867, Lib 3: 174, 300, 432, 568, 592, 633, 729, 867, Lib
4: 174, 432, 592, 729, Lib 5: 300, 568, 633, 867, Lib 6: 592,
568.
[0100] FIG. 22 shows the results of an EGFR and TfnR
internalization assay when single gene knockdowns are performed.
The Y-axis represents percent internalization relative to
control.
[0101] FIG. 23 shows the results of an EGFR and TfnR
internalization assay when multiple genes are knocked down (e.g.,
Rab5a, b, c). The Y-axis represents the percent internalization
relative to control.
[0102] FIG. 24 shows the simultaneous knockdown of four different
genes. siRNAs directed against G6PD, GAPDH, PLK, and UQC were
simultaneously introduced into cells. Twenty-four hours later,
cultures were harvested and assayed for mRNA target levels for each
of the four genes. A comparison is made between cells transfected
with individual siRNAs vs. a pool of siRNAs directed against all
four genes.
[0103] FIG. 25 shows the functionality of ten siRNAs at 0.3 nM
concentrations.
DETAILED DESCRIPTION
Definitions
[0104] Unless stated otherwise, the following terms and phrases
have the meanings provided below:
Complementary
[0105] The term "complementary" refers to the ability of
polynucleotides to form base pairs with one another. Base pairs are
typically formed by hydrogen bonds between nucleotide units in
antiparallel polynucleotide strands. Complementary polynucleotide
strands can base pair in the Watson-Crick manner (e.g., A to T, A
to U, C to G), or in any other manner that allows for the formation
of duplexes. As persons skilled in the art are aware, when using
RNA as opposed to DNA, uracil rather than thymine is the base that
is considered to be complementary to adenosine. However, when a U
is denoted in the context of the present invention, the ability to
substitute a T is implied, unless otherwise stated.
[0106] Perfect complementarity or 100% complementarity refers to
the situation in which each nucleotide unit of one polynucleotide
strand can hydrogen bond with a nucleotide unit of a second
polynucleotide strand. Less than perfect complementarity refers to
the situation in which some, but not all, nucleotide units of two
strands can hydrogen bond with each other. For example, for two
20-mers, if only two base pairs on each strand can hydrogen bond
with each other, the polynucleotide strands exhibit 10%
complementarity. In the same example, if 18 base pairs on each
strand can hydrogen bond with each other, the polynucleotide
strands exhibit 90% complementarity.
Deoxynucleotide
[0107] The term "deoxynucleotide" refers to a nucleotide or
polynucleotide lacking a hydroxyl group (OH group) at the 2' and/or
3' position of a sugar moiety. Instead, it has a hydrogen bonded to
the 2' and/or 3' carbon. Within an RNA molecule that comprises one
or more deoxynucleotides, "deoxynucleotide" refers to the lack of
an OH group at the 2' position of the sugar moiety, having instead
a hydrogen bonded directly to the 2' carbon.
Deoxyribonucleotide
[0108] The terms "deoxyribonucleotide" and "DNA" refer to a
nucleotide or polynucleotide comprising at least one sugar moiety
that has an H, rather than an OH, at its 2' and/or 3' position.
Duplex Region
[0109] The phrase "duplex region" refers to the region in two
complementary or substantially complementary polynucleotides that
form base pairs with one another, either by Watson-Crick base
pairing or any other manner that allows for a stabilized duplex
between polynucleotide strands that are complementary or
substantially complementary. For example, a polynucleotide strand
having 21 nucleotide units can base pair with another
polynucleotide of 21 nucleotide units, yet only 19 bases on each
strand are complementary or substantially complementary, such that
the "duplex region" has 19 base pairs. The remaining bases may, for
example, exist as 5' and 3' overhangs. Further, within the duplex
region, 100% complementarity is not required; substantial
complementarity is allowable within a duplex region. Substantial
complementarity refers to 79% or greater complementarity. For
example, a mismatch in a duplex region consisting of 19 base pairs
results in 94.7% complementarity, rendering the duplex region
substantially complementary.
Filters
[0110] The term "filter" refers to one or more procedures that are
performed on sequences that are identified by the algorithm. In
some instances, filtering includes in silico procedures where
sequences identified by the algorithm can be screened to identify
duplexes carrying desirable or undesirable motifs. Sequences
carrying such motifs can be selected for, or selected against, to
obtain a final set with the preferred properties. In other
instances, filtering includes wet lab experiments. For instance,
sequences identified by one or more versions of the algorithm can
be screened using any one of a number of procedures to identify
duplexes that have hyperfunctional traits (e.g., they exhibit a
high degree of silencing at subnanomolar concentrations and/or
exhibit high degrees of silencing longevity).
Gene Silencing
[0111] The phrase "gene silencing" refers to a process by which the
expression of a specific gene product is lessened or attenuated.
Gene silencing can take place by a variety of pathways. Unless
specified otherwise, as used herein, gene silencing refers to
decreases in gene product expression that results from RNA
interference (RNAi), a defined, though partially characterized
pathway whereby small inhibitory RNA (siRNA) act in concert with
host proteins (e.g., the RNA induced silencing complex, RISC) to
degrade messenger RNA (mRNA) in a sequence-dependent fashion. The
level of gene silencing can be measured by a variety of means,
including, but not limited to, measurement of transcript levels by
Northern Blot Analysis, B-DNA techniques, transcription-sensitive
reporter constructs, expression profiling (e.g., DNA chips), and
related technologies. Alternatively, the level of silencing can be
measured by assessing the level of the protein encoded by a
specific gene. This can be accomplished by performing a number of
studies including Western Analysis, measuring the levels of
expression of a reporter protein that has e.g., fluorescent
properties (e.g., GFP) or enzymatic activity (e.g., alkaline
phosphatases), or several other procedures.
miRNA
[0112] The term "miRNA" refers to microRNA.
Nucleotide
[0113] The term "nucleotide" refers to a ribonucleotide or a
deoxyribonucleotide or modified form thereof, as well as an analog
thereof. Nucleotides include species that comprise purines, e.g.,
adenine, hypoxanthine, guanine, and their derivatives and analogs,
as well as pyrimidines, e.g., cytosine, uracil, thymine, and their
derivatives and analogs.
[0114] Nucleotide analogs include nucleotides having modifications
in the chemical structure of the base, sugar and/or phosphate,
including, but not limited to, 5-position pyrimidine modifications,
8-position purine modifications, modifications at cytosine
exocyclic amines, and substitution of 5-bromo-uracil; and
2'-position sugar modifications, including but not limited to,
sugar-modified ribonucleotides in which the 2'-OH is replaced by a
group such as an H, OR, R, halo, SH, SR, NH.sub.2, NHR, NR.sub.2,
or CN, wherein R is an alkyl moiety. Nucleotide analogs are also
meant to include nucleotides with bases such as inosine, queuosine,
xanthine, sugars such as 2'-methyl ribose, non-natural
phosphodiester linkages such as methylphosphonates,
phosphorothioates and peptides.
[0115] Modified bases refer to nucleotide bases such as, for
example, adenine, guanine, cytosine, thymine, uracil, xanthine,
inosine, and queuosine that have been modified by the replacement
or addition of one or more atoms or groups. Some examples of types
of modifications that can comprise nucleotides that are modified
with respect to the base moieties include but are not limited to,
alkylated, halogenated, thiolated, aminated, amidated, or
acetylated bases, individually or in combination. More specific
examples include, for example, 5-propynyluridine,
5-propynylcytidine, 6-methyladenine, 6-methylguanine,
N,N,-dimethyladenine, 2-propyladenine, 2-propylguanine,
2-aminoadenine, 1-methylinosine, 3-methyluridine, 5-methylcytidine,
5-methyluridine and other nucleotides having a modification at the
5 position, 5-(2-amino)propyl uridine, 5-halocytidine,
5-halouridine, 4-acetylcytidine, 1-methyladenosine,
2-methyladenosine, 3-methylcytidine, 6-methyluridine,
2-methylguanosine, 7-methylguanosine, 2,2-dimethylguanosine,
5-methylaminoethyluridine, 5-methyloxyuridine, deazanucleotides
such as 7-deaza-adenosine, 6-azouridine, 6-azocytidine,
6-azothymidine, 5-methyl-2-thiouridine, other thio bases such as
2-thiouridine and 4-thiouridine and 2-thiocytidine, dihydrouridine,
pseudouridine, queuosine, archaeosine, naphthyl and substituted
naphthyl groups, any O- and N-alkylated purines and pyrimidines
such as N6-methyladenosine, 5-methylcarbonylmethyluridine, uridine
5-oxyacetic acid, pyridine-4-one, pyridine-2-one, phenyl and
modified phenyl groups such as aminophenol or 2,4,6-trimethoxy
benzene, modified cytosines that act as G-clamp nucleotides,
8-substituted adenines and guanines, 5-substituted uracils and
thymines, azapyrimidines, carboxyhydroxyalkyl nucleotides,
carboxyalkylaminoalkyl nucleotides, and alkylcarbonylalkylated
nucleotides. Modified nucleotides also include those nucleotides
that are modified with respect to the sugar moiety, as well as
nucleotides having sugars or analogs thereof that are not ribosyl.
For example, the sugar moieties may be, or be based on, mannoses,
arabinoses, glucopyranoses, galactopyranoses, 4'-thioribose, and
other sugars, heterocycles, or carbocycles.
[0116] The term nucleotide is also meant to include what are known
in the art as universal bases. By way of example, universal bases
include but are not limited to 3-nitropyrrole, 5-nitroindole, or
nebularine. The term "nucleotide" is also meant to include the N3'
to P5' phosphoramidate, resulting from the substitution of a
ribosyl 3' oxygen with an amine group.
[0117] Further, the term nucleotide also includes those species
that have a detectable label, such as for example a radioactive or
fluorescent moiety, or mass label attached to the nucleotide.
Off-Target Silencing and Off-Target Interference
[0118] The phrases "off-target silencing" and "off-target
interference" are defined as degradation of mRNA other than the
intended target mRNA due to overlapping and/or partial homology
with secondary mRNA messages.
Polynucleotide
[0119] The term "polynucleotide" refers to polymers of nucleotides,
and includes but is not limited to DNA, RNA, DNA/RNA hybrids
including polynucleotide chains of regularly and/or irregularly
alternating deoxyribosyl moieties and ribosyl moieties (i.e.,
wherein alternate nucleotide units have an --OH, then and --H, then
an --OH, then an --H, and so on at the 2' position of a sugar
moiety), and modifications of these kinds of polynucleotides,
wherein the attachment of various entities or moieties to the
nucleotide units at any position are included.
Polyribonucleotide
[0120] The term "polyribonucleotide" refers to a polynucleotide
comprising two or more modified or unmodified ribonucleotides
and/or their analogs. The term "polyribonucleotide" is used
interchangeably with the term "oligoribonucleotide."
Ribonucleotide and Ribonucleic Acid
[0121] The term "ribonucleotide" and the phrase "ribonucleic acid"
(RNA), refer to a modified or unmodified nucleotide or
polynucleotide comprising at least one ribonucleotide unit. A
ribonucleotide unit comprises an hydroxyl group attached to the 2'
position of a ribosyl moiety that has a nitrogenous base attached
in N-glycosidic linkage at the 1' position of a ribosyl moiety, and
a moiety that either allows for linkage to another nucleotide or
precludes linkage.
siRNA
[0122] The term "siRNA" refers to small inhibitory RNA duplexes
that induce the RNA interference (RNAi) pathway. These molecules
can vary in length (generally 18-30 base pairs) and contain varying
degrees of complementarity to their target mRNA in the antisense
strand. Some, but not all, siRNA have unpaired overhanging bases on
the 5' or 3' end of the sense strand and/or the antisense strand.
The term "siRNA" includes duplexes of two separate strands, as well
as single strands that can form hairpin structures comprising a
duplex region.
[0123] siRNA may be divided into five (5) groups (non-functional,
semi-functional, functional, highly functional, and
hyper-functional) based on the level or degree of silencing that
they induce in cultured cell lines. As used herein, these
definitions are based on a set of conditions where the siRNA is
transfected into said cell line at a concentration of 100 nM and
the level of silencing is tested at a time of roughly 24 hours
after transfection, and not exceeding 72 hours after transfection.
In this context, "non-functional siRNA" are defined as those siRNA
that induce less than 50% (<50%) target silencing.
"Semi-functional siRNA" induce 50-79% target silencing. "Functional
siRNA" are molecules that induce 80-95% gene silencing.
"Highly-functional siRNA" are molecules that induce greater than
95% gene silencing. "Hyperfunctional siRNA" are a special class of
molecules. For purposes of this document, hyperfunctional siRNA are
defined as those molecules that: (I) induce greater than 95%
silencing of a specific target when they are transfected at
subnanomolar concentrations (i.e., less than one nanomolar); and/or
(2) induce functional (or better) levels of silencing for greater
than 96 hours. These relative functionalities (though not intended
to be absolutes) may be used to compare siRNAs to a particular
target for applications such as functional genomics, target
identification and therapeutics.
SMARTSCORE.TM., or siRNA Rank
[0124] The term "SMARTSCORE.TM.", or "siRNA rank" refers to a
number determined by applying any of the formulas to a given siRNA
sequence. The term "SMART-selected" or "rationally selected" or
"rational selection" refers to siRNA that have been selected on the
basis of their SMARTSCORES.TM., or siRNA ranking.
Substantially Similar
[0125] The phrase "substantially similar" refers to a similarity of
at least 90% with respect to the identity of the bases of the
sequence.
Target
[0126] The term "target" is used in a variety of different forms
throughout this document and is defined by the context in which it
is used. "Target mRNA" refers to a messenger RNA to which a given
siRNA can be directed against. "Target sequence" and "target site"
refer to a sequence within the mRNA to which the sense strand of an
siRNA shows varying degrees of homology and the antisense strand
exhibits varying degrees of complementarity. The phrase "siRNA
target" can refer to the gene, mRNA, or protein against which an
siRNA is directed. Similarly, "target silencing" can refer to the
state of a gene, or the corresponding mRNA or protein.
Transfection
[0127] The term "transfection" refers to a process by which agents
are introduced into a cell. The list of agents that can be
transfected is large and includes, but is not limited to, siRNA,
sense and/or anti-sense sequences, DNA encoding one or more genes
and organized into an expression plasmid, proteins, protein
fragments, and more. There are multiple methods for transfecting
agents into a cell including, but not limited to, electroporation,
calcium phosphate-based transfections, DEAE-dextran-based
transfections, lipid-based transfections, molecular conjugate-based
transfections (e.g., polylysine-DNA conjugates), microinjection and
others.
[0128] The present invention is directed to improving the
efficiency of gene silencing by siRNA. Through the inclusion of
multiple siRNA sequences that are targeted to a particular gene
and/or selecting an siRNA sequence based on certain defined
criteria, improved efficiency may be achieved.
[0129] The present invention will now be described in connection
with preferred embodiments. These embodiments are presented in
order to aid in an understanding of the present invention and are
not intended, and should not be construed, to limit the invention
in any way. All alternatives, modifications and equivalents that
may become apparent to those of ordinary skill upon reading this
disclosure are included within the spirit and scope of the present
invention.
[0130] Furthermore, this disclosure is not a primer on RNA
interference. Basic concepts known to persons skilled in the art
have not been set forth in detail.
[0131] The present invention is directed to increasing the
efficiency of RNAi, particularly in mammalian systems. Accordingly,
the present invention provides kits, siRNAs and methods for
increasing siRNA efficacy.
[0132] According to a first embodiment, the present invention
provides a kit for gene silencing, wherein said kit is comprised of
a pool of at least two siRNA duplexes, each of which is comprised
of a sequence that is complementary to a portion of the sequence of
one or more target messenger RNA, and each of which is selected
using non-target specific criteria. Each of the at least two siRNA
duplexes of the kit complementary to a portion of the sequence of
one or more target mRNAs is preferably selected using Formula
X.
[0133] According to a second embodiment, the present invention
provides a method for selecting an siRNA, said method comprising
applying selection criteria to a set of potential siRNA that
comprise 18-30 base pairs, wherein said selection criteria are
non-target specific criteria, and said set comprises at least two
siRNAs and each of said at least two siRNAs contains a sequence
that is at least substantially complementary to a target gene; and
determining the relative functionality of the at least two
siRNAs.
[0134] In one embodiment, the present invention also provides a
method wherein said selection criteria are embodied in a formula
comprising:
(-14)*G.sub.13-13*A.sub.1-12*U.sub.7-11*U.sub.2-10*A.sub.11-10*U.sub.4-10-
*C.sub.3-10*C.sub.5-10*C.sub.6-9*A.sub.10-9*U.sub.9-9*C.sub.18-8*G.sub.10--
7*U.sub.1-7*U.sub.16-7*C.sub.17-7*C.sub.19+7*U.sub.17+8*A.sub.2+8*A.sub.4+-
8*A.sub.5+8*C.sub.4+9*G.sub.8+10*A.sub.7+10*U.sub.18+11*A.sub.19+11*C.sub.-
9+15*G.sub.1+18*A.sub.3+19*U.sub.10-Tm-3*(GC.sub.total)-6*(GC.sub.15-19)-3-
0*X; or Formula VIII
(-8)*A1+(-1)*A2+(12)*A3+(7)*A4+(18)*A5+(12)*A6+(19)*A7+(6)*A8+(-4)*A9+(-5-
)*A10+(-2)*A11+(-5)*A12+(17)*A13+(-3)*A14+(4)*A15+(2)*A16+(8)*A17+(11)*A18-
+(30)*A19+(-13)*U1+(-10)*U2+(2)*U3+(-2)*U4+(-5)*U5+(5)*U6+(-2)*U7+(-10)*U8-
+(-5)*U9+(15)*U10+(-1)*U11+(0)*U12+(10)*U13+(-9)*U14+(-13)*U15+(-10)*U16+(-
3)*U17+(9)*U18+(9)*U19+(7)*C1+(3)*C2+(-21)*C3+(5)*C4+(-9)*C5+(-20)*C6+(-18-
)*C7+(-5)*C8+(5)*C9+(1)*C10+(2)*C11+(-5)*C12+(-3)*C13+(-6)*C14+(-2)*C15+(--
5)*C16+(-3)*C17+(-12)*C18+(-18)*C19+(14)*G1+(8)*G2+(7)*G3+(-10)*G4+(-4)*G5-
+(2)*G6+(1)*G7+(9)*G8+(5)*G9+(-11)*G10+(1)*G11+(9)*G12+(-24)*G13+(18)*G14+-
(11)*G15+(13)*G16+(-7)*G17+(-9)*G18+(-22)*G19+6*(number of A+U in
position 15-19)-3*(number of G+C in whole siRNA), Formula X
[0135] wherein position numbering begins at the 5-most position of
a sense strand, and
[0136] A.sub.1=1 if A is the base at position 1 of the sense
strand, otherwise its value is 0; [0137] A.sub.2=1 if A is the base
at position 2 of the sense strand, otherwise its value is 0; [0138]
A.sub.3=1 if A is the base at position 3 of the sense strand,
otherwise its value is 0; [0139] A.sub.4=1 if A is the base at
position 4 of the sense strand, otherwise its value is 0; [0140]
A.sub.5=1 if A is the base at position 5 of the sense strand,
otherwise its value is 0; [0141] A.sub.6=1 if A is the base at
position 6 of the sense strand, otherwise its value is 0; [0142]
A.sub.7=1 if A is the base at position 7 of the sense strand,
otherwise its value is 0; [0143] A.sub.10=1 if A is the base at
position 10 of the sense strand, otherwise its value is 0; [0144]
A.sub.11=1 if A is the base at position 11 of the sense strand,
otherwise its value is 0; [0145] A.sub.13=1 if A is the base at
position 13 of the sense strand, otherwise its value is 0; [0146]
A.sub.19=1 if A is the base at position 19 of the sense strand,
otherwise if another base is present or the sense strand is only 18
base pairs in length, its value is 0;
[0147] C.sub.3=1 if C is the base at position 3 of the sense
strand, otherwise its value is 0; [0148] C.sub.4=1 if C is the base
at position 4 of the sense strand, otherwise its value is 0; [0149]
C.sub.5=1 if C is the base at position 5 of the sense strand,
otherwise its value is 0; [0150] C.sub.6=1 if C is the base at
position 6 of the sense strand, otherwise its value is 0; [0151]
C.sub.7=1 if C is the base at position 7 of the sense strand,
otherwise its value is 0; [0152] C.sub.9=1 if C is the base at
position 9 of the sense strand, otherwise its value is 0; [0153]
C.sub.17=1 if C is the base at position 17 of the sense strand,
otherwise its value is 0; [0154] C.sub.18=1 if C is the base at
position 18 of the sense strand, otherwise its value is 0; [0155]
C.sub.19=1 if C is the base at position 19 of the sense strand,
otherwise if another base is present or the sense strand is only 18
base pairs in length, its value is 0;
[0156] G.sub.1=1 if G is the base at position 1 on the sense
strand, otherwise its value is 0; [0157] G.sub.2=1 if G is the base
at position 2 of the sense strand, otherwise its value is 0; [0158]
G.sub.8=1 if G is the base at position 8 on the sense strand,
otherwise its value is 0; [0159] G.sub.10=1 if G is the base at
position 10 on the sense strand, otherwise its value is 0; [0160]
G.sub.13=1 if G is the base at position 13 on the sense strand,
otherwise its value is 0; [0161] G.sub.19=1 if G is the base at
position 19 of the sense strand, otherwise if another base is
present or the sense strand is only 18 base pairs in length, its
value is 0;
[0162] U.sub.1=1 if U is the base at position 1 on the sense
strand, otherwise its value is 0; [0163] U.sub.2=1 if U is the base
at position 2 on the sense strand, otherwise its value is 0; [0164]
U.sub.3=1 if U is the base at position 3 on the sense strand,
otherwise its value is 0; [0165] U.sub.4=1 if U is the base at
position 4 on the sense strand, otherwise its value is 0; [0166]
U.sub.7=1 if U is the base at position 7 on the sense strand,
otherwise its value is 0; [0167] U.sub.9=1 if U is the base at
position 9 on the sense strand, otherwise its value is 0; [0168]
U.sub.10=1 if U is the base at position 10 on the sense strand,
otherwise its value is 0; [0169] U.sub.15=1 if U is the base at
position 15 on the sense strand, otherwise its value is 0; [0170]
U.sub.16=1 if U is the base at position 16 on the sense strand,
otherwise its value is 0; [0171] U.sub.17=1 if U is the base at
position 17 on the sense strand, otherwise its value is 0; [0172]
U.sub.18=1 if U is the base at position 18 on the sense strand,
otherwise its value is 0.
[0173] GC.sub.15-19=the number of G and C bases within positions
15-19 of the sense strand, or within positions 15-18 if the sense
strand is only 18 base pairs in length;
[0174] GC.sub.total=the number of G and C bases in the sense
strand;
[0175] Tm=100 if the siRNA oligo has the internal repeat longer
then 4 base pairs, otherwise its value is 0; and
[0176] X=the number of times that the same nucleotide repeats four
or more times in a row.
[0177] Any of the methods of selecting siRNA in accordance with the
invention can further comprise comparing the internal stability
profiles of the siRNAs to be selected, and selecting those siRNAs
with the most favorable internal stability profiles. Any of the
methods of selecting siRNA can further comprise selecting either
for or against sequences that contain motifs that induce cellular
stress. Such motifs include, for example, toxicity motifs. Any of
the methods of selecting siRNA can further comprise either
selecting for or selecting against sequences that comprise
stability motifs.
[0178] In another embodiment, the present invention provides a
method of gene silencing, comprising introducing into a cell at
least one siRNA selected according to any of the methods of the
present invention. The siRNA can be introduced by allowing passive
uptake of siRNA, or through the use of a vector.
[0179] According to a third embodiment, the invention provides a
method for developing an algorithm for selecting siRNA, said method
comprising: (a) selecting a set of siRNA; (b) measuring gene
silencing ability of each siRNA from said set; (c) determining
relative functionality of each siRNA; (d) determining improved
functionality by the presence or absence of at least one variable
selected from the group consisting of the presence or absence of a
particular nucleotide at a particular position, the total number of
As and Us in positions 15-19, the number of times that the same
nucleotide repeats within a given sequence, and the total number of
Gs and Cs; and (e) developing an algorithm using the information of
step (d).
[0180] In another embodiment, the invention provides a method for
selecting an siRNA with improved functionality, comprising using
the above-mentioned algorithm to identify an siRNA of improved
functionality.
[0181] According to a fourth embodiment, the present invention
provides a kit, wherein said kit is comprised of at least two
siRNAs, wherein said at least two siRNAs comprise a first optimized
siRNA and a second optimized siRNA, wherein said first optimized
siRNA and said second optimized siRNA are optimized according a
formula comprising Formula X.
[0182] According to a fifth embodiment, the present invention
provides a method for identifying a hyperfunctional siRNA,
comprising applying selection criteria to a set of potential siRNA
that comprise 18-30 base pairs, wherein said selection criteria are
non-target specific criteria, and said set comprises at least two
siRNAs and each of said at least two siRNAs contains a sequence
that is at least substantially complementary to a target gene;
determining the relative functionality of the at least two siRNAs
and assigning each of the at least two siRNAs a functionality
score; and selecting siRNAs from the at least two siRNAs that have
a functionality score that reflects greater than 80 percent
silencing at a concentration in the picomolar range, wherein said
greater than 80 percent silencing endures for greater than 120
hours.
[0183] In other embodiments, the invention provides kits and/or
methods wherein the siRNA are comprised of two separate
polynucleotide strands; wherein the siRNA are comprised of a single
contiguous molecule such as, for example, a unimolecular siRNA
(comprising, for example, either a nucleotide or non-nucleotide
loop); wherein the siRNA are expressed from one or more vectors;
and wherein two or more genes are silenced by a single
administration of siRNA.
[0184] According to a sixth embodiment, the present invention
provides a hyperfunctional siRNA that is capable of silencing Bcl
2.
[0185] According to a seventh embodiment, the present invention
provides a method for developing an siRNA algorithm for selecting
functional and hyperfunctional siRNAs for a given sequence. The
method comprises:
[0186] (a) selecting a set of siRNAs;
[0187] (b) measuring the gene silencing ability of each siRNA from
said set;
[0188] (c) determining the relative functionality of each
siRNA;
[0189] (d) determining the amount of improved functionality by the
presence or absence of at least one variable selected from the
group consisting of the total GC content, melting temperature of
the siRNA, GC content at positions 15-19, the presence or absence
of a particular nucleotide at a particular position, relative
thermodynamic stability at particular positions in a duplex, and
the number of times that the same nucleotide repeats within a given
sequence; and
[0190] (e) developing an algorithm using the information of step
(d).
[0191] According to this embodiment, preferably the set of siRNAs
comprises at least 90 siRNAs from at least one gene, more
preferably at least 180 siRNAs from at least two different genes,
and most preferably at least 270 and 360 siRNAs from at least three
and four different genes, respectively. Additionally, in step (d)
the determination is made with preferably at least two, more
preferably at least three, even more preferably at least four, and
most preferably all of the variables. The resulting algorithm is
not target sequence specific.
[0192] In another embodiment, the present invention provides
rationally designed siRNAs identified using the formulas above.
[0193] In yet another embodiment, the present invention is directed
to hyperfunctional siRNA.
[0194] The ability to use the above algorithms, which are not
sequence or species specific, allows for the cost-effective
selection of optimized siRNAs for specific target sequences.
Accordingly, there will be both greater efficiency and reliability
in the use of siRNA technologies.
[0195] The methods disclosed herein can be used in conjunction with
comparing internal stability profiles of selected siRNAs, and
designing an siRNA with a desirable internal stability profile;
and/or in conjunction with a selection either for or against
sequences that contain motifs that induce cellular stress, for
example, cellular toxicity.
[0196] Any of the methods disclosed herein can be used to silence
one or more genes by introducing an siRNA selected, or designed, in
accordance with any of the methods disclosed herein. The siRNA(s)
can be introduced into the cell by any method known in the art,
including passive uptake or through the use of one or more
vectors.
[0197] Any of the methods and kits disclosed herein can employ
either unimolecular siRNAs, siRNAs comprised of two separate
polynucleotide strands, or combinations thereof. Any of the methods
disclosed herein can be used in gene silencing, where two or more
genes are silenced by a single administration of siRNA(s). The
siRNA(s) can be directed against two or more target genes, and
administered in a single dose or single transfection, as the case
may be.
Optimizing siRNA
[0198] According to one embodiment, the present invention provides
a method for improving the effectiveness of gene silencing for use
to silence a particular gene through the selection of an optimal
siRNA. An siRNA selected according to this method may be used
individually, or in conjunction with the first embodiment, i.e.,
with one or more other siRNAs, each of which may or may not be
selected by this criteria in order to maximize their
efficiency.
[0199] The degree to which it is possible to select an siRNA for a
given mRNA that maximizes these criteria will depend on the
sequence of the mRNA itself. However, the selection criteria will
be independent of the target sequence. According to this method, an
siRNA is selected for a given gene by using a rational design. That
said, rational design can be described in a variety of ways.
Rational design is, in simplest terms, the application of a proven
set of criteria that enhance the probability of identifying a
functional or hyperfunctional siRNA. In one method, rationally
designed siRNA can be identified by maximizing one or more of the
following criteria:
[0200] (1) A low GC content, preferably between about 30-52%.
[0201] (2) At least 2, preferably at least 3 A or U bases at
positions 15-19 of the siRNA on the sense strand. [0202] (3) An A
base at position 19 of the sense strand. [0203] (4) An A base at
position 3 of the sense strand. [0204] (5) A U base at position 10
of the sense strand. [0205] (6) An A base at position 14 of the
sense strand. [0206] (7) A base other than C at position 19 of the
sense strand. [0207] (8) A base other than G at position 13 of the
sense strand.
[0208] (9) A Tm, which refers to the character of the internal
repeat that results in inter- or intramolecular structures for one
strand of the duplex, that is preferably not stable at greater than
50.degree. C., more preferably not stable at greater than
37.degree. C., even more preferably not stable at greater than
30.degree. C. and most preferably not stable at greater than
20.degree. C. [0209] (10) A base other than U at position 5 of the
sense strand. [0210] (11) A base other than A at position 11 of the
sense strand. [0211] (12) A base other than an A at position 1 of
the sense strand. [0212] (13) A base other than an A at position 2
of the sense strand. [0213] (14) An A base at position 4 of the
sense strand. [0214] (15) An A base at position 5 of the sense
strand. [0215] (16) An A base at position 6 of the sense strand.
[0216] (17) An A base at position 7 of the sense strand. [0217]
(18) An A base at position 8 of the sense strand. [0218] (19) A
base other than an A at position 9 of the sense strand. [0219] (20)
A base other than an A at position 10 of the sense strand. [0220]
(21) A base other than an A at position 11 of the sense strand.
[0221] (22) A base other than an A at position 12 of the sense
strand. [0222] (23) An A base at position 13 of the sense strand.
