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Definitions

• the present invention generally relates to a inhibition method of target mRNA expression using small interfering RNA (hereinafter, referred to as "siRNA”), and more specifically, to a inhibition method of target mRNA expression using siRNA comprising the steps of selecting complementary siRNA predicted to show the maximal target inhibition efficiency by analyzing a relative binding energy pattern between adjacent and nonadjacent portions of nucleotide sequence of candidate siRNAs and inhibiting target mRNA expression by treating said selected siRNA.
• siRNA small interfering RNA
• RNA interference refers to a phenomenon of decomposing target mRNA in a cytoplasm by double-stranded RNA (hereinafter, referred to as "dsRNA”) having complementary nucleotide sequence of the target mRNA.
• dsRNA double-stranded RNA
• siRNA small interfering RNA
• the small interfering RNA was granted to the No. 1 of Breakthrough of the Year of the Science Journal in 2002 year (Jennifer Couzin, BREAKTHROUGH OF THE YEAR: Small RNAs Make Big Splash, Jennifer Couzin, Science 20 December 2002: 2296-2297).
• siRNA has some advantages as a tool of therapeutics and functional genomics over conventional antisense RNA.
• siRNA has been known to inhibit the expression of genes effectively at a lower concentration than antisense RNA. It means that a smaller amount of siRNA can be used for study and higher therapeutic effect can be expected.
• inhibition of gene expression by RNAi is a natural mechanism in a body and its action is very specific.
• RNAi experiment includes siRNA design (target site selection), cell culture experiment (cell culture assay, target mRNA degradation rate, the most effective siRNA selection), animal experiment (stability, modification, delivery, pharmacokinetics, toxicology) and clinical test.
• the most important step is selecting effective siRNA sequence(s) and delivering selected siRNA into a target tissue (drug delivery).
• the selection of siRNA sequence having high efficiency is important because different siRNAs show different efficiency and only a siRNA having high efficiency results in an accurate experimental result and can be used for therapy.
• the efficient nucleotide sequence can be selected by a computer-aided scoring method and an experimental method.
• the experimental method is directed to select nucleotide sequences that combine well with target mRNA synthesized by in vitro transcription.
• the mRNA structure obtained from in vitro transcription may be different from that of the mRNA in a cell, and various proteins may be bonded to the mRNA in a cell so that a result obtained from the experiment using mRNA obtained by in vitro transcription may not reflect an actual result. Therefore, developing an algorithm for searching an effective siRNA sequence is important and this can be done by considering various elements that influence the effectiveness of siRNA sequence.
• siRNA design has been performed according to the Tuschl rule which considers 3'overhang type, GC ratio, repetition of specific nucleotide, SNP (single nucleotide polymorphism) in a sequence, secondary structure of RNA, homology with un-targeted mRNA sequence (S.M. Elbashir, J. Harborth, W. Lendeckel, A. Yalcin, Klaus Weber, T. Tuschl, Nature, 411, 494-498, 2001a; S.M. Elbashir, W. Lendeckel, T. Tuschl, Genes & Dev., 15, 188-200, 2001b; S.M. Elbashir, J. Martinez, A. Patkaniowska, W. Lendeckel, T.
• siRNA efficiency could be predicted by calculating the energy differences between 5 '-end and 3 '-end of candidate siRNA (Schwarz DS, Hutvagner G, Du T, Xu Z, Aronin N, Zamore PD., Cell, 115(2), 199-208, 2003, see Fig. 1).
• E A , E B , E C and E D with respect to each dsRNA which are mean binding energy values of l st -2 nd (A), 3 rd -7 th (B), 8 th - 15 th (C) and 16 th - 18 th (D) in the base sequence of the dsRNA,
• X (A-B) is a value corresponding to a difference between mean binding energy E A of the section (A) and mean binding energy E B of the section (B), and the same goes for Y(B-C), Y(C-D) and Y (A -D);
• Wj is a predetermined weight allotted to each factor based on Wi;
• gene expression inhibition data using siRNA are collected from two papers.
• the one is from Khvorova's paper (Khvorova A, Reynolds A, Jayasena SD, Cell, 115(4), 505, 2003) and the other is from Amarzguioui's paper (Amarzguioui M, Prydz H, Biochem. Biophys. Res. Commun., 316(4), 1050-8, 2004).
