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WO2019232640A1 - Method for the identification and design of rna interference agents - Google Patents

Method for the identification and design of rna interference agents Download PDF

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Publication number
WO2019232640A1
WO2019232640A1 PCT/CA2019/050798 CA2019050798W WO2019232640A1 WO 2019232640 A1 WO2019232640 A1 WO 2019232640A1 CA 2019050798 W CA2019050798 W CA 2019050798W WO 2019232640 A1 WO2019232640 A1 WO 2019232640A1
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score
ssrna
rna
molecule
target
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Yifei Yan
Gerardo Ferbeyre
François MAJOR
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Universite de Montreal
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Universite de Montreal
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/70—Carbohydrates; Sugars; Derivatives thereof
    • A61K31/7088—Compounds having three or more nucleosides or nucleotides
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • 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
    • 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
    • C12N15/63—Introduction of foreign genetic material using vectors; Vectors; Use of hosts therefor; Regulation of expression
    • 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
    • G16B40/30—Unsupervised 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 nucleic acids [NA]
    • 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
    • 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
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/10—Nucleic acid folding
    • 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
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids

Definitions

  • the present invention generally relates to gene silencing, and more specifically to the identification and design of RNA interference (RNAi) agents such as microRNAs (miRNAs).
  • RNAi RNA interference
  • miRNAs microRNAs
  • RNAi Double-stranded RNA-mediated interference
  • a miRNA binds to an Argonaute protein (AGO) to form an RNA-induced silencing complex (miRISC), which uses only about seven-nucleotide complementarity to recognize and target numerous mRNAs.
  • a fully complementary miRNA sequence to its target mRNA allows the AGO to cleave it, whereas a partially complementary one represses its translation.
  • MiRNAs with the same seed matching sequence but various mismatches introduced beyond the seed silence their targets at different efficiencies.
  • sequence complementarity and silencing efficiency is poorly understood. Despite complementarity patterns deduced from CLIP-based experimental data, one cannot accurately predict the efficiency of a guide strand from sequence.
  • RNAi RNA interference
  • the present disclosure provides the following items 1 to 36:
  • a method for the design of a single-stranded RNA (ssRNA) molecule capable of inducing translational repression or degradation of a plurality of target RNAs comprising:
  • list_of_regions (B, C, A, D);
  • %pair paired bases to target mRNA of current_region / total number of bases of current_region;
  • region B corresponds to nucleotides 12 to 14 of said ssRNA molecules
  • region C corresponds to nucleotides 15 to 17 of said ssRNA molecules
  • region A corresponds to nucleotides 9 to 11 of said ssRNA molecules
  • region D corresponds to nucleotides 18 to 21 of said ssRNA molecules
  • the ssRNA molecules capable of inducing translational repression or degradation of said plurality of target RNAs comprises the sequences exhibiting the highest scores.
  • a method for assessing the potential of a candidate single-stranded RNA (ssRNA) molecule to induce translational repression or degradation of a target RNA comprising:
  • list_of_regions (B, C, A, D);
  • %pair paired bases to target mRNA of current_region / total number of bases of current_region;
  • region B corresponds to nucleotides 12 to 14 of said candidate ssRNA molecule
  • region C corresponds to nucleotides 15 to 17 of said candidate ssRNA molecule
  • region A corresponds to nucleotides 9 to 1 1 of said candidate ssRNA molecule
  • region D corresponds to nucleotides 18 to 21 of said candidate ssRNA molecule
  • the score positively correlates with the potential of the candidate ssRNA molecule to induce translational repression or degradation of the target RNA.
  • step (ii) is performed using an miRNA target prediction program.
  • the method of item 4 further comprising calculating an miScore for the ssRNA molecule, wherein said miScore corresponds to the sum of: (i) the score obtained according to the algorithm defined in any one of items 1 to 4; (ii) a score corresponding to X * Y, wherein X is a constant and Y is the number of Watson-Crick base-pairing between the nucleotides from the seed region and the nucleotides from the target mRNA; and (iii) a D module score that is 1 if there is at least one mismatch in the D module and 0 if the D module is fully complementary to the target mRNA.
  • step (iii) aligning the sequences of the ssRNA molecules and of the target RNA and/or folding the sequences of the ssRNA molecules and of the target RNA.
  • a computational analysis system comprising the computer-readable medium according to item 22.
  • a computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
  • an input module wherein said input module permits to input the RNA sequences of a plurality of target RNAs; a data analysis module, wherein said data analysis module is coupled to said input module and is capable of identifying candidate ssRNA molecules and calculating a score for said candidate ssRNA molecules according to the method defined in any one of items 1 to 14;
  • an output module wherein said output module is coupled to said data analysis module and said output module is capable of providing to a user the scores of said candidate ssRNA molecules.
  • a computer program product for use in conjunction with a computer system comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
  • an input module wherein said input module permits to input the RNA sequences of a target RNA and of a ssRNA molecule;
  • a data analysis module wherein said data analysis module is coupled to said input module and is capable of calculating a score for said candidate ssRNA molecule according to the method defined in any one of items 1 to 14;
  • an output module wherein said output module is coupled to said data analysis module and said output module is capable of providing to a user the score of said ssRNA molecule.
  • a kit comprising (i) the computer-readable medium according to item 22; and (b) instructions for the design of a single-stranded RNA (ssRNA) molecule capable of inducing translational repression or degradation of a plurality of target RNAs, or for assessing the potential of a candidate ssRNA molecule to induce translational repression or degradation of a target RNA, using the computer program recorded on the computer-readable medium.
  • ssRNA single-stranded RNA
  • RNA molecule comprising one of the following sequences: SEQ ID NO: 23, SEQ ID NO: 27, SEQ ID NO: 31 , SEQ ID NO: 35, or SEQ ID NO: 39.
  • RNA molecule of item 27 comprising one of the following sequences: SEQ ID NO: 31 , SEQ ID NO: 35, or SEQ ID NO: 39, preferably SEQ ID NO: 31 .
  • RNA molecule of item 27 or 28 which is single-stranded RNA (ssRNA) molecule.
  • RNA molecule of item 29 which is a microRNA (miRNA), pre-miRNAs or pri-miRNA, preferably a miRNA.
  • a method for inhibiting the expression of an E2F transcription factor in a cell comprising contacting said cell with the RNA molecule of any one of items 27-30.
  • E2F transcription factor is E2F1 , E2F2 and/or E2F3.
  • RNA molecule of any one of items 27-30 for inhibiting the expression of an E2F transcription factor in a cell.
  • RNA molecule of any one of items 27-30 for the manufacture of a medicament for inhibiting the expression of an E2F transcription factor in a cell.
  • E2F transcription factor is E2F1 , E2F2 and/or E2F3.
  • the tumor is an osteosarcoma, a small cell lung carcinoma, a breast carcinoma, a prostate carcinoma, or a glioblastoma.
  • FIGs. 1A-J show the silencing profile of the coding region and 3’UTR sites in the reporter plasmid.
  • FIG. 1A MiB and designed single-module guides. The perfect complementary shRNA miB; then, from top to bottom, the mismatched guide RNAs miB-A (nts 9-1 1); miB-B (nts 12-14); miB-C (nts 15-17); and, miB-D (nts 18-21).
  • FIG. 1 B The pPRIME vector used to clone all shRNAs. The guide strand is located at the 3’-half of the stem loop structure (arrow).
  • FIG. 1 C The pNL-luc plasmid is a luciferase reporter that contains the HIV-1 genome.
  • FIG. 1 D The dual luciferase reporter plasmid FR(-)TS in which the cloning site of the target sequence is located in the 3’ UTR of the firefly reporter gene. The firefly and renilla luciferases are transcribed in opposite directions.
  • FIG. 1 E The repression profile of mismatched shRNA on the pNL-luc reporter. The plus sign (+) indicates the student t-test for the comparing columns yields p ⁇ 0.05; double plus signs (++) p ⁇ 0.01. The same convention is followed for FIGs. 1 F-H.
  • FIG. 1 F-H The same convention is followed for FIGs. 1 F-H.
  • FIG. 1 F The repression profile of mismatched shRNAs on the FR(-)TS reporter.
  • FIG. 1 G The repression profile of the pNL-luc reported perturbed by exogenously expressed D597A mutant AG02.
  • FIG. 1 H The repression profile of the FR(-)TS reporter perturbed by the expression of the AG02 mutant.
  • FIG. 11 Titration of the 3’UTR FR(-)TS reporter has limited effects on silencing.
  • FIG. 1J Titration of the HIV pNL- luc target reporter has limited effects on silencing.
  • FIG. 2A shows that single-nt mutations in the seed of miB shRNA abolish its ability to repress FR(-)TS reporter expression.
  • FIG. 2B shows that RT-PCR confirms that the change in repression profile of the FR(- )TS reporter occurs at the RNA level.
  • Asterisk shows the line-indicated bars are not significantly different in height (p>0.05).
  • FIG. 2C shows that ectopically expressed recombinant wild-type AG02 carrying the same peptide tag as the D597A AGo2 mutant shows decreased repression efficiency at for miB- A, -B, and -C.
  • FIG. 2D shows titration of four different shRNA constructs (miB-A, -B, -C, and -D), across eight-fold concentration difference.
  • FIG. 2E shows the quantification of mature guides of miB and its module-altered variants by TaqMan RT-qPCR. Significant differences are shown on as p-values on top of horizontal bars for significant or near-significant differences.
  • FIG. 2F shows titration of miB shRNA construct concentrations, covering 8-fold difference, used in combination with four different MRE reporters.
  • FIG. 2G shows the TaqMan RT-qPCR determination of the level of mature miB at the transfection concentrations used in reporter assay in FIG. 2F.
  • the relative quantities are normalized to the background levels of miB in the negative control.
  • FIGs. 3A-H show that the silencing profiles in FR(-)tat and pNL-luc reporters are similar.
  • FIG. 3A Dual luciferase construct
  • FR(-)tat contains the first exon of the tat gene of HIV upstream of the renilla luciferase, creating a fusion protein of tat and renilla luciferase; the miB MRE is in the tat gene.
  • FIG. 3B Repression profile on the FR (-)tat reporter. The plus sign (+) indicates the student t-test for the comparing columns yields p ⁇ 0.05; double plus signs (++) p ⁇ 0.01. The same convention is followed for FIG. 3D.
  • FIG. 3D Dual luciferase construct
  • FR(-)tat contains the first exon of the tat gene of HIV upstream of the renilla luciferase, creating a fusion protein of tat and renilla
  • FIG. 3D The silencing profile is more sensitive to MRE location than MRE repeat numbers. Reporter expression levels of pNL- luc vector (left bars), or FR(-)TS vector that contains the target sequence either one time (middle bars) or six times in tandem (right bars) in the presence of mismatched miB variants.
  • FIG. 3E Base pairing between engineered sites that mismatch the miB shRNA at modules A-D. Each site is cloned into the same FR-reporter with the flanking regions from the tat gene.
  • FIG. 3G A table of shRNAs used in combination with each target site reporter to reconstitute the same mismatched positions in modules A, B, C, and D. The first row of the table indicates which module is mismatched in the guide::target duplex. The first column on the left is a list of mismatched module site reporters. Each entry in the table is a miB-modified shRNA with mutated modules used in combination with the target site of that row.
  • FIG. 4A The sequences of the guide::target duplexes are listed in FIG. 4A.
  • FIG. 3H Synthesized repression profile from reporter assays results by testing the 25 guide-target combinations in the table. Indistinguishable columns heights are indicated by bars on top of the figure.
  • FIGs. 4A-D show the combination of different sets of shRNAs with the no mismatch sequence (n.m.) and four mismatched modules in four different sites in the reporter constructs. Each site was tested to derive its own repression profile. These four profiles were combined to the wild-type profile to generate the synthesized profile shown in FIG. 3G.
  • FIG. 4E shows the statistical significance of pairwise comparison of the mismatched expression levels from mismatched modules in FIG. 3H. Each entry is a p-value obtained by two- tailed unpaired student t-test assuming unequal variance
  • FIGs. 5A-D show that the combined effects of mismatches reveal the interdependency between the modules.
  • FIG. 5A MiB and double-module guides. All possible combinations of two mismatched modules are listed.
  • FIG. 5C 3-D representation of the p-values of the student t-test results of comparing the efficiencies of all miB variants. When the pairwise comparison is not able to distinguish the two guide RNAs by their residual reporter expression levels, a large p-value shows up as a tall column on the graph.
  • FIG. 5D State diagram of the proposed sequential recognition model for AG02 slicing. Double circles represent the accepted state, which is defined as the most efficient slicing state.
  • FIG. 6A shows Student’s t-test p-values are obtained by comparing the repression levels using single- and double-module mismatched guide RNAs; double-module shown with each corresponding single-module. Asterisks on top indicate the expression levels that are indistinguishable.
  • FIG. 6B shows the tabulated p-values used in the above graph from the pairwise comparisons.
  • FIGs. 7A-G show the validation of the non-seed model.
  • FIG. 7A Pearson correlation between reporter assay results and miScores (single- and double-module guides).
  • FIG. 7B Pearson correlation between reporter assay results and free energy changes calculated using PITA (single- and double-module guides).
  • FIG. 7C RLU values of the guides in Table 2 as measured in the FR(-)TS reporter assay.
  • FIG. 7D Pearson correlation between the reporter assay results and the miScores without alignment (13 additional guides).
  • FIG. 7E Pearson correlation between the reporter assay results and free energy changes calculated using PITA (13 additional guides).
  • FIG. 7F Pearson correlation between published silencing efficiencies and miScores.
  • FIG. 7G PC3 cell growth curves of miR-20a, MT1 , and sm3. Juxtaposed are the Western blots of E2F factors when miR-20a, MT 1 , and sm3 were present.
  • FIG. 8A shows that including target site secondary structure calculations did not improve predictions by the conventional free-energy model.
  • FIG. 8B shows that the MicroAlign algorithm robustly predicts the efficiency of guide strands with good accuracy without the alignment step.
  • FIG. 8C shows that when more than two trinucleotide modules are mismatched at the same time, alternative bps can occur between the strands in each alignment.
  • the guide sequence is listed on top and the target sequence at the bottom.
  • the guide sequence is written in the 3’ to 5’ direction and the target sequence in the 5’ to 3’ direction.
  • FIG. 8D shows that comparing the measured and the predicted silencing efficiency of the guide sequences that mismatch in at least three modules, the program produced a better ranking than the conventional free-energy model
  • FIG. 8E shows Pearson correlation between miScores and K cat / K m values (Wee et al., 22) calculated without the alignment step.
  • FIG. 8F shows Pearson correlation calculated with the alignment step for the same guide sequences in FIG. 8E.
  • FIG. 8G shows Pearson correlation between miScores and luciferase assay results (Robertson et al., 21) calculated without the alignment step.
  • FIG. 8H shows Pearson correlation calculated for the same guide sequences in FIG. 8G calculated alignment step.
  • FIG. 8I shows the mean log 2 fold changes in protein levels of the miScore-evaluated targets were pooled from cells that were transfected with miR-124, miR-181 , and miR-1 , respectively.
  • the mean target protein level was taken for 293 proteins.
  • the pooled protein levels were placed in three equal size bins: top, mid, and bottom.
  • the mean value of the top 30 targets evaluated by miScore was also computed (top 30).
  • FIG. 9A shows that enrichment of smartRNA designs further validates the model.
  • Top smartRNAs designed by MultiTar then selected using miScores.
  • the guide RNA sequence is on top, in 3’ to 5’ direction, and the target sequence is at the bottom in 5’ to 3’ direction.
  • ) and Wobble (*) base pairs are indicated.
  • FIG. 9B shows Western blot of the target E2F proteins in the presence of each designed multi-targeting guide RNAs.
  • FIG. 9C shows the quantification of target protein levels from the Western blot in the top panel, which shows all the target protein levels of the five tested top designs.
  • Left bars E2F1
  • middle bars E2F2
  • right bars E2F3.
  • FIG. 9D shows growth curves of all five newly tested new multi-targeting guides relative to best previously tested ones.
  • FIG. 9E shows relative protein levels are plotted against the predicted miScores.
  • Data point shape corresponds to the target gene: diamond represents E2F-1 , square represents E2F- 2, and triangle represents E2F-3.
  • the horizontal line represents 70% expression level threshold of an “effective” knockdown.
  • the vertical line is the suggested miScore cut-off for selecting efficient guide designs.
  • FIG. 9F shows mean log 2 fold changes of the targeted mRNAs (top two panels) and proteins (bottom two panels) in miR-124 transfected cells binned by miScores (left) and scores not considering the modular order of the base pairs (right).
  • FIG. 9G shows the same as FIG. 9F, but for miR-181 transfected cells.
  • the present inventors have identified a pattern revealing how base pairing from non-seed nucleotides of miRNAs contributes to gene silencing efficiency.
  • the present disclosure provides method for assessing the potential of a candidate single-stranded RNA (ssRNA) molecule (e.g., a RNA interference agent such as an miRNA) to induce translational repression or degradation of a target RNA (e.g., target mRNA), the method comprising:
  • list_of_regions (B, C, A, D);
  • %pair paired bases to target mRNA of current_region / total number of bases of current_region;
  • region B corresponds to nucleotides 12 to 14 of said ssRNA molecule
  • region C corresponds to nucleotides 15 to 17 of said ssRNA molecule
  • region A corresponds to nucleotides 9 to 1 1 of said ssRNA molecule
  • region D corresponds to nucleotides 18 to 21 of said ssRNA molecule
  • the score correlates with the potential of the candidate ssRNA molecule to induce translational repression or degradation of the target RNA (e.g., target mRNA).
  • target RNA e.g., target mRNA
  • the present disclosure also provides a method for assessing one or more RNA (e.g., mRNA) targeted by an miRNA molecule (i.e., the one or more RNAs that is/are repressed or degraded by the miRNA) in a cell, the method comprising:
  • RNAs e.g., mRNAs
  • miRNAs e.g., mRNAs
  • RNAs e.g., mRNAs
  • the present disclosure also provides a method for identifying one or more miRNA response elements (MRE) for an miRNA molecule (i.e., the one or more binding sites of the miRNA) in RNAs (e.g., mRNAs) expressed by a cell, the method comprising:
  • RNAs e.g., mRNAs
  • RNAs e.g., mRNAs
  • RNAs e.g., mRNAs
  • the present disclosure also provides a method for the design of a synthetic single- stranded RNA (ssRNA) molecule (e.g., a mature miRNA) capable of inducing translational repression or degradation or of a plurality of target RNAs (e.g., mRNAs), the method comprising:
  • RNA sequences of the plurality of target RNAs e.g., mRNAs
  • RNA means a molecule comprising at least one ribonucleotide residue.
  • ribonucleotide is meant a nucleotide with a hydroxyl group at the 2' position of a beta-D-ribo-furanose moiety.
  • the terms include double-stranded RNA, single-stranded RNA, isolated RNA such as partially purified RNA, essentially pure RNA, synthetic RNA, recombinantly- produced RNA, as well as altered RNA that differs from naturally occurring RNA by the addition, deletion, substitution and/or alteration of one or more nucleotides.
  • alterations can include addition of non-nucleotide material, such as to the end(s) of the siRNA or internally, for example at one or more nucleotides of the RNA.
  • Nucleotides in the RNA molecules of the present disclosure can also comprise non-standard nucleotides, such as non-naturally occurring nucleotides or chemically synthesized nucleotides or deoxynucleotides. These altered RNAs can be referred to as analogs or analogs of naturally-occurring RNA.
  • the RNA is an miRNA.
  • double-stranded RNA or “dsRNA” as used herein refers to a ribonucleic acid duplex, including but not limited to, endogenous and artificial siRNA duplexes, short hairpin RNA duplexes (shRNAs) and miRNA duplexes.
  • single-stranded RNA or “ssRNA” as used herein refers to a ribonucleic acid, including but not limited to, endogenous and artificial mature single-stranded siRNA molecules, mature single-stranded shRNAs and mature single-stranded miRNAs.
  • RNA short interfering RNA
  • siRNA a nucleic acid molecule capable of modulating, by inhibiting or down regulating, gene expression, through RNAi or gene silencing via sequence-specific-mediated cleavage of one or more target mRNA strands.
  • miRNA refers to a nucleic acid molecule capable of modulating, by inhibiting or down-regulating, gene expression through sequence-specific- mediated translational suppression and subsequent polyA removal and degradation of one or more target mRNA strands. miRNAs are typically partially complementary to binding sites in the 3'UTR of mRNAs, but may also bind to 5'UTRs and Coding regions (CDS) of mRNAs, as well as non-coding RNAs (ncRNAs).
  • CDS Coding regions
  • RNA interference refers to sequence-specific inhibition of gene expression and/or reduction in target RNA levels mediated by an RNA molecule, which RNA comprises a portion that is substantially complementary to a target RNA.
  • RNAi can occur via selective intracellular degradation of RNA.
  • RNAi can occur by translational repression.
  • RNA interference agent refers to an RNA molecule, optionally including one or more nucleotide analogs or modifications, having a structure characteristic of molecules that can mediate inhibition of gene expression through an RNAi mechanism.
  • RNAi agents mediate inhibition of gene expression by causing degradation of target transcripts.
  • RNAi agents mediate inhibition of gene expression by inhibiting translation of target transcripts.
  • an RNAi agent includes a portion that is substantially complementary to a target RNA.
  • RNAi agents are single-stranded.
  • RNAi agents are at least partly double- stranded.
  • exemplary RNAi agents can include siRNA, shRNA, and/or miRNA.
  • the RNAi agent is an miRNA.
  • RNAi agents may be composed entirely of natural RNA nucleotides (i.e., adenine, guanine, cytosine, and uracil).
  • RNAi agents may include one or more non-natural RNA nucleotides (e.g., nucleotide analogs, DNA nucleotides, etc.). Inclusion of non-natural RNA nucleic acid residues may be used to make the RNAi agent more resistant to cellular degradation than RNA.
  • RNAi agent may refer to any RNA molecule, RNA molecule derivative, and/or nucleic acid encoding an RNA molecule that induces an RNAi effect (e.g., degradation of target RNA and/or inhibition of translation).
  • an RNAi agent may comprise a blunt-ended (i.e., without overhangs) dsRNA that can act as a Dicer substrate.
  • siRNA functions by mediating the cleavage of mRNA target sequences which possess a region of complete or near complete complementarity to the "guide strand" of the siRNA molecule.
