Conversion or editing of adenosine (A) into inosine (I) catalyzed by specialized cellular enzymes represents one of the most common post-transcriptional RNA modifications with emerging connection to disease. A-to-I conversions can happen at specific sites and lead to increase in proteome diversity and changes in RNA stability, splicing, and regulation. Such sites can be detected as adenine-to-guanine sequence changes by next-generation RNA sequencing which resulted in millions reported sites from multiple genome-wide surveys. Nonetheless, the lack of extensive independent validation in such endeavors, which is critical considering the relatively high error rate of next-generation sequencing, leads to lingering questions about the validity of the current compendiums of the editing sites and conclusions based on them.Strikingly, we found that the current analytical methods suffer from very high false positive rates and that a significant fraction of sites in the public databases cannot be validated. In this work, we present potential solutions to these problems and provide a comprehensive and extensively validated list of A-to-I editing sites in a human cancer cell line. Our findings demonstrate that most of true A-to-I editing sites in a human cancer cell line are located in the non-coding transcripts, the so-called RNA 'dark matter'. On the other hand, many ADAR editing events occurring in exons of human protein-coding mRNAs, including those that can recode the transcriptome, represent false positives and need to be interpreted with caution. Nonetheless, yet undiscovered authentic ADAR sites that increase the diversity of human proteome exist and warrant further identification.Accurate identification of human ADAR sites remains a challenging problem, particularly for the sites in exons of protein-coding mRNAs. As a result, genome-wide surveys of ADAR editome must still be accompanied by extensive Sanger validation efforts. However, given the vast number of unknown human ADAR sites, there is a need for further developments of the analytical techniques, potentially those that are based on deep learning solutions, in order to provide a quick and reliable identification of the editome in any sample.The online version contains supplementary material available at 10.1186/s12915-023-01651-w. RNA editing refers to a suite of epitranscriptomic molecular phenomena that result in changes to sequences of specific RNA molecules via insertion, deletion, or substitution of nucleotides at specific positions . A subty2CR [Mammalian A-to-I type of RNA editing has two major well-characterized physiological functions mediated by two ADARs: ADAR1 and ADAR2 . First, 2CR  and GLI12CR .All in all, this type of post-transcriptional RNA modification has attracted a significant amount of research interest, including multiple genome-wide studies focused on mapping ADAR editing events and measuring editing levels across multiple cell types and species. The advent of next-generation sequencing (NGS) has caused an explosion of the A-to-I editing sites with millions identified the in the human genome , 19–24 uTherefore, based on these considerations, in this study, we have performed a genomic survey of ADAR editome that was different from most previous endeavors in two major ways. First, instead of generating a wide survey of editing sites in many different cell or tissue types, we focused on generating as comprehensive collection of ADAR event in a single cell type as possible. Second, and more importantly, we made authenticity of the detected editing sites the top priority in this study by performing extensive Sanger validation of the candidate sites Fig. . We haveADAR-mediated editing of the same transcript can be influenced by a cell type , at leasIn each sample, RNA-seq analysis was performed on total RNA containing both polyA+ and polyA− fractions to ensure detection of ADAR editing events in the non-coding transcriptome that tends to be non-polyadenylated . OverallAs expected, a significant fraction (76.5%) of the total 5,604,679 sites detected by at least one method mapped to repeats of which majority (80.5%) were located in the Alu repeats  of the annotated sites were detected in only 1 of the 130 samples, this ratio increased to 95.3% in the unannotated sites  and 23.5% (8/34) of the respectively unannotated and annotated sites detected in just one sample could be validated . Instead, we used the large number of independent samples to obtain as complete and authentic of a compendium of ADAR editing sites in a cancer cell line as possible. Furthermore, in addition to the failure to predict authentic editing sites from individual samples, there was little overlap among the 3 analytical techniques: as shown in the Fig. In terms of the annotated editing sites, the three analytical tools differed mostly in terms of the sensitivity, but less so in the accuracy. Of the 3196 unique annotated detected sites, 1643 (51.4%), 1765 (55.2%), and 2872 (89.9%) were found by respectively REDItools, SPRINT, and RED-ML , 457 (5.2%), and 1827 (20.9%) were found by respectively REDItools, SPRINT, and RED-ML  mapped to the exons, of which 424 mapped to coding regions (CDSs), 1978 mapped to 3′ untranslated regions (UTRs) and 333 to 5′ UTRs. For comparison, only 464 (14.5%) annotated sites mapped to exons of which 32, 291 and 141 mapped to CDSs, 3′ and 5′ UTRs respectively  of the unannotated sites mapping to the non-coding genome were mostly detected by either only REDItools (3410) or RED-ML (263) or by both methods  and unannotated (97.7%) ADAR editing sites mapped to the non-coding transcripts, the so-called RNA 'dark matter' , 54–56. Of the remaining sites, 32 and 192 respectively mapped to exons and introns of annotated lncRNAs, of which 38 sites mapping to the latter were unannotated  in the -1 position (the base immediately 5′ to the editing site) . TherefoAs mentioned above, the validation ratios for the various types of annotated editing sites were consistently higher than those for the unannotated ones. Still, out of the 3160 annotated editing sites detected in this work, we failed to validate 25.5%, leaving the question of the authenticity of these ~806 sites open. Furthermore, it is important to stress that the 3160 annotated sites were independently detected by different investigators in different cell types and by us in at least two different K562 samples. As such, it is almost certain that there is an additional hidden population of false annotated sites that were not detected in this work and therefore not even subjected to Sanger validation. Therefore, to account for such sites, at least partially, and to estimate the minimal number of potential false positives, we included 2630 annotated sites detected in just one sample out of which, based on the results of validation, we expect 2012 or 76.5%, to be false , which is the same dataset that was used to derive annotated K562 sites in the CLAIRE (Cell Line A-to-I RNA Editing) database which is a part of REDIportal  Fig. 7)7). We peThese surprising results could be explained by the high false positive rates for the newly discovered sites found by the REDItools methods used to generate millions of annotated sites , 24. TheEven though HPC-REDItools was more sensitive in the detection of the annotated sites than the other 3 methods, the validation rate for those sites was lower than those for sites found only by RED-ML 81%) or SPRINT (71.4%)  mapping to the corresponding site.As can be seen in the Additional file The availability of multiple true positive and negative ADAR sites from the Sanger validation has also allowed us to test feasibility of predicting such sites solely based on genomic sequences, bypassing the need to generate costly RNA-seq data. Recently, a Deep Learning-based method, EditPredict, that used Convolutional Neural Network models has been developed for such purpose . HoweverOverall, the fraction of the sites predicted by EditPredict was higher for the true ADAR sites validated by Sanger (34.5% vs 24.3%). However, these results also demonstrated the problems with the method. First, the sensitivity of detection was relatively low (< 50%) even for the annotated sites. Second, and more importantly, the specificity of the method was also low, which would result in generation of a significant fraction of false positive sites. These results illustrated that, while promising, this and similar methods require further development and cannot yet substitute for methods based on analysis of the wetlab data such as RNA-seq. The reasons for this likely include (1) binding of ADARs to their substrates strongly depends on proper RNA folds that are still challenging to model , and (2)In this work, we focused on generating a compendium of authentic ADAR sites in a human cell line K562 with the goals of (1) illustrating technological challenges that could arise during such an endeavor due to the current state of sequencing and analytical techniques; (2) providing potential solutions to these problems; and (3) exploring possible biological implications of analyzing cancer editome. The unique aspect of our work is extensive independent validation of the results of the analytical editing site prediction pipeline using the highly accurate Sanger sequencing of 500 of predicted editing sites. Such magnitude of independent validation is very rare in genome-wide ADAR prediction studies but, as the results presented here have shown, is essential since the fraction of false positives can be very high. Surprisingly, even already annotated sites appear to contain a large fraction, > 40%, of questionable editing events that cannot be validated even in the same cell type. This fraction is much higher among the newly discovered sites and can reach over 90% depending on the analytical method used. It is, however, theoretically possible that some of the annotated K562 sites that failed to be detected in this work do in fact represent real ADAR sites that are edited only under very specific biological conditions. Still, at the very least, our results suggest that interpreting results obtained from genome-wide editome surveys has to be done with caution even when using sites deposited in the public databases.It is theoretically possible that the failure to validate many candidate editing sites could be due to a low sensitivity of our validation method. However, we think that this is unlikely for the following reasons. First, we only considered sites for which we could obtain high-quality Sanger electropherograms with low background as illustrated in the Figs. Also, it is possible that true ADAR sites that occur only in specific subpopulations of cells would result in very low overall editing levels in the bulk cell population that would be below the level of detection by the Sanger validation. However, in this work, we limited our survey to editing sites, both annotated and unannotated, that had editing levels > 0.2 which should be detectable on the Sanger sequencing traces . Taken tTo address this issue, in this study we have identified potential solutions that adopted in an analytical pipeline shown in the Fig. One of the most striking conclusions from this study is that ADAR sites that can recode mRNAs to make novel proteins are very rare in human cells, consistent with at least one previous study . MoreoveOn the other hand, it is fairly clear from this work, that most of the newly discovered true human editing sites would likely occur in non-coding transcripts, the RNA 'dark matter' , 54–56. 2. For drug treatments, K562 cells (5 × 105 cells/ml) were cultured in 6-well plates using 3 ml of the medium per well. After 16 h, drugs or DMSO/water controls were added at the different concentrations and for various amounts of time as listed in the Additional file The details of RNA-seq samples used in this study are listed in the Additional file Genomic DNA from our K562 clone was resequenced on the Illumina NovaSeq platform using the PE150 strategy and 90-GB scale by Novogene Corporation (Beijing). Only reads that passed quality filtering were aligned to the human genome by BWA (version: 0.7.8-r455) and then further filtered for by Samblaster (version 0.1.21) to remove improper alignments. Duplicate reads were removed by SAMtools (version 1.0) and Sambamba (version 0.4.7). Finally, the remaining alignments were used for variant calling by SAMtools. The above analysis was done by Novogene Corporation (Beijing).Quality filtered NGS reads were trimmed with fastx toolkit (version 0.0.13)  and aligPutative ADAR editing sites were filtered against the common SNPs from dbSNP 151  and K562The editing level for each site in each sample was determined by each method. If a site was detected by multiple methods in the same sample, its editing level was calculated as the average of editing levels estimated by each method that detected it. RNA editing candidates with the maximum editing level among the 130 samples of > 0.2 and found in at least two samples by any method were used for the downstream analyses. A site was considered to be found by only one method if it was detected in all samples using that method only. If a site was found by more than one method in different samples, it was considered as found by different methods even if the editing in the sample where the editing level passed the threshold was found only by one method. In other words, if a site was found in one sample using RED-ML with editing level of 0.1 and in another sample using SPRINT with the editing level of 0.3, it was considered as detected by both of these methods even though only one method detected it with the editing level exceeding the threshold of 0.2.Specific editing sites were validated by PCR amplifying 146–593 bp regions containing the sites from the corresponding RNA samples and the K562 genomic DNA using nested primers. The PCR products were purified by VAHTS DNA clean beads  and then sequenced directly using Sanger platform by Sangon Biotech (Shanghai) and Biosune (Xiamen). The resulting electropherograms were scored manually for the presence of true editing sites. Only electropherograms with low background were considered in the analysis.The annotated ADAR RNA-editing sites were downloaded from the following databases: (1) REDIportal v2.0 , (2) RADADAR sites located within exons or introns of known genes or lncRNAs and vlincRNAs had to map to the same genomic strands as the corresponding transcripts. When necessary, the coordinates of datasets were converted from HG19 to HG38 using the LiftOver tool from the UCSC Genome Browser. The overlaps between the RNA-editing sites and the different genomic element were calculated using the “intersect” function of the BEDTools (version 2.30.0) , 81.The sequence within ± 5 bp region around each site was extracted using the “getfasta” function of BEDTools . Then, tAdditional file 1: TableS1. Description of the 130 RNA-seq samples. Table S2. Description of the drugs usedto treat cells in the RNA-seq analyses. TableS3. Fractions of candidate sites located in repeats/Alus. Table S4. The numbers of candidatesites predicted by different methods. TableS5. The number of candidate sites predicted in only one or at least twosamples with the maximum editing level >0.2. Table S6. The validation ratios of candidate sites detected in only1 or at least 2 samples. Table S7.The list of sites that were positive in the Sanger validation. Table S8. The list of sites that werenegative in the Sanger validation. TableS9. The validation ratios of candidate sites detected by one or moremethods. Table S10. The validationratios of candidate sites in exonic regions. Table S11. The validation ratios of unannotated candidate sites innon-exonic regions. Table S12. Theodds ratios of enrichment of the 3160 annotated editing candidate sites indifferent genomic features. Table S13.Genomic landscape of RNA editing candidate sites with the maximum editing level>0.2 and detected in at least 2 samples. Table S14. List of the 3160 annotated sites. Table S15. List of the 989 unannotated sites. Table S16. Sequence motifs around the sites positive or negative inthe Sanger validation and all sites predicted by the pipeline. Table S17. Overlap between the 237annotated editing sites predicted in the CCLE K562 RNA-seq samples with thesites predicted in our 130 K562 RNA-seq samples.Additional file 2: SupplementaryFigure 1. Schematics of the initial ADAR site identificationpipeline used in this study. The pipeline shown here was used to generateannotated and unannotated sites shown in the Figure 2 prior to the subsequentrefinement based on the Sanger validation. The diagram shows the 3 major stepsin pipeline: (1) the initial calling of the candidate editing sites fromRNA-seq data, (2) filtration of false positives and (3) overlap with annotateddatabases.Additional file 3: SupplementaryFigure 2. Distribution of sites subjected to the Sangervalidation. Source data are provided as a Source data file.Additional file 4: SupplementaryFigure 3. Sanger electropherograms of sites that were positivein the Sanger validation. Electropherograms for the target sites (blue dashedboxes) detected by each method only or by multiple methods are shown for PCRproducts amplified from RNA or genomic DNA (gDNA). Adjacent editing sitesidentified by Sanger only are shown in yellow dashed boxes.Additional file 5: SupplementaryFigure 4. Sanger electropherograms of sites that were negativein the Sanger validation. Electropherograms for the target sites (blue dashedboxes) detected by each method or by multiple methods are shown for PCR productsamplified from either (1) only RNA for the sites that did not show evidence ofRNA editing, or (2) both RNA and genomic DNA (gDNA) for the sites that turnedout to represent sequence variants. Adjacent editing sites identