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EP1694843A1 - Procedes de prediction d'efficacite en matiere d'arni - Google Patents

Procedes de prediction d'efficacite en matiere d'arni

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Publication number
EP1694843A1
EP1694843A1 EP04820427A EP04820427A EP1694843A1 EP 1694843 A1 EP1694843 A1 EP 1694843A1 EP 04820427 A EP04820427 A EP 04820427A EP 04820427 A EP04820427 A EP 04820427A EP 1694843 A1 EP1694843 A1 EP 1694843A1
Authority
EP
European Patent Office
Prior art keywords
rnai
potency
reagent
previous
target gene
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP04820427A
Other languages
German (de)
English (en)
Inventor
Jonathan Hall
Dieter Huesken
Jörg Bernd LANGE
Francois Jean-Charles Natt
Mischa Werner Henri Marie Reinhardt
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Novartis Pharma GmbH
Novartis AG
Original Assignee
Novartis Pharma GmbH
Novartis AG
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Filing date
Publication date
Application filed by Novartis Pharma GmbH, Novartis AG filed Critical Novartis Pharma GmbH
Publication of EP1694843A1 publication Critical patent/EP1694843A1/fr
Withdrawn legal-status Critical Current

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C12N15/111—General methods applicable to biologically active non-coding nucleic acids
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A61P25/28—Drugs for disorders of the nervous system for treating neurodegenerative disorders of the central nervous system, e.g. nootropic agents, cognition enhancers, drugs for treating Alzheimer's disease or other forms of dementia
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2310/00—Structure or type of the nucleic acid
    • C12N2310/10—Type of nucleic acid
    • C12N2310/14—Type of nucleic acid interfering N.A.
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2320/00—Applications; Uses
    • C12N2320/10—Applications; Uses in screening processes
    • C12N2320/11—Applications; Uses in screening processes for the determination of target sites, i.e. of active nucleic acids

Definitions

  • the present invention relates generally to the field of RNA interference (RNAi) and particularly to methods for obtaining RNAi reagents of high RNAi potency using an algorithm trained by an artificial neuronal network.
  • RNAi RNA interference
  • RNAi mechanism is not fully characterized and that many additional parameters may have an impact on siRNA potency, it would be beneficial to acquire larger functional data sets in order to have a better understanding of the sequence-activity relationship.
  • the present invention pertains to a method of making an algorithm for the prediction of the RNAi potency of a RNAi reagent comprising: a) determining experimentally the potency of a plurality of RNAi reagent to down regulate a reporter protein readout; and b) using said potency data set to train a artificial neuronal network.
  • the present invention pertains to an algorithm obtained by a method according to the present invention.
  • the present invention pertains to a method for predicting the RNAi potency of a RNAi reagent comprising: a) providing a plurality of RNAi reagent sequences comprising a region complementary to a given target gene; b) applying a trained artificial neuronal network according to the present invention to said RNAi reagent sequences; and c) selecting the RNAi reagent sequence(s) which are predicted to be potent.
  • the present invention pertains to a method for inhibiting the expression of a given target gene, comprising: a) providing a plurality of RNAi reagent sequences comprising a region complementary to a given target gene; b) applying a trained artificial neuronal network according to the present invention to a said RNAi reagent sequences; c) selecting the RNAi reagent sequence(s) which are predicted to be potent; d) synthesizing the RNAi reagent(s) selected in c); e) exposing cells expressing the target gene with the RNAi reagent(s) of d); and f) measuring the activity of the RNAi reagent or other phenotypes which may be induced by the down regulation of the target gene.
  • Figure 1 An example of a normalized data set. 79 siRNA targeting a 3'-UTR insert of a YFP mRNA were co-transfected with the reporter fusion mRNA (H1299, 50 nM, readout at 50h). Grey bars are 3'-UTR targeting siRNA, black bars are positive and negative controls. Negative control was arbitrary set at 10% potency and positive control at 90% potency. Each siRNA potencies were normalized according to these controls.
  • Figure 2 Illustrates the filtering of screening data.
  • Figure 3 Comparison of prediction versus screening measures for the training set.
