Deaminase-based RNA sensor

Abstract

An RNA editing tool for use in a system designed to measure RNA in vivo and manipulate specific cell types is disclosed herein. The RNA sensor system comprises: a) a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, wherein the ssRNA sensor optionally further comprises a normalization gene; and b) an adenosine deaminase (ADAR) deaminase acting on RNA, wherein the sensor may bind to an ssRNA target to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for the ADAR deaminase; wherein the substrate comprises a mispair within the stop codon; wherein the mispair is editable by the ADAR deaminase, and the editable can effectively remove the stop codon to enable translation and expression of the payload. A method for quantifying ribonucleic acid (RNA) levels using the RNA sensor system is also disclosed.

Description

Technology Field

[0001] Cross-reference regarding related applications

[0002] This application claims the priority advantage of U.S. provisional application serial number 63 / 267,177 filed January 26, 2022 and U.S. provisional application serial number 63 / 210,829 filed June 15, 2021. The entirety of these applications is incorporated herein by reference.

[0003] Statements regarding federally sponsored research or development

[0004] This invention was made with government support under Grant No. 5DP5OD024583 awarded by the National Institutes of Health (NIH). The government retains specific rights to this invention. Background Technology

[0005] The ability to edit nucleic acids in a precise and programmable manner has become sophisticated in recent years. New technologies in vivo Enabling such precise editing opens up the possibility of treating patients at the genotype level. However, there are no viable tools to measure and track RNA levels in vivo without genetic engineering or tagging. Genetic engineering often requires genome manipulation rather than being a rigorous observational sensor system capable of measuring changes, which can have unexpected consequences for expression and overall cellular activity. Furthermore, to fully integrate sensors, genome-level manipulation requires genetically modified organisms, which is an unfeasible approach in many scenarios.

[0006] While recent advancements have made it possible to determine numerous specific cell types, the ability to track and manipulate these cells remains lacking. The present disclosure relates to an RNA editing tool for use in systems designed to measure RNA in vivo and manipulate specific cell types. The problem to be solved

[0007] The present disclosure relates to an RNA sensor system. The present disclosure provides an RNA sensor system comprising: a) a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, wherein the ssRNA sensor optionally further comprises a normalizing gene; and b) an adenosine deaminase (ADAR) deaminase acting on RNA; wherein the sensor may bind to an ssRNA target to form a double-stranded RNA (dsRNA) duplex that becomes a substrate for the ADAR deaminase; wherein the substrate comprises a mispairing within the stop codon; wherein the mispairing is editable by the ADAR deaminase, and the editing effectively removes the stop codon to enable translation and expression of the payload. means of solving the problem

[0008] The present disclosure provides an RNA sensor system, wherein the mispair between the ssRNA sensor and the ssRNA target includes adenine:cytidine mispair of the dsRNA duplex.

[0009] The present disclosure provides an RNA sensor system, wherein the mispair between the ssRNA sensor and the ssRNA target comprises an adenine:cytidine mispair, and an ADAR deaminase edits the adenine to inosine in the mispair of the dsRNA duplex. In one embodiment, the RNA system comprises one or more mispairs.

[0010] The sensor strand of an RNA sensor system may include a payload, wherein the payload includes a reporter protein, a transcription factor, an enzyme, a transgene protein, or a therapeutic protein. The present disclosure also provides an RNA sensor system, wherein the payload includes a fluorescent reporter. The present disclosure also provides an RNA sensor system, wherein the payload includes an EGFP reporter or a luciferase reporter. The present disclosure also provides an RNA sensor system, wherein the payload includes a caspase.

[0011] The present disclosure also provides an RNA sensor system, wherein ADAR is endogenous or exogenous. The present disclosure also provides an RNA sensor system, wherein ADAR is a modified ADAR.

[0012] The present disclosure also provides an RNA sensor system, wherein ADAR comprises an RNA edit (REPAIR) molecule for programmable A to I(G) replacement, a Cas13b-ADAR fusion molecule, a Cas13d-ADAR fusion molecule, a Cas7-11-ADAR fusion molecule, and an MS2-ADAR fusion molecule, a deaminase domain of ADAR2, full-length ADAR2, or truncated ADAR2.

[0013] The present disclosure also provides an RNA sensor system comprising a plurality of RNA sensors.

[0014] The present disclosure also comprises: a) an AND gate comprising an ssRNA sensor comprising a plurality of stop codons complementary to different ssRNA targets and one or more payloads, wherein the ssRNA sensor may bind to the ssRNA targets to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for ADAR deaminase; wherein the substrate comprises a mispair within each stop codon; wherein the mispair within each stop codon is editable by ADAR deaminase and the editable stop codon can be effectively removed to enable translation and expression of one or more payloads; and / or b) an OR gate comprising a plurality of independent ssRNA sensors, wherein each of the plurality of independent ssRNA sensors comprises a stop codon and a payload complementary to one or more different RNA targets; wherein each ssRNA sensor may bind to a different ssRNA target to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for ADAR deaminase; The present invention provides a cell logic system comprising an OR gate, wherein the substrate contains a mispair within each stop codon; and the mispair within each stop codon is editable by an ADAR deaminase, and the editable stop codon is effectively removed to enable the translation and expression of one or more payloads.The present disclosure also comprises: a) an AND gate comprising an ssRNA sensor comprising a plurality of stop codons complementary to different ssRNA targets and one or more payloads, wherein the ssRNA sensor may bind to the ssRNA targets to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for ADAR deaminase; wherein the substrate includes a mispair within each stop codon; wherein the mispair within each stop codon is editable by ADAR deaminase and the editable stop codon can be effectively removed to enable translation and expression of one or more payloads; or b) an OR gate comprising a plurality of independent ssRNA sensors comprising stop codons and payloads complementary to one or more different RNA targets, wherein each ssRNA sensor may bind to the ssRNA targets to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for ADAR deaminase; wherein the substrate includes a mispair within each stop codon; A cell logic system comprising an OR gate is disclosed, wherein the above-mentioned mispairing within each stop codon can be edited by ADAR deaminase, and the editing can effectively remove the stop codon to enable the translation and expression of one or more payloads.

[0015] The present disclosure provides a method for detecting or quantifying ribonucleic acid (RNA) levels using an RNA sensor system, comprising: a) providing a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, wherein the ssRNA sensor optionally further comprises a normalization gene; and b) providing an adenosine deaminase (ADAR) deaminase acting on RNA; wherein the sensor may bind to an ssRNA target of interest to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for the ADAR deaminase; wherein the substrate comprises a mispair within the stop codon; wherein the mispair is editable by the ADAR deaminase and the editable can effectively remove the stop codon to enable translation and expression of the payload.

[0016] The present disclosure also provides an RNA sensor system, wherein the mispairing comprises an adenine:cytidine mispairing of a dsRNA duplex.

[0017] The present disclosure provides a method for detecting or quantifying ribonucleic acid (RNA) levels using an RNA sensor system, wherein the mispair comprises adenine versus cytidine, and ADAR deaminase edits adenine to inosine in a dsRNA duplex. The present disclosure provides a method for detecting or quantifying ribonucleic acid (RNA) levels using an RNA sensor system, wherein the RNA sensor system comprises one or more mispairs.

[0018] The present disclosure provides a method for detecting or quantifying ribonucleic acid (RNA) levels using an RNA sensor system comprising a payload, wherein the payload is translated into a reporter protein, a transcription factor, an enzyme, a gene-transferred protein, or a therapeutic protein.

[0019] The present disclosure also provides an RNA sensor system, wherein ADAR is endogenous or exogenous. In some embodiments, ADAR is a modified ADAR. In some embodiments, ADAR is an ADAR that is endogenous to the cell type in which the sensor may be used.

[0020] The present disclosure also provides an RNA sensor system comprising: a) a single-stranded RNA (ssRNA) sensor comprising at least a first stop codon and a payload, wherein the single-stranded RNA (ssRNA) sensor optionally further comprises a normalization gene; and b) an adenosine deaminase (ADAR) deaminase acting on RNA; wherein the sensor may bind to an ssRNA target to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for the ADAR deaminase; wherein the substrate comprises a mispair within the first stop codon; wherein the mispair is editable by the ADAR deaminase and the editable stop codon can be effectively removed to enable translation and expression of the payload.

[0021] The present disclosure also provides an RNA sensor system, wherein the single-stranded RNA sensor comprises one or more stop codons. The present disclosure also provides an RNA sensor system, wherein the single-stranded RNA sensor further comprises a second stop codon. The present disclosure also provides an RNA sensor system, wherein the single-stranded RNA sensor further comprises a third stop codon.

[0022] The present disclosure also provides an RNA sensor system, wherein the mismatch comprises a CCA on the target strand and a TAG / UAG on the sensor strand. The present disclosure also provides an RNA sensor system, wherein the sensor strand comprises a TAG / UAG stop codon but does not mismatch with a CCA codon on the target strand. The present disclosure also provides an RNA sensor system, wherein the sensor strand comprises a stop codon capable of producing a match or mismatch with a codon on the target strand selected from the group consisting of ACA, ACT, ACC, ACG, TCA, TCT, TCC, TCG, GCA, GCT, GCC, GCG, CCA, CCT, CCC, and CCG.

[0023] The present disclosure also provides a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, wherein the ssRNA sensor optionally further comprises a normalization gene; and b) an adenosine deaminase (ADAR) deaminase acting on RNA; wherein the sensor may bind to an ssRNA target to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for the ADAR deaminase; wherein the substrate comprises a stop codon editable by the ADAR deaminase, and the stop codon may be effectively removed so as to enable translation and expression of the payload. In some embodiments, the ssRNA sensor comprises a TAG / UAG stop codon. In some embodiments, the TAG / UAG stop codon forms a dsRNA duplex with an ssRNA target at a codon having the chemical formula nCn, where n is any nucleotide and C is cytidine.

[0024] The present disclosure also provides an RNA sensor system as described herein, wherein the ssRNA sensor is 50 nt or more, 100 nt or more, 150 nt or more, 200 nt or more, 250 nt or more, 300 nt or more, or 500 nt or more. In some embodiments, the ssRNA sensor is 51 nt. In some embodiments, the ssRNA sensor is 81 nt. In some embodiments, the ssRNA sensor is 171 nt. In some embodiments, the ssRNA sensor is 225 nt. In some embodiments, the ssRNA sensor is 279 nt. In some embodiments, the ssRNA sensor is longer than 279 nt.

[0025] The present disclosure also provides an RNA sensor system as described herein, wherein the ssRNA sensor is a circular sensor. In some embodiments, the circular sensor is a Rolling Circle Translation Sensor. In some embodiments, the circular sensor is a Regular Circular Sensor.

[0026] The present disclosure also provides an RNA sensor system as described herein, wherein the ssRNA sensor comprises two stop codons. In some embodiments, the ssRNA sensor comprises three stop codons. In some embodiments, the ssRNA sensor comprises two stop codons, wherein only one stop codon is targeted for ADAR editing. In some embodiments, the ssRNA sensor comprises three stop codons, wherein only one stop codon is targeted for ADAR editing.

[0027] The present disclosure also provides an RNA sensor system as described herein, wherein the ssRNA sensor comprises at least one avidity binding region. In some embodiments, the ssRNA sensor comprises at least three avidity binding regions. In some embodiments, the ssRNA sensor comprises at least five avidity binding regions. In some embodiments, the ssRNA sensor comprises at least seven avidity binding regions. In some embodiments, the ssRNA sensor comprises seven or more avidity binding regions. In some embodiments, the avidity binding regions are separated by MS2 hairpin regions.

[0028] The present disclosure also provides an RNA sensor system as described herein, wherein the payload comprises a Cre recombinase. In some embodiments, the payload comprises a Cas protein. In some embodiments, the payload comprises Cas9. In some embodiments, the payload comprises a transcription factor. In some embodiments, the payload comprises the ADAR payload. In some embodiments, the payload is a reporter for a cellular stress response.

[0029] The present disclosure also provides a composition comprising an RNA sensor system and a delivery vehicle as described herein. In some embodiments, the composition comprises an RNA sensor system and lipid nanoparticles, wherein the RNA sensor system comprises: a) a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, optionally wherein the ssRNA sensor further comprises a normalization gene; and b) an adenosine deaminase (ADAR) deaminase acting on RNA; wherein the sensor may bind to an ssRNA target to form a double-stranded RNA (dsRNA) duplex that serves as a substrate for the ADAR deaminase; wherein the substrate comprises a stop codon editable by the ADAR deaminase, and the editing may effectively remove the stop codon to enable translation and expression of the payload, and the RNA sensor system is encapsulated in lipid nanoparticles.

[0030] The present disclosure also provides a method for killing a specific cell or cell type, wherein the method comprises the step of supplying a single-stranded RNA (ssRNA) sensor or guide comprising a stop codon and a payload, wherein the ssRNA sensor further comprises a normalization gene; wherein the payload is a self-dimerizing caspase, and the ssRNA sensor or guide may form a double-stranded RNA duplex that binds to an ssRNA target and becomes a substrate for an adenosine deaminase (ADAR) deaminase acting on RNA, and the ssRNA target is enriched in the expression of the specific cell or cell type.

[0031] The present disclosure also provides an RNA sensor system comprising: a) an RNA sensor comprising a stop codon and a payload, wherein the RNA sensor optionally further comprises a normalization gene; and b) an adenosine deaminase (ADAR) deaminase acting on RNA; wherein the sensor may bind to an RNA target to form a double-stranded RNA (dsRNA) duplex region that serves as a substrate for the ADAR deaminase; wherein the substrate comprises a mispair within the stop codon; wherein the mispair is editable by the ADAR deaminase and the editable stop codon can be effectively removed to enable translation and expression of the payload. In some embodiments, the RNA sensor is a single-stranded RNA. In some embodiments, the RNA sensor comprises one or more double-stranded RNA (dsRNA) domains. In some embodiments, the RNA target is a single-stranded RNA. In some embodiments, the RNA comprises one or more double-stranded RNA (dsRNA) domains.

[0032] The present disclosure also provides an RNA sensor system as described herein, wherein the RNA sensor is 50 nt or more, 100 nt or more, 150 nt or more, 200 nt or more, 250 nt or more, 300 nt or more, or 500 nt or more. In some embodiments, the RNA sensor is 51 nt. In some embodiments, the ssRNA sensor is 81 nt. In some embodiments, the ssRNA sensor is 171 nt. In some embodiments, the ssRNA sensor is 225 nt. In some embodiments, the ssRNA sensor is 279 nt. In some embodiments, the ssRNA sensor is longer than 279 nt.

[0033] The present disclosure also provides an RNA sensor system as described herein, wherein the ssRNA sensor is a circular sensor. In some embodiments, the circular sensor is a rolling circle translation sensor. In some embodiments, the circular sensor is a general circular sensor. In some embodiments, the RNA sensor includes two stop codons. In some embodiments, the RNA sensor includes three stop codons. In some embodiments, the RNA sensor includes two stop codons, wherein only one stop codon is targeted for ADAR editing. In some embodiments, the RNA sensor includes three stop codons, wherein only one stop codon is targeted for ADAR editing.

[0034] The present disclosure also provides an RNA sensor system as described herein, wherein the RNA sensor comprises at least one antigen-antibody binding region. In some embodiments, the RNA sensor comprises at least three antigen-antibody binding regions. In some embodiments, the RNA sensor comprises at least five antigen-antibody binding regions. In some embodiments, the RNA sensor comprises at least seven antigen-antibody binding regions. In some embodiments, the RNA sensor comprises seven or more antigen-antibody binding regions. In some embodiments, the antigen-antibody binding regions are separated by MS2 hairpin regions.

[0035] In some embodiments, the payload includes a Cre recombinase. In some embodiments, the payload includes a Cas protein. In some embodiments, the payload includes Cas9. In some embodiments, the payload includes a transcription factor. In some embodiments, the payload includes the ADAR payload. In some embodiments, the payload is a reporter for a cellular stress response.

