Patent Application: US-15127702-A

Abstract:
the use of antisense oligodeoxyribonucleotides to inhibit translation of mrnas promises to be an important means of controlling gene expression and disease processes . odns are about 20 nucleotides long , so hundreds of possible targets are available in a given mrna . an elusive goal has been to efficiently predict the best in vivo antisense target without having to study a large pool of possible odn sequences for each mrna . it would be a breakthrough if odn selection could be accurately guided by the application of sequence specific parameters to an mrna sequence . the selection of the best odn sequence is complicated since cellular uptake , conditions at the mrna target site , non - sequence - specific effects , sequence redundancy , and mrna secondary structures are difficult to predict . thermodynamic parameters for nearest - neighbor duplex stabilities , from in vitro studies , have not been adequate predictors of in vivo hybridization . the methodology of this application shows that it is possible to obtain parameters for in vivo motifs , which are defined as combinations of next - nearest - neighbors , that are correlated with efficient antisense targeting . these parameters can be used to identify mrna sequences that are binding sites for effective antisense odns . next - nearest - neighbor nucleotide parameters can be derived directly from cell culture inhibition data so that in vivo conditions are taken into account .

Description:
( 1 ) a database of antisense effects must be obtained for a number of sequences that is greater than the number of next - nearest - neighbor parameters that need to be determined . the database must have at least one example of each nnn triplet in at least one sequence . the method of the present invention is unique in that the database may consist of data obtained for in vivo antisense effects , but those skilled in the art will realize that other databases may be used to determine nnn parameters . from our published theory [ gray , 1997a ; gray 1997b ], if end effects are significant , 84 nnn parameters are needed to describe an array of sequence - dependent data for short oligomers . in the case of relatively long odns of 20 - nucleotides , one can treat the sequences as closed sequences with no ends , which reduces the number of independent parameters to 49 [ gray , 1997a ; gray 1997b ]. in this latter case , there are fewer than 64 (= 4 3 ) nnn parameters because there are 15 constraints on arranging triplets in a closed sequence . a search of the literature and 42 references from tu et al . ( 1998 ), revealed no single database large enough for such an analysis . in addition , only a few data sets of 20 or more included individual errors , which are needed to weight the % inhibition values for a singular value decomposition ( svd ) analysis . in the present invention , data were obtained for a total of 102 antisense odn sequences that contained representative numbers of all 64 nnn triplets . the odns were uniformly modified to contain phosphorothioate linkages between each nucleotide , with no phosphate groups at either end . four gene products were targeted : c - rafl , akt2 , bcl - 2 , and pkcα , and the data was obtained from antisense treatments of two cell lines , t24 bladder cancer cells and a549 lung cancer cells . the specific sequence positions on the mrnas and the inhibition data of protein levels are given in table 1 . * from a preliminary nnn fit by svd , the % inhibition data for five additional s - odns targeted to c - rafl mrna had squared deviations of twice the average and were omitted from the final set . ** errors are ranges from duplicate western blots or standard deviations from three or more measurements *** where no error is shown , the data are from single measurements and for the purpose of svd analysis a maximum error of 15 % was assumed . ( 2 ) each of the target mrna sequences is separated into its constituent nnn triplets . this is illustrated in fig2 a , 2 b , and 2 c for three of the target sequences in c - rafl mrna . the total number of nnn triplets is 20 for each sequence , but , in general , they are different for each sequence . the array shown in fig2 a , 2 b , and 2 c makes a 3 × 64 matrix . an additional row is added for each additional sequence . additional columns in the matrix may be added to allow for differences in the data sets for different genes or cell lines or any other parameter than one wants to distinguish . for example , we added a 65th column with a “ 0 ” for every sequence that was used to inhibit the level of a gene product in t24 cells and a “ 1 ” for every sequence that was used to inhibit the level of a gene product in a549 cells . for the sequences in table 1 , the resulting matrix was 102 (# of sequences )× 65 (# of parameters ). ( 3 ) solve the matrix equation . the matrix