  • Figure 4 Comparison of prediction versus screening measures for the testing set.
  • Figure 5 Dependence of the prediction - measure correlation (on test set) from the size of the training set.
  • RNAi reagent and “oligoribonucleotide” are used interchangeably and mean an oligomer or polymer of ribonucleic acid (RNA) or deoxyribonucleic acid (DNA) or a mimetic thereof.
  • RNAi reagent may also comprise modified ribonucleotide residues. Suitable modifications are known in the art. See, for instance, Uhlmann, Current Opin. Drug Discovery Dev., Vol. 3, No. 2, pp. 203-213 (2000); and Uhlmann and Peyman, Chem. Rev., Washington, DC, Vol. 90, No. 4, pp. 543-584 (1990).
  • RNAi reagent encompasses both single-stranded and double-stranded nucleic acid molecules.
  • Double-stranded nucleic acid molecules may be composed of two separate strands or of one strand comprising two regions which can form a double-stranded structure and a spacer region between the two regions forming a hairpin loop.
  • the RNAi reagents are preferably of double-stranded structure and comprise a sequence complementary to a target gene.
  • the present invention is not limited to double stranded structures and includes also single-stranded RNAi reagents capable of inducing RNAi. See Schwarz et al., Mol. Cell, Vol. 10, No. 3, pp. 537-548 (2002).
  • the present invention provides a method of making an algorithm for the prediction of the RNAi potency of a RNAi reagent comprising: a) determining experimentally the potency of a plurality of RNAi reagent to down regulate a reporter protein readout; and b) using said potency data set to train a artificial neuronal network.
  • the method of making an algorithm for the prediction of the RNAi potency of a RNAi reagent comprises the steps of: a) determining experimentally the RNAi potency of a plurality of RNAi reagents comprising a sequence complementary to at least one target gene; b) generating a data set of the potency of said RNAi reagents with the experimentally determined RNAi potency of a), wherein said data set though obtained and different targets (reporter fusion-mRNA) has a common protein read-out which is normalizable by mean reporter specific positive and negative controls; and c) training an artificial neuronal net using said read-out.
  • algorithm means a set of equations and a set of rules which can be applied to the data automatically and can be implemented as a code which executes on a computer.
  • RNAi potency or “potency” is a term of the art and means the relative ability of a given siRNA to down-regulate a given protein or mRNA upon its transfection in a cellular assay.
  • potency of a siRNA is determined by measuring the level of expression of the target mRNA or protein and is usually expressed as a percentage of the negative control.
  • a high potency means that the RNAi reagent is capable of efficiently inhibiting, i.e., decreasing, the expression of a target gene
  • a low potency means that the expression of the target gene is not or only little inhibited.
  • a potent RNAi reagent inhibits the expression of a target gene more than 50% as compared to negative control, preferably more than 60%, more than 70%, more than 80%, most preferably more than 90%.
  • RNAi reagent in accordance with the present invention is a RNAi reagent suitable for RNAi experimentation.
  • RNAi reagents suitable for RNAi are known in the art. See Dykxhoorn et al., Nature Rev., Vol. 4, pp. 457-467 (2003).
  • Such a RNAi reagent comprises a sequence that is complementary to a target gene.
  • a target gene comprising a sequence that is complementary to an RNA transcribed from the DNA sequence of the target gene, including pre-mRNA, mRNA, cDNA.
  • target gene is meant to include any DNA sequence which is expressed, i.e., transcribed to RNA, in a cell, tissue or organism.
  • the expressed sequence needs not necessarily be translated into a protein and includes for instance also pre-mRNAs, regulatory RNAs, rRNAs, etc.
  • the sequence complementary to the target gene typically is from about 19-23 nucleotides long, but may also be longer.
  • the complementary sequence is less than 50 nucleotides long, more preferably from 15-35 nucleotides long or from 18-25 nucleotides long.
  • the complementary sequence is preferably 100% identical to the corresponding sequence of the target gene, i.e., there is no mismatch between the complementary sequence and the corresponding sequence of the target gene.
  • the complementary sequence may comprise one, two, three, four, five or more mismatches, if these mismatches do not abolish the RNAi activity of the RNAi reagent.
  • the RNAi reagent used for RNAi is preferably double-stranded and may be composed of two separate strands, but may also be composed of one strand forming a hairpin loop.
  • the double-strand RNA region of the RNAi reagent may contain mismatches and function in a miRNA like mechanism. Examples for types of RNAi reagents that mediate RNAi are for instance siRNAs or miRNA (microRNA) or small hairpin RNAs (shRNAs).
  • the RNA potency of a plurality of RNAi reagents is experimentally determined in accordance with the methods of the present invention.
  • it is advantageous to provide a large number of RNAi reagents as there is a positive correlation between the number of RNAi reagents that are provided and the quality of the algorithm.
  • the number of RNAi reagents should not be below a minimum number, below which the algorithm is not properly trainable.
  • RNAi reagents at least 10 RNAi reagents, at least 50 RNAi reagents or at least 100 RNAi reagents are provided, in a more preferred embodiment at least 200 RNAi reagents, at least 500 RNAi reagents, at least 1000 RNAi reagents or at least 2000 RNAi reagents are provided. In another preferred embodiment, less than 10000 RNAi reagents, preferably less than 5000 RNAi reagents or more preferably less than 3000 RNAi reagents are provided. In another preferred embodiment, the RNAi reagents are randomly selected.
  • the RNAi reagents may be overlapping or not overlapping, in a preferred embodiment, the RNAi reagents are not overlapping.
  • the RNAi reagents comprise a region complementary to a target gene.
  • Several RNAi reagents may comprise a region complementary to the same target gene, overlapping or not overlapping.
  • all RNAi reagents comprise a region complementary to the same target gene, overlapping or not overlapping.
  • the RNAi reagents comprise a region complementary to more than one target gene, preferably to at least 2 target genes, at least 5 target genes or at least 10 target genes.
  • RNAi specificity or “specificity” is a term of the art and refers in the context of the present invention to the selectivity of a RNAi reagent, i.e., to the capability of a RNAi reagent to selectively inhibit or decrease the expression of a given target gene without inhibiting or decreasing the expression of other genes expressed in a cell, tissue or organism.
  • specific RNAi reagents only inhibit the expression of the target gene and leave the expression of all other genes expressed in a cell, tissue or organism unaffected.
  • the specific RNAi reagents advantageously comprise a fully complementary sequence to the target gene, i.e., a complementary sequence with no mismatch to the target sequence, but no fully complementary sequence to all other genes expressed in a cell or an organism, i.e., said complementary sequences have at least one mismatch, preferably at least two mismatches, more preferably at least three mismatches to the all sequences expressed in a cell, tissue or organism other than the target gene.
  • Pre-selection of the specificity of the RNAi reagents can for instance be done by computationally comparing the sequence of the RNAi reagents of interest to all known expressed sequences available in the databases for a given cell, tissue or organism using a suitable software for sequence comparison.
  • RNAi potency of a RNAi reagent Numerous methods suitable for experimentally determining RNAi potency of a RNAi reagent are known in the art.
  • a double-stranded RNAi reagent comprising a complementary region to a given target gene is transfected into a cell expressing the target gene.
  • different methods such as for instance electroporation, use of cationic lipids or cationic polymers as helpers for transfection may be used.
  • the cells are then incubated under suitable conditions allowing the expression of the target gene.
  • the expression of the target gene is subsequently measured using a suitable technique, such as, for instance, RT-PCR or measuring the amount of a reporter protein.
  • a fusion-mRNA coding for a reporter is co-transfected with the siRNA.
  • the target nucleotide sequence is inserted in 3'-UTR of the reporter coding sequence.
  • the reporter protein is a Yellow Fluorescent protein (YFP).
  • negative and reporter specific controls i.e., a siRNA targeting the coding region of the reporter protein and thereby silencing the reporter protein with a similar potency independently of the 3'-UTR insert, are used and allow the comparison of the expression level of the reporter protein obtained with each siRNA with the expression levels of the negative control and the reporter specific positive control.
  • the cell is a eukaryotic cell, preferably an animal cell, more preferably a mammalian cell and most preferably a human cell.