[0036] In some embodiments, ADAR is selected from the group consisting of ADAR2, ADAR1, ADAR1 p150, ADAR1 p110, ADAR2 R455G, ADAR2 R455G, ADAR2 S486T, ADAR2 T375G E488Q T490A, ADAR2 T375G, ADAR2 T375S, ADAR2 N473D, ADAR2 deaminase domain, ADAR2 T490S, ADAR2 T490A, MCP-ADAR2 deaminase domain, ADAR2 R455E, ADAR2 T375G T490A, ADAR2 E488Q, MCP-ADAR2 deaminase domain E488Q T490A, ADAR2 R510E, ADAR2 R455S, ADAR2 V351L, and derivatives or modified variants thereof. In some embodiments, ADAR is expressed endogenously in target cells where an RNA sensor system can be used. Brief explanation of the drawing

[0037] Fig. 1 silver This is a graphic diagram illustrating how an RNA sensor utilizing ADAR technology provides novel gene output. The sensor RNA contains a random marker protein, a guide RNA region with a stop codon (red octagon), and a downstream payload protein. The sensor binding with the target RNA forms a duplex with AC mismatch, which serves as a substrate for RNA editing by the ADAR protein (brown). For example, RNA editing can convert the UAG stop codon to UIG, allowing for the translation of the payload (green protein). Fig. 2a Is This is a graphical diagram of the design of a dual transcriptome ADAR sensor. The luciferase dual transcriptome ADAR sensor contains a normalized protein under constitutive expression and a payload protein under ADAR sensor control. The ratio fold change can be calculated by determining the normalized sensor activation (gluc / cluc) and then normalizing it to the ratio value in the absence of a target. The target can be delivered via exogenous transfection under the control of a doxycycline-inducible promoter. The sensor can detect target EGFP transcripts by recruiting endogenous ADARs or by utilizing exogenously delivered ADARs to detect target transcripts with enhanced sensitivity. do 2b Is This is a graphical comparison of the multiple increase in luciferase activation values ​​in HEK293FT cells transfected with eGFP recognition sensor strands, as well as eGFP plasmid or control plasmid, in the presence or absence of supplemental ADAR. do 2c This is a graphic diagram comparing the luciferase values ​​of a non-target sensor, a target sensor, or a constitutively active plasmid in the presence or absence of supplemental ADAR. Fig. 2d Is This is a graphic of next-generation sequencing results quantifying the editing of UAG stop codons in target sensors, non-target sensors, and constitutively active plasmids. Fig. 3a This is a schematic diagram showing a fluorescent ADAR sensor having a single transcript design containing a normalized fluorescent protein (mCherry) constitutively expressed upstream of an ADAR sensor guide controlling a second fluorescent protein (mNeon). Due to dual fluorescence in the single transcript, non-functional eGFP must be used in this format. do 3b Is This is a series of representative images of HEK293FT cells transfected in the presence or absence of ADAR supplemented with a target sensor or a non-target sensor, or non-functional eGFP. do 3c Is It is the quantification of the multiplicity increase of mNeon activation by measuring fluorescence. do 3d This is a graphic of next-generation sequencing results quantifying the editing of UAG stop codons in the presence of supplementary ADARs for both non-target and target sensors. Fig. 4 Is Luciferase expression ( a ) and multiple increase in luciferase expression compared to negative control in HEK293FT cells ( b )of This is a graphic diagram. The ratio multiplier change can be calculated by calculating the normalized sensor activation (gluc / cluc) and then normalizing it to the ratio value in the absence of the target. a The expression of the Gluc luciferase gene was quantified in HEK293FT cells into which two distinct guide strands (Design 2 and Design 4) targeting EGFP or a negative control scrambled sequence (negative control) were introduced. b The increase in EGFP expression was quantified compared to the negative control guide strand. Fig. 5 Is This is a graphical representation of the increase in luciferase expression of experimental guide strands 1-4 targeting EGFP transcripts in the presence of endogenous ADAR2 (Fig. 3, blue bars), endogenous ADAR2 supplemented by the deaminase domain of exogenously supplied ADAR2 (ADAR2dd; Fig. 2, white bars), or a fusion construct expressing a catalytically inactive Cas13b enzyme fused to the deaminase domain of ADAR2 (dPspCas13b-ADAR2dd; Fig. 2, red bars). All multiple increases are associated with a scrambled guide designed not to target EGFP, which serves as a negative control. Fig. 6is a visual depiction of the ADAR variant and catalytic domain mutation screen. a Schematic diagram of different ADARs tested, including ADAR1p150, ADAR1p110, ADAR2, and MS2 coat protein (MCP)-ADAR fusion protein (MCP-ADAR), from left to right. fl = full length. DD = deaminase domain. Catalytic domain mutations are not shown in the schematic diagram; all are located in the deaminase domain. b ). Bar graph showing the activation of exogenously transfected sensors in the presence of exogenously transfected iRFP and different ADAR variants. ADAR variants selected for screening across targets are shown in red, along with RNA sequencing data showing the conversion of TAG stop codons to TIG in the presence and absence of targets. Error bars represent the standard deviation of n = 3 technical replicates. Fig. 7 silver This is a graphical representation of the activation of ADAR sensors by eGFP and iRFP targets. a ) Testing of ADAR variants on a 69nt iRFP sensor. The indicated dilution change represents the fluorescence ratio value in the presence of a target (mNeon / mCherry) divided by the ratio value in the absence of a target. b ) Denormalized mNeon / mCherry fluorescence ratio values ​​for the data shown in Fig. 7a. c ) Testing of ADAR variants on a 51nt eGFP sensor. The indicated dilution change represents the fluorescence ratio value in the presence of a target (mNeon / mCherry) divided by the ratio value in the absence of a target. d ) Denormalized mNeon / mCherry fluorescence ratio values ​​for the data shown in Fig. 7c. Error bars represent the standard deviation of the n = 3 technical replicas. Fig. 8 silver ( a ) MCP-ADAR2dd exogenous supplement, (b ) ADAR1 p150 isomorphic exogenous supplement, ( c ) ADAR2 exogenous supplementation and ( d ) This is a graphical depiction of the edit rate at the stop codon of the sensor panels for the +target group and -target group for the absence of exogenous ADAR supplementation. Fig. 9 Is This is a heatmap showing the results of experiments in which HEK293FT cells were transfected with plasmids expressing ADAR p150, target transcripts, and target detection ADAR sensor constructs in combinations indicated on the y and x axes, respectively. The displayed data represents the fold change calculated as the fluorescence ratio (mNeon / mCherry) at the + target divided by the - target (pUC19) condition. All conditions represent data from n = 3 technical replicas. Fig. 10 ( a ) Selected ADAR variants screened for four different targets in combination with each RNA sensor. The numbers in the heat map indicate proportional multiplier changes. All conditions represent data from n = 3 technical replicas. ( b Representative images of the neuropeptide Y (NPY) target for ADAR1 p150 are displayed. Cells were transfected with the NPY sensor, ADAR variants, and targets in the combinations listed around the image. Image data obtained via confocal microscopy of HEK293 cells at 10x magnification and digitally enhanced by 4x. Fig. 11 silver Normalized for the target and ADAR sensor combination when tested in HEK293FT cells ( a, c, e, g ) fluorescence ratio and denormalized( b, d, f, h ) This is a graphical representation of the fluorescence values. The tested target is iRFP( a, b ), eGFP( c, d ), Neuropeptide Y( e, f ) and dCas9( g, h It was. ( a, c, e, g ): For each target and sensor combination, the change in the sensor fluorescence ratio (mNeon / mCherry) multiple representing the + target condition normalized to the - target condition. ( b, d, f, h ): Denormalized mNeon / mCherry fluorescence ratios for each sensor and target combination with different ADAR variants. Next-generation sequencing data of RNA sensors for UAG to UIG conversion for iRFP and EGFP targets. Edit % indicates the percentage of A->I edited reads. All conditions represent data from n = 3 technical replicas. Fig. 12 is in the absence of a target ( Fig. 12a ) or target exists ( Fig. 12b This is a representative image of the entire 10x magnification image with inserts for the ADAR p150 image shown in Fig. 10b. The scale bar is 100 μm. Fig. 13 silver ( a ) MCP-ADAR2dd exogenous supplement, ( b ) ADAR1 p150 isomorphic exogenous supplement, ( c ) ADAR2 exogenous supplementation and ( d ) This is a graphical comparison of normalized luciferase values ​​in the sensor panel in the +target group and -target group for no exogenous ADAR supplementation. Fig. 14 This is a heatmap of the titration of exogenously supplemented MCP-ADAR2dd versus the IL6 target sensor against 20 ng of transfected tetracycline-inducible human IL6 introducer gene in IL6. The fold change represents the normalized luciferase ratio between the +target group and the -target group. Fig. 15Figure 1 is a graphic showing the fold increase in luciferase expression for guide strands 1 and 2 targeted with EGFP compared to a scrambled guide strand designed not to specifically target EGFP. Each guide strand was introduced into HEK293T cells. The cells were then tested using the Cas13b enzyme fused to the deaminase domain of ADAR2 (RNA editing for programmable A to I(G) substitution (REPAIR)) or the catalytically inactive version (REPAIR K370A). The guide strands were tested in cells containing only endogenous ADAR2 (Fig. 15, blue bars), cells containing endogenous ADAR2 and the exogenous catalytically inactive REPAIR molecule (REPAIR K370A; Fig. 15, white bars), or cells containing the catalytically active REPAIR molecule (REPAIR; Fig. 15, red bars). Fig. 16 silver This is a heatmap showing the magnification increase of luciferase activation when tested in HEK293FT cells (white, lowest magnification increase vs. dark blue, highest magnification increase). The y-axis represents guide strands of various different designs with different lengths and mismatches targeting EGFP. The x-axis represents the exogenous ADAR molecules tested (none = endogenous only, ADAR2fl = full length of ADAR2, REPAIR = Cas13b enzyme fused to the deaminase domain of ADAR2; MS2-ADAR2dd = MS2 binding protein fused to ADAR2dd; dDisCas7-11-ADAR2dd = catalytically inactive Cas7-11 fused to the deaminase domain of ADAR2). Fig. 17 (a) This is a schematic diagram showing sensors of different lengths screened for iRFP target transcriptomes. ( b)A bar graph showing sensor activation increasing with increasing sensor length. Sensor activation indicates that for each sensor, the normalized fluorescence (mNeon / mCherry) value in the presence of a target is divided by the normalized fluorescence (mNeon / mCherry) value in the absence of a target. ( c) The mNeon-positive fraction represents the proportion of cells with expression higher than a predefined threshold. All conditions represent data from n = 3 technical replicas. Fig. 18 (a) Single-cell image analysis similar to fluorescent cell analysis for iRFP-targeted ADAR sensors with guide lengths of 69, 249, and 600 nt. The histogram shows the population density of mNeon expression across all cells for +iRFP (blue) and -iRFP (pink) targeting conditions. The dotted line shows the constant intensity threshold across all conditions gating individual cells as mNeon(+) or mNeon(-). The colored boxes represent the % mNeon-positive cells for +iRFP (blue) and -iRFP (pink) targeting conditions. b A representative image for ) (a) is displayed. Cells were transfected with iRFP targets, ADAR p150, and different ADAR sensor guide lengths in the combinations listed around the image. Scale bar, 100 microns Fig. 19(ab) is a representative image of the full 10x magnification image with an inset for the image shown in Fig. 18b. The scale bar is 100 μm. Fig. 20 This is a graphical comparison of different exogenous supplement ADAR variants in an IL6 target sensor containing a transiently transfected tetracycline-inducible human IL6 introducer gene. do 21(ad) Is (a) MCP-ADAR2dd (E488Q, T490A) exogenous supplementation, (b) ADAR1 p150 isomorphic exogenous supplement, (c) ADAR2 exogenous supplementation,(d) This is a series of graphic diagrams showing the normalized luciferase values ​​of the sensor panels of the +target group and -target group for the absence of exogenous ADAR supplementation. Fig. 22 This is a comparative graphic between a sensor containing a normal guide for a human IL6 target with endogenous ADAR1, exogenous supplemented ADAR1 p150 isoform, full-length ADAR2, or MCP-ADAR2dd (E488Q, T490A) in HEK293 cells, and a guide containing multiple binding sites and an MS2 hairpin loop. The fold change is calculated as the normalized luciferase value (Gluc / Cluc) of the +target condition versus the -target condition. Fig. 23 silver This is a visual representation of ADAR sensor operation with an MS2 hairpin loop and coupling force area. The addition of the MS2 hairpin loop and coupling force improves the sensitivity and dynamic range of the ADAR sensor. Fig. 24 bonding force This is a schematic diagram of the step-by-step creation of three coupling force ADAR sensors spaced 5nt apart between guide areas. Fig. 25 (a) Is This is a schematic diagram of various linker lengths outside the target area. (b) This is a graphical representation of the effect of linker length between coupling regions. Linker lengths of 5nt, 30nt, and 50nt between coupling regions of the MS2 hairpin connection-5 coupling sensor for IL6 were tested. Fig. 26 (a) Is This is a schematic diagram of a dual and single stop codon coupling force / MS2 hairpin sensor. (b) Is This is a comparison of sensor multiplication activation between a standard MS2 hairpin-connected 7-coupling force sensor and a double stop codon 7-coupling force sensor with a 3' downstream stop codon inserted inside the last force region. Fig. 27 (a) This is a comparison of background versus activation for the coupling force sensor for the naive ("long") sensor. do 27 (b)This is a scatter plot of the multiplication change versus background luciferase values ​​for the binding force sensor versus the naive ("long") sensor. do 28(a) Is This is a bar plot showing a comparison between a 5-partitioned coupling force sensor versus a 7-partitioned coupling force dual stop codon sensor across MCP-ADAR2dd (E488Q, T490A) and ADAR1 p150. do 28(b) This is a bar plot showing a comparison between the activation and background signals of the sensor with 7 coupling sites, coupling strength single stop codon versus 7 coupling strength double stop codon. Fig. 29 Is This is a comparison of target mismatch tolerances for all 16 possible mismatches between the Naive 51bp sensor, 3-binding force, and 5-binding force sensor designs. (The 16 targets include 5' or 3' nucleotide changes from standard CCA). (a) is a discrepancy This is a rough representation of the tolerance. (b) Is This is a heatmap showing the log10 normalized tolerance of different target mismatches for the native CCA target (red) and the log multiplier activation of all three sensor designs across 16 target mismatches (blue). Fig. 30 This is a heatmap showing the normalized preference of sensor designs within each target mismatch combination between the Naive 51bp sensor, the triple coupling site sensor, and the five coupling site sensors. Fig. 31 (a) Is This is a visual representation of the generation and activation of the circular sensor. A standard circular sensor is generated from a twister ribozyme backbone driven by the U6 promoter for in vitro autocirculation. The autocirculation of the sensor-hibit tag utilizes the mammalian cell RtcB ligase. A rolling circle translation version of the circular sensor is created by deleting the stop codon at the C-terminus of the hibit protein and inserting a T2A peptide to allow for circular ribosomal readthrough. ( b) Sensors of various lengths between 50 nt and 120 nt are compared for changes in sensor activation multiplicity upon induction of a gene introduction target (human IL6). Fig. 32 (a) Is This is a schematic diagram of the evaluated RNA modifications. b Heatmap comparing different mRNA modifications of a synthetic mRNA ADAR sensor detecting IL6 introduced gene expression in HEK293FT cells when MCP-ADAR2dd (E488Q, T490A) was supplemented by transient plasmid transfection 24 hours prior to mRNA sensor transfection. c Heatmap comparing different mRNA modifications for a synthetic mRNA ADAR sensor detecting IL6 introduction gene expression in HEK293FT cells when supplemented with MCP-ADAR2dd(E488Q, T490A) mRNA upon sensor transfection. Fig. 33 Figure 33 is a graphic depicting the expression of EGFP in HEK293FT cells (Figs. 33a, 6b) and the multiplication of GFP expression (Fig. 33c). EGFP expression was either constitutive or expressed as a gradient using doxycycline-inducible EGFP constructs. Then, HEK203T cells were exposed to doxycycline concentrations ranging from 8 ng / mL to 200 ng / mL. Fig. 34This is a graphic depicting the dose-dependent luciferase activity of guide strand 1(a) and guide strand 3(b) as a function of doxycycline dose in HEK293FT cells simultaneously exposed to guide strand targeting EGFP and full-length-ADAR2 under the control of a doxycycline-inducible promoter. Fig. 35 This is a graphic depicting the luciferase activity of guide strand 1(a) and guide strand 3(b) as a function of GFP fluorescence in HEK293FT cells simultaneously exposed to guide strand targeting EGFP and full-length-ADAR2 under the control of a doxycycline-inducible promoter. Fig. 36 silver This is a visual representation of the combined treatment result of tetracycline-inducible IL6 and stable lentivirus integration. b Next, the relative expression of IL6 is quantified using a double stop codon 7-strength IL6 sensor, and the corresponding change in luciferase fold is plotted against the Cq value of IL6 expression detected by quantitative polymerase chain reaction (QCPR). Fig. 37 silver This is a scatter plot indicating that the double stop-codon 7-power IL6 sensor is useful for quantifying the relative expression of IL6 over a wide dynamic range. The target expression range is generated through a combination of transient overexpression of tetracycline-inducible IL6 in HEK293FT cells and a stable lentivirus-integrated tetracycline-IL6 cassette. Changes in ADAR sensor fold relative to the baseline are plotted against changes in IL6 gene expression determined by quantitative polymerase chain reaction (qPCR). Fig. 38 This is a scatter plot showing linear regression of the change in sensor activation fold relative to the change in gene expression fold detected by qPCR. Fig. 39 Is Figure 38 shows the corresponding edit of adenosine at the UAG stop codon of the sensor across different IL6 gene expression levels. Fig. 40 (a) Is This is a schematic representation of an AND gate. b )Is This is a schematic representation of an OR gate. c )Is This is a graphical diagram comparing the activation multiplier changes of the naive 51nt guide AND gate sensor and the 5 binding force guide AND gate sensors across all four combinations of IL6 and EGFP target induction. Fig. 41 (a) Is This is a graphical diagram of the normalized sensor activation of the AND-gated ADAR sensor for EGFP and IL6 transcriptome inputs across all four possible target combinations. ( b ) is a graphical diagram of normalized sensor activation of an OR-gate ADAR sensor for EGFP and IL6 transcriptome inputs across different target combinations. Fig. 42 (a) Is This is a schematic diagram of an IL6-reactive caspase using an ADAR sensor equipped with a 5-binding affinity sensor targeting the human IL6 transcript. Sensor activation expresses FKBP autodimerizing caspase 9. b ) is a graphical representation of the fold change in apoptosis in response to ADAR sensor activation by IL6 transcript detection. The positive control sensor contains a scramble guide sequence preceding the i-caspase that lacks a stop codon in the frame. The fold change in apoptosis is determined by calculating the fold change in cell viability at the + target compared to the - target condition. c )Is This is a bar plot comparing the percentage cell survival values ​​of the IL6-responsive i-caspase ADAR sensor and the control group without stop codons in the +target and -target groups. Fig. 43 This is a visual representation of an experiment investigating the efficiency of ADAR sensors in thermal shock testing. a) Heat shock of Hela cells at 42°C induces upregulation of HSP40 and HSP70 gene expression. HSP70 and HSP40 target sensors are applied to Hela cells, either together with or alone, followed by 24 hours at 42°C or 37°C. b ) qPCR validation of upregulated HSP40 and HSP70 levels after 24-hour heat shock. c Sensor activation is calculated between the 42°C and 37°C groups and normalized to a sensor with a scrambled non-target guide to account for changes in protein degradation. Fig. 44 Is This is a visual representation of the SERPINA1 assay in three cell types differentially expressing SERPINA1. a )Is This is a bar plot comparing SERPINA1 expression across HEK293FT, SERPINA1, and Hela cells. b ) The five SERPINA1-sensing binding affinity sensors are transfected in three different cell types (HEK293FT, Hela, and HepG2 cells) with or without exogenous MCP-ADAR2dd (E488Q, T490A). c ) is a bar plot comparing the change in sensor activation fold between Hela cells and HepG2 cells across SERPINA1 sensors targeting different CCA sites. d Sensor activation is determined by calculating the raw luciferase value of the SERPINA1 sensor normalized to a scrambled non-targeted guide sensor to account for differences in protein