[0223] (24) A base other than an A at position 14 of the sense
strand. [0224] (25) An A base at position 15 of the sense strand
[0225] (26) An A base at position 16 of the sense strand. [0226]
(27) An A base at position 17 of the sense strand. [0227] (28) An A
base at position 18 of the sense strand. [0228] (29) A base other
than a U at position 1 of the sense strand. [0229] (30) A base
other than a U at position 2 of the sense strand. [0230] (31) A U
base at position 3 of the sense strand. [0231] (32) A base other
than a U at position 4 of the sense strand. [0232] (33) A base
other than a U at position 5 of the sense strand. [0233] (34) A U
base at position 6 of the sense strand. [0234] (35) A base other
than a U at position 7 of the sense strand. [0235] (36) A base
other than a U at position 8 of the sense strand. [0236] (37) A
base other than a U at position 9 of the sense strand. [0237] (38)
A base other than a U at position 11 of the sense strand. [0238]
(39) A U base at position 13 of the sense strand. [0239] (40) A
base other than a U at position 14 of the sense strand. [0240] (41)
A base other than a U at position 15 of the sense strand. [0241]
(42) A base other than a U at position 16 of the sense strand.
[0242] (43) A U base at position 17 of the sense strand. [0243]
(44) A U base at position 18 of the sense strand. [0244] (45) A U
base at position 19 of the sense strand. [0245] (46) A C base at
position 1 of the sense strand. [0246] (47) A C base at position 2
of the sense strand. [0247] (48) A base other than a C at position
3 of the sense strand. [0248] (49) A C base at position 4 of the
sense strand. [0249] (50) A base other than a C at position 5 of
the sense strand. [0250] (51) A base other than a C at position 6
of the sense strand. [0251] (52) A base other than a C at position
7 of the sense strand. [0252] (53) A base other than a C at
position 8 of the sense strand. [0253] (54) A C base at position 9
of the sense strand. [0254] (55) A C base at position 10 of the
sense strand. [0255] (56) A C base at position 11 of the sense
strand. [0256] (57) A base other than a C at position 12 of the
sense strand. [0257] (58) A base other than a C at position 13 of
the sense strand. [0258] (59) A base other than a C at position 14
of the sense strand. [0259] (60) A base other than a C at position
15 of the sense strand. [0260] (61) A base other than a C at
position 16 of the sense strand. [0261] (62) A base other than a C
at position 17 of the sense strand. [0262] (63) A base other than a
C at position 18 of the sense strand. [0263] (64) A G base at
position 1 of the sense strand. [0264] (65) A G base at position 2
of the sense strand. [0265] (66) A G base at position 3 of the
sense strand. [0266] (67) A base other than a G at position 4 of
the sense strand. [0267] (68) A base other than a G at position 5
of the sense strand. [0268] (69) A G base at position 6 of the
sense strand. [0269] (70) A G base at position 7 of the sense
strand. [0270] (71) A G base at position 8 of the sense strand.
[0271] (72) A G base at position 9 of the sense strand. [0272] (73)
A base other than a G at position 10 of the sense strand. [0273]
(74) A G base at position 11 of the sense strand. [0274] (75) A G
base at position 12 of the sense strand. [0275] (76) A G base at
position 14 of the sense strand. [0276] (77) A G base at position
15 of the sense strand. [0277] (78) A G base at position 16 of the
sense strand. [0278] (79) A base other than a G at position 17 of
the sense strand. [0279] (80) A base other than a G at position 18
of the sense strand. [0280] (81) A base other than a G at position
19 of the sense strand.
[0281] The importance of various criteria can vary greatly. For
instance, a C base at position 10 of the sense strand makes a minor
contribution to duplex functionality. In contrast, the absence of a
C at position 3 of the sense strand is very important. Accordingly,
preferably an siRNA will satisfy as many of the aforementioned
criteria as possible.
[0282] With respect to the criteria, GC content, as well as a high
number of AU in positions 15-19 of the sense strand, may be
important for easement of the unwinding of double stranded siRNA
duplex. Duplex unwinding has been shown to be crucial for siRNA
functionality in vivo.
[0283] With respect to criterion 9, the internal structure is
measured in terms of the melting temperature of the single strand
of siRNA, which is the temperature at which 50% of the molecules
will become denatured. With respect to criteria 2-8 and 10-11, the
positions refer to sequence positions on the sense strand, which is
the strand that is identical to the mRNA.
[0284] In one preferred embodiment, at least criteria 1 and 8 are
satisfied. In another preferred embodiment, at least criteria 7 and
8 are satisfied. In still another preferred embodiment, at least
criteria 1, 8 and 9 are satisfied.
[0285] It should be noted that all of the aforementioned criteria
regarding sequence position specifics are with respect to the 5'
end of the sense strand. Reference is made to the sense strand,
because most databases contain information that describes the
information of the mRNA. Because according to the present invention
a chain can be from 18 to 30 bases in length, and the
aforementioned criteria assumes a chain 19 base pairs in length, it
is important to keep the aforementioned criteria applicable to the
correct bases.
[0286] When there are only 18 bases, the base pair that is not
present is the base pair that is located at the 3' of the sense
strand. When there are twenty to thirty bases present, then
additional bases are added at the 5' end of the sense chain and
occupy positions .sup.-1 to .sup.-11. Accordingly, with respect to
SEQ. ID NO. 0001 NNANANNNNUCNAANNNNA and SEQ. ID NO. 0028
GUCNNANANNNNUCNAANNNNA, both would have A at position 3, A at
position 5, U at position 10, C at position 11, A and position 13,
A and position 14 and A at position 19. However, SEQ. ID NO. 0028
would also have C at position -1, U at position -2 and G at
position -3.
[0287] For a 19 base pair siRNA, an optimal sequence of one of the
strands may be represented below, where N is any base, A, C, G, or
U: TABLE-US-00001 SEQ. ID NO. 0001. NNANANNNNUCNAANNNNA SEQ. ID NO.
0002. NNANANNNNUGNAANNNNA SEQ. ID NO. 0003. NNANANNNNUUNAANNNNA
SEQ. ID NO. 0004. NNANANNNNUCNCANNNNA SEQ. ID NO. 0005.
NNANANNNNUGNCANNNNA SEQ. ID NO. 0006. NNANANNNNUUNCANNNNA SEQ. ID
NO. 0007. NNANANNNNUCNUANNNNA SEQ. ID NO. 0008. NNANANNNNUGNUANNNNA
SEQ. ID NO. 0009. NNANANNNNUUNUANNNNA SEQ. ID NO. 0010.
NNANCNNNNUCNAANNNNA SEQ. ID NO. 0011. NNANCNNNNUGNAANNNNA SEQ. ID
NO. 0012. NNANCNNNNUUNAANNNNA SEQ. ID NO. 0013. NNANCNNNNUCNCANNNNA
SEQ. ID NO. 0014. NNANCNNNNUGNCANNNNA SEQ. ID NO. 0015.
NNANCNNNNUUNCANNNNA SEQ. ID NO. 0016. NNANCNNNNUCNUANNNNA SEQ. ID
NO. 0017. NNANCNNNNUGNUANNNNA SEQ. ID NO. 0018. NNANCNNNNUUNUANNNNA
SEQ. ID NO. 0019. NNANGNNNNUCNAANNNNA SEQ. ID NO. 0020.
NNANGNNNNUGNAANNNNA SEQ. ID NO. 0021. NNANGNNNNUUNAANNNNA SEQ. ID
NO. 0022. NNANGNNNNUCNCANNNNA SEQ. ID NO. 0023. NNANGNNNNUGNCANNNNA
SEQ. ID NO. 0024. NNANGNNNNUUNCANNNNA SEQ. ID NO. 0025.
NNANGNNNNUCNUANNNNA SEQ. ID NO. 0026. NNANGNNNNUGNUANNNNA SEQ. ID
NO. 0027. NNANONNNNNUNUANNNNA
[0288] In one embodiment, the sequence used as an siRNA is selected
by choosing the siRNA that score highest according to one of the
following seven algorithms that are represented by Formulas I-VI:
Relative functionality of
siRNA=-(GC/3)+(AU.sub.15-19)-(Tm.sub.20.degree.
C.)*3-(G.sub.13)*3-(C.sub.19)+(A.sub.19)*2+(A.sub.3)+(U10)+(A.sub.14)-(U.-
sub.5)-(A.sub.11) Formula I Relative functionality of
siRNA=-(GC/3)-(AU.sub.15-19)*3-(G.sub.13)*3-(C.sub.19)+(A.sub.19)*2+(A.su-
b.3) Formula II Relative functionality of
siRNA=-(GC/3)+(AU.sub.15-19)-(Tm.sub.20.degree. C.)*3 Formula III
Relative functionality of
siRNA=-GC/2+(AU.sub.15-19)/2-(Tm.sub.20.degree.
C.)*2-(G.sub.13)*3-(C.sub.19)+(A.sub.19)*2+(A.sub.3)+(U.sub.10)+(A.sub.14-
)-(U.sub.5)-(A.sub.11) Formula IV Relative functionality of
siRNA=-(G.sub.13)*3-(C.sub.19)+(A.sub.19)*2+(A.sub.3)+(U.sub.10)+(A.sub.1-
4)-(U.sub.5)-(A.sub.11) Formula V Relative functionality of
siRNA=-(G.sub.13)*3-(C.sub.19)+(A.sub.19)*2+(A.sub.3) Formula VI
Relative functionality of
siRNA=-(GC/2)+(AU.sub.15-19)/2-(Tm.sub.20.degree.
C.)*1-(G.sub.13)*3-(C.sub.19)+(A.sub.19)*3+(A.sub.3)*3+(U.sub.10)/2+(A.su-
b.14)/2-(U.sub.5)/2-(A.sub.11)/2 Formula VII
[0289] In Formulas I-VI:
[0290] wherein A.sub.19=1 if A is the base at position 19 on the
sense strand, otherwise its value is 0,
[0291] AU.sub.15-19=0-5 depending on the number of A or U bases on
the sense strand at positions 15-19;
[0292] G.sub.13=1 if G is the base at position 13 on the sense
strand, otherwise its value is 0;
[0293] C.sub.19=1 if C is the base at position 19 of the sense
strand, otherwise its value is 0;
[0294] GC=the number of G and C bases in the entire sense
strand;
[0295] Tm.sub.20.degree. C.=1 if the Tm is greater than 20.degree.
C.; [0296] A.sub.3=1 if A is the base at position 3 on the sense
strand, otherwise its value is 0; [0297] U.sub.10=1 if U is the
base at position 10 on the sense strand, otherwise its value is 0;
[0298] A.sub.14=1 if A is the base at position 14 on the sense
strand, otherwise its value is 0; [0299] U.sub.5=1 if U is the base
at position 5 on the sense strand, otherwise its value is 0; and
[0300] A.sub.11=1 if A is the base at position 11 of the sense
strand, otherwise its value is 0.
[0301] Formulas I-VII provide relative information regarding
functionality. When the values for two sequences are compared for a
given formula, the relative functionality is ascertained; a higher
positive number indicates a greater functionality. For example, in
many applications a value of 5 or greater is beneficial.
[0302] Additionally, in many applications, more than one of these
formulas would provide useful information as to the relative
functionality of potential siRNA sequences. However, it is
beneficial to have more than one type of formula, because not every
formula will be able to help to differentiate among potential siRNA
sequences. For example, in particularly high GC mRNAs, formulas
that take that parameter into account would not be useful and
application of formulas that lack GC elements (e.g., formulas V and
VI) might provide greater insights into duplex functionality.
Similarly, formula II might by used in situations where hairpin
structures are not observed in duplexes, and formula IV might be
applicable for sequences that have higher AU content. Thus, one may
consider a particular sequence in light of more than one or even
all of these algorithms to obtain the best differentiation among
sequences. In some instances, application of a given algorithm may
identify an unusually large number of potential siRNA sequences,
and in those cases, it may be appropriate to re-analyze that
sequence with a second algorithm that is, for instance, more
stringent. Alternatively, it is conceivable that analysis of a
sequence with a given formula yields no acceptable siRNA sequences
(i.e. low SMARTSCORES.TM., or siRNA ranking). In this instance, it
may be appropriate to re-analyze that sequences with a second
algorithm that is, for instance, less stringent. In still other
instances, analysis of a single sequence with two separate formulas
may give rise to conflicting results (i.e. one formula generates a
set of siRNA with high SMARTSCORES.TM., or siRNA ranking, while the
other formula identifies a set of siRNA with low SMARTSCORES.TM.,
or siRNA ranking). In these instances, it may be necessary to
determine which weighted factor(s) (e.g. GC content) are
contributing to the discrepancy and assessing the sequence to
decide whether these factors should or should not be included.
Alternatively, the sequence could be analyzed by a third, fourth,
or fifth algorithm to identify a set of rationally designed
siRNA.
[0303] The above-referenced criteria are particularly advantageous
when used in combination with pooling techniques as depicted in
Table I: TABLE-US-00002 TABLE I FUNCTIONAL PROBABILITY OLIGOS POOLS
CRITERIA >95% >80% <70% >95% >80% <70% CURRENT
33.0 50.0 23.0 79.5 97.3 0.3 NEW 50.0 88.5 8.0 93.8 99.98 0.005
(GC) 28.0 58.9 36.0 72.8 97.1 1.6
[0304] The term "current" used in Table I refers to Tuschl's
conventional siRNA parameters (Elbashir, S. M. et al. (2002)
"Analysis of gene function in somatic mammalian cells using small
interfering RNAs" Methods 26: 199-213). "New" refers to the design
parameters described in Formulas I-VII. "GC" refers to criteria
that select siRNA solely on the basis of GC content.
[0305] As Table I indicates, when more functional siRNA duplexes
are chosen, siRNAs that produce <70% silencing drops from 23% to
8% and the number of siRNA duplexes that produce >80% silencing
rises from 50% to 88.5%. Further, of the siRNA duplexes with
>80% silencing, a larger portion of these siRNAs actually
silence >95% of the target expression (the new criteria
increases the portion from 33% to 50%). Using this new criteria in
pooled siRNAs, shows that, with pooling, the amount of silencing
>95% increases from 79.5% to 93.8% and essentially eliminates
any siRNA pool from silencing less than 70%.
[0306] Table II similarly shows the particularly beneficial results
of pooling in combination with the aforementioned criteria.
However, Table II, which takes into account each of the
aforementioned variables, demonstrates even a greater degree of
improvement in functionality. TABLE-US-00003 TABLE II FUNCTIONAL
PROBABILITY OLIGOS POOLS FUNC- NON- FUNC- AV- NON- TION- AVER-
FUNC- TION- ER- FUNC- AL AGE TIONAL AL AGE TIONAL RANDOM 20 40 50
67 97 3 CRITERIA 1 52 99 0.1 97 93 0.0040 CRITERIA 4 89 99 0.1 99
99 0.0000
[0307] The terms "functional," "Average," and "Non-functional" used
in Table II, refer to siRNA that exhibit >80%, >50%, and
<50% functionality, respectively. Criteria 1 and 4 refer to
specific criteria described above.
[0308] The above-described algorithms may be used with or without a
computer program that allows for the inputting of the sequence of
the mRNA and automatically outputs the optimal siRNA. The computer
program may, for example, be accessible from a local terminal or
personal computer, over an internal network or over the
Internet.
[0309] In addition to the formulas above, more detailed algorithms
may be used for selecting siRNA. Preferably, at least one RNA
duplex of 18-30 base pairs is selected such that it is optimized
according a formula selected from:
(-14)*G.sub.13-13*A.sub.1-12*U.sub.7-11*U.sub.2-10*A.sub.11-10*U.sub.4-10-
*C.sub.3-10*C.sub.5-10*C.sub.6-9*A.sub.10-9*U.sub.9-9*C.sub.18-8*G.sub.10--
7*U.sub.1-7*U.sub.16-7*C.sub.17-7*C.sub.19+7*U.sub.17+8*A.sub.2+8*A.sub.4+-
8*A.sub.5+8*C.sub.4+9*G.sub.8+10*A.sub.7+10*U.sub.18+11*A.sub.19+11*C.sub.-
9+15*G.sub.1+18*A.sub.3+19*U.sub.10-Tm-3*(GC.sub.total)-6*(GC.sub.15-19)-3-
0*X; and Formula VIII
(14.1)*A.sub.3+(14.9)*A.sub.6+(17.6)*A.sub.13+(24.7)*A.sub.19+(14.2)*U.su-
b.10+(10.5)*C.sub.9+(23.9)*G.sub.1+(16.3)*G.sub.2+(-12.3)*A.sub.11+(-19.3)-
*U.sub.1+(-12.1)*U.sub.2+(-11)*U.sub.3+(-15.2)*U.sub.15+(-11.3)*U.sub.16+(-
-11.8)*C.sub.3+(-17.4)*C.sub.6+(-10.5)*C.sub.7+(-13.7)*G.sub.13+(-25.9)*G.-
sub.19-Tm-3*(GC.sub.total)-6*(GC.sub.15-19)-30*X; and Formula IX
(-8)*A1+(-1)*A2+(12)*A3+(7)*A4+(18)*A5+(12)*A6+(19)*A7+(6)*A8+(-4)*A9+(-5-
)*A10+(-2)*A11+(-5)*A12+(17)*A13+(-3)*A14+(4)*A15+(2)*A16+(8)*A17+(11)*A18-
+(30)*A19+(-13)*U1+(-10)*U2+(2)*U3+(-2)*U4+(-5)*U5+(5)*U6+(-2)*U7+(-10)*U8-
+(-5)*U9+(15)*U10+(-1)*U11+(0)*U12+(10)*U13+(-9)*U14+(-13)*U15+(-10)*U16+(-
3)*U17+(9)*U18+(9)*U19+(7)*C1+(3)*C2+(-21)*C3+(5)*C4+(-9)*C5+(-20)*C6+(-18-
)*C7+(-5)*C8+(5)*C9+(1)*C10+(2)*C11+(-5)*C12+(-3)*C13+(-6)*C14+(-2)*C15+(--
5)*C16+(-3)*C17+(-12)*C18+(-18)*C19+(14)*G1+(8)*G2+(7)*G3+(-10)*G4+(-4)*G5-
+(2)*G6+(1)*G7+(9)*G8+(5)*G9+(-11)*G10+(1)*G11+(9)*G12+(-24)*G13+(18)*G14+-
(11)*G15+(13)*G16+(-7)*G17+(-9)*G18+(-22)*G19+6*(number of A+U in
position 15-19)-3*(number of G+C in whole siRNA). Formula X
[0310] wherein
[0311] A.sub.1=1 if A is the base at position 1 of the sense
strand, otherwise its value is 0; [0312] A.sub.2=1 if A is the base
at position 2 of the sense strand, otherwise its value is 0; [0313]
A.sub.3=1 if A is the base at position 3 of the sense strand,
otherwise its value is 0; [0314] A.sub.4=1 if A is the base at
position 4 of the sense strand, otherwise its value is 0; [0315]
A.sub.5=1 if A is the base at position 5 of the sense strand,
otherwise its value is 0; [0316] A.sub.6=1 if A is the base at
position 6 of the sense strand, otherwise its value is 0; [0317]
A.sub.7=1 if A is the base at position 7 of the sense strand,
otherwise its value is 0; [0318] A.sub.10=1 if A is the base at
position 10 of the sense strand, otherwise its value is 0; [0319]
A.sub.11=1 if A is the base at position 11 of the sense strand,
otherwise its value is 0; [0320] A.sub.13=1 if A is the base at
position 13 of the sense strand, otherwise its value is 0; [0321]
A.sub.19=1 if A is the base at position 19 of the sense strand,
otherwise if another base is present or the sense strand is only 18
base pairs in length, its value is 0;
[0322] C.sub.3=1 if C is the base at position 3 of the sense
strand, otherwise its value is 0; [0323] C.sub.4=1 if C is the base
at position 4 of the sense strand, otherwise its value is 0; [0324]
C.sub.5=1 if C is the base at position 5 of the sense strand,
otherwise its value is 0; [0325] C.sub.6=1 if C is the base at
position 6 of the sense strand, otherwise its value is 0; [0326]
C.sub.7=1 if C is the base at position 7 of the sense strand,
otherwise its value is 0; [0327] C.sub.9=1 if C is the base at
position 9 of the sense strand, otherwise its value is 0; [0328]
C.sub.17=1 if C is the base at position 17 of the sense strand,
otherwise its value is 0; [0329] C.sub.18=1 if C is the base at
position 18 of the sense strand, otherwise its value is 0; [0330]
C.sub.19=1 if C is the base at position 19 of the sense strand,
otherwise if another base is present or the sense strand is only 18
base pairs in length, its value is 0;
[0331] G.sub.1=1 if G is the base at position 1 on the sense
strand, otherwise its value is 0; [0332] G.sub.2=1 if G is the base
at position 2 of the sense strand, otherwise its value is 0; [0333]
G8=1 if G is the base at position 8 on the sense strand, otherwise
its value is 0; [0334] G.sub.10=1 if G is the base at position 10
on the sense strand, otherwise its value is 0; [0335] G.sub.13=1 if
G is the base at position 13 on the sense strand, otherwise its
value is 0; [0336] G.sub.19=1 if G is the base at position 19 of
the sense strand, otherwise if another base is present or the sense
strand is only 18 base pairs in length, its value is 0;
[0337] U.sub.1=1 if U is the base at position 1 on the sense
strand, otherwise its value is 0; [0338] U.sub.2=1 if U is the base
at position 2 on the sense strand, otherwise its value is 0; [0339]
U.sub.3=1 if U is the base at position 3 on the sense strand,
otherwise its value is 0; [0340] U.sub.4=1 if U is the base at
position 4 on the sense strand, otherwise its value is 0; [0341]
U.sub.7=1 if U is the base at position 7 on the sense strand,
otherwise its value is 0; [0342] U.sub.9=1 if U is the base at
position 9 on the sense strand, otherwise its value is 0; [0343]
U.sub.10=1 if U is the base at position 10 on the sense strand,
otherwise its value is 0; [0344] U.sub.15=1 if U is the base at
position 15 on the sense strand, otherwise its value is 0; [0345]
U.sub.16=1 if U is the base at position 16 on the sense strand,
otherwise its value is 0; [0346] U.sub.17=1 if U is the base at
position 17 on the sense strand, otherwise its value is 0; [0347]
U.sub.18=1 if U is the base at position 18 on the sense strand,
otherwise its value is 0;
[0348] GC.sub.15-19=the number of G and C bases within positions
15-19 of the sense strand, or within positions 15-18 if the sense
strand is only 18 base pairs in length;
[0349] GC.sub.total=the number of G and C bases in the sense
strand;
[0350] Tm=100 if the siRNA oligo has the internal repeat longer
then 4 base pairs, otherwise its value is 0; and
[0351] X=the number of times that the same nucleotide repeats four
or more times in a row.
[0352] The above formulas VIII, IX, and X, as well as formulas
I-VII, provide methods for selecting siRNA in order to increase the
efficiency of gene silencing. A subset of variables of any of the
formulas may be used, though when fewer variables are used, the
optimization hierarchy becomes less reliable.
[0353] With respect to the variables of the above-referenced
formulas, a single letter of A or C or G or U followed by a
subscript refers to a binary condition. The binary condition is
that either the particular base is present at that particular
position (wherein the value is "1") or the base is not present
(wherein the value is "0"). Because position 19 is optional, i.e.,
there might be only 18 base pairs, when there are only 18 base
pairs, any base with a subscript of 19 in the formulas above would
have a zero value for that parameter. Before or after each variable
is a number followed by *, which indicates that the value of the
variable is to be multiplied or weighed by that number.
[0354] The numbers preceding the variables A, or G, or C, or U in
Formulas VIII, IX, and X (or after the variables in Formula I-VII)
were determined by comparing the difference in the frequency of
individual bases at different positions in functional siRNA and
total siRNA. Specifically, the frequency in which a given base was
observed at a particular position in functional groups was compared
with the frequency that that same base was observed in the total,
randomly selected siRNA set. If the absolute value of the
difference between the functional and total values was found to be
greater than 6%, that parameter was included in the equation. Thus,
for instance, if the frequency of finding a "G" at position 13
(G.sub.13) is found to be 6% in a given functional group, and the
frequency of G.sub.13 in the total population of siRNAs is 20%, the
difference between the two values is 6%-20%=-14%. As the absolute
value is greater than six (6), this factor (-14) is included in the
equation. Thus, in Formula VIII, in cases where the siRNA under
study has a G in position 13, the accrued value is (-14)*(1)=-14.
In contrast, when a base other than G is found at position 13, the
accrued value is (-14)*(0)=0.
[0355] When developing a means to optimize siRNAs, the inventors
observed that a bias toward low internal thermodynamic stability of
the duplex at the 5'-antisense (AS) end is characteristic of
naturally occurring miRNA precursors. The inventors extended this
observation to siRNAs for which functionality had been assessed in
tissue culture.
[0356] With respect to the parameter GC.sub.15-19, a value of 0-5
will be ascribed depending on the number of G or C bases at
positions 15 to 19. If there are only 18 base pairs, the value is
between 0 and 4.
[0357] With respect to the criterion GC.sub.total content, a number
from 0-30 will be ascribed, which correlates to the total number of
G and C nucleotides on the sense strand, excluding overhangs.
Without wishing to be bound by any one theory, it is postulated
that the significance of the GC content (as well as AU content at
positions 15-19, which is a parameter for formulas III-VII) relates
to the easement of the unwinding of a double-stranded siRNA duplex.
Duplex unwinding is believed to be crucial for siRNA functionality
in vivo and overall low internal stability, especially low internal
stability of the first unwound base pair is believed to be
important to maintain sufficient processivity of RISC
complex-induced duplex unwinding. If the duplex has 19 base pairs,
those at positions 15-19 on the sense strand will unwind first if
the molecule exhibits a sufficiently low internal stability at that
position. As persons skilled in the art are aware, RISC is a
complex of approximately twelve proteins; Dicer is one, but not the
only, helicase within this complex. Accordingly, although the GC
parameters are believed to relate to activity with Dicer, they are
also important for activity with other RISC proteins.
[0358] The value of the parameter Tm is 0 when there are no
internal repeats longer than (or equal to) four base pairs present
in the siRNA duplex; otherwise the value is 1. Thus for example, if
the sequence ACGUACGU, or any other four nucleotide (or more)
palindrome exists within the structure, the value will be one (1).
Alternatively if the structure ACGGACG, or any other 3 nucleotide
(or less) palindrome exists, the value will be zero (0).
[0359] The variable "X" refers to the number of times that the same
nucleotide occurs contiguously in a stretch of four or more units.
If there are, for example, four contiguous As in one part of the
sequence and elsewhere in the sequence four contiguous Cs, X=2.
Further, if there are two separate contiguous stretches of four of
the same nucleotides or eight or more of the same nucleotides in a
row, then X=2. However, X does not increase for five, six or seven
contiguous nucleotides.
[0360] Again, when applying Formula VIII, Formula IX, or Formula X,
to a given mRNA, (the "target RNA" or "target molecule"), one may
use a computer program to evaluate the criteria for every sequence
of 18-30 base pairs or only sequences of a fixed length, e.g., 19
base pairs. Preferably the computer program is designed such that
it provides a report ranking of all of the potential siRNAs 18-30
base pairs, ranked according to which sequences generate the
highest value. A higher value refers to a more efficient siRNA for
a particular target gene. The computer program that may be used may
be developed in any computer language that is known to be useful
for scoring nucleotide sequences, or it may be developed with the
assistance of commercially available product such as Microsoft's
PRODUCT.NET. Additionally, rather than run every sequence through
one and/or another formula, one may compare a subset of the
sequences, which may be desirable if for example only a subset are
available. For instance, it may be desirable to first perform a
BLAST (Basic Local Alignment Search Tool) search and to identify
sequences that have no homology to other targets. Alternatively, it
may be desirable to scan the sequence and to identify regions of
moderate GC context, then perform relevant calculations using one
of the above-described formulas on these regions. These
calculations can be done manually or with the aid of a
computer.
[0361] As with Formulas I-VII, either Formula VIII, Formula IX, or
Formula X may be used for a given mRNA target sequence. However, it
is possible that according to one or the other formula more than
one siRNA will have the same value. Accordingly, it is beneficial
to have a second formula by which to differentiate sequences.
Formulas IX and X were derived in a similar fashion as Formula
VIII, yet used a larger data set and thus yields sequences with
higher statistical correlations to highly functional duplexes. The
sequence that has the highest value ascribed to it may be referred
to as a "first optimized duplex." The sequence that has the second
highest value ascribed to it may be referred to as a "second
optimized duplex." Similarly, the sequences that have the third and
fourth highest values ascribed to them may be referred to as a
third optimized duplex and a fourth optimized duplex, respectively.
When more than one sequence has the same value, each of them may,
for example, be referred to as first optimized duplex sequences or
co-first optimized duplexes. Formula X is similar to Formula IX,
yet uses a greater numbers of variables and for that reason,
identifies sequences on the basis of slightly different
criteria.
[0362] It should also be noted that the output of a particular
algorithm will depend on several of variables including: (1) the
size of the data base(s) being analyzed by the algorithm, and (2)
the number and stringency of the parameters being applied to screen
each sequence. Thus, for example, in U.S. patent application Ser.
No. 10/714,333, entitled "Functional and Hyperfunctional siRNA,"
filed Nov. 14, 2003, Formula VIII was applied to the known human
genome (NCBI REFSEQ database) through ENTREZ (EFETCH). As a result
of these procedures, roughly 1.6 million siRNA sequences were
identified. Application of Formula VIII to the same database in
March of 2004 yielded roughly 2.2 million sequences, a difference
of approximately 600,000 sequences resulting from the growth of the
database over the course of the months that span this period of
time. Application of other formulas (e.g., Formula X) that change
the emphasis of, include, or eliminate different variables can
yield unequal numbers of siRNAs. Alternatively, in cases where
application of one formula to one or more genes fails to yield
sufficient numbers of siRNAs with scores that would be indicative
of strong silencing, said genes can be reassessed with a second
algorithm that is, for instance, less stringent.
[0363] siRNA sequences identified using Formula VIII and Formula X
(minus sequences generated by Formula VIII) are contained within
the sequence listing. The data included in the sequence listing is
described more fully below. The sequences identified by Formula
VIII and Formula X that are disclosed in the sequence listing may
be used in gene silencing applications.
[0364] It should be noted that for Formulas VIII, IX, and X all of
the aforementioned criteria are identified as positions on the
sense strand when oriented in the 5' to 3' direction as they are
identified in connection with Formulas I-VII unless otherwise
specified.
[0365] Formulas I-X, may be used to select or to evaluate one, or
more than one, siRNA in order to optimize silencing. Preferably, at
least two optimized siRNAs that have been selected according to at
least one of these formulas are used to silence a gene, more
preferably at least three and most preferably at least four. The
siRNAs may be used individually or together in a pool or kit.