  • an experimental potency score correlating to the experimentally determined RNAi potency will be created for each RNAi reagent leading to a read-out which can be compiled in a data set of the experiments.
  • the experimental scores are obtained by measurement of the reporter protein.
  • all data of the experimental read-out come from a single type of experimental setting under homogenous conditions. The data is preferably based on a measurement on protein level and not mRNA level, i.e., the amount of protein expressed by the target gene is measured upon exposure with the RNAi reagent and not the amount of mRNA.
  • RNAi potency in accordance with the present invention is described hereinbelow in the Examples.
  • a reporter assay is used for the experimental determination of RNAi activity.
  • the use of a reporter assay in accordance with the present invention allows to screen a large number of siRNA against a broad panel of targets with a common experimental read-out.
  • Such an assay is described in H ⁇ sken et al., Nucleic Acids Res., Vol. 31 , No. 17, p. e102 (2003).
  • an mRNA fusion transcript construct comprising of a full-length reporter gene with a target region of interest inserted into the 3'-untranslated region is provided.
  • Luciferase and Fluorescent reporter genes can be used in the constructs.
  • the RNAi reagents to be tested for RNAi potency comprise a sequence complementary of interest inserted into the 3'-untranslated region.
  • the RNAi reagent is then transfected with a suitable transfection method into cells expressing transiently or constitutively the reporter gene construct and cultured under suitable conditions allowing the expression of the reporter gene.
  • the level of protein expressed by the reporter gene is subsequently measured.
  • Such an assay allows measuring the RNAi potency of large numbers of RNAi reagents at the protein level against a broad panel of targets with a common read-out. As such, the generated data set is homogeneous and all potency data are comparable each together.
  • the above described data set is used for training of an artificial neuronal network.
  • Artificial neuronal networks are known in the art, see, for instance, Zell, Simulation neuronaler Netztechnike, Addison Wesley (1994); and Rumelhart and McLelland, Parallel Distributed Processing, Vol.1 , MIT Press, Cambridge, MA (1986), and available, for instance, on http://www-ra.informatik.uni-tuebingen.de/SNNS.
  • Statistical information will be extracted from the siRNA sequence antisense strand by artificial neuronal networks and correlated with the screening measures.
  • a trained network can be applied to an arbitrary input sequence to give an estimate of the 'would-be' screening measure.
  • a 3 layer feed-forward network with back propagation training 7-8 can be used.
  • the input layer consists of 4 lanes of ordered nodes, see Figure 1. There is one lane per nucleic base type and always 4 nodes of different base type referring to the same input sequence position. The number of positions is the length of the input sequence.
  • exactly one node at any given position is activated.
  • the activities along the ordered lanes then represent the input sequence.
  • the signals of activated nodes are propagated from the input layer into the second layer, also called layer of hidden units. During this propagation the signals of the input layer, either 0 or 1 , are weighted differently, summed into the signals of the hidden units.
  • the weights are the memory elements to represent the statistical knowledge. Initially the weights are randomly set, and result in output signals for siRNA antisense sequence which deviate from the true screening signals. The difference between current network output signal and the experimental is used to change all weights to reduce the differences. Back propagation comes into place by the network for having a 'true' target signal for hidden units in the second layer.
  • Another aspect of the present invention provides a computer system comprising computer hardware and the algorithm of the present invention.
  • a further aspect of the present invention provides a computer readable medium comprising the algorithm of the present invention.
  • Another aspect of the present invention provides methods for obtaining RNAi reagents with an enhanced likelihood of RNAi potency against a given target gene, i.e., an enhanced likelihood of inhibiting the expression of a pre-selected target gene.
  • an enhanced likelihood of RNAi potency against a given target gene i.e., an enhanced likelihood of inhibiting the expression of a pre-selected target gene.
  • a higher percentage will be potent using this method to design RNAi reagents than if the RNAi reagents were designed randomly.
  • fewer RNAi reagents have to be designed and screened to find a given number of RNAi reagents of high RNAi potency useful for specifically inhibiting the expression of a target gene.
  • the method according to the present invention comprises the steps of: a) providing a plurality of RNAi reagent sequences comprising a region complementary to a given target gene; b) applying the trained algorithm according to the present invention to said RNAi reagent sequences using a neuronal network; and c) selecting the RNAi reagent sequence(s) which are predicted to be potent.
  • RNAi reagents sequences comprising a sequence complementary to the gene to be targeted, i.e. to the gene to be inhibited by RNAi.
  • the RNAi reagents may be prescreened for specificity or for the presence or absence of certain sequence motives. They may be overlapping or non-overlapping.
  • the candidate RNAi reagents are randomly chosen.
  • the potency of the RNAi reagents provided in the first step is predicted using the neuronal network which is trained with an algorithm in accordance with the present invention.
  • the RNAi reagent sequences which are predicted to be potent are selected.
  • the three or five or 10 most potent RNAi reagents can be chosen.
  • all RNAi reagent sequences with a prediction score above a certain threshold score are chosen.
  • the threshold score is at least 0.7, at least 0.75, at least 0.8 or at least 0.85.
  • the selected RNAi reagents can now be experimentally assayed for their RNAi potency.
  • RNAi reagents suitable for RNAi comprising the sequences which were predicted to be active are synthesized.
  • the RNAi reagents are chemically synthesized.
  • RNAi reagents may also be synthesized using biochemical methods such as in vitro transcription or vector based systems. Suitable cells expressing the target gene can now be exposed to the synthesized RNAi reagents (or to the vector comprising the sequence of interest in case of a vector based system), incubated under suitable conditions and the expression level of the target gene can be measured with suitable methods. As a control, the expression level of the target gene of cells which were not exposed to the sequence of interest can be compared.
  • siRNA targeting 34 different mRNA have been screened for potency in a cellular assay.
  • One feature of this study has been to generate a homogeneous data set for subsequent analysis of potency-sequence relationship. This has been possible by the use of a fusion-mRNA reporter assay. See H ⁇ sken et al. (2003), supra. In this assay, a plasmid encoding a reporter fusion-mRNA for a reporter protein where the target sequence has been inserted in 3'-UTR of the reporter mRNA is transfected followed by the transfection of the RNAi reagent.
  • the result of down regulation of the target sequence has no biological consequences; b) in all assays, the potency readout is at protein level and as the same reporter protein is the whole study, the potency data are not biased by different read-outs; and c) the use of common positive and negative controls in all assays allows the normalization of all potency data.
  • a artificial neuronal network has been used to investigate the sequence potency relationship of this homogeneous potency data se.
  • the trained artificial neuronal network is able to predict the potency of any siRNA solely based on its nucleotide sequence.
  • this approach will be applicable to other RNAi reagents like, for instance, shRNAs or miRNAs.
  • RNAi reagents used in that study were 21-mer double-stranded RNAi reagents with a 19 base-pairing RNA region and dideoxynucleotide overhangs on 3' of each strand.
  • the overhang of the sense strand was systematically a dithymidine whereas the overhangs of the antisense was a dideoxynucleotide designed to be complementary to the target.
  • the enhanced cyan and yellow fluorescent protein (eCFP, eYFP) dual reporter based vector pNAS-092 was constructed to contain a multiple cloning site after the stop codon of the eYFP for inserting the appropriate cDNAs or ESTs of interest.
  • the eCFP reporter serves for normalization measurements driven under the elongation factor 1 alpha (EF-1 ⁇ promoter and the eYFP reporter was used to monitor activity of siRNA driven under the CMV promoter.
  • the origin of the vector was a plasmid pBudCE4 (Invitrogen) which contains a hCMV and a EF-1 ⁇ promoter.
  • pNAS-092 was generated by inserting the eCFP gene derived from peCFP-N1 (Clontech) and by transferring the eYFP gene derived from peFP-N1 (Clontech) together with a synthetic DNA fragment bearing the cloning site (EcoRV, Notl, Hindlll, Kpnl, Xbal). The synthetic DNA that was used in the pNAS-092 was confirmed by sequencing.