• Khvorova's paper discloses a nucleotide sequence represented by the SEQ. ID. NO:1 corresponding to 193-390 nucleotide sequence of human cyclophilin gene (hCyPB), a nucleotide sequence represented by the SEQ. ID.
• N x the number of variation of the effective group
• locations which are the center of the section are designated using the p-value of Fig. 6.
• the following standards are applied.
• the section (1) is set as follows.
• the number of effective siRNAs is N f and the number of ineffective siRNAs is N n , the efficiency is i (T in case of siRNA of the effective group, 'n' in case of siRNA of the ineffective group).
• the mean binding energy per one binding energy that the jth (to have a number of l ⁇ Nf or 1-N n as a value) siRNA has in a section k (one of A, B, C and D) is defined as E,_, k . That is, the mean energy per one binding energy is represented as E f3B in the section B of the 3 rd siRNA of the effective group.
• Each E 1J k is obtained using experimental data.
• E 1 (B-C) and E 1 (C-D) may be obtained using the Equation 2.
• Ef(A-B) is a value that represents binding energy per one binding energy location in the sections A and B of siRNAs of the effective group
• E n (A-B) is that of the ineffective group. That is, if a section is taken to increase an absolute value of Ef (A- B) - E n (A-B), a difference of the mean binding energy between the effective siRNA group and the ineffective siRNA group in the sections A and B becomes larger. As a result, a section can be selected using the above- described characteristic. The same goes for B-C and C-D.
• each R value of the two sections are added so that the R value in the section A-B is set as 3.
• the two sections set through (1) and (2) are selected by distinguishing a relative binding energy pattern with the adjacent section.
• the t-test is performed on six combinations of A-B, B-C, C-D, A-C, A-D and B-D obtained by the difference of the four sections A, B, C and D. Table 6 shows the t-test results.
• the obtained data are divided into the sections obtained by the above-described process to obtain E,( A-B) , E 1 (B-Q, E,( C -D) and E,( A- D) from the equation 2.
• These values mean energy values obtained by averaging values on difference of the average energy in eachgroup.
• each value has distribution values which are S 1(A-B)9 S 1(B-C) , S 1 (C-D) and S 1 (A -D )-
• N 1 The number of siRNA experimental data is defined as N 1 .
• Table 7 shows values E I(A- B), E l(B- c), E 1(C- D), E I(A-D ), values S I(A- B), S l(B-C ), S I(C- D), S, (A- D), N 1 , and t- values and p-values through the t-test.
• the data set is p-value ⁇ 0.05 in all sections, it can be used in the scoring system for dividing the effective siRNA and the ineffective siRNA.
• the equation 3 can be applied to all of X,(A-B) > XI(B-Q 5 XI(C-D) and X 1 (A-D) 5 and also each range of values X 1 (A-B) 5 Xi(B-C) 5 Xi(C-D) and X 1 (A-D) can be obtained as shown in Fig. 11.
• the efficiency of unknown siRNA is scored through the relative binding energy pattern under consideration of the results by:
• Each score is defined as Y(A-B) 5 Y(B-C), Y(C-D) and Y(A-D> Referring to Fig. 11, in the continuous section, if -0.02 ⁇ X (A- B ) ⁇ 0.38, -0.29 ⁇ X(B-C) ⁇ -0.01, 0.00 ⁇ X(C-D) ⁇ 0.35, 0.07 ⁇ X (A -D) ⁇ 0.37, then Y (A-B) , Y (B- c), Y(c-D) and Y (A-D) are individually given 10 points, and if -0.63 ⁇ X (A - B ) ⁇ -0.21, 0.05 ⁇ X (B -c ) ⁇ 0.44, - 0.47 ⁇ X(C-D) ⁇ -0.09, -0.67 ⁇ X (A-D) ⁇ -0.23, then Y (A-B) , Y (B
• the binding energy pattern of siRNA is scored depending on how the weighting factors W( A- B), W(B-C), W( C -D) and W( A-D ) in each section are set.
• the t- value between the effective siRNA group and the ineffective siRNA is examined as each weighting factor is increased from 0 to 1 by 0.01.