  • miRNA functions by mediating translational suppression and subsequent polyA removal and degradation of mRNA target sequences which possess seed sites within their 3' untranslated regions (3' UTR) which are complementary to nucleotides 2 to 8 from the 5' end of the miRNA's guide strand (the seed region).
  • 3' UTR 3' untranslated regions
  • complementarity and “complementary” are meant that a nucleic acid can form hydrogen bond(s) with another nucleic acid for example by Watson-Crick base pairing.
  • a nucleic acid which can form hydrogen bond(s) with another nucleic acid through non-Watson-Crick base pairing also falls within the definition of having complementarity.
  • a percent complementarity indicates the percentage of residues in a nucleic acid molecule that can form hydrogen bonds (e.g., Watson-Crick base pairing) with a second nucleic acid sequence (e.g., 5, 6, 7, 8, 9, 10 out of 10 being 50%, 60%, 70%, 80%, 90%, and 100% complementary).
  • Perfectly complementary or “fully complementary” means that all sequential residues of a nucleic acid sequence will form hydrogen bonds with the same number of sequential residues in a second nucleic acid sequence.
  • the seed region will have no more than 1 , most preferably no mismatches with the target mRNAs' seed site(s).
  • seed site is meant a nucleotide sequence present in the 3' UTR of a target mRNA sequence which is complementary to the seed region of at least one strand of a RNAi agent (e.g., miRNA) molecule and which has the potential to mediate miRNA-like translational suppression and/or polyA removal and subsequent degradation of the mRNA strand it is contained within when hybridized to its complementary seed region.
  • a RNAi agent e.g., miRNA
  • seed region is meant a nucleotide sequence present on a strand of the RNAi agent (e.g., miRNA) molecules described herein which is complementary to one or more seed sites present in the coding region or preferably the 3' UTR region of one or more target mRNA molecules.
  • the seed region comprises nucleotides 1 to 8 or 2 to 8 from the 5' end of the dsRNA strand, i.e. 7 or 8 nucleotides in length, however the seed region may be 6 to 10 residues, preferably 6 to 8 residues, in length. Seed regions will preferably start at nucleotide 2 from the 5’ end.
  • RNAi agents e.g., miRNAs
  • shorter seed regions are likely to result in weaker miRNA-like down-regulation but are likely to increase the number of candidate RNAi agents (e.g., miRNAs) identified by the model described herein.
  • the skilled man will be able to decide what length of seed region to use.
  • the residue at position 1 from the 5' end of the RNAi agent (e.g., miRNA) strand is adenosine.
  • a given seed region may be complementary to seed sites in more than one target mRNA molecule.
  • the seed sites may be within the coding region or preferably the 3' UTR region of the same mRNA molecule which is targeted for cleavage/translation repression by the RNAi agent (e.g., miRNA) molecule incorporating a corresponding seed region, or the seed sites may be within the coding region or preferably the 3' UTR region of different mRNA molecules targeted for cleavage/translation repression by the RNAi agent (e.g., miRNA) molecule incorporating a corresponding seed region.
  • the RNAi agent e.g., miRNA
  • a “target gene” or “gene of interest” is a gene whose expression is desired to be modulated.
  • the term includes any nucleotide sequence, which may or may not contain identified gene(s), including, but not limited to, coding region(s), non-coding region(s), untranscribed region(s), intron(s), exon(s) and transgenes(s).
  • the target gene can be a gene derived from a cell, an endogenous gene, a transgene or exogenous genes such as genes of a pathogen (e.g., a virus), which is present in the cell after infection thereof.
  • the cell containing the target gene can be derived from or contained in any organism, e.g., bacteria, fungi, animals (mice, human).
  • the target gene encodes a protein that causes a disease or disorder. In certain embodiments, the target gene encodes a protein that is overexpressed or overactive. In certain embodiments, the target gene encodes a protein whose aberrant expression causes a diseased state, oncogenic transformation or promotes viral infection.
  • a "target mRNA” sequence is an mRNA sequence derived from a target gene.
  • the target mRNA may be a cytoplasmic mRNA, a mitochondrial mRNA, a viral mRNA.
  • the ssRNAs e.g., mature miRNAs designed or identified by the methods/algorithm described herein may target a single gene, or multiple target genes.
  • the target site in the target RNA may be of the same length as the RNAi agent (e.g., miRNA), i.e. the RNAi agent binds to contiguous residues in the target RNA.
  • the RNAi agent e.g., miRNA
  • the target site in the target RNA may alternatively be longer than the RNAi agent, i.e. the complementary modules are separated by up to 5 nucleotides (1 , 2, 3, 4 or 5 nucleotides).
  • the target site comprises one or more bulges (internal loops).
  • the RNA sequences of the plurality of target mRNA comprise a plurality of different target mRNA sequences.
  • the plurality of mRNA sequences are transcribed from more than one gene of interest. For example, more effective down regulation of an intended target gene may likely be achieved by also targeting other genes, for example genes encoding proteins having a function in the cell that is redundant with the intended target gene, genes encoding proteins involved in the same cellular pathway as the intended target gene, or genes which encode transcription factors that positively regulate the expression levels or activity of the intended target gene.
  • the RNA sequences of the plurality of target mRNA may also comprise mRNA sequences which are transcribed from a single target gene, for instance by alternative gene splicing.
  • the determination of the score for predicting miRNA silencing specificity/efficacy for a target RNA (based on base-pairing beyond the seed) using the method/algorithm defined herein may be illustrated as follows.
  • RNA comprising the following sequence:
  • the miRNA comprises a seed region defined by nucleotides 1-8 (UAUAUAUA), and the non-seed region (nucleotides 9-21) is divided into 4 modules: nucleotides 9-11 defining module A, nucleotides 12-14 defining module B, nucleotides 15-17 defining module C, and nucleotides 18-21 defining module D. Accordingly, miRNA 1 has one mismatch in the B region and one mismatch in the C region, whereas miRNA 2 has only one mismatch in the A region.
  • the base-pairing with the target in modules A to D for the miRNA candidates are as follows:
  • %pair paired bases to target mRNA of current_region / total number of bases of current_region;
  • Evalute_score is a recursive function that evaluates the fraction of Watson-Crick basepairing in the current module (in the order B, C, A and D), which may be 0/3, 1/3, 2/3 or 3/3 for modules A-C.
  • miRNA 2 has a higher score than miRNA 1 , it is predicted to be superior to miRNA 1 for inducing translational repression or degradation of the target RNA.
  • the constant (pairing_score) used in the algorithm is 1 , 2, 3, 4, 5, 6, 7, 8, 9 or 10, for example 2, 3, 4, 5 or 6, preferably 3, 4 or 5.
  • the methods further comprise calculating a score for the base-pairing in the seed region (seed score).
  • seed score is combined to the score calculated with the algorithm described herein (for non-seed region).
  • the seed score may be calculated by adding a number of points for each Watch-Crick base-pairing in the seed region. In an embodiment, from 1 to 10, 2 to 9, 3 to 8, or 4 to 7 points are added for each Watch-Crick basepairing in the seed region. In a further embodiment, 6 points are added for each Watch-Crick base-pairing in the seed region.
  • the methods further comprise calculating a score for the base-pairing in the D module, nucleotides 18-21 (D score).
  • D score nucleotides 18-21
  • the seed score, the score calculated with the algorithm described herein (for non-seed region) and the D score are combined to obtain a miScore for the miRNA.
  • miScores of miRNA 1 and miRNA 2 exemplified above may be calculated as follows.
  • miRNA 2 has a higher miScore than miRNA 1 , it is predicted to be superior to miRNA 1 for inducing translational repression or degradation of the target RNA.
  • the methods described herein may also comprise additional steps or analysis, for example to improve the predictive capacity of the algorithm/methods. Such additional steps/analysis may be useful, for example, for pre-selecting miRNA candidates to be analysed by the algorithm/methods described herein.
  • the methods include a nucleotide sequence alignment step of the sequences of the ssRNA molecules and of the target RNA.
  • Optimal alignment of sequences may be conducted using a variety of tools/algorithms, such as the local homology algorithm of Smith and Waterman, 1981 , Adv. Appl. Math 2: 482, the homology alignment algorithm of Needleman and Wunsch, 1970, J. Mol. Biol. 48: 443, the search for similarity method of Pearson and Lipman, 1988, Proc. Natl. Acad. Sci.
  • Such analysis may be performed using the miRBooking tool/algorithm (see, e.g., Weill et al. Nucleic Acids Res. 2015; 43(14):6730-8), which infers the whole set of miRNA/mRNA interactions by simulating the RNA competition to hybridize, finding the stable state at equilibrium (microtargetome), and estimating the miRNA-induced silencing (miS) levels applied to each mRNA.
  • miRBooking tool/algorithm see, e.g., Weill et al. Nucleic Acids Res. 2015; 43(14):6730-8
  • sequences from mature miRNA are obtained from databases (e.g., miRBase for miRNAs and NCBI RefSeq for human mRNAs). Nucleotides 2-8 of the mature miRNAs are defined the seed sequences.
  • the free energies of the duplexes seed/MRE, AG may be computed using the MC-Fold software (Parisien M., Major F. Nature. 2008;452:51 -55), which incorporates the energy contributions of non-canonical base pairs that can possibly form at mismatch positions.
  • the energies of the duplexes may be normalized assuming a Boltzmann distribution, and converted into a hybridization probability, HP:
  • miRNA-induced silencing on a given mRNA is proportional to the sum of the contributions of all miRNA copies occupying the multiple copies of this mRNA, and it is computed as the sum of each hybridization probability (HP) multiplied by a MRE location factor, W:
  • miRNA target prediction programs such as MC-fold (Parisien M., Major F. Nature. 2008;452:51 -55), BayMiR, microDoR, PicTar, TargetScan, PITATop, and PITAAII, may also be used in combination with the algorithm (see, e.g., Alexiou et al., Bioinformatics. 2009 Dec 1 ;25(23):3049-55). Aspects of the disclosure may be implemented in hardware or software, or a combination of both.
  • the algorithms and methods defined herein are implemented in one or more computer programs executing on programmable computers each comprising at least one processor, at least one data storage system (including volatile and nonvolatile memory and/or storage elements), at least one input device, and at least one output device.
  • Program code is applied to input data to perform the algorithms and processes described herein and generate output information.
  • the output information is applied to one or more output devices, in known fashion.
  • the steps/algorithms of the methods described herein may be performed by a computer program product, encoding instructions to perform at least one or more the steps/algorithms described herein.
  • the computer program product may be embodied on a computer readable medium, e.g., a hard disk drive, flash device, a random access memory, a tape, or any other suitable medium used to store data.
  • a suitable operating system for example a Windows ® , Google ® or Apple ® operating system, which may be executed by a suitable system or device, embodied for example as a personal computer, a server, a console, a cell phone, or any other suitable computing device, or combination of devices.
  • the methods and systems disclosed herein may be implemented in localized and distributed forms consistent with computing technology known to the skilled person.
  • the disclosure provides a computer program, stored on a computer-readable medium, for performing the algorithms and methods defined herein.
  • the present disclosure provides a method of producing a single- stranded or double-stranded RNAi molecule which comprises performing the methods and algorithms as defined above and then synthesizing one or more of the RNA molecules generated or identified by said methods or algorithms.
  • a double-stranded RNA (dsRNA) is synthesized based on the ssRNAs identified by the methods and algorithms as defined above, i.e. comprising a strand comprising a sequence complementary to the sequence of the ssRNAs.
  • the algorithm and methods described herein can optionally comprise one or more additional steps in which modifications to the ssRNA molecules are designed.
  • the two strands of the dsRNA molecule may be linked by a linking component such as a chemical linking group or an oligonucleotide linker with the result that the resulting structure of the dsRNA is a hairpin structure.
  • the linking component must not block or otherwise negatively affect the activity of the dsRNA, for instance by blocking loading of strands into the RISC complex or association with Dicer.
  • Many suitable chemical linking groups are known in the art. If an oligonucleotide linker is used, it may be of any sequence or length provided that full functionality of the dsRNA is retained.
  • the ssRNA molecules identified by the method described herein may be modified by inserting one or more mutations (substitutions) in the base/original nucleotide sequence.
  • the one or more mutations are incorporated into the D module of the ssRNA molecule.
  • the antisense strand can be modified for Dicer processing by suitable modifiers located at the 3' end of the antisense strand, i.e., the dsRNA is designed to direct orientation of Dicer binding and processing.
  • suitable modifiers include nucleotides such as deoxyribonucleotides, dideoxyribonucleotides, acyclonucleotides and the like and sterically hindered molecules, such as fluorescent molecules and the like.
  • Acyclonucleotides substitute a 2-hydroxyethoxymethyl group for the 2'-deoxyribofuranosyl sugar normally present in dNMPs.
  • nucleotide modifiers could include 3'-deoxyadenosine (cordycepin), 3'-azido-3'-deoxythymidine (AZT), 2', 3'- dideoxyinosine (ddl), thiacytidine (3TC), 2',3'-didehydro-2',3'-dideoxythymidine (d4T) and the monophosphate nucleotides of 3'-azido-3'-deoxythymidine (AZT), 2',3'-dideoxy-3'-thiacytidine (3TC) and 2',3'-didehydro-2',3'-dideoxythymidine (d4T).
  • Deoxynucleotides can be used as the modifiers. When nucleotide modifiers are utilized, 1 -3 nucleotide modifiers, or 2 nucleotide modifiers are substituted for the ribonucleotides on the 3' end of the antisense strand. When sterically hindered molecules are utilized, they are attached to the ribonucleotide at the 3' end of the antisense strand. Thus, the length of the strand does not change with the incorporation of the modifiers.
  • the invention contemplates substituting two DNA bases in the dsRNA to direct the orientation of Dicer processing.
  • two terminal DNA bases are located on the 3' end of the antisense strand in place of two ribonucleotides forming a blunt end of the duplex on the 5' end of the sense strand and the 3' end of the antisense strand, and a two-nucleotide RNA overhang is located on the 3'-end of the sense strand.
  • This is an asymmetric composition with DNA on the blunt end and RNA bases on the overhanging end.
  • modifications contemplated for the phosphate backbone include phosphonates, including methylphosphonate, phosphorothioate, and phosphotriester modifications such as alkylphosphotriesters, and the like.
  • modifications contemplated for the sugar moiety include 2'-alkyl pyrimidine, such as 2'-0-methyl, 2'-fluoro, amino, and deoxy modifications and the like.
  • modifications contemplated for the base groups include abasic sugars, 2-O-alkyl modified pyrimidines, 4-thiouracil, 5-bromouracil, 5- iodouracil, and 5-(3-aminoallyl)-uracil and the like.
  • Locked nucleic acids, or LNA's could also be incorporated. Many other modifications are known and can be used so long as the above criteria are satisfied. Examples of modifications are also disclosed in U.S. Pat. Nos. 5,684, 143, 5,858,988 and 6,291 ,438 and in U.S. published patent application No. 2004/0203145 A1 . Other modifications are disclosed in Herdewijn (2000) Antisense Nucleic Acid Drug Dev 10: 297-310, Eckstein (2000) Antisense Nucleic Acid Drug Dev 10: 1 17-21 , Rusckowski et al.
  • the ssRNAs or dsRNAs can be obtained using a number of techniques known to those of skill in the art.
  • the ssRNAs or dsRNAs can be chemically synthesized or recombinantly produced using methods known in the art, such as the Drosophila in vitro system described in U.S.
  • ssRNAs or dsRNAs may be chemically synthesized using appropriately protected ribonucleoside phosphoramidites and a conventional DNA/RNA synthesizer.
  • the dsRNAs can be synthesized as two separate, complementary RNA molecules, or as a single RNA molecule with two complementary regions.
  • Commercial suppliers of synthetic RNA molecules or synthesis reagents include Proligo (Hamburg, Germany), Dharmacon Research (Lafayette, Colo., USA), Pierce Chemical (part of Perbio Science, Rockford, III., USA), Glen Research (Sterling, Va., USA), ChemGenes (Ashland, Mass., USA) and Cruachem (Glasgow, UK).
  • the dsRNAs can also be expressed from recombinant circular or linear DNA vectors or plasmids using any suitable promoter.
  • suitable promoters for expressing dsRNAs from a plasmid include, for example, the U6 or H1 RNA pol III promoter sequences and the cytomegalovirus promoter. Selection of other suitable promoters is within the skill in the art.
  • the recombinant plasmids or vectors may also comprise inducible or regulatable promoters for expression of the dsRNA in a particular cell, tissue or in a particular intracellular environment.
  • the dsRNAs expressed from recombinant plasmids can either be isolated from cultured cell expression systems by standard techniques, or can be expressed intracellularly at or near the area of disease in vivo.
  • the dsRNAs can be expressed from a recombinant plasmid either as two separate, complementary RNA molecules, or as a single RNA molecule with two complementary regions.
  • plasmids suitable for expressing dsRNAs of the invention are within the skill in the art. See, for example Tuschl, T. (2002), Nat. Biotechnoi. 20: 446-448; Brummelkamp T R et al. (2002), Science 296: 550-553; Miyagishi M et al. (2002), Nat. Biotechnoi. 20: 497-500; Paddison P J et al. (2002), Genes Dev. 16: 948-958; Lee N S et al. (2002), Nat. Biotechnoi. 20: 500-505; and Paul C P et al. (2002), Nat. Biotechnoi. 20: 505-508).
  • the dsRNAs can also be expressed from, recombinant viral vectors intracellularly in vivo.
  • the recombinant viral vectors of the invention comprise sequences encoding the dsRNAs of the invention and any suitable promoter for expressing the dsRNA sequences. Suitable promoters include, for example, the U6 or H1 RNA pol III promoter sequences and the cytomegalovirus promoter. Selection of other suitable promoters is within the skill in the art.
  • the recombinant viral vectors of the invention can also comprise inducible or regulatable promoters for expression of the dsRNAs in a particular tissue or in a particular intracellular environment.
  • dsRNAs can be expressed from a recombinant viral vector either as two separate, complementary RNA molecules, or as a single RNA molecule with two complementary regions.
  • Any viral vector capable of accepting the coding sequences for the dsRNAs molecule(s) to be expressed can be used, for example vectors derived from adenovirus (AV); adeno-associated virus (AAV); retroviruses (e.g., lentiviruses (LV), Rhabdoviruses, murine leukemia virus); herpes virus, and the like.
  • AV adenovirus
  • AAV adeno-associated virus
  • retroviruses e.g., lentiviruses (LV), Rhabdoviruses, murine leukemia virus
  • herpes virus and the like.
  • the tropism of viral vectors can be modified by pseudotyping the vectors with envelope proteins or other surface antigens from other viruses, or by substituting different viral capsid proteins, as appropriate
  • Mature miRNAs derived from miRNA precursors e.g., precursor-miRNA (pre-miRNAs) and primary miRNA (pri-miRNAs), which are processed by the cellular machinery to produce the mature miRNAs.
  • miRNA genes are usually transcribed by RNA polymerase II (Pol II). The polymerase often binds to a promoter found near the DNA sequence, encoding what will become the hairpin loop of the pre-miRNA. The resulting transcript is capped with a specially modified nucleotide at the 5’ end, polyadenylated with multiple adenosines poly(A) tail), and spliced.
  • pri-miRNA Animal miRNAs are initially transcribed as part of one arm of an ⁇ 80 nucleotide RNA stem- loop that in turn forms part of a several hundred nucleotide-long miRNA precursor referred to a primary miRNA (pri-miRNA).
  • a single pri-miRNA may contain from one to six miRNA precursors.
  • the pri-miRNA is recognized and processed by a protein complex referred to as the microprocessor complex comprising DGCR8 (or "Pasha" in invertebrates) and the RNAse Drosha, to produce the pre-miRNA that no longer contains the 5’ cap and poly(A) tail of the pri- miRNA.
  • the pre-miRNA hairpin is cleaved by the RNase III enzyme Dicer, yielding an miRNA:miRNA duplex about 22 nucleotides in length. Dicer then unwinds the duplex, and one of the strand of the duplex (the mature miRNA) is incorporated into the RNA-induced silencing complex (RISC).
  • RISC RNA-induced silencing complex
  • the present disclosure also provides a pre-miRNA or pri-miRNA comprising a sequence encoding the ssRNA (miRNA) identified by the methods described herein, as well as a plasmid or vector comprising a sequence encoding such pre-miRNA or pri-miRNA.
  • a pre-miRNA or pri-miRNA comprising a sequence encoding the ssRNA (miRNA) identified by the methods described herein, as well as a plasmid or vector comprising a sequence encoding such pre-miRNA or pri-miRNA.
  • the present disclosure provides the use of the ssRNAs, dsRNAs, miRNAs, pre-miRNAs, pri-miRNAs, or plasmids/vectors encoding same, identified by the methods described herein, for inhibiting or reducing the expression of a target gene in a cell.
  • the ability of a dsRNA containing a given target sequence to cause RNAi-mediated degradation of the target mRNA can be evaluated using standard techniques for measuring the levels of RNA or protein in cells.
  • dsRNA of the invention can be delivered to cultured cells, and the levels of target mRNA can be measured by Northern blot or dot blotting techniques, or by quantitative RT-PCR.
  • the levels of protein encoded by the target gene in the cultured cells can be measured by ELISA or Western blot, for example.
  • the present disclosure provides an RNA molecule comprising or consisting of one of the following sequences: SEQ ID NO: 23, SEQ ID NO: 27, SEQ ID NO: 31 , SEQ ID NO: 35, or SEQ ID NO: 39.
  • the RNA molecule comprises or consists of SEQ ID NO: 23.
  • the RNA molecule comprises or consists of SEQ ID NO: 27.
  • the RNA molecule comprises or consists of SEQ ID NO: 31 .
  • the RNA molecule comprises or consists of SEQ ID NO: 35.
  • the RNA molecule comprises or consists of SEQ ID NO: 39.
  • the RNA molecule is a single-stranded RNA (ssRNA) molecule, such as a miRNA, pre-miRNAs or pri-miRNA.
  • ssRNA single-stranded RNA
  • the RNA molecule comprises 200 nucleotides or less, 150 nucleotides or less, 100 nucleotides or less, 90 nucleotides or less, 80 nucleotides or less, 70 nucleotides or less, 60 nucleotides or less, 50 nucleotides or less, 40 nucleotides or less, or 30 nucleotides or less.