  • pNAS-092 was converted to a GatewayTM destination vector pNAS-097 by inserting the attR1 and attR2 cloning sites according to the manufacture protocol (Invitrogen) after the eYFP stop codon. All plasmids used for the final reporter assay were constructed by inserting into the cloning site the c-DNA via ligation (pNAS-092) or recombination (pNAS-097).
  • H-1299 The human non-small cell lung carcinoma cell line H-1299 (CRL-5803) was purchased from ATCC (Rockville, MD). H-1299 cells was maintained in 5% humidified CO 2 atmosphere at 37°C in RPMI 1640 medium (Life Technologies) containing 10% fetal bovine serum plus 1 % L-Glutamine. H1299 cells were split 48 hours prior to transfection reaching a 80% subconfluent stage. Cells were trypsinized, washed and equally dispensed (50 ⁇ L) in black 96-well assay plates (Costar, clear bottom) a day before the transfection.
  • Fluorescent protein assays with dual reporter construct carrying a reference gene Intracellular normalization
  • Lipofectamine-PLUS reagent was incubated with the plasmid diluted in OptiMEM-l (22 ng/ ⁇ L plasmid, 4.4 mL/mL Lipofectamine-PLUS) and then this mix was diluted 11 -fold with OptiMEM-l.
  • Lipofectamine was 1.3-fold pre-diluted with HEPES (20 mM, pH 7.2) and diluted further 28.6-fold with OptiMEM-l (26.6 ⁇ lJmL Lipofectamine) and kept for 15 minutes. Both mixtures were combined 1 :1 and incubated for 15 minutes, further diluted 10-fold with OptiMEM-l.
  • the medium was aspirated and 100 ⁇ L was added to the cells (0.2 ⁇ L/mL Lipofectamine-PLUS, 13.3 ⁇ L/mL Lipofectamine, 1 ng/ ⁇ L plasmid). After 2 hours, 50 ⁇ L siRNA transfection mixture was added to the cells which was then further incubated for 2 hours.
  • Oligofectamin was diluted with OptiMEM-l (60 ⁇ L/mL) were mixed and incubated for 30 minutes at room temperature.
  • the siRNA was diluted to 600 ⁇ M from the hybridized stock solution with hybridization buffer (30 mM HEPES, 100 mM Potassium Acetate, 2 mM Magnesium Acetate: pH 7.63 at room temperature. Annealing 2 minutes at 90°C followed by 1 hour at 37°C).
  • Diluted Oligofectamin and siRNA were combined 2 volumes plus 1 volume, and incubated for 15 minutes.
  • the siRNA-Oligofectamin mix was further diluted 1 :1 with OptiMEM-l and transferred onto the cells (50 ⁇ L) (final cone.
  • siRNA treatments of the positive standard YFP-specific siRNA NAS-12842/58
  • negative standard luciferase siRNA NAS-8548/9
  • reporter plasmids with 34 different inserts were selected. Size of the inserts varied between 344 nucleotides and 3784 nucleotides.
  • siRNA per plate we have 3160 in total.
  • the sequences were designed to walk the inserts randomly, allowing for overlap of varying size (0-20 bases). With an insert size of 27k bases even regularly chosen positions of the 3160 siRNA would result in significant overlap (13 bases) in the siRNA sequences. Sequences with long polynucleotide stretch (five or more consecutive nucleotides) were not considered. In cases of long inserts, two sets of 79 siRNA were designed.
  • siRNA sequence set The total set of 3160 siRNA sequences was checked for its nucleic content. It was found that all possible motifs up to tetra nucleotides were present in the siRNA sequence set.
  • siRNA plate contains 79 siRNA, a negative control (anti-luciferase siRNA NAS-8549), two reporter specific siRNA (anti-YFP siRNA NAS-12842 and NAS-12847).
  • Control siRNA were pipetted in from one single batch in triplicates. Eight wells were left empty and will be used for "plasmid only" negative controls.