Further, they may be applied to a cell simultaneously or
separately. Preferably, the at least two siRNAs are applied
simultaneously. Pools are particularly beneficial for many research
applications. However, for therapeutics, it may be more desirable
to employ a single hyperfunctional siRNA as described elsewhere in
this application.
[0366] When planning to conduct gene silencing, and it is necessary
to choose between two or more siRNAs, one should do so by comparing
the relative values when the siRNA are subjected to one of the
formulas above. In general a higher scored siRNA should be
used.
[0367] Useful applications include, but are not limited to, target
validation, gene functional analysis, research and drug discovery,
gene therapy and therapeutics. Methods for using siRNA in these
applications are well known to persons of skill in the art.
[0368] Because the ability of siRNA to function is dependent on the
sequence of the RNA and not the species into which it is
introduced, the present invention is applicable across a broad
range of species, including but not limited to all mammalian
species, such as humans, dogs, horses, cats, cows, mice, hamsters,
chimpanzees and gorillas, as well as other species and organisms
such as bacteria, viruses, insects, plants and C. elegans.
[0369] The present invention is also applicable for use for
silencing a broad range of genes, including but not limited to the
roughly 45,000 genes of a human genome, and has particular
relevance in cases where those genes are associated with diseases
such as diabetes, Alzheimer's, cancer, as well as all genes in the
genomes of the aforementioned organisms.
[0370] The siRNA selected according to the aforementioned criteria
or one of the aforementioned algorithms are also, for example,
useful in the simultaneous screening and functional analysis of
multiple genes and gene families using high throughput strategies,
as well as in direct gene suppression or silencing.
Development of the Algorithms
[0371] To identify siRNA sequence features that promote
functionality and to quantify the importance of certain currently
accepted conventional factors--such as G/C content and target site
accessibility--the inventors synthesized an siRNA panel consisting
of 270 siRNAs targeting three genes, Human Cyclophilin, Firefly
Luciferase, and Human DBI. In all three cases, siRNAs were directed
against specific regions of each gene. For Human Cyclophilin and
Firefly Luciferase, ninety siRNAs were directed against a 199 bp
segment of each respective mRNA. For DBI, 90 siRNAs were directed
against a smaller, 109 base pair region of the mRNA. The sequences
to which the siRNAs were directed are provided below.
[0372] It should be noted that in certain sequences, "t" is
present. This is because many databases contain information in this
manner. However, the t denotes a uracil residue in mRNA and siRNA.
Any algorithm will, unless otherwise specified, process a t in a
sequence as a u.
[0373] Human Cyclophilin: 193-390, M60857 TABLE-US-00004 SEQ. ID
NO. 29: gttccaaaaa cagtggataa ttttgtggcc ttagctacag gagagaaagg
atttggctac aaaaacagca aattccatcg tgtaatcaag gacttcatga tccagggcgg
agacttcacc aggggagatg gcacaggagg aaagagcatc tacggtgagc gcttccccga
tgagaacttc aaactgaagc actacgggcc tggctggg
[0374] Firefly Luciferase: 1434-1631, U47298 (pGL3, Promega)
TABLE-US-00005 SEQ. ID NO. 30: tgaacttccc gccgccgttg ttgttttgga
gcacggaaag acgatgacgg aaaaagagat cgtggattac gtcgccagtc aagtaacaac
cgcgaaaaag ttgcgcggag gagttgtgtt tgtggacgaa gtaccgaaag gtcttaccgg
aaaactcgac gcaagaaaaa toagagagat cctcataaag gccaagaagg
[0375] DBI, NM.sub.--020548 (202-310) (Every Position)
TABLE-US-00006 SEQ. ID N0. 0031: acgggcaagg ccaagtggga tgcctggaat
gagctgaaag ggacttccaa ggaagatgcc atgaaagctt acatcaacaa agtagaagag
ctaaagaaaa aatacggg
[0376] A list of the siRNAs appears in Table III (see Examples
Section, Example II)
[0377] The set of duplexes was analyzed to identify correlations
between siRNA functionality and other biophysical or thermodynamic
properties. When the siRNA panel was analyzed in functional and
non-functional subgroups, certain nucleotides were much more
abundant at certain positions in functional or non-functional
groups. More specifically, the frequency of each nucleotide at each
position in highly functional siRNA duplexes was compared with that
of nonfunctional duplexes in order to assess the preference for or
against any given nucleotide at every position. These analyses were
used to determine important criteria to be included in the siRNA
algorithms (Formulas VIII, IX, and X).
[0378] The data set was also analyzed for distinguishing
biophysical properties of siRNAs in the functional group, such as
optimal percent of GC content, propensity for internal structures
and regional thermodynamic stability. Of the presented criteria,
several are involved in duplex recognition, RISC activation/duplex
unwinding, and target cleavage catalysis.
[0379] The original data set that was the source of the
statistically derived criteria is shown in FIG. 2. Additionally,
this figure shows that random selection yields siRNA duplexes with
unpredictable and widely varying silencing potencies as measured in
tissue culture using HEK293 cells. In the figure, duplexes are
plotted such that each x-axis tick-mark represents an individual
siRNA, with each subsequent siRNA differing in target position by
two nucleotides for Human Cyclophilin B and Firefly Luciferase, and
by one nucleotide for Human DBI. Furthermore, the y-axis denotes
the level of target expression remaining after transfection of the
duplex into cells and subsequent silencing of the target.
[0380] siRNA identified and optimized in this document work equally
well in a wide range of cell types. FIG. 3A shows the evaluation of
thirty siRNAs targeting the DBI gene in three cell lines derived
from different tissues. Each DBI siRNA displays very similar
functionality in HEK293 (ATCC, CRL-1573, human embryonic kidney),
HeLa (ATCC, CCL-2, cervical epithelial adenocarcinoma) and DU145
(HTB-81, prostate) cells as determined by the B-DNA assay. Thus,
siRNA functionality is determined by the primary sequence of the
siRNA and not by the intracellular environment. Additionally, it
should be noted that although the present invention provides for a
determination of the functionality of siRNA for a given target, the
same siRNA may silence more than one gene. For example, the
complementary sequence of the silencing siRNA may be present in
more than one gene. Accordingly, in these circumstances, it may be
desirable not to use the siRNA with highest SMARTSCORE.TM., or
siRNA ranking. In such circumstances, it may be desirable to use
the siRNA with the next highest SMARTSCORE.TM., or siRNA
ranking.
[0381] To determine the relevance of G/C content in siRNA function,
the G/C content of each duplex in the panel was calculated and the
functional classes of siRNAs (<F50, .gtoreq.F50, .gtoreq.F80,
.gtoreq.F95 where F refers to the percent gene silencing) were
sorted accordingly. The majority of the highly-functional siRNAs
(.gtoreq.F95) fell within the G/C content range of 36%-52% (FIG.
3B). Twice as many non-functional (<F50) duplexes fell within
the high G/C content groups (>57% GC content) compared to the
36%-52% group. The group with extremely low GC content (26% or
less) contained a higher proportion of non-functional siRNAs and no
highly-functional siRNAs. The G/C content range of 30%-52% was
therefore selected as Criterion I for siRNA functionality,
consistent with the observation that a G/C range 30%-70% promotes
efficient RNAi targeting. Application of this criterion alone
provided only a marginal increase in the probability of selecting
functional siRNAs from the panel: selection of F50 and F95 siRNAs
was improved by 3.6% and 2.2%, respectively. The siRNA panel
presented here permitted a more systematic analysis and
quantification of the importance of this criterion than that used
previously.
[0382] A relative measure of local internal stability is the A/U
base pair (bp) content; therefore, the frequency of A/U bp was
determined for each of the five terminal positions of the duplex
(5' sense (S)/5' antisense (AS)) of all siRNAs in the panel.
Duplexes were then categorized by the number of A/U bp in positions
1-5 and 15-19 of the sense strand. The thermodynamic flexibility of
the duplex 5'-end (positions 1-5; S) did not appear to correlate
appreciably with silencing potency, while that of the 3'-end
(positions 15-19; S) correlated with efficient silencing. No
duplexes lacking A/U bp in positions 15-19 were functional. The
presence of one A/U bp in this region conferred some degree of
functionality, but the presence of three or more A/Us was
preferable and therefore defined as Criterion II. When applied to
the test panel, only a marginal increase in the probability of
functional siRNA selection was achieved: a 1.8% and 2.3% increase
for F50 and F95 duplexes, respectively (Table IV).
[0383] The complementary strands of siRNAs that contain internal
repeats or palindromes may form internal fold-back structures.
These hairpin-like structures exist in equilibrium with the
duplexed form effectively reducing the concentration of functional
duplexes. The propensity to form internal hairpins and their
relative stability can be estimated by predicted melting
temperatures. High Tm reflects a tendency to form hairpin
structures. Lower Tm values indicate a lesser tendency to form
hairpins. When the functional classes of siRNAs were sorted by
T.sub.m (FIG. 3C), the following trends were identified: duplexes
lacking stable internal repeats were the most potent silencers (no
F95 duplex with predicted hairpin structure T.sub.m>60.degree.
C.). In contrast, about 60% of the duplexes in the groups having
internal hairpins with calculated T.sub.m values less than
20.degree. C. were F80. Thus, the stability of internal repeats is
inversely proportional to the silencing effect and defines
Criterion III (predicted hairpin structure
T.sub.m.ltoreq.20.degree. C.).
Sequence-Based Determinants of siRNA Functionality
[0384] When the siRNA panel was sorted into functional and
non-functional groups, the frequency of a specific nucleotide at
each position in a functional siRNA duplex was compared with that
of a nonfunctional duplex in order to assess the preference for or
against a certain nucleotide. FIG. 4 shows the results of these
queries and the subsequent resorting of the data set (from FIG. 2).
The data is separated into two sets: those duplexes that meet the
criteria, a specific nucleotide in a certain position--grouped on
the left (Selected) and those that do not--grouped on the right
(Eliminated). The duplexes are further sorted from most functional
to least functional with the y-axis of FIG. 4a-4e representing the
% expression i.e., the amount of silencing that is elicited by the
duplex (Note: each position on the X-axis represents a different
duplex). Statistical analysis revealed correlations between
silencing and several sequence-related properties of siRNAs. FIG. 4
and Table IV show quantitative analysis for the following five
sequence-related properties of siRNA: (A) an A at position 19 of
the sense strand; (B) an A at position 3 of the sense strand; (C) a
U at position 10 of the sense strand; (D) a base other than G at
position 13 of the sense strand; and (E) a base other than C at
position 19 of the sense strand.
[0385] When the siRNAs in the panel were evaluated for the presence
of an A at position 19 of the sense strand, the percentage of
non-functional duplexes decreased from 20% to 11.8%, and the
percentage of F95 duplexes increased from 21.7% to 29.4% (Table
IV). Thus, the presence of an A in this position defined Criterion
IV.
[0386] Another sequence-related property correlated with silencing
was the presence of an A in position 3 of the sense strand (FIG.
4b). Of the siRNAs with A3, 34.4% were F95, compared with 21.7%
randomly selected siRNAs. The presence of a U base in position 10
of the sense strand exhibited an even greater impact (FIG. 4c). Of
the duplexes in this group, 41.7% were F95. These properties became
criteria V and VI, respectively.
[0387] Two negative sequence-related criteria that were identified
also appear on FIG. 4. The absence of a G at position 13 of the
sense strand, conferred a marginal increase in selecting functional
duplexes (FIG. 4d). Similarly, lack of a C at position 19 of the
sense strand also correlated with functionality (FIG. 4e). Thus,
among functional duplexes, position 19 was most likely occupied by
A, and rarely occupied by C. These rules were defined as criteria
VII and VIII, respectively.
[0388] Application of each criterion individually provided marginal
but statistically significant increases in the probability of
selecting a potent siRNA. Although the results were informative,
the inventors sought to maximize potency and therefore consider
multiple criteria or parameters. Optimization is particularly
important when developing therapeutics. Interestingly, the
probability of selecting a functional siRNA based on each
thermodynamic criteria was 2%-4% higher than random, but 4%-8%
higher for the sequence-related determinates. Presumably, these
sequence-related increases reflect the complexity of the RNAi
mechanism and the multitude of protein-RNA interactions that are
involved in RNAi-mediated silencing. TABLE-US-00007 TABLE IV
PERCENT IMPROVEMENT CRITERION FUNCTIONAL OVER RANDOM (%) I. 30%-52%
G/C Content <F50 16.4 -3.6 .gtoreq.F50 83.6 3.6 .gtoreq.F80 60.4
4.3 .gtoreq.F95 23.9 2.2 II. At least 3 A/U bases at <F50 18.2
-1.8 positions 15-19 of the sense .gtoreq.F50 81.8 1.8 strand
.gtoreq.F80 59.7 3.6 .gtoreq.F95 24.0 2.3 III. Absence of internal
<F50 16.7 -3.3 repeats, as measured by Tm of .gtoreq.F50 83.3
3.3 secondary structure .ltoreq.20.degree. C. .gtoreq.F80 61.1 5.0
.gtoreq.F95 24.6 2.9 IV. An A base at position 19 <F50 11.8 -8.2
of the senses trand .gtoreq.F50 88.2 8.2 .gtoreq.F80 75.0 18.9
.gtoreq.F95 29.4 7.7 V. An A base at position 3 of <F50 17.2
-2.8 the sense strand .gtoreq.F50 82.8 2.8 .gtoreq.F80 62.5 6.4
.gtoreq.F95 34.4 12.7 VI. A U base at position 10 <F50 13.9 -6.1
of the sense strand .gtoreq.F50 86.1 6.1 .gtoreq.F80 69.4 13.3
.gtoreq.F95 41.7 20 VII. A base other than C at <F50 18.8 -1.2
position 19 of the sense strand .gtoreq.F50 81.2 1.2 .gtoreq.F80
59.7 3.6 .gtoreq.F95 24.2 2.5 VIII. A base other than G at <F50
15.2 -4.8 position 13 of the sense strand .gtoreq.F50 84.8 4.8
.gtoreq.F80 61.4 5.3 .gtoreq.F95 26.5 4.8
The siRNA Selection Algorithm
[0389] In an effort to improve selection further, all identified
criteria, including but not limited to those listed in Table IV
were combined into the algorithms embodied in Formula VIII, Formula
IX, and Formula X. Each siRNA was then assigned a score (referred
to as a SMARTSCORE.TM., or siRNA ranking) according to the values
derived from the formulas. Duplexes that scored higher than 0 or
-20 (unadjusted), for Formulas VIII and IX, respectively,
effectively selected a set of functional siRNAs and excluded all
non-functional siRNAs. Conversely, all duplexes scoring lower than
0 and -20 (minus 20) according to formulas VIII and IX,
respectively, contained some functional siRNAs but included all
non-functional siRNAs. A graphical representation of this selection
is shown in FIG. 5. It should be noted that the scores derived from
the algorithm can also be provided as "adjusted" scores. To convert
Formula VIII unadjusted scores into adjusted scores it is necessary
to use the following equation: (160+unadjusted score)/2.25
[0390] When this takes place, an unadjusted score of "0" (zero) is
converted to 75. Similarly, unadjusted scores for Formula X can be
converted to adjusted scores. In this instance, the following
equation is applied: (228+unadjusted score)/3.56
[0391] When these manipulations take place, an unadjusted score of
38 is converted to an adjusted score of 75.
[0392] The methods for obtaining the seven criteria embodied in
Table IV are illustrative of the results of the process used to
develop the information for Formulas VIII, IX, and X. Thus similar
techniques were used to establish the other variables and their
multipliers. As described above, basic statistical methods were use
to determine the relative values for these multipliers.
[0393] To determine the value for "Improvement over Random" the
difference in the frequency of a given attribute (e.g., GC content,
base preference) at a particular position is determined between
individual functional groups (e.g., <F50) and the total siRNA
population studied (e.g., 270 siRNA molecules selected randomly).
Thus, for instance, in Criterion I (30%-52% GC content) members of
the <F50 group were observed to have GC contents between 30-52%
in 16.4% of the cases. In contrast, the total group of 270 siRNAs
had GC contents in this range, 20% of the time. Thus for this
particular attribute, there is a small negative correlation between
30%-52% GC content and this functional group (i.e.,
16.4%-20%=-3.6%). Similarly, for Criterion VI, (a "U" at position
10 of the sense strand), the >F95 group contained a "U" at this
position 41.7% of the time. In contrast, the total group of 270
siRNAs had a "U" at this position 21.7% of the time, thus the
improvement over random is calculated to be 20% (or
41.7%-21.7%).
Identifying the Average Internal Stability Profile of Strong
siRNA
[0394] In order to identify an internal stability profile that is
characteristic of strong siRNA, 270 different siRNAs derived from
the cyclophilin B, the diazepam binding inhibitor (DBI), and the
luciferase gene were individually transfected into HEK293 cells and
tested for their ability to induce RNAi of the respective gene.
Based on their performance in the in vivo assay, the sequences were
then subdivided into three groups, (i)>95% silencing; (ii)
80-95% silencing; and (iii) less than 50% silencing. Sequences
exhibiting 51-84% silencing were eliminated from further
consideration to reduce the difficulties in identifying relevant
thermodynamic patterns.
[0395] Following the division of siRNA into three groups, a
statistical analysis was performed on each member of each group to
determine the average internal stability profile (AISP) of the
siRNA. To accomplish this the Oligo 5.0 Primer Analysis Software
and other related statistical packages (e.g., Excel) were exploited
to determine the internal stability of pentamers using the nearest
neighbor method described by Freier et al., (1986) Improved
free-energy parameters for predictions of RNA duplex stability,
Proc Natl. Acad. Sci. USA 83(24): 9373-7. Values for each group at
each position were then averaged, and the resulting data were
graphed on a linear coordinate system with the Y-axis expressing
the .DELTA.G (free energy) values in kcal/mole and the X-axis
identifying the position of the base relative to the 5' end.
[0396] The results of the analysis identified multiple key regions
in siRNA molecules that were critical for successful gene
silencing. At the 3'-most end of the sense strand (5' antisense),
highly functional siRNA (>95% gene silencing, see FIG. 6a,
>F95) have a low internal stability (AISP of position
19=.about.-7.6 kcal/mol). In contrast low-efficiency siRNA (i.e.,
those exhibiting less than 50% silencing, <F50) display a
distinctly different profile, having high .DELTA.G values
(.about.-8.4 kcal/mol) for the same position. Moving in a 5' (sense
strand) direction, the internal stability of highly efficient siRNA
rises (position 12=.about.-8.3 kcal/mole) and then drops again
(position 7=.about.-7.7 kcal/mol) before leveling off at a value of
approximately -8.1 kcal/mol for the 5' terminus. siRNA with poor
silencing capabilities show a distinctly different profile. While
the AISP value at position 12 is nearly identical with that of
strong siRNAs, the values at positions 7 and 8 rise considerably,
peaking at a high of .about.-9.0 kcal/mol. In addition, at the 5'
end of the molecule the AISP profile of strong and weak siRNA
differ dramatically. Unlike the relatively strong values exhibited
by siRNA in the >95% silencing group, siRNAs that exhibit poor
silencing activity have weak AISP values (-7.6, -7.5, and -7.5
kcal/mol for positions 1, 2 and 3 respectively).
[0397] Overall the profiles of both strong and weak siRNAs form
distinct sinusoidal shapes that are roughly 180.degree.
out-of-phase with each other. While these thermodynamic
descriptions define the archetypal profile of a strong siRNA, it
will likely be the case that neither the .DELTA.G values given for
key positions in the profile or the absolute position of the
profile along the Y-axis (i.e., the .DELTA.G-axis) are absolutes.
Profiles that are shifted upward or downward (i.e., having on an
average, higher or lower values at every position) but retain the
relative shape and position of the profile along the X-axis can be
foreseen as being equally effective as the model profile described
here. Moreover, it is likely that siRNA that have strong or even
stronger gene-specific silencing effects might have exaggerated
.DELTA.G values (either higher or lower) at key positions. Thus,
for instance, it is possible that the 5'-most position of the sense
strand (position 19) could have .DELTA.G values of 7.4 kcal/mol or
lower and still be a strong siRNA if, for instance, a
G-C.fwdarw.G-T/U mismatch were substituted at position 19 and
altered duplex stability. Similarly, position 12 and position 7
could have values above 8.3 kcal/mol and below 7.7 kcal/mole,
respectively, without abating the silencing effectiveness of the
molecule. Thus, for instance, at position 12, a stabilizing
chemical modification (e.g., a chemical modification of the 2'
position of the sugar backbone) could be added that increases the
average internal stability at that position. Similarly, at position
7, mismatches similar to those described previously could be
introduced that would lower the .DELTA.G values at that
position.
[0398] Lastly, it is important to note that while functional and
non-functional siRNA were originally defined as those molecules
having specific silencing properties, both broader or more limiting
parameters can be used to define these molecules. As used herein,
unless otherwise specified, "non-functional siRNA" are defined as
those siRNA that induce less than 50% (<50%) target silencing,
"semi-functional siRNA" induce 50-79% target silencing, "functional
siRNA" are molecules that induce 80-95% gene silencing, and
"highly-functional siRNA" are molecules that induce great than 95%
gene silencing. These definitions are not intended to be rigid and
can vary depending upon the design and needs of the application.
For instance, it is possible that a researcher attempting to map a
gene to a chromosome using a functional assay, may identify an
siRNA that reduces gene activity by only 30%. While this level of
gene silencing may be "non-functional" for, e.g., therapeutic
needs, it is sufficient for gene mapping purposes and is, under
these uses and conditions, "functional." For these reasons,
functional siRNA can be defined as those molecules having greater
than 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% silencing
capabilities at 100 nM transfection conditions. Similarly,
depending upon the needs of the study and/or application,
non-functional and semi-functional siRNA can be defined as having
different parameters. For instance, semi-functional siRNA can be
defined as being those molecules that induce 20%, 30%, 40%, 50%,
60%, or 70% silencing at 100 nM transfection conditions. Similarly,
non-functional siRNA can be defined as being those molecules that
silence gene expression by less than 70%, 60%, 50%, 40%, 30%, or
less. Nonetheless, unless otherwise stated, the descriptions stated
in the "Definitions" section of this text should be applied.
[0399] Functional attributes can be assigned to each of the key
positions in the AISP of strong siRNA. The low 5' (sense strand)
AISP values of strong siRNAs may be necessary for determining which
end of the molecule enters the RISC complex. In contrast, the high
and low AISP values observed in the central regions of the molecule
may be critical for siRNA-target mRNA interactions and product
release, respectively.
[0400] If the AISP values described above accurately define the
thermodynamic parameters of strong siRNA, it would be expected that
similar patterns would be observed in strong siRNA isolated from
nature. Natural siRNAs exist in a harsh, RNase-rich environment and
it can be hypothesized that only those siRNA that exhibit
heightened affinity for RISC (i.e., siRNA that exhibit an average
internal stability profile similar to those observed in strong
siRNA) would survive in an intracellular environment. This
hypothesis was tested using GFP-specific siRNA isolated from N.
benthaniana. Llave et al. (2002) Endogenous and
Silencing-Associated Small RNAs in Plants, The Plant Cell 14,
1605-1619, introduced long double-stranded GFP-encoding RNA into
plants and subsequently re-isolated GFP-specific siRNA from the
tissues. The AISP of fifty-nine of these GFP-siRNA were determined,
averaged, and subsequently plotted alongside the AISP profile
obtained from the cyclophilin B/DBI/luciferase siRNA having >90%
silencing properties (FIG. 6b). Comparison of the two groups show
that profiles are nearly identical. This finding validates the
information provided by the internal stability profiles and
demonstrates that: (1) the profile identified by analysis of the
cyclophilin B/DBI/luciferase siRNAs are not gene specific; and (2)
AISP values can be used to search for strong siRNAs in a variety of
species.
[0401] Both chemical modifications and base-pair mismatches can be
incorporated into siRNA to alter the duplex's AISP and
functionality. For instance, introduction of mismatches at
positions 1 or 2 of the sense strand destabilized the 5' end of the
sense strand and increases the functionality of the molecule (see
Luc, FIG. 7). Similarly, addition of 2'-O-methyl groups to
positions 1 and 2 of the sense strand can also alter the AISP and
(as a result) increase both the functionality of the molecule and
eliminate off-target effects that results from sense strand
homology with the unrelated targets (FIGS. 8a, 8b).
Rationale for Criteria in a Biological Context
[0402] The fate of siRNA in the RNAi pathway may be described in 5
major steps: (1) duplex recognition and pre-RISC complex formation;
(2) ATP-dependent duplex unwinding/strand selection and RISC
activation; (3) mRNA target identification; (4) mRNA cleavage, and
(5) product release (FIG. 1). Given the level of nucleic
acid-protein interactions at each step, siRNA functionality is
likely influenced by specific biophysical and molecular properties
that promote efficient interactions within the context of the
multi-component complexes. Indeed, the systematic analysis of the
siRNA test set identified multiple factors that correlate well with
functionality. When combined into a single algorithm, they proved
to be very effective in selecting active siRNAs.
[0403] The factors described here may also be predictive of key
functional associations important for each step in RNAi. For
example, the potential formation of internal hairpin structures
correlated negatively with siRNA functionality. Complementary
strands with stable internal repeats are more likely to exist as
stable hairpins thus decreasing the effective concentration of the
functional duplex form. This suggests that the duplex is the
preferred conformation for initial pre-RISC association. Indeed,
although single complementary strands can induce gene silencing,
the effective concentration required is at least two orders of
magnitude higher than that of the duplex form.
[0404] siRNA-pre-RISC complex formation is followed by an
ATP-dependent duplex unwinding step and "activation" of the RISC.
The siRNA functionality was shown to correlate with overall low
internal stability of the duplex and low internal stability of the
3' sense end (or differential internal stability of the 3' sense
compare to the 5' sense strand), which may reflect strand selection
and entry into the RISC. Overall duplex stability and low internal
stability at the 3' end of the sense strand were also correlated
with siRNA functionality. Interestingly, siRNAs with very high and
very low overall stability profiles correlate strongly with
non-functional duplexes. One interpretation is that high internal
stability prevents efficient unwinding while very low stability
reduces siRNA target affinity and subsequent mRNA cleavage by the
RISC.
[0405] Several criteria describe base preferences at specific
positions of the sense strand and are even more intriguing when
considering their potential mechanistic roles in target recognition
and mRNA cleavage. Base preferences for A at position 19 of the
sense strand but not C, are particularly interesting because they
reflect the same base preferences observed for naturally occurring
miRNA precursors. That is, among the reported miRNA precursor
sequences 75% contain a U at position 1 which corresponds to an A
in position 19 of the sense strand of siRNAs, while G was
under-represented in this same position for miRNA precursors. These
observations support the hypothesis that both miRNA precursors and
siRNA duplexes are processed by very similar if not identical
protein machinery. The functional interpretation of the
predominance of a U/A base pair is that it promotes flexibility at
the 5' antisense ends of both siRNA duplexes and miRNA precursors
and facilitates efficient unwinding and selective strand entrance
into an activated RISC.
[0406] Among the criteria associated with base preferences that are
likely to influence mRNA cleavage or possibly product release, the
preference for U at position 10 of the sense strand exhibited the
greatest impact, enhancing the probability of selecting an F80
sequence by 13.3%. Activated RISC preferentially cleaves target
mRNA between nucleotides 10 and 11 relative to the 5' end of the
complementary targeting strand. Therefore, it may be that U, the
preferred base for most endoribonucleases, at this position
supports more efficient cleavage. Alternatively, a U/A bp between
the targeting siRNA strand and its cognate target mRNA may create
an optimal conformation for the RISC-associated "slicing"
activity.
Post Algorithm Filters
[0407] According to another embodiment, the output of any one of
the formulas previously listed can be filtered to remove or select
for siRNAs containing undesirable or desirable motifs or
properties, respectively. In one example, sequences identified by
any of the formulas can be filtered to remove any and all sequences
that induce toxicity or cellular stress. Introduction of an siRNA
containing a toxic motif into a cell can induce cellular stress
and/or cell death (apoptosis) which in turn can mislead researchers
into associating a particular (e.g., nonessential) gene with, e.g.,
an essential function. Alternatively, sequences generated by any of
the before mentioned formulas can be filtered to identify and
retain duplexes that contain toxic motifs. Such duplexes may be
valuable from a variety of perspectives including, for instance,
uses as therapeutic molecules. A variety of toxic motifs exist and
can exert their influence on the cell through RNAi and non-RNAi
pathways. Examples of toxic motifs are explained more fully in
commonly assigned U.S. Provisional Patent Application Ser. No.
60/538,874, entitled "Identification of Toxic Sequences," filed
Jan. 23, 2004. Briefly, toxic motifs include A/G UUU A/G/U, G/C AAA
G/C, and GCCA, or a complement of any of the foregoing.
[0408] In another instance, sequences identified by any of the
before mentioned formulas can be filtered to identify duplexes that
contain motifs (or general properties) that provide serum stability
or induce serum instability. In one envisioned application of siRNA
as therapeutic molecules, duplexes targeting disease-associated
genes will be introduced into patients intravenously. As the
half-life of single and double stranded RNA in serum is short,
post-algorithm filters designed to select molecules that contain
motifs that enhance duplex stability in the presence of serum
and/or (conversely) eliminate duplexes that contain motifs that
destabilize siRNA in the presence of serum, would be
beneficial.
[0409] In another instance, sequences identified by any of the
before mentioned formulas can be filtered to identify duplexes that
are hyperfunctional. Hyperfunctional sequences are defined as those
sequences that (1) induce greater than 95% silencing of a specific
target when they are transfected at subnanomolar concentrations
(i.e., less than one nanomolar); and/or (2) induce functional (or
better) levels of silencing for greater than 96 hours. Filters that
identify hyperfunctional molecules can vary widely. In one example,
the top ten, twenty, thirty, or forty siRNA can be assessed for the
ability to silence a given target at, e.g., concentrations of 1 nM
and 0.5 nM to identify hyperfunctional molecules.
Pooling
[0410] According to another embodiment, the present invention
provides a pool of at least two siRNAs, preferably in the form of a
kit or therapeutic reagent, wherein one strand of each of the
siRNAs, the sense strand comprises a sequence that is substantially
similar to a sequence within a target mRNA. The opposite strand,
the antisense strand, will preferably comprise a sequence that is
substantially complementary to that of the target mRNA. More
preferably, one strand of each siRNA will comprise a sequence that
is identical to a sequence that is contained in the target mRNA.
Most preferably, each siRNA will be 19 base pairs in length, and
one strand of each of the siRNAs will be 100% complementary to a
portion of the target mRNA.