equation nnn hk × p k = i h , where the nnn matrix has dimensions of h = 102 and k = 65 , the p vector has k = 65 values , one for each of the 64 nnn triplexes and one for the cell line , and i is the vector of % inhibition values for each sequence . each hth row of the nnn hk matrix and the hth value of the i h vector is divided by the error for the hth sequence . this equation was solved as in our other work ( gray 1997b ) using standard procedures ( press et al ., 1992 ) to give values for the 65 parameters of the p vector and , hence , for the 49 independent combinations of the nnn triplets . the values for these parameters are listed in table 2 for our specific data set . those skilled in the art will realize that other sets of p parameters may be obtained for other data sets by the same procedure . the p parameters show the significance of various nnn triplets to the antisense inhibitory effect of the sequences in the data set . negative values mean that some triplets , or combinations of triplets , are actually counterproductive to a maximum antisense effect . one may also note that from the last column in table 2 that the number of occurrences of the various triplets ranged from 6 to 76 in the sequences used in this database , so all nnn triplets were well represented . ( 4 ) assess the importance of combinations of the nnn triplets . table 3 shows the relative importance of the 10 simplest independent combinations of nnn in the target sequences that were analyzed . because there are constraints linking the nnn , only values for the four triplets that are homopurine or homopyrimidine can be individually determined ( three left - hand columns of table 3 ). the other 60 triplet values are interrelated and must be expressed as combinations , the simplest of which constitute six repeating sequences (( cg ) n , ( ac ) n , etc . ), and these are listed in the three right - hand columns of table 3 . the values in table 3 reveal that : ( a ) triplets of rna purines ( ggg , aaa , and aga + gag ) are all more important than those with rna pyrimidines ( ccc , uuu , and ucu + cuc ), consistent with in vitro hybrid oligomer stabilities ; ( b ) ggg is one of the most stable triplets , in agreement with tu &# 39 ; s analysis ; ( c ) aaa also has an unusually high positive effect when it is present in antisense odns ; and ( d ) uuu has a negative contribution , in agreement with the well - known instability of ru : da pairs , which plays an important role in transcription termination in prokaryotes . overall , the results of this analysis show that such an approach can give parameters pertinent to s - odn : rna hybridization in vivo and that results from more extensive data sets will lead to new insights regarding mrna target selection . ( 5 ) apply the derived nnn values to the prediction of antisense effectiveness of other targeted genes . this procedure simply involves the multiplication of each nnn parameter from table 2 with the number of occurrence of that nnn in the sequence for which a predicted value is desired . if the nnn values have been derived from sequences that are 20 nucleotides long and a predicted value is desired for sequences that are l nucleotides long where l is not 20 , the nnn values should be multiplied by l / 20 . examples are in the following section . fig3 a shows the fit of hybrid nn free energy values , δg °( 37 ° c . ), typical of nn stability predictions , to the measured inhibition values for the 102 antisense odn sequences used to inhibit four gene products in two cell lines ( data from table 1 ). fig3 b shows the fit of the derived nnn values in table 2 , multiplied by the nnn triplets in each of the 102 odn sequences , to the measured inhibition values . the fit to the nnn data set is better , as is shown by the correlation coefficients , r , and the significance values from the t - text , p , in the first row in table 4 . that is , the nn fit has a regression coefficient of 0 . 309 , while the nnn fit gives a better regression coefficient of 0 . 778 , and a lower value of p , although both fits are significant ( below the p = 0 . 05 level ). * r is the coefficient of correlation and p is the significance of r using the t - test . the smaller the value of p the more significant the correlation ; p is the level at which the null hypothesis can be rejected . in this example , the predictions from the nnn parameters are compared with those from the nn free energy δg ° for a data set that was not used in deriving the nnn parameters of table 2 . the data are published inhibition data from western blots of pkcα protein taken after treatments with 20 antisense s - odns ( dean et al ., 1994 ). as shown in fig4 a and 4b , and in the second line of data in table 4 , the experimental inhibition data are better approximated by those of the in vivo nnn values than by the in vitro nn parameters . it is noteworthy that the nnn parameters give a fit that is significant ( p = 0 . 015 ), while the nn parameters give a fit that is not significant at the p = 0 . 05 level ( p = 0 . 110 ). in this example , the predictions from the nnn parameters are compared with those from the nn free energy δg ° for a second data set that was not used in deriving the nnn parameters of table 2 . the data are published inhibition data from northern blots for two adhesion molecule mrnas ( e - selectin and icam - 1 ) taken after treatments with 33 antisense s - odns ( bennett et al ., 1994 ). as shown in fig5 a and 5b , and in the third line of data in table 4 , this experimental inhibition data is also better approximated by those of the in vivo nnn values than by the in vitro nn parameters . however , in neither case is the p value below the desired 0 . 05 value , indicating that additional factors remain to be identified by using a larger data set . in this example , the predictions from the nnn parameters are compared with those from the nn free energy δg ° for a fourth data set that was not used in deriving the nnn parameters of table 2 . the data are published inhibition data from inhibition of p - glycoprotein function in 22 antisense dna experiments using a rhodamine flux assays ( ho et al ., 1996 ). in this case , as may be seen in fig5 a and 5b , and in the last line of data in table 4 , the experimental inhibition data are better approximated by those of the in vitro nn parameters ( p & lt ; 0 . 05 ), although the in vivo nnn parameters give a fit with p = 0 . 068 , close to the desired p = 0 . 05 level . the odn sequences used in this study were preselected by an in vitro library of oligonucleotides to test for rnase h sensitive sites . this case serves to illustrate that the nnn methodology differs from methods based on nn stabilities of dna : mrna hybrids and is sensitive to different factors during in vivo antisense treatments . prior art in identifying effective antisense sequence combinations may be divided into two categories . in the first category , the majority of predictive routines are based on the knowledge that the thermodynamic properties of nucleic acid duplexes reside in the interactions of neighboring bases or base pairs , called nearest - neighbor ( nn ) properties . the predictive algorithms provided in computer programs including gray & amp ; clark , 1999 ; gray & amp ; clark , 2001 , hybsimulator ™ software ( rnature , inc ; mitsuhashi et al ., 1994 ), oligowalk ( mathews et al ., 1999 ), a recent program by walton et al . ( 2002 ) and all similar programs , to the knowledge of the inventor , rely on nn properties of nucleic acid duplexes , sometimes augmented with other factors such as changes in the folding of the target mrna sequence . there are only 13 independent combinations of nn properties in closed sequences . the present invention differs in that it relies on nnn triplet properties of sequences . there are 49 independent nnn triplet combinations for closed sequences . in the second category , researchers have searched for motifs of three or more nucleotides that are present in effective antisense molecules ( tu et al ., 1998 ; matveeva et al ., 2000 ). the results of such searches do not , however , show how to optimize motif combinations in a given target sequence . nor do such results allow one to objectively rank the antisense effectiveness of all sequences regardless of differences in their motif combinations . in fact , the theory of nucleotide sequence combinations ( gray 1997a ; 1997b ) shows that motifs cannot be combined in all combinations . the present invention allows for the derivation of a minimal set of 64 parameters for 64 nnn triplets that can be used to assign parameters for up to 49 independent nnn triplet combinations ( i . e . motifs ). values such as those in table 2 above are sufficient to account for all nnn triplet combinations ( i . e . motifs ) needed to unambiguously rank the effectiveness of any nucleotide sequence , as long as the sequence is long enough to be considered a closed , circular sequence . to those knowledgeable in the art , the present method may be expanded to derive 49 nnn combinations from any database with at least 49 sequences for closed sequences . the method may be expanded to cover sequences that are not closed and to derive next - next - nearest - neighbor ( nnnn ) quadruplet properties . basu , s ., & amp ; e . wickstrom ( 1997 ) nucl . acids res . 25 , 1327 - 1332 . beltinger , c ., h . saragovi , r . smith , l lesauteur , n . shah , l . dedionisio , l . christensen , a . raible , l . jarett , & amp ; a . gewirtz ( 1995 ) j . clin . invest . 95 , 1814 - 1823 . bennett , c . f ., t . p . condon , s . grimm , h . chan , & amp ; m .- y . chiang ( 1994 ) j . immun . 152 , 3530 - 3540 . bernstein , p . 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