  • the present disclosure provides a composition, such as a pharmaceutical composition, comprising the above-mentioned RNA molecule and a carrier or excipient, in a further embodiment a pharmaceutically acceptable carrier or excipient.
  • Supplementary active compounds can also be incorporated into the compositions.
  • the carrier/excipient can be suitable, for example, for intravenous, parenteral, subcutaneous, intramuscular, intracranial, intraorbital, ophthalmic, intraventricular, intracapsular, intraspinal, intrathecal, epidural, intracisternal, intraperitoneal, intranasal or pulmonary (e.g., aerosol) administration (see Remington: The Science and Practice of Pharmacy, by Loyd V AIIen, Jr, 2012, 22 nd edition, Pharmaceutical Press; Handbook of Pharmaceutical Excipients, by Rowe et al., 2012, 7 th edition, Pharmaceutical Press).
  • Therapeutic formulations are prepared using standard methods known in the art by mixing the active ingredient having the desired degree of purity with one or more optional pharmaceutically acceptable carriers, excipients and/or stabilizers.
  • excipient has its normal meaning in the art and is any ingredient that is not an active ingredient (drug) itself. Excipients include for example binders, lubricants, diluents, fillers, thickening agents, disintegrants, plasticizers, coatings, barrier layer formulations, lubricants, stabilizing agent, release-delaying agents and other components. "Pharmaceutically acceptable excipient” as used herein refers to any excipient that does not interfere with effectiveness of the biological activity of the active ingredients and that is not toxic to the subject, i.e. , is a type of excipient and/or is for use in an amount which is not toxic to the subject.
  • the composition comprises excipients, including for example and without limitation, one or more binders (binding agents), thickening agents, surfactants, diluents, release-delaying agents, colorants, flavoring agents, fillers, disintegrants/dissolution promoting agents, lubricants, plasticizers, silica flow conditioners, glidants, anti-caking agents, anti-tacking agents, stabilizing agents, anti-static agents, swelling agents and any combinations thereof.
  • binders binding agents
  • thickening agents surfactants
  • diluents release-delaying agents
  • colorants colorants
  • flavoring agents fillers
  • disintegrants/dissolution promoting agents lubricants
  • plasticizers plasticizers
  • silica flow conditioners silica flow conditioners
  • glidants anti-caking agents
  • anti-tacking agents stabilizing agents
  • anti-static agents swelling agents and any combinations thereof.
  • RNA molecule comprising the sequences of SEQ ID NO: 23, SEQ ID NO: 27, SEQ ID NO: 31 , SEQ ID NO: 35 and SEQ ID NO: 39 have been shown to inhibit the expression of E2F transcription factors, e.g., E2F1 , E2F2 and/or E2F3.
  • E2F transcription factors e.g., E2F1 , E2F2 and/or E2F3.
  • the present disclosure provides a method for inhibiting the expression of an E2F transcription factor in a cell comprising contacting said cell with the RNA molecule defined above.
  • the present disclosure provides the use of the RNA molecule defined above for inhibiting the expression of an E2F transcription factor in a cell, or for the manufacture of a medicament for inhibiting the expression of an E2F transcription factor in a cell.
  • the method/use permits to inhibit the expression of E2F1 , E2F2 and/or E2F3, in a further embodiment the expression of E2F1 , E2F
  • the cell is a tumor cell.
  • the present disclosure provides a method for treating cancer in a subject in need thereof comprising administering to the subject an effective amount of the RNA molecule defined above, or of a pharmaceutical composition comprising the RNA molecule defined above.
  • the present disclosure provides the use of the RNA molecule defined above, or of a pharmaceutical composition comprising the RNA molecule defined above, for treating cancer in a subject in need thereof, or for the manufacture of a medicament for treating cancer in a subject in need thereof.
  • the cancer is a cancer associated with dysregulated E2F expression/activity or dysregulated Rb/E2F pathway activity.
  • the cancer is associated with a mutation in the retinoblastoma tumor suppressor protein (Rb).
  • Rb mutations causing loss of Rb function have been identified in a wide spectrum of tumors including osteosarcomas, small cell lung carcinomas, breast carcinomas, prostate carcinomas, glioblastomas and others.
  • the Renilla luciferase control vector, SVR was obtained by replacing the CMV promoter in the pcDNA-Rlucll plasmid (from the laboratory of Sylvie Mader at the Institut detician en immunologie et en cancerologie (IRIC), see Breton, B. , et al., Biophys J, 2010. 99(12): p. 4037- 46) with an SV40 promoter. Briefly, the CMV promoter was removed by restriction enzymes Spel and Hindlll (New England Biolabs ® ). The resulting linearized vector was gel-purified with QIAEX II ® Gel Extraction Kit.
  • the SV40 promoter from the pGL3-control luciferase vector fragment was obtained by digesting the vector with Nhel and Hindlll. Gel purified SV40 promoter fragment was inserted upstream of the Rlucll gene in pcDNA-RLucll vector by ligation using T4 DNA ligase (NEB).
  • the firefly-ren/7/a opposite-sense target site reporter is referred to as the FR(-)TS construct, which contains both firefly and renilla luciferase reporter genes oriented in the opposite directions.
  • FR(-)TS construct contains both firefly and renilla luciferase reporter genes oriented in the opposite directions.
  • a 76 bp region of the HIV genome containing the miB shRNA target site in the center pNL4-3 vector, Accession number: AF324493, nts 5968-6044
  • Cloning of the target site was carried out by inserting the annealed oligonucleotides into the Xbal site upstream of the poly-A signal in the pGL3-Ctl reporter.
  • the annealed oligos are the following: the forward oligo sequence is CTAGAATGGCAGGAAGAAGCGGAGACAGCGACGAAGAGCTCATCAGAACAGTCAGACTC ATCAAGCTTCTCTATCAAAGCAT (SEQ ID NO: 71); and, the reverse oligo sequence is CTAGATGCTTTGATAGAGAAGCTTGATGAGTCTGACTGTTCTGATGAGCTCTTCGTCGCTG T CT CCGCTT CTT CCTGCCATT (SEQ ID NO: 72).
  • Bold letters represent the miB binding site.
  • the reporter that contains six times of the target site does not include the flanking regions; rather, the 3’UTR insert is a tandem repeat of the target site only.
  • Renilla luciferase gene was removed from pcDNA-Rlucll plasmid by digesting the vector with Spel and Xbal restriction enzymes. The gel purified (QIAEX II ® Gel Extraction Kit) Renilla luciferase fragment was then inserted in the Nhel site in Promega ® pGL3-control luciferase vector.
  • the FR(-)tat dual luciferase vector was constructed as follows.
  • the FR(-)TS vector without insertion of the miB shRNA target site from the previous step was used as a starting material.
  • the vector was digested with restriction enzymes Xbal and Hindlll from NEB and gel purified using QIAEX II ® Gel Extraction Kit.
  • the first exon of the tat gene was amplified from pNL4.3-luc vector (from the laboratory of Eric Cohen at the Institut de mecanics Cliniques de Montreal (IRCM)) with forward primer (5’ to 3’): ATCCAAGCTTCCCGCCACCATGGCAGGAAGAAGCGGA (SEQ ID NO: 69), and reverse primer (5’ to 3’): CGACTCTAGATGCTTTGATAGAGAAGCT (SEQ ID NO: 70).
  • the PCR was carried out using 55 °C as annealing temperature.
  • the amplified fragment was ethanol precipitated and digested with restriction enzymes Xbal and Hindlll. Upon gel purification, the fragment was ligated with the digested vector at 16°C overnight. The ligation mix was transformed into DH10B.
  • the vector pPRIME (from the laboratory of Jerry Pelletier, McGill University) has been previously optimized for shRNA cloning (60-62). Designed guide-RNAs were cloned into the vector following miR-30-based shRNA cloning protocols (40). Briefly, complementary oligonucleotides that contain the shRNA sequences (Biocorp, oligonucleotides are listed in Table 1) were diluted to 100 mM in deionized water. Annealing reaction was carried out at 95°C in annealing buffer for 5 minutes followed by slow cooling to room temperature. The annealed double-stranded oligonucleotides were then phosphorylated by T4 PNK (NEB).
  • T4 PNK T4 PNK
  • Ligation reaction was performed by combining doubly digested pPRIME by Xhol and EcoRI with the phosphorylation product of annealed oligonucleotides in T4 DNA ligase (NEB) reaction mix at 16 °C overnight.
  • Upper case letter are the Xbal site protruding ends complementary sequences.
  • Underlined sequences are the miB-targeting sequences with seeds binding sequence in bold.
  • the mutant sites contain inverted seed sequences.
  • HEK 293T HEK 293T (d 7) cells (from ATCC ® ) were maintained according to established conditions (50). Briefly, cells were grown in DMEM (+L-glutamine) (Life Technologies ® ) supplemented with 10% FBS, 100 U/mL penicillin/streptomycin at 37 °C and 5% C0 2 . Cells were grown to confluency before plating. For testing the efficiencies of mismatched guides, cells were plated in 96-well plates at ⁇ 20,000 cells per well 24 hours prior to the transfection. For assays that required growth in 24-well plates, cells were plated at ⁇ 100,000 cells per well.
  • the reporter plasmids and the shRNA plasmids were co-transfected into the cells using LipofectamineTM 2000 (Invitrogen ® ) according to the manufacturer's instructions.
  • 10 ng of shRNA plasmid 5 ng of pNL-luc and 2 ng of SVR control vector were co-transfected into each 96 well; alternatively, 50 ng of the shRNA construct, 20 ng of the pNL-luc, and 10 ng of the SVR control vector were co-transfected into each 24-well.
  • 10 ng of the target construct and 50 ng of the shRNA construct were combined and transfected into each 96- well.
  • 25 ng of the Ago2D597A vector (43) 25 ng of the Ago2D597A vector (43) (from the laboratory of Sven Diederichs, University of Freiburg) was combined with the DNA mix described above and subsequently co-transfected into the cells.
  • Luciferase assays were performed accordingly to established protocols adapted from the Dual-Glo ® Luciferase System (Promega ® ). 48 hours post-transfection, cells were lysed with 1 * Passive lysis buffer (Promega ® ) and luciferase activity was assayed using the Dual-Glo ® Luciferase System (Promega ® ). Luminescent light was measured on Veritas Microplate Luminometer (Turner Biosystems ® ). The ratio between the reporter and the control luciferase bioluminescence light was taken and then normalized to that of the negative control shRNA or empty vector, resulting in the percentage residual expression of the reporter gene.
  • RNA extraction was performed using TRIzol ® reagent following manufacturer’s protocol. RNA was extracted from the plates of the same cells used in the luciferase assay. Both oligo-dT primer and random primers were used for the synthesis of cDNA from total RNA extracted according to previously established protocols (63). Briefly, 800 ng of total RNA was used for each synthesis reaction in 20 pL of total volume using Invitrogen reagents (M-MLV Reverse Transcriptase, Cat. No. 28025-021 , Invitrogen ® ). RNA was extracted from the same cells that were used in the luciferase assay and M-MLV was used to perform the cDNA synthesis.
  • M-MLV Reverse Transcriptase M-MLV Reverse Transcriptase
  • the newly synthesized cDNA was diluted 100 times prior to real-time PCR.
  • Each real-time PCR reaction mixture contained the diluted cDNA (1 pi), forward and reverse primers (250 nM), MgCh (2.5 mM), dNTPs (0.2 mM), SYBR green (0.33X), buffer for Jumpstart ® Taq DNA polymerase and Jumpstart ® Taq DNA polymerase (0.25 U; Sigma) in a final volume of 10 mI. After denaturation at 95 °C for 6 min, samples went through 50 cycles of amplification (20 s at 95 °C, 20 s at 58 °C and 30 s at 72 °C). Melt curves were determined for each reaction and qPCR was performed using a LightCycler ® 480 (Roche Applied Science ® , Canada). Data was normalized using Renilla and HPRT as controls.
  • RNA guide molecules The detection of mature RNA guide molecules was performed following the polyA-based RT-qPCR protocol established previously (48, 64). Briefly, 20 pl_ of reaction contained 1 mI_ of reverse transcription products diluted 10-fold, 10 mM of forward primer, and 10 mM of universal reverse primer, 2 mI_ of Taq polymerase buffer (10X), 4 mI_ of 2.5 mM each dNTP, 0.6 U Taq and 10 mM of universal TaqMan ® probe. The mix is heated to 95 °C for 2 minutes prior to entering 45 cycles of 95 °C for 15 seconds followed by 60 °C for 1 minute. The reactions were carried out and measurements were taken on a StepOnePlusTM Real-Time System from Applied Biosciences ® .
  • the forward primer sequences are as follows:
  • miB GTG CTGTTCT GAT G AGCT CTT CGT C (SEQ ID NO:43);
  • miB-A GTGCTGTTCTGAACTGCTCTTCGTC (SEQ ID NO:44);
  • miB-B GTG CTGTTCTG AT G ACG ACTT CGTC (SEQ ID NO:45);
  • miB-C GTGCTGTTCTGATGAGCTGAACGTC (SEQ ID NO:46);
  • miB-D GTG CTGTTCTG ATG AG CTCTTGCAG (SEQ ID NO:47);
  • PC3 were obtained from American Type Culture Collection (ATCC ® ) and cultured in RPM1 (Wisent) supplemented with 10% FBS (Wisent ® ), 1 % penicillin/streptomycin sulfate (Wisent), and 2 mmol/L L-glutamine (Wisent ® ) at 37°C and 5% C0 2 .
  • Gene transfer was performed using retroviral particles produced in Phoenix packaging cells. Phoenix cells were transfected by calcium-phosphate precipitation with 20 pg of a retroviral plasmid (15 hrs at 37°C).
  • the plasmids used were: shNTC (non-targeting control), MiR20, MT E2F(1), E2F Afa, E2F Afb, E2F Afc, E2F Afd and E2F Afe.
  • shNTC non-targeting control
  • MiR20 MT E2F(1), E2F Afa, E2F Afb, E2F Afc, E2F Afd and E2F Afe.
  • the virus-containing medium was filtered (0.45 pm filter, Millipore ® ) and supplemented with 4 pg/ml polybrene (Sigma ® ) (first supernatant). Viruses were collected for an additional 8 hrs as before (second supernatant).
  • the culture medium was replaced by the appropriate first and second supernatant on PC3 cells.
  • infected cell populations were purified by selection with 2 pg/ml puromycin for 48 hours.
  • PC3 cells were washed with cold PBS and then scraped on ice into 500 pl_ of PBS buffer containing IX Complete-EDTA free Protease Inhibitor Cocktail (Roche Applied Science ® ) and 1X PhosSTOP ® Phosphatase Inhibitor Cocktail (Roche Applied Science ® ). Cells were spun at maximum speed for 5 min. The pellet was resuspended in 100 pi of Laemmli-p-mercaptoethanol buffer, sonicated 5 seconds at a low intensity, heated 5 min at 95°C, and then cleared by centrifugation at 13,000 RPM for 10 min.
  • the proteins were quantified with the Bradford reagent and 30 pg were loaded on a 10% SDS-PAGE and transferred to Immobilon-P PVDF membranes (Millipore ® ). Membranes were blocked 1 hour at room temperature in PBS containing 0.1 % TweenTM 20 (PBS-T) and 5% dry milk and then washed 3 times 5 min with PBS-T. The membranes were incubated with the primary antibodies diluted in PBS-T + 3% BSA + 0.05% Na- azide overnight at 4°C.
  • anti-E2F1 (1 : 1 ,000, clone H- 137; rabbit polyclonal; #SC22820, Santa Cruz ® ); anti-E2F2 (1 :1 ,000, clone L-20; rabbit polyclonal; #SC632, Santa Cruz ® ); anti-E2F3 (1 :1000; clone PG-37, mouse monoclonal, #5551 , Millipore ® ); anti-a-tubulin (1 :20,000, mouse monoclonal clone B-5-1 -2, T6074, Sigma-Aldrich ® ).
  • Membranes were washed 3 times 5 min with PBS-T and then incubated with the secondary antibodies diluted in PBS-T + 5% dry milk 1 hour at room temperature.
  • the following secondary antibodies were used: goat anti-rabbit IgG conjugated to HRP (1 :3000, #170-6515, Bio-Rad) or goat anti-mouse IgG conjugated to HRP (1 :3,000, #170-6516, Bio-Rad ® ). Finally, the membranes were washed 3 times 5 min with PBS-T.
  • Immunoblots were visualized using enhanced chemiluminescence (ECL) detection systems and Super RX X-Ray films (Fujifilm ® ) or a ChemiDocTM MP system (Bio-Rad ® ). Band quantification was done using ImageJTM or Image LabTM 4.0 (Bio-Rad ® ).
  • the evaluation program MicroAlgin was implemented in Microsoft Visual Studio Express 2012 C++ as a stand-alone windows application. Experimental measurements were plotted against the predicted miScore s (see code below) to calculate Pearson correlations.
  • the catalytic efficiency (Kcat/Km) measured for AG02 was used as a proxy to infer relative target expression levels (22). The less efficient the catalysis, the higher is the expression of the siRNA target. As for the Fold inhibition dataset, mismatched guide siRNAs in the first position and seed region were excluded from this dataset. For the sake of uniformity, the catalytic efficiency values were normalized to the most efficient siRNA guide to get K cat /K m percentage values comparable to the other datasets. Relative target expression was defined as 100 minus the percentage catalytic efficiency of the siRNA guide.
  • the pseudocode of MicroAlign evaluation algorithm implements the DFA described in FIG. 3D.
  • the start state is qO and the accepted state is q4.
  • the configuration of base pairs between the miRNA and the target now can be regarded as a regular expression that is recognized by this DFA, simulating the AG02 mechanism.
  • the bps are predicted by Needleman-Wunch algorithm and evaluated by regions following the discovered order. This DFA was described using a recursive algorithm. The“bottom” of the recursion is the evaluation of region-D, where the contribution of bps is little for accessible 3’UTR sites.
  • Hstjofjregions (B, C, A, D);
  • %pair paired bases of current_region /total number of bases of current_region;
  • miB shRNA
  • MRE is located in exon 1 of the HIV-1 tat gene (nts 5993-6013).
  • miB's nts in the non-seed region were mutated in short stretches of 3 or 4 nts at a time (modules) such that they mismatch the corresponding nts in the target sequence: from 5’ to 3’ module A (nts 9-1 1 ), B (nts 12-14), C (nts 15-17), and D (nts 18-21) (FIG.
  • the mismatched positions were engineered by copying the nt from the target strand (A:A, G:G, C:C, and U:U).
  • the guide strands containing these mismatches were named miB-A, -B, -C, and -D, respectively, and were cloned into pPRIME (40), an shRNA expression vector based on the miR-30 backbone (FIG. 1 B).
  • the pNL4.3-luc reporter construct which contains the complete HIV genome with a disabled env gene, was used (FIG. 1C). Since effective endogenous MREs are often located in the 3’UTR of their mRNA (41), a dual luciferase reporter, FR(-)TS, which embeds the miB MRE in the 3’UTR of the firefly luciferase (FIG. 1 D), was constructed.
  • the MRE is located 29 nts downstream of the firefly luciferase stop codon, which is within a region (15-300 nts from the stop codon) associated with a high density of mRNA-bound AG02 protein in the HITS-CLIP assays previously conducted (42).
  • This reporter construct functions properly, individual nts were mutated to their complementary nts in the seed of miB between position 1 and 6. As a result, a significant abolishing effect of the repression was observed relative to miB (FIG. 2A), confirming the reporter system is capable of measuring one- nt mismatch effects.
  • miB effectively repressed pNL-4.3 reporter gene expression, with a 75-80% knockdown efficiency relative to vector-only transfected cells (14).
  • reporter gene silencing by mismatched small RNA guide was greatly abolished except for miB-D, which retained more than 50% of the silencing capability (FIG. 1 E).
  • ShRNAs (or miRNAs) that partially base pair with the HIV target sequences in the non-seed regions were strikingly ineffective in repressing the viral target (15,37). At least 80% loss of repression was observed due to a mismatch of three nts in module A, B, or C.
  • the mutant greatly abolished the silencing by miB and miB-D shRNAs for both target constructs (FIG. 1G and H), indicating that these two shRNAs mainly depend on the cleaving AG02 to downregulate the target.
  • Example 3 Variation in target concentration is not a dominant factor that perturbs the silencing efficiencies
  • RNA genome of the HIV is known to contain rich secondary structure (45).
  • exonl of the tat gene was cloned into the dual luciferase construct after removing the miB MRE.
  • Exon 1 of tat is inserted in-frame with and upstream of the renilla luciferase (FIG. 3A). This vector was named FR-faf.
  • a fusion protein of tat and renilla luciferase is synthesized upon translation.
  • the renilla luciferase remains active and its expression is still sensitive to the downregulation of miB shRNA (FIG. 3B).
  • the miB target site was inserted into the FR(-)TS vector six times in tandem, and the 1- and 6-MRE target constructs were tested side by side with the pNL-luc reporter (FIG. 3D).
  • the 6-MRE in the 3’UTR has enhancing effects on silencing for miB, miB-A, and miB-D. However, no significant changes were observed for miB-B and miB-C. It was concluded that the number of MREs in the 3’UTR influences the silencing efficiency, but to a significantly less extent than their location.
  • Example 5 The pattern of repression levels is not associated with the levels of mature guide RNAs
  • shRNA constructs with eight-fold differences in quantity were transfected. Downregulation levels appeared to be resistant to such perturbations, indicating that the guide- AG02 biogenesis pathway was already saturated at half of the amount of guide RNA constructs used (i.e. 20 ng) (FIG. 2D). To further confirm that the pattern was not due to differences in mature guide RNA levels, they were measured using TaqMan ® RT-qPCR (48) (FIG. 2E). No significant differences were observed for miB, miB-C, or miB-D. However, the levels of miB-A and miB-B were significantly different, respectively 1 .5 and 0.4 times that of miB.
  • the sequence in the target, rather than the guide was altered to create the same mismatches in the four modules when using miB as a guide.
  • four mutated target sequences, tat-A, -B, -C, and D were cloned into the 3’UTR of the same dual luciferase reporter (FIG. 2E), and a similar profile was observed (FIG. 3F).