[0411] By increasing the number of siRNAs directed to a particular
target using a pool or kit, one is able both to increase the
likelihood that at least one siRNA with satisfactory functionality
will be included, as well as to benefit from additive or
synergistic effects. Further, when two or more siRNAs directed
against a single gene do not have satisfactory levels of
functionality alone, if combined, they may satisfactorily promote
degradation of the target messenger RNA and successfully inhibit
translation. By including multiple siRNAs in the system, not only
is the probability of silencing increased, but the economics of
operation are also improved when compared to adding different
siRNAs sequentially. This effect is contrary to the conventional
wisdom that the concurrent use of multiple siRNA will negatively
impact gene silencing (e.g., Holen, T. et al. (2003) Similar
behavior of single strand and double strand siRNAs suggests they
act through a common RNAi pathway. NAR 31: 2401-21407).
[0412] In fact, when two siRNAs were pooled together, 54% of the
pools of two siRNAs induced more than 95% gene silencing. Thus, a
2.5-fold increase in the percentage of functionality was achieved
by randomly combining two siRNAs. Further, over 84% of pools
containing two siRNAs induced more than 80% gene silencing.
[0413] More preferably, the kit is comprised of at least three
siRNAs, wherein one strand of each siRNA comprises a sequence that
is substantially similar to a sequence of the target mRNA and the
other strand comprises a sequence that is substantially
complementary to the region of the target mRNA. As with the kit
that comprises at least two siRNAs, more preferably one strand will
comprise a sequence that is identical to a sequence that is
contained in the mRNA and another strand that is 100% complementary
to a sequence that is contained in the mRNA. During experiments,
when three siRNAs were combined together, 60% of the pools induced
more than 95% gene silencing and 92% of the pools induced more than
80% gene silencing.
[0414] Further, even more preferably, the kit is comprised of at
least four siRNAs, wherein one strand of each siRNA comprises a
sequence that is substantially similar to a region of the sequence
of the target mRNA, and the other strand comprises a sequence that
is substantially complementary to the region of the target mRNA. As
with the kit or pool that comprises at least two siRNAs, more
preferably one strand of each of the siRNA duplexes will comprise a
sequence that is identical to a sequence that is contained in the
mRNA, and another strand that is 100% complementary to a sequence
that is contained in the mRNA.
[0415] Additionally, kits and pools with at least five, at least
six, and at least seven siRNAs may also be useful with the present
invention. For example, pools of five siRNA induced 95% gene
silencing with 77% probability and 80% silencing with 98.8%
probability. Thus, pooling of siRNAs together can result in the
creation of a target-specific silencing reagent with almost a 99%
probability of being functional. The fact that such high levels of
success are achievable using such pools of siRNA, enables one to
dispense with costly and time-consuming target-specific validation
procedures.
[0416] For this embodiment, as well as the other aforementioned
embodiments, each of the siRNAs within a pool will preferably
comprise 18-30 base pairs, more preferably 18-25 base pairs, and
most preferably 19 base pairs. Within each siRNA, preferably at
least 18 contiguous bases of the antisense strand will be 100%
complementary to the target mRNA. More preferably, at least 19
contiguous bases of the antisense strand will be 100% complementary
to the target mRNA. Additionally, there may be overhangs on either
the sense strand or the antisense strand, and these overhangs may
be at either the 5' end or the 3' end of either of the strands, for
example there may be one or more overhangs of 1-6 bases. When
overhangs are present, they are not included in the calculation of
the number of base pairs. The two nucleotide 3' overhangs mimic
natural siRNAs and are commonly used but are not essential.
Preferably, the overhangs should consist of two nucleotides, most
often dTdT or UU at the 3' end of the sense and antisense strand
that are not complementary to the target sequence. The siRNAs may
be produced by any method that is now known or that comes to be
known for synthesizing double stranded RNA that one skilled in the
art would appreciate would be useful in the present invention.
Preferably, the siRNAs will be produced by Dharmacon's proprietary
ACE.RTM. technology. However, other methods for synthesizing siRNAs
are well known to persons skilled in the art and include, but are
not limited to, any chemical synthesis of RNA oligonucleotides,
ligation of shorter oligonucleotides, in vitro transcription of RNA
oligonucleotides, the use of vectors for expression within cells,
recombinant Dicer products and PCR products.
[0417] The siRNA duplexes within the aforementioned pools of siRNAs
may correspond to overlapping sequences within a particular mRNA,
or non-overlapping sequences of the mRNA. However, preferably they
correspond to non-overlapping sequences. Further, each siRNA may be
selected randomly, or one or more of the siRNA may be selected
according to the criteria discussed above for maximizing the
effectiveness of siRNA.
[0418] Included in the definition of siRNAs are siRNAs that contain
substituted and/or labeled nucleotides that may, for example, be
labeled by radioactivity, fluorescence or mass. The most common
substitutions are at the 2' position of the ribose sugar, where
moieties such as H (hydrogen) F, NH.sub.3, OCH.sub.3 and other O--
alkyl, alkenyl, alkynyl, and orthoesters, may be substituted, or in
the phosphorous backbone, where sulfur, amines or hydrocarbons may
be substituted for the bridging of non-bridging atoms in the
phosphodiester bond. Examples of modified siRNAs are explained more
fully in commonly assigned U.S. patent application Ser. No.
10/613,077, filed Jul. 1, 2003.
[0419] Additionally, as noted above, the cell type into which the
siRNA is introduced may affect the ability of the siRNA to enter
the cell; however, it does not appear to affect the ability of the
siRNA to function once it enters the cell. Methods for introducing
double-stranded RNA into various cell types are well known to
persons skilled in the art.
[0420] As persons skilled in the art are aware, in certain species,
the presence of proteins such as RdRP, the RNA-dependent RNA
polymerase, may catalytically enhance the activity of the siRNA.
For example, RdRP propagates the RNAi effect in C. elegans and
other non-mammalian organisms. In fact, in organisms that contain
these proteins, the siRNA may be inherited. Two other proteins that
are well studied and known to be a part of the machinery are
members of the Argonaute family and Dicer, as well as their
homologues. There is also initial evidence that the RISC complex
might be associated with the ribosome so the more efficiently
translated mRNAs will be more susceptible to silencing than
others.
[0421] Another very important factor in the efficacy of siRNA is
mRNA localization. In general, only cytoplasmic mRNAs are
considered to be accessible to RNAi to any appreciable degree.
However, appropriately designed siRNAs, for example, siRNAs
modified with internucleotide linkages or 2'-O-methyl groups, may
be able to cause silencing by acting in the nucleus. Examples of
these types of modifications are described in commonly assigned
U.S. patent application Ser. Nos. 10/431,027 and 10/613,077.
[0422] As described above, even when one selects at least two
siRNAs at random, the effectiveness of the two may be greater than
one would predict based on the effectiveness of two individual
siRNAs. This additive or synergistic effect is particularly
noticeable as one increases to at least three siRNAs, and even more
noticeable as one moves to at least four siRNAs. Surprisingly, the
pooling of the non-functional and semi-functional siRNAs,
particularly more than five siRNAs, can lead to a silencing mixture
that is as effective if not more effective than any one particular
functional siRNA.
[0423] Within the kits of the present invention, preferably each
siRNA will be present in a concentration of between 0.001 and 200
.mu.M, more preferably between 0.01 and 200 nM, and most preferably
between 0.1 and 10 nM.
[0424] In addition to preferably comprising at least four or five
siRNAs, the kits of the present invention will also preferably
comprise a buffer to keep the siRNA duplex stable. Persons skilled
in the art are aware of buffers suitable for keeping siRNA stable.
For example, the buffer may be comprised of 100 mM KCl, 30 mM
HEPES-pH 7.5, and 1 mM MgCl.sub.2. Alternatively, kits might
contain complementary strands that contain any one of a number of
chemical modifications (e.g., a 2'-O-ACE) that protect the agents
from degradation by nucleases. In this instance, the user may (or
may not) remove the modifying protective group (e.g., deprotect)
before annealing the two complementary strands together.
[0425] By way of example, the kits may be organized such that pools
of siRNA duplexes are provided on an array or microarray of wells
or drops for a particular gene set or for unrelated genes. The
array may, for example, be in 96 wells, 384 wells or 1284 wells
arrayed in a plastic plate or on a glass slide using techniques now
known or that come to be known to persons skilled in the art.
Within an array, preferably there will be controls such as
functional anti-lamin A/C, cyclophilin and two siRNA duplexes that
are not specific to the gene of interest.
[0426] In order to ensure stability of the siRNA pools prior to
usage, they may be retained in lyophilized form at minus twenty
degrees (-20.degree. C.) until they are ready for use. Prior to
usage, they should be resuspended; however, even once resuspended,
for example, in the aforementioned buffer, they should be kept at
minus twenty degrees, (-20.degree. C.) until used. The
aforementioned buffer, prior to use, may be stored at approximately
4.degree. C. or room temperature. Effective temperatures at which
to conduct transfections are well known to persons skilled in the
art and include for example, room temperature.
[0427] The kits may be applied either in vivo or in vitro.
Preferably, the siRNA of the pools or kits is applied to a cell
through transfection, employing standard transfection protocols.
These methods are well known to persons skilled in the art and
include the use of lipid-based carriers, electroporation, cationic
carriers, and microinjection. Further, one could apply the present
invention by synthesizing equivalent DNA sequences (either as two
separate, complementary strands, or as hairpin molecules) instead
of siRNA sequences and introducing them into cells through vectors.
Once in the cells, the cloned DNA could be transcribed, thereby
forcing the cells to generate the siRNA. Examples of vectors
suitable for use with the present application include but are not
limited to the standard transient expression vectors, adenoviruses,
retroviruses, lentivirus-based vectors, as well as other
traditional expression vectors. Any vector that has an adequate
siRNA expression and procession module may be used. Furthermore,
certain chemical modifications to siRNAs, including but not limited
to conjugations to other molecules, may be used to facilitate
delivery. For certain applications it may be preferable to deliver
molecules without transfection by simply formulating in a
physiological acceptable solution.
[0428] This embodiment may be used in connection with any of the
aforementioned embodiments. Accordingly, the sequences within any
pool may be selected by rational design.
Multigene Silencing
[0429] In addition to developing kits that contain multiple siRNA
directed against a single gene, another embodiment includes the use
of multiple siRNA targeting multiple genes. Multiple genes may be
targeted through the use of high- or hyper-functional siRNA. High-
or hyper-functional siRNA that exhibit increased potency, require
lower concentrations to induce desired phenotypic (and thus
therapeutic) effects. This circumvents RISC saturation. It
therefore reasons that if lower concentrations of a single siRNA
are needed for knockout or knockdown expression of one gene, then
the remaining (uncomplexed) RISC will be free and available to
interact with siRNA directed against two, three, four, or more,
genes. Thus in this embodiment, the authors describe the use of
highly functional or hyper-functional siRNA to knock out three
separate genes. More preferably, such reagents could be combined to
knockout four distinct genes. Even more preferably, highly
functional or hyperfunctional siRNA could be used to knock out five
distinct genes. Most preferably, siRNA of this type could be used
to knockout or knockdown the expression of six or more genes.
Hyperfunctional siRNA
[0430] The term hyperfunctional siRNA (hf-siRNA) describes a subset
of the siRNA population that induces RNAi in cells at low- or
sub-nanomolar concentrations for extended periods of time. These
traits, heightened potency and extended longevity of the RNAi
phenotype, are highly attractive from a therapeutic standpoint.
Agents having higher potency require lesser amounts of the molecule
to achieve the desired physiological response, thus reducing the
probability of side effects due to "off-target" interference. In
addition to the potential therapeutic benefits associated with
hyperfunctional siRNA, hf-siRNA are also desirable from an economic
perspective. Hyperfunctional siRNA may cost less on a per-treatment
basis, thus reducing overall expenditures to both the manufacturer
and the consumer.
[0431] Identification of hyperfunctional siRNA involves multiple
steps that are designed to examine an individual siRNA agent's
concentration- and/or longevity-profiles. In one non-limiting
example, a population of siRNA directed against a single gene are
first analyzed using the previously described algorithm (Formula
VIII). Individual siRNA are then introduced into a test cell line
and assessed for the ability to degrade the target mRNA. It is
important to note that when performing this step it is not
necessary to test all of the siRNA. Instead, it is sufficient to
test only those siRNA having the highest SMARTSCORES.TM., or siRNA
ranking (i.e., SMARTSCORES.TM., or siRNA ranking >-10).
Subsequently, the gene silencing data is plotted against the
SMARTSCORES.TM., or siRNA rankings (see FIG. 9). siRNA that (1)
induce a high degree of gene silencing (i.e., they induce greater
than 80% gene knockdown) and (2) have superior SMARTSCORES.TM.
(i.e., a SMARTSCORE.TM., or siRNA ranking, of >-10, suggesting a
desirable average internal stability profile) are selected for
further investigations designed to better understand the molecule's
potency and longevity. In one, non-limiting study dedicated to
understanding a molecule's potency, an siRNA is introduced into one
(or more) cell types in increasingly diminishing concentrations
(e.g., 3.0.fwdarw.0.3 nM). Subsequently, the level of gene
silencing induced by each concentration is examined and siRNA that
exhibit hyperfunctional potency (i.e., those that induce 80%
silencing or greater at, e.g., picomolar concentrations) are
identified. In a second study, the longevity profiles of siRNA
having high (>-10) SMARTSCORES.TM., or siRNA rankings and
greater than 80% silencing are examined. In one non-limiting
example of how this is achieved, siRNA are introduced into a test
cell line and the levels of RNAi are measured over an extended
period of time (e.g., 24-168 hrs). siRNAs that exhibit strong RNA
interference patterns (i.e., >80% interference) for periods of
time greater than, e.g., 120 hours, are thus identified. Studies
similar to those described above can be performed on any and all of
the >106 siRNA included in this document to further define the
most functional molecule for any given gene. Molecules possessing
one or both properties (extended longevity and heightened potency)
are labeled "hyperfunctional siRNA," and earmarked as candidates
for future therapeutic studies.
[0432] While the example(s) given above describe one means by which
hyperfunctional siRNA can be isolated, neither the assays
themselves nor the selection parameters used are rigid and can vary
with each family of siRNA. Families of siRNA include siRNAs
directed against a single gene, or directed against a related
family of genes.
[0433] The highest quality siRNA achievable for any given gene may
vary considerably. Thus, for example, in the case of one gene (gene
X), rigorous studies such as those described above may enable the
identification of an siRNA that, at picomolar concentrations,
induces 99.sup.+% silencing for a period of 10 days. Yet identical
studies of a second gene (gene Y) may yield an siRNA that at high
nanomolar concentrations (e.g., 100 nM) induces only 75% silencing
for a period of 2 days. Both molecules represent the very optimum
siRNA for their respective gene targets and therefore are
designated "hyperfunctional." Yet due to a variety of factors
including but not limited to target concentration, siRNA stability,
cell type, off-target interference, and others, equivalent levels
of potency and longevity are not achievable. Thus, for these
reasons, the parameters described in the before mentioned assays
can vary. While the initial screen selected siRNA that had
SMARTSCORES.TM. above -10 and a gene silencing capability of
greater than 80%, selections that have stronger (or weaker)
parameters can be implemented. Similarly, in the subsequent studies
designed to identify molecules with high potency and longevity, the
desired cutoff criteria (i.e., the lowest concentration that
induces a desirable level of interference, or the longest period of
time that interference can be observed) can vary. The
experimentation subsequent to application of the rational criteria
of this application is significantly reduced where one is trying to
obtain a suitable hyperfunctional siRNA for, for example,
therapeutic use. When, for example, the additional experimentation
of the type described herein is applied by one skilled in the art
with this disclosure in hand, a hyperfunctional siRNA is readily
identified.
[0434] The siRNA may be introduced into a cell by any method that
is now known or that comes to be known and that from reading this
disclosure, persons skilled in the art would determine would be
useful in connection with the present invention in enabling siRNA
to cross the cellular membrane. These methods include, but are not
limited to, any manner of transfection, such as, for example,
transfection employing DEAE-Dextran, calcium phosphate, cationic
lipids/liposomes, micelles, manipulation of pressure,
microinjection, electroporation, immunoporation, use of vectors
such as viruses, plasmids, cosmids, bacteriophages, cell fusions,
and coupling of the polynucleotides to specific conjugates or
ligands such as antibodies, antigens, or receptors, passive
introduction, adding moieties to the siRNA that facilitate its
uptake, and the like.
[0435] Having described the invention with a degree of
particularity, examples will now be provided. These examples are
not intended to and should not be construed to limit the scope of
the claims in any way.
EXAMPLES
General Techniques and Nomenclatures
[0436] siRNA nomenclature. All siRNA duplexes are referred to by
sense strand. The first nucleotide of the 5-end of the sense strand
is position 1, which corresponds to position 19 of the antisense
strand for a 19-mer. In most cases, to compare results from
different experiments, silencing was determined by measuring
specific transcript mRNA levels or enzymatic activity associated
with specific transcript levels, 24 hours post-transfection, with
siRNA concentrations held constant at 100 nM. For all experiments,
unless otherwise specified, transfection efficiency was ensured to
be over 95%, and no detectable cellular toxicity was observed. The
following system of nomenclature was used to compare and report
siRNA-silencing functionality: "F" followed by the degree of
minimal knockdown. For example, F50 signifies at least 50%
knockdown, F80 means at least 80%, and so forth. For this study,
all sub-F50 siRNAs were considered non-functional.
[0437] Cell culture and transfection. 96-well plates are coated
with 50 .mu.l of 50 mg/ml poly-L-lysine (Sigma) for 1 hr, and then
washed 3.times. with distilled water before being dried for 20 min.
HEK293 cells or HEK293Lucs or any other cell type of interest are
released from their solid support by trypsinization, diluted to
3.5.times.10.sup.5 cells/ml, followed by the addition of 100 .mu.L
of cells/well. Plates are then incubated overnight at 37.degree.
C., 5% CO.sub.2. Transfection procedures can vary widely depending
on the cell type and transfection reagents. In one non-limiting
example, a transfection mixture consisting of 2 mL Opti-MEM I
(Gibco-BRL), 80 .mu.l Lipofectamine 2000 (Invitrogen), 15 .mu.L
SUPERNasin at 20 U/.mu.l (Ambion), and 1.5 .mu.l of reporter gene
plasmid at 1 .mu.g/.mu.l is prepared in 5-ml polystyrene round
bottom tubes. One hundred .mu.l of transfection reagent is then
combined with 100 .mu.l of siRNAs in polystyrene deep-well titer
plates (Beckman) and incubated for 20 to 30 min at room
temperature. Five hundred and fifty microliters of Opti-MEM is then
added to each well to bring the final siRNA concentration to 100
nM. Plates are then sealed with parafilm and mixed. Media is
removed from HEK293 cells and replaced with 95 .mu.l of
transfection mixture. Cells are incubated overnight at 37.degree.
C., 5% CO.sub.2.
[0438] Quantification of gene knockdown. A variety of
quantification procedures can be used to measure the level of
silencing induced by siRNA or siRNA pools. In one non-limiting
example: to measure mRNA levels 24 hrs post-transfection,
QuantiGene branched-DNA (bDNA) kits (Bayer) (Wang, et al,
Regulation of insulin preRNA splicing by glucose. Proc. Natl. Acad.
Sci. USA 1997, 94:4360.) are used according to manufacturer
instructions. To measure luciferase activity, media is removed from
HEK293 cells 24 hrs post-transfection, and 50 .mu.l of Steady-GLO
reagent (Promega) is added. After 5 minutes, plates are analyzed on
a plate reader.
Example I
Sequences Used to Develop the Algorithm
[0439] Anti-Firefly and anti-Cyclophilin siRNAs panels (FIG. 5a, b)
sorted according to using Formula VIII predicted values. All siRNAs
scoring more than 0 (formula VIII) and more then 20 (formula IX)
are fully functional. All ninety sequences for each gene (and DBI)
appear below in Table III. TABLE-US-00008 TABLE III Cyclo 1 SEQ. ID
0032 GUUCCAAAAACAGUGGAUA Cyclo 2 SEQ. ID 0033 UCCAAAAACAGUGGAUAAU
Cyclo 3 SEQ. ID 0034 CAAAAACAGUGGAUAAUUU Cyclo 4 SEQ. ID 0035
AAAACAGUGGAUAAUUUUG Cyclo 5 SEQ. ID 0036 AACAGUGGAUAAUUUUGUG Cyclo
6 SEQ. ID 0037 CAGUGGAUAAUUUUGUGGC Cyclo 7 SEQ. ID 0038
GUGGAUAAUUUUGUGGCCU Cyclo 8 SEQ. ID 0039 GGAUAAUUUUGUGGCCUUA Cyclo
9 SEQ. ID 0040 AUAAUUUUGUGGCCUUAGC Cyclo 10 SEQ. ID 0041
AAUUUUGUGGCCUUAGCUA Cyclo 11 SEQ. ID 0042 UUUUGUGGCCUUAGCUACA Cyclo
12 SEQ. ID 0043 UUGUGGCCUUAGCUACAGG Cyclo 13 SEQ. ID 0044
GUGGCCUUAGCUACAGGAG Cyclo 14 SEQ. ID 0045 GGCCUUAGCUACAGGAGAG Cyclo
15 SEQ. ID 0046 CCUUAGCUACAGGAGAGAA Cyclo 16 SEQ. ID 0047
UUAGCUACAGGAGAGAAAG Cyclo 17 SEQ. ID 0048 AGCUACAGGAGAGAAAGGA Cyclo
18 SEQ. ID 0049 CUACAGGAGAGAAAGGAUU Cyclo 19 SEQ. ID 0050
ACAGGAGAGAAAGGAUUUG Cyclo 20 SEQ. ID 0051 AGGAGAGAAAGGAUUUGGC Cyclo
21 SEQ. ID 0052 GAGAGAAAGGAUUUGGCUA Cyclo 22 SEQ. ID 0053
GAGAAAGGAUUUGGCUACA Cyclo 23 SEQ. ID 0054 GAAAGGAUUUGGCUACAAA Cyclo
24 SEQ. ID 0055 AAGGAUUUGGCUACAAAAA Cyclo 25 SEQ. ID 0056
GGAUUUGGCUACAAAAACA Cyclo 26 SEQ. ID 0057 AUUUGGCUACAAAAACAGC Cyclo
27 SEQ. ID 0058 UUGGCUACAAAAACAGCAA Cyclo 28 SEQ. ID 0059
GGCUACAAAAACAGCAAAU Cyclo 29 SEQ. ID 0060 CUACAAAAACAGCAAAUUC Cyclo
30 SEQ. ID 0061 ACAAAAACAGCAAAUUCCA Cyclo 31 SEQ. ID 0062
AAAAACAGCAAAUUCCAUC Cyclo 32 SEQ. ID 0063 AAACAGCAAAUUCCAUCGU Cyclo
33 SEQ. ID 0064 ACAGCAAAUUCCAUCGUGU Cyclo 34 SEQ. ID 0065
AGCAAAUUCCAUCGUGUAA Cyclo 35 SEQ. ID 0066 CAAAUUCCAUCGUGUAAUC Cyclo
36 SEQ. ID 0067 AAUUCCAUCGUGUAAUCAA Cyclo 37 SEQ. ID 0068
UUCCAUCGUGUAAUCAAGG Cyclo 38 SEQ. ID 0069 CCAUCGUGUAAUCAAGGAC Cyclo
39 SEQ. ID 0070 AUCGUGUAAUCAAGGACUU Cyclo 40 SEQ. ID 0071
CGUGUAAUCAAGGACUUCA Cyclo 41 SEQ. ID 0072 UGUAAUCAAGGACUUCAUG Cyclo
42 SEQ. ID 0073 UAAUCAAGGACUUCAUGAU Cyclo 43 SEQ. ID 0074
AUCAAGGACUUCAUGAUCC Cyclo 44 SEQ. ID 0075 CAAGGACUUCAUGAUCCAG Cyclo
45 SEQ. ID 0076 AGGACUUCAUGAUCCAGGG Cyclo 46 SEQ. ID 0077
GACUUCAUCAUCCAGGGCG Cyclo 47 SEQ. ID 0078 CUUCAUGAUCCAGGGCGGA Cyclo
48 SEQ. ID 0079 UCAUGAUCCAGGGCGGAGA Cyclo 49 SEQ. ID 0080
AUGAUCCAGGGCGGAGACU Cyclo 50 SEQ. ID 0081 GAUCCAGGGCGGAGACUUC Cyclo
51 SEQ. ID 0082 UCCAGGGCGGAGACUUCAC Cyclo 52 SEQ. ID 0083
CAGGGCGGAGACUUCACCA Cyclo 53 SEQ. ID 0084 GGGCGGAGACUUCACCAGG Cyclo
54 SEQ. ID 0085 GCGGAGACUUCACCAGGGG Cyclo 55 SEQ. ID 0086
GGAGACUUCACCAGGGGAG Cyclo 56 SEQ. ID 0087 AGACUUCACCAGGGGAGAU Cyclo
57 SEQ. ID 0088 ACUUCACCAGGGGAGAUGG Cyclo 58 SEQ. ID 0089
UUCACCAGGGGAGAUGGCA Cyclo 59 SEQ. ID 0090 CACCAGGGGAGAUGGCACA Cyclo
60 SEQ. ID 0091 CCAGGGGAGAUGGCACAGG Cyclo 61 SEQ. ID 0092
AGGGGAGAUGGCACAGGAG Cyclo 62 SEQ. ID 0093 GGGAGAUGGCACAGGAGGA Cyclo
63 SEQ. ID 0094 GAGAUGGCACAGGAGGAAA Cyclo 64 SEQ. ID 0095
GAUGGCACAGGAGGAAAGA Cyclo 65 SEQ. ID 0096 UGGCACAGGAGGAAAGAGC Cyclo
66 SEQ. ID 0097 GCACAGGAGGAAAGAGCAU Cyclo 67 SEQ. ID 0098
ACAGGAGGAAAGAGCAUCU Cyclo 68 SEQ. ID 0099 AGGAGGAAAGAGCAUCUAC Cyclo
69 SEQ. ID 0100 GAGGAAAGAGCAUCUACGG Cyclo 70 SEQ. ID 0101
GGAAAGAGCAUCUACGGUG Cyclo 71 SEQ. ID 0102 AAAGAGCAUCUACGGUGAG Cyclo
72 SEQ. ID 0103 AGAGCAUCUACGGUGAGCG Cyclo 73 SEQ. ID 0104
AGCAUCUACGGUGAGCGCU Cyclo 74 SEQ. ID 0105 CAUCUACGGUGAGCGCUUC Cyclo
75 SEQ. ID 0106 UCUACGGUGAGCGCUUCCC Cyclo 76 SEQ. ID 0107
UACGGUGAGCGCUUCCCCG Cyclo 77 SEQ. ID 0108 CGGUGAGCGCUUCCCCGAU Cyclo
78 SEQ. ID 0109 GUGAGCGCUUCCCCGAUGA Cyclo 79 SEQ. ID 0110
GAGCGCUUCCCCGAUGAGA Cyclo 80 SEQ. ID 0111 GCGCUUCCCCGAUGAGAAC Cyclo
81 SEQ. ID 0112 GCUUCCCCGAUGAGAACUU Cyclo 82 SEQ. ID 0113
UUCCCCGAUGAGAACUUCA Cyclo 83 SEQ. ID 0114 CCCCGAUGAGAACUUCAAA Cyclo
84 SEQ. ID 0115 CCGAUGAGAACUUCAAACU Cyclo 85 SEQ. ID 0116
GAUGAGAACUUCAAACUGA Cyclo 86 SEQ. ID 0117 UGAGAACUUCAAACUGAAG Cyclo
87 SEQ. ID 0118 AGAACUUCAAACUGAAGCA Cyclo 88 SEQ. ID 0119
AACUUCAAACUGAAGCACU Cyclo 89 SEQ. ID 0120 CUUCAAACUGAAGCACUAC Cyclo
90 SEQ. ID 0121 UCAAACUGAAGCACUACGG DB 1 SEQ. ID 0122
ACGGGCAAGGCCAAGUGGG DB 2 SEQ. ID 0123 CGGGCAAGGCCAAGUGGGA DB 3 SEQ.
ID 0124 GGGCAAGGCCAAGUGGGAU DB 4 SEQ. ID 0125 GGCAAGGCCAAGUGGGAUG
DB 5 SEQ. ID 0126 GCAAGGCCAAGUGGGAUGC DB 6 SEQ. ID 0127
CAAGGCCAAGUGGGAUGCC DB 7 SEQ. ID 0128 AAGGCCAAGUGGGAUGCCU DB 8 SEQ.
ID 0129 AGGCCAAGUGGGAUGCCUG DB 9 SEQ. ID 0130 GGCCAAGUGGGAUGCCUGG
DB 10 SEQ. ID 0131 GCCAAGUGGGAUGCCUGGA DB 11 SEQ. ID 0132
CCAAGUGGGAUGCCUGGAA DB 12 SEQ. ID 0133 CAAGUGGGAUGCCUGGAAU DB 13
SEQ. ID 0134 AAGUGGGAUGCCUGGAAUG DB 14 SEQ. ID 0135
AGUGGGAUGCCUGGAAUGA DB 15 SEQ. ID 0136 GUGGGAUGCCUGGAAUGAG DB 16
SEQ. ID 0137 UGGGAUGCCUGGAAUGAGC DB 17 SEQ. ID 0138
GGGAUGCCUGGAAUGAGCU DB 18 SEQ. ID 0139 GGAUGCCUGGAAUGAGCUG DB 19
SEQ. ID 0140 GAUGCCUGGAAUGAGCUGA DB 20 SEQ. ID 0141
AUGCCUGGAAUGAGCUGAA DB 21 SEQ. ID 0142 UGCCUGGAAUGAGCUGAAA DB 22
SEQ. ID 0143 GCCUGGAAUGAGCUGAAAG DB 23 SEQ. ID 0144
CCUGGAAUGAGCUGAAAGG DB 24 SEQ. ID 0145 CUGGAAUGAGCUGAAAGGG DB 25
SEQ. ID 0146 UGGAAUGAGCUGAAAGGGA DB 26 SEQ. ID 0147
GGAAUGAGCUGAAAGGGAC DB 27 SEQ. ID 0148 GAAUGAGCUGAAAGGGACU DB 28
SEQ. ID 0149 AAUGAGCUGAAAGGGACUU DB 29 SEQ. ID 0150
AUGAGCUGAAAGGGACUUC DB 30 SEQ. ID 0151 UGAGCUGAAAGGGACUUCC DB 31
SEQ. ID 0152 GAGCUGAAAGGGACUUCCA DB 32 SEQ. ID 0153
AGCUGAAAGGGACUUCCAA
DB 33 SEQ. ID 0154 GCUGAAAGGGACUUCCAAG DB 34 SEQ. ID 0155
CUGAAAGGGACUUCCAAGG DB 35 SEQ. ID 0156 UGAAAGGGACUUCCAAGGA DB 36
SEQ. ID 0157 GAAAGGGACUUCCAAGGAA DB 37 SEQ. ID 0158
AAAGGGACUUCCAAGGAAG DB 38 SEQ. ID 0159 AAGGGACUUCCAAGGAAGA DB 39
SEQ. ID 0160 AGGGACUUCCAAGGAAGAU DB 40 SEQ. ID 0161
GGGACUUCCAAGGAAGAUG DB 41 SEQ. ID 0162 GGACUUCCAAGGAAGAUGC DB 42
SEQ. ID 0163 GACUUCCAAGGAAGAUGCC DB 43 SEQ. ID 0164
ACUUCCAAGGAAGAUGCCA DB 44 SEQ. ID 0165 CUUCCAAGGAAGAUGCCAU DB 45
SEQ. ID 0166 UUCCAAGGAAGAUGCCAUG DB 46 SEQ. ID 0167
UCCAAGGAAGAUGCCAUGA DB 47 SEQ. ID 0168 CCAAGGAAGAUGCCAUGAA DB 48
SEQ. ID.0169 CAAGGAAGAUGCCAUGAAA DB 49 SEQ. ID 0170
AAGGAAGAUGCCAUGAAAG DB 50 SEQ. ID 0171 AGGAAGAUGCCAUGAAAGC DB 51
SEQ. ID 0172 GGAAGAUGCCAUGAAAGCU DB 52 SEQ. ID 0173
GAAGAUGCCAUGAAAGCUU DB 53 SEQ. ID 0174 AAGAUGCCAUGAAAGCUUA DB 54
SEQ. ID 0175 AGAUGCCAUGAAAGCUUAC DB 55 SEQ. ID 0176
GAUGCCAUGAAAGCUUACA DB 56 SEQ. ID 0177 AUGCCAUGAAAGCUUACAU DB 57
SEQ. ID 0178 UGCCAUGAAAGCUUACAUC DB 58 SEQ. ID 0179
GCCAUGAAAGCUUACAUCA DB 59 SEQ. ID 0180 CCAUGAAAGCUUACAUCAA DB 60
SEQ. ID 0181 CAUGAAAGCUUACAUCAAC DB 61 SEQ. ID 0182
AUGAAAGCUUACAUCAACA DB 62 SEQ. ID 0183 UGAAAGCUUACAUCAACAA DB 63
SEQ. ID 0184 GAAAGCUUACAUCAACAAA DB 64 SEQ. ID 0185
AAAGCUUACAUCAACAAAG DB 65 SEQ. ID 0186 AAGCUUACAUCAACAAAGU DB 66
SEQ. ID 0187 AGCUUACAUCAACAAAGUA DB 67 SEQ. ID 0188
GCUUACAUCAACAAAGUAG DB 68 SEQ. ID 0189 CUUACAUCAACAAAGUAGA DB 69
SEQ. ID 0190 UUACAUCAACAAAGUAGAA DB 70 SEQ. ID 0191
UACAUCAACAAAGUAGAAG DB 71 SEQ. ID 0192 ACAUCAACAAAGUAGAAGA DB 72
SEQ. ID 0193 CAUCAACAAAGUAGAAGAG DB 73 SEQ. ID 0194
AUCAACAAAGUAGAAGAGC DB 74 SEQ. ID 0195 UCAACAAAGUAGAAGAGCU DB 75
SEQ. ID 0196 CAACAAAGUAGAAGAGCUA DB 76 SEQ. ID 0197
AACAAAGUAGAAGAGCUAA DB 77 SEQ. ID 0198 ACAAAGUAGAAGAGCUAAA DB 78
SEQ. ID 0199 CAAAGUAGAAGAGCUAAAG DB 79 SEQ. ID 0200
AAAGUAGAAGAGCUAAAGA DB 80 SEQ. ID 0201 AAGUAGAAGAGCUAAAGAA DB 81
SEQ. ID 0202 AGUAGAAGAGCUAAAGAAA DB 82 SEQ. ID 0203
GUAGAAGAGCUAAAGAAAA DB 83 SEQ. ID 0204 UAGAAGAGCUAAAGAAAAA DB 84
SEQ. ID 0205 AGAAGAGCUAAAGAAAAAA DB 85 SEQ. ID 0206
GAAGAGCUAAAGAAAAAAU DB 86 SEQ. ID 0207 AAGAGCUAAAGAAAAAAUA OB 87
SEQ. ID 0208 AGAGCUAAAGAAAAAAUAC DB 88 SEQ. ID 0209
GAGCUAAAGAAAAAAUACG DB 89 SEQ. ID 0210 AGCUAAAGAAAAAAUACGG DB 90
SEQ. ID 0211 GCUAAAGAAAAAAUACGGG Luc 1 SEQ. ID 0212
AUCCUCAUAAAGGCCAAGA Luc 2 SEQ. ID 0213 AGAUCCUCAUAAAGGCCAA Luc 3
SEQ. ID 0214 AGAGAUCCUCAUAAAGGCC Luc 4 SEQ. ID 0215
AGAGAGAUCCUCAUAAAGG Luc 5 SEQ. ID 0216 UCAGAGAGAUCCUCAUAAA Luc 6
SEQ. ID 0217 AAUCAGAGAGAUCCUCAUA Luc 7 SEQ. ID 0218
AAAAUCAGAGAGAUCCUCA Luc 8 SEQ. ID 0219 GAAAAAUCAGAGAGAUCCU Luc 9
SEQ. ID 0220 AAGAAAAAUCAGAGAGAUC Luc 10 SEQ. ID 0221
GCAAGAAAAAUCAGAGAGA Luc 11 SEQ. ID 0222 ACGCAAGAAAAAUCAGAGA Luc 12
SEQ. ID 0223 CGACGCAAGAAAAAUCAGA Luc 13 SEQ. ID 0224
CUCGACGCAAGAAAAAUCA Luc 14 SEQ. ID 0225 AACUCGACGCAAGAAAAAU Luc 15
SEQ. ID 0226 AAAACUCGACGCAAGAAAA Luc 16 SEQ. ID 0227
GGAAAACUCGACGCAAGAA Luc 17 SEQ. ID 0228 CCGGAAAACUCGACGCAAG Luc 18
SEQ. ID 0229 UACCGGAAAACUCGACGCA Luc 19 SEQ. ID 0230
CUUACCGGAAAACUCGACG Luc 20 SEQ. ID 0231 GUCUUACCGGAAAACUCGA Luc 21
SEQ. ID 0232 AGGUCUUACCGGAAAACUC Luc 22 SEQ. ID 0233
AAACGUCUUACCGGAAAAC Luc 23 SEQ. ID 0234 CGAAAGGUCUUACCGGAAA Luc 24
SEQ. ID 0235 ACCGAAAGGUCUUACCGGA Luc 25 SEQ. ID 0236
GUACCGAAACGUCUUACCG Luc 26 SEQ. ID 0237 AAGUACCGAAAGGUCUUAC Luc 27
SEQ. ID 0238 CGAAGUACCGAAAGGUCUU Luc 28 SEQ. ID 0239
GACGAAGUACCGAAAGGUC Luc 29 SEQ. ID 0240 UGGACGAAGUACCGAAAGG Luc 30
SEQ. ID 0241 UGUGGACGAAGUACCGAAA Luc 31 SEQ. ID 0242
UUUGUGGACGAAGUACCGA Luc 32 SEQ. ID 0243 UGUUUGUGGACGAAGUACC Luc 33
SEQ. ID 0244 UGUGUUUGUGGACGAAGUA Luc 34 SEQ. ID 0245
GUUGUGUUUGUGGACGAAG Luc 35 SEQ. ID 0246 GAGUUGUGUUUGUGGACGA Luc 36
SEQ. ID 0247 AGGAGUUGUGUUUGUGGAC Luc 37 SEQ. ID 0248
GGAGGAGUUGUGUUUGUGG Luc 38 SEQ. ID 0249 GCGGAGGAGUUGUGUUUGU Luc 39
SEQ. ID 0250 GCGCGGAGGAGUUGUGUUU Luc 40 SEQ. ID 0251
UUGCGCGGAGGAGUUGUGU Luc 41 SEQ. ID 0252 AGUUGCGCGGAGGAGUUGU Luc 42
SEQ. ID 0253 AAAGUUGCGCGGAGGAGUU Luc 43 SEQ. ID 0254
AAAAAGUUGCGCGGAGGAG Luc 44 SEQ. ID 0255 CGAAAAAGUUGCGCGGAGG Luc 45
SEQ. ID 0256 CGCGAAAAAGUUGCGCGGA Luc 46 SEQ. ID 0257
ACCGCGAAAAAGUUGCGCG Luc 47 SEQ. ID 0258 CAACCGCGAAAAAGUUGCG Luc 48
SEQ. ID 0259 AACAACCGCGAAAAAGUUG Luc 49 SEQ. ID 0260
GUAACAACCGCGAAAAAGU Luc 50 SEQ. ID 0261 AAGUAACAACCGCGAAAAA Luc 51
SEQ. ID 0262 UCAAGUAACAACCGCGAAA LuG 52 SEQ. ID 0263
AGUCAAGUAACAACCGCGA Luc 53 SEQ. ID 0264 CCAGUCAAGUAACAACCGC Luc 54
SEQ. ID 0265 CGCCAGUCAAGUAACAACC Luc 55 SEQ. ID 0266
GUCGCCAGUCAAGUAACAA Luc 56 SEQ. ID 0267 ACGUCGCCAGUCAAGUAAC Luc 57
SEQ. ID 0268 UUACGUCGCCAGUCAAGUA Luc 58 SEQ. ID 0269
GAUUACGUCGCCAGUCAAG Luc 59 SEQ. ID 0270 UGGAUUACGUCGCCAGUCA Luc 60
SEQ. ID 0271 CGUGGAUUACGUCGCCAGU Luc 61 SEQ. ID 0272
AUCGUGGAUUACGUCGCCA Luc 62 SEQ. ID 0273 AGAUCGUGGAUUACGUCGC Luc 63
SEQ. ID 0274 AGAGAUCGUGGAUUACGUC Luc 64 SEQ. ID 0275
AAAGAGAUCGUGGAUUACG Luc 65 SEQ. ID 0276 AAAAAGAGAUCGUGGAUUA Luc 66
SEQ. ID 0277 GGAAAAAGAGAUCGUGGAU Luc 67 SEQ. ID 0278
ACGGAAAAAGAGAUCGUGG
Luc 68 SEQ. ID 0279 UGACGGAAAAAGAGAUCGU Luc 69 SEQ. ID 0280
GAUGACGGAAAAAGAGAUC Luc 70 SEQ. ID 0281 ACGAUGACGGAAAAAGAGA Luc 71
SEQ. ID 0282 AGACGAUGACGGAAAAAGA Luc 72 SEQ. ID 0283
AAAGACGAUGACGGAAAAA Luc 73 SEQ. ID 0284 GGAAAGACGAUGACGGAAA Luc 74
SEQ. ID 0285 ACGGAAAGACGAUGACGGA Luc 75 SEQ. ID 0286
GCACGGAAAGACGAUGACG Luc 76 SEQ. ID 0287 GAGCACGGAAAGACGAUGA Luc 77
SEQ. ID 0288 UGGAGCACGGAAAGACGAU Luc 78 SEQ. ID 0289
UUUGGAGCACGGAAAGACG Luc 79 SEQ. ID 0290 GUUUUGGAGCACGGAAAGA Luc 80
SEQ. ID 0291 UUGUUUUGGAGCACGGAAA Luc 81 SEQ. ID 0292
UGUUGUUUUGGAGCACGGA Luc 82 SEQ. ID 0293 GUUGUUGUUUUGGAGCACG Luc 83
SEQ. ID 0294 CCGUUGUUGUUUUGGAGCA Luc 84 SEQ. ID 0295
CGCCGUUGUUGUUUUGGAG Luc 85 SEQ. ID 0296 GCCGCCGUUGUUGUUUUGG Luc 86
SEQ. ID 0297 CCGCCGCCGUUGUUGUUUU Luc 87 SEQ. ID 0298
UCCCGCCGCCGUUGUUGUU Luc 88 SEQ. ID 0299 CUUCCCGCCGCCGUUGUUG Luc 89
SEQ. ID 0300 AACUUCCCGCCGCCGUUGU Luc 90 SEQ. ID 0301
UGAACUUCCCGCCGCCGUU
Example II
Validation of the Algorithm Using DBI, Luciferase, PLK, EGFR, and
SEAP
[0440] The algorithm (Formula VIII) identified siRNAs for five
genes, human DBI, firefly luciferase (fLuc), renilla luciferase
(rLuc), human PLK, and human secreted alkaline phosphatase (SEAP).
Four individual siRNAs were selected on the basis of their
SMARTSCORES.TM. derived by analysis of their sequence using Formula
VIII (all of the siRNAs would be selected with Formula IX as well)
and analyzed for their ability to silence their targets'
expression. In addition to the scoring, a BLAST search was
conducted for each siRNA. To minimize the potential for off-target
silencing effects, only those target sequences with more than three
mismatches against un-related sequences were selected. Semizarov,
et al. (2003) Specificity of short interfering RNA determined
through gene expression signatures, Proc. Natl. Acad. Sci. USA,
100:6347. These duplexes were analyzed individually and in pools of
4 and compared with several siRNAs that were randomly selected. The
functionality was measured as a percentage of targeted gene
knockdown as compared to controls. All siRNAs were transfected as
described by the methods above at 100 nM concentration into HEK293
using Lipofectamine 2000. The level of the targeted gene expression
was evaluated by B-DNA as described above and normalized to the
non-specific control. FIG. 10 shows that the siRNAs selected by the
algorithm disclosed herein were significantly more potent than
randomly selected siRNAs. The algorithm increased the chances of
identifying an F50 siRNA from 48% to 91%, and an F80 siRNA from 13%
to 57%. In addition, pools of SMART siRNA silence the selected
target better than randomly selected pools (see FIG. 10F).
Example III
Validation of the Algorithm Using Genes Involved in
Clathrin-Dependent Endocytosis
[0441] Components of clathrin-mediated endocytosis pathway are key
to modulating intracellular signaling and play important roles in
disease. Chromosomal rearrangements that result in fusion
transcripts between the Mixed-Lineage Leukemia gene (MLL) and CALM
(clathrin assembly lymphoid myeloid leukemia gene) are believed to
play a role in leukemogenesis. Similarly, disruptions in Rab7 and
Rab9, as well as HIP1 (Huntingtin-interacting protein), genes that
are believed to be involved in endocytosis, are potentially
responsible for ailments resulting in lipid storage, and neuronal
diseases, respectively. For these reasons, siRNA directed against
clathrin and other genes involved in the clathrin-mediated
endocytotic pathway are potentially important research and
therapeutic tools.
[0442] siRNAs directed against genes involved in the
clathrin-mediated endocytosis pathways were selected using Formula
VIII. The targeted genes were clathrin heavy chain (CHC, accession
# NM.sub.--004859), clathrin light chain A (CLCa, NM.sub.--001833),
clathrin light chain B (CLCb, NM.sub.--001834), CALM (U45976),
.beta.2 subunit of AP-2 (.beta.2, NM.sub.--001282), Eps15
(NM.sub.--001981), Eps15R (NM.sub.--021235), dynamin II (DYNII,
NM.sub.--004945), Rab5a (BC001267), Rab5b (NM.sub.--002868), Rab5c
(AF141304), and EEA.1 (XM.sub.--018197).
[0443] For each gene, four siRNAs duplexes with the highest scores
were selected and a BLAST search was conducted for each of them
using the Human EST database. In order to minimize the potential
for off-target silencing effects, only those sequences with more
than three mismatches against un-related sequences were used. All
duplexes were synthesized at Dharmacon, Inc. as 21-mers with 3'-UU
overhangs using a modified method of 2'-ACE chemistry, Scaringe
(2000) Advanced 5'-silyl-2'-orthoester approach to RNA
oligonucleotide synthesis, Methods Enzymol. 317:3, and the
antisense strand was chemically phosphorylated to insure maximized
activity.
[0444] HeLa cells were grown in Dulbecco's modified Eagle's medium
(DMEM) containing 10% fetal bovine serum, antibiotics and
glutamine. siRNA duplexes were resuspended in 1.times. siRNA
Universal buffer (Dharmacon, Inc.) to 20 .mu.M prior to
transfection. HeLa cells in 12-well plates were transfected twice
with 4 .mu.l of 20 .mu.M siRNA duplex in 3 .mu.l Lipofectamine 2000
reagent (Invitrogen, Carlsbad, Calif., USA) at 24-hour intervals.
For the transfections in which 2 or 3 siRNA duplexes were included,
the amount of each duplex was decreased, so that the total amount
was the same as in transfections with single siRNAs. Cells were
plated into normal culture medium 12 hours prior to experiments,
and protein levels were measured 2 or 4 days after the first
transfection.
[0445] Equal amounts of lysates were resolved by electrophoresis,
blotted, and stained with the antibody specific to targeted
protein, as well as antibodies specific to unrelated proteins, PP1
phosphatase and Tsg1011 (not shown). The cells were lysed in Triton
X-100/glycerol solubilization buffer as described previously.
Tebar, Bohlander, & Sorkin (1999) Clathrin Assembly Lymphoid
Myeloid Leukemia (CALM) Protein: Localization in Endocytic-coated
Pits, Interactions with Clathrin, and the Impact of Overexpression
on Clathrin-mediated Traffic, Mol. Biol. Cell, 10:2687. Cell
lysates were electrophoresed, transferred to nitrocellulose
membranes, and Western blotting was performed with several
antibodies followed by detection using enhanced chemiluminescence
system (Pierce, Inc). Several x-ray films were analyzed to
determine the linear range of the chemiluminescence signals, and
the quantifications were performed using densitometry and
AlphaImager v5.5 software (Alpha Innotech Corporation). In
experiments with Eps15R-targeted siRNAs, cell lysates were
subjected to immunoprecipitation with Ab860, and Eps15R was
detected in immunoprecipitates by Western blotting as described
above.
[0446] The antibodies to assess the levels of each protein by
Western blot were obtained from the following sources: monoclonal
antibody to clathrin heavy chain (TD.1) was obtained from American
Type Culture Collection (Rockville, Md., USA); polyclonal antibody
to dynamin II was obtained from Affinity Bioreagents, Inc. (Golden,
Colo., USA); monoclonal antibodies to EEA.1 and Rab5a were
purchased from BD Transduction Laboratories (Los Angeles, Calif.,
USA); the monoclonal antibody to Tsg101 was purchased from Santa
Cruz Biotechnology, Inc. (Santa Cruz, Calif., USA); the monoclonal
antibody to GFP was from ZYMED Laboratories Inc. (South San
Francisco, Calif., USA); the rabbit polyclonal antibodies Ab32
specific to .alpha.-adaptins and Ab20 to CALM were described
previously (Sorkin et al. (1995) Stoichiometric Interaction of the
Epidermal Growth Factor Receptor with the Clathrin-associated
Protein Complex AP-2, J. Biol. Chem., 270:619), the polyclonal
antibodies to clathrin light chains A and B were kindly provided by
Dr. F. Brodsky (UCSF); monoclonal antibodies to PP1 (BD
Transduction Laboratories) and .alpha.-Actinin (Chemicon) were
kindly provided by Dr. M. Dell'Acqua (University of Colorado);
Eps15 Ab577 and Eps15R Ab860 were kindly provided by Dr. P. P. Di
Fiore (European Cancer Institute).
[0447] FIG. 11 demonstrates the in vivo functionality of 48
individual siRNAs, selected using Formula VIII (most of them will
meet the criteria incorporated by Formula IX as well) targeting 12
genes. Various cell lines were transfected with siRNA duplexes
(Dup1-4) or pools of siRNA duplexes (Pool), and the cells were
lysed 3 days after transfection with the exception of CALM (2 days)
and .beta.2 (4 days).
[0448] Note a .beta.1-adaptin band (part of AP-1 Golgi adaptor
complex) that runs slightly slower than .beta.2 adaptin. CALM has
two splice variants, 66 and 72 kD. The full-length Eps15R (a
doublet of .about.130 kD) and several truncated spliced forms of
.about.100 kD and .about.70 kD were detected in Eps15R
immunoprecipitates (shown by arrows). The cells were lysed 3 days
after transfection. Equal amounts of lysates were resolved by
electrophoresis and blotted with the antibody specific to a
targeted protein (GFP antibody for YFP fusion proteins) and the
antibody specific to unrelated proteins PP1 phosphatase or
.alpha.-actinin, and TSG101. The amount of protein in each specific
band was normalized to the amount of non-specific proteins in each
lane of the gel. Nearly all of them appear to be functional, which
establishes that Formula VIII and IX can be used to predict siRNAs'
functionality in general in a genome wide manner.
[0449] To generate the fusion of yellow fluorescent protein (YFP)
with Rab5b or Rab5c (YFP-Rab5b or YFP-Rab5c), a DNA fragment
encoding the full-length human Rab5b or Rab5c was obtained by PCR
using Pfu polymerase (Stratagene) with a SacI restriction site
introduced into the 5' end and a KpnI site into the 3' end and
cloned into pEYFP-C1 vector (CLONTECH, Palo Alto, Calif., USA).
GFP-CALM and YFP-Rab5a were described previously (Tebar, Bohlander,
& Sorkin (1999) Clathrin Assembly Lymphoid Myeloid Leukemia
(CALM) Protein: Localization in Endocytic-coated Pits, Interactions
with Clathrin, and the Impact of Overexpression on
Clathrin-mediated Traffic, Mol. Biol. Cell 10:2687).
Example IV
Validation of the Algorithm Using Eg5, GADPH, ATE1, MEK2, MEK1, QB,
LaminA/C, c-Myc, Human Cyclophilin, and Mouse Cyclophilin
[0450] A number of genes have been identified as playing
potentially important roles in disease etiology. Expression
profiles of normal and diseased kidneys has implicated Edg5 in
immunoglobulin A neuropathy, a common renal glomerular disease.
Myc1, MEK1/2 and other related kinases have been associated with
one or more cancers, while lamins have been implicated in muscular
dystrophy and other diseases. For these reasons, siRNA directed
against the genes encoding these classes of molecules would be
important research and therapeutic tools.
[0451] FIG. 12 illustrates four siRNAs targeting 10 different genes
(Table V for sequence and accession number information) that were
selected according to the Formula VIII and assayed as individuals
and pools in HEK293 cells. The level of siRNA induced silencing was
measured using the B-DNA assay. These studies demonstrated that
thirty-six out of the forty individual SMART-selected siRNA tested
are functional (90%) and all 10 pools are fully functional.
Example V
Validation of the Algorithm Using Bcl2
[0452] Bcl-2 is a .about.25 kD, 205-239 amino acid, anti-apoptotic
protein that contains considerable homology with other members of
the BCL family including BCLX, MCL1, BAX, BAD, and BIK. The protein
exists in at least two forms (Bcl2a, which has a hydrophobic tail
for membrane anchorage, and Bcl2b, which lacks the hydrophobic
tail) and is predominantly localized to the mitochondrial membrane.
While Bcl2 expression is widely distributed, particular interest
has focused on the expression of this molecule in B and T cells.
Bcl2 expression is down-regulated in normal germinal center B cells
yet in a high percentage of follicular lymphomas, Bcl2 expression
has been observed to be elevated. Cytological studies have
identified a common translocation ((14; 18)(q32; q32)) amongst a
high percentage (>70%) of these lymphomas. This genetic lesion
places the Bcl2 gene in juxtaposition to immunoglobulin heavy chain
gene (IgH) encoding sequences and is believed to enforce
inappropriate levels of gene expression, and resistance to
programmed cell death in the follicle center B cells. In other
cases, hypomethylation of the Bcl2 promoter leads to enhanced
expression and again, inhibition of apoptosis. In addition to
cancer, dysregulated expression of Bcl-2 has been correlated with
multiple sclerosis and various neurological diseases.
[0453] The correlation between Bcl-2 translocation and cancer makes
this gene an attractive target for RNAi. Identification of siRNA
directed against the bcl2 transcript (or Bcl2-IgH fusions) would
further our understanding Bcl2 gene function and possibly provide a
future therapeutic agent to battle diseases that result from
altered expression or function of this gene.
In Silico Identification of Functional siRNA
[0454] To identify functional and hyperfunctional siRNA against the
Bcl2 gene, the sequence for Bcl-2 was downloaded from the NCBI
Unigene database and analyzed using the Formula VIII algorithm. As
a result of these procedures, both the sequence and
SMARTSCORES.TM., or siRNA rankings of the Bcl2 siRNA were obtained
and ranked according to their functionality. Subsequently, these
sequences were BLAST'ed (database) to insure that the selected
sequences were specific and contained minimal overlap with
unrelated genes. The SMARTSCORES.TM., or siRNA rankings for the top
10 Bcl-2 siRNA are identified in FIG. 13.
In Vivo Testing of Bcl-2 SiRNA
[0455] Bcl-2 siRNAs having the top ten SMARTSCORES.TM., or siRNA
rankings were selected and tested in a functional assay to
determine silencing efficiency. To accomplish this, each of the ten
duplexes were synthesized using 2'-O-ACE chemistry and transfected
at 100 nM concentrations into cells. Twenty-four hours later assays
were performed on cell extracts to assess the degree of target
silencing. Controls used in these experiments included mock
transfected cells, and cells that were transfected with a
non-specific siRNA duplex.
[0456] The results of these experiments are presented below (and in
FIG. 14) and show that all ten of the selected siRNA induce 80% or
better silencing of the Bcl2 message at 100 nM concentrations.
These data verify that the algorithm successfully identified
functional Bcl2 siRNA and provide a set of functional agents that
can be used in experimental and therapeutic environments.
TABLE-US-00009 siRNA 1 GGGAGAUAGUGAUGAAGUA SEQ. ID NO. 302 siRNA 2
GAAGUACAUCCAUUAUAAG SEQ. ID NO. 303 siRNA 3 GUACGACAACCGGGAGAUA
SEQ. ID NO. 304 siRNA 4 AGAUAGUGAUGAAGUACAU SEQ. ID NO. 305 siRNA 5
UGAAGACUCUGCUCAGUUU SEQ. ID NO. 306 siRNA 6 GCAUGCGGCCUCUGUUUGA
SEQ. ID NO. 307 siRNA 7 UGCGGCCUCUGUUUGAUUU SEQ. ID NO. 308 siRNA 8
GAGAUAGUGAUGAAGUACA SEQ. ID NO. 309 siRNA 9 GGAGAUAGUGAUGAAGUAC
SEQ. ID NO. 310 siRNA 10 GAAGACUCUGCUCAGUUUG SEQ. ID NO. 311
[0457] Bcl2 siRNA: Sense Strand, 5'.fwdarw.3'
Example VI
Sequences Selected by the Algorithm
[0458] Sequences of the siRNAs selected using Formulas (Algorithms)
VIII and IX with their corresponding ranking, which have been
evaluated for the silencing activity in vivo in the present study
(Formula VIII and IX, respectively) are shown in Table V. It should
be noted that the "t" residues in Table V, and elsewhere, when
referring to siRNA, should be replaced by "u" residues.
TABLE-US-00010 TABLE V FORMULA FORMULA GENE Name SEQ. ID No.