• Fig. 12 shows distribution of combinations depending on each weighting factor value among the upper 100 t- values which are arranged in a descending order. Referring to the distribution of Fig. 12, a location for maximizing a t- value, that is, a location for maximizing a difference of the binding energy variation between the effective siRNA group and the ineffective siRNA group can be found.
• the combination OfW(A-B), W (B - Q , W (C-D) and W (A - D) for maximizing the t-value between the two groups is ranging from 0.90 to 1.00, 0.2 to 0.4, 0.2 to 0.3 and 0.7 to 0.9, preferably, 1.00, 0.37, 0.20, 0.90 in the continuous section, and ranging from 0.5 to 0.7, 0.3 to 0.5, 0.3 to 0.5 and 0.9 to 1.0, preferably, 0.65, 0.48, 0.48 and 0.90 in the discontinuous section. If it is set beyond a threshold value in each case, the t-value is rapidly decreased even to insignificant level for discriminating in the scoring method.
• the present inventors considered how the relative binding energy pattern can be combined with other factors (GC content, T m , absolute scores of binding energy, homology with other mRNA, secondary structure of RNA) to obtain a system for predicting the overall efficiency of siRNA.
• the following linear equation basically the same way of scoring the relative binding energy pattern is used as a scoring method.
• Z 1 comprising various factors for affecting inhibition of target mRNA includes the relative binding energy as an essential factor and one or more factors selected from the group comprising the number of A/U in 5 bases of 3'-end, the presence of G/C at 1 st position, the presence of AAJ at 19 th position, the content of G/C, T m , secondary structure of RNA, the homology with other mRNA and the like as an optional factor.
• the optional factors are not necessarily included in allotting the Z value but factors for inducing better prediction with the relative binding energy can be included without limitation.
• Z J the score (Y) of the relative binding energy
• Z 2 the number of A/U in 5 bases of 3'-end
• Z 3 the presence of G/C at 1 st position
• Z 4 the presence of A/U at 19 th position
• Z 5 the score of G/C content.
• Z 1 is the calculated score Y
• Z 2 is the number of A/U in 5 bases of 3'-end
• Z 3 is 1 when the base of 5' end is G/C or 0 when it isn't
• Z 4 is 1 when the base of 3' end is A/U or 0 when it isn't
• Z 5 is 10 when the content of G/C ranges from 36 to 53% and 0 when it does not belong within the range.
• Fig. 13 is a graph for optimizing the weighting factor Wj on each score in the same way of the scoring the relative binding energy pattern as in Fig. 12.
• the combination of Wi, W 2 , W 3 , W 4 and W 5 optimized through this process ranges from 0.9 to 1.0, from 0.0 to 0.2, from 0.1 to 0.3 and from 0.0 to 0.2, preferably, 0.90, 0.07, 0.15, 0.19 and 0.11.
• the Z value obtained through the above process can be an index for distinguishing which relative binding energy pattern unknown siRNA has.
• only the analysis of the base sequence enables evaluation of the binding energy, thereby maximizing the design and production efficiency of siRNA.
• the expression of target mRNA can be effectively inhibited by applying a selected siRNA having an excellent inhibition efficiency, preferably a selected siRNA having a Z value within upper 10% to the target mRNA using the above-described method.
• the above numerical value can be any value and may be flexibly applied depending on sample size of a candidate siRNA group, experimental conditions and the like.
• Fig. 1 is a diagram illustrating inhibition efficiency of gene expression of siRNA changes depending on combination patterns of RISC enzyme.
• Fig. 2 is a diagram illustrating a method for scoring the relationship between the inhibition efficiency of gene expression and the binding energy of siRNA.
• Fig. 3 is a diagram illustrating binding energydistribution of binding energy of siRNA in INN-HB nearest neighbor model.
• Fig. 4 illustrates binding energy values in INN-HB nearest neighbor model.
• Fig. 5 is a graph illustrating the mean of the binding energy in each location of collected siRNA data: axis X; from the 1 st to 18 th positions, axis Y; mean of the binding energy (- ⁇ G), solid line; when the inhibition efficiency of gene expression is 90% or more, dotted line; when the inhibition efficiency of gene expression is below 50%.