  • the miB construct was titrated at eight different fold concentrations. Again, the same profile emerged (FIG. 2F). Then, using TaqMan ® RT-qPCR, the mature miB was quantified at each transfected concentration, and it was found that variations in mature miB abundance is not related to the observed pattern (FIG. 2G). 20 ng of each shRNA construct was used for transfection, where the mature levels can vary linearly with that of tranfected DNA. However, within the variation range, no significant difference in repression levels could be detected.
  • the mismatches of the four modules were reconstituted in different target sites and shRNA-target combinations.
  • four additional sites were tested in combination with five sets of guides.
  • 25 different combinations were tested in total (FIG. 2G).
  • the nts at the mismatches as well as the surrounding sequences of the modules differ in each combination (FIG. 4A).
  • the repression values obtained from all sites was averaged to produce a synthesized repression profile (FIG. 3H). Again, the four modules were pair-wise distinguishable (FIG. 4B).
  • Example 6 Sequence alteration in the non-seed region display a decidable pattern on repression levels
  • Example 7 Establishing a computation model using the pattern
  • AG02 can be modeled as a multi-state machine, depicted in a Deterministic Finite Automaton (DFA) (FIG. 5D).
  • DFA Deterministic Finite Automaton
  • the guide-loaded AG02 first recognizes bps in the seed. Seed pairing is followed by base pairing of the nts in module-B. When the bps in the module-B are recognized, AG02 transitions to the next state, allowing base pairing of the nts in the C-module to be recognized, followed by the A-module.
  • DFA Deterministic Finite Automaton
  • the DFA describes a recursive algorithm that asserts the rule of evaluating the efficiency of a guide RNA. This model was implemented in a program called MicroAlign as a stand-alone Windows application. The first step of the program is to align the guide and the target strands to make sure that a reasonable conformation of the duplex is scored. Then, the miScore, which quantitatively reflects the silencing efficiency, is calculated.
  • miScores were compared with free energy changes (calculated using PITA) (49).
  • the miScores capture accurately the silencing efficiency.
  • a very strong correlation between miScores and the expression levels of the reporters was observed (FIG. 7A; r 2 > 0.98, p ⁇ 2.6x10 _1 °), as compared to free energy changes (FIG. 7B; r 2 ⁇ 0.60, p ⁇ 6.3x10 3 ).
  • RNA guides that contain at least three of the four mismatched modules were engineered (Table 2, rows 2-6), as well as combinations of random mismatches (Table 2, rows 7-14). From the reporter assay results (FIG. 7C), a high accuracy of the miScores was again observed (FIG. 7D, r 2 ⁇ 0.50, p ⁇ 0.01), relative to changes in free energy (FIG. 7E, r 2 ⁇ 0.20, p > 0.05). This holds even when no alignment is performed (FIG. 8B). Inaccuracies of the free energy model mostly occur when the mismatches are in more than two modules (FIG. 8A), whereas our alignment algorithm identifies alternative bps (FIG. 8C) that improves the ranking of predicted activities of such guides (FIG. 8D).
  • miB-BCD UGUUCUGAUGACGAGAAGCAG (SEQ ID NO: 50) miB-ACD UGUUCUGAACUGCUGAAGCAG (SEQ ID NO: 51) miB-ABD UGUUCUGAACUCGACUUGCAC (SEQ ID NO: 52) miB-ABC UGUUCUGAACUCGAGAACGUC (SEQ ID NO: 53) miB-ABCD UGUUCUGAACUCGAGAAGCAG (SEQ ID NO: 54) miB-mod1 UGUUCUGAUAAACGCUGAGUC (SEQ ID NO: 55) miB-mod2 UGUUCUGAUGGAGUCUUAGAG (SEQ ID NO: 56) miB-mod3 UGUUCUGAAGACCUAUCCGUC (SEQ ID NO: 57) miB-mod4 UGUUCUGAUGGCCUCCCAGUC (SEQ ID NO: 58) miB-12D+18 UGUUCUGAUGACCUCUUCCAG (SEQ ID NO: 59) miB-13D+18 U
  • the mRNA and protein levels of the three miRNA target genes were pooled, and the mean differential repression levels were calculated as log 2 fold changes.
  • the mean of the 293 protein targets was first established, which is -0.15 (FIG. 8I).
  • the target protein levels were sorted by their miScores and split into three equal sized bins, which were labeled‘top’,‘mid’ and ‘bottom’.
  • the mean of each bin was calculated (FIG. 8I) and enriched repression efficiencies in the top and mid bins (near -0.2) was found.
  • the mid bin significantly differs from the bottom bin (P ⁇ 0.05). This shows that the miScores significantly enrich for more effectively repressed targets in the top two bins.
  • MicroAlign program was modified so it calculates scores according to the total number of base pairs, without considering their order.
  • the enrichment for the miR-124 and miR-181 mRNA targets in the three bins was considered.
  • MicroAlign resolves the difference in repression efficiency of the targets solely based on the hierarchical order of base pairing beyond the seed.
  • this modular base pairing mechanism beyond the seed is playing a significant role in the targeting process of cellular miRNAs and can be used to determine the repression efficiency of AG02-dependent miRNA silencing guides.
  • siRNAs can function as miRNAs.
  • MicroRNA targeting specificity in mammals determinants beyond seed pairing. Molecular cell, 27, 91 -105. 14. Boden, D., Pusch, O., Silbermann, R., Lee, F., Tucker, L. and Ramratnam, B. (2004) Enhanced gene silencing of HIV-1 specific siRNA using microRNA designed hairpins. Nucleic acids research, 32, 1 154-1 158.
  • Robertson, B. Dalby, A.B. , Karpilow, J., Khvorova, A., Leake, D. and Vermeulen, A.
  • microRNA target sites in mammalian mRNAs Elife, 4. 26. Majoros, W.H., Lekprasert, P. , Mukherjee, N., Skalsky, R.L., Corcoran, D.L. , Cullen, B.R. and Ohler, U. (2013) MicroRNA target site identification by integrating sequence and binding information. Nature methods, 10, 630-633.
  • HITS-CLIP decodes microRNA-mRNA interaction maps. Nature, 460, 479-486.
  • RNA silencing RNA, 17, 1858-1869.
  • RNAi screening uncovers Dhx9 as a modifier of ABT-737 resistance in an Emu-myc/Bcl-2 mouse model. Blood, 121 , 3402- 3412.

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Abstract

A novel method for assessing the potential of miRNAs to induce translational repression or degradation of a target mRNA is described. The method is based on the identification of a pattern revealing how base pairing from non-seed nucleotides of miRNAs contributes to gene silencing, and more particularly of the hierarchical involved of modules in the non-seed nucleotides in target RNA silencing. Base-pairing in the module corresponding to nucleotides 12- 14 is shown to be of particular importance, followed by based-pairing in the modules corresponding to nucleotides 15-17, 9-11 and 17-21, in decreasing order. The use of the method to more accurately and efficiently decode mi RNA targets at the genome level, and to enrich de novo design of efficient multi-targeting RNA silencing guides, is also described.

Description

METHOD FOR THE IDENTIFICATION AND DESIGN OF RNA INTERFERENCE
AGENTS
CROSS REFERENCE TO RELATED APPLICATIONS
The present application claims the benefit of United States Provisional Patent Application No. 62/682,385 filed on June 8, 2018, which is incorporated herein by reference in its entirety.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
This invention was made with Government support under Grant No. R01 GM088813 awarded by the National Institute of Health (NIH). The Government has certain rights in this invention.
TECHNICAL FIELD
The present invention generally relates to gene silencing, and more specifically to the identification and design of RNA interference (RNAi) agents such as microRNAs (miRNAs).
BACKGROUND ART
Double-stranded RNA-mediated interference (RNAi) is a simple and rapid method of silencing gene expression in a range of organisms. The silencing of a gene is a consequence of processing of RNA into short RNAs that bind to homologous mRNA, leading to repression of its translation or its degradation, and ultimately to the inhibition of protein expression.
In humans, a miRNA binds to an Argonaute protein (AGO) to form an RNA-induced silencing complex (miRISC), which uses only about seven-nucleotide complementarity to recognize and target numerous mRNAs. A fully complementary miRNA sequence to its target mRNA allows the AGO to cleave it, whereas a partially complementary one represses its translation. MiRNAs with the same seed matching sequence but various mismatches introduced beyond the seed silence their targets at different efficiencies. However, the relation between sequence complementarity and silencing efficiency is poorly understood. Despite complementarity patterns deduced from CLIP-based experimental data, one cannot accurately predict the efficiency of a guide strand from sequence. The systematic introduction of mismatches in the nucleotides beyond the seed revealed key positions contributing either positively or negatively to downregulation. Based on this realization, positional scoring schemes were incorporated into miRNA target prediction programs by assigning weights to each position. However, these refinements only moderately improved predictions. The limited predictability of miRISC off-targets not only hampered the decoding of the miRNA function, but also restrained the application of RNA interference (RNAi) technology due to unexpected toxicity. As the result, RNAi technology is regarded less specific and efficient comparing to the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 technology, which tends to become the preferred method for gene knockout.
There is thus a need for a better understanding of the features of miRNA sequences that determine gene silencing efficiency, and for improved methods for the identification and design of target-specific miRNAs.
The present description refers to a number of documents, the content of which is herein incorporated by reference in their entirety.
SUMMARY OF THE INVENTION
The present disclosure provides the following items 1 to 36:
1 . A method for the design of a single-stranded RNA (ssRNA) molecule capable of inducing translational repression or degradation of a plurality of target RNAs, the method comprising:
(i) inputting the mRNA sequences of the plurality of target RNAs;
(ii) identifying all subsequences of 6 to 8 contiguous nucleotides in length that (i) are identical within all target RNAs, or (ii) that differs by one nucleotide, wherein the complement of said 6 to 8 contiguous nucleotides is comprised in the seed region of said candidate ssRNA molecules;
(iii) identifying the sequences exhibiting the highest score among the candidate ssRNA molecules of (ii), wherein said score is calculated using the following algorithm: int pairing_score; //can be defined as any constant
list_of_regions = (B, C, A, D);
Evaluate_score (list_of_regions) {
current_region = car(list_of_regions);
%pair = paired bases to target mRNA of current_region / total number of bases of current_region;
if (current_region == D)
score = 0;
else if (%pair > 0)
score = %pair *(pairing_score + Evaluate_score(cdr(list_of_regions)));
return score;
}
wherein region B corresponds to nucleotides 12 to 14 of said ssRNA molecules; region C corresponds to nucleotides 15 to 17 of said ssRNA molecules; region A corresponds to nucleotides 9 to 11 of said ssRNA molecules; and region D corresponds to nucleotides 18 to 21 of said ssRNA molecules; and wherein the ssRNA molecules capable of inducing translational repression or degradation of said plurality of target RNAs comprises the sequences exhibiting the highest scores.
2. A method for assessing the potential of a candidate single-stranded RNA (ssRNA) molecule to induce translational repression or degradation of a target RNA, the method comprising:
(i) inputting the RNA sequence of the target RNA and the sequence of the candidate ssRNA;
(ii) identify all subsequences in the target RNA that are complementary to at least 5 nucleotides of the seed region from the candidate ssRNA;
(iii) calculating the score of the candidate ssRNA molecule using the following algorithm: int pairing_score; //can be defined as any constant
list_of_regions = (B, C, A, D);
Evaluate_score (list_of_regions) {
current_region = car(list_of_regions);
%pair = paired bases to target mRNA of current_region / total number of bases of current_region;
if (current_region == D)
score = 0;
else if (%pair > 0)
score = %pair *(pairing_score + Evaluate_score(cdr(list_of_regions)));
return score;
}
wherein region B corresponds to nucleotides 12 to 14 of said candidate ssRNA molecule; region C corresponds to nucleotides 15 to 17 of said candidate ssRNA molecule; region A corresponds to nucleotides 9 to 1 1 of said candidate ssRNA molecule; and region D corresponds to nucleotides 18 to 21 of said candidate ssRNA molecule;
and wherein the score positively correlates with the potential of the candidate ssRNA molecule to induce translational repression or degradation of the target RNA.
3. The method of item 1 , wherein step (ii) is performed using an miRNA target prediction program.
4. The method of any one of items 1 to 3, wherein the pairing_score constant is from 1 to 10, preferably 2 to 6.
5. The method of item 4, wherein the pairing_score constant is 3.
6. The method of item 4, further comprising calculating an miScore for the ssRNA molecule, wherein said miScore corresponds to the sum of: (i) the score obtained according to the algorithm defined in any one of items 1 to 4; (ii) a score corresponding to X * Y, wherein X is a constant and Y is the number of Watson-Crick base-pairing between the nucleotides from the seed region and the nucleotides from the target mRNA; and (iii) a D module score that is 1 if there is at least one mismatch in the D module and 0 if the D module is fully complementary to the target mRNA.
7. The method of item 6, wherein X is from 5 to 7.
8. The method of item 7, wherein X is 6.
9. The method of any one items 6 to 8, wherein the miScore of the ssRNA molecule is at least 50.
10. The method of item 9, wherein the miScore of the ssRNA molecule is at least 55.
1 1 . The method of item 10, wherein the miScore of the ssRNA molecule is at least 60, 65, 70 or 75.
12. The method of any one of items 1 to 1 1 , wherein the method further comprises, prior to step (iii), aligning the sequences of the ssRNA molecules and of the target RNA and/or folding the sequences of the ssRNA molecules and of the target RNA.
13. The method of any one of items 1 to 12, wherein the ssRNA molecule is a mature miRNA.
14. The method of any one of items 1 to 13, wherein the sequence of the ssRNA molecule is complementary to a sequence located in the 3' untranslated regions (3' UTR) of the target mRNA or plurality of target RNAs.
15. The method of any one of items 1 to 14, further comprising synthesizing or producing said ssRNA molecule, or a precursor thereof.
16. The method of item 15, wherein said precursor is a miRNA duplex, a pre-miRNA or a pri- miRNA.
17. The method of item 16, wherein said pre-miRNA or pri-miRNA is encoded by a vector.
18. The method of any one of items 1 to 17, further comprising incorporating one or more modifications into the sequence or backbone of the ssRNA molecule.
19. The method of item 18, wherein said one or more modifications into the sequence of the ssRNA molecule is/are in the D region.
20. Use of the ssRNA identified or produced by the methods of any one of items 1 to 19, for inhibiting or reducing the expression of a target gene in a cell.
21 . A computer program for implementing the method of any one of items 1 to 14.
22. A computer-readable medium having recorded thereon the computer program of item 21 .
23. A computational analysis system comprising the computer-readable medium according to item 22.
24. A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
an input module, wherein said input module permits to input the RNA sequences of a plurality of target RNAs; a data analysis module, wherein said data analysis module is coupled to said input module and is capable of identifying candidate ssRNA molecules and calculating a score for said candidate ssRNA molecules according to the method defined in any one of items 1 to 14;
an output module, wherein said output module is coupled to said data analysis module and said output module is capable of providing to a user the scores of said candidate ssRNA molecules.
25. A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
an input module, wherein said input module permits to input the RNA sequences of a target RNA and of a ssRNA molecule;
a data analysis module, wherein said data analysis module is coupled to said input module and is capable of calculating a score for said candidate ssRNA molecule according to the method defined in any one of items 1 to 14;
an output module, wherein said output module is coupled to said data analysis module and said output module is capable of providing to a user the score of said ssRNA molecule.
26. A kit comprising (i) the computer-readable medium according to item 22; and (b) instructions for the design of a single-stranded RNA (ssRNA) molecule capable of inducing translational repression or degradation of a plurality of target RNAs, or for assessing the potential of a candidate ssRNA molecule to induce translational repression or degradation of a target RNA, using the computer program recorded on the computer-readable medium.
27. An RNA molecule comprising one of the following sequences: SEQ ID NO: 23, SEQ ID NO: 27, SEQ ID NO: 31 , SEQ ID NO: 35, or SEQ ID NO: 39.
28. The RNA molecule of item 27, comprising one of the following sequences: SEQ ID NO: 31 , SEQ ID NO: 35, or SEQ ID NO: 39, preferably SEQ ID NO: 31 .
29. The RNA molecule of item 27 or 28, which is single-stranded RNA (ssRNA) molecule.
30. The RNA molecule of item 29, which is a microRNA (miRNA), pre-miRNAs or pri-miRNA, preferably a miRNA.
31 . A method for inhibiting the expression of an E2F transcription factor in a cell comprising contacting said cell with the RNA molecule of any one of items 27-30.
32. The method of item 31 , wherein said E2F transcription factor is E2F1 , E2F2 and/or E2F3.
33. The method of item 31 or 32, wherein said cell is a tumor cell.
34. Use of the RNA molecule of any one of items 27-30 for inhibiting the expression of an E2F transcription factor in a cell.
35. Use of the RNA molecule of any one of items 27-30 for the manufacture of a medicament for inhibiting the expression of an E2F transcription factor in a cell. 36. The use of item 34 or 35, wherein said E2F transcription factor is E2F1 , E2F2 and/or E2F3.
37. The use of any one of items 34-36, wherein said cell is a tumor cell.
38. The method of claim 33 or use of claim 37, wherein the tumor is an osteosarcoma, a small cell lung carcinoma, a breast carcinoma, a prostate carcinoma, or a glioblastoma.
Other objects, advantages and features of the present invention will become more apparent upon reading of the following non-restrictive description of specific embodiments thereof, given by way of example only with reference to the accompanying drawings.
BRIEF DESCRIPTION OF DRAWINGS
In the appended drawings:
FIGs. 1A-J show the silencing profile of the coding region and 3’UTR sites in the reporter plasmid. FIG. 1A: MiB and designed single-module guides. The perfect complementary shRNA miB; then, from top to bottom, the mismatched guide RNAs miB-A (nts 9-1 1); miB-B (nts 12-14); miB-C (nts 15-17); and, miB-D (nts 18-21). FIG. 1 B: The pPRIME vector used to clone all shRNAs. The guide strand is located at the 3’-half of the stem loop structure (arrow). FIG. 1 C: The pNL-luc plasmid is a luciferase reporter that contains the HIV-1 genome. The luciferase reporter gene (FFluc) is located nearthe 3’ LTR of the viral genome. FIG. 1 D: The dual luciferase reporter plasmid FR(-)TS in which the cloning site of the target sequence is located in the 3’ UTR of the firefly reporter gene. The firefly and renilla luciferases are transcribed in opposite directions. FIG. 1 E: The repression profile of mismatched shRNA on the pNL-luc reporter. The plus sign (+) indicates the student t-test for the comparing columns yields p < 0.05; double plus signs (++) p < 0.01. The same convention is followed for FIGs. 1 F-H. FIG. 1 F: The repression profile of mismatched shRNAs on the FR(-)TS reporter. FIG. 1 G: The repression profile of the pNL-luc reported perturbed by exogenously expressed D597A mutant AG02. FIG. 1 H: The repression profile of the FR(-)TS reporter perturbed by the expression of the AG02 mutant. FIG. 11: Titration of the 3’UTR FR(-)TS reporter has limited effects on silencing. FIG. 1J: Titration of the HIV pNL- luc target reporter has limited effects on silencing.
FIG. 2A shows that single-nt mutations in the seed of miB shRNA abolish its ability to repress FR(-)TS reporter expression.
FIG. 2B shows that RT-PCR confirms that the change in repression profile of the FR(- )TS reporter occurs at the RNA level. Asterisk shows the line-indicated bars are not significantly different in height (p>0.05).
FIG. 2C shows that ectopically expressed recombinant wild-type AG02 carrying the same peptide tag as the D597A AGo2 mutant shows decreased repression efficiency at for miB- A, -B, and -C. FIG. 2D shows titration of four different shRNA constructs (miB-A, -B, -C, and -D), across eight-fold concentration difference.
FIG. 2E shows the quantification of mature guides of miB and its module-altered variants by TaqMan RT-qPCR. Significant differences are shown on as p-values on top of horizontal bars for significant or near-significant differences.
FIG. 2F shows titration of miB shRNA construct concentrations, covering 8-fold difference, used in combination with four different MRE reporters.
FIG. 2G shows the TaqMan RT-qPCR determination of the level of mature miB at the transfection concentrations used in reporter assay in FIG. 2F. The relative quantities are normalized to the background levels of miB in the negative control.
FIGs. 3A-H show that the silencing profiles in FR(-)tat and pNL-luc reporters are similar. FIG. 3A: Dual luciferase construct FR(-)tat contains the first exon of the tat gene of HIV upstream of the renilla luciferase, creating a fusion protein of tat and renilla luciferase; the miB MRE is in the tat gene. FIG. 3B: Repression profile on the FR (-)tat reporter. The plus sign (+) indicates the student t-test for the comparing columns yields p < 0.05; double plus signs (++) p < 0.01. The same convention is followed for FIG. 3D. FIG. 3C: Secondary structure calculation of the miB target sequence within the tat gene by SFold. The vertical axis indicates the probability of being single-stranded. A horizontal line indicates the threshold of p = 0.5. FIG. 3D: The silencing profile is more sensitive to MRE location than MRE repeat numbers. Reporter expression levels of pNL- luc vector (left bars), or FR(-)TS vector that contains the target sequence either one time (middle bars) or six times in tandem (right bars) in the presence of mismatched miB variants. FIG. 3E: Base pairing between engineered sites that mismatch the miB shRNA at modules A-D. Each site is cloned into the same FR-reporter with the flanking regions from the tat gene. FIG. 3F: Dual luciferase assay when miB shRNA construct is used in combination with all four site reporters. Firefly luciferase levels were first normalized to renilla light, then normalized to the non-repressed level of each particular reporter construct (N=4). FIG. 3G: A table of shRNAs used in combination with each target site reporter to reconstitute the same mismatched positions in modules A, B, C, and D. The first row of the table indicates which module is mismatched in the guide::target duplex. The first column on the left is a list of mismatched module site reporters. Each entry in the table is a miB-modified shRNA with mutated modules used in combination with the target site of that row. The sequences of the guide::target duplexes are listed in FIG. 4A. FIG. 3H: Synthesized repression profile from reporter assays results by testing the 25 guide-target combinations in the table. Indistinguishable columns heights are indicated by bars on top of the figure.
FIGs. 4A-D show the combination of different sets of shRNAs with the no mismatch sequence (n.m.) and four mismatched modules in four different sites in the reporter constructs. Each site was tested to derive its own repression profile. These four profiles were combined to the wild-type profile to generate the synthesized profile shown in FIG. 3G. FIG. 4E shows the statistical significance of pairwise comparison of the mismatched expression levels from mismatched modules in FIG. 3H. Each entry is a p-value obtained by two- tailed unpaired student t-test assuming unequal variance
FIGs. 5A-D show that the combined effects of mismatches reveal the interdependency between the modules. FIG. 5A: MiB and double-module guides. All possible combinations of two mismatched modules are listed. FIG. 5B: The repression profile of all miB variants (N=4). FIG. 5C: 3-D representation of the p-values of the student t-test results of comparing the efficiencies of all miB variants. When the pairwise comparison is not able to distinguish the two guide RNAs by their residual reporter expression levels, a large p-value shows up as a tall column on the graph. FIG. 5D: State diagram of the proposed sequential recognition model for AG02 slicing. Double circles represent the accepted state, which is defined as the most efficient slicing state.
FIG. 6A shows Student’s t-test p-values are obtained by comparing the repression levels using single- and double-module mismatched guide RNAs; double-module shown with each corresponding single-module. Asterisks on top indicate the expression levels that are indistinguishable.
FIG. 6B shows the tabulated p-values used in the above graph from the pairwise comparisons.
FIGs. 7A-G show the validation of the non-seed model. FIG. 7A: Pearson correlation between reporter assay results and miScores (single- and double-module guides). FIG. 7B: Pearson correlation between reporter assay results and free energy changes calculated using PITA (single- and double-module guides). FIG. 7C: RLU values of the guides in Table 2 as measured in the FR(-)TS reporter assay. FIG. 7D: Pearson correlation between the reporter assay results and the miScores without alignment (13 additional guides). FIG. 7E: Pearson correlation between the reporter assay results and free energy changes calculated using PITA (13 additional guides). FIG. 7F: Pearson correlation between published silencing efficiencies and miScores. miB-based guides (diamonds); Wee et al. (22) dataset (triangles); and, Robertson et al. (21) dataset (squares). FIG. 7G: PC3 cell growth curves of miR-20a, MT1 , and sm3. Juxtaposed are the Western blots of E2F factors when miR-20a, MT 1 , and sm3 were present.
FIG. 8A shows that including target site secondary structure calculations did not improve predictions by the conventional free-energy model.
FIG. 8B shows that the MicroAlign algorithm robustly predicts the efficiency of guide strands with good accuracy without the alignment step.
FIG. 8C shows that when more than two trinucleotide modules are mismatched at the same time, alternative bps can occur between the strands in each alignment. The guide sequence is listed on top and the target sequence at the bottom. The guide sequence is written in the 3’ to 5’ direction and the target sequence in the 5’ to 3’ direction. FIG. 8D shows that comparing the measured and the predicted silencing efficiency of the guide sequences that mismatch in at least three modules, the program produced a better ranking than the conventional free-energy model
FIG. 8E shows Pearson correlation between miScores and Kcat / Km values (Wee et al., 22) calculated without the alignment step.
FIG. 8F shows Pearson correlation calculated with the alignment step for the same guide sequences in FIG. 8E.
FIG. 8G shows Pearson correlation between miScores and luciferase assay results (Robertson et al., 21) calculated without the alignment step.
FIG. 8H shows Pearson correlation calculated for the same guide sequences in FIG. 8G calculated alignment step.
FIG. 8I shows the mean log2 fold changes in protein levels of the miScore-evaluated targets were pooled from cells that were transfected with miR-124, miR-181 , and miR-1 , respectively. The mean target protein level was taken for 293 proteins. The pooled protein levels were placed in three equal size bins: top, mid, and bottom. The mean value of the top 30 targets evaluated by miScore was also computed (top 30).
FIG. 9A shows that enrichment of smartRNA designs further validates the model. Top smartRNAs designed by MultiTar then selected using miScores. The guide RNA sequence is on top, in 3’ to 5’ direction, and the target sequence is at the bottom in 5’ to 3’ direction. Predicted Watson-Crick (|) and Wobble (*) base pairs are indicated.
FIG. 9B shows Western blot of the target E2F proteins in the presence of each designed multi-targeting guide RNAs.
FIG. 9C shows the quantification of target protein levels from the Western blot in the top panel, which shows all the target protein levels of the five tested top designs. Left bars = E2F1 , middle bars = E2F2, right bars = E2F3.
FIG. 9D shows growth curves of all five newly tested new multi-targeting guides relative to best previously tested ones.
FIG. 9E shows relative protein levels are plotted against the predicted miScores. Data point shape corresponds to the target gene: diamond represents E2F-1 , square represents E2F- 2, and triangle represents E2F-3. The horizontal line represents 70% expression level threshold of an “effective” knockdown. The vertical line is the suggested miScore cut-off for selecting efficient guide designs.
FIG. 9F shows mean log2 fold changes of the targeted mRNAs (top two panels) and proteins (bottom two panels) in miR-124 transfected cells binned by miScores (left) and scores not considering the modular order of the base pairs (right).
FIG. 9G shows the same as FIG. 9F, but for miR-181 transfected cells. DISCLOSURE OF INVENTION
In the studies described herein, the present inventors have identified a pattern revealing how base pairing from non-seed nucleotides of miRNAs contributes to gene silencing efficiency. The have developed a model for assessing the potential of miRNAs to induce translational repression or degradation of a target mRNA, which may be used to more accurately and efficiently decode miRNA targets at the genome level, and to enrich de novo design of efficient multitargeting RNA silencing guides.
Accordingly, in a first aspect, the present disclosure provides method for assessing the potential of a candidate single-stranded RNA (ssRNA) molecule (e.g., a RNA interference agent such as an miRNA) to induce translational repression or degradation of a target RNA (e.g., target mRNA), the method comprising:
(i) input the mRNA sequence of the target mRNA and the sequence of the ssRNA;
(ii) calculating the score of the candidate ssRNA molecule using the following algorithm:
int pairing_score; //can be defined as any constant
list_of_regions = (B, C, A, D);
Evaluate_score (list_of_regions) {
current_region = car(list_of_regions);
%pair = paired bases to target mRNA of current_region / total number of bases of current_region;
if (current_region == D)
score = 0;
else if (%pair > 0)
score = %pair *(pairing_score + Evaluate_score(cdr(list_of_regions)));
return score;
}
wherein region B corresponds to nucleotides 12 to 14 of said ssRNA molecule; region C corresponds to nucleotides 15 to 17 of said ssRNA molecule; region A corresponds to nucleotides 9 to 1 1 of said ssRNA molecule; and region D corresponds to nucleotides 18 to 21 of said ssRNA molecule;
and wherein the score correlates with the potential of the candidate ssRNA molecule to induce translational repression or degradation of the target RNA (e.g., target mRNA).
The present disclosure also provides a method for assessing one or more RNA (e.g., mRNA) targeted by an miRNA molecule (i.e., the one or more RNAs that is/are repressed or degraded by the miRNA) in a cell, the method comprising:
(i) input the sequences of the RNAs (e.g., mRNAs) expressed by said cell and the sequence of the miRNA; (ii) calculating the score of the miRNA molecule for the RNAs (e.g., mRNAs) expressed by said cell using the algorithm defined above:
wherein the one or more RNAs (e.g., mRNAs) associated with the highest scores are targeted by the miRNA molecule.
The present disclosure also provides a method for identifying one or more miRNA response elements (MRE) for an miRNA molecule (i.e., the one or more binding sites of the miRNA) in RNAs (e.g., mRNAs) expressed by a cell, the method comprising:
(i) input the sequences of the RNAs (e.g., mRNAs) expressed by said cell and the sequence of the miRNA;
(ii) calculating the score of the miRNA molecule for one or more regions of the RNAs (e.g., mRNAs) expressed by said cell using the algorithm defined above:
wherein the one or more regions of the RNAs (e.g., mRNAs) associated with the highest scores are MRE for the miRNA molecule.
The present disclosure also provides a method for the design of a synthetic single- stranded RNA (ssRNA) molecule (e.g., a mature miRNA) capable of inducing translational repression or degradation or of a plurality of target RNAs (e.g., mRNAs), the method comprising:
(i) input the RNA sequences of the plurality of target RNAs (e.g., mRNAs);
(ii) identify all subsequences of 6 to 8 contiguous nucleotides in length that are present within all target RNAs (i.e., putative miRNA response elements, MRE), wherein the complement of said 6 to 8 contiguous nucleotides is comprised in the seed region said of candidate ssRNA molecules;
(iii) identifying the ssRNA molecules exhibiting the highest score among the candidate ssRNA molecules of (ii), wherein said score is calculated using the algorithm defined above; wherein the ssRNA molecules exhibiting the highest score are capable of inducing translational repression or degradation of said plurality of target RNAs (e.g., mRNAs).
As used herein, the term "RNA" means a molecule comprising at least one ribonucleotide residue. By "ribonucleotide" is meant a nucleotide with a hydroxyl group at the 2' position of a beta-D-ribo-furanose moiety. The terms include double-stranded RNA, single-stranded RNA, isolated RNA such as partially purified RNA, essentially pure RNA, synthetic RNA, recombinantly- produced RNA, as well as altered RNA that differs from naturally occurring RNA by the addition, deletion, substitution and/or alteration of one or more nucleotides. Such alterations can include addition of non-nucleotide material, such as to the end(s) of the siRNA or internally, for example at one or more nucleotides of the RNA. Nucleotides in the RNA molecules of the present disclosure can also comprise non-standard nucleotides, such as non-naturally occurring nucleotides or chemically synthesized nucleotides or deoxynucleotides. These altered RNAs can be referred to as analogs or analogs of naturally-occurring RNA. In an embodiment, the RNA is an miRNA. The term "double-stranded RNA" or "dsRNA" as used herein refers to a ribonucleic acid duplex, including but not limited to, endogenous and artificial siRNA duplexes, short hairpin RNA duplexes (shRNAs) and miRNA duplexes.
The term "single-stranded RNA" or "ssRNA" as used herein refers to a ribonucleic acid, including but not limited to, endogenous and artificial mature single-stranded siRNA molecules, mature single-stranded shRNAs and mature single-stranded miRNAs.
The term "short interfering RNA" or "siRNA" as used herein refers to a nucleic acid molecule capable of modulating, by inhibiting or down regulating, gene expression, through RNAi or gene silencing via sequence-specific-mediated cleavage of one or more target mRNA strands.
The term "microRNA" or "miRNA" refers to a nucleic acid molecule capable of modulating, by inhibiting or down-regulating, gene expression through sequence-specific- mediated translational suppression and subsequent polyA removal and degradation of one or more target mRNA strands. miRNAs are typically partially complementary to binding sites in the 3'UTR of mRNAs, but may also bind to 5'UTRs and Coding regions (CDS) of mRNAs, as well as non-coding RNAs (ncRNAs).
As used herein, the term "RNA interference" or "RNAi" refers to sequence-specific inhibition of gene expression and/or reduction in target RNA levels mediated by an RNA molecule, which RNA comprises a portion that is substantially complementary to a target RNA. In various embodiments, RNAi can occur via selective intracellular degradation of RNA. In various embodiments, RNAi can occur by translational repression.
As used herein, the term "RNA interference agent" or“RNAi agent” refers to an RNA molecule, optionally including one or more nucleotide analogs or modifications, having a structure characteristic of molecules that can mediate inhibition of gene expression through an RNAi mechanism. In various embodiments, RNAi agents mediate inhibition of gene expression by causing degradation of target transcripts. In various embodiments, RNAi agents mediate inhibition of gene expression by inhibiting translation of target transcripts. Generally, an RNAi agent includes a portion that is substantially complementary to a target RNA. In various embodiments, RNAi agents are single-stranded. In various embodiments, RNAi agents are at least partly double- stranded. In various embodiments, exemplary RNAi agents can include siRNA, shRNA, and/or miRNA. In an embodiment, the RNAi agent is an miRNA. In various embodiments, RNAi agents may be composed entirely of natural RNA nucleotides (i.e., adenine, guanine, cytosine, and uracil). In various embodiments, RNAi agents may include one or more non-natural RNA nucleotides (e.g., nucleotide analogs, DNA nucleotides, etc.). Inclusion of non-natural RNA nucleic acid residues may be used to make the RNAi agent more resistant to cellular degradation than RNA. In various embodiments, the term "RNAi agent" may refer to any RNA molecule, RNA molecule derivative, and/or nucleic acid encoding an RNA molecule that induces an RNAi effect (e.g., degradation of target RNA and/or inhibition of translation). In various embodiments, an RNAi agent may comprise a blunt-ended (i.e., without overhangs) dsRNA that can act as a Dicer substrate.
Typically, siRNA functions by mediating the cleavage of mRNA target sequences which possess a region of complete or near complete complementarity to the "guide strand" of the siRNA molecule. Typically, miRNA functions by mediating translational suppression and subsequent polyA removal and degradation of mRNA target sequences which possess seed sites within their 3' untranslated regions (3' UTR) which are complementary to nucleotides 2 to 8 from the 5' end of the miRNA's guide strand (the seed region). However, because siRNA and miRNA molecules are structurally related, siRNAs can function as miRNAs and vice versa.
By "complementarity" and "complementary" are meant that a nucleic acid can form hydrogen bond(s) with another nucleic acid for example by Watson-Crick base pairing. A nucleic acid which can form hydrogen bond(s) with another nucleic acid through non-Watson-Crick base pairing also falls within the definition of having complementarity. A percent complementarity indicates the percentage of residues in a nucleic acid molecule that can form hydrogen bonds (e.g., Watson-Crick base pairing) with a second nucleic acid sequence (e.g., 5, 6, 7, 8, 9, 10 out of 10 being 50%, 60%, 70%, 80%, 90%, and 100% complementary). "Perfectly complementary" or "fully complementary" means that all sequential residues of a nucleic acid sequence will form hydrogen bonds with the same number of sequential residues in a second nucleic acid sequence.
In the context of complementarity between seed regions and seed sites, the seed region will have no more than 1 , most preferably no mismatches with the target mRNAs' seed site(s).
By "seed site" is meant a nucleotide sequence present in the 3' UTR of a target mRNA sequence which is complementary to the seed region of at least one strand of a RNAi agent (e.g., miRNA) molecule and which has the potential to mediate miRNA-like translational suppression and/or polyA removal and subsequent degradation of the mRNA strand it is contained within when hybridized to its complementary seed region.
By "seed region" is meant a nucleotide sequence present on a strand of the RNAi agent (e.g., miRNA) molecules described herein which is complementary to one or more seed sites present in the coding region or preferably the 3' UTR region of one or more target mRNA molecules. Typically, the seed region comprises nucleotides 1 to 8 or 2 to 8 from the 5' end of the dsRNA strand, i.e. 7 or 8 nucleotides in length, however the seed region may be 6 to 10 residues, preferably 6 to 8 residues, in length. Seed regions will preferably start at nucleotide 2 from the 5’ end. Longer seed regions may result in stronger miRNA-like down-regulation but are likely to reduce the number of candidate RNAi agents (e.g., miRNAs) identified by the model described herein. In contrast, shorter seed regions are likely to result in weaker miRNA-like down-regulation but are likely to increase the number of candidate RNAi agents (e.g., miRNAs) identified by the model described herein. The skilled man will be able to decide what length of seed region to use. Preferably the residue at position 1 from the 5' end of the RNAi agent (e.g., miRNA) strand is adenosine.
A given seed region may be complementary to seed sites in more than one target mRNA molecule. The seed sites may be within the coding region or preferably the 3' UTR region of the same mRNA molecule which is targeted for cleavage/translation repression by the RNAi agent (e.g., miRNA) molecule incorporating a corresponding seed region, or the seed sites may be within the coding region or preferably the 3' UTR region of different mRNA molecules targeted for cleavage/translation repression by the RNAi agent (e.g., miRNA) molecule incorporating a corresponding seed region.
A "target gene" or "gene of interest" is a gene whose expression is desired to be modulated. The term includes any nucleotide sequence, which may or may not contain identified gene(s), including, but not limited to, coding region(s), non-coding region(s), untranscribed region(s), intron(s), exon(s) and transgenes(s). The target gene can be a gene derived from a cell, an endogenous gene, a transgene or exogenous genes such as genes of a pathogen (e.g., a virus), which is present in the cell after infection thereof. The cell containing the target gene can be derived from or contained in any organism, e.g., bacteria, fungi, animals (mice, human). In certain embodiments, the target gene encodes a protein that causes a disease or disorder. In certain embodiments, the target gene encodes a protein that is overexpressed or overactive. In certain embodiments, the target gene encodes a protein whose aberrant expression causes a diseased state, oncogenic transformation or promotes viral infection.
A "target mRNA" sequence is an mRNA sequence derived from a target gene. The target mRNA may be a cytoplasmic mRNA, a mitochondrial mRNA, a viral mRNA. The ssRNAs (e.g., mature miRNAs) designed or identified by the methods/algorithm described herein may target a single gene, or multiple target genes.
The target site in the target RNA may be of the same length as the RNAi agent (e.g., miRNA), i.e. the RNAi agent binds to contiguous residues in the target RNA.
The target site in the target RNA may alternatively be longer than the RNAi agent, i.e. the complementary modules are separated by up to 5 nucleotides (1 , 2, 3, 4 or 5 nucleotides). In other words, the target site comprises one or more bulges (internal loops).
In an embodiment, the RNA sequences of the plurality of target mRNA comprise a plurality of different target mRNA sequences. In an embodiment, the plurality of mRNA sequences are transcribed from more than one gene of interest. For example, more effective down regulation of an intended target gene may likely be achieved by also targeting other genes, for example genes encoding proteins having a function in the cell that is redundant with the intended target gene, genes encoding proteins involved in the same cellular pathway as the intended target gene, or genes which encode transcription factors that positively regulate the expression levels or activity of the intended target gene. The RNA sequences of the plurality of target mRNA may also comprise mRNA sequences which are transcribed from a single target gene, for instance by alternative gene splicing.
The determination of the score for predicting miRNA silencing specificity/efficacy for a target RNA (based on base-pairing beyond the seed) using the method/algorithm defined herein may be illustrated as follows.
Assuming a target RNA comprising the following sequence:
AUAUAUAUGGGGGGGGGGAAAA (SEQ ID NO: 73)
and 2 miRNA candidates comprising the following sequences:
miRNA 1 - U AU AU AU ACCCACC ACCU U U U (SEQ ID NO: 74)
miRNA 2 - U AU AU AU ACACCCCCCC U U U U (SEQ ID NO: 75)
As described throughout the present specification, the miRNA comprises a seed region defined by nucleotides 1-8 (UAUAUAUA), and the non-seed region (nucleotides 9-21) is divided into 4 modules: nucleotides 9-11 defining module A, nucleotides 12-14 defining module B, nucleotides 15-17 defining module C, and nucleotides 18-21 defining module D. Accordingly, miRNA 1 has one mismatch in the B region and one mismatch in the C region, whereas miRNA 2 has only one mismatch in the A region. The base-pairing with the target in modules A to D for the miRNA candidates are as follows:
Figure imgf000017_0001
int pairing_score; //can be defined as any constant.
The constant 1 will be used for the present illustration (the constant 3 was used in the studies described herein). list_of_regions = (B, C, A, D);
Evaluatejscore (list_of_regions) {
current_region = car(list_of_regions);
%pair = paired bases to target mRNA of current_region / total number of bases of current_region;
if (current_region == D)
score = 0;
else if (%pair > 0)
score = %pair *(pairing_score + Evaluate_score(cdr(list_of_regions))); return score;
}
Evalute_score is a recursive function that evaluates the fraction of Watson-Crick basepairing in the current module (in the order B, C, A and D), which may be 0/3, 1/3, 2/3 or 3/3 for modules A-C. The pairing in module D is not taken into account (i.e. it has no effect) in the determination the score in the non-seed region (score = 0).
Calculation for miRNA 1 :
score( modules B, C, A and D) = 2/3 * ( 1 + score( modules C, A and D) ) score( modules C, A and D ) = 2/3 * ( 1 + score( modules A and D ) ) score( modules A and D ) = 3/3 * ( 1 + score( modules D ) ) score( module D ) = 0.
Following the recursivity:
score( modules A and D ) = 3/3 * ( 1 + 0 ) = 3/3 score( modules C, A and D ) = 2/3 * ( 1
+ 3/3 ) = 2/3 * 6/3 = 12/9 = 4/3 score( modules B, C, A and D ) = 2/3 * ( 1 + 4/3 ) = 2/3 * 7/3 = 14/9 1.56
Calculation for miRNA 2:
score( modules B, C, A et D) = 3/3 * ( 1 + score( modules C, A et D) ) score( modules C, A et D ) = 3/3 * ( 1 + score( modules A et D ) ) score( modules A et D ) = 2/3 * ( 1 + score( modules D ) ) score( module D ) = 0
Following the recursivity:
score( modules A and D ) = 2/3 * ( 1 + 0 ) = 2/3 score( modules C, A and D ) = 3/3 * ( 1
+ 2/3 ) = 3/3 * 5/3 = 15/9 = 5/3 score( modules B, C, A and D ) = 3/3 * ( 1 + 5/3 ) = 3/3 * 8/3 = 24/9 2.66
Because miRNA 2 has a higher score than miRNA 1 , it is predicted to be superior to miRNA 1 for inducing translational repression or degradation of the target RNA.
In an embodiment, the constant (pairing_score) used in the algorithm is 1 , 2, 3, 4, 5, 6, 7, 8, 9 or 10, for example 2, 3, 4, 5 or 6, preferably 3, 4 or 5.
In an embodiment, the methods further comprise calculating a score for the base-pairing in the seed region (seed score). In an embodiment, this seed score is combined to the score calculated with the algorithm described herein (for non-seed region). The seed score may be calculated by adding a number of points for each Watch-Crick base-pairing in the seed region. In an embodiment, from 1 to 10, 2 to 9, 3 to 8, or 4 to 7 points are added for each Watch-Crick basepairing in the seed region. In a further embodiment, 6 points are added for each Watch-Crick base-pairing in the seed region.
In an embodiment, the methods further comprise calculating a score for the base-pairing in the D module, nucleotides 18-21 (D score). In a further embodiment, no point is added for Watch-Crick base-pairing in the D region, and 1 point is added for no base-pairing Watch-Crick base-pairing in the D region, to obtain the D score.
In an obtain, the seed score, the score calculated with the algorithm described herein (for non-seed region) and the D score are combined to obtain a miScore for the miRNA.