production / secretion and background ADAR activity between cell types, and then normalizing it to the gluc / cluc ratio in HEK293FT cells. Fig. 45 is a graphical representation of the normalized multiple change in editing speed at five SERPINA1 binding force sensors in different cell types (HEK293, Hela, and HepG2). Fig. 46 This is a bar plot comparing the change in mRNA SERPINA1 sensor activation folds targeting different CCA sites on the SERPINA1 transcript in Hepa1-6 cells transiently transfected with tetracycline-inducible SERPINA1 expression. Fig. 47 (a) Is This is a schematic diagram of an in vivo sensing experiment of the human SERPINA1 transcript using the SERPINA1 mADAR sensor construct. SERPINA1 target sensor mRNA with Akaluc output is produced in vitro with 25% 5-methylcytosine and 0% pseudouridine. Constitutive Akaluc (no stop codon) and non-target guide (with stop codon) sensor constructs are synthesized using the same protocol. All mRNAs are packaged in lipid nanoparticles and injected via the tail vein into wild-type mice or NSG-Piz mice containing the human SERPINA1 PiZ mutant cassette. In vivo sensor activation is measured 18 hours after injection. b )Is Representative images of sensor activation for various synthetic mRNA ADAR sensor constructs are shown. Fig. 48 (a) Is This is a graphic plot of Akaluc-generated light intensity calculated for the liver and compared between wild-type and NSG-PiZ mutant mice. The fold change between NGS-PiZ mice and WT mice is calculated for each ADAR sensor construct. Significance was determined via a two-tailed t-test, N = 2 mice. *, p<0.05. P values ​​less than 0.05 are indicated by an asterisk for statistical significance. ( b ) is a bar plot comparing Akaluc luminescence in NSG-PiZ mice and WT mice across non-target sensor, constitutive sensor, SERPINA1 CCA35 target sensor and SERPINA1 CCA30 target sensor. Fig. 49 Is The minimum number of genes required to classify tissues according to genes ( a ) as well as the number of protein-coding genes that are abundant in specific tissues, enhanced in specific tissues, or have low specificity ( b This is a graphical representation of differential genetic analysis for 37 tissues using the Human Protein Atlas and GTEX datasets for ). c )Is This is a heatmap showing the relative transcriptome abundance of 34 mRNAs that uniquely define tissues across 34 different tissue types. Fig. 50 This is a graphic diagram illustrating the characteristics of RADARS safety regarding immune response and endogenous RNA knockdown. a, b ) Effects of sensor-target duplex formation on innate antiviral pathways. RADARS sensors were transfected in the presence or absence of complementary target sequences. Total RNA was analyzed using quantitative PCR (qPCR) to determine MDA5( a ) and IFN-β ( b Determined the relative expression level of ). c, d Effect of sensor-target duplex formation on the abundance of endogenous target transcripts. The relative abundance of NEFM and PPIP transcripts upon transfection with complementary or non-target RADARS sensors was evaluated by qPCR. Data are presented as mean ± sd (n = 4); unpaired two-sided Students' t-test, ns, p > 0.05. Fig. 51 silver This is a graphic showing the degradation of the RADARS signal corresponding to endogenous target knockdown. a Schematic diagram of siRNA knockdown of endogenous transcripts. b) qPCR and fluorescent RADARS detected differences in expression between siRNAs targeting PPIB or NEFM and control non-target siRNAs in HEK293FT cells. For RADAR, sensor activation was calculated for the target siRNA and normalized to the control siRNA. Data are the mean ± sd of technical replicates (n ≥ 3). Fig. 52 Is This is a graphical comparison of the ploidy activation of #CCA8 IL6 engineered guide RNA with 171nt guide and 4 MS2 loops when used with exogenous ADAR1p150 supplement or endogenous ADAR. Data are the mean ± sem of technical replicas (n=3). Fig. 53a Is This is a graphical comparison of mRNA RADARS sensor activation and pluripotency when detecting IL6 transcripts with plasmid ADAR1p150 transfection. Synthetic mRNA sensors are synthesized with different bases chemically modified at different incorporation levels ranging from 0 to 100%. Data are the mean ± sem of technical copies (n=3). do 53b Is This is a graphical comparison of mRNA RADARS activation multiplicity when detecting IL6 transcription using endogenous ADAR synthesized with different bases chemically modified at different incorporation levels ranging from 0 to 100%. Data are the mean ± sem of technical copies (n=3). do 53c Is This is a graphic comparison of the induction of interferon beta responses by mRNA RADARS transfection. Synthetic mRNA is synthesized with different levels of chemically modified bases, and the interferon response is measured by the plasmid (One-Glo luciferase) reporter assay (Gentili et al., 2015). Fig. 54a Is This is a visual characterization of the evolution of different RADARS sensor designs used with exogenous ADAR1p150 supplementation. The inset depicts the backbone of the different RADARS designs. RADARS duplicative activation is calculated as the ratio (Gluc / Cluc) of Gausia luciferase (Gluc) luminescence to constitutive Cypridina luciferase (Cluc) luminescence in the presence of the IL6 target compared to the case in the absence of the target (see Methods). The sensor, target (IL6), and ADAR1p150 are co-delivered via transient transfection. Data are the mean ± sem of technical replicas (n=3). Fig. 54b Is This is a graphical comparison of the Gluc / Cluc ratios between +target and -target conditions for IL6 #CCA8 targeting engineered guide RNAs with different lengths and different MS2 hairpin loops while maintaining the 0aa 5' peptide length. Error bars represent the standard error of the mean. (n=3 technical replicas). Fig. 55a This is a graphical comparison of Gluc / Cluc ratios between +target and -target conditions for engineered guide RNAs with 5 binding sites (4 MS2 loops) and 9 binding sites (8 MS2 loops) of varying 5' peptide lengths. Error bars represent the standard error of the mean. (n=3 technical replicates). do 55b Is This is a graphical comparison of the Gluc / Cluc ratio between the absence of an out-of-frame stop codon and the addition of two out-of-frame stop codons for guide RNA engineered with five binding sites containing a 200aa 5' peptide residue. The last column represents the constitutive gluc driven under the Ef1-alpha promoter. Error bars represent the standard error of the mean. (n=3 technical replicas). Fig. 56aThis is a graphical depiction of the multiplication activation of IL6, EGFP, and NPY-targeted RADARS with exogenous ADAR1p150 supplementation. For each transcript, 12 engineered guide RNAs were engineered to target different CCA sites across the transcript. The depicted CCA site numbers follow the rule that #CCAx indicates the number of CCA triplicates counted at the 5' end of the transcriptome coding region. Each dot represents the average of three technical replicas for an individual sensor. The horizontal solid line represents the average of all 12 engineered guide RNAs. do 56b Is Graphically depicts the percentage of edits of target adenosine at UAG stop codons for non-target engineered guide RNAs and 14 IL6 targeted engineered guide RNAs tiling the CCA site on IL6 with exogenous ADAR1p150 supplementation using a RADARSv2 design in the presence and absence of target IL6 transcripts. Error bars represent the standard error of the mean (n=3 technical replicas). Fig. 57 This is a graphical depiction of RADARSv2 with engineered guide RNAs targeting the high TPM gene (RPS5), low TPM gene (KRAS), or non-target scrambled sequences, used in combination with exogenous ADAR1p150 supplementation or endogenous ADAR to detect the downregulation of corresponding genes via gene-specific siRNAs. Activation is calculated by the activation of the payload in the on-target siRNA group relative to the non-target siRNA group. Data are the mean ± sem of technical replicas (n=3). Fig. 58aThis is a visual depiction of a schematic diagram (top) showing a fluorescent output RADARS construct containing a normalized fluorescent protein (mCherry) constitutively expressed upstream of a RADARS engineered guide RNA controlling an mNeon fluorescent protein, and an image of a fluorescent RADARS (EGFP outside the frame) showing HEK293FT cells expressing only the mNeon payload in the presence of a target transcript. HEK293FT cells are transfected with EGFP-targeted RADARS, ADAR1p150, and ±target (EGFP outside the frame) as indicated (bottom). Scale bar, 100 microns. do 58b d) is a visual depiction of the flow cytometry analysis of fluorescent RADARS showing the mNeon / mCherry fluorescence histogram for transfected HEK293FT cells, where the beige and blue distributions indicate the absence and presence of the target, respectively. Fig. 59a This is a visual depiction of the gating strategy used for the flow cytometry analysis of fluorescent RADARS in HEK293 cells. The gate is drawn using a control population transfected with the pUC19 plasmid. do 59b Is This is a visual depiction of a gate superimposed on a population of cells transfected with EGFP-targeted RADARS, ADARp150, and pUC19 plasmids. do 59c Is This is a visual depiction of gates superimposed on cell populations transfected with EGFP-targeted RADARS, ADARp150, and EGFP-targeted (frame-shifted) plasmids. Fig. 60a Is This is a visual depiction of RADARS duplicative activation relative to baseline conditions (0 ng / mL doxycycline in integrated HEK293FT cells) plotted against changes in IL6 gene expression determined by quantitative polymerase chain reaction (qPCR) on a log10-log10 scale. The blue dashed line represents the linear regression result of the data. Data are the mean ± sem of descriptive replicates (n=3). Fig. 60b This is a visual depiction of the raw Cq values ​​of the IL6 introduction gene normalized by subtracting the GAPDH gene Cq number and the corresponding RADARS duplication activation. Error bars represent the standard error of the mean (n=3 biological replicates). do 60c Is This is a visual depiction of the effect of titrating the optimal IL6 engineered guide RNA RADARS sensor amount on activation and total protein production generated under target conditions (gluc / cluc ratio). For conditions of less than 40 ng, the residual plasmid amount was replaced with pUC19 plasmid. Error bars represent the standard error of the mean (n=3 technical replicas). do 61 silver This is a visual depiction of the experimental results of siRNA knockdown of 10 endogenous transcripts measured by qPCR expression. The fold change is calculated by the gene expression of the target transcript in the on-target siRNA group relative to the non-target siRNA group (n=3 biological copies). Fig. 62a(Top) is a visual depiction of gene expression of transcripts per million (TPM) on a log scale across 10 genes ranging from 10,381 TPM (RSP5) to 13 TPM (KRAS). Bottom: Detection of RADARSv2 transcripts in cells treated with 100 nM of a target siRNA pool or a non-target siRNA pool. Bars indicate the ploidy activation of target RADARS and non-target RADARS constructs (Gluc / Cluc ratio of RADARS within the target siRNA group relative to the non-target siRNA group). Significance was determined via a non-paired t-test between target and non-target RADARS using Welch correction, assuming individual variances for each group (*, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001). do 62b is a graphical depiction of the editing rate of the UAG stop codon at the best-performing sensor in each gene group from b. One-sided unpaired t-test performed between the on-target siRNA group and the non-target siRNA group. (*, p<0.05. **, p<0.01. ***, p<0.001. ****, p<0.0001) Error bars represent the standard error of the mean (n=3 technical replicates). Fig. 63a Figure 63 is a graphical depiction of the performance of eight randomly selected engineered guide RNAs and eight randomly selected non-targeted engineered guide RNAs targeting ten endogenous transcripts. The ploidy activation of RADARS is calculated as the Gluc / Cluc ratio in the on-target siRNA group versus the non-targeted siRNA group. The best-performing target sensors for each gene are labeled in yellow and are examined in Figure 63. The horizontal lines represent the mean of each group. Fig. 63b Is This is a visual depiction of RADARSv2 targeting RPL41, GAPDH, ACTB, HSP90AA1, PPIB, and KRAS tracking the expression of these transcripts across various siRNA concentrations. The blue and beige lines represent the fold activation of the RADARS (Gluc / Cluc) ratio and the fold change in qPCR-quantified expression for 0 nM siRNA, respectively. The gray line represents the fold activation of non-target engineered guide RNA (non-complementary to target transcripts). Data are the mean ± SEM of descriptive replicates (n=3). (R values ​​represent the Pearson correlation between qPCR and target RADARS, *, p<0.05. **, p<0.01. ***, p<0.001). Fig. 64a Is This is a visual schematic diagram of the upregulation of the heat shock protein family gene HSP70 during heat shock at 42℃. do 64b This is a visual depiction of the results of experiments conducted at 42°C or 37°C for 24 hours following transfection of HeLa cells with exogenous ADAR1p150 supplementation, using four HSP70-targeted engineered guide RNAs and scrambled non-targeted (NT) engineered guide RNAs targeting different CCA sites. qPCR and RADARSv2 detected differences in HSP70 expression between the 37°C (control) and 42°C (heat shock) groups. Sensor activation was calculated between the 42°C and 37°C groups and normalized to the NT condition. Data are the mean ± sem of technical replicates (n=3). Fig. 65a Is This is a visual schematic diagram of a two-input AND gate with RADARS. do 65b is a graphical depiction of the normalized sensor activation of AND-gate RADARS for EGFP and IL6 transcriptome inputs across all four possible target combinations. Data are the mean ± sem of technical replicas (n=3). Fig. 66a Is This is a visual schematic diagram of a two-input OR gate logic with RADARS. Fig. 66b Is This is a graphical depiction of the sensor activation of OR gate RADARS for all possible EGFP and IL6 transcriptome input combinations. Data are the mean ± sem of technical replicas (n=3). Fig. 67a Is This is a schematic diagram of a SERPINA1-targeted RADARS with an inductive caspase 9 payload. Fig. 67b Is This is a graphical depiction of the cell viability of A549, Hela, and HepG2 cells after transfection with SERPINA1-sensing RADARS-expressing i-caspase 9 along with exogenous ADAR1p150. The non-target control engineered guide RNA contains a scrambled sequence with a stop codon preceding the payload. Data are the mean ± sem of technical copies (n=3). do 67c This is a graphical depiction of the cell viability of HepG2, Hela, and A549 cells measured 48 hours after transfection with SERPINA1 or non-targeted RADARS-expressing i-caspase 9 constructs and ADAR1p150 using the MTS assay and normalized to a control transfected only with a GFP-expressing plasmid. Fig. 68 This is a graphical depiction of the results of experiments conducted at 42°C or 37°C for up to 24 hours after transfecting HeLa cells with the best HSP70-targeted RADARS construct and scrambled non-targeted (NT) RADARS construct from the HSP70 transcript and without exogenous ADAR. qPCR and RADARSv2 detected differences in HSP70 expression between the 37°C (control) and 42°C (heat shock) groups. Sensor activation was calculated between the 42°C and 37°C groups and normalized to the NT condition, which is a sensor containing scrambled non-targeted engineered guide RNA (NT) to control changes in protein production. Data are the mean ± sem of technical replicas (n=3). Fig. 69a Is This is a visual schematic of the dual loxP EGFP Cre reporter and IL6 RADARS-CRE. Right: HEK293FT fluorescence 48 hours after transfection of the dual loxP EGFP reporter, ADAR1p150, and IL6-targeted RADARS by Cre payloads with or without the IL6 target. Images for the - target and + target conditions are shown. The white scale bar represents 100 microns. do 69b Figure 69a is a visual depiction of the results of an experiment in which cells were collected for flow analysis of EGFP expression. Fig. 70a Is This is a visual schematic of a SERPINA1-targeted RADARS construct with a Cre payload. Fig. 70b Is This is a visual depiction of the results for the percentage of GFP+ cells analyzed by flow cytometry on Hela, HepG2, and A549 cells 48 hours after transfection with SERPINA1-targeted RADARS constructs expressing Cre along with exogenous ADAR1p150. Data are the mean ± sem of technical replicates (n=3). do 70c Is This is a visual depiction of EGFP expression quantified by flow cytometry 48 hours after transfection of CRE reporters, ADAR1p150, and IL6-targeted RADARS with Cre payloads containing or not containing IL6 targets. The distribution of EGFP signals is analyzed by flow cytometry on Hela, A549, and HepG2 cells. For all three cell types, GFP-positive cells have FITC channel EGFP intensity of 10 7 It is defined as a group of abnormal cells. Fig. 71a Is This is a visual depiction of bioluminescent images of sensor activation for various synthetic mRNA RADARS constructs. do 71b Is This is a graphical comparison of intrahepatic Akaluc luminescence intensities in NSG-PiZ and NSG-WT mice across substrate background, constitutive sensors, and SERPINA1 #CCA32 targeted RADARS. Data are the mean ± SEM of descriptive replicates (n=3). Significance was determined between NSG-WT and NSG-PiZ sample intensities using a two-sided unpaired t-test, N=3 mice. (NS, p>0.05. **, p<0.01.) do 71c Is This is a graphical comparison of Akaluc-generated light intensity calculated for liver and compared between wild-type and NSG-PiZ mutant mice. Pluripotency between NGS-PiZ mice and NSG-WT mice is calculated for each RADARS construct. Significance is tested for each group against substrate background using a two-sided unpaired t-test, N = 3 mice (NS, p>0.05. ***, p<0.001.). Fig. 72a Is This is a graphical depiction of transcriptome expression levels of 10 endogenous genes in HEK293FT cells transfected with targeted or non-targeted (NT) RADARS constructs along with exogenous ADAR1p150 supplementation, as quantified by qPCR. The data shown are normalized to the targeted RADARS. Significance between the targeted and non-targeted RADARS is determined via a non-paired t-test using Welch correction, assuming individual variances for each group (Ns, p>0.05). Fig. 72b Is This is a graphical depiction of protein expression of endogenous ACTB and PPIB in HEK293FT cells transfected with ACTB / PPIB targeted RADARS or non-targeted RADARS (including ADAR1p150 supplementation), as quantified by Western blot. Significance between targeted and non-targeted RADARS is determined by a non-paired t-test using Welch correction assuming individual variances for each group (Ns, p>0.05). do72c and Fig. 72d Is This is a visual depiction of the sensor-target hybridization effect on protein production being analyzed by Western blot. ACTB (Fig. 72c) and PPIB (Fig. 72d) protein levels are shown in response to RADARS hybridization, and GAPDH is used as a normalized protein control. Fig. 73a is a graphical depiction of the qPCR-detected expression levels of interferon beta, OAS1, RIG-1, and MDA5 upon transfection with IL6, ACTB, RPS5, or PPIB, non-targeted (NT) RADARS, and RADARS constructs targeting high molecular weight poly(I:C). Significance was determined by a one-way ANOVA test between the untreated group, the RADARS group, and the poly(I:C) group (Ns, p>0.05. ****, p<0.0001). do 73b Is This is a graphical depiction of the qPCR-detected gene expression fold changes of four dsRNA-responsive genes (IFNb, OAS1, MDA5, and RIG-1) that respond to RADARS in HEK293FT cells using ACTB as a normalization gene. do 73c Is This is a graphical depiction of the qPCR-detected gene expression fold changes of four dsRNA-responsive genes (IFNb, OAS1, MDA5, and RIG-1) in response to RADARS or poly(I:C) in HepG2 cells using GAPDH as a normalization gene. Fig. 74a Is This is a visual depiction of the quantification of RNA editing in the 200 bp hybridization region of RADARS for target transcripts against ACTB, PPIB, and NT-engineered guide RNAs. A to I(G) transformations are depicted in the heatmap. (NS, p>0.05) do 74bThis is a scatter plot analysis of transcriptome-wide off-target editing. The scatter plot shows the allelic fractions of A->G mutations in (i) PPIB sensor and ADAR1p150 overexpression versus non-transfected HEK293T cells (n = 3.17 x 10⁶ regions); (ii) identical to (i) but with an IL6 sensor (n = 3.07 x 10⁶ regions); and (iii) identical to (i) but without ADAR overexpression and with a non-target RADAR sensor (n = 3.14 x 10⁶ regions). Regions are colored by fdr-corrected p-values ​​(to the left of the color bar). For i and iii, experiments were generated from three independent duplicates. do 74c Is This is a scatter plot analysis of whole transcriptome off-target editing with MCP-ADAR2 (E488Q) using the dataset from NCBI Geo accession number GSE123905 (Katrekar et al., 2019). The scatter plot shows the allelic fraction of A->G mutations in MCP-ADAR2 (E488Q) overexpression versus non-transfected HEK293T cells (n = 2.35 x 10⁶ sites). Sites are colored by fdr-corrected p-values ​​(left of color bars). Fig. 75a Is This is a graphical depiction of the sequence homology analysis between transcriptome off-targets (n=23 sites) and targeted PPIB homologous regions. Corresponding fdr-corrected p-values ​​for the significance of local alignment (Monte Carlo permutation test) between the 200 bp surrounding each off-target site and the PPIB homologous regions targeted by the engineered guide RNA (the red line indicates p=0.05). do 75b Is This is a visual depiction of the sequence logo analysis of off-target editing sites from ADAR1p150 overexpression with PPIB-targeted RADARS. Specific details for implementing the invention

[0038] The present disclosure provides a system and a sensor for detecting and quantifying RNA. The present disclosure also provides a system and a method for gene editing. Additionally, a system for in vivo imaging of RNA expression is disclosed. The present disclosure provides a sensor system for converting adenine to inosine. Inosine is recognized as guanosine (G) by a translation machine. The conversion of adenine to inosine is the result of hydrolytic deamination of adenosine (Cox et al. Science 358(6366):1019-1027 (2017)). Accordingly, the adenosine deaminase (ADAR) family acting on the RNA of an enzyme can modify codons within a transcript to functionally alter the translation product. The present disclosure provides a transcript edit that enables the expression of a payload by removing a stop codon, among other functional alterations.

[0039] definition

[0040] Unless otherwise specified, the terms and technologies used in this application have the meanings generally known to those skilled in the art.

[0041] As used herein, the term “about” is understood to mean a variation of a specified value. Unless otherwise explicitly stated, the term “about” is understood to mean a variation of a specified value by + / - 10%. As used herein, the term “about” applied to a range modifies two endpoints of the range. For example, a range of “about 5 to 10” is understood to mean “about 5 to about 10”.

[0042] The terms “sensor” and “sensor strand” as used herein are used interchangeably. The terms “sensor” or “sensor strand” as used herein are understood to relate to a single-stranded RNA containing at least a stop codon, wherein the RNA strand may hybridize with another RNA strand or form a duplex. The terms “sensor” and “guide” strand may be used interchangeably throughout this disclosure.

[0043] As used herein, the terms “ADAR,” “ADAR enzyme,” and “deamination enzyme” are used interchangeably unless explicitly stated otherwise. Accordingly, within this disclosure, “wherein, the ADAR enzyme is a prokaryotic RNA editing enzyme” is also understood to mean “wherein, the deamination enzyme is a prokaryotic RNA editing enzyme.”

[0044] As used herein, the term “RNA sensor system” or “sensor system” is understood to mean (i) hybridization of a single-stranded RNA into a transcript of interest such that the hybridized RNA produced contains at least one mispair and at least one stop codon, (ii) recognition of the hybridized RNA as a substrate, and (iii) the minimum components required for editing the single-stranded RNA to remove the stop codon.

[0045] As used herein, the terms “cell logic system” or “logic system” refer to a system composed of a plurality of individual sensor systems. The cell logic system or logic system of this disclosure is a complex system and may be composed of one or more individual RNA sensor systems. When an individual RNA sensor system is integrated into a larger cell logic system, it may rely on the individual RNA sensor system of the same cell for activation. Alternatively, a plurality of individual RNA sensor systems may be integrated into the cell logic system so that the individual RNA sensor system is not required in another system.

[0046] Unless explicitly stated otherwise, the term “payload” as used herein is generally understood to mean a portion of single-stranded RNA that is hybridized to another single-stranded RNA, an intrusive strand to an already duplexed RNA molecule, or a portion of single-stranded RNA that can be translated to express a protein. Accordingly, where an embodiment of the present disclosure specifies, for example, that “the payload comprises a therapeutic protein,” the payload is generally understood to be a fragment or portion of single-stranded RNA that can be translated to express a therapeutic protein.

[0047] As used herein, "cell-specific," "cell type specific," and "activable by a specific cell type" will be understood by those skilled in the art to mean that the activation of the RNA sensor requires the presence of a factor present in a specific cell type at a substantially higher level than in other cell types. Those skilled in the art will recognize that while the expression of such factors may occur in other cell types and thereby enable the activation of the RNA sensor, the likelihood is not high.

[0048] As used herein and unless otherwise specified, “binding region” or “binding binding region” may be used interchangeably to describe a region on a guide strand having a degree of complementarity to a target transcriptome. A binding region refers to the design of a plurality of binding sites within the guide. These binding sites may be separated by a linker. A binding region may optionally be separated from the main sensor region of the guide strand by one or more secondary structures, including a hairpin structure. In some embodiments, the hairpin structure is an MS2 hairpin. A binding region may optionally contain a stop codon that is not targeted for editing by ADAR. In some embodiments, a binding region includes a linker sequence. In some embodiments, a binding region includes one or more linker sequences.

[0049] Adenosine acting on RNA Deaminases ( ADAR ) and other RNA editing enzymes.

[0050] ADAR enzymes are evolutionarily conserved among animals. Mammals possess three known ADAR enzymes: ADAR1, ADAR2, and ADAR3. ADAR1 and ADAR2 are known to be catalytically active. In contrast, ADAR3, despite its significant similarity to ADAR2, is generally considered catalytically inactive (see: Savva et al. Genome Biol. 13(12):252. (2012)). The ADAR enzymes considered in this disclosure include mammalian ADAR enzymes or modified enzymes derived therefrom. In some embodiments, the ADAR enzyme of the RNA sensor is human ADAR1. In some embodiments, the ADAR enzyme of the RNA sensor is modified human ADAR1. In some embodiments, the ADAR enzyme of the RNA sensor is human ADAR2. In some embodiments, the ADAR enzyme of the RNA sensor is modified human ADAR2. In some embodiments, the ADAR enzyme of the RNA sensor is modified human ADAR3. In some embodiments, the ADAR enzyme of the RNA sensor is a synthetic enzyme. In some embodiments, the ADAR enzyme of the RNA sensor is a non-mammalian ADAR enzyme.

[0051] The ADAR enzyme of the present disclosure comprises a modified enzyme. The ADAR enzyme considered in the present disclosure comprises an enzyme modified to increase the enzyme's affinity for the sensor strand. In some embodiments, the ADAR is modified to include an additional RNA binding domain. In some embodiments, the ADAR is modified to exclude one or more non-catalytic domains. In some embodiments, the ADAR enzyme of the sensor system comprises an ADAR2 deaminase domain. In some embodiments, the ADAR consists of an ADAR2 deaminase domain. In some embodiments, the ADAR comprises an ADAR2 deaminase domain fused to an MS2 binding protein. In some embodiments, the ADAR consists of an ADAR2 deaminase domain fused to an MS2 binding protein. In some embodiments, the ADAR is fused to a CRISPR-associated protein (Cas protein), or a fragment or derivative thereof. In some embodiments, the ADAR is fused to a modified Cas protein. In some embodiments, the modified Cas protein is mutated to lack catalytic activity. In some embodiments, ADAR is fused to a modified Cas13. In some embodiments, ADAR is fused to Cas13b containing a mutation at the amino acid corresponding to K370. In some embodiments, ADAR is fused to Cas13b containing a K370A mutation. In some embodiments, ADAR is fused to a modified Cas13d. In some embodiments, ADAR is fused to a modified Cas7-11.

[0052] The ADAR enzyme of the present disclosure may be endogenous to the cell to which the sensor is delivered. The ADAR enzyme considered in the present disclosure may be exogenous and may be delivered to the cell simultaneously with or separately from the sensor. In some embodiments, the exogenous ADAR is delivered separately from the sensor. In some embodiments, the exogenous ADAR is delivered simultaneously with the sensor. In some embodiments, the exogenous ADAR may be used to supplement the endogenous ADAR. In some embodiments, one or more exogenous ADARs are supplied to the cell.

[0053] Additional RNA editing enzymes

[0054] Additional deamination enzymes may be used in the method of the present disclosure. In some embodiments, the deamination enzyme may be a modified ADAR enzyme. The modified ADAR enzyme may include an ADAR enzyme modified to have increased cytidine deamination activity, such as RESCUE (see: Abudayyeh et al., Science 365, 382-386 (2019)).

[0055] In some embodiments, the deamination enzyme is a modified cytidine-to-uracil editing enzyme. In some embodiments, the deamination enzyme may be a member of the cytidine deaminase apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC) family. In some embodiments, the deamination enzyme is a modified APOBEC1. In some embodiments, the deamination enzyme is a modified APOBEC2. In some embodiments, the deamination enzyme is a modified APOBEC3. In some embodiments, the deamination enzyme is a modified APOBEC3A. In some embodiments, the deamination enzyme is a modified APOBEC3B. In some embodiments, the deamination enzyme is a modified APOBEC3C. In some embodiments, the deamination enzyme is a modified APOBEC3D. In some embodiments, the deamination enzyme is a modified APOBEC3E. In some embodiments, the deamination enzyme is a modified APOBEC3F. In some embodiments, the deamination enzyme is modified APOBEC3G. In some embodiments, the deamination enzyme is modified APOBEC3H.

[0056] In some embodiments, the deamination enzyme may be a prokaryotic RNA editing enzyme. In some embodiments, the deamination enzyme is Escherichia coli ( Escherichia coli )(i. coli( E. coli It originates from ))

[0057] Sensor / Sensor wire

[0058] The sensor of the present disclosure comprises at least one stop codon. The sensor may be located on the same RNA strand as the payload, the normalization gene, or both the payload and the normalization gene. The sensor of the present disclosure may be designed such that when a single-stranded RNA (ssRNA) sensor strand binds to a target ssRNA strand to generate a double-stranded RNA (dsRNA) duplex, the duplex includes a mispair within a region corresponding to the stop codon of the sensor strand. The disclosed sensor may be modified in various ways. In some embodiments, the sensor is provided to a cell as a DNA template, which may subsequently be transcribed into a single-stranded RNA sensor molecule.

[0059] Additionally, a sensor strand comprising one or more stop codons is provided by the present disclosure. In some embodiments, the sensor strand comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 stop codons. In some embodiments, the sensor strand comprises 10 or more stop codons. In some embodiments, the sensor strand comprises 2 stop codons. In some embodiments, the sensor strand comprises 3 stop codons. In some embodiments, the sensor strand comprises 4 stop codons. In some embodiments, the sensor strand comprises 5 stop codons. In some embodiments, the sensor strand comprises 6 stop codons. In some embodiments, the sensor strand comprises 7 stop codons. In some embodiments, the sensor strand comprises 8 stop codons. In some embodiments, the sensor strand comprises 9 stop codons. In some embodiments, the sensor strand comprises 10 stop codons.

[0060] Additionally, a sensor / guide strand comprising one or more antigen-antibody binding regions is provided in this disclosure. In some embodiments, the sensor strand comprises three binding regions. In some embodiments, the sensor strand comprises five binding regions. In some embodiments, the sensor strand comprises seven binding regions. The binding regions may incorporate stop codons that are not targets of ADAR editing. The antigen-antibody binding regions may also contain stop codons that are targets of ADAR editing. In some embodiments, the antigen-antibody binding regions include stop codons intended for ADAR editing.

[0061] Additionally, the present disclosure provides a sensor / guide strand incorporating one or more MS2 hairpins. In some embodiments, the sensor strand comprises two MS2 hairpins. In some embodiments, the sensor strand comprises three MS2 hairpins.

[0062] In some embodiments, the sensor / guide strand includes both a coupling force region and an MS2 hairpin region.

[0063] In some embodiments, the sensor / guide strand comprises an RNA modification. In some embodiments, the modified RNA comprises 5-methicytosine. In some embodiments, the modified RNA comprises pseudouridine.

[0064] payload

[0065] In some embodiments, the payload includes a reporter transcript. In some embodiments, the payload consists of a reporter transcript. In some embodiments, the reporter transcript is a fluorescent reporter. In some embodiments, the reporter transcript includes a luciferase transcript. In some embodiments, the reporter transcript includes a GFP transcript.

[0066] The sensor system of the present disclosure may be designed to deliver a payload encoding a therapeutic protein. In some embodiments, the therapeutic protein may be used in combination with another therapeutic agent.

[0067] In some embodiments, the payload includes a transcription factor. In some embodiments, the payload includes an enzyme. In some embodiments, the payload includes a gene-transferred protein.

[0068] In some embodiments, the payload includes a protein for use in editing the genome of a cell. In some embodiments, the payload includes a Cas protein. In some embodiments, the payload includes a Cas9 protein.

[0069] In some embodiments, the payload includes a protein capable of converting one cell type into another cell type.

[0070] In some embodiments, the payload includes an ADAR. In some embodiments, the payload includes an ADAR capable of initiating a positive feedback loop.

[0071] In some embodiments, the payload includes a protein capable of killing a specific cell type. In some embodiments, the payload includes a protein capable of killing tumor cells. In some embodiments, the payload includes an immunomodulatory protein.