FTLLSEQTENCE VIII IX CLTC NM_004859 0312 GAAAGAATCTGTAGAGAAA 76
94.2 CLTC NM_004859 0313 GCAATGAGCTGTTTGAAGA 65 39.9 CLTC NM_004859
0314 TGACAAAGGTGGATAAATT 57 38.2 CLTC NM_004859 0315
GGAAATGGATCTCTTTGAA 54 49.4 CLTA NM_001833 0316 GGAAAGTAATGGTCCAACA
22 55.5 CLTA NM_001833 0317 AGACAGTTATGCAGCTATT 4 22.9 CLTA
NM_001833 0318 CCAATTCTCGGAAGCAAGA 1 17 CLTA NM_001833 0319
GAAAGTAATGGTCCAACAG -1 -13 CLTB NM_001834 0320 GCGCCAGAGTGAACAAGTA
17 57.5 CLTB NM_001834 0321 GAAGGTGGCCCAGCTATGT 15 -8.6 CLTB
NM_001834 0322 GGAACCAGCGCCAGAGTGA 13 40.5 CLTB NM_001834 0323
GAGCGAGATTGCAGGCATA 20 61.7 CALM U45976 0324 GTTAGTATCTGATGACTTG 36
-34.6 CALM U45976 0325 GAAATGGAACCACTAAGAA 33 46.1 CALM U45976 0326
GGAAATGGAACCACTAAGA 30 61.2 CALM U45976 0327 CAACTACACTTTCCAATGC 28
6.8 EPS15 NM_001981 0328 CCACCAAGATTTCATGATA 48 25.2 EPS15
NM_001981 0329 GATCGGAACTCCAACAAGA 43 49.3 EPS15 NM_001981 0330
AAACGGAGCTACAGATTAT 39 11.5 EPS15 NM_001981 0331
CCACACAGCATTCTTGTAA 33 -23.6 EPS15R NM_021235 0332
GAAGTTACCTTGAGCAATC 48 33 EPS15R NM_021235 0333 GGACTTGGCCGATCCAGAA
27 33 EPS15R NM_021235 0334 GCACTTGGATCGAGATGAG 20 1.3 EPS15R
NM_021235 0335 CAAAGACCAATTCGCGTTA 17 27.7 DNM2 NM_004945 0336
CCGAATCAATCGCATCTTC 6 -29.6 DNM2 NM_004945 0337 GACATGATCCTGCAGTTCA
5 -14 DNM2 NM_004945 0338 GAGCGAATCGTCACCACTT 5 24 DNM2 NM_004945
0339 CCTCCGAGCTGGCGTCTAC -4 -63.6 ARF6 AF93885 0340
TCACATGGTTAACCTCTAA 27 -21.1 ARF6 AF93885 0341 GATGAGGGACGCCATAATC
7 -38.4 ARF6 AF93885 0342 CCTCTAACTACAAATCTTA 4 16.9 ARF6 AF93885
0343 GGAAGGTGCTATCCAAAAT 4 11.5 RAB5A BC001267 0344
GCAAGCAAGTCCTAACATT 40 25.1 RAB5A BC001267 0345 GGAAGAGGAGTAGACCTTA
17 50.1 RAB5A BC001267 0346 AGGAATCAGTGTTGTAGTA 16 11.5 RAB5A
BC001267 0347 GAAGAGGAGTAGACCTTAC 12 7 RAB5B NM_002868 0348
GAAAGTCAAGCCTGGTATT 14 18.1 RAB5B NM_002868 0349
AAAGTCAAGCCTGGTATTA 6 -17.8 RAB5B NM_002868 0350
GCTATGAACGTGAATGATC 3 -21.1 RAB5B NM_002868 0351
CAAGCCTGGTATTACGTTT -7 -37.5 RAB5C AF141304 0352
GGAACAAGATCTGTCAATT 38 51.9 RAB5C AF141304 0353 GCAATGAACGTGAACGAAA
29 43.7 RAB5C AF141304 0354 CAATGAACGTGAACGAAAT 18 43.3 RAB5C
AF141304 0355 GGACAGGAGCGGTATCACA 6 18.2 EEA1 XM_018197 0356
AGACAGAGCTTGAGAATAA 67 64.1 EEA1 XM_018197 0357 GAGAAGATCTTTATGCAAA
60 48.7 EEA1 XM_018197 0358 GAAGAGAAATCAGCAGATA 58 45.7 EEA1
XM_018197 0359 GCAAGTAACTCAACTAACA 56 72.3 AP2B1 NM_001282 0360
GAGCTAATCTGCCACATTG 49 -12.4 AP2B1 NM_001282 0361
GCAGATGAGTTACTAGAAA 44 48.9 AP2B1 NM_001282 0362
CAACTTAATTGTCCAGAAA 41 28.2 AP2B1 NM_001282 0363
CAACACAGGATTCTGATAA 33 -5.8 PLK NM_005030 0364 AGATTGTGCCTAAGTCTCT
-35 -3.4 PLK NM_005030 0365 ATGAAGATCTGGAGGTGAA 0 -4.3 PLK
NM_005030 0366 TTTGAGACTTCTTGCCTAA -5 -27.7 PLK NM_005030 0367
AGATCACCCTCCTTAAATA 15 72.3 GAPDH NM_002046 0368
CAACGGATTTGGTCGTATT 27 -2.8 GAPDH NM_002046 0369
GAAATCCCATCACCATCTT 24 3.9 GAPDH NM_002046 0370 GACCTCAACTACATGGTTT
22 -22.9 GAPDH NM_002046 0371 TGGTTTACATGTTCCAATA 9 9.8 c-Myc 0372
GAAGAAATCGATGTTGTTT 31 -11.7 c-Myc 0373 ACACAAACTTGAACAGCTA 22 51.3
c-Myc 0374 GGAAGAAATCGATGTTGTT 18 26 c-Myc 0375 GAAACGACGAGAACAGTTG
18 -8.9 MAP2K1 NM_002755 0376 GCACATGGATGGAGGTTCT 26 16 MAP2K1
NM_002755 0377 GCAGAGAGAGCAGATTTGA 16 0.4 MAP2K1 NM_002755 0378
GAGGTTCTCTGGATCAAGT 14 15.5 MAP2K1 NM_002755 0379
GAGCAGATTTGAAGCAACT 14 18.5 MAP2K2 NM_030662 0380
CAAAGACGATGACTTCGAA 37 26.4 MAP2K2 NM_030662 0381
GATCAGCATTTGCATGGAA 24 -0.7 MAP2K2 NM_030662 0382
TCCAGGAGTTTGTCAATAA 17 -4.5 MAP2K2 NM_030662 0383
GGAAGCTGATCCACCTTGA 16 59.2 KNSL1 (EG5) NM_004523 0384
GCAGAAATCTAAGGATATA 53 35.8 KNSL1 (EG5) NM_004523 0385
CAACAAGGATGAAGTCTAT 50 18.3 KNSL1 (EG5) NM_004523 0386
CAGCAGAAATCTAAGGATA 41 32.7 KNSL1 (EG5) NM_004523 0387
CTAGATGGCTTTCTCAGTA 39 3.9 CyclophilinA NM_021130 0388
AGACAAGGTCCCAAAGACA -16 58.1 CyclophilinA NM_021130 0389
GGAATGGCAAGACCAGCAA -6 36 CyclophilinA NM_021130 0390
AGAATTATTCCAGGGTTTA -3 16.1 CyclophilinA NM_021130 0391
GCAGACAAGGTCCCAAAGA 8 8.9 LAMIN A/C NM_170707 0392
AGAACCAGCTTCAGGATGA 31 38.8 LAMIN A/C NM_170707 0393
GAGCTTGACTTCCAGAAGA 33 22.4 LAMIN A/C NM_170707 0394
CCACCGAAGTTCACCCTAA 21 27.5 LAMIN A/C NM_170707 0395
GAGAAGAGCTCCTCCATCA 55 30.1 CyclophilinB M60857 0396
GAAAGAGCATCTACGGTGA 41 83.9 CyclophilinB M60857 0397
GAAAGGATTTGGCTACAAA 53 59.1 CyclophilinB M60857 0398
ACAGCAAATTCCATCGTGT 20 28.8 CyclophilinB M60857 0399
GGAAAGACTGTTCCAAAAA 2 27 DBI1 NM_020548 0400 CAACACGCCTCATCCTCTA 27
7.6 DBI2 NM_020548 0401 CATGAAAGCTTACATCAAC 25 -30.8 DBI3 NM_020548
0402 AAGATGCCATGAAAGCTTA 17 22 DBI4 NM_020548 0403
GCACATACCGCCTGAGTCT 15 3.9 rLUC1 0404 GATCAAATCTGAAGAAGGA 57 49.2
rLUC2 0405 GCCAAGAAGTTTCCTAATA 50 13.7 rLUC3 0406
CAGCATATCTTGAACCATT 41 -2.2 rLUC4 0407 GAACAAAGGAAACGGATGA 39 29.2
SeAP1 NM_031313 0408 CGGAAACGGTCCAGGCTAT 6 26.9 SeAP2 NM_031313
0409 GCTTCGAGCAGACATGATA 4 -11.2 SeAP3 NM_031313 0410
CCTACACGGTCCTCCTATA 4 4.9 SeAP4 NM_031313 0411 GCCAAGAACCTCATCATCT
1 -9.9 fLUC1 0412 GATATGGGCTGAATACAAA 54 40.4 fLUC2 0413
GCACTCTGATTGACAAATA 47 54.7 fLUC3 0414 TGAAGTCTCTGATTAAGTA 46 34.5
fLUC4 0415 TCAGAGAGATCCTCATAAA 40 11.4 mCyclo_1 NM_008907 0416
GCAAGAAGATCACCATTTC 52 46.4 mCyclo_2 NM_008907 0417
GAGAGAAATTTGAGGATGA 36 70.7 mCyclo_3 NM_008907 0418
GAAAGGATTTGGCTATAAG 35 -1.5 mCyclo_4 NM_008907 0419
GAAAGAAGGCATGAACATT 27 10.3 BCL2_1 NM_000633 0420
GGGAGATAGTGATGAAGTA 21 72 BCL2_2 NM_000633 0421 GAAGTACATCCATTATAAG
1 3.3 BCL2_3 NM_000633 0422 GTACGACAACCGGGAGATA 1 35.9 BCL2_4
NM_000633 0423 AGATAGTGATGAAGTACAT -12 22.1 BCL2_5 NM_000633 0424
TGAAGACTCTGCTCAGTTT 36 19.1 BCL2_6 NM_000633 0425
GCATGCGGCCTCTGTTTGA 5 -9.7 QB1 NM_003365.1 0426 GCACACAGCUUACUACAUC
52 -4.8 QB2 NM_003365.1 0427 GAAAUGCCCUGGUAUCUCA 49 22.1 QB3
NM_003365.1 0428 GAAGGAACGUGAUGUGAUC 34 22.9 QB4 NM_003365.1 0429
GCACUACUCCUGUGUGUGA 28 20.4 ATE1-1 NM_007041 0430
GAACCCAGCUGGAGAACUU 45 15.5
ATET-2 NM_007041 0431 GAUAUACAGUGUGAUCUUA 40 12.2 ATE1-3 NM_007041
0432 GUACUACGAUCCUGAUUAU 37 32.9 ATE1-4 NM_007041 0433
GUGCCGACCUUUACAAUUU 35 18.2 EGFR-1 NM_005228 0434
GAAGGAAACTGAATTCAAA 68 79.4 EGFR-1 NM_005228 0435
GGAAATATGTACTACGAAA 49 49.5 EGFR-1 NM_005228 0436
CCACAAAGCAGTGAATTTA 41 7.6 EGFR-1 NM_005228 0437
GTAACAAGCTCACGCAGTT 40 25.9
[0459] Many of the genes to which the described siRNA are directed
play critical roles in disease etiology. For this reason, the
siRNAs listed in the sequence listing may potentially act as
therapeutic agents. A number of prophetic examples follow and
should be understood in view of the siRNA that are identified in
the sequence listing. To isolate these siRNAs, the appropriate
message sequence for each gene is analyzed using one of the before
mentioned formulas (preferably formula VIII) to identify potential
siRNA targets. Subsequently these targets are BLAST'ed to eliminate
homology with potential off-targets.
Example VII
Evidence for the Benefits of Pooling
[0460] Evidence for the benefits of pooling have been demonstrated
using the reporter gene, luciferase. Ninety siRNA duplexes were
synthesized using Dharmacon proprietary ACE.RTM. chemistry against
one of the standard reporter genes: firefly luciferase. The
duplexes were designed to start two base pairs apart and to cover
approximately 180 base pairs of the luciferase gene (see sequences
in Table III). Subsequently, the siRNA duplexes were co-transfected
with a luciferase expression reporter plasmid into HEK293 cells
using standard transfection protocols and luciferase activity was
assayed at 24 and 48 hours.
[0461] Transfection of individual siRNAs showed standard
distribution of inhibitory effect. Some duplexes were active, while
others were not. FIG. 15 represents a typical screen of ninety
siRNA duplexes (SEQ. ID NO. 0032-0120) positioned two base pairs
apart. As the figure suggests, the functionality of the siRNA
duplex is determined more by a particular sequence of the
oligonucleotide than by the relative oligonucleotide position
within a gene or excessively sensitive part of the mRNA, which is
important for traditional anti-sense technology.
[0462] When two continuous oligonucleotides were pooled together, a
significant increase in gene silencing activity was observed (see
FIGS. 16A and B). A gradual increase in efficacy and the frequency
of pools functionality was observed when the number of siRNAs
increased to 3 and 4 (FIGS. 16A, 16B, 17A, and 17B). Further, the
relative positioning of the oligonucleotides within a pool did not
determine whether a particular pool was functional (see FIGS. 18A
and 18B, in which 100% of pools of oligonucleotides distanced by 2,
10 and 20 base pairs were functional).
[0463] However, relative positioning may nonetheless have an
impact. An increased functionality may exist when the siRNA are
positioned continuously head to toe (5' end of one directly
adjacent to the 3' end of the others).
[0464] Additionally, siRNA pools that were tested performed at
least as well as the best oligonucleotide in the pool, under the
experimental conditions whose results are depicted in FIG. 19.
Moreover, when previously identified non-functional and marginally
(semi) functional siRNA duplexes were pooled together in groups of
five at a time, a significant functional cooperative action was
observed (see FIG. 20). In fact, pools of semi-active
oligonucleotides were 5 to 25 times more functional than the most
potent oligonucleotide in the pool. Therefore, pooling several
siRNA duplexes together does not interfere with the functionality
of the most potent siRNAs within a pool, and pooling provides an
unexpected significant increase in overall functionality
Example VIII
Additional Evidence of the Benefits of Pooling
[0465] Experiments were performed on the following genes:
.beta.-galactosidase, Renilla luciferase, and Secreted alkaline
phosphatase, which demonstrates the benefits of pooling. (see FIG.
21). Individual and pools of siRNA (described in Figure legend 21)
were transfected into cells and tested for silencing efficiency.
Approximately 50% of individual siRNAs designed to silence the
above-specified genes were functional, while 100% of the pools that
contain the same siRNA duplexes were functional.
Example IX
Highly Functional siRNA
[0466] Pools of five siRNAs in which each two siRNAs overlap to
10-90% resulted in 98% functional entities (>80% silencing).
Pools of siRNAs distributed throughout the mRNA that were evenly
spaced, covering an approximate 20-2000 base pair range, were also
functional. When the pools of siRNA were positioned continuously
head to tail relative to mRNA sequences and mimicked the natural
products of Dicer cleaved long double stranded RNA, 98% of the
pools evidenced highly functional activity (>95% silencing).
Example X
Human Cyclophilin B
[0467] Table III above lists the siRNA sequences for the human
cyclophilin B protein. A particularly functional siRNA may be
selected by applying these sequences to any of Formula I to VII
above.
[0468] Alternatively, one could pool 2, 3, 4, 5 or more of these
sequences to create a kit for silencing a gene. Preferably, within
the kit there would be at least one sequence that has a relatively
high predicted functionality when any of Formulas I-VII is
applied.
Example XI
Sample Pools of siRNAs and their Application to Human Disease
[0469] The genetic basis behind human disease is well documented
and siRNA may be used as both research or diagnostic tools and
therapeutic agents, either individually or in pools. Genes involved
in signal transduction, the immune response, apoptosis, DNA repair,
cell cycle control, and a variety of other physiological functions
have clinical relevance and therapeutic agents that can modulate
expression of these genes may alleviate some or all of the
associated symptoms. In some instances, these genes can be
described as a member of a family or class of genes and siRNA
(randomly, conventionally, or rationally designed) can be directed
against one or multiple members of the family to induce a desired
result.
[0470] To identify rationally designed siRNA to each gene, the
sequence was analyzed using Formula VIII or Formula X to identify
rationally designed siRNA. To confirm the activity of these
sequences, the siRNA are introduced into a cell type of choice
(e.g., HeLa cells, HEK293 cells) and the levels of the appropriate
message are analyzed using one of several art proven techniques.
siRNA having heightened levels of potency can be identified by
testing each of the before mentioned duplexes at increasingly
limiting concentrations. Similarly, siRNA having increased levels
of longevity can be identified by introducing each duplex into
cells and testing functionality at 24, 48, 72, 96, 120, 144, 168,
and 192 hours after transfection. Agents that induce >95%
silencing at sub-nanomolar concentrations and/or induce functional
levels of silencing for >96 hours are considered
hyperfunctional.
Example XII
Validation of Multigene Knockout Using Rab5 and Eps
[0471] Two or more genes having similar, overlapping functions
often leads to genetic redundancy. Mutations that knockout only one
of, e.g., a pair of such genes (also referred to as homologs)
results in little or no phenotype due to the fact that the
remaining intact gene is capable of fulfilling the role of the
disrupted counterpart. To fully understand the function of such
genes in cellular physiology, it is often necessary to knockout or
knockdown both homologs simultaneously. Unfortunately, concomitant
knockdown of two or more genes is frequently difficult to achieve
in higher organisms (e.g., mice) thus it is necessary to introduce
new technologies dissect gene function. One such approach to
knocking down multiple genes simultaneously is by using siRNA. For
example, FIG. 11 showed that rationally designed siRNA directed
against a number of genes involved in the clathrin-mediated
endocytosis pathway resulted in significant levels of protein
reduction (e.g., >80%). To determine the effects of gene
knockdown on clathrin-related endocytosis, internalization assays
were performed using epidermal growth factor and transferrin.
Specifically, mouse receptor-grade EGF (Collaborative Research
Inc.) and iron-saturated human transferrin (Sigma) were iodinated
as described previously (Jiang, X., Huang, F., Marusyk, A. &
Sorkin, A. (2003) Mol Biol Cell 14, 858-70). HeLa cells grown in
12-well dishes were incubated with .sup.125I-EGF (1 ng/ml) or
.sup.125I-transferrin (1 .mu.g/ml) in binding medium (DMEM, 0.1%
bovine serum albumin) at 37.degree. C., and the ratio of
internalized and surface radioactivity was determined during 5-min
time course to calculate specific internalization rate constant
k.sub.e as described previously (Jiang, X et al.). The measurements
of the uptakes of radiolabeled transferrin and EGF were performed
using short time-course assays to avoid influence of the recycling
on the uptake kinetics, and using low ligand concentration to avoid
saturation of the clathrin-dependent pathway (for EGF Lund, K. A.,
Opresko, L. K., Strarbuck, C., Walsh, B. J. & Wiley, H. S.
(1990) J. Biol. Chem. 265, 15713-13723).
[0472] The effects of knocking down Rab5a, 5b, 5c, Eps, or Eps 15R
(individually) are shown in FIG. 22 and demonstrate that disruption
of single genes has little or no effect on EGF or Tfn
internalization. In contrast, simultaneous knock down of Rab5a, 5b,
and 5c, or Eps and Eps 15R, leads to a distinct phenotype (note:
total concentration of siRNA in these experiments remained constant
with that in experiments in which a single siRNA was introduced,
see FIG. 23). These experiments demonstrate the effectiveness of
using rationally designed siRNA to knockdown multiple genes and
validates the utility of these reagents to override genetic
redundancy.
Example XIII
Validation of Multigene Targeting Using G6PD, GAPDH, PLK, and
UQC
[0473] Further demonstration of the ability to knock down
expression of multiple genes using rationally designed siRNA was
performed using pools of siRNA directed against four separate
genes. To achieve this, siRNA were transfected into cells (total
siRNA concentration of 100 nM) and assayed twenty-four hours later
by B-DNA. Results shown in FIG. 24 show that pools of rationally
designed molecules are capable of simultaneously silencing four
different genes.
Example XIV
Validation of Multigene Knockouts as Demonstrated by Gene
Expression Profiling, a Prophetic Example
[0474] To further demonstrate the ability to concomitantly
knockdown the expression of multiple gene targets, single siRNA or
siRNA pools directed against a collection of genes (e.g., 4, 8, 16,
or 23 different targets) are simultaneously transfected into cells
and cultured for twenty-four hours. Subsequently, mRNA is harvested
from treated (and untreated) cells and labeled with one of two
fluorescent probes dyes (e.g., a red fluorescent probe for the
treated cells, a green fluorescent probe for the control cells.).
Equivalent amounts of labeled RNA from each sample is then mixed
together and hybridized to sequences that have been linked to a
solid support (e.g., a slide, "DNA CHIP"). Following hybridization,
the slides are washed and analyzed to assess changes in the levels
of target genes induced by siRNA.
Example XV
Identifying Hyperfunctional siRNA
Identification of Hyperfunctional Bcl-2 siRNA
[0475] The ten rationally designed Bcl2 siRNA (identified in FIG.
13, 14) were tested to identify hyperpotent reagents. To accomplish
this, each of the ten Bcl-2 siRNA were individually transfected
into cells at a 300 pM (0.3 nM) concentrations. Twenty-four hours
later, transcript levels were assessed by B-DNA assays and compared
with relevant controls. As shown in FIG. 25, while the majority of
Bcl-2 siRNA failed to induce functional levels of silencing at this
concentration, siRNA 1 and 8 induced >80% silencing, and siRNA 6
exhibited greater than 90% silencing at this subnanomolar
concentration.
[0476] By way of prophetic examples, similar assays could be
performed with any of the groups of rationally designed genes
described in the Examples. Thus for instance, rationally designed
siRNA sequences directed against a gene of interest could be
introduced into cells at increasingly limiting concentrations to
determine whether any of the duplexes are hyperfunctional.
Example XVI
Gene Silencing
Prophetic Example
[0477] Below is an example of how one might transfect a cell.
[0478] Select a cell line. The selection of a cell line is usually
determined by the desired application. The most important feature
to RNAi is the level of expression of the gene of interest. It is
highly recommended to use cell lines for which siRNA transfection
conditions have been specified and validated.
[0479] Plate the cells. Approximately 24 hours prior to
transfection, plate the cells at the appropriate density so that
they will be approximately 70-90% confluent, or approximately
1.times.10.sup.5 cells/ml at the time of transfection. Cell
densities that are too low may lead to toxicity due to excess
exposure and uptake of transfection reagent-siRNA complexes. Cell
densities that are too high may lead to low transfection
efficiencies and little or no silencing. Incubate the cells
overnight. Standard incubation conditions for mammalian cells are
37.degree. C. in 5% CO.sub.2. Other cell types, such as insect
cells, require different temperatures and CO.sub.2 concentrations
that are readily ascertainable by persons skilled in the art. Use
conditions appropriate for the cell type of interest.
[0480] siRNA re-suspension. Add 20 .mu.l siRNA universal buffer to
each siRNA to generate a final concentration of 50 .mu.M.
[0481] siRNA-lipid complex formation. Use RNase-free solutions and
tubes. Using the following table, Table XI: TABLE-US-00011 TABLE XI
96-WELL 24-WELL MIXTURE 1 (TRANSIT-TKO-PLASMID DILUTION MIXTURE)
Opti-MEM 9.3 .mu.l 46.5 .mu.l TransIT-TKO (1 .mu.g/.mu.l) 0.5 .mu.l
2.5 .mu.l MIXTURE 1 FINAL VOLUME 10.0 .mu.l 50.0 .mu.l MIXTURE 2
(SIRNA DILUTION MIXTURE) Opti-MEM 9.0 .mu.l 45.0 .mu.l siRNA (1
.mu.M) 1.0 .mu.l 5.0 .mu.l MIXTURE 2 FINAL VOLUME 10.0 .mu.l 50.0
.mu.l MIXTURE 3 (SIRNA-TRANSFECTION REAGENT MIXTURE) Mixture 1 10
.mu.l 50 .mu.l Mixture 2 10 .mu.l 50 .mu.l MIXTURE 3 FINAL VOLUME
20 .mu.l 100 .mu.l Incubate 20 minutes at room temperature MIXTURE
4 (MEDIA-SIRNA/TRANSFECTION REAGENT MIXTURE) Mixture 3 20 .mu.l 100
.mu.l Complete media 80 .mu.l 400 .mu.l MIXTURE 4 FINAL VOLUME 100
.mu.l 500 .mu.l Incubate 48 hours at 37.degree. C.
[0482] Transfection. Create a Mixture 1 by combining the specified
amounts of OPTI-MEM serum free media and transfection reagent in a
sterile polystyrene tube. Create a Mixture 2 by combining specified
amounts of each siRNA with OPTI-MEM media in sterile 1 ml tubes.
Create a Mixture 3 by combining specified amounts of Mixture 1 and
Mixture 2. Mix gently (do not vortex) and incubate at room
temperature for 20 minutes. Create a Mixture 4 by combining
specified amounts of Mixture 3 to complete media. Add appropriate
volume to each cell culture well. Incubate cells with transfection
reagent mixture for 24-72 hours at 37.degree. C. This incubation
time is flexible. The ratio of silencing will remain consistent at
any point in the time period. Assay for gene silencing using an
appropriate detection method such as RT-PCR, Western blot analysis,
immunohistochemistry, phenotypic analysis, mass spectrometry,
fluorescence, radioactive decay, or any other method that is now
known or that comes to be known to persons skilled in the art and
that from reading this disclosure would useful with the present
invention. The optimal window for observing a knockdown phenotype
is related to the mRNA turnover of the gene of interest, although
24-72 hours is standard. Final Volume reflects amount needed in
each well for the desired cell culture format. When adjusting
volumes for a Stock Mix, an additional 10% should be used to
accommodate variability in pipetting, etc. Duplicate or triplicate
assays should be carried out when possible.
Example XVII
siRNAs that Target Minichromosome Maintenance Deficient 2, Mitotin
(MCM2)
[0483] siRNAs that target MCM2 [NCBI accession number
NM.sub.--004526], having sequences generated in silico by the
algorithms herein, are provided. In various embodiments, the siRNAs
are rationally designed. In various embodiments, the siRNAs are
functional or hyperfunctional. These siRNA that have been generated
by the algorithms of the present invention include: TABLE-US-00012
AAGCUGACCUGGAAGAGAA; (SEQ. ID NO. 438) AAGCUUGUCUGAUCGUUCA; (SEQ.
ID NO. 439) AAUGUUAGCCUCAAUUUCU; (SEQ. ID NO. 440)
ACAAGGGCCUCUUCUGUGA; (SEQ. ID NO. 441) ACAAUGACAUCUUUGAAUC; (SEQ.
ID NO. 442) ACACAGCACCAGAAUGUAU; (SEQ. ID NO. 443)
ACACUGGUUUGAAGAAUGU; (SEQ. ID NO. 444) ACAGGAAGAUGUACGGAGA; (SEQ.
ID NO. 445) ACGGCGAGGUCAUGAAGAA; (SEQ. ID NO. 446)
AGAACAUUAAGAAGGGCAA; (SEQ. ID NO. 447) AGACAAUGACAUCUUUGAA; (SEQ.
ID NO. 448) AGACAUACCGAGCUAAAUU; (SEQ. ID NO. 449)
AGACCCAACUAUGAUUAGA; (SEQ. ID NO. 450) AGACCUGUGCCUGCCAUUA; (SEQ.
ID NO. 451) AGCAUGAAAUGGAUACUUA; (SEQ. ID NO. 452)
AGCUAAAUUCUGUGGAGUA; (SEQ. ID NO. 453) AGGAAGAUGUACGGAGACA; (SEQ.
ID NO. 454) AUACCGAGCUAAAUUCUGU; (SEQ. ID NO. 455)
AUAUGGAAAUUCUCUCAGA; (SEQ. ID NO. 456) AUAUUAAGGUGUGGCUUUA; (SEQ.
ID NO. 457) AUGAACAAAUGGCCUUUAU; (SEQ. ID NO. 458)
AUGAAGACAUACCGAGCUA; (SEQ. ID NO. 459) AUGUAAAGCUUGUCUGAUC; (SEQ.
ID NO. 460) CAAGUGAAUUUCCUGUAAC; (SEQ. ID NO. 461)
CAAGUUGUUCCUUAAGUCA; (SEQ. 10 NO. 462) CACAGCACCAGAAUGUAUA; (SEQ.
ID NO. 463) CACCAUAGGUAGAAUGUAA; (SEQ. ID NO. 464)
CAGAAAGGUUAGUAUCAUC; (SEQ. ID NO. 465) CAGUAGCACAAGUUAUUUA; (SEQ.
ID NO. 466) CAUCAGAUAGCUCUUAUAC; (SEQ. ID NO. 467)
CCAACUAUGAUUAGAGCCA; (SEQ. ID NO. 468) CCACCCGGGUUACCAAUGA; (SEQ.
ID NO. 469) CCAGACCCAACUAUGAUUA; (SEQ. ID NO. 470)
CCAGAGAGUGAGAGACAUU; (SEQ. ID NO. 471) CCAGAGCAGCUGCAAGUAC; (SEQ.
ID NO. 472) CCUGCAGGCUAGAGAAGCA; (SEQ. ID NO. 473)
CCUGCUAUUUGAAGUGUAA; (SEQ. ID NO. 474) CGAGUAAUAUGCCUGCUAU; (SEQ.
ID NO. 475) CGAUUAGACUGGACAGCUU; (SEQ. ID NO. 476)
CGGAGACAAUGACAUCUUU; (SEQ. ID NO. 477) CGGCUUACCGACUGGAAGA; (SEQ.
ID NO. 478) CUAGGUAGGAAAUGUGGUA; (SEQ. ID NO. 479)
CUAUAUAGCUGAUCAGUUU; (SEQ. ID NO. 480) CUCCAAGCCUAUCAAGUUU; (SEQ.
ID NO. 481) GAAAAGAUUCCCACCCAAU; (SEQ. ID NO. 482)
GAAAUGGAUACUUAUAUGG; (SEQ. ID NO. 483) GAACAAAUGGCCUUUAUUA; (SEQ.
ID NO. 484) GAACACCAUAGGUAGAAUG; (SEQ. ID NO. 485)
GAACAUUAAGAAGGGCAAA; (SEQ. ID NO. 486) GAACUGAUUCACAUCUCAU; (SEQ.
ID NO. 487) GAAGAAUGUUAAGACUUGA; (SEQ. ID NO. 488)
GAAGACAUACCGAGCUAAA; (SEQ. ID NO. 489) GAAGCUGACCUGGAAGAGA; (SEQ.
ID NO. 490) GAAGUGUAAUUGAGAAGGA; (SEQ. ID NO. 491)
GAAUGACACUGUUCAGGAA; (SEQ. ID NO. 492) GAAUGUAAAGCUUGUCUGA; (SEQ.
ID NO. 493) GACAAUGACAUCUUUGAAU; (SEQ. ID NO. 494)
GACACUGGUUUGAAGAAUG; (SEQ. ID NO. 495) GACAGUUUAUUUGUUGAGA; (SEQ.
ID NO. 496) GACAUACCGAGCUAAAUUC; (SEQ. ID NO. 497)
GACAUUAACUCAUUAGACU; (SEQ. ID NO. 498) GACCAAAAGUUACAUGUUU; (SEQ.
ID NO. 499) GACCUGGAAGAGAACAUUA; (SEQ. ID NO. 500)
GACCUGUGCCUGCCAUUAC; (SEQ. ID NO. 501) GACUGGAACUUGAACUGAU; (SEQ.
ID NO. 502) GAGACAAUGACAUCUUUGA; (SEQ. ID NO. 503)
GAGAGACAUUAACUCAUUA; (SEQ. ID NO. 504) GAGUCAAGUUGUUCCUUAA; (SEQ.
ID NO. 505) GAUAGCUCUUAUACGAGUA; (SEQ. ID NO. 506)
GCAAGUGAAUUUCCUGUAA; (SEQ. ID NO. 507) GCACAAGGGCCUCUUCUGU; (SEQ.
ID NO. 508) GCACCAGAAUGUAUAUUAA; (SEQ. ID NO. 509)
GCACCAGCAUGAAGACAUA; (SEQ. ID NO. 510) GCAGAAAGGUUAGUAUCAU; (SEQ.
ID NO. 511) GCAGGCUGAUUUCUAGGUA; (SEQ. ID NO. 512)
GCAUGAAAUGGAUACUUAU; (SEQ. ID NO. 513) GCAUGAAGCCAGAGAGUGA; (SEQ.
ID NO. 514) GCAUUUGUUUCUACUUUGA; (SEQ. ID NO. 515)
GCCCAGACCCAACUAUGAU; (SEQ. ID NO. 516) GCGAAGCUGACCUGGAAGA; (SEQ.
ID NO. 517) GCGAGGAGUGGGUGUGUGA; (SEQ. ID NO. 518)
GCGAGGUCAUGAAGAAGAA; (SEQ. ID NO. 519) GCUAAAUUCUGUGGAGUAU; (SEQ.
ID NO. 520) GCUCUGACAUUCUGAUUCG; (SEQ. 10 NO. 521)
GCUCUUAUACGAGUAAUAU; (SEQ. ID NO. 522) GCUGAUUUCUAGGUAGGAA; (SEQ.
ID NO. 523) GGAAAUUCUCUCAGAUAGA; (SEQ. ID NO. 524)
GGAAGAGAACAUUAAGAAG; (SEQ. ID NO. 525) GGAAGCAUUUGUUUCUACU; (SEQ.
ID NO. 526) GGAGACAAUGACAUCUUUG; (SEQ. ID NO. 527)
GGCCCAGACCCAACUAUGA; (SEQ. ID NO. 528) GGCGAGGUCAUGAAGAAGA; (SEQ.
ID NO. 529) GGGAAAUGCUGCGAGGAGU; (SEQ. ID NO. 530)
GGGUGUGUGACGAGCCCAA; (SEQ. ID NO. 531) GGUACCAGCAGAAAGGUUA; (SEQ.
ID NO. 532) GGUAGAAUGUAAAGCUUGU; (SEQ. ID NO. 533)
GGUCAGGCCUUGCGAAGCU; (SEQ. ID NO. 534) GUAAAUAUUGUGUGUGUGU; (SEQ.
ID NO. 535) GUACACAGCACCAGAAUGU; (SEQ. ID NO. 536)
GUCCUUGGCAGGCUGAUUU; (SEQ. ID NO. 537) GUGACGAGCCCAAGGACCA; (SEQ.
ID NO. 538) GUGCAUCCGUACUCCCAAA; (SEQ. ID NO. 539)
GUGUGUGUGUGUAUAUAUA; (SEQ. ID NO. 540) GUGUGUGUGUGUGUAUAUA; (SEQ.
ID NO. 541) GUUACAUGUUUGCACCUUU; (SEQ. ID NO. 542)
GUUUGCACCUUUCUAGUUG; (SEQ. ID NO. 543) UAAAUUCUGUGGAGUAUGU; (SEQ.
ID NO. 544) UAGGUAGAAUGUAAAGCUU; (SEQ. ID NO. 545)
UAUAUUAAGGUGUGGCUUU; (SEQ. ID NO. 546) UCAAGUUGUUCCUUAAGUC; (SEQ.
ID NO. 547) UCAAUGUUAGCCUCAAUUU; (SEQ. ID NO. 548)
UCAGAUAGCUCUUAUACGA; (SEQ. ID NO. 549) UCAUUAGACUGGAACUUGA; (SEQ.
ID NO. 550) UCGGACAGUUUAUUUGUUG; (SEQ. ID NO. 551)
UCUCCAAGCCUAUCAAGUU; (SEQ. ID NO. 552) UGAAGAAUGUUAAGACUUG; (SEQ.
ID NO. 553) UGACAGUGGAACUACAUUA; (SEQ. ID NO. 554)
UGAGUCAAGUUGUUCCUUA; (SEQ. ID NO. 555) UGGAAAUUCUCUCAGAUAG; (SEQ.
ID NO. 556) UGGAAGAGAACAUUAAGAA; (SEQ. ID NO. 557)
UUACAUGUUUGCACCUUUC; (SEQ. ID NO. 558) and UUGCGAAGCUGACCUGGAA.
(SEQ. ID NO. 559)
[0484] Thus, consistent with Example XVII, the present invention
provides an siRNA that targets CTGF is provided, wherein the siRNA
is selected from the group consisting of SEQ. ID NOs. 438-559.
[0485] In another embodiment, an siRNA is provided, said siRNA
comprising a sense region and an antisense region, wherein said
sense region and said antisense region are at least 90%
complementary, said sense region and said antisense region together
form a duplex region comprising 18-30 base pairs, and said sense
region comprises a sequence that is at least 90% similar to a
sequence selected from the group consisting of: SEQ. ID NOs
438-559.
[0486] In another embodiment, an siRNA is provided wherein the
siRNA comprises a sense region and an antisense region, wherein
said sense region and said antisense region are at least 90%
complementary, said sense region and said antisense region together
form a duplex region comprising 18-30 base pairs, and said sense
region comprises a sequence that is identical to a contiguous
stretch of at least 18 bases of a sequence selected from the group
consisting of: SEQ. ID NOs 438-559.
[0487] In another embodiment, an siRNA is provided wherein the
siRNA comprises a sense region and an antisense region, wherein
said sense region and said antisense region are at least 90%
complementary, said sense region and said antisense region together
form a duplex region comprising 19-30 base pairs, and said sense
region comprises a sequence that is identical to a contiguous
stretch of at least 18 bases of a sequence selected from the group
consisting of: SEQ. ID NOs 438-559.
[0488] In another embodiment, a pool of at least two siRNAs is
provided, wherein said pool comprises a first siRNA and a second
siRNA, said first siRNA comprises a duplex region of length 18-30
base pairs that has a first sense region that is at least 90%
similar to 18 bases of a first sequence selected from the group
consisting of: SEQ. ID NOs 438-559 and said second siRNA comprises
a duplex region of length 18-30 base pairs that has a second sense
region that is at least 90% similar to 18 bases of a second
sequence selected from the group consisting of: SEQ. ID NOs 438-559
and wherein said first sense region and said second sense region
are not identical.
[0489] In another embodiment, a pool of at least two siRNAs is
provided, wherein said pool comprises a first siRNA and a second
siRNA, said first siRNA comprises a duplex region of length 18-30
base pairs that has a first sense region that is identical to at
least 18 bases of a sequence selected from the group consisting of:
SEQ. ID NOs 438-559 and wherein the second siRNA comprises a second
sense region that comprises a sequence that is identical to at
least 18 bases of a sequence selected from the group consisting of:
SEQ. ID NOs 438-559.
[0490] In another embodiment, a pool of at least two siRNAs is
provided, wherein said pool comprises a first siRNA and a second
siRNA, said first siRNA comprises a duplex region of length 19-30
base pairs and has a first sense region comprising a sequence that
is at least 90% similar to a sequence selected from the group
consisting of: SEQ. ID NOs 438-559, and said duplex of said second
siRNA is 19-30 base pairs and comprises a second sense region that
comprises a sequence that is at least 90% similar to a sequence
selected from the group consisting of: SEQ. ID NOs 438-559.
[0491] In another embodiment, a pool of at least two siRNAs is
provided, wherein said pool comprises a first siRNA and a second
siRNA, said first siRNA comprises a duplex region of length 19-30
base pairs and has a first sense region comprising a sequence that
is identical to at least 18 bases of a sequence selected the group
consisting of: SEQ. ID NOs 438-559 and said duplex of said second
siRNA is 19-30 base pairs and comprises a second sense region
comprising a sequence that is identical to a sequence selected from
the group consisting of: SEQ. ID NOs 438-559.
[0492] While the invention has been described in connection with
specific embodiments thereof, it will be understood that it is
capable of further modifications and this application is intended
to cover any variations, uses, or adaptations of the invention
following, in general, the principles of the invention and
including such departure from the present disclosure as come within
known or customary practice within the art to which the invention
pertains and as may be applied to the essential features
hereinbefore set forth and as follows in the scope of the appended
claims.
Sequence CWU 1
1
559 1 19 RNA Artificial Sequence Synthetic misc_feature (1)...(2) n
is any nucleotide misc_feature 4 n is any nucleotide misc_feature
(6)...(9) n is any nucleotide misc_feature 12 n is any nucleotide
misc_feature 15-18 n is any nucleotide 1 nnanannnnu cnaannnna 19 2
19 RNA Artificial Sequence Synthetic misc_feature (1)...(2) n is
any nucleotide misc_feature 4 n is any nucleotide misc_feature
(6)...(9) n is any nucleotide misc_feature 12 n is any nucleotide
misc_feature 15- 18 n is any nucleotide 2 nnanannnnu gnaannnna 19 3
19 RNA Artificial Sequence Synthetic misc_feature (1)...(2) n is
any nucleotide misc_feature 4 n is any nucleotide misc_feature
(6)...(9) n is any nucleotide misc_feature 12 n is any nucleotide
misc_feature 15-18 n is any nucleotide 3 nnanannnnu unaannnna 19 4
19 RNA Artificial Sequence Synthetic misc_feature 1, 2, 4, 6-9, 12,
15 - 18 N is any nucleotide 4 nnanannnnu cncannnna 19 5 19 RNA
Artificial Sequence Synthetic misc_feature 1,2, 4, 6 -9, 12, 15 -
18 N is any nucleotide 5 nnanannnnu gncannnna 19 6 19 RNA
Artificial Sequence Synthetic misc_feature 1, 2, 4, 6-9, 12, 15 -
18 N is any nucleotide 6 nnanannnnu uncannnna 19 7 19 RNA
Artificial Sequence Synthetic misc_feature 1, 2, 4, 6-19, 12, 15
-18 N is any nucleotide 7 nnanannnnu cnuannnna 19 8 19 RNA
Artificial Sequence Synthetic misc_feature 1, 2, 4, 6-9, 12, 15 -18
N is any nucleotide 8 nnanannnnu gnuannnna 19 9 19 RNA Artificial
Sequence Synthetic misc_feature 1, 2, 4, 6-9, 12 , 15 - 18 N is any
nucleotide 9 nnanannnnu unuannnna 19 10 19 RNA Artificial Sequence
Synthetic misc_feature 1, 2, 4, 6-9, 12, 15-18 N is any nucleotide
10 nnancnnnnu cnaannnna 19 11 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 11
nnancnnnnu gnaannnna 19 12 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15-18 N is any nucleoitde 12
nnancnnnnu unaannnna 19 13 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 13
nnancnnnnu cncannnna 19 14 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 14
nnancnnnnu gncannnna 19 15 19 RNA Artificial Sequence Synthetic
misc_feature 1 -2, 4, 6-9, 12, 15 - 18 N is any nucleotde 15
nnancnnnnu uncannnna 19 16 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 16
nnancnnnnu cnuannnna 19 17 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 17
nnancnnnnu gnuannnna 19 18 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 18
nnancnnnnu unuannnna 19 19 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 19
nnangnnnnu cnaannnna 19 20 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 20
nnangnnnnu gnaannnna 19 21 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 21
nnangnnnnu unaannnna 19 22 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 22
nnangnnnnu cncannnna 19 23 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 -18 N is any nucleotide 23
nnangnnnnu gncannnna 19 24 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 24
nnangnnnnu uncannnna 19 25 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15-18 N is any nucleotide 25
nnangnnnnu cnuannnna 19 26 19 RNA Artificial Sequence Synthetic
misc_feature 1, 2, 4, 6-9, 12, 15 - 18 N is any nucleotide 26
nnangnnnnu gnuannnna 19 27 19 RNA Artificial Sequence Synthetic
misc_feature 1,2, 4, 6-9, 10, 12, 15 - 18 N is any nucleotide 27
nnangnnnnn unuannnna 19 28 22 RNA Artificial Sequence Synthetic
misc_feature 4, 5, 7, 9 -12, 15, 18 -21 N is any nucleotide 28
gucnnanann nnucnaannn na 22 29 208 DNA Homo Sapiens misc_feature
(0)...(0) Human cyclophilin RNA fragment 29 gttccaaaaa cagtggataa
ttttgtggcc ttagctacag gagagaaagg atttggctac 60 aaaaacagca
aattccatcg tgtaatcaag gacttcatga tccagggcgg agacttcacc 120
aggggagatg gcacaggagg aaagagcatc tacggtgagc gcttccccga tgagaacttc
180 aaactgaagc actacgggcc tggctggg 208 30 200 DNA Phonitus pyralis
misc_feature (0)...(0) Firefly luciferase 30 tgaacttccc gccgccgttg
ttgttttgga gcacggaaag acgatgacgg aaaaagagat 60 cgtggattac
gtcgccagtc aagtaacaac cgcgaaaaag ttgcgcggag gagttgtgtt 120
tgtggacgaa gtaccgaaag gtcttaccgg aaaactcgac gcaagaaaaa tcagagagat
180 cctcataaag gccaagaagg 200 31 108 DNA Homo sapiens misc_feature
(0)...(0) Human DBL RNA fragment 31 acgggcaagg ccaagtggga
tgcctggaat gagctgaaag ggacttccaa ggaagatgcc 60 atgaaagctt
acatcaacaa agtagaagag ctaaagaaaa aatacggg 108 32 19 RNA Artificial
Sequence Synthetic 32 guuccaaaaa caguggaua 19 33 19 RNA Artificial
Sequence Synthetic 33 uccaaaaaca guggauaau 19 34 19 RNA Artificial
Sequence Synthetic 34 caaaaacagu ggauaauuu 19 35 19 RNA Artificial
Sequence Synthetic 35 aaaacagugg auaauuuug 19 36 19 RNA Artificial
Sequence Synthetic 36 aacaguggau aauuuugug 19 37 19 RNA Artificial
Sequence Synthetic 37 caguggauaa uuuuguggc 19 38 19 RNA Artificial
Sequence Synthetic 38 guggauaauu uuguggccu 19 39 19 RNA Artificial
Sequence Synthetic 39 ggauaauuuu guggccuua 19 40 19 RNA Artificial
Sequence Synthetic 40 auaauuuugu ggccuuagc 19 41 19 RNA Artificial
Sequence Synthetic 41 aauuuugugg ccuuagcua 19 42 19 RNA Artificial
Sequence Synthetic 42 uuuuguggcc uuagcuaca 19 43 19 RNA Artificial
Sequence Synthetic 43 uuguggccuu agcuacagg 19 44 19 RNA Artificial
Sequence Synthetic 44 guggccuuag cuacaggag 19 45 19 RNA Artificial
Sequence Synthetic 45 ggccuuagcu acaggagag 19 46 19 RNA Artificial
Sequence Synthetic 46 ccuuagcuac aggagagaa 19 47 19 RNA Artificial
Sequence Synthetic 47 uuagcuacag gagagaaag 19 48 19 RNA Artificial
Sequence Synthetic 48 agcuacagga gagaaagga 19 49 19 RNA Artificial
Sequence Synthetic 49 cuacaggaga gaaaggauu 19 50 19 RNA Artificial
Sequence Synthetic 50 acaggagaga aaggauuug 19 51 19 RNA Artificial
Sequence Synthetic 51 aggagagaaa ggauuuggc 19 52 19 RNA Artificial
Sequence Synthetic 52 gagagaaagg auuuggcua 19 53 19 RNA Artificial
Sequence Synthetic 53 gagaaaggau uuggcuaca 19 54 19 RNA Artificial
Sequence Synthetic 54 gaaaggauuu ggcuacaaa 19 55 19 RNA Artificial
Sequence Synthetic 55 aaggauuugg cuacaaaaa 19 56 19 RNA Artificial
Sequence Synthetic 56 ggauuuggcu acaaaaaca 19 57 19 RNA Artificial
Sequence Synthetic 57 auuuggcuac aaaaacagc 19 58 19 RNA Artificial
Sequence Synthetic 58 uuggcuacaa aaacagcaa 19 59 19 RNA Artificial
Sequence Synthetic 59 ggcuacaaaa acagcaaau 19 60 19 RNA Artificial
Sequence Synthetic 60 cuacaaaaac agcaaauuc 19 61 19 RNA Artificial
Sequence Synthetic 61 acaaaaacag caaauucca 19 62 19 RNA Artificial
Sequence Synthetic 62 aaaaacagca aauuccauc 19 63 19 RNA Artificial
Sequence Synthetic 63 aaacagcaaa uuccaucgu 19 64 19 RNA Artificial
Sequence Synthetic 64 acagcaaauu ccaucgugu 19 65 19 RNA Artificial
Sequence Synthetic 65 agcaaauucc aucguguaa 19 66 19 RNA Artificial
Sequence Synthetic 66 caaauuccau cguguaauc 19 67 19 RNA Artificial
Sequence Synthetic 67 aauuccaucg uguaaucaa 19 68 19 RNA Artificial
Sequence Synthetic 68 uuccaucgug uaaucaagg 19 69 19 RNA Artificial
Sequence Synthetic 69 ccaucgugua aucaaggac 19 70 19 RNA Artificial
Sequence Synthetic 70 aucguguaau caaggacuu 19 71 19 RNA Artificial
Sequence Synthetic 71 cguguaauca aggacuuca 19 72 19 RNA Artificial
Sequence Synthetic 72 uguaaucaag gacuucaug 19 73 19 RNA Artificial
Sequence Synthetic 73 uaaucaagga cuucaugau 19 74 19 RNA Artificial
Sequence Synthetic 74 aucaaggacu ucaugaucc 19 75 19 RNA Artificial
Sequence Synthetic 75 caaggacuuc augauccag 19 76 19 RNA Artificial
Sequence Synthetic 76 aggacuucau gauccaggg 19 77 19 RNA Artificial
Sequence Synthetic 77 gacuucauga uccagggcg 19 78 19 RNA Artificial
Sequence Synthetic 78 cuucaugauc cagggcgga 19 79 19 RNA Artificial
Sequence Synthetic 79 ucaugaucca gggcggaga 19 80 19 RNA Artificial
Sequence Synthetic 80 augauccagg gcggagacu 19 81 19 RNA Artificial
Sequence Synthetic 81 gauccagggc ggagacuuc 19 82 19 RNA Artificial
Sequence Synthetic 82 uccagggcgg agacuucac 19 83 19 RNA Artificial
Sequence Synthetic 83 cagggcggag acuucacca 19 84 19 RNA Artificial
Sequence Synthetic 84 gggcggagac uucaccagg 19 85 19 RNA Artificial
Sequence Synthetic 85 gcggagacuu caccagggg 19 86 19 RNA Artificial
Sequence Synthetic 86 ggagacuuca ccaggggag 19 87 19 RNA Artificial
Sequence Synthetic 87 agacuucacc aggggagau 19 88 19 RNA Artificial
Sequence Synthetic 88 acuucaccag gggagaugg 19 89 19 RNA Artificial
Sequence Synthetic 89 uucaccaggg gagauggca 19 90 19 RNA Artificial
Sequence Synthetic 90 caccagggga gauggcaca 19 91 19 RNA Artificial
Sequence Synthetic 91 ccaggggaga uggcacagg 19 92 19 RNA Artificial
Sequence Synthetic 92 aggggagaug gcacaggag 19 93 19 RNA Artificial
Sequence Synthetic 93 gggagauggc acaggagga 19 94 19 RNA Artificial
Sequence Synthetic 94 gagauggcac aggaggaaa 19 95 19 RNA Artificial
Sequence Synthetic 95 gauggcacag gaggaaaga 19 96 19 RNA Artificial
Sequence Synthetic 96 uggcacagga ggaaagagc 19 97 19 RNA Artificial
Sequence Synthetic 97 gcacaggagg aaagagcau 19 98 19 RNA Artificial
Sequence Synthetic 98 acaggaggaa agagcaucu 19 99 19 RNA Artificial
Sequence Synthetic 99 aggaggaaag agcaucuac 19 100 19 RNA Artificial
Sequence Synthetic 100 gaggaaagag caucuacgg 19 101 19 RNA
Artificial Sequence Synthetic 101 ggaaagagca ucuacggug 19 102 19
RNA Artificial Sequence Synthetic 102 aaagagcauc uacggugag 19 103
19 RNA Artificial Sequence Synthetic 103 agagcaucua cggugagcg 19
104 19 RNA Artificial Sequence Synthetic 104 agcaucuacg gugagcgcu
19 105 19 RNA Artificial Sequence Synthetic 105 caucuacggu
gagcgcuuc 19 106 19 RNA Artificial Sequence Synthetic 106
ucuacgguga gcgcuuccc 19 107 19 RNA Artificial Sequence Synthetic
107 uacggugagc gcuuccccg 19 108 19 RNA Artificial Sequence
Synthetic 108 cggugagcgc uuccccgau 19 109 19 RNA Artificial
Sequence Synthetic 109 gugagcgcuu ccccgauga 19 110 19 RNA
Artificial Sequence Synthetic 110 gagcgcuucc ccgaugaga 19 111 19
RNA Artificial Sequence Synthetic 111 gcgcuucccc gaugagaac 19 112
19 RNA Artificial Sequence Synthetic 112 gcuuccccga ugagaacuu 19
113 19 RNA Artificial Sequence Synthetic 113 uuccccgaug agaacuuca
19 114 19 RNA Artificial Sequence Synthetic 114 ccccgaugag
aacuucaaa 19 115 19 RNA Artificial Sequence Synthetic 115
ccgaugagaa cuucaaacu 19 116 19 RNA Artificial Sequence Synthetic
116 gaugagaacu ucaaacuga 19 117 19 RNA Artificial Sequence
Synthetic 117 ugagaacuuc aaacugaag 19 118 19 RNA Artificial
Sequence Synthetic 118 agaacuucaa acugaagca 19 119 19 RNA
Artificial Sequence Synthetic 119 aacuucaaac ugaagcacu 19 120 19
RNA Artificial Sequence Synthetic 120 cuucaaacug aagcacuac 19 121
19 RNA Artificial Sequence Synthetic 121 ucaaacugaa gcacuacgg 19
122 19 RNA Artificial Sequence Synthetic 122 acgggcaagg ccaaguggg
19 123 19 RNA Artificial Sequence Synthetic 123 cgggcaaggc
caaguggga 19 124 19 RNA Artificial Sequence Synthetic 124
gggcaaggcc aagugggau 19 125 19 RNA Artificial Sequence Synthetic
125 ggcaaggcca agugggaug 19 126 19 RNA Artificial Sequence
Synthetic 126 gcaaggccaa gugggaugc 19 127 19 RNA Artificial
Sequence Synthetic 127 caaggccaag ugggaugcc 19 128 19 RNA
Artificial Sequence Synthetic 128 aaggccaagu gggaugccu 19 129 19
RNA Artificial Sequence Synthetic 129 aggccaagug ggaugccug 19 130
19 RNA Artificial Sequence Synthetic 130 ggccaagugg gaugccugg 19
131 19 RNA Artificial Sequence Synthetic 131 gccaaguggg
augccugga
19 132 19 RNA Artificial Sequence Synthetic 132 ccaaguggga
ugccuggaa 19 133 19 RNA Artificial Sequence Synthetic 133
caagugggau gccuggaau 19 134 19 RNA Artificial Sequence Synthetic
134 aagugggaug ccuggaaug 19 135 19 RNA Artificial Sequence
Synthetic 135 agugggaugc cuggaauga 19 136 19 RNA Artificial
Sequence Synthetic 136 gugggaugcc uggaaugag 19 137 19 RNA
Artificial Sequence Synthetic 137 ugggaugccu ggaaugagc 19 138 19
RNA Artificial Sequence Synthetic 138 gggaugccug gaaugagcu 19 139
19 RNA Artificial Sequence Synthetic 139 ggaugccugg aaugagcug 19
140 19 RNA Artificial Sequence Synthetic 140 gaugccugga augagcuga
19 141 19 RNA Artificial Sequence Synthetic 141 augccuggaa
ugagcugaa 19 142 19 RNA Artificial Sequence Synthetic 142
ugccuggaau gagcugaaa 19 143 19 RNA Artificial Sequence Synthetic
143 gccuggaaug agcugaaag 19 144 19 RNA Artificial Sequence
Synthetic 144 ccuggaauga gcugaaagg 19 145 19 RNA Artificial
Sequence Synthetic 145 cuggaaugag cugaaaggg 19 146 19 RNA
Artificial Sequence Synthetic 146 uggaaugagc ugaaaggga 19 147 19
RNA Artificial Sequence Synthetic 147 ggaaugagcu gaaagggac 19 148
19 RNA Artificial Sequence Synthetic 148 gaaugagcug aaagggacu 19
149 19 RNA Artificial Sequence Synthetic 149 aaugagcuga aagggacuu
19 150 19 RNA Artificial Sequence Synthetic 150 augagcugaa
agggacuuc 19 151 19 RNA Artificial Sequence Synthetic 151
ugagcugaaa gggacuucc 19 152 19 RNA Artificial Sequence Synthetic
152 gagcugaaag ggacuucca 19 153 19 RNA Artificial Sequence
Synthetic 153 agcugaaagg gacuuccaa 19 154 19 RNA Artificial
Sequence Synthetic 154 gcugaaaggg acuuccaag 19 155 19 RNA
Artificial Sequence Synthetic 155 cugaaaggga cuuccaagg 19 156 19
RNA Artificial Sequence Synthetic 156 ugaaagggac uuccaagga 19 157
19 RNA Artificial Sequence Synthetic 157 gaaagggacu uccaaggaa 19
158 19 RNA Artificial Sequence Synthetic 158 aaagggacuu ccaaggaag
19 159 19 RNA Artificial Sequence Synthetic 159 aagggacuuc
caaggaaga 19 160 19 RNA Artificial Sequence Synthetic 160
agggacuucc aaggaagau 19 161 19 RNA Artificial Sequence Synthetic
161 gggacuucca aggaagaug 19 162 19 RNA Artificial Sequence
Synthetic 162 ggacuuccaa ggaagaugc 19 163 19 RNA Artificial
Sequence Synthetic 163 gacuuccaag gaagaugcc 19 164 19 RNA
Artificial Sequence Synthetic 164 acuuccaagg aagaugcca 19 165 19
RNA Artificial Sequence Synthetic 165 cuuccaagga agaugccau 19 166
19 RNA Artificial Sequence Synthetic 166 uuccaaggaa gaugccaug 19
167 19 RNA Artificial Sequence Synthetic 167 uccaaggaag augccauga
19 168 19 RNA Artificial Sequence Synthetic 168 ccaaggaaga
ugccaugaa 19 169 19 RNA Artificial Sequence Synthetic 169
caaggaagau gccaugaaa 19 170 19 RNA Artificial Sequence Synthetic
170 aaggaagaug ccaugaaag 19 171 19 RNA Artificial Sequence
Synthetic 171 aggaagaugc caugaaagc 19 172 19 RNA Artificial
Sequence Synthetic 172 ggaagaugcc augaaagcu 19 173 19 RNA
Artificial Sequence Synthetic 173 gaagaugcca ugaaagcuu 19 174 19
RNA Artificial Sequence Synthetic 174 aagaugccau gaaagcuua 19 175
19 RNA Artificial Sequence Synthetic 175 agaugccaug aaagcuuac 19
176 19 RNA Artificial Sequence Synthetic 176 gaugccauga aagcuuaca
19 177 19 RNA Artificial Sequence Synthetic 177 augccaugaa
agcuuacau 19 178 19 RNA Artificial Sequence Synthetic 178
ugccaugaaa gcuuacauc 19 179 19 RNA Artificial Sequence Synthetic
179 gccaugaaag cuuacauca 19 180 19 RNA Artificial Sequence
Synthetic 180 ccaugaaagc uuacaucaa 19 181 19 RNA Artificial
Sequence Synthetic 181 caugaaagcu uacaucaac 19 182 19 RNA
Artificial Sequence Synthetic 182 augaaagcuu acaucaaca 19 183 19
RNA Artificial Sequence Synthetic 183 ugaaagcuua caucaacaa 19 184
19 RNA Artificial Sequence Synthetic 184 gaaagcuuac aucaacaaa 19
185 19 RNA Artificial Sequence Synthetic 185 aaagcuuaca ucaacaaag
19 186 19 RNA Artificial Sequence Synthetic 186 aagcuuacau
caacaaagu 19 187 19 RNA Artificial Sequence Synthetic 187
agcuuacauc aacaaagua 19 188 19 RNA Artificial Sequence Synthetic
188 gcuuacauca acaaaguag 19 189 19 RNA Artificial Sequence
Synthetic 189 cuuacaucaa caaaguaga 19 190 19 RNA Artificial
Sequence Synthetic 190 uuacaucaac aaaguagaa 19 191 19 RNA
Artificial Sequence Synthetic 191 uacaucaaca aaguagaag 19 192 19
RNA Artificial Sequence Synthetic 192 acaucaacaa aguagaaga 19 193
19 RNA Artificial Sequence Synthetic 193 caucaacaaa guagaagag 19
194 19 RNA Artificial Sequence Synthetic 194 aucaacaaag uagaagagc
19 195 19 RNA Artificial Sequence Synthetic 195 ucaacaaagu
agaagagcu 19 196 19 RNA Artificial Sequence Synthetic 196
caacaaagua gaagagcua 19 197 19 RNA Artificial Sequence Synthetic
197 aacaaaguag aagagcuaa 19 198 19 RNA Artificial Sequence
Synthetic 198 acaaaguaga agagcuaaa 19 199 19 RNA Artificial
Sequence Synthetic 199 caaaguagaa gagcuaaag 19 200 19 RNA
Artificial Sequence Synthetic 200 aaaguagaag agcuaaaga 19 201 19
RNA Artificial Sequence Synthetic 201 aaguagaaga gcuaaagaa 19 202
19 RNA Artificial Sequence Synthetic 202 aguagaagag cuaaagaaa 19
203 19 RNA Artificial Sequence Synthetic 203 guagaagagc uaaagaaaa
19 204 19 RNA Artificial Sequence Synthetic 204 uagaagagcu
aaagaaaaa 19 205 19 RNA Artificial Sequence Synthetic 205
agaagagcua aagaaaaaa 19 206 19 RNA Artificial Sequence Synthetic
206 gaagagcuaa agaaaaaau 19 207 19 RNA Artificial Sequence
Synthetic 207 aagagcuaaa gaaaaaaua 19 208 19 RNA Artificial
Sequence Synthetic 208 agagcuaaag aaaaaauac 19 209 19 RNA
Artificial Sequence Synthetic 209 gagcuaaaga aaaaauacg 19 210 19
RNA Artificial Sequence Synthetic 210 agcuaaagaa aaaauacgg 19 211
19 RNA Artificial Sequence Synthetic 211 gcuaaagaaa aaauacggg 19
212 19 RNA Artificial Sequence Synthetic 212 auccucauaa aggccaaga
19 213 19 RNA Artificial Sequence Synthetic 213 agauccucau
aaaggccaa 19 214 19 RNA Artificial Sequence Synthetic 214
agagauccuc auaaaggcc 19 215 19 RNA Artificial Sequence Synthetic
215 agagagaucc ucauaaagg 19 216 19 RNA Artificial Sequence
Synthetic 216 ucagagagau ccucauaaa 19 217 19 RNA Artificial
Sequence Synthetic 217 aaucagagag auccucaua 19 218 19 RNA
Artificial Sequence Synthetic 218 aaaaucagag agauccuca 19 219 19
RNA Artificial Sequence Synthetic 219 gaaaaaucag agagauccu 19 220
19 RNA Artificial Sequence Synthetic 220 aagaaaaauc agagagauc 19
221 19 RNA Artificial Sequence Synthetic 221 gcaagaaaaa ucagagaga
19 222 19 RNA Artificial Sequence Synthetic 222 acgcaagaaa
aaucagaga 19 223 19 RNA Artificial Sequence Synthetic 223
cgacgcaaga aaaaucaga 19 224 19 RNA Artificial Sequence Synthetic
224 cucgacgcaa gaaaaauca 19 225 19 RNA Artificial Sequence
Synthetic 225 aacucgacgc aagaaaaau 19 226 19 RNA Artificial
Sequence Synthetic 226 aaaacucgac gcaagaaaa 19 227 19 RNA
Artificial Sequence Synthetic 227 ggaaaacucg acgcaagaa 19 228 19
RNA Artificial Sequence Synthetic 228 ccggaaaacu cgacgcaag 19 229
19 RNA Artificial Sequence Synthetic 229 uaccggaaaa cucgacgca 19
230 19 RNA Artificial Sequence Synthetic 230 cuuaccggaa aacucgacg
19 231 19 RNA Artificial Sequence Synthetic 231 gucuuaccgg
aaaacucga 19 232 19 RNA Artificial Sequence Synthetic 232
aggucuuacc ggaaaacuc 19 233 19 RNA Artificial Sequence Synthetic
233 aaaggucuua ccggaaaac 19 234 19 RNA Artificial Sequence
Synthetic 234 cgaaaggucu uaccggaaa 19 235 19 RNA Artificial
Sequence Synthetic 235 accgaaaggu cuuaccgga 19 236 19 RNA
Artificial Sequence Synthetic 236 guaccgaaag gucuuaccg 19 237 19
RNA Artificial Sequence Synthetic 237 aaguaccgaa aggucuuac 19 238
19 RNA Artificial Sequence Synthetic 238 cgaaguaccg aaaggucuu 19
239 19 RNA Artificial Sequence Synthetic 239 gacgaaguac cgaaagguc
19 240 19 RNA Artificial Sequence Synthetic 240 uggacgaagu
accgaaagg 19 241 19 RNA Artificial Sequence Synthetic 241
uguggacgaa guaccgaaa 19 242 19 RNA Artificial Sequence Synthetic
242 uuuguggacg aaguaccga 19 243 19 RNA Artificial Sequence
Synthetic 243 uguuugugga cgaaguacc 19 244 19 RNA Artificial
Sequence Synthetic 244 uguguuugug gacgaagua 19 245 19 RNA
Artificial Sequence Synthetic 245 guuguguuug uggacgaag 19 246 19
RNA Artificial Sequence Synthetic 246 gaguuguguu uguggacga 19 247
19 RNA Artificial Sequence Synthetic 247 aggaguugug uuuguggac 19
248 19 RNA Artificial Sequence Synthetic 248 ggaggaguug uguuugugg
19 249 19 RNA Artificial Sequence Synthetic 249 gcggaggagu
uguguuugu 19 250 19 RNA Artificial Sequence Synthetic 250
gcgcggagga guuguguuu 19 251 19 RNA Artificial Sequence Synthetic
251 uugcgcggag gaguugugu 19 252 19 RNA Artificial Sequence
Synthetic 252 aguugcgcgg aggaguugu 19 253 19 RNA Artificial
Sequence Synthetic 253 aaaguugcgc ggaggaguu 19 254 19 RNA
Artificial Sequence Synthetic 254 aaaaaguugc gcggaggag 19 255 19
RNA Artificial Sequence Synthetic 255 cgaaaaaguu gcgcggagg 19 256
19 RNA Artificial Sequence Synthetic 256 cgcgaaaaag uugcgcgga 19
257 19 RNA Artificial Sequence Synthetic 257 accgcgaaaa aguugcgcg
19 258 19 RNA Artificial Sequence Synthetic 258 caaccgcgaa
aaaguugcg 19 259 19 RNA Artificial Sequence Synthetic 259
aacaaccgcg aaaaaguug 19 260 19 RNA Artificial Sequence Synthetic
260 guaacaaccg cgaaaaagu 19 261 19 RNA Artificial Sequence
Synthetic 261 aaguaacaac cgcgaaaaa 19 262 19 RNA Artificial
Sequence Synthetic 262 ucaaguaaca accgcgaaa 19 263 19 RNA
Artificial Sequence Synthetic 263 agucaaguaa caaccgcga 19 264 19
RNA Artificial Sequence Synthetic 264 ccagucaagu aacaaccgc 19 265
19 RNA Artificial Sequence Synthetic 265 cgccagucaa guaacaacc 19
266 19 RNA Artificial Sequence Synthetic 266 gucgccaguc aaguaacaa
19 267 19 RNA Artificial Sequence Synthetic 267 acgucgccag
ucaaguaac 19 268 19 RNA Artificial Sequence Synthetic 268
uuacgucgcc agucaagua 19 269 19 RNA Artificial Sequence Synthetic
269 gauuacgucg ccagucaag 19 270 19 RNA Artificial Sequence
Synthetic 270 uggauuacgu cgccaguca 19 271 19 RNA Artificial
Sequence Synthetic 271 cguggauuac gucgccagu 19 272 19 RNA
Artificial Sequence Synthetic 272 aucguggauu acgucgcca 19 273 19
RNA Artificial Sequence Synthetic 273 agaucgugga uuacgucgc 19 274
19 RNA Artificial Sequence Synthetic 274 agagaucgug gauuacguc 19
275 19 RNA Artificial Sequence Synthetic 275 aaagagaucg uggauuacg
19 276 19 RNA Artificial Sequence Synthetic 276 aaaaagagau
cguggauua 19 277 19 RNA Artificial Sequence Synthetic 277
ggaaaaagag aucguggau 19 278 19 RNA Artificial Sequence Synthetic
278 acggaaaaag agaucgugg 19 279 19 RNA Artificial Sequence
Synthetic 279 ugacggaaaa agagaucgu 19 280 19 RNA Artificial
Sequence Synthetic 280 gaugacggaa aaagagauc 19 281 19 RNA
Artificial Sequence Synthetic 281 acgaugacgg aaaaagaga 19 282 19
RNA Artificial Sequence Synthetic 282
agacgaugac ggaaaaaga 19 283 19 RNA Artificial Sequence Synthetic
283 aaagacgaug acggaaaaa 19 284 19 RNA Artificial Sequence
Synthetic 284 ggaaagacga ugacggaaa 19 285 19 RNA Artificial
Sequence Synthetic 285 acggaaagac gaugacgga 19 286 19 RNA
Artificial Sequence Synthetic 286 gcacggaaag acgaugacg 19 287 19
RNA Artificial Sequence Synthetic 287 gagcacggaa agacgauga 19 288
19 RNA Artificial Sequence Synthetic 288 uggagcacgg aaagacgau 19
289 19 RNA Artificial Sequence Synthetic 289 uuuggagcac ggaaagacg
19 290 19 RNA Artificial Sequence Synthetic 290 guuuuggagc
acggaaaga 19 291 19 RNA Artificial Sequence Synthetic 291
uuguuuugga gcacggaaa 19 292 19 RNA Artificial Sequence Synthetic
292 uguuguuuug gagcacgga 19 293 19 RNA Artificial Sequence
Synthetic 293 guuguuguuu uggagcacg 19 294 19 RNA Artificial
Sequence Synthetic 294 ccguuguugu uuuggagca 19 295 19 RNA
Artificial Sequence Synthetic 295 cgccguuguu guuuuggag 19 296 19
RNA Artificial Sequence Synthetic 296 gccgccguug uuguuuugg 19 297
19 RNA Artificial Sequence Synthetic 297 ccgccgccgu uguuguuuu 19
298 19 RNA Artificial Sequence Synthetic 298 ucccgccgcc guuguuguu
19 299 19 RNA Artificial Sequence Synthetic 299 cuucccgccg
ccguuguug 19 300 19 RNA Artificial Sequence Synthetic 300
aacuucccgc cgccguugu 19 301 19 RNA Artificial Sequence Synthetic
301 ugaacuuccc gccgccguu 19 302 19 RNA Artificial Sequence
Synthetic 302 gggagauagu gaugaagua 19 303 19 RNA Artificial
Sequence Synthetic 303 gaaguacauc cauuauaag 19 304 19 RNA
Artificial Sequence Synthetic 304 guacgacaac cgggagaua 19 305 19
RNA Artificial Sequence Synthetic 305 agauagugau gaaguacau 19 306
19 RNA Artificial Sequence Synthetic 306 ugaagacucu gcucaguuu 19
307 19 RNA Artificial Sequence Synthetic 307 gcaugcggcc ucuguuuga
19 308 19 RNA Artificial Sequence Synthetic 308 ugcggccucu
guuugauuu 19 309 19 RNA Artificial Sequence Synthetic 309
gagauaguga ugaaguaca 19 310 19 RNA Artificial Sequence Synthetic
310 ggagauagug augaaguac 19 311 19 RNA Artificial Sequence
Synthetic 311 gaagacucug cucaguuug 19 312 19 DNA Artificial
Sequence Synthetic 312 gaaagaatct gtagagaaa 19 313 19 DNA
Artificial Sequence Synthetic 313 gcaatgagct gtttgaaga 19 314 19
DNA Artificial Sequence Synthetic 314 tgacaaaggt ggataaatt 19 315
19 DNA Artificial Sequence Synthetic 315 ggaaatggat ctctttgaa 19
316 19 DNA Artificial Sequence Synthetic 316 ggaaagtaat ggtccaaca
19 317 19 DNA Artificial Sequence Synthetic 317 agacagttat
gcagctatt 19 318 19 DNA Artificial Sequence Synthetic 318
ccaattctcg gaagcaaga 19 319 19 DNA Artificial Sequence Synthetic
319 gaaagtaatg gtccaacag 19 320 19 DNA Artificial Sequence
Synthetic 320 gcgccagagt gaacaagta 19 321 19 DNA Artificial
Sequence Synthetic 321 gaaggtggcc cagctatgt 19 322 19 DNA
Artificial Sequence Synthetic 322 ggaaccagcg ccagagtga 19 323 19
DNA Artificial Sequence Synthetic 323 gagcgagatt gcaggcata 19 324
19 DNA Artificial Sequence Synthetic 324 gttagtatct gatgacttg 19
325 19 DNA Artificial Sequence Synthetic 325 gaaatggaac cactaagaa
19 326 19 DNA Artificial Sequence Synthetic 326 ggaaatggaa
ccactaaga 19 327 19 DNA Artificial Sequence Synthetic 327
caactacact ttccaatgc 19 328 19 DNA Artificial Sequence Synthetic
328 ccaccaagat ttcatgata 19 329 19 DNA Artificial Sequence
Synthetic 329 gatcggaact ccaacaaga 19 330 19 DNA Artificial
Sequence Synthetic 330 aaacggagct acagattat 19 331 19 DNA
Artificial Sequence Synthetic 331 ccacacagca ttcttgtaa 19 332 19
DNA Artificial Sequence Synthetic 332 gaagttacct tgagcaatc 19 333
19 DNA Artificial Sequence Synthetic 333 ggacttggcc gatccagaa 19
334 19 DNA Artificial Sequence Synthetic 334 gcacttggat cgagatgag
19 335 19 DNA Artificial Sequence Synthetic 335 caaagaccaa
ttcgcgtta 19 336 19 DNA Artificial Sequence Synthetic 336
ccgaatcaat cgcatcttc 19 337 19 DNA Artificial Sequence Synthetic
337 gacatgatcc tgcagttca 19 338 19 DNA Artificial Sequence
Synthetic 338 gagcgaatcg tcaccactt 19 339 19 DNA Artificial
Sequence Synthetic 339 cctccgagct ggcgtctac 19 340 19 DNA
Artificial Sequence Synthetic 340 tcacatggtt aacctctaa 19 341 19
DNA Artificial Sequence Synthetic 341 gatgagggac gccataatc 19 342
19 DNA Artificial Sequence Synthetic 342 cctctaacta caaatctta 19
343 19 DNA Artificial Sequence Synthetic 343 ggaaggtgct atccaaaat
19 344 19 DNA Artificial Sequence Synthetic 344 gcaagcaagt
cctaacatt 19 345 19 DNA Artificial Sequence Synthetic 345
ggaagaggag tagacctta 19 346 19 DNA Artificial Sequence Synthetic
346 aggaatcagt gttgtagta 19 347 19 DNA Artificial Sequence
Synthetic 347 gaagaggagt agaccttac 19 348 19 DNA Artificial
Sequence Synthetic 348 gaaagtcaag cctggtatt 19 349 19 DNA
Artificial Sequence Synthetic 349 aaagtcaagc ctggtatta 19 350 19
DNA Artificial Sequence Synthetic 350 gctatgaacg tgaatgatc 19 351
19 DNA Artificial Sequence Synthetic 351 caagcctggt attacgttt 19
352 19 DNA Artificial Sequence Synthetic 352 ggaacaagat ctgtcaatt
19 353 19 DNA Artificial Sequence Synthetic 353 gcaatgaacg
tgaacgaaa 19 354 19 DNA Artificial Sequence Synthetic 354
caatgaacgt gaacgaaat 19 355 19 DNA Artificial Sequence Synthetic
355 ggacaggagc ggtatcaca 19 356 19 DNA Artificial Sequence
Synthetic 356 agacagagct tgagaataa 19 357 19 DNA Artificial
Sequence Synthetic 357 gagaagatct ttatgcaaa 19 358 19 DNA
Artificial Sequence Synthetic 358 gaagagaaat cagcagata 19 359 19
DNA Artificial Sequence Synthetic 359 gcaagtaact caactaaca 19 360
19 DNA Artificial Sequence Synthetic 360 gagctaatct gccacattg 19
361 19 DNA Artificial Sequence Synthetic 361 gcagatgagt tactagaaa
19 362 19 DNA Artificial Sequence Synthetic 362 caacttaatt
gtccagaaa 19 363 19 DNA Artificial Sequence Synthetic 363
caacacagga ttctgataa 19 364 19 DNA Artificial Sequence Synthetic
364 agattgtgcc taagtctct 19 365 19 DNA Artificial Sequence
Synthetic 365 atgaagatct ggaggtgaa 19 366 19 DNA Artificial
Sequence Synthetic 366 tttgagactt cttgcctaa 19 367 19 DNA
Artificial Sequence Synthetic 367 agatcaccct ccttaaata 19 368 19
DNA Artificial Sequence Synthetic 368 caacggattt ggtcgtatt 19 369
19 DNA Artificial Sequence Synthetic 369 gaaatcccat caccatctt 19
370 19 DNA Artificial Sequence Synthetic 370 gacctcaact acatggttt
19 371 19 DNA Artificial Sequence Synthetic 371 tggtttacat
gttccaata 19 372 19 DNA Artificial Sequence Synthetic 372
gaagaaatcg atgttgttt 19 373 19 DNA Artificial Sequence Synthetic
373 acacaaactt gaacagcta 19 374 19 DNA Artificial Sequence
Synthetic 374 ggaagaaatc gatgttgtt 19 375 19 DNA Artificial
Sequence Synthetic 375 gaaacgacga gaacagttg 19 376 19 DNA
Artificial Sequence Synthetic 376 gcacatggat ggaggttct 19 377 19
DNA Artificial Sequence Synthetic 377 gcagagagag cagatttga 19 378
19 DNA Artificial Sequence Synthetic 378 gaggttctct ggatcaagt 19
379 19 DNA Artificial Sequence Synthetic 379 gagcagattt gaagcaact
19 380 19 DNA Artificial Sequence Synthetic 380 caaagacgat
gacttcgaa 19 381 19 DNA Artificial Sequence Synthetic 381
gatcagcatt tgcatggaa 19 382 19 DNA Artificial Sequence Synthetic
382 tccaggagtt tgtcaataa 19 383 19 DNA Artificial Sequence
Synthetic 383 ggaagctgat ccaccttga 19 384 19 DNA Artificial
Sequence Synthetic 384 gcagaaatct aaggatata 19 385 19 DNA
Artificial Sequence Synthetic 385 caacaaggat gaagtctat 19 386 19
DNA Artificial Sequence Synthetic 386 cagcagaaat ctaaggata 19 387
19 DNA Artificial Sequence Synthetic 387 ctagatggct ttctcagta 19
388 19 DNA Artificial Sequence Synthetic 388 agacaaggtc ccaaagaca
19 389 19 DNA Artificial Sequence Synthetic 389 ggaatggcaa
gaccagcaa 19 390 19 DNA Artificial Sequence Synthetic 390
agaattattc cagggttta 19 391 19 DNA Artificial Sequence Synthetic
391 gcagacaagg tcccaaaga 19 392 19 DNA Artificial Sequence
Synthetic 392 agaagcagct tcaggatga 19 393 19 DNA Artificial
Sequence Synthetic 393 gagcttgact tccagaaga 19 394 19 DNA
Artificial Sequence Synthetic 394 ccaccgaagt tcaccctaa 19 395 19
DNA Artificial Sequence Synthetic 395 gagaagagct cctccatca 19 396
19 DNA Artificial Sequence Synthetic 396 gaaagagcat ctacggtga 19
397 19 DNA Artificial Sequence Synthetic 397 gaaaggattt ggctacaaa
19 398 19 DNA Artificial Sequence Synthetic 398 acagcaaatt
ccatcgtgt 19 399 19 DNA Artificial Sequence Synthetic 399
ggaaagactg ttccaaaaa 19 400 19 DNA Artificial Sequence Synthetic
400 caacacgcct catcctcta 19 401 19 DNA Artificial Sequence
Synthetic 401 catgaaagct tacatcaac 19 402 19 DNA Artificial
Sequence Synthetic 402 aagatgccat gaaagctta 19 403 19 DNA
Artificial Sequence Synthetic 403 gcacataccg cctgagtct 19 404 19
DNA Artificial Sequence Synthetic 404 gatcaaatct gaagaagga 19 405
19 DNA Artificial Sequence Synthetic 405 gccaagaagt ttcctaata 19
406 19 DNA Artificial Sequence Synthetic 406 cagcatatct tgaaccatt
19 407 19 DNA Artificial Sequence Synthetic 407 gaacaaagga
aacggatga 19 408 19 DNA Artificial Sequence Synthetic 408
cggaaacggt ccaggctat 19 409 19 DNA Artificial Sequence Synthetic
409 gcttcgagca gacatgata 19 410 19 DNA Artificial Sequence
Synthetic 410 cctacacggt cctcctata 19 411 19 DNA Artificial
Sequence Synthetic 411 gccaagaacc tcatcatct 19 412 19 DNA
Artificial Sequence Synthetic 412 gatatgggct gaatacaaa 19 413 19
DNA Artificial Sequence Synthetic 413 gcactctgat tgacaaata 19 414
19 DNA Artificial Sequence Synthetic 414 tgaagtctct gattaagta 19
415 19 DNA Artificial Sequence Synthetic 415 tcagagagat cctcataaa
19 416 19 DNA Artificial Sequence Synthetic 416 gcaagaagat
caccatttc 19 417 19 DNA Artificial Sequence Synthetic 417
gagagaaatt tgaggatga 19 418 19 DNA Artificial Sequence Synthetic
418 gaaaggattt ggctataag 19 419 19 DNA Artificial Sequence
Synthetic 419 gaaagaaggc atgaacatt 19 420 19 DNA Artificial
Sequence Synthetic 420 gggagatagt gatgaagta 19 421 19 DNA
Artificial Sequence Synthetic 421 gaagtacatc cattataag 19 422 19
DNA Artificial Sequence Synthetic 422 gtacgacaac cgggagata 19 423
19 DNA Artificial Sequence Synthetic 423 agatagtgat gaagtacat 19
424 19 DNA Artificial Sequence Synthetic 424 tgaagactct gctcagttt
19 425 19 DNA Artificial Sequence Synthetic 425 gcatgcggcc
tctgtttga 19 426 19 RNA Artificial Sequence Synthetic 426
gcacacagcu uacuacauc 19 427 19 RNA Artificial Sequence Synthetic
427 gaaaugcccu gguaucuca 19 428 19 RNA Artificial Sequence
Synthetic 428 gaaggaacgu gaugugauc 19 429 19 RNA Artificial
Sequence Synthetic 429 gcacuacucc uguguguga 19 430 19 RNA
Artificial Sequence Synthetic 430 gaacccagcu ggagaacuu 19 431 19
RNA Artificial Sequence Synthetic 431 gauauacagu gugaucuua 19 432
19 RNA Artificial Sequence Synthetic 432 guacuacgau ccugauuau
19
433 19 RNA Artificial Sequence Synthetic 433 gugccgaccu uuacaauuu
19 434 19 DNA Artificial Sequence Synthetic 434 gaaggaaact
gaattcaaa 19 435 19 DNA Artificial Sequence Synthetic 435
ggaaatatgt actacgaaa 19 436 19 DNA Artificial Sequence Synthetic
436 ccacaaagca gtgaattta 19 437 19 DNA Artificial Sequence
Synthetic 437 gtaacaagct cacgcagtt 19 438 19 RNA Artificial
Sequence Synthetic 438 aagcugaccu ggaagagaa 19 439 19 RNA
Artificial Sequence Synthetic 439 aagcuugucu gaucguuca 19 440 19
RNA Artificial Sequence Synthetic 440 aauguuagcc ucaauuucu 19 441
19 RNA Artificial Sequence Synthetic 441 acaagggccu cuucuguga 19
442 19 RNA Artificial Sequence Synthetic 442 acaaugacau cuuugaauc
19 443 19 RNA Artificial Sequence Synthetic 443 acacagcacc
agaauguau 19 444 19 RNA Artificial Sequence Synthetic 444
acacugguuu gaagaaugu 19 445 19 RNA Artificial Sequence Synthetic
445 acaggaagau guacggaga 19 446 19 RNA Artificial Sequence
Synthetic 446 acggcgaggu caugaagaa 19 447 19 RNA Artificial
Sequence Synthetic 447 agaacauuaa gaagggcaa 19 448 19 RNA
Artificial Sequence Synthetic 448 agacaaugac aucuuugaa 19 449 19
RNA Artificial Sequence Synthetic 449 agacauaccg agcuaaauu 19 450
19 RNA Artificial Sequence Synthetic 450 agacccaacu augauuaga 19
451 19 RNA Artificial Sequence Synthetic 451 agaccugugc cugccauua
19 452 19 RNA Artificial Sequence Synthetic 452 agcaugaaau
ggauacuua 19 453 19 RNA Artificial Sequence Synthetic 453
agcuaaauuc uguggagua 19 454 19 RNA Artificial Sequence Synthetic
454 aggaagaugu acggagaca 19 455 19 RNA Artificial Sequence
Synthetic 455 auaccgagcu aaauucugu 19 456 19 RNA Artificial
Sequence Synthetic 456 auauggaaau ucucucaga 19 457 19 RNA
Artificial Sequence Synthetic 457 auauuaaggu guggcuuua 19 458 19
RNA Artificial Sequence Synthetic 458 augaacaaau ggccuuuau 19 459
19 RNA Artificial Sequence Synthetic 459 augaagacau accgagcua 19
460 19 RNA Artificial Sequence Synthetic 460 auguaaagcu ugucugauc
19 461 19 RNA Artificial Sequence Synthetic 461 caagugaauu
uccuguaac 19 462 19 RNA Artificial Sequence Synthetic 462
caaguuguuc cuuaaguca 19 463 19 RNA Artificial Sequence Synthetic
463 cacagcacca gaauguaua 19 464 19 RNA Artificial Sequence
Synthetic 464 caccauaggu agaauguaa 19 465 19 RNA Artificial
Sequence Synthetic 465 cagaaagguu aguaucauc 19 466 19 RNA
Artificial Sequence Synthetic 466 caguagcaca aguuauuua 19 467 19
RNA Artificial Sequence Synthetic 467 caucagauag cucuuauac 19 468
19 RNA Artificial Sequence Synthetic 468 ccaacuauga uuagagcca 19
469 19 RNA Artificial Sequence Synthetic 469 ccacccgggu uaccaauga
19 470 19 RNA Artificial Sequence Synthetic 470 ccagacccaa
cuaugauua 19 471 19 RNA Artificial Sequence Synthetic 471
ccagagagug agagacauu 19 472 19 RNA Artificial Sequence Synthetic
472 ccagagcagc ugcaaguac 19 473 19 RNA Artificial Sequence
Synthetic 473 ccugcaggcu agagaagca 19 474 19 RNA Artificial
Sequence Synthetic 474 ccugcuauuu gaaguguaa 19 475 19 RNA
Artificial Sequence Synthetic 475 cgaguaauau gccugcuau 19 476 19
RNA Artificial Sequence Synthetic 476 cgauuagacu ggacagcuu 19 477
19 RNA Artificial Sequence Synthetic 477 cggagacaau gacaucuuu 19
478 19 RNA Artificial Sequence Synthetic 478 cggcuuaccg acuggaaga
19 479 19 RNA Artificial Sequence Synthetic 479 cuagguagga
aauguggua 19 480 19 RNA Artificial Sequence Synthetic 480
cuauauagcu gaucaguuu 19 481 19 RNA Artificial Sequence Synthetic
481 cuccaagccu aucaaguuu 19 482 19 RNA Artificial Sequence
Synthetic 482 gaaaagauuc ccacccaau 19 483 19 RNA Artificial
Sequence Synthetic 483 gaaauggaua cuuauaugg 19 484 19 RNA
Artificial Sequence Synthetic 484 gaacaaaugg ccuuuauua 19 485 19
RNA Artificial Sequence Synthetic 485 gaacaccaua gguagaaug 19 486
19 RNA Artificial Sequence Synthetic 486 gaacauuaag aagggcaaa 19
487 19 RNA Artificial Sequence Synthetic 487 gaacugauuc acaucucau
19 488 19 RNA Artificial Sequence Synthetic 488 gaagaauguu
aagacuuga 19 489 19 RNA Artificial Sequence Synthetic 489
gaagacauac cgagcuaaa 19 490 19 RNA Artificial Sequence Synthetic
490 gaagcugacc uggaagaga 19 491 19 RNA Artificial Sequence
Synthetic 491 gaaguguaau ugagaagga 19 492 19 RNA Artificial
Sequence Synthetic 492 gaaugacacu guucaggaa 19 493 19 RNA
Artificial Sequence Synthetic 493 gaauguaaag cuugucuga 19 494 19
RNA Artificial Sequence Synthetic 494 gacaaugaca ucuuugaau 19 495
19 RNA Artificial Sequence Synthetic 495 gacacugguu ugaagaaug 19
496 19 RNA Artificial Sequence Synthetic 496 gacaguuuau uuguugaga
19 497 19 RNA Artificial Sequence Synthetic 497 gacauaccga
gcuaaauuc 19 498 19 RNA Artificial Sequence Synthetic 498
gacauuaacu cauuagacu 19 499 19 RNA Artificial Sequence Synthetic
499 gaccaaaagu uacauguuu 19 500 19 RNA Artificial Sequence
Synthetic 500 gaccuggaag agaacauua 19 501 19 RNA Artificial
Sequence Synthetic 501 gaccugugcc ugccauuac 19 502 19 RNA
Artificial Sequence Synthetic 502 gacuggaacu ugaacugau 19 503 19
RNA Artificial Sequence Synthetic 503 gagacaauga caucuuuga 19 504
19 RNA Artificial Sequence Synthetic 504 gagagacauu aacucauua 19
505 19 RNA Artificial Sequence Synthetic 505 gagucaaguu guuccuuaa
19 506 19 RNA Artificial Sequence Synthetic 506 gauagcucuu
auacgagua 19 507 19 RNA Artificial Sequence Synthetic 507
gcaagugaau uuccuguaa 19 508 19 RNA Artificial Sequence Synthetic
508 gcacaagggc cucuucugu 19 509 19 RNA Artificial Sequence
Synthetic 509 gcaccagaau guauauuaa 19 510 19 RNA Artificial
Sequence Synthetic 510 gcaccagcau gaagacaua 19 511 19 RNA
Artificial Sequence Synthetic 511 gcagaaaggu uaguaucau 19 512 19
RNA Artificial Sequence Synthetic 512 gcaggcugau uucuaggua 19 513
19 RNA Artificial Sequence Synthetic 513 gcaugaaaug gauacuuau 19
514 19 RNA Artificial Sequence Synthetic 514 gcaugaagcc agagaguga
19 515 19 RNA Artificial Sequence Synthetic 515 gcauuuguuu
cuacuuuga 19 516 19 RNA Artificial Sequence Synthetic 516
gcccagaccc aacuaugau 19 517 19 RNA Artificial Sequence Synthetic
517 gcgaagcuga ccuggaaga 19 518 19 RNA Artificial Sequence
Synthetic 518 gcgaggagug gguguguga 19 519 19 RNA Artificial
Sequence Synthetic 519 gcgaggucau gaagaagaa 19 520 19 RNA
Artificial Sequence Synthetic 520 gcuaaauucu guggaguau 19 521 19
RNA Artificial Sequence Synthetic 521 gcucugacau ucugauucg 19 522
19 RNA Artificial Sequence Synthetic 522 gcucuuauac gaguaauau 19
523 19 RNA Artificial Sequence Synthetic 523 gcugauuucu agguaggaa
19 524 19 RNA Artificial Sequence Synthetic 524 ggaaauucuc
ucagauaga 19 525 19 RNA Artificial Sequence Synthetic 525
ggaagagaac auuaagaag 19 526 19 RNA Artificial Sequence Synthetic
526 ggaagcauuu guuucuacu 19 527 19 RNA Artificial Sequence
Synthetic 527 ggagacaaug acaucuuug 19 528 19 RNA Artificial
Sequence Synthetic 528 ggcccagacc caacuauga 19 529 19 RNA
Artificial Sequence Synthetic 529 ggcgagguca ugaagaaga 19 530 19
RNA Artificial Sequence Synthetic 530 gggaaaugcu gcgaggagu 19 531
19 RNA Artificial Sequence Synthetic 531 ggguguguga cgagcccaa 19
532 19 RNA Artificial Sequence Synthetic 532 gguaccagca gaaagguua
19 533 19 RNA Artificial Sequence Synthetic 533 gguagaaugu
aaagcuugu 19 534 19 RNA Artificial Sequence Synthetic 534
ggucaggccu ugcgaagcu 19 535 19 RNA Artificial Sequence Synthetic
535 guaaauauug ugugugugu 19 536 19 RNA Artificial Sequence
Synthetic 536 guacacagca ccagaaugu 19 537 19 RNA Artificial
Sequence Synthetic 537 guccuuggca ggcugauuu 19 538 19 RNA
Artificial Sequence Synthetic 538 gugacgagcc caaggacca 19 539 19
RNA Artificial Sequence Synthetic 539 gugcauccgu acucccaaa 19 540
19 RNA Artificial Sequence Synthetic 540 gugugugugu guauauaua 19
541 19 RNA Artificial Sequence Synthetic 541 gugugugugu guguauaua
19 542 19 RNA Artificial Sequence Synthetic 542 guuacauguu
ugcaccuuu 19 543 19 RNA Artificial Sequence Synthetic 543
guuugcaccu uucuaguug 19 544 19 RNA Artificial Sequence Synthetic
544 uaaauucugu ggaguaugu 19 545 19 RNA Artificial Sequence
Synthetic 545 uagguagaau guaaagcuu 19 546 19 RNA Artificial
Sequence Synthetic 546 uauauuaagg uguggcuuu 19 547 19 RNA
Artificial Sequence Synthetic 547 ucaaguuguu ccuuaaguc 19 548 19
RNA Artificial Sequence Synthetic 548 ucaauguuag ccucaauuu 19 549
19 RNA Artificial Sequence Synthetic 549 ucagauagcu cuuauacga 19
550 19 RNA Artificial Sequence Synthetic 550 ucauuagacu ggaacuuga
19 551 19 RNA Artificial Sequence Synthetic 551 ucggacaguu
uauuuguug 19 552 19 RNA Artificial Sequence Synthetic 552
ucuccaagcc uaucaaguu 19 553 19 RNA Artificial Sequence Synthetic
553 ugaagaaugu uaagacuug 19 554 19 RNA Artificial Sequence
Synthetic 554 ugacagugga acuacauua 19 555 19 RNA Artificial
Sequence Synthetic 555 ugagucaagu uguuccuua 19 556 19 RNA
Artificial Sequence Synthetic 556 uggaaauucu cucagauag 19 557 19
RNA Artificial Sequence Synthetic 557 uggaagagaa cauuaagaa 19 558
19 RNA Artificial Sequence Synthetic 558 uuacauguuu gcaccuuuc 19
559 19 RNA Artificial Sequence Synthetic 559 uugcgaagcu gaccuggaa
19
* * * * *