The miScores of miRNA 1 and miRNA 2 exemplified above may be calculated as follows.
Seed score: 8 Watch-Crick base-pairings * 6 points/base-pairing = 48 points for both miRNA 1 and miRNA 2.
Evalute_score (non-seed region) - using 5 as the constant (pairing_score) for miRNA 1 :
score( modules A and D ) = 3/3 * ( 5 + 0 ) = 5 score( modules C, A and D ) = 2/3 * ( 5 + 5 ) = 2/3 * 10 = 20/3 score( modules B, C, A and D ) = 2/3 * ( 5 + 20/3 ) = 2/3 * 35/3 = 70/3 = 23.3 for miRNA 2:
score( modules A and D ) = 2/3 * ( 5 + 0 ) = 10/3 score( modules C, A and D ) = 3/3 * ( 5 + 10/3 ) = 3/3 * 25/3 = 25/3 score( modules B, C, A and D ) = 3/3 * ( 5 + 25/3 ) = 3/3 * 40/3 = 120/3
= 40
D region score: perfect base pairing in the D module = 0 point for both miRNA 1 and miRNA 2.
miScore for miRNA 1 = 48 + 23.3 + 0 = 71.3
miScore for miRNA 2 = 48 + 40 + 0 = 88
Again, because miRNA 2 has a higher miScore than miRNA 1 , it is predicted to be superior to miRNA 1 for inducing translational repression or degradation of the target RNA.
The methods described herein may also comprise additional steps or analysis, for example to improve the predictive capacity of the algorithm/methods. Such additional steps/analysis may be useful, for example, for pre-selecting miRNA candidates to be analysed by the algorithm/methods described herein.
In an embodiment, the methods include a nucleotide sequence alignment step of the sequences of the ssRNA molecules and of the target RNA. Optimal alignment of sequences may be conducted using a variety of tools/algorithms, such as the local homology algorithm of Smith and Waterman, 1981 , Adv. Appl. Math 2: 482, the homology alignment algorithm of Needleman and Wunsch, 1970, J. Mol. Biol. 48: 443, the search for similarity method of Pearson and Lipman, 1988, Proc. Natl. Acad. Sci. USA 85: 2444, and the computerised implementations of these algorithms (such as GAP, BESTFIT, FASTA and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, Madison, Wl, U.S.A.). Sequence identity may also be determined using the BLAST algorithm, described in Altschul et ai, 1990, J. Mol. Biol. 215: 403- 10. Tools for performing sequence alignment analyses are available through the National Center for Biotechnology Information or the European Bioinformatics Institute (EMBL-EBI), for example. In another embodiment, prior to performing the algorithm described herein, putative seed/MicroRNA Recognition Element (MRE) interactions in a population of miRNAs and mRNA may be identified using suitable methods. Such analysis may be performed using the miRBooking tool/algorithm (see, e.g., Weill et al. Nucleic Acids Res. 2015; 43(14):6730-8), which infers the whole set of miRNA/mRNA interactions by simulating the RNA competition to hybridize, finding the stable state at equilibrium (microtargetome), and estimating the miRNA-induced silencing (miS) levels applied to each mRNA.
To estimate seed/MRE hybridization probabilities, sequences from mature miRNA are obtained from databases (e.g., miRBase for miRNAs and NCBI RefSeq for human mRNAs). Nucleotides 2-8 of the mature miRNAs are defined the seed sequences. The free energies of the duplexes seed/MRE, AG, may be computed using the MC-Fold software (Parisien M., Major F. Nature. 2008;452:51 -55), which incorporates the energy contributions of non-canonical base pairs that can possibly form at mismatch positions. The energies of the duplexes may be normalized assuming a Boltzmann distribution, and converted into a hybridization probability, HP:
Figure imgf000020_0002
where the sum is done over all 47 heptamers, k s the Boltzmann constant, and Tthe temperature of the system.
To compute miRNA-induced silencing on each gene, it is assumed that the miRNA- induced silencing on a given mRNA is proportional to the sum of the contributions of all miRNA copies occupying the multiple copies of this mRNA, and it is computed as the sum of each hybridization probability (HP) multiplied by a MRE location factor, W:
miS
Figure imgf000020_0001
where C is the cellular context; m is the mRNA expressed at n copies in C; x is the seed and y the MRE; HP(x::y) is the hybridization probability of forming the duplex x::y; and, W(y) is a contribution factor due to the location of y in m: W(y) = 0.1 if y is in the 5' UTR or coding region (CDS), and W(y) = 1.0 if y is in the 3' UTR, as this model is in agreement with experimental observations (Fang Z., Rajewsky N., PLoS One. 201 1 ;6:e18067). HP(x::y) is calculated from the free energy of folding, AG, of the duplex x::y.
Other miRNA target prediction programs, such as MC-fold (Parisien M., Major F. Nature. 2008;452:51 -55), BayMiR, microDoR, PicTar, TargetScan, PITATop, and PITAAII, may also be used in combination with the algorithm (see, e.g., Alexiou et al., Bioinformatics. 2009 Dec 1 ;25(23):3049-55). Aspects of the disclosure may be implemented in hardware or software, or a combination of both. However, preferably, the algorithms and methods defined herein are implemented in one or more computer programs executing on programmable computers each comprising at least one processor, at least one data storage system (including volatile and nonvolatile memory and/or storage elements), at least one input device, and at least one output device. Program code is applied to input data to perform the algorithms and processes described herein and generate output information. The output information is applied to one or more output devices, in known fashion.
The steps/algorithms of the methods described herein may be performed by a computer program product, encoding instructions to perform at least one or more the steps/algorithms described herein. The computer program product may be embodied on a computer readable medium, e.g., a hard disk drive, flash device, a random access memory, a tape, or any other suitable medium used to store data. One skilled in the art will appreciate that the algorithm or computer program product may be implemented as an application for any suitable operating system, for example a Windows®, Google® or Apple® operating system, which may be executed by a suitable system or device, embodied for example as a personal computer, a server, a console, a cell phone, or any other suitable computing device, or combination of devices. The methods and systems disclosed herein may be implemented in localized and distributed forms consistent with computing technology known to the skilled person.
Thus, in another embodiment, the disclosure provides a computer program, stored on a computer-readable medium, for performing the algorithms and methods defined herein.
In a yet further aspect, the present disclosure provides a method of producing a single- stranded or double-stranded RNAi molecule which comprises performing the methods and algorithms as defined above and then synthesizing one or more of the RNA molecules generated or identified by said methods or algorithms. In an embodiment, a double-stranded RNA (dsRNA) is synthesized based on the ssRNAs identified by the methods and algorithms as defined above, i.e. comprising a strand comprising a sequence complementary to the sequence of the ssRNAs.
The algorithm and methods described herein can optionally comprise one or more additional steps in which modifications to the ssRNA molecules are designed. For instance, the two strands of the dsRNA molecule may be linked by a linking component such as a chemical linking group or an oligonucleotide linker with the result that the resulting structure of the dsRNA is a hairpin structure. The linking component must not block or otherwise negatively affect the activity of the dsRNA, for instance by blocking loading of strands into the RISC complex or association with Dicer. Many suitable chemical linking groups are known in the art. If an oligonucleotide linker is used, it may be of any sequence or length provided that full functionality of the dsRNA is retained. The ssRNA molecules identified by the method described herein may be modified by inserting one or more mutations (substitutions) in the base/original nucleotide sequence. In an embodiment, the one or more mutations are incorporated into the D module of the ssRNA molecule.
The antisense strand can be modified for Dicer processing by suitable modifiers located at the 3' end of the antisense strand, i.e., the dsRNA is designed to direct orientation of Dicer binding and processing. Suitable modifiers include nucleotides such as deoxyribonucleotides, dideoxyribonucleotides, acyclonucleotides and the like and sterically hindered molecules, such as fluorescent molecules and the like. Acyclonucleotides substitute a 2-hydroxyethoxymethyl group for the 2'-deoxyribofuranosyl sugar normally present in dNMPs. Other nucleotide modifiers could include 3'-deoxyadenosine (cordycepin), 3'-azido-3'-deoxythymidine (AZT), 2', 3'- dideoxyinosine (ddl), thiacytidine (3TC), 2',3'-didehydro-2',3'-dideoxythymidine (d4T) and the monophosphate nucleotides of 3'-azido-3'-deoxythymidine (AZT), 2',3'-dideoxy-3'-thiacytidine (3TC) and 2',3'-didehydro-2',3'-dideoxythymidine (d4T). Deoxynucleotides can be used as the modifiers. When nucleotide modifiers are utilized, 1 -3 nucleotide modifiers, or 2 nucleotide modifiers are substituted for the ribonucleotides on the 3' end of the antisense strand. When sterically hindered molecules are utilized, they are attached to the ribonucleotide at the 3' end of the antisense strand. Thus, the length of the strand does not change with the incorporation of the modifiers. The invention contemplates substituting two DNA bases in the dsRNA to direct the orientation of Dicer processing. In a further invention, two terminal DNA bases are located on the 3' end of the antisense strand in place of two ribonucleotides forming a blunt end of the duplex on the 5' end of the sense strand and the 3' end of the antisense strand, and a two-nucleotide RNA overhang is located on the 3'-end of the sense strand. This is an asymmetric composition with DNA on the blunt end and RNA bases on the overhanging end.
Examples of modifications contemplated for the phosphate backbone include phosphonates, including methylphosphonate, phosphorothioate, and phosphotriester modifications such as alkylphosphotriesters, and the like. Examples of modifications contemplated for the sugar moiety include 2'-alkyl pyrimidine, such as 2'-0-methyl, 2'-fluoro, amino, and deoxy modifications and the like. Examples of modifications contemplated for the base groups include abasic sugars, 2-O-alkyl modified pyrimidines, 4-thiouracil, 5-bromouracil, 5- iodouracil, and 5-(3-aminoallyl)-uracil and the like. Locked nucleic acids, or LNA's, could also be incorporated. Many other modifications are known and can be used so long as the above criteria are satisfied. Examples of modifications are also disclosed in U.S. Pat. Nos. 5,684, 143, 5,858,988 and 6,291 ,438 and in U.S. published patent application No. 2004/0203145 A1 . Other modifications are disclosed in Herdewijn (2000) Antisense Nucleic Acid Drug Dev 10: 297-310, Eckstein (2000) Antisense Nucleic Acid Drug Dev 10: 1 17-21 , Rusckowski et al. (2000) Antisense Nucleic Acid Drug Dev 10: 333-345, Stein et al., (2001) Antisense Nucleic Acid Drug Dev 1 1 : 317- 325 and Vorobjev et al. (2001 ) Antisense Nucleic Acid Drug Dev 1 1 : 77-85. The ssRNAs or dsRNAs can be obtained using a number of techniques known to those of skill in the art. For example, the ssRNAs or dsRNAs can be chemically synthesized or recombinantly produced using methods known in the art, such as the Drosophila in vitro system described in U.S. published application 2002/0086356 of Tuschl et al., or the methods of synthesizing RNA molecules described in Verma and Eckstein (1998) Annu Rev Biochem 67: 99- 134. The ssRNAs or dsRNAs may be chemically synthesized using appropriately protected ribonucleoside phosphoramidites and a conventional DNA/RNA synthesizer.
The dsRNAs can be synthesized as two separate, complementary RNA molecules, or as a single RNA molecule with two complementary regions. Commercial suppliers of synthetic RNA molecules or synthesis reagents include Proligo (Hamburg, Germany), Dharmacon Research (Lafayette, Colo., USA), Pierce Chemical (part of Perbio Science, Rockford, III., USA), Glen Research (Sterling, Va., USA), ChemGenes (Ashland, Mass., USA) and Cruachem (Glasgow, UK).
The dsRNAs can also be expressed from recombinant circular or linear DNA vectors or plasmids using any suitable promoter. Suitable promoters for expressing dsRNAs from a plasmid include, for example, the U6 or H1 RNA pol III promoter sequences and the cytomegalovirus promoter. Selection of other suitable promoters is within the skill in the art. The recombinant plasmids or vectors may also comprise inducible or regulatable promoters for expression of the dsRNA in a particular cell, tissue or in a particular intracellular environment.
The dsRNAs expressed from recombinant plasmids can either be isolated from cultured cell expression systems by standard techniques, or can be expressed intracellularly at or near the area of disease in vivo. The dsRNAs can be expressed from a recombinant plasmid either as two separate, complementary RNA molecules, or as a single RNA molecule with two complementary regions.
Selection of plasmids suitable for expressing dsRNAs of the invention, methods for inserting nucleic acid sequences for expressing the dsRNAs into the plasmid, and methods of delivering the recombinant plasmid to the cells of interest are within the skill in the art. See, for example Tuschl, T. (2002), Nat. Biotechnoi. 20: 446-448; Brummelkamp T R et al. (2002), Science 296: 550-553; Miyagishi M et al. (2002), Nat. Biotechnoi. 20: 497-500; Paddison P J et al. (2002), Genes Dev. 16: 948-958; Lee N S et al. (2002), Nat. Biotechnoi. 20: 500-505; and Paul C P et al. (2002), Nat. Biotechnoi. 20: 505-508).
The dsRNAs can also be expressed from, recombinant viral vectors intracellularly in vivo. The recombinant viral vectors of the invention comprise sequences encoding the dsRNAs of the invention and any suitable promoter for expressing the dsRNA sequences. Suitable promoters include, for example, the U6 or H1 RNA pol III promoter sequences and the cytomegalovirus promoter. Selection of other suitable promoters is within the skill in the art. The recombinant viral vectors of the invention can also comprise inducible or regulatable promoters for expression of the dsRNAs in a particular tissue or in a particular intracellular environment. dsRNAs can be expressed from a recombinant viral vector either as two separate, complementary RNA molecules, or as a single RNA molecule with two complementary regions. Any viral vector capable of accepting the coding sequences for the dsRNAs molecule(s) to be expressed can be used, for example vectors derived from adenovirus (AV); adeno-associated virus (AAV); retroviruses (e.g., lentiviruses (LV), Rhabdoviruses, murine leukemia virus); herpes virus, and the like. The tropism of viral vectors can be modified by pseudotyping the vectors with envelope proteins or other surface antigens from other viruses, or by substituting different viral capsid proteins, as appropriate.
Selection of recombinant viral vectors suitable for use in the invention, methods for inserting nucleic acid sequences for expressing the dsRNA into the vector, and methods of delivering the viral vector to the cells of interest are within the skill in the art. See, for example, Dornburg R (1995), Gene Therap. 2: 301 -310; Eglitis MA (1988), Biotechniques 6: 608-614; Miller A D (1990), Hum Gene Therap. 1 : 5-14; and Anderson WF (1998), Nature 392: 25-30.
Mature miRNAs derived from miRNA precursors, e.g., precursor-miRNA (pre-miRNAs) and primary miRNA (pri-miRNAs), which are processed by the cellular machinery to produce the mature miRNAs. miRNA genes are usually transcribed by RNA polymerase II (Pol II). The polymerase often binds to a promoter found near the DNA sequence, encoding what will become the hairpin loop of the pre-miRNA. The resulting transcript is capped with a specially modified nucleotide at the 5’ end, polyadenylated with multiple adenosines poly(A) tail), and spliced. Animal miRNAs are initially transcribed as part of one arm of an ~80 nucleotide RNA stem- loop that in turn forms part of a several hundred nucleotide-long miRNA precursor referred to a primary miRNA (pri-miRNA). A single pri-miRNA may contain from one to six miRNA precursors. The pri-miRNA is recognized and processed by a protein complex referred to as the microprocessor complex comprising DGCR8 (or "Pasha" in invertebrates) and the RNAse Drosha, to produce the pre-miRNA that no longer contains the 5’ cap and poly(A) tail of the pri- miRNA. The pre-miRNA hairpin is cleaved by the RNase III enzyme Dicer, yielding an miRNA:miRNA duplex about 22 nucleotides in length. Dicer then unwinds the duplex, and one of the strand of the duplex (the mature miRNA) is incorporated into the RNA-induced silencing complex (RISC).
Accordingly, the present disclosure also provides a pre-miRNA or pri-miRNA comprising a sequence encoding the ssRNA (miRNA) identified by the methods described herein, as well as a plasmid or vector comprising a sequence encoding such pre-miRNA or pri-miRNA.
In another aspect, the present disclosure provides the use of the ssRNAs, dsRNAs, miRNAs, pre-miRNAs, pri-miRNAs, or plasmids/vectors encoding same, identified by the methods described herein, for inhibiting or reducing the expression of a target gene in a cell. The ability of a dsRNA containing a given target sequence to cause RNAi-mediated degradation of the target mRNA can be evaluated using standard techniques for measuring the levels of RNA or protein in cells. For example, dsRNA of the invention can be delivered to cultured cells, and the levels of target mRNA can be measured by Northern blot or dot blotting techniques, or by quantitative RT-PCR. Alternatively, the levels of protein encoded by the target gene in the cultured cells can be measured by ELISA or Western blot, for example.
In another aspect, the present disclosure provides an RNA molecule comprising or consisting of one of the following sequences: SEQ ID NO: 23, SEQ ID NO: 27, SEQ ID NO: 31 , SEQ ID NO: 35, or SEQ ID NO: 39. In an embodiment, the RNA molecule comprises or consists of SEQ ID NO: 23. In an embodiment, the RNA molecule comprises or consists of SEQ ID NO: 27. In an embodiment, the RNA molecule comprises or consists of SEQ ID NO: 31 . In an embodiment, the RNA molecule comprises or consists of SEQ ID NO: 35. In an embodiment, the RNA molecule comprises or consists of SEQ ID NO: 39.
In an embodiment, the RNA molecule is a single-stranded RNA (ssRNA) molecule, such as a miRNA, pre-miRNAs or pri-miRNA.
In an embodiment, the RNA molecule comprises 200 nucleotides or less, 150 nucleotides or less, 100 nucleotides or less, 90 nucleotides or less, 80 nucleotides or less, 70 nucleotides or less, 60 nucleotides or less, 50 nucleotides or less, 40 nucleotides or less, or 30 nucleotides or less.
In another aspect, the present disclosure provides a composition, such as a pharmaceutical composition, comprising the above-mentioned RNA molecule and a carrier or excipient, in a further embodiment a pharmaceutically acceptable carrier or excipient. Supplementary active compounds can also be incorporated into the compositions. The carrier/excipient can be suitable, for example, for intravenous, parenteral, subcutaneous, intramuscular, intracranial, intraorbital, ophthalmic, intraventricular, intracapsular, intraspinal, intrathecal, epidural, intracisternal, intraperitoneal, intranasal or pulmonary (e.g., aerosol) administration (see Remington: The Science and Practice of Pharmacy, by Loyd V AIIen, Jr, 2012, 22nd edition, Pharmaceutical Press; Handbook of Pharmaceutical Excipients, by Rowe et al., 2012, 7th edition, Pharmaceutical Press). Therapeutic formulations are prepared using standard methods known in the art by mixing the active ingredient having the desired degree of purity with one or more optional pharmaceutically acceptable carriers, excipients and/or stabilizers.
An "excipient," as used herein, has its normal meaning in the art and is any ingredient that is not an active ingredient (drug) itself. Excipients include for example binders, lubricants, diluents, fillers, thickening agents, disintegrants, plasticizers, coatings, barrier layer formulations, lubricants, stabilizing agent, release-delaying agents and other components. "Pharmaceutically acceptable excipient" as used herein refers to any excipient that does not interfere with effectiveness of the biological activity of the active ingredients and that is not toxic to the subject, i.e. , is a type of excipient and/or is for use in an amount which is not toxic to the subject. Excipients are well known in the art, and the present composition is not limited in these respects. In certain embodiments, the composition comprises excipients, including for example and without limitation, one or more binders (binding agents), thickening agents, surfactants, diluents, release-delaying agents, colorants, flavoring agents, fillers, disintegrants/dissolution promoting agents, lubricants, plasticizers, silica flow conditioners, glidants, anti-caking agents, anti-tacking agents, stabilizing agents, anti-static agents, swelling agents and any combinations thereof. As those of skill would recognize, a single excipient can fulfill more than two functions at once, e.g., can act as both a binding agent and a thickening agent. As those of skill will also recognize, these terms are not necessarily mutually exclusive.
RNA molecule comprising the sequences of SEQ ID NO: 23, SEQ ID NO: 27, SEQ ID NO: 31 , SEQ ID NO: 35 and SEQ ID NO: 39 have been shown to inhibit the expression of E2F transcription factors, e.g., E2F1 , E2F2 and/or E2F3. Accordingly, in another aspect, the present disclosure provides a method for inhibiting the expression of an E2F transcription factor in a cell comprising contacting said cell with the RNA molecule defined above. In another aspect, the present disclosure provides the use of the RNA molecule defined above for inhibiting the expression of an E2F transcription factor in a cell, or for the manufacture of a medicament for inhibiting the expression of an E2F transcription factor in a cell. In an embodiment, the method/use permits to inhibit the expression of E2F1 , E2F2 and/or E2F3, in a further embodiment the expression of E2F1 , E2F2 and E2F3.
Dysregulated E2F expression/activity or Rb/E2F pathway has been shown to be associated to cancer. Thus, in an embodiment, the cell is a tumor cell.
In another aspect, the present disclosure provides a method for treating cancer in a subject in need thereof comprising administering to the subject an effective amount of the RNA molecule defined above, or of a pharmaceutical composition comprising the RNA molecule defined above. In another aspect, the present disclosure provides the use of the RNA molecule defined above, or of a pharmaceutical composition comprising the RNA molecule defined above, for treating cancer in a subject in need thereof, or for the manufacture of a medicament for treating cancer in a subject in need thereof. In an embodiment, the cancer is a cancer associated with dysregulated E2F expression/activity or dysregulated Rb/E2F pathway activity. In an embodiment, the cancer is associated with a mutation in the retinoblastoma tumor suppressor protein (Rb). Rb mutations causing loss of Rb function have been identified in a wide spectrum of tumors including osteosarcomas, small cell lung carcinomas, breast carcinomas, prostate carcinomas, glioblastomas and others. MODE(S) FOR CARRYING OUT THE INVENTION
The present invention is illustrated in further details by the following non-limiting examples.
Example 1 : Materials and Methods
Plasmid Construction
The Renilla luciferase control vector, SVR, was obtained by replacing the CMV promoter in the pcDNA-Rlucll plasmid (from the laboratory of Sylvie Mader at the Institut de recherche en immunologie et en cancerologie (IRIC), see Breton, B. , et al., Biophys J, 2010. 99(12): p. 4037- 46) with an SV40 promoter. Briefly, the CMV promoter was removed by restriction enzymes Spel and Hindlll (New England Biolabs®). The resulting linearized vector was gel-purified with QIAEX II® Gel Extraction Kit. The SV40 promoter from the pGL3-control luciferase vector fragment was obtained by digesting the vector with Nhel and Hindlll. Gel purified SV40 promoter fragment was inserted upstream of the Rlucll gene in pcDNA-RLucll vector by ligation using T4 DNA ligase (NEB).
The firefly-ren/7/a opposite-sense target site reporter is referred to as the FR(-)TS construct, which contains both firefly and renilla luciferase reporter genes oriented in the opposite directions. In addition, a 76 bp region of the HIV genome containing the miB shRNA target site in the center (pNL4-3 vector, Accession number: AF324493, nts 5968-6044) was inserted into the 3’UTR of the firefly gene. Cloning of the target site was carried out by inserting the annealed oligonucleotides into the Xbal site upstream of the poly-A signal in the pGL3-Ctl reporter. For FR(- )TS vector, the annealed oligos are the following: the forward oligo sequence is CTAGAATGGCAGGAAGAAGCGGAGACAGCGACGAAGAGCTCATCAGAACAGTCAGACTC ATCAAGCTTCTCTATCAAAGCAT (SEQ ID NO: 71); and, the reverse oligo sequence is CTAGATGCTTTGATAGAGAAGCTTGATGAGTCTGACTGTTCTGATGAGCTCTTCGTCGCTG T CT CCGCTT CTT CCTGCCATT (SEQ ID NO: 72). Bold letters represent the miB binding site. The reporter that contains six times of the target site does not include the flanking regions; rather, the 3’UTR insert is a tandem repeat of the target site only. Renilla luciferase gene was removed from pcDNA-Rlucll plasmid by digesting the vector with Spel and Xbal restriction enzymes. The gel purified (QIAEX II® Gel Extraction Kit) Renilla luciferase fragment was then inserted in the Nhel site in Promega® pGL3-control luciferase vector.
The FR(-)tat dual luciferase vector was constructed as follows. The FR(-)TS vector without insertion of the miB shRNA target site from the previous step was used as a starting material. The vector was digested with restriction enzymes Xbal and Hindlll from NEB and gel purified using QIAEX II ® Gel Extraction Kit. The first exon of the tat gene was amplified from pNL4.3-luc vector (from the laboratory of Eric Cohen at the Institut de Recherches Cliniques de Montreal (IRCM)) with forward primer (5’ to 3’): ATCCAAGCTTCCCGCCACCATGGCAGGAAGAAGCGGA (SEQ ID NO: 69), and reverse primer (5’ to 3’): CGACTCTAGATGCTTTGATAGAGAAGCT (SEQ ID NO: 70).
The PCR was carried out using 55 °C as annealing temperature. The amplified fragment was ethanol precipitated and digested with restriction enzymes Xbal and Hindlll. Upon gel purification, the fragment was ligated with the digested vector at 16°C overnight. The ligation mix was transformed into DH10B.
The vector pPRIME (from the laboratory of Jerry Pelletier, McGill University) has been previously optimized for shRNA cloning (60-62). Designed guide-RNAs were cloned into the vector following miR-30-based shRNA cloning protocols (40). Briefly, complementary oligonucleotides that contain the shRNA sequences (Biocorp, oligonucleotides are listed in Table 1) were diluted to 100 mM in deionized water. Annealing reaction was carried out at 95°C in annealing buffer for 5 minutes followed by slow cooling to room temperature. The annealed double-stranded oligonucleotides were then phosphorylated by T4 PNK (NEB). Ligation reaction was performed by combining doubly digested pPRIME by Xhol and EcoRI with the phosphorylation product of annealed oligonucleotides in T4 DNA ligase (NEB) reaction mix at 16 °C overnight.
Table 1 : Oligonucleotides used for the construction of the miB target site
Oligo name Oligo sequence 5’->3’
for_HIV_148_TS CTAGAataacaaaaaaaaacaaaaacaacaacaaaaaactcatcaaaacaatcaaa
ctcatcaagcttctctatcaaagcaT (SEQ ID NO: 63)
rev_HIV_148_TS CTAGAtactttaataaaaaaacttaataaatctaactatctaataaactcttcatcactatctc
cgcttcttcctgccatT (SEQ ID NO: 64) for_mut_HIV_148_TS CTAGAataacaaaaaaaaacaaaaacaacaacaaaaaactcacaaaactaatcaaa
ctcatcaagcttctctatcaaagcaT (SEQ ID NO: 65) rev mut HIV 148 TS CTAGAtactttaataaaaaaacttaataaatctaactaqtctqtaaactcttcatcactatctc
cgcttcttcctgccatT (SEQ ID NO: 66)
For_HIV148TSwtX3 CTAGAaacaacaaaaaactcatcaaaacaatcaacaacaaaaaactcatcaaaaca
atcaacaacaaaaaactcatcaqaacaatcT (SEQ ID NO: 67)
Rev_HIV148TSwtX3 CTAGAaactqtctqataaactcttcatcactaactqtctqataaactcttcatcactaacta
tctaataaactcttcatcactT (SEQ ID NO: 68)
Note: Upper case letter are the Xbal site protruding ends complementary sequences. Underlined sequences are the miB-targeting sequences with seeds binding sequence in bold. The mutant sites contain inverted seed sequences.
Cell culture and monitoring shRNA efficiencies HEK 293T (d 7) cells (from ATCC®) were maintained according to established conditions (50). Briefly, cells were grown in DMEM (+L-glutamine) (Life Technologies®) supplemented with 10% FBS, 100 U/mL penicillin/streptomycin at 37 °C and 5% C02. Cells were grown to confluency before plating. For testing the efficiencies of mismatched guides, cells were plated in 96-well plates at ~20,000 cells per well 24 hours prior to the transfection. For assays that required growth in 24-well plates, cells were plated at ~100,000 cells per well. The reporter plasmids and the shRNA plasmids were co-transfected into the cells using Lipofectamine™ 2000 (Invitrogen®) according to the manufacturer's instructions. Along with 10 ng of shRNA plasmid, 5 ng of pNL-luc and 2 ng of SVR control vector were co-transfected into each 96 well; alternatively, 50 ng of the shRNA construct, 20 ng of the pNL-luc, and 10 ng of the SVR control vector were co-transfected into each 24-well. For assays using all FR(-)TS constructs and FR(-)tat constructs, 10 ng of the target construct and 50 ng of the shRNA construct were combined and transfected into each 96- well. When an Ago2 expression construct is used, 25 ng of the Ago2D597A vector (43) (from the laboratory of Sven Diederichs, University of Freiburg) was combined with the DNA mix described above and subsequently co-transfected into the cells.
Luciferase assays were performed accordingly to established protocols adapted from the Dual-Glo® Luciferase System (Promega®). 48 hours post-transfection, cells were lysed with 1 * Passive lysis buffer (Promega®) and luciferase activity was assayed using the Dual-Glo® Luciferase System (Promega®). Luminescent light was measured on Veritas Microplate Luminometer (Turner Biosystems®). The ratio between the reporter and the control luciferase bioluminescence light was taken and then normalized to that of the negative control shRNA or empty vector, resulting in the percentage residual expression of the reporter gene.
Measuring reporter transcript abundance using qRT-PCR
RNA extraction was performed using TRIzol® reagent following manufacturer’s protocol. RNA was extracted from the plates of the same cells used in the luciferase assay. Both oligo-dT primer and random primers were used for the synthesis of cDNA from total RNA extracted according to previously established protocols (63). Briefly, 800 ng of total RNA was used for each synthesis reaction in 20 pL of total volume using Invitrogen reagents (M-MLV Reverse Transcriptase, Cat. No. 28025-021 , Invitrogen®). RNA was extracted from the same cells that were used in the luciferase assay and M-MLV was used to perform the cDNA synthesis. The newly synthesized cDNA was diluted 100 times prior to real-time PCR. Each real-time PCR reaction mixture contained the diluted cDNA (1 pi), forward and reverse primers (250 nM), MgCh (2.5 mM), dNTPs (0.2 mM), SYBR green (0.33X), buffer for Jumpstart® Taq DNA polymerase and Jumpstart® Taq DNA polymerase (0.25 U; Sigma) in a final volume of 10 mI. After denaturation at 95 °C for 6 min, samples went through 50 cycles of amplification (20 s at 95 °C, 20 s at 58 °C and 30 s at 72 °C). Melt curves were determined for each reaction and qPCR was performed using a LightCycler® 480 (Roche Applied Science®, Canada). Data was normalized using Renilla and HPRT as controls.
The detection of mature RNA guide molecules was performed following the polyA-based RT-qPCR protocol established previously (48, 64). Briefly, 20 pl_ of reaction contained 1 mI_ of reverse transcription products diluted 10-fold, 10 mM of forward primer, and 10 mM of universal reverse primer, 2 mI_ of Taq polymerase buffer (10X), 4 mI_ of 2.5 mM each dNTP, 0.6 U Taq and 10 mM of universal TaqMan® probe. The mix is heated to 95 °C for 2 minutes prior to entering 45 cycles of 95 °C for 15 seconds followed by 60 °C for 1 minute. The reactions were carried out and measurements were taken on a StepOnePlus™ Real-Time System from Applied Biosciences®. The forward primer sequences are as follows:
miB: GTG CTGTTCT GAT G AGCT CTT CGT C (SEQ ID NO:43);
miB-A: GTGCTGTTCTGAACTGCTCTTCGTC (SEQ ID NO:44);
miB-B: GTG CTGTTCTG AT G ACG ACTT CGTC (SEQ ID NO:45);
miB-C: GTGCTGTTCTGATGAGCTGAACGTC (SEQ ID NO:46);
miB-D: GTG CTGTTCTG ATG AG CTCTTGCAG (SEQ ID NO:47);
U6: ACGCAAATTCGTGAAGCGTTCCAT (SEQ ID NO:48);
Puromycin: TGACCGAGTACAAGCCCAC (SEQ ID NO:49).
Cells and Retroviral-Mediated Gene Transfer
PC3 were obtained from American Type Culture Collection (ATCC®) and cultured in RPM1 (Wisent) supplemented with 10% FBS (Wisent®), 1 % penicillin/streptomycin sulfate (Wisent), and 2 mmol/L L-glutamine (Wisent®) at 37°C and 5% C02. Gene transfer was performed using retroviral particles produced in Phoenix packaging cells. Phoenix cells were transfected by calcium-phosphate precipitation with 20 pg of a retroviral plasmid (15 hrs at 37°C). The plasmids used were: shNTC (non-targeting control), MiR20, MT E2F(1), E2F Afa, E2F Afb, E2F Afc, E2F Afd and E2F Afe. After 48 hrs, the virus-containing medium was filtered (0.45 pm filter, Millipore®) and supplemented with 4 pg/ml polybrene (Sigma®) (first supernatant). Viruses were collected for an additional 8 hrs as before (second supernatant). For infections, the culture medium was replaced by the appropriate first and second supernatant on PC3 cells. Sixteen hours later, infected cell populations were purified by selection with 2 pg/ml puromycin for 48 hours.
Growth Curve
20,000 cells per well were plated into 6 well plates. At the indicated times, cells were washed with PBS, fixed in 4% formaldehyde, and rinsed with distilled water. Cells were stained with 0.1 % crystal violet (Sigma®) for 30 min, rinsed extensively, and dried. Cell-associated dye was extracted with 2.0 ml 10% acetic acid. Aliquots were diluted 1 :4 with H20, transferred to 96- well microtiter plates, and the optical density at 590 nm was determined. Values were normalized to the optical density at day 0 for the appropriate condition. Within an experiment, each point was determined in triplicate.
Western blot
PC3 cells were washed with cold PBS and then scraped on ice into 500 pl_ of PBS buffer containing IX Complete-EDTA free Protease Inhibitor Cocktail (Roche Applied Science®) and 1X PhosSTOP® Phosphatase Inhibitor Cocktail (Roche Applied Science®). Cells were spun at maximum speed for 5 min. The pellet was resuspended in 100 pi of Laemmli-p-mercaptoethanol buffer, sonicated 5 seconds at a low intensity, heated 5 min at 95°C, and then cleared by centrifugation at 13,000 RPM for 10 min. The proteins were quantified with the Bradford reagent and 30 pg were loaded on a 10% SDS-PAGE and transferred to Immobilon-P PVDF membranes (Millipore®). Membranes were blocked 1 hour at room temperature in PBS containing 0.1 % Tween™ 20 (PBS-T) and 5% dry milk and then washed 3 times 5 min with PBS-T. The membranes were incubated with the primary antibodies diluted in PBS-T + 3% BSA + 0.05% Na- azide overnight at 4°C. The following primary antibodies were used: anti-E2F1 (1 : 1 ,000, clone H- 137; rabbit polyclonal; #SC22820, Santa Cruz®); anti-E2F2 (1 :1 ,000, clone L-20; rabbit polyclonal; #SC632, Santa Cruz®); anti-E2F3 (1 :1000; clone PG-37, mouse monoclonal, #5551 , Millipore®); anti-a-tubulin (1 :20,000, mouse monoclonal clone B-5-1 -2, T6074, Sigma-Aldrich®). Membranes were washed 3 times 5 min with PBS-T and then incubated with the secondary antibodies diluted in PBS-T + 5% dry milk 1 hour at room temperature. The following secondary antibodies were used: goat anti-rabbit IgG conjugated to HRP (1 :3000, #170-6515, Bio-Rad) or goat anti-mouse IgG conjugated to HRP (1 :3,000, #170-6516, Bio-Rad®). Finally, the membranes were washed 3 times 5 min with PBS-T. Immunoblots were visualized using enhanced chemiluminescence (ECL) detection systems and Super RX X-Ray films (Fujifilm®) or a ChemiDoc™ MP system (Bio-Rad®). Band quantification was done using ImageJ™ or Image Lab™ 4.0 (Bio-Rad®).
Implementation and validation of MicroAlian and the miScore
The evaluation program MicroAlgin was implemented in Microsoft Visual Studio Express 2012 C++ as a stand-alone windows application. Experimental measurements were plotted against the predicted miScore s (see code below) to calculate Pearson correlations.
Fold inhibition of miR-21 , miR-122 and miR-22 were taken from (21). For each miRNA, the dataset chosen represented what the authors defined as the inhibitor concentration whose efficacy most accurately captured the effects of the dinucleotide mismatches. The concentrations were: 20 nM for miR-21 , 2 nM for miR-122 and 0.3 nM for miR-22. As a pre-filtering step, mismatched inhibitors in the first position and seed region (nts 2-8) were excluded. The data were transformed into residual target proportions (1 / Fold-inhibition), and because all 3 miRNAs do not share the same concentration, the residual target proportions had to be linearly scaled to give relative target expression levels. The linear scaling was performed by fixing the lowest residual target proportion to 0 and the positive control value, represented by the fully matched inhibitor, to 100.
The catalytic efficiency (Kcat/Km) measured for AG02 was used as a proxy to infer relative target expression levels (22). The less efficient the catalysis, the higher is the expression of the siRNA target. As for the Fold inhibition dataset, mismatched guide siRNAs in the first position and seed region were excluded from this dataset. For the sake of uniformity, the catalytic efficiency values were normalized to the most efficient siRNA guide to get Kcat/Km percentage values comparable to the other datasets. Relative target expression was defined as 100 minus the percentage catalytic efficiency of the siRNA guide.
The pseudocode of MicroAlign evaluation algorithm implements the DFA described in FIG. 3D. The set of transition functions (d) of the DFA is described in the figure, where state set Q = {q0, q1 , q2, q3, q4}, alphabet set å = {seed, A, B, C, D}, and transition function set d: Q X å -> Q. The start state is qO and the accepted state is q4. The configuration of base pairs between the miRNA and the target now can be regarded as a regular expression that is recognized by this DFA, simulating the AG02 mechanism.
The bps are predicted by Needleman-Wunch algorithm and evaluated by regions following the discovered order. This DFA was described using a recursive algorithm. The“bottom” of the recursion is the evaluation of region-D, where the contribution of bps is little for accessible 3’UTR sites. The algorithm is implemented as a Windows application and a copy of it is available online: http://major.iric.ca/MajorLabEn/MiR-Tools.html: pairing_score = 3
Hstjofjregions = (B, C, A, D);
Evaluatejscore (list_of_regions) {
current_region = car(list_of_regions);
%pair = paired bases of current_region /total number of bases of current_region;
if (current_region == D)
score = 0;
else if (%pair > 0)
score = %pair *(pairing_score + Evaluate _score( cdr( list_of_regions )));
return score;
}
For the calculation of the miScores of the miRNAs, 6 points were added for each Watson- Crick pairing in the seed, 0 point was added for full base-pairing in module D (4/4), and 1 point was added for one mismatch or more in module D (0/4, 1/4, 2/4 or 3/4). Student t-test for the luciferase reporter assay
The tests were performed in an unpaired and two-tailed fashion using 0.05 as the threshold p-value, assuming non-equal variance.
Example 2: Mismatched modules cause disturbance in silencing efficiency
To study the role of base pairing as a determinant of the efficiency of AG02-mediated silencing, we chose an shRNA, miB, which was reported to target a structurally open region of the HIV genome and inhibit viral gene expression (14, 38, 39). Its MRE is located in exon 1 of the HIV-1 tat gene (nts 5993-6013). miB's nts in the non-seed region were mutated in short stretches of 3 or 4 nts at a time (modules) such that they mismatch the corresponding nts in the target sequence: from 5’ to 3’ module A (nts 9-1 1 ), B (nts 12-14), C (nts 15-17), and D (nts 18-21) (FIG. 1 A). The mismatched positions were engineered by copying the nt from the target strand (A:A, G:G, C:C, and U:U). The guide strands containing these mismatches were named miB-A, -B, -C, and -D, respectively, and were cloned into pPRIME (40), an shRNA expression vector based on the miR-30 backbone (FIG. 1 B).
To test the mismatched shRNAs, the pNL4.3-luc reporter construct, which contains the complete HIV genome with a disabled env gene, was used (FIG. 1C). Since effective endogenous MREs are often located in the 3’UTR of their mRNA (41), a dual luciferase reporter, FR(-)TS, which embeds the miB MRE in the 3’UTR of the firefly luciferase (FIG. 1 D), was constructed. The MRE is located 29 nts downstream of the firefly luciferase stop codon, which is within a region (15-300 nts from the stop codon) associated with a high density of mRNA-bound AG02 protein in the HITS-CLIP assays previously conducted (42). To test whether this reporter construct functions properly, individual nts were mutated to their complementary nts in the seed of miB between position 1 and 6. As a result, a significant abolishing effect of the repression was observed relative to miB (FIG. 2A), confirming the reporter system is capable of measuring one- nt mismatch effects.
miB effectively repressed pNL-4.3 reporter gene expression, with a 75-80% knockdown efficiency relative to vector-only transfected cells (14). However, reporter gene silencing by mismatched small RNA guide was greatly abolished except for miB-D, which retained more than 50% of the silencing capability (FIG. 1 E). ShRNAs (or miRNAs) that partially base pair with the HIV target sequences in the non-seed regions were strikingly ineffective in repressing the viral target (15,37). At least 80% loss of repression was observed due to a mismatch of three nts in module A, B, or C. When FR(-)TS was used as the target construct, all guide strands showed improved silencing efficiency compared to the pNL-luc reporter construct (FIG. 1 F). Also, a profile of repression efficiency emerges: miB and miB-D were the most efficient, followed by miB-A, then miB-C and miB-B, with more than 60% remaining expression. Example 2: AG02 is involved in the reporter level pattern exhibited by mismatches
A construct containing an RNase-imparied mutant of AG02, D597A, which lacks the key aspartate residue required to cleave the target, was exogenously supplied to cells. The mutant greatly abolished the silencing by miB and miB-D shRNAs for both target constructs (FIG. 1G and H), indicating that these two shRNAs mainly depend on the cleaving AG02 to downregulate the target.
As the functional difference between wild-type and the mutant AG02 lies in the RNase activity, perturbation in the RNA levels was assessed. qRT-PCR was performed to measure the abundance of the reporter transcript in cells expressing FR(-)TS (FIG. 2B). Though less salient, the pattern is coherent with that observed in luciferase assay (FIG. 1 H). The fact that the mutant abolishes repression for miB-A is surprising since module-A contains mismatches that include the scissile phosphate (between nts 10-1 1), and that repression should be slicer-independent. It was suspected that the recombinant version of the AG02 protein is somewhat compromised for its slicer-independent activity due to its amino acid sequence modifications, namely, the addition of the FLAG/HA tag. To confirm, a slicer-intact version of the recombinant AG02 that carries the same FLAG/HA tag was exogenously supplied. Repression levels of miB and miB-D were improved. However, the miB-A, -B, and -C repression was compromised relative to the samples where only the endogenous AG02 was present (FIG. 2C), confirming that the peptide tag has decreased the slicer-independent activity of AG02.
Example 3: Variation in target concentration is not a dominant factor that perturbs the silencing efficiencies
Previous studies have shown that concentration of the target or the miRNA affects the repression efficiency due to threshold effects and competition from ceRNAs (15, 44). The assay conditions were optimized so that target concentration will not affect repression signficantly in this study. Both the pNL-luc and FR(-)TS reporters were titrated at a concentration range of 25-fold difference (4 ng, 20 ng, and 100 ng) with no significant alteration of the repression pattern. At higher concentrations of the target, the downregulation was less efficient in general (FIG. 11). However, the efficiency was maintained with the FR(-)TS reporter in cells transfected with miB, miB-A or -D (FIG. 1J), even at the highest level. For these three guide sequences, the enhancement of repression did not exeed 20% for any guide even when the target concentration decreased 25-fold. The perturbation in the repression profile using the FR(-)TS reporter (FIG. 1 J) constrasts that obtained by using exogenous AG02 proteins: the downregulation achieved by miB, miB-A, and -D were significantly abolished (up to two-fold) when a mutant AG02 was coexpressed, even in the presence of the endogenous wild-type AG02; no such abolishment in silencing could be achieved by increasing the target concentration. Example 4: Confirmation of the effects of MRE location, accessibility, and repeats
Local structures in the target RNA may hinder the action of miRNA (13), and the RNA genome of the HIV is known to contain rich secondary structure (45). To make sure that the improvement of silencing efficiency when moving the MRE from the pNL-4.3 to FR(-)TS construct is not due to the removal of global structure of the viral mRNA, exonl of the tat gene was cloned into the dual luciferase construct after removing the miB MRE. Exon 1 of tat is inserted in-frame with and upstream of the renilla luciferase (FIG. 3A). This vector was named FR-faf. As the result, a fusion protein of tat and renilla luciferase is synthesized upon translation. Despite reduced light intensity, the renilla luciferase remains active and its expression is still sensitive to the downregulation of miB shRNA (FIG. 3B).