[0072] Logic gate

[0073] The present disclosure also relates to a complex multi-sensor reporter system. In some embodiments, such a multi-sensor reporter system uses logic gates. Such logic gates may be composed of AND gates, OR gates, or AND OR gates as individual decision points in the same reporter system.

[0074] An AND gate for use in the reporter system of the present disclosure can be implemented by having multiple guide strand binding sections on the same ssRNA sensor. In this type of AND gate, each individual guide section detects a separate endogenous transcript in the cell. In this type of gate, "activation" of the AND gate is equivalent to the activation of the entire ssRNA sensor strand; that is, the removal of the stop codon and the expression of the terminal payload. This type of logic gate requires that each guide section interacts with the target sequence and ADAR or other deamination molecules. Deamination of the stop codon in each guide section will allow for the full expression of the payload. In some embodiments, each guide section is further separated by an individual reporter. In some embodiments, each guide section is further separated by an individual, distinct reporter. In some embodiments, each reporter on the ssRNA sensor for use in the AND gate is a distinct fluorescent reporter.

[0075] In some embodiments, the AND gate may operate in a sequential manner. In this type of AND gate, each multi-guide strand binding section is located in a separate ssRNA sensor. In this type of AND gate, activation of the gate involves the activation of multiple sensors in a defined order. In this type of AND gate, activation of the first sensor results in the expression of an intermediate payload. This intermediate payload allows for the expression of a second RNA sensor in a specific cellular environment. In this system, the cascade of RNA sensor activation can occur only at the time of activation and in the context of a specifically determined cellular stimulus.

[0076] An OR gate for use in the reporter system of the present disclosure can be implemented through the use of multiple independent ssRNA sensors within the same cell. Each of the multiple independent sensors can deliver a payload without activating another sensor.

[0077] delivery system

[0078] The present disclosure also provides a system for delivering ADAR sensors.

[0079] In some embodiments, the ADAR sensor is delivered directly to the cell. In some embodiments, the ADAR sensor is encapsulated in lipid nanoparticles. In some embodiments, the ADAR sensor is delivered via a viral vector.

[0080] In some embodiments, the ADAR sensor is a circular RNA.

[0081] Method of disclosure

[0082] The present disclosure also provides a method for using the sensor system described herein.

[0083] In some embodiments, the RNA sensor carries a payload that can be optically observed. In some embodiments, the RNA sensor is tracked via an imaging system. The imaging system may be any suitable imaging system compatible with the system. In some embodiments, the imaging system uses fluorescent molecules. In some embodiments, the RNA sensor system includes multiple fluorescent molecules. In other embodiments, the imaging system is non-invasive. In some embodiments, the RNA sensor system is compatible with a fluorescence activated cell sorting (FACS) system.

[0084] In some embodiments, the RNA sensor system is tracked via a non-invasive imaging system. In some embodiments, the imaging system tracks dark red luciferase.

[0085] The present disclosure also provides a method for quantifying RNA in vivo. The quantification method of the present disclosure may rely on the incorporation of a normalization gene into the sensor strand. The translation of this normalization gene occurs independently of RNA editing. Quantification of the normalization gene provides a reference to the total sensor strand delivered to each individual cell. Referencing this normalization gene allows for the determination of the activated RNA sensor as a percentage of the total delivered sensor.

[0086] The present disclosure also considers imaging of living cells. In some embodiments, a fluorescent reporter is visualized. In some embodiments, multiple fluorescent reporters are tracked. The present disclosure also provides long-term cell lineage tracking. In some embodiments, activation of the RNA sensor system causes a permanent change in the expression of reporter molecules so that the RNA sensor system can subsequently identify cells in which it was previously activated.

[0087] In some embodiments, the RNA sensor system may be used to target a specific cell type. In some embodiments, the RNA sensor system is engineered to be activated by a specific cell type. In some embodiments, the RNA sensor system targets a specific cell type. In some embodiments, the RNA sensor system targets tumor cells. In some embodiments, the RNA sensor system delivers a payload that kills a specific cell type. In some embodiments, the RNA sensor system delivers a payload that converts one cell type into another cell type. In some embodiments, the RNA sensor system delivers a payload that edits the genome of a cell.

[0088] In some embodiments, the RNA sensor system is drug-administerable. In some embodiments, the RNA sensor system is drug-sensitive. In some embodiments, the RNA sensor system is activated only in the presence of a single drug or compound. In some embodiments, the RNA sensor system is activated only in the presence of multiple drugs or compounds.

[0089] The present disclosure also considers the use of the RNA sensor system described herein for use in in vitro diagnostic testing. For example, the RNA sensor system may be in a diagnostic test, wherein the payload comprises a fluorescent protein, a luciferase protein, an antigen, or an epitope. In some embodiments, the diagnostic test is a lateral flow strip.

[0090] The technology has now been described in detail and will be more clearly understood by referring to the following examples. The following examples are included merely for illustrative purposes and are not to be construed as limiting embodiments of the technology. All patents and publications mentioned herein are expressly incorporated by reference.

[0091] Examples

[0092] Examples 1. of the examples method

[0093] Unless otherwise specified, the following experimental techniques were used in the embodiments provided herein.

[0094] Luciferase Measurement of activity.

[0095] Medium containing secreted luciferase was collected 48 hours after transfection unless otherwise specified. Luciferase activity was measured using 20 μL of medium on a Biotek Synergy 4 plate reader with an injection protocol using the Targeting System Cypridinia and Targeting System Gaucia luciferase assay kits (Targeting System). All replicates were performed using biological replicas.

[0096] Transfection for fluorescent sensors.

[0097] Cells were plated at 10K on Corning 96-well tissue culture-treated plates (black) the day before transfection to achieve approximately 40-50% confluence on the day of transfection. For all fluorescent sensors, HEK293FT cells were transfected with 100 ng of total plasmid DNA using TransIT-LT1 according to the manufacturer's specifications (ratio 1 μg DNA: 3 μL trans reagent). Unless otherwise specified, ADAR sensors, ADAR, and target plasmids were mixed at equal concentrations (33.3 ng / condition), and for experiments where one or more of the previous were absent, pUC19 was substituted to maintain the total DNA concentration at 100 ng.

[0098] fluorescence ADAR sensor's Confocal Microscopic examination

[0099] 48 hours after transfection, all wells were measured by confocal microscopy under the following settings. For each well, 2x2 images were collected at 10x magnification and stitched around the center point. Images were collected at 488 nm (32.8% power, 100 ms exposure), 561 nm (35.2% power, 100 ms exposure), 640 nm (80% power, 100 ms exposure), and the brightfield channel (25 ms exposure).

[0100] Quantification of fluorescence signals from images.

[0101] The images were opened in Matlab and segmented via the watershed of the mCherry channel. For each segmented cell, the total pixel area and average pixel intensity were calculated for the mNeon (488 nM), mCherry (561 nM), and iRFP (640 nM) channels and output to an aggregated CSV file. The CSV files were batch processed in R according to the following steps: all CSV files were merged, conditions with small aggregated areas (low cell counts or not transfected with the sensor) were merged, the fluorescence background of each channel was subtracted from all conditions of that channel, and the average fluorescence intensity was obtained by dividing the aggregated value for each condition by the area. The standard deviation was calculated by comparing the mean values ​​of three technical transfected copies. For the mNeon / mCherry ratio value, the average mNeon fluorescence intensity for a condition was divided by the average mCherry value for that same condition. For the fluorescence ratio value and the ratio multiplier change value, the error was propagated according to the following formula:

[0102]

[0103] Confocal In the image mNeon Quantification of the percentage of positive cells.

[0104] Some consistent leakage of mNeon was observed in the fluorescence sensor due to very low levels of plasmid contamination or ribosome slippage. Therefore, the detection of mNeon-positive cells was gated at 30 AU above the background, and the percentage of mCherry-positive cells was determined under conditions expressing mNeon higher than this threshold. The mNeon values ​​were plotted as a base-10 logarithm as a histogram with kernel density smoothing to generate the plot in Fig. 2e.

[0105] RNA extraction and ADAR Next-generation sequencing of sensors.

[0106] To calculate the editing rate of the ADAR SENSOR, cells were collected 48 hours after imaging and transfection. Total RNA was extracted using the RNeasy 96 kit (Qiagen) with DNase treatment. cDNA was prepared using SuperScript IV reverse transcriptase (Invitrogen) and sensor-specific primers. The sensor's guide region was amplified, indexed, and sequenced on the Illumina MiSeq platform. Reads were demultiplexed and aligned to each sensor, and the A-to-I editing rate was calculated using an in-house MATLAB pipeline.

[0107] Quantification of protein expression.

[0108] Two days after transfection of HEK293FT cells, the Nano-Glo HiBiT lysis detection system (Promega) was used to quantify HiBiT tags in cell lysates. For the preparation of the Nano-Glo HiBiT lysis reagent, the Nano-Glo HiBiT lysis buffer (Promega) was mixed with the Nano-Glo HiBiT lysis substrate (Promega) and LgBiT protein (Promega) according to the manufacturer's protocol. The volume of the added Nano-Glo HiBiT lysis reagent was equal to the culture medium present in each well, and the samples were placed in an orbital shaker at 600 rpm for 3 minutes. After incubation at room temperature for 10 minutes, readings were performed using a plate reader (Biotek Synergy Neo 2) with a gain of 125 and an integration time of 2 seconds. The control background was subtracted from the final measurements.

[0109] mRNA synthesis.

[0110] Prior to in vitro transcription, DNA templates were obtained by PCR using targeted forward primers containing the T7 promoter. Sensor mRNA and MCP-ADAR2dd mRNA were transcribed via HiScribe supplemented with 50% 5-methyl-CTP and pseudo-UTP (Jena Biosciences) according to the manufacturer's protocol. TM Transcribed and poly-A-tailed using the T7 ARCA mRNA kit (NEB, E2065S). Then, the mRNA was cleared with MEGAclear TM It was purified using a transfer cleanup kit (Thermo Fisher, AM1908).

[0111] Collection and quantification of total RNA PCR .

[0112] For gene expression experiments in mammalian cells, cell collection for cDNA generation and reverse transcription were performed 48 hours after transfection using a previously described variant of the commercial cell-to-Ct kit (Thermo Fisher Scientific) (Joung et al., 2017). Transcriptome expression was then quantified by qPCR using the Rapid Master Mix (Thermo Fisher Scientific) and TaqMan qPCR probe (Thermo Fisher Scientific) in combination with the GAPDH control probe (Thermo Fisher Scientific). All qPCR reactions were performed in 10 μl reactions using two technical replicas of the 384-well format and read using a LightCycler 480 Instrument II (Roche). For multi-target reactions, reads for different targets were performed in separate wells. Expression levels were calculated by subtracting the housekeeping control (GAPDH) cycle threshold (Ct) value from the target Ct value to normalize to the total input, resulting in △Ct levels. Relative transcriptome abundance was calculated as 2-△Ct. All copies were performed using biological copies.

[0113] Automatic generation of bonding force sensors.

[0114] The coupling force sensor is generated using the Python script from the following repository (https: / / github.com / abugoot-lab / ADAR SENSOR). A schematic diagram for generating a typical three coupling force guide ADAR sensor with a 5nt gap between two MS2 hairpin loops and a guide area (on the target) is shown in FIG. 24.

[0115] Livestock Industry and Animal Protocols.

[0116] All experiments were conducted with female B6(Cg)-Tyrc-2J / J(Albino B6) and NOD.Cg-Prkdcscid Il2rgtm1Wjl Tg(SERPINA1 ) with free access to food and water. * The study was performed on E342K)#Slcw / SzJ (NSG-PiZ) (The Jackson Laboratory) mice. NSG-PiZ mice express the human SERPINA1 mutant in an immunodeficient NOD scid gamma background. All mice were housed in individually ventilated cages in a temperature-controlled animal facility (normal 12:12 light-dark cycle) and used according to procedures approved by the MIT Animal Care Committee.

[0117] RADARSv2 design

[0118] Stop codons were engineered in the +1 and +2 frames following a guide RNA region engineered to capture translation ribosomes across all frames. This out-of-frame stop codon design synergistically with the long 5' peptide to significantly reduce background, generating approximately 200-fold activation. The inventors selected this sensor design, named RADARSv2, which incorporates a structured guide, upstream peptide, and out-of-frame stop codon as an integrated structure for future sensors ( Fig. 54a) .

[0119] Examples 2. Luciferase and development of fluorescent sensors EGFP transcript Detection

[0120] Luciferase sensor's Cloning

[0121] The luciferase sensor was cloned by Gibson assembly of the PCR product. The sensor backbone is generated by cloning both Cypridinia luciferase (Cluc) under the expression of the CMV promoter and Gaussian luciferase (Gluc) under the expression of the EF1-a promoter in a single vector. The expression of both luciferases in a single vector allowed one luciferase to act as a dosing control for normalizing the knockdown of the other luciferase, thereby controlling for mutations caused by transfection conditions. After ordering the short sensor with primers, it was phosphorylated and annealed using T4 PNK. The annealed oligo was ligated to the backbone using T4 DNA ligase (NEB) in a typical 10 μL ligation reaction with 1 μL of T4 DNA ligase, 30 ng of insert, 50 ng of backbone, and 1 μL of 10x ligation buffer for 20 minutes at room temperature. The long binding affinity sensor region was ordered directly as an Eblock from Integrated DNA Technologies (IDT). The PCR product was purified by gel extraction (Monarch Gel Extraction Kit, NEB) and assembled onto the backbone using the NEB HiFi DNA Assembly Master Mix Kit with 2.5 μL of master mix, 30 ng of backbone, and 5 ng of insert in a 5 μL reaction. The reaction mixture was incubated in a thermocycler at 50°C for 30 minutes, and 2 μL of the assembled reaction mixture was transformed with 20 μL of qualified Stbl3 produced by the Mix and Go! Competence Kit (Zymo) and plated on agar plates supplemented with appropriate antibiotics. After growing overnight at 37°C, colonies were placed in Terrific Broth (TB) medium (Thermo Fisher Scientific) and incubated at 37°C for 24 hours with shaking. Cultures were collected using the QIAprep Spin Miniprep Kit (Qiagen) according to the manufacturer's instructions.

[0122] This luciferase ADAR sensor contains a 51nt EGFP transcript detection guide and a Gaussian luciferase (Gluc) payload (Fig. 2a). Constitutive cypridina luciferase (Cluc) is included in a separate transcript, enabling ratiometric control over transfection dispersion. This dual reporter, dual transcript luciferase reporter system targets functional eGFP under the control of a doxycycline-inducible promoter. The inventors tested this ADAR sensor design with scrambled guide controls in the presence or absence of an exogenous ADAR2 deaminase domain containing the hypersensitive mutant E488Q and the specific mutant T490A fused to the MS2 coat protein (MCP-ADAR2dd (E488Q, T490A)) (Kuttan and Bass 2012; Cox et al. 2017). The ADAR sensor expression plasmid was co-transfected into HEK293FT cells using an EGFP expression plasmid or a control plasmid. The inventors observed that when relying solely on endogenous ADAR, the ADAR sensor resulted in a maximum 5-fold increase in normalized luciferase values, and when supplemented with exogenous MCP-ADAR2dd (E488Q, T490A), it resulted in a 51-fold activation of the signal (fold change in luciferase expression in the presence / absence of the target) (Fig. 2a). Furthermore, the inventors observed that during ADAR sensor induction supplemented with exogenous ADAR, the luciferase signal was similar to a constitutive expression transcript without an upstream stop codon (approx. 78%, Fig. 2c). Thus, such high protein production released upon ADAR sensor activation validates the ADAR sensor for applications requiring high absolute payload expression.To confirm that payload expression depends on RNA editing, the inventors collected RNA from cells and quantified the editing by next-generation sequencing, and observed an approximately 24-fold increase in the editing of the UAG stop codon in the EGFP-targeted sensor, but a negligible increase in the editing of the non-targeted sensor (Fig. 2d).

[0123] fluorescent sensor Cloning

[0124] The fluorescent ADAR sensor matrix was cloned into three fragments via Gibson assembly using a pcDNA3.1(+) cleavage containing HindIII and NotI as the backbone. mCherry was amplified from Addgene vector 109427, and T2A mNeon was ordered from IDT to gBlock. All fluorescent ADAR sensors were down-cloned into the matrix fluorescent plasmid via Golden Gate cloning using the enzyme Esp3I (the isoschizomer of BsmBI). The inserts were ordered as complementary strands with overhangs and produced by phosphorylation or annealing via PCR. The Golden Gate reaction was performed using the NEB BsmBIv2 Golden Gate Assembly Kit or by component assembly in a 20 μL reaction containing 25 ng of vector and 2 μL of 1:200 diluted inserts (approx. 5-10 ng). The reaction mixtures were heat-cycled for 1 hour, alternating between 25°C and 37°C for 5 minutes each, and then 0.75 μL of the reaction mixture was transfected into 12.5 μL of Zymo Mix and Go qualified cells. The transfected cells were diluted 1:1 using SOC medium, and 10 μL was striped onto a 50 μg / mL carbenicillin agar plate. After overnight incubation at 37°C, a single colony was placed in 4 mL of Luria broth (LB) supplemented with 50 μg / mL carbenicillin. A plasmid for the luciferase sensor was prepared from the culture as described above.