  • Each plate was assayed in duplicates and YFP level was measured by fluorimetry at two time points. For each plate, linear correlation within the duplicate was checked, as well as the level of inhibition of the positive and negative controls. Data were accepted when a correlation within the duplicate was superior to 0.7 and when the positive control was down regulating YFP to at least 60% as compared to negative control (see Figure 3). Data set from five assays were rejected according to that filter. Remaining data set (2717 sequences) was divided in a training set and a testing set and further filtered by checking each siRNA individually and excluding siRNA displaying a variation higher than 30% within the duplicate. As such, approximately 15% of data were further removed. The remaining duplicate data points were averaged to result in a data set with reduced noise characteristics.
  • the siRNA sequence data is presented to the input layer and the screening reporter signals are used to adjust the weights between the network nodes.
  • Each siRNA sequence and its screening measure is presented a total of 10 times.
  • the weights of the network are updated synchronously with a learning rate of 0.1 and a momentum of 0.1. See Zell, Simulation Neuronaler Netztechnike, Addison Wesley (1994).
  • the resulting five trained networks weights differed but all five networks showed consistently only slightly varying prediction output.
  • a final output was achieved by averaging the signals of the respective output node of all five networks. We will refer to the average output as 'the' output of 'the' network instead of looking at properties of any of the individual networks for simplicity.
  • the trained network predicted the experimental inhibition activity with a correlation of 0.63 when applied to the testing set, while it showed moderately higher correlation of 0.665 when applied to the training set.
  • Figure 3 graphs the concordance of the two.
  • the performance of the algorithm can be evaluated by setting a threshold for experimentally active siRNA and for predicted active siRNA.
  • the threshold for experimentally active siRNA was set at 75% normalized potency (0.75).
  • the threshold for predicted active siRNA was set at a score above 0.8.
  • These thresholds form four quadrants containing true negatives (predicted inactive and inactive), false negative (predicted inactive but active), false positive (predicted active but inactive) and true positive (predicted active and active) sequences.
  • the predictor performance can be measured by its sensitivity and selectivity.
  • the training data does not need to be taken completely for training, which allows investigating the degradation of the BlOpred prediction performance with fading set size.
  • the size of the testing set was constant.
  • the correlation for a training data set as small as 265 data points still gives correlations of about 0.53.

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Abstract

L'invention concerne des procédés permettant de mettre au point un algorithme pour prédire l'efficacité de l'ARNi d'un réactif ARNi. L'invention concerne également des procédés permettant de prédire l'efficacité d'un réactif ARNi, ainsi que des procédés permettant d'inhiber l'expression d'un gène cible donné, à l'aide d'un tel algorithme.
EP04820427A 2003-12-10 2004-12-09 Procedes de prediction d'efficacite en matiere d'arni Withdrawn EP1694843A1 (fr)

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PCT/EP2004/014037 WO2005059132A1 (fr) 2003-12-10 2004-12-09 Procedes de prediction d'efficacite en matiere d'arni

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KR (1) KR20060126499A (fr)
CN (1) CN1890370A (fr)
AU (1) AU2004298527A1 (fr)
BR (1) BRPI0417489A (fr)
CA (1) CA2545675A1 (fr)
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JP2010513246A (ja) * 2006-12-15 2010-04-30 ノバルティス アーゲー Ｇｐｒ４の阻害
WO2008122314A1 (fr) * 2007-04-10 2008-10-16 Qiagen Gmbh Étiquettes d'interférence arn
WO2009118300A1 (fr) 2008-03-25 2009-10-01 Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute For Biomedical Research Traitement du cancer par régulation à la baisse de frizzled-4 et/ou frizzled-1
EP2405920A1 (fr) 2009-03-06 2012-01-18 Novartis Forschungsstiftung, Zweigniederlassung Friedrich Miescher Institute For Biomedical Research Nouvelle thérapie contre l'anxiété
EP2241323A1 (fr) 2009-04-14 2010-10-20 Novartis Forschungsstiftung, Zweigniederlassung Friedrich Miescher Institute For Biomedical Research Tenascine-W et cancers du cerveau
CN101625732B (zh) * 2009-08-03 2011-11-30 杭州电子科技大学 江河潮水水位的预测方法
EP2292266A1 (fr) 2009-08-27 2011-03-09 Novartis Forschungsstiftung, Zweigniederlassung Traitement du cancer en modulant la copine III
US20120244170A1 (en) 2009-09-22 2012-09-27 Rafal Ciosk Treating cancer by modulating mex-3
WO2011045352A2 (fr) 2009-10-15 2011-04-21 Novartis Forschungsstiftung Tyrosine kinase de la rate et cancers du cerveau
US20130004519A1 (en) 2010-03-05 2013-01-03 Ruth Chiquet-Ehrismann Smoci, tenascin-c and brain cancers
EP2561076A1 (fr) 2010-04-19 2013-02-27 Novartis Forschungsstiftung, Zweigniederlassung Friedrich Miescher Institute For Biomedical Research Modulation de xrn1
US20130089538A1 (en) 2010-06-10 2013-04-11 Novartis Forschungsstiftung, Zweigniederlassung Friedrich Miescher Institute forBiomedical Researh Treating cancer by modulating mammalian sterile 20-like kinase 3
WO2012168259A1 (fr) 2011-06-06 2012-12-13 Novartis Forschungsstiftung, Zweigniederlassung Protéine tyrosine phosphatase de type non-récepteur 11 (ptpn11), et cancer du sein triple négatif
WO2013068431A1 (fr) 2011-11-08 2013-05-16 Novartis Forschungsstiftung, Zweigniederlassung, Friedrich Miescher Institute For Biomedical Research Traitement inédit contre les maladies neurodégénératives
RU2014124184A (ru) 2011-11-15 2015-12-27 Новартис Аг Комбинация ингибитора фосфоинозитид-3-киназы и модулятора пути янус-киназы 2 - проводника сигнала и активатора транскрипции 5
WO2013102825A1 (fr) 2012-01-02 2013-07-11 Novartis Ag Cdcp1 et cancer du sein
WO2013144240A1 (fr) 2012-03-29 2013-10-03 Friedrich Miescher Institute For Biomedical Research Inhibition de l'interleukine 8 et/ou de ses récepteurs cxcrl dans le traitement du cancer du sein surexprimant her2/her3
US20150218238A1 (en) 2012-06-29 2015-08-06 Novartis Forschungsstiftung, Zweigniederlassung Friedrich Miescher Institute For Biomedical Resear Treating diseases by modulating a specific isoform of mkl1
WO2014006114A1 (fr) 2012-07-05 2014-01-09 Novartis Forschungsstiftung, Zweigniederlassung Friedrich Miescher Institute For Biomedical Research Nouveau traitement pour maladies neurodégénératives
WO2014006115A1 (fr) 2012-07-06 2014-01-09 Novartis Ag Combinaison d'un inhibiteur de phosphoinositide 3-kinase et d'un inhibiteur de l'interaction il-8/cxcr
WO2015019286A1 (fr) 2013-08-07 2015-02-12 Friedrich Miescher Institute For Biomedical Research Nouvelle méthode de criblage pour le traitement de l'ataxie de friedreich
EP3154579A1 (fr) 2014-06-13 2017-04-19 Friedrich Miescher Institute for Biomedical Research Nouveau traitement dirigé contre le virus de la grippe
WO2016001830A1 (fr) 2014-07-01 2016-01-07 Friedrich Miescher Institute For Biomedical Research Combinaison d'un inhibiteur de braf v600e et d'un inhibiteur de mertk pour le traitement du mélanome
WO2016046768A1 (fr) 2014-09-24 2016-03-31 Friedrich Miescher Institute For Biomedical Research Lats et cancer du sein
WO2017072669A1 (fr) 2015-10-28 2017-05-04 Friedrich Miescher Institute For Biomedical Research Ténascine w et cancers du tractus biliaire
CN109360604B (zh) * 2018-11-21 2021-09-24 南昌大学 一种卵巢癌分子分型预测系统

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RU2006124515A (ru) 2008-01-20
AU2004298527A1 (en) 2005-06-30
CN1890370A (zh) 2007-01-03
BRPI0417489A (pt) 2007-05-22
WO2005059132A1 (fr) 2005-06-30
JP2007523632A (ja) 2007-08-23
KR20060126499A (ko) 2006-12-07
CA2545675A1 (fr) 2005-06-30

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