• Fig. 4 illustrates binding energy values in INN-HB nearest neighbor model.
• Fig. 5 is a graph illustrating the mean of the binding energy in each location of collected siRNA data: axis X; from the 1 st to 18 th positions, axis Y; mean of the binding energy (- ⁇ G), solid line; when the inhibition efficiency of gene expression is 90% or more, dotted line; when the inhibition efficiency of gene expression is below 50%.
• FIG. 6 is a graph illustrating t-test result of the binding energy in each location of collected siRNA data: axis X; from the 1 st to 18 th positions, axis Y; p-value, solid line; pGL3 gene, dotted line; hCyPB gene dash-dot line; complex gene extracted from Amarzguioui's paper.
• Fig. 7 is a graph illustrating t-test result of the binding energy in each location of collected siRNA data: axis X; from the 1 st to 18 th positions, axis Y; t- value, solid line; pGL3 gene, dotted line; hCyPB gene dash-dot line; complex gene extracted from Amarzguioui's paper.
• Fig. 8 is a graph illustrating various information on sections A(I ⁇ 2), B(3 ⁇ 7), C(8 ⁇ 15) and D(16 ⁇ 18) obtained by analyzing binding energy data through the process (1).
• Fig. 9 is a graph illustrating ratio distribution where the p-value is less than 0.05 among the combination of A-B, B-C and C-D having a specific R value.
• Fig. 10 is a diagram illustrating a section selected through the processes (1) and (2).
• Fig. 11 illustrates a graph (A) that shows a reliable section of a relative difference between the mean binding energy of ineffective siRNA and effective siRNA in the sections A-B, B-C, C-D and A-D selected through the process (1) and a graph (B) that shows a reliable section between a relative difference of the mean binding energy of ineffective siRNA and effective siRNA in the sections A-B, B-C, C-D and A-D selected through the process (2).
• Fig. 12 is a graph illustrating the relationship between weighting factor and the t- value in the score of relative binding energy pattern, wherein the combination of weighting factors are arranged in a descending order depending on the t-value to show the number of the weighting factors of the upper 100 combinations in each section.
• Fig. 13 shows a graph for optimizing the weighting factor Wj on each score in the same way of scoring the relative binding energy pattern as shown in Fig. 12.
• the above value can be obtained using a statistical program R (http://www.R- project.org) ([1] Richard A. Becker, John M. Chambers, and Allan R. Wilks. The New S Language. Chapman & Hall, London, 1988; [2] John M. Chambers and Trevor J. Hastie. Statistical Models in S. Chapman & Hall, London, 1992; [3] John M. Chambers. Programming with Data. Springer, New York, 1998. ISBN 0-387-98503-4; [4] William N. Venables and Brian D. Ripley. Modern Applied Statistics with S. Fourth Edition. Springer, 2002. ISBN 0-387-95457-0; [5] William N. Venables and Brian D. Ripley.
• the success rate of prediction is shown to be higher by 10% in the scoring method binding energyaccording to the present invention using the relative binding energy pattern than in the conventional scoring method of siRNA efficiency in both cases of LDA and QDA.
• Example 2 Inhibition experiment of Survivin gene expression
• 36 siRNAs for inhibiting surviving gene expression were designed, and then the inhibition experiment of the surviving gene expression was performed.
• the resultant data set was divided into effective/ineffective groups on a basis of 75% inhibition efficiency of expression.
• the three data sets obtained from the Khvorova's paper and the Amarzguioui's paper were used as train sets, and the survivin data set was used as a test set.
• Example 9 In the same way of Example 1, the score of siRNA was marked, and the success rate of prediction of the efficiency of siRNAs was calculated through LDA (Linear discriminant analysis) and QDA (Quadratic discriminant analysis) using the statistical program R. As a result, the sucess rate of prediction was 0.64 in both cases of LDA and QDA to show almost the same results of Example 1 (see Table 9). Table 9
• a researcher or an experimenter can analyzes patterns of a relative binding energy on base sequences of unknown siRNA without actual experiments to determine whether the siRNA is effective or ineffective rapidly, thereby design and production efficiency of siRNA can be maximized and target mRNA expression can be effectively inhibited with efficient siRNA to the target mRNA.

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