[0125] This dual reporter, single transcriptome fluorescent sensor contains a single transcriptome fluorescent reporter constitutively expressing mCherry upstream of a 51 bp eGFP sensor, and the downstream mNeon reporter is activated only upon interaction with the target (Fig. 3a). HEK293 cells were transfected with non-functional eGFP under the control of a doxycycline-inducible promoter. Cells were also transfected with the dual reporter, single transcriptome target sensor or non-target sensor in the presence of 1 μg / mL doxycycline. Fig. 3b shows representative images from experiments in the presence and absence of a target, and in the presence and absence of exogenous ADAR. Fig. 3c shows the quantification of the EGFP fluorescence fold change upon target induction; the ratio fold change indicates that for the ADAR variant, the mNeon / mCherry fluorescence value (fluorescence ratio value) in the presence of the target was divided by the fluorescence ratio value in the absence of the target. In the presence of exogenous MCP-ADAR2dd (E488Q, T490A), the target ADAR sensor exhibited an excess 21-fold increase in activation. Additionally, there was low background activation in the absence of the target ADAR sensor. The TAG->TIG editing rate of sensor TAG codons was also measured in the presence or absence of the target. In the presence of exogenous ADAR, UAG stop codons were edited at a rate of 9.4% in the presence of the target ADAR sensor, whereas UAG stop codons were edited at a rate of 0.2% in the absence of the target ADAR sensor, suggesting that target-driven editing induces fluorescent payload expression.

[0126] To further establish the proof of principle for the biological luciferase sensor, three guide strands were introduced into HEK293FT cells simultaneously with exogenous EGFP reporter transcripts. The ADAR sensor was a dual-transcript Gaussian luciferase (Gluc) / Cypridina luciferase (Cluc) transcript, enabling ratiometric control over transfection variability. The dual-reporter, dual-transcript luciferase reporter system targeted exogenous eGFP reporter transcripts. Designs 2 and 4 are different guides targeting EGFP transcripts. Exogenous ADAR was not introduced into the cells. The negative control is a guide strand that does not recognize EGFP. When EGFP expression was analyzed after the introduction of the three guide strands, both Design 2 and Design 4 showed a significant increase in luciferase expression levels (Fig. 4a). Both Design 2 and Design 4 guide strands showed a significant increase in luciferase signal compared to the negative control scrambled guide (Fig. 4b).

[0127] Examples 3. Exogenous ADAR2 After administration Transcript Increased expression

[0128] To determine whether the introduction of additional ADAR molecules increases luciferase expression, five guide strands were introduced into HEK293FT cells simultaneously with exogenous EGFP reporter transcripts. Guides 1–4 are different guides targeting EGFP transcripts. The negative control is a scrambled control designed not to recognize EGFP. Each guide strand was tested under three experimental conditions. First, each guide was introduced into HEK293FT cells to which exogenous ADAR had not been introduced (Fig. 5, blue bars). Next, each guide was introduced into HEK293FT cells simultaneously with the deaminase domain of ADAR2 (ADARdd) (Fig. 5, white bars). Finally, each guide was introduced into HEK293FT cells simultaneously with the ADAR2dd overexpressing transcript dPspCas13b-ADAR2dd (Fig. 5, red bars).

[0129] Guide strands 1–4 showed a 1.5 to 2-fold increase in luciferase expression without the addition of any exogenous ADAR molecules when normalized to the negative control (Fig. 5, blue bars). When the deaminase domain of ADAR2 (ADAR2dd) was simultaneously introduced into cells, guides 1, 2, and 4 showed an increase in luciferase expression similar to that of the same guides in the presence of endogenous ADAR (Fig. 5, white bars). Guide strand 3 showed a 3-fold increase in luciferase expression in the presence of additional ADAR2dd molecules (Fig. 5, white bars).

[0130] When the ADAR2dd overexpression vector was introduced simultaneously with guide strands 1-4, luciferase expression increased at least twofold (Fig. 5, red bar). Specifically, guide strand 3 showed a fourfold increase in luciferase expression compared to the negative control. This data highlights the potential to utilize exogenous and modified ADAR molecules to enhance sensor function.

[0131] Examples4. ADAR Optimization and length screening reduce the background in the presence of a target and increase ADAR sensor activation.

[0132] During the validation of these ADAR sensors, the inventors observed that, for some guides, activation could occur in the presence of exogenous ADAR despite the absence of target RNA (Figs. 2c, 3b). The inventors began to determine whether ADAR activity could be optimized to increase activation and reduce background. To optimize the ADAR sensors and minimize this background, the inventors selected and tested a panel of different ADAR1 and ADAR2 mutants along with 69nt guides targeting frameshifted EGFP transcripts or iRFP transcripts (Figs. 6, 7). Fig. 6a shows a schematic diagram of the different ADARs tested, including the p150 isoform of ADAR1, the p110 isoform of ADAR1, ADAR2, and the MS2 coat protein (MCP)-ADAR fusion protein (MCP-ADAR), from left to right. fl = full length. DD = deaminase domain. Catalytic domain mutations are not shown in the schematic diagram; all are located in the deaminase domain.

[0133] The inventors screened full-length human ADAR isoforms (ADAR1 p110, ADAR1 p150, and ADAR2) (Galipon et al. 2017; Merkle et al. 2019) and their catalytic deaminase domains, along with specific mutants designed to destabilize ADAR-dsRNA interactions to reduce non-specific editing (Cox et al. 2017; Matthews et al. 2016). While the initial exogenous ADAR selector, MCP-ADAR2dd (E488Q, T490A), performed best on frameshifted EGFP transcripts, several candidates from this screen showed similar activation upon target co-infection (Fig. 6b), and background was reduced in two target sets (Fig. 7). The inventors also investigated stop codon editing rates using various sensors for (Fig. 8a) MCP-ADAR2dd exogenous supplementation, (Fig. 8b) ADAR1 p150 isoform exogenous supplementation, (Fig. 8c) ADAR2 exogenous supplementation, and (Fig. 8d) no exogenous ADAR supplementation. The editing rates of these candidates were also investigated. Editing rates are calculated using RNA sequencing data showing the conversion of UAG stop codons to UIG in the presence and absence of targets of ADAR variants selected for further screening (Fig. 8).

[0134] Since guide selection can affect the overall sensitivity of the sensor, the inventors screened top ADAR candidates for several guide sequences and targets in an orthogonal panel (Fig. 10). First, the inventors observed activation above the background only in exactly matched ADAR sensor and target transcripts (Fig. 9). ADAR1 p150 showed the highest double activation in three out of four targets driven by generally low overall background signals in the absence of a target, whereas MCP-ADAR2dd (E488Q, T490A) performed best in the EGFP target due to generally high levels of absolute signal but struggled with higher backgrounds in other targets that reduced overall activation (Figs. 6, 10, 11, 12).

[0135] Analysis of background versus activation of the sensor panel was also performed using various sensors for (Fig. 13a) MCP-ADAR2dd exogenous supplementation, (Fig. 13b) ADAR1 p150 isoform exogenous supplementation, (Fig. 13c) ADAR2 exogenous supplementation, and (Fig. 13d) no exogenous ADAR supplementation.

[0136] The optimal amount of exogenous ADAR was also investigated (Fig. 14). In the titration experiment, HEK293 cells were transfected with varying amounts of MCP-ADAR2dd ranging from 10 ng to 100 ng, as well as varying amounts of 3-site binding affinity IL6 sensor strands ranging from 10 ng to 100 ng, in a tetracycline-induced IL6 experiment where the amount of target (IL6) was fixed at 20 ng. The fold change represents the normalized luciferase ratio between the +target group and the -target group.

[0137] Examples 5. Use of RNA editing (repair) systems for programmable A to I(G) substitution for biological sensors

[0138] Next, the feasibility of the RNA editing (REPAIR) system for programmable A-to-I(G) substitution was evaluated in relation to whether it could be utilized as a mechanism to trigger these gene sensors. The REPAIR system consists of the fusion of a catalytically active Cas13b enzyme fused to the deaminase domain of the ADAR2 molecule. A catalytically inactive Cas13b enzyme incorporating the K370A mutation is also fused to the deaminase domain of the ADAR2 molecule to form a fusion protein (REPAIR K370A) lacking Cas13b activity.

[0139] Two guide strands designed to target EGFP, as well as a negative control designed not to target EGFP, were introduced into HEK293FT cells without the addition of any exogenous ADAR molecules (Fig. 15, blue bars). The guide strands were also introduced into HEK293FT cells simultaneously with the REPAIR molecule (Fig. 15, red bars) or with the catalytically inactive REPAIR K370A molecule (Fig. 15, white bars). Guides 1 and 2, designed to target Cas13b, did not show an increase in luciferase expression in the cells compared to cells relying solely on endogenous ADAR expression. However, when Cas13 activity was deactivated (REPAIR K370A), Guide 1 showed a fourfold increase in luciferase expression compared to cells relying solely on endogenous ADAR expression. This increase in expression highlights the potential for the REPAIR system to be used with the gene sensors of this disclosure.

[0140] Examples 6. Luciferase Investigation of various guide strand characteristics for increased expression

[0141] Next, the characteristics of the guide strand design were modified to determine which variables could be adjusted to increase the efficiency and expression of the gene sensor. Figure 16 shows a heatmap representing ploid luciferase expression for various guide / ADAR combinations. Exogenously introduced full-length ADAR (Column 2) consistently showed the highest ploid increase in luciferase expression. However, this was not the case for the guide strand designed as an MS2 agonist, which showed generally consistent expression regardless of the introduced ADAR.

[0142] By selecting a lower background level in Example 4, the inventors began to further optimize the sensor with an ADAR1 p150 structure. To improve both binding stability and target search time, increasing the guide length around an early stop codon (Qu et al. 2019) was tested for constitutive iRFP targets. As the guide length increased from 51 nt to 600 nt, sensor activation improved by 2.2 to 18.22 times (Fig. 17b). Additionally, a significant shift in the distribution of mNeon expression levels per cell was observed for all guide lengths in the presence of the target (Fig. 17c, Fig. 18a), and a more substantial mNeon(+) population was observed at longer guide lengths. Meanwhile, the percentage of mNeon(+) cells in the absence of the target remained consistently below 5% for all guide lengths. At a guide length of 600 nt, 66.7% of mNeon(+) cells were observed in the presence of a target and 1.6% in the absence of a target, suggesting a powerful ability to separate cell populations based on target mRNA expression.

[0143] In Example 4, the two best-performing ADARS, ADAR1 p150 and MCP-ADAR2dd (E488Q,T490A), achieved optimal signals by reducing background or increasing activation. Since MCP-ADAR2dd (E488Q,T490A) showed the highest activation as a luciferase sensor (Fig. 20) when manipulating the ADAR enzyme, the inventors hypothesized that a guided manipulation strategy that lowers background would result in the most optimal sensor activation when combined with MCP-ADAR2dd (E488Q,T490A).

[0144] A novel sensor targeting IL6 mRNA, a transcript that is rarely expressed in HEK293FT cells, was designed (Uhlen et al. 2015) and can regulate low levels of IL6 expression for sensitivity testing by enabling both the supplementation of IL6 mRNA through exogenous transfection and the generation of integrated lines containing IL6 mRNA under the control of a dox-inducible promoter (see Fig. 20).

[0145] Due to the increase in background signal in the absence of a target, potentially leading to overtranslation of stop codons within a longer guide region (Fig. 21 (a) exogenous supplementation of MCP-ADAR2dd(E488Q, T490A), (b) exogenous supplementation of ADAR1 p150 isoform, (c) exogenous supplementation of ADAR2, and (d) no exogenous ADAR supplementation), the inventors modified the guide region to block abnormal translation by introducing an MS2 hairpin loop (Chao et al. 2008), which provides the additional advantage of recruiting the MCP-ADAR2dd(E488Q, T490A) protein to the guide:target duplex (Fig. 23). The inventors repeated this investigation into increased guide regions with a luciferase sensor targeting an IL6 transcript and found a significant decrease in the pluripotent change of ADAR sensor activation across 81nt guides using the MCP-ADAR2dd (E488Q, T490A) construct (Fig. 22) when only one binding site was present on the guide strand. However, structural additions and modifications to the guide strand design implemented to determine whether the addition of an MS2 hairpin loop and an additional engineered guide binding region (referred to as the "binding force binding region") to the sensor / guide strand improved sensitivity resulted in a significant increase in ADAR sensor activation (Fig. 22). Fig. 22 shows the results of experiments to determine the pluripotent change for different types of guide / sensor strands.HEK293 cells were transfected with sensors containing MS2 hairpins with additional binding regions (x-axis; 51 bp sensor, sensor with a continuous 171 bp binding region, sensor with a 171 bp binding region separated by two MS2 hairpins, sensor with a single continuous 225 bp binding region, sensor with a 225 bp binding region separated by four MS2 hairpins, sensor with a continuous 279 bp binding region, sensor with a 279 bp binding region separated by six MS2 hairpins, and non-target sensor) (Lipofectamin 3000, Thermo Fisher Scientific). Separation of the binding region by MS2 hairpins significantly increased the expression of the target at all various binding region lengths. Several types of ADAR proteins were tested, including full-length ADAR 2 (ADAR2 FL), the p150 isoform of ADAR1, endogenous ADAR1, and a fusion protein of an MS2 coat-binding protein fused to the deaminase domain of human ADAR2. The fold change in expression (y-axis) is calculated as the raw luciferase value of the +target condition versus the -target condition.

[0146] ADAR sensor activation was highest in the 5-site binding force binding guide, achieving approximately 70-fold activation and significantly lower background compared to the uninterrupted guide design (Fig. 27). While the binding force binding guide improved performance for all exogenous ADAR constructs, maximum performance improvement was demonstrated with MCP-ADAR2dd supplementation. Binding force binding guides with 5 or 7 binding sites were able to generate detectable activation relying solely on endogenous ADAR. Fig. 23 illustrates various forms of possible MS2 hairpin / binding force variants, and Fig. 24 provides a design guide for the binding force sensor and an easy-to-use software program for automatically generating the binding force sensor for input target sequences (github.com / abugoot-lab / ADAR SENSOR).

[0147] The inventors tested whether the highest-performance IL6-targeted engineered guide RNA could detect synthetic IL6 targets transfected into cells using endogenous ADAR. Although supplementation with exogenous ADAR1p150 improved the performance of RADARSv2, the inventors observed more than 50-fold activation of the payload using endogenous ADAR ( Fig. 52 ).

[0148] To further explore the binding force binding guide concept, the inventors varied the spacing between binding sites (5, 30, and 50 nt). The length between binding sites represents the number of nucleotides on the guide strand starting just before the MS2 hairpin to another region of complementarity. Binding sites closer together in the target transcript resulted in maximum activation (Fig. 25).

[0149] The inventors also explored whether improvements to the binding force binding guide, combined with orthogonal methods, could block translational overtranslations such as additional stop codons. The inventors compared a single-stop 7-binding force region sensor or a double-stop 7-binding force region sensor with additional stop codons in the final binding force region (Fig. 26). Fig. 26b shows the pluripotent change of the luciferase payload between the single and double stop sensors. The double stop sensor showed a significant increase in pluripotent change compared to the single stop sensor according to ADAR. The inventors found that additional stop codons increased the pluripotent activation of the 7-binding force binding guide, exceeding the performance of the 5-site binding force binding guide (Fig. 28a). This improvement was driven by both a decrease in background activation rate and an increase in the edit rate of stop codons in the presence of a target (Fig. 28b, Fig. 8).

[0150] Despite the abundance of CCA codons in potential target transcripts, the inventors explored whether progressive manipulation of the binding force guide design could improve mismatch tolerance and increase targeting flexibility. The inventors also tested target mismatch tolerance (Fig. 29A) across 16 possible mismatches (from standard CCA) between naive 51bp sensors, 3-binding force sensors, and 5-binding force sensors. The inventors designed 16 targets encompassing all nucleotide variations for 5' cytosine or 3' adenosine (nCn). Testing guides containing UAG across these various codons, the inventors found that guanine or cytosine mismatches were generally more tolerant than adenosine or uridine mismatches (Fig. 29b). Additionally, with the exception of ACA and ACU targets, the 5-site binding force binding guide ADAR sensor design exhibited the best activation multiplier variation (Fig. 30).

[0151] The modularity of protein payloads also allows for small payloads, such as the Hibit payload (Schwinn et al. 2018), which permits the in vivo circulation of transcripts. Circular RNA provides a platform for enhanced retention time and minimal immunotoxicity (Katrekar et al. 2019), and the inventors hypothesized that these characteristics could be utilized by circularizing ADAR sensors with small payloads (Fig. 31a). Two forms of short circular sensors were initially developed. A standard circular sensor was used in vitro in the presence of RtcB ligase The backbone is a dual-twister ribozyme system (Litke and Jaffrey 2019) driven by a circular U6 promoter. The standard circular sensor also contains a Hibit tag, with a stop codon at the c-terminal end of the Hibit. The inventors discovered that the circular ADAR sensor expressed the Hibit in a target (IL6)-specific manner (Fig. 31b). For signal amplification, the inventors augmented this circular ADAR sensor into an infinite ADAR sensor by removing the stop codon following the payload and inserting a 2A peptide at one end of the Hibit tag to allow expression via rolling circle translation (RCT) (Abe et al. 2015). This rolling circle translation sensor is similar to the standard circular sensor except that the stop codon is removed from the Hibit and a T2ToA peptide is inserted to allow ribosomal overtranslation in a circular manner. These circular sensors (IL6 target) were transfected into HEK293 cells, and sensors of various lengths were compared for sensor activation. The longer sensor consistently increased the sensor activation multiplier change. The inventors discovered that the rolling circle sensor can express proteins in a target-specific manner with minimal background leakage (Fig. 31b).