Mismatches in module A, B, or C greatly abolish silencing. This resembles the repression pattern displayed when pNL4.3-luc viral genome construct was used as a target. Energy calculation following the approach previously developed using sFold (46) indicated the absence of stable local RNA structure (FIG. 3C, p > 0.5), rendering the MRE accessible (seed position 41 - 47). This corroborates with the high throughput screen results from another study, where an shRNAs library tiling the entire genome of the HIV was screened to probe for the accessible regions of the viral RNA genome (38). Therefore, the enhancement of repression reflects the fact that the MRE has been moved from the coding to a non-coding region of the mRNA, rather than the removal of either global or local secondary structure.
To see whether the number of MREs on each target RNA could alter the repression profile significantly, the miB target site was inserted into the FR(-)TS vector six times in tandem, and the 1- and 6-MRE target constructs were tested side by side with the pNL-luc reporter (FIG. 3D). The 6-MRE in the 3’UTR has enhancing effects on silencing for miB, miB-A, and miB-D. However, no significant changes were observed for miB-B and miB-C. It was concluded that the number of MREs in the 3’UTR influences the silencing efficiency, but to a significantly less extent than their location.
Example 5: The pattern of repression levels is not associated with the levels of mature guide RNAs
shRNA constructs with eight-fold differences in quantity were transfected. Downregulation levels appeared to be resistant to such perturbations, indicating that the guide- AG02 biogenesis pathway was already saturated at half of the amount of guide RNA constructs used (i.e. 20 ng) (FIG. 2D). To further confirm that the pattern was not due to differences in mature guide RNA levels, they were measured using TaqMan® RT-qPCR (48) (FIG. 2E). No significant differences were observed for miB, miB-C, or miB-D. However, the levels of miB-A and miB-B were significantly different, respectively 1 .5 and 0.4 times that of miB. To address the concern of whether the profile of repression efficiency truthfully reflects the positional effects of the mismatched nts during the targeting process rather than the efficiency of processing and AGO-loading, the sequence in the target, rather than the guide, was altered to create the same mismatches in the four modules when using miB as a guide. Using the same design rationale for mismatches in the guide, four mutated target sequences, tat-A, -B, -C, and D, were cloned into the 3’UTR of the same dual luciferase reporter (FIG. 2E), and a similar profile was observed (FIG. 3F). To confirm that this profile is stable with different amounts of mature miB guide RNA, the miB construct was titrated at eight different fold concentrations. Again, the same profile emerged (FIG. 2F). Then, using TaqMan® RT-qPCR, the mature miB was quantified at each transfected concentration, and it was found that variations in mature miB abundance is not related to the observed pattern (FIG. 2G). 20 ng of each shRNA construct was used for transfection, where the mature levels can vary linearly with that of tranfected DNA. However, within the variation range, no significant difference in repression levels could be detected. This confirmed that although the mature levels of the guide RNAs may differ by up to 1 .5 times, such as in the cases of miB-A and miB-B in the previous experiments, the repression profile is not affected and is solely due to the positional effects of the mismatches in the targeting process.
To ensure that these observations were not biased by a particular guide RNA or a particular MRE, the mismatches of the four modules were reconstituted in different target sites and shRNA-target combinations. Along with the wild-type, four additional sites were tested in combination with five sets of guides. Along with the fully complementary guide for each site, 25 different combinations were tested in total (FIG. 2G). The nts at the mismatches as well as the surrounding sequences of the modules differ in each combination (FIG. 4A). For each module, the repression values obtained from all sites was averaged to produce a synthesized repression profile (FIG. 3H). Again, the four modules were pair-wise distinguishable (FIG. 4B).
Example 6: Sequence alteration in the non-seed region display a decidable pattern on repression levels
To grade the relative importance of each module in their ability to influence gene silencing, the wild-type site was combined with all six possible double-module variants of miB: miB-AC, -BD, -AD, -AB, -BC, and -CD (FIG. 5A). This produced a spectrum of silencing effects when luciferase expression was monitored (FIG. 5B). The reporter level of each single-module was grouped with those of the double-module variants that contain it (FIG. 6A). A pattern of indistinguishable reporter levels emerged from these expression levels (FIG. 5B), as well as from their associated p-values (FIG. 5C; Student’s f-test; p-values in FIG. 6B). The following information was deciphered: when the seed is perfectly matched, the B-module has the most decisive effect on silencing because it determines how base pairing in the rest of the non-seed nts contribute to silencing. Such decisive power of the modules decreases following the order of module C, A, and D.
Example 7: Establishing a computation model using the pattern
To consistently apply this rule to evaluate the targeting efficiency of miRNA-mediated repression, a computational tool that emulates the decision-making process of AG02 was built. AG02 can be modeled as a multi-state machine, depicted in a Deterministic Finite Automaton (DFA) (FIG. 5D). The guide-loaded AG02 first recognizes bps in the seed. Seed pairing is followed by base pairing of the nts in module-B. When the bps in the module-B are recognized, AG02 transitions to the next state, allowing base pairing of the nts in the C-module to be recognized, followed by the A-module. Since a mismatched module-D is indistinguishable from miB, the slicer activity is likely to be fully functional once modules A, B, and C are all base paired. For this reason, the accepted state of the DFA, q4, i.e. where slicing can occur, was defined. The DFA describes a recursive algorithm that asserts the rule of evaluating the efficiency of a guide RNA. This model was implemented in a program called MicroAlign as a stand-alone Windows application. The first step of the program is to align the guide and the target strands to make sure that a reasonable conformation of the duplex is scored. Then, the miScore, which quantitatively reflects the silencing efficiency, is calculated. miScores were compared with free energy changes (calculated using PITA) (49). The miScores capture accurately the silencing efficiency. A very strong correlation between miScores and the expression levels of the reporters was observed (FIG. 7A; r2 > 0.98, p < 2.6x10_1°), as compared to free energy changes (FIG. 7B; r2 < 0.60, p <6.3x10 3).
Example 8: Validation of the model
RNA guides that contain at least three of the four mismatched modules were engineered (Table 2, rows 2-6), as well as combinations of random mismatches (Table 2, rows 7-14). From the reporter assay results (FIG. 7C), a high accuracy of the miScores was again observed (FIG. 7D, r2 ~ 0.50, p < 0.01), relative to changes in free energy (FIG. 7E, r2 ~ 0.20, p > 0.05). This holds even when no alignment is performed (FIG. 8B). Inaccuracies of the free energy model mostly occur when the mismatches are in more than two modules (FIG. 8A), whereas our alignment algorithm identifies alternative bps (FIG. 8C) that improves the ranking of predicted activities of such guides (FIG. 8D).
Table 2: Guide strand sequences of all the shRNAs tested in the study described herein
Oligo Name 5’->3’ guide sequences
itϊΪB UGUUCUGAUGAGCUCUUCGUC (SEQ ID NO: 1)
miB-BCD UGUUCUGAUGACGAGAAGCAG (SEQ ID NO: 50) miB-ACD UGUUCUGAACUGCUGAAGCAG (SEQ ID NO: 51) miB-ABD UGUUCUGAACUCGACUUGCAC (SEQ ID NO: 52) miB-ABC UGUUCUGAACUCGAGAACGUC (SEQ ID NO: 53) miB-ABCD UGUUCUGAACUCGAGAAGCAG (SEQ ID NO: 54) miB-mod1 UGUUCUGAUAAACGCUGAGUC (SEQ ID NO: 55) miB-mod2 UGUUCUGAUGGAGUCUUAGAG (SEQ ID NO: 56) miB-mod3 UGUUCUGAAGACCUAUCCGUC (SEQ ID NO: 57) miB-mod4 UGUUCUGAUGGCCUCCCAGUC (SEQ ID NO: 58) miB-12D+18 UGUUCUGAUGACCUCUUCCAG (SEQ ID NO: 59) miB-13D+18 UGUUCUGAUGAGGUCUUCCAG (SEQ ID NO: 60) miB-D+18 UGUUCUGAUGAGCUCUUCCAG (SEQ ID NO: 61 ) miB-AD+18 UGUUCUGAACUGCUCUUCCAG (SEQ ID NO: 62)
The analysis of third-party published data further confirmed the strong correlation between miScores and silencing. The following data was used: i) the catalytic efficiency (KCaf/Km) measured for AG02 in vitro, where mismatches were systematically generated in the guide RNA (22); and, ii) miRNA sponges engineered with dinucleotide mismatches tiling the entire non-seed region (21) (see FIGs. 8E and G; and with alignment FIGs. 8F and H). This data was pooled and computed the Pearson correlation between miScores and experimental expression levels (FIG. 7F; r2 > 0.5; p < 10 13).
The model was further validated by showing how it enriches the design of efficient smartRNAs. As previously established, when the synthesis of multiple isoforms of the E2F protein is inhibited using smartRNAs, PC3 cell growth and proliferation are compromised (50). In the previous study, the program MultiTar developed in our group was used to obtain a list of possible guide sequences against three E2F isoforms (E2F1-3). Here, with the same design principles of MultiTar, MicroAlign was used to score the efficiency of the designed anti-E2F smartRNAs. The top five scored designed smartRNAs, sm1-5 (FIG. 9A), were then tested alongside with the previous best smartRNA tested, MT1. The protein levels of the E2Fs (FIG. 9B) were compared, and it was found that three smartRNAs, sm3-5, significantly knockdown (> 30%) all three E2F isoforms (FIG. 9C). Plotting relative protein levels against the predicted miScores, it was found that a cut-off miScores of 55 selects efficient guide strands (FIG. 9E). Relative to the positive control, MT 1 , three of the five new smartRNAs knocked down E2F 1 to a similar degree or more, while four of the five new ones knocked down E2F2 or E2F3 more effectively. Following a nine- day growth assay of PC3 cells, sm3 inhibited cell growth more efficiently than MT1 (FIG. 7G), while sm4 and sm5 inhibit cell growth comparably to MT1 (FIG. 9D).
To further validate the suggested modular base pairing mechanism beyond the seed is playing a significant role in the targeting process of cellular miRNAs, standard public data used to benchmark miRNA target prediction programs (50) was used. These data were generated by transfecting cells with three miRNAs, miR-124, miR-181 and miR-1 , followed by mRNA and protein quantification, using, respectively, expression profiling and Stable Isotope Labeling with Amino acids in Cell culture (SILAC) and LC-MS/MS (liquid chromatography-mass spectrometry/mass-spectrometry). If the modular order of base pairing beyond the seed is a significant factor in target repression efficiency, then the targets that are top-ranked by the MicroAlign program should be enriched by effectively repressed mRNAs and proteins.
The mRNA and protein levels of the three miRNA target genes were pooled, and the mean differential repression levels were calculated as log2 fold changes. The mean of the 293 protein targets was first established, which is -0.15 (FIG. 8I). Then, the target protein levels were sorted by their miScores and split into three equal sized bins, which were labeled‘top’,‘mid’ and ‘bottom’. The mean of each bin was calculated (FIG. 8I) and enriched repression efficiencies in the top and mid bins (near -0.2) was found. The mid bin significantly differs from the bottom bin (P < 0.05). This shows that the miScores significantly enrich for more effectively repressed targets in the top two bins. Then, the mean of the top-30 proteins from each transfected sample were taken, and the enrichment was consistently observed (mean < -0.22, P < 0.05, Mann-Whitney U test). Previously, similar mean repression at the protein level was achieved by the top-scored target predictions from PicTar and PITA; and an even better mean was observed from those of TargetScan (near -0.28). As for the programs that do not consider evolutionary conservation, they mostly yielded less significant means (> -0.1) (50).
To confirm that the enrichment is due to the base pairing order beyond the seed, MicroAlign program was modified so it calculates scores according to the total number of base pairs, without considering their order. The enrichment for the miR-124 and miR-181 mRNA targets in the three bins was considered. Using the non-modified MicroAlign to analyze 1334 miR-124 targets, it was consistently observed that the top and middle bins were enriched in more efficiently repressed targets (FIG. 9F, top left panel), as well as a significant difference between the bottom and the top two bins (P < 0.05 in both cases, Mann-Whitney test). When the base pairing order was removed, the enrichment of efficiently repressed targets weakened in the top and middle bins, while the bottom bin got more efficiently repressed targets (FIG. 9F, top right panel). The same pattern was observed for the 98 protein levels measured by SILAC (FIG. 9F, two bottom panels).
For the 1308 miR-181 mRNA targets, the same gradual enrichment from the bottom to the top bin was observed (FIG. 9G, top left), with a significant difference between the bottom and the two top bins (P < 0.02 and P < 0.002, respectively). Once again, more efficiently repressed targets were found in the bottom bin when the base pairing order was not considered (FIG. 9G, top right). The same pattern was observed at the protein expression levels (FIG. 9G, bottom panels).
Taken together, when miRNAs were ectopically expressed, MicroAlign resolves the difference in repression efficiency of the targets solely based on the hierarchical order of base pairing beyond the seed. Hence, this modular base pairing mechanism beyond the seed is playing a significant role in the targeting process of cellular miRNAs and can be used to determine the repression efficiency of AG02-dependent miRNA silencing guides.
Although the present invention has been described hereinabove by way of specific embodiments thereof, it can be modified, without departing from the spirit and nature of the subject invention as defined in the appended claims. In the claims, the word "comprising" is used as an open-ended term, substantially equivalent to the phrase "including, but not limited to". The singular forms "a", "an" and "the" include corresponding plural references unless the context clearly dictates otherwise.
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Claims

WHAT IS CLAIMED IS:
1 . A method for the design of a single-stranded RNA (ssRNA) molecule capable of inducing translational repression or degradation of a plurality of target RNAs, the method comprising:
(i) inputting the RNA sequences of the plurality of target RNAs;
(ii) identify all subsequences of 6 to 8 contiguous nucleotides in length that (i) are identical within all target RNAs, or (ii) that differs by one nucleotide, wherein the complement of said 6 to 8 contiguous nucleotides is comprised in the seed region of said candidate ssRNA molecules;
(iii) identifying the sequences exhibiting the highest score among the candidate ssRNA molecules of (ii), wherein said score is calculated using the following algorithm:
int pairing_score; //can be defined as any constant
list_of_regions = (B, C, A, D);
Evaluate_score (list_of_regions) {
current_region = car(list_of_regions);
%pair = paired bases to target mRNA of current_region / total number of bases of current_region;
if (current_region == D)
score = 0;
else if (%pair > 0)
score = %pair *(pairing_score + Evaluate_score(cdr(list_of_regions)));
return score;
}
wherein region B corresponds to nucleotides 12 to 14 of said ssRNA molecules; region C corresponds to nucleotides 15 to 17 of said ssRNA molecules; region A corresponds to nucleotides 9 to 1 1 of said ssRNA molecules; and region D corresponds to nucleotides 18 to 21 of said ssRNA molecules;
and wherein the ssRNA molecules capable of inducing translational repression or degradation of said plurality of target RNAs comprises the sequences exhibiting the highest score.
2. A method for assessing the potential of a candidate single-stranded RNA (ssRNA) molecule to induce translational repression or degradation of a target RNA, the method comprising:
(i) inputting the RNA sequence of the target RNA and the sequence of the candidate ssRNA;
(ii) identify all subsequences in the target RNA that are complementary to at least 5 nucleotides of the seed region from the candidate ssRNA;
(iii) calculating the score of the candidate ssRNA molecule using the following algorithm: int pairing_score; //can be defined as any constant
list_of_regions = (B, C, A, D);
Evaluate_score (list_of_regions) {
current_region = car(list_of_regions);
%pair = paired bases to target mRNA of current_region / total number of bases of current_region;
if (current_region == D)
score = 0;
else if (%pair > 0)
score = %pair *(pairing_score + Evaluate_score(cdr(list_of_regions)));
return score;
}
wherein region B corresponds to nucleotides 12 to 14 of said candidate ssRNA molecule; region C corresponds to nucleotides 15 to 17 of said candidate ssRNA molecule; region A corresponds to nucleotides 9 to 1 1 of said candidate ssRNA molecule; and region D corresponds to nucleotides 18 to 21 of said candidate ssRNA molecule;
and wherein the score positively correlates with the potential of the candidate ssRNA molecule to induce translational repression or degradation of the target RNA.
3. The method of claim 1 , wherein step (ii) is performed using an miRNA target prediction program.
4. The method of any one of claims 1 to 3, wherein the pairing_score constant is from 1 to 10, preferably from 2 to 6.
5. The method of claim 4, wherein the pairing_score constant is 3.
6. The method of claim 4, further comprising calculating an miScore for the ssRNA molecule, wherein said miScore corresponds to the sum of: (i) the score obtained according to the algorithm defined in any one of claims 1 to 5; (ii) a score corresponding to X * Y, wherein X is a constant and Y is the number of Watson-Crick base-pairing between the nucleotides from the seed region and the nucleotides from the target mRNA; and (iii) a D module score that is 1 if there is at least one mismatch in the D module and 0 if the D module is fully complementary to the target mRNA.
7. The method of claim 6, wherein X is from 5 to 7.
8. The method of claim 7, wherein X is 6.
9. The method of any one claims 6 to 8, wherein the miScore of the ssRNA molecule is at least 50.
10. The method of claim 9, wherein the miScore of the ssRNA molecule is at least 55.
1 1 . The method of claim 10, wherein the miScore of the ssRNA molecule is at least 60, 65, 70 or 75.
12. The method of any one of claims 1 to 1 1 , wherein the method further comprises, prior to step (iii), aligning the sequences of the ssRNA molecules and of the target RNA and/or folding the sequences of the ssRNA molecules and of the target RNA.
13. The method of any one of claims 1 to 12, wherein the ssRNA molecule is a mature miRNA.
14. The method of any one of claims 1 to 13, wherein the sequence of the ssRNA molecule is complementary to a sequence located in the 3' untranslated regions (3' UTR) of the target mRNA or plurality of target RNAs.
15. The method of any one of claims 1 to 14, further comprising synthesizing or producing said ssRNA molecule, or a precursor thereof.
16. The method of claim 15, wherein said precursor is a miRNA duplex, a pre-miRNA or a pri- miRNA.
17. The method of claim 16, wherein said pre-miRNA or pri-miRNA is encoded by a vector.
18. The method of any one of claims 1 to 17, further comprising incorporating one or more modifications into the sequence or backbone of the ssRNA molecule.
19. The method of claim 18, wherein said one or more modifications into the sequence of the ssRNA molecule is/are in the D region.
20. Use of the ssRNA identified or produced by the methods of any one of claims 1 to 19, for inhibiting or reducing the expression of a target gene in a cell.
21 . A computer program for implementing the method of any one of claims 1 to 14.
22. A computer-readable medium having recorded thereon the computer program of claim 21 .
23. A computational analysis system comprising the computer-readable medium according to claim 22.
24. A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
an input module, wherein said input module permits to input the RNA sequences of a plurality of target RNAs;
a data analysis module, wherein said data analysis module is coupled to said input module and is capable of identifying candidate ssRNA molecules and calculating a score for said candidate ssRNA molecules according to the method defined in any one of claims 1 to 14; an output module, wherein said output module is coupled to said data analysis module and said output module is capable of providing to a user the scores of said candidate ssRNA molecules.
25. A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:
an input module, wherein said input module permits to input the RNA sequences of a target RNA and of a ssRNA molecule;
a data analysis module, wherein said data analysis module is coupled to said input module and is capable of calculating a score for said candidate ssRNA molecule according to the method defined in any one of claims 1 to 14;
an output module, wherein said output module is coupled to said data analysis module and said output module is capable of providing to a user the score of said ssRNA molecule.
26. A kit comprising (i) the computer-readable medium according to claim 22; and (b) instructions for the design of a single-stranded RNA (ssRNA) molecule capable of inducing translational repression or degradation of a plurality of target RNAs, or for assessing the potential of a candidate ssRNA molecule to induce translational repression or degradation of a target RNA, using the computer program recorded on the computer-readable medium.
27. An RNA molecule comprising one of the following sequences: SEQ ID NO: 23, SEQ ID NO: 27, SEQ ID NO: 31 , SEQ ID NO: 35, or SEQ ID NO: 39.
28. The RNA molecule of claim 27, comprising one of the following sequences: SEQ ID NO: 31 , SEQ ID NO: 35, or SEQ ID NO: 39, preferably SEQ ID NO: 31 .
29. The RNA molecule of claim 27 or 28, which is single-stranded RNA (ssRNA) molecule.
30. The RNA molecule of claim 29, which is a microRNA (miRNA), pre-miRNAs or pri-miRNA.
31. A method for inhibiting the expression of an E2F transcription factor in a cell comprising contacting said cell with the RNA molecule of any one of claims 27-30.
32. The method of claim 31 , wherein said E2F transcription factor is E2F1 , E2F2 and/or E2F3.
33. The method of claim 31 or 32, wherein said cell is a tumor cell.
34. Use of the RNA molecule of any one of claims 27-30 for inhibiting the expression of an E2F transcription factor in a cell.
35. Use of the RNA molecule of any one of claims 27-30 for the manufacture of a medicament for inhibiting the expression of an E2F transcription factor in a cell.
36. The use of claim 34 or 35, wherein said E2F transcription factor is E2F1 , E2F2 and/or
E2F3.
37. The use of any one of claims 34-36, wherein said cell is a tumor cell.
38. The method of claim 33 or use of claim 37, wherein the tumor is an osteosarcoma, a small cell lung carcinoma, a breast carcinoma, a prostate carcinoma, or a glioblastoma.
PCT/CA2019/050798 2018-06-08 2019-06-07 Method for the identification and design of rna interference agents Ceased WO2019232640A1 (en)

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