[0152] The inventors investigated the effects of mRNA modifications on changes in sensor activation quotients. Although synthetic mRNA has emerged as a useful therapeutic modifier, there is no method to control payload expression in a transcript-specific manner. The inventors explored the application of synthetic mRNA ADAR sensors for transcript-specific expression in a mouse model expressing the human SERPINA1 transcript in mouse hepatocytes. When delivering mRNA, the incorporation of base modifications such as 5' methylcytosine (5mc) and pseudouridine (Ψ) is essential to reduce the host immune response (Kauffman et al., 2016), but these modifications can interfere with ADAR activity and affect mADAR sensor function.

[0153] The incorporation of 5-methylcytosine and pseudouridine was analyzed in HEK293 cells. 24 hours prior to mRNA transfection, HEK293 cells were supplemented with MCP-ADAR2dd as a plasmid or directly as mRNA. An IL6 sensor containing tetracycline-inducible IL6 was also used. The inventors found that while increased amounts of Ψ reduced the activation of the ADAR sensor, 5mc was more tolerant with 25% incorporation of 5mc, which exhibited peak signal activation (Fig. 32).

[0154] Using this inductive IL6 system to measure sensor activation, the inventors of the exogenous ADAR1p150 ( Fig. 53a ) or endogenous ADAR( Fig. 53b The effects on mRNA RADARS activation for different levels of incorporation of a broad panel of chemically modified bases and transfected modified IL6-sensing mRNA RADARS were further evaluated. The inventors found that all tested modifications reduced mRNA RADARS activation, likely due to interference with ADAR1p150's ability to edit modified mRNA. Among the modifications, the inventors found that 50% incorporation of modified bases, such as 5-methylcytosine or 5-methyluridine, was the most resistant for exogenous ADARp150, while 100% incorporation of 5-methylcytosine showed peak activation for endogenous ADAR. To determine whether these levels of modification were sufficient to reduce the host immune response, the induction of interferon beta-related genes by chemically modified mRNA RADARS was evaluated. The inventors observed that minimal induction of inflammatory genes was achieved even at a level of 25% incorporation of modified bases. Fig. 53c ).

[0155] The inventors have constitutive in separate transcripts to provide ratiometric control over transfection dispersion Cypridina LuciferaseRADARSv1, along with (Cluc) Gausia Luciferase (Gluc) Tested a sensor containing a 51nt IL6 transcriptome detection guide in front of the payload( Fig. 54a With this RADARSv1 design and co-transfection with exogenous ADAR1p150, the inventors increased the Gluc / Cluc ratio in the presence of exogenous IL6 target expression ( Fig. 54b As quantified by the change in the Gluc / Cluc ratio between conditions defined as RADARS multiplier activation in the remainder of the manuscript, approximately 5-fold activation ( Fig. 54a ) was observed. Since ADAR1p150 prefers long double-stranded RNA as a substrate, the inventors optimized the length of the guide region from 51nt to 279nt, resulting in a gradual increase in activation at 81nt, and at longer lengths, activation decreased due to increased background payload expression in the absence of target RNA ( Fig. 54a , Fig. 54b ).

[0156] Three strategies were employed to prevent dsRNA formation in the absence of a target, partially due to translational over-translation and self-folding. First, the inventors introduced multiple MS2 hairpin loop interspersed binding sites into the guide region to create a secondary structure that prevents self-folding and enables multivalent binding. The inventors optimized this engineered guide, referred to as organized guide RNA (engineered guide RNA), by varying the number of MS2 loops and binding sites in the guide. RADARS activation was highest in the engineered guide RNA containing five interspersed MS2 hairpin loop binding sites, which reduced background payload expression in the absence of a target and achieved approximately 20-fold activation compared to the uninterrupted guide design. Fig. 54a , Fig. 54b ).

[0157] Second, the inventors increased the length of the translateable open read frame (ORF) preceding the engineered guide RNA to promote termination and prevent ribosomal reactivation, which is known to depend on the upstream ORF length. The inventors tested 5' peptide lengths at 0, 100, and 200 residues, and at 200 residues, significantly reduced background translational excess ( Fig. 55a We discovered that we were able to achieve activation exceeding 100 times. .

[0158] Finally, the inventors manipulated stop codons at +1 and +2 frames along a engineered guide RNA region to capture translation ribosomes across all frames. This out-of-frame stop codon design synergistically with the long 5' peptide to significantly reduce background, generating approximately 200-fold activation. The inventors selected this sensor design, named RADARSv2, which incorporates the structured guide, upstream peptide, and out-of-frame stop codon, as an integrated structure for future sensors. Figs. 54a, 55b ).

[0159] The inventors evaluated the RADARSv2 design across exogenously expressed IL6, EGFP, and neuropeptide Y (NPY) targets by tiling engineered guide RNA across 14 spaced CCA sites across the transcriptome. The inventors found that while RADARS activation depends on the hybridization sites selected for a given target, most sensors exhibited significant payload activation with up to 1,000-fold activation in the presence of the target, demonstrating the generalizability of the RADARSv2 design ( Fig. 56aTo confirm that payload expression is due to RNA editing, the inventors collected RNA from cells transfected with a panel of 14 IL6-targeted engineered guide RNAs and quantified the editing using next-generation sequencing. In the presence of target transcripts, all 14 engineered guide RNAs exhibited editing exceeding 15%, with an average of 35.1% + / - 11.4%. In the absence of target transcripts, 13 of the 14 engineered guide RNAs exhibited minimal editing (0.32% + / - 0.34%). The inventors also observed minimal editing in non-targeted sensors, reaffirming that RNA editing of the RADARS sensor requires specific engineered guide RNA target recognition ( Fig. 56b ).

[0160] Examples 7. Quantitative and Correlation analyze

[0161] To determine whether the above gene sensor could be accurately used as a "dose-sensitive" sensor, inducible EGFP transcripts were introduced into HEK293FT cells. These EGFP transcripts were placed under the control of a doxycycline-inducible promoter, and then the expression of the EGFP transcripts was altered by exposing the cells to 0 ng / mL, 8 ng / mL, 40 ng / mL, or 200 ng / mL of doxycycline. In HEK293FT cells without the introduction of exogenous ADAR (Fig. 6a), neither guide strand showed a significant difference in luciferase activity. However, guide strand 3 showed a tendency toward dose sensitivity (Fig. 33a, white bar).

[0162] When full-length ADAR2 was introduced into cells simultaneously with the guide strand (Fig. 33b), guide strand 3 exhibited clear dose sensitivity, whereas guide strand 1 showed slight dose sensitivity but to a lesser degree (Figs. 33b-c, white bars). Cells administered a dose of 200 ng / mL of doxycycline showed luciferase activity similar to that of cells constitutively expressing EGFP transcripts. As the dose of doxycycline decreased, there was a corresponding decrease in the luciferase activity identified in the cells. The same trend is observed in cells simultaneously exposed to full-length ADAR2 and guide strand 1 (Fig. 33b, blue bars). Fig. 34 depicts dose-dependent reporter expression when EGFP targeting guide strand 1 (Fig. 34a) and guide strand 3 (Fig. 34b) is introduced into cells. Luciferase ploidy activation also follows this dose-dependent trajectory (Fig. 33c). Figure 35 depicts the level of luciferase activity as a function of GFP fluorescence. These results indicate that this gene sensor can be valuable not merely as an "on-off" sensor, but as a quantitative sensor at the transcriptome level.

[0163] The inventors compared the target expression of the highest per million transcript (TPM) gene (RPS5) and the lowest expressed gene (KRAS) in siRNA disturbance experiments to compare how the performance of RADARSv2 differs between exogenous ADAR1p150 and endogenous ADAR. The inventors found that both sensors detected siRNA knockdown without exogenous ADARp150 supplementation, but the RPS5 sensor benefited more from exogenous ADAR ( Fig. 57 ), observed that this suggests that larger changes in expression may lead to greater benefits from exogenous ADAR.

[0164] To further investigate the quantitative values ​​of the ADAR sensor, the inventors generated a wide range of expression levels with both the transformed and virus-integrated versions of the inducible IL-6 expression system and measured the luciferase response of the 7-site binding force guide with a double stop codon (Fig. 36a). The inventors found that ADAR sensor luciferase activation was linearly correlated with the concentration of the target introduced gene, as confirmed by qPCR (Figs. 37, 38, R2 = 0.96). Accordingly, RNA editing of the first stop codon in the ADAR sensor guide showed a strong correlation with the gene expression level (Fig. 39), which demonstrates that the ADAR sensor can quantitatively detect transcripts at both RNA editing and payload levels.

[0165] To enable single-cell measurements with RADARSv2, the inventors engineered a fluorescent payload for microscopic examination and flow-based reading ( Fig. 58a The inventors designed a fluorescent sensor as a single transcript containing an upstream mCherry normalized control, separated from the best EGFP-targeted engineered guide RNA by the self-cleaving peptide P2A sequence, and then separated by the self-cleaving peptide T2A sequence in front of the mNeon payload. The inventors transfected HEK293FT cells with EGFP-targeted RADARS with or without a combination of exogenous ADAR1p150 or frameshifted non-fluorescent EGFP-targeted transcripts. The inventors observed the mNeon fluorescent signal by microscopy in the presence of the target transcripts and observed a negligible background in the absence of the target transcripts ( Fig. 58aQuantification of fluorescence signals by flow cytometry revealed a shift in the distribution of mNeon / mCherry ratios from 1.00% of mNeon / mCherry positive cells in the absence of target transcripts to 56.1% of mNeon / mCherry positive cells in the presence of target transcripts, and a 38-fold increase in the geometric mean ratio ( Fig. 58b , Figs. 59a-c) .

[0166] To further explore the quantitative accuracy of RADARS, the inventors used transfected and virus-integrated versions of the tetracycline-inducible IL-6 expression system to generate a wide range of expression levels and measured the luciferase response with the best IL-6-sensing engineered guide RNA. RADARS luciferase activation was quantitative and had a linear correlation with the concentration of the target introduction gene, as confirmed by qPCR ( Fig. 60a , Fig. 60b , R2 = 0.95). In addition, RADARS activation was invariant with respect to the amount of transfected sensor, and the robust activation rate over large titrations of sensor load enabled adjustment of the total sensor output independently of overall sensor activation ( Fig. 60c ).

[0167] The inventors also designed sensors for a panel of 10 different transcripts ranging from approximately 10,000 to approximately 10 TPMs in HEK293FT cells for verification. For each transcript, the inventors compared 8 different targeted engineered guide RNAs with 8 non-targeted engineered guide RNAs. After siRNA transfection, the inventors verified the knockdown of these 10 genes by qPCR ( Fig. 61), and a significant decrease in RADARS signals for each transcript was observed compared to the non-targeted sensor control. The robustness of the engineered guide RNA was related to expression: for highly expressed genes, most of the eight different targeted engineered guide RNAs detected target transcript knockdown, but as expression levels decreased, fewer engineered guide RNAs successfully detected knockdown. Fig. 63a Despite the decrease in RADARS sensitivity at low TPM, at least one of the eight engineered guide RNAs tested was able to significantly detect transcriptome knockdown ( Fig. 62a These data suggest that RADARS is sensitive to relative changes in gene expression across a wide range of expression levels. By measuring the editing rate of UAG stop codons for the best-performing sensor for each of the 10 target transcripts ( Fig. 62b The inventors found that while the overall editing speed is low, there is a statistically significant decrease in editing speed when the target is knocked down for all 10 genes. Since RADARS is overexpressed compared to endogenous targets with low turnover speeds, the editing speed of the stop codon will be less sensitive to changes in the copy number of the target.

[0168] Next, the inventors intended to determine the sensitivity of RADARS by measuring changes in gene expression of endogenous targets. To test RADARS across various levels of endogenous transcriptome expression, the inventors applied a sensor to measure transcriptional downregulation via siRNA ( Fig. 63The inventors utilized a commercially validated siRNA pool targeting six endogenous genes, divided into highly expressed genes RPL41, GAPDH, and ACTB, and moderately to low-expression genes HSP90AA1, PPIB, and KRAS. For each transcript, the inventors first compared eight different engineered guide RNAs for maximum sensitivity to knockdown ( Fig. 63a Next, the inventors titrated the amount of siRNA to generate various expression levels, confirmed them by qPCR, and tracked changes in expression levels using the best engineered guide RNA supplemented with exogenous ADAR1p150. For all six genes, the inventors found high Pearson correlations (R>0.86, RADARS). Fig. 61b )as We observed that qPCR measurements track transcriptome levels. The inventors found that for KRAS expressed at 13 TPM (transcriptome per million) in HEK293FT cells (Karlsson et al., 2021), the raw RADARS ploidy deviates from qPCR-measured ploidy changes, likely due to a loss of sensitivity at these low expression levels. However, the RADARS response was still highly correlated with qPCR-determined KRAS levels (R=0.93). Fig. 61b ).

[0169] Next, the inventors investigated whether RADARS can detect the upregulation of endogenous transcripts using a cellular heat shock model that induces the upregulation of heat shock series genes. The inventors identified a dynamic heat shock response protein. HSP70 second We designed RADARSv2-engineered guide RNAs targeting the virus, transfected HeLa cells with exogenous ADAR1p150, and then exposed the cells to heat shock at 42°C ( Fig. 64aRADARS showed strong agreement with qPCR, and the best HSP70-targeted engineered guide RNA was measured by qPCR HSP70 It generates 5.9-fold activation in response to heat shock compared to a 7.2-fold increase in transcriptome expression levels ( Fig. 64b These results suggest that RADARS is sensitive to the upregulation of endogenous transcripts and can detect changes in relative gene expression with high fidelity.

[0170] Examples 8. Logic Gates

[0171] The inventors also set out to determine whether the ADAR sensor of the present disclosure could be multiplexed into a logic system that may include AND gates and OR gates. Such AND OR approaches are schematically illustrated in FIG. 40a (AND) and FIG. 40b (OR). An AND gate can fully deliver a payload only when both target strands are present. However, an OR gate can deliver a payload in the presence of either one of the target strands, but not both. To create a basic AND gate, the inventors connected two single 51nt guides targeting EGFP and IL6, respectively, with an MS2 hairpin loop. However, this design performed poorly due to a combination of low signal and background overtranslation ( FIG. 40a).

[0172] To improve the AND gate signal, the inventors used a RADARSv2 design and found that the resulting AND gate sensor operates in a target-specific manner, ensuring that both targets are fully activated with minimal leakage under single-target conditions (Figs. 65a, 65b). The AND logic strand exhibited 36-fold activation in the presence of two target transcripts, along with only 1.3-1.5-fold activation in the presence of a single target RNA ( Fig. 65b ).

[0173] To manipulate the OR gate logic, the inventors co-transfected two 5-binding site binding affinity sensors targeting EGFP and IL6. These sensors responded to EGFP or IL6 target transcripts in a manner consistent with the OR gate (Fig. 41b). The OR logic sensors individually exhibited significantly increased fold changes in the presence of each gene, but not in the absence of both genes. Overall, these results suggest that the modularity of the ADAR sensor allows logical calculations to be performed on the mRNA of living cells.

[0174] To improve OR gate logic ( Fig. 66a ), the inventors co-transfected two engineered guide RNAs RADARSv2 (upstream ORF, out-of-frame stop codon) targeting EGFP and IL6 transcripts and confirmed that the sensor responds to the EGFP or IL6 target transcript in a manner consistent with the OR gate ( Fig. 66b ).

[0175] Examples . 9 In target cells Apoptosis To induce ADAR Use of sensors

[0176] To determine whether the ADAR sensor of the present disclosure could be used with a non-reporter payload, the inventors determined whether the ADAR sensor could induce apoptotic cell death in a target cell group. To apply the ADAR sensor for cell state-specific death, the inventors engineered the payload with therapeutically relevant i-caspase-9 (Straathof et al. 2005) (Fig. 42a). The i-caspase payload was engineered into a sensor strand capable of targeting human IL6. Mammalian cells were transfected with the caspase ADAR sensor, the target, and MCP-ADAR2dd. 24 hours after transfection, cells were split 1:5 in fresh medium and supplemented with 10 nM AP20187 (Sigma Aldrich) in the drug sample. After an additional 24 hours of growth, cells were evaluated for viability using the CellTiter-Glo luminescent cell viability assay (Promega). The control group caspase is a sensor strand with a scrambled sensor region (i.e., The caspases were those that did not specifically target IL6 and lacked an intervention stop codon. Using the CellTiter-Glo assay (Promega), apoptosis was measured as a multiple change in the luminescence values ​​of cell lysates from the +target group relative to the -target group. The inventors discovered that fusing the IL6 sensor with a double stop codon 7-element binding affinity guide in front of the caspase selectively killed IL6-expressing cells with minimal toxicity in the absence of IL6 induction (Fig. 42b, c). IL6-responsive caspases showed a significant increase in the induction of apoptotic cell death, indicating that ADAR sensors can be used to induce apoptosis in target cell groups. See Fig. 42b. The percentage of cell survival was also analyzed for cells treated with IL6-responsive caspases and cells with caspases but without stop codons in cells with and without target transcripts (Fig. 42c).

[0177] Next, the inventors used a highly specific SERPINA1-targeted engineered guide RNA for cell-specific death by binding the engineered guide RNA to an i-caspase-9 payload ( Fig. 67a )(Straathof et al., 2005). The inventors co-transfected A549, HeLa, and HepG2 cells with SERPINA1-i-Caspase 9 RADARS containing ADARp150 and evaluated cell viability 48 hours after transfection. The inventors found that SERPINA1-targeted RADARS-i-Caspase selectively killed HepG2 cells with minimal toxicity in other cell types, and that non-targeted negative controls did not show differential death ( Fig. 67b , Fig. 67c ).

[0178] Examples 10. To track cell status and cell type ADAR Use the sensor

[0179] To determine whether the developed ADAR sensor could be used to track cell status, the heat shock response of HeLa cells was first investigated. Two sets of HeLa cells were transfected with ADAR sensors accompanied by guides designed to target heat shock family genes, including HSP70 and HSP40. HSP70 and HSP40 can be upregulated in an in vitro heat shock model (Fig. 43a, b). ADAR sensors with 5-site or 7-site binding affinity guide designs detected both HSP70 and HSP40 upregulation in cells exposed to heat shock (Fig. 43a). HeLa cells (ATCC CCL-2) were transfected with HSP40 or HSP70 ADAR sensors. Twenty-four hours after transfection, a portion of the cells were moved to 42°C (5% CO2) for 24 hours. The medium was collected after the 24-hour heat shock period, and luciferase measurements were performed. To control non-specific changes in translation as a result of heat shock, the inventors transfected a scrambled non-target guide. Normalized with respect to the non-target guide, the inventors discovered up to 3-fold activation of the ADAR sensor in response to heat shock (Fig. 43c).

[0180] The inventors of the present invention conducted a thermal shock test ( Fig. 64 )second With the RADARSv2 design, only the RADARSv2 sensor was delivered without ADAR supplementation and the designed sensor. The inventors discovered that the best HSP70 sensor (CCA42) can track the upregulation of HSP70 during heat shock by utilizing endogenous ADAR within Hela cells ( Fig. 68 ), therefore, it demonstrates the feasibility of a single-component RADARSv2 system to be deployed using endogenous ADAR.

[0181] Differences in cell types represent major variations in gene expression within tissues. Therefore, the inventors began to determine whether ADAR sensors could accurately track differences in cell types. First, to identify marker transcripts for cell type differentiation, the inventors performed differential genetic analysis between HEK293, Hela, and HepG2 cells (Fig. 44a) and selected SERPINA1, a liver serine protease inhibitor with a therapeutically relevant pathogenic variant (Boelle et al., 2019), as a marker expressed only in HepG2 cells and not in other cell lines (Fig. 44b). The inventors designed an ADAR sensor panel with a guide targeting SERPINA1 and tested its ability to distinguish between HepG2 and Hela cells, finding that the CCA30 guide design exhibited the highest activation fold change between HepG2 and Hela cells (Fig. 44c). The inventors transfected three different cell types with a SERPINA1 (CCA30) targeting sensor along with a non-targeting scramble sensor designed to control background ADAR editing, transfection dispersion, protein production, and secretion differences between the three cell types. Each cell type was transfected with a CCA30 SERPINA1 sensor having an MS2 hairpin connected to five binding regions, with or without MCP-ADAR2dd. The fold change (Fig. 44d) was calculated by the raw luciferase value of the SERPINA1 sensor normalized to the scrambled non-targeting sensor to account for differences in protein production / secretion and background ADAR kinetics between cell types, and then normalized to the proportion of HEK cells for cell type comparison.

[0182] The normalized fold change in editing rate in all three cell types was also analyzed in the presence of endogenous ADAR, supplemental ADAR, and control (Fig. 45). Various CCA sites of the SERPINA1 transcript were also used as targets.

[0183] To model liver-specific cell targeting in vitro, the inventors [developed] human Hepa-1-6 cells SERPINA1 Expresses transcripts, and upper CCA SERPINA1 The sensor was synthesized in vitro as mRNA, and the mRNA sensor alone was transfected into Hepa-1-6 cells. The inventors have two targeting CCA30 and CCA35 SERPINA1 All sensors recruit endogenous ADAR from Hepa-1-6 cells SERPINA1 It was discovered that the induction of transcriptomes can be detected (Fig. 46).

[0184] To evaluate RADARSv2 for cell type identification, the inventors first engineered a system for permanent genetic labeling of cell populations by leveraging the modular nature of RADARS. The inventors designed a dual loxP system for conditionally permanently labeled cells containing EGFP upon Cre expression and tested this reporter in HEK293FT cells with ADAR1p150 and IL6-targeted engineered guide RNA containing a Cre payload. Upon IL6 induction, the inventors observed significant production of EGFP protein with minimal signal in the absence of the target RNA ( Fig. 69a , Fig. 69b ).

[0185] Then, the inventors used the RADARSv2 design SERPINA1 second SERPINA1 It was identified as a differentially expressed marker gene in the liver-derived cell line HepG2 compared to two non-hepatic cell lines, A549 and HeLa, which lack expression (Karlsson et al., 2021). By selectively activating Cre in HepG2 cells using SERPINA1-targeted engineered guide RNA ( Fig. 70a), the inventors co-transfected this sensor into HepG2, Hela, and A549 cells using ADAR1p150 and Cre loxP reporters and benchmarked activation against non-targeted RADARS constructs. Non-targeted engineered guide RNA did not show reporter activation in any cell type, whereas targeted engineered guide RNA showed significant EGFP reporter activation only in HepG2 cells ( Fig. 70b , Fig. 70c These results establish that the RADARS system can distinguish cell types based on specific markers, and that engineered guide RNAs and payloads can be modularly combined for cell-type specific expression of various introduced genes.

[0186] Examples 11. ADAR sensor's In vivo use

[0187] To determine whether the ADAR sensor could be used in vivo, the inventors next tested the SERPINA1 sensor in mice. The inventors synthesized an ADAR sensor targeting the CCA30 and CCA35 sites of human SERPINA1 in a construct expressing akaluciferase (Akalu), which allows for easy, non-invasive luminescence imaging to confirm cell-specific ADAR sensor activation (Yeh et al. 2019) (Fig. 47). Prior to bioluminescence imaging, 8 to 10-week-old Albino B6 and NSG-PiZ mice were anesthetized with 3% isoflurane, and 5 μg of the synthesized mRNA was injected via retro-orbital injection using an in vivo-jetRNA transfection reagent (Polyplus). Eighteen hours after injection, mice were re-anesthetized with 3% isoflurane, and 100 μl of 15 mM acalumin-HCl (Sigma Aldrich) was immediately administered for imaging. Abdominal bioluminescent images were acquired using the IVIS Spectrum In Vivo Imaging System (PerkinElmer). The following conditions were used for image acquisition: exposure time = 60 sec, binning = median: 4, field of view = 12.5 x 12.5 cm, and f / stop = 1. Bioluminescent images were analyzed using Living Image 4.3 software (PerkinElmer). Since albino B6 mice do not express human SERPINA1, they represent negative controls (lacking sites for CCA30 or CCA35 binding). To determine whether endogenous ADAR alone could edit the administered ADAR sensor, no additional ADAR enzymes were administered to the mice. The SERPINA1-sensing mRNA RADARS design resulted in significant activation of Akaluc expression in NSG-PiZ mice compared to NSG-WT mice (p = 0.(007, N=3 mice, one-way ANOVA) and the inventors observed that there was no significant difference between the two strains under substrate-only background luciferase and constitutive Akaluc mRNA RADARS conditions. Figs. 71a-c ).

[0188] In addition to ADAR sensors targeting the CCA30 and CCA35 sites of SERPINA1, the inventors also designed an Akaluc payload using a constitutive ADAR sensor expressing akaluciferase lacking a stop codon or a scrambled non-target guide (see Fig. 47b). Three sensor systems were investigated. The SERPINA1-sensing ADAR sensor design showed significant activation of Akaluc expression in NSG-PiZ mice compared to WT mice (p = 0.04, N=2 mice, two-sided non-paired t-test), while the control guide showed no significant difference between the two strains (Figs. 47b, Fig. 48). This activation demonstrates that the ADAR sensor can be delivered as synthetic mRNA to detect cell status in vivo using endogenous ADAR.

[0189] Additional considerations

[0190] Analysis of publicly available tissue gene expression data (GTEx Consortium 2013) showed that 34 out of 37 tissues could be distinguished as single genes with a sensor with 3x sensitivity (Fig. 49a), and 3 additional tissues were classified as combinations of genes, demonstrating direct applicability to both the sensitivity and logical input of the ADAR sensor (Fig. 49).

[0191] Examples 12. Off Target Testing of effects and general cell process interruption

[0192] Because the RADARSv2 mechanism involves the formation of a long hybridization region between the sensor-engineered guide RNA and the target transcript, the inventors investigated whether this duplex disrupts target transcript levels through Dicer knockdown or transcript stabilization. The inventors compared target expression for each siRNA-knockdowned endogenous transcript between the upstream-engineered guide RNA condition and the non-target-engineered guide RNA sensor (Fig. 63a ) We found no significant change in target transcript expression ( Fig. 72a In addition, to confirm that the generated engineered guide RNA-target hybridization did not interfere with endogenous translation, the inventors co-transfected HEK293FT cells with ADAR1p150 and ACTB or PPIB targeted engineered guide RNAs and quantified target protein levels by Western blot. Similar to mRNA levels, the inventors observed that the protein levels of ACTB and PPIB remained unchanged under targeted engineered guide RNA conditions compared to non-targeted engineered guide RNAs, demonstrating that there was no notable effect on target expression by RADARS ( Figs. 72b, Figs. 72c-d ).

[0193] The formation of dsRNA in cells can activate immune response pathways, and therefore, the inventors next investigated the RADARS-induced upregulation of major endogenous innate immune signaling pathways (IFNB1, MDA5, OAS1, and RIG-1) involved in the dsRNA response by qPCR using ACTB and GAPDH as normalization genes ( Figs. 73a, 73b). To compare with a positive control, the inventors tested RADARS constructs targeting ACTB, PPIB, RPS5, and exogenously introduced IL6 gene, along with high molecular weight poly(I:C), which acts as an analog dsRNA and activates these four pathways. The inventors found that while the RADARS constructs did not significantly upregulate any of the four dsRNA-responsive transcripts, poly(I:C) induced significant activation of all four pathways ( Fig. 73a ). To generalize this finding across cell lines, the inventors tested the same set of RADARS constructs in the HepG2 human hepatocellular carcinoma cell line and observed that the RADARS sensors did not induce any dsRNA response ( Fig. 73c ).

[0194] Finally, the inventors investigated whether ADAR1p150 overexpression generated off-target editing in the transcriptome, which has been observed with therapeutic ADAR-based RNA editing approaches (Cox et al., 2017; Qu et al., 2019; Reautschnig et al., 2022). The inventors first profiled the region surrounding the hybridization duplex of PPIB and ACTB transcripts using RADARS-engineered guide RNA and found that there was no significant off-target editing due to sensor hybridization or ADAR1p150 overexpression ( Fig. 74a ). To investigate potential off-targets in an unbiased manner, the inventors next performed polyA mRNA sequencing on cells expressing the PPIB sensor and ADAR1p150. The inventors found that the combination of ADAR1p150 overexpression and the PPIB-targeted RADARS resulted in only 23 detectable sites in the transcriptome with less than 10% editing ( Fig. 74b). Furthermore, in the absence of ADARp150 overexpression by non-target engineered guide RNA, the inventors found no significant editing of sites. In contrast, when the inventors analyzed the disclosed RNA-seq data from the overexpression of the MCP-ADAR2(E488Q) deaminase domain, they detected over 10,000 sites with significant A->I RNA editing, highlighting the effect of deaminase construct selection on the off-target profile ( Fig. 74c ).

[0195] The inventors did not find significant homology between the engineered guide RNA and the sequence surrounding the off-target editing site ( Fig. 75a ). Performing analysis on different engineered guide RNAs targeting exogenous IL6 targets in the presence of identical sequencing and ADARp150 overexpression revealed low, significant off-target editing rates (less than 10% editing at all 42 sites, Fig. 74b). Importantly, 22 / 23 PPIB off-target sites were shared between the two different engineered guide RNA samples. Finally, the inventors found that the sequence motif of the bases surrounding the editing sites is very similar to the preferred substrate of ADAR1 (Eggington et al., 2011)( do 75b Overall, these observations suggest that RADARS generates relatively little non-specific RNA editing in the transcriptome when used with ADARp150 overexpression.

Claims

Claim 1 a) a single-stranded RNA (ssRNA) sensor strand comprising a stop codon and a downstream portion of the single-stranded RNA located on the same RNA strand that can be translated to express a payload protein; and b) an RNA sensor system comprising an adenosine deaminase (ADAR) deaminase acting on RNA; wherein the ssRNA sensor strand can bind to an ssRNA target, and the ssRNA target is an endogenous transcript within the cell that forms a double-stranded RNA (dsRNA) duplex that serves as a substrate for the ADAR deaminase; wherein the substrate comprises a mispairing within a stop codon between the ssRNA sensor strand and the ssRNA target; wherein the mispairing is editable by the ADAR deaminase, and the editing effectively removes the stop codon to enable translation and expression of the downstream payload protein. Claim 2 An RNA sensor system according to claim 1, wherein the mispairing within the stop codon of the ssRNA sensor strand comprises an adenosine-to-cytosine mispairing comprising an adenosine within the ssRNA sensor strand located directly opposite to the cytosine within the endogenous ssRNA target in the dsRNA duplex. Claim 3 In paragraph 2, an RNA sensor system in which the adenosine of the ssRNA sensor strand within the mispair in the stop codon is edited into inosine by ADAR deaminase. Claim 4 An RNA sensor system according to claim 1, comprising one or more mispairs within a dsRNA duplex. Claim 5 An RNA sensor system according to claim 1, wherein the payload comprises a reporter protein, a transcription factor, an enzyme, a transgene protein, or a therapeutic protein. Claim 6 In paragraph 5, an RNA sensor system in which the payload comprises a therapeutic protein. Claim 7 An RNA sensor system according to claim 1, wherein the payload includes a fluorescent reporter. Claim 8 In claim 7, an RNA sensor system wherein the payload comprises an eGFP reporter or a luciferase reporter. Claim 9 An RNA sensor system according to claim 1, wherein the payload comprises a caspase. Claim 10 In paragraph 1, an RNA sensor system in which the ADAR is endogenous or exogenous. Claim 11 An RNA sensor system according to claim 1, comprising an RNA editing (REPAIR) molecule for programmable replacement of ADAR deaminase from A to I(G), a Cas13b-ADAR fusion molecule, a Cas13d-ADAR fusion molecule, a Cas7-11-ADAR fusion molecule, and an MS2-ADAR fusion molecule, a deaminase domain of ADAR2, full-length ADAR2, or truncated ADAR2. Claim 12 An RNA sensor system according to claim 1, wherein the ssRNA sensor strand further comprises a normalization gene. Claim 13 An RNA sensor system according to claim 1, wherein the ssRNA sensor strand is circular RNA. Claim 14 A cell logic system comprising the following: a) as an AND gate: (i) a synthetic single-stranded (ssRNA) sensor construct comprising (1) one or more payloads, and (2) a sensor domain comprising a plurality of stop codons editable by ADAR deaminase, and a first hybridization region and a second hybridization region, wherein the first hybridization region and the second hybridization region are capable of hybridizing to different ssRNA targets; and (ii) an adenosine deaminase (ADAR) deaminase acting on RNA; wherein the first hybridization region of the synthetic ssRNA sensor domain is capable of hybridizing to a first endogenous ssRNA target to form an RNA complex of the first hybridization region to a first endogenous ssRNA, said complex comprising one of a plurality of stop codons editable by ADAR deaminase and is a substrate of the ADAR deaminase; A second hybridization region of a synthetic ssRNA sensor can hybridize to a second endogenous ssRNA target to form an RNA complex of the second hybridization region to a second endogenous ssRNA target, said complex comprising one of a plurality of stop codons editable by ADAR deaminase and being a substrate of ADAR deaminase; said substrate comprising a mispair within a stop codon editable by ADAR deaminase; said editing capable of effectively removing a stop codon to enable translation of one or more payloads, an AND gate; or b) as an OR gate: (i) a plurality of independent synthetic ssRNA sensor constructs, each comprising (1) one or more payloads, (2) a stop codon editable by ADAR deaminase, and (3) a first hybridization region capable of hybridizing to one or more endogenous ssRNA targets; and (ii) adenosine deaminase (ADAR) deaminase that acts on RNA;A first hybridization region of each synthetic ssRNA sensor construct can hybridize to one or more endogenous ssRNA targets to form a double-stranded RNA (dsRNA) duplex of the synthetic ssRNA and the endogenous ssRNA target, said duplex is a substrate of an ADAR deaminase containing a mispair within one or more stop codons editable by the ADAR deaminase of the synthetic sensor domain; said substrate contains a mispair within a stop codon editable by the ADAR deaminase; and said editing can effectively remove the stop codon to enable translation of one or more payloads, an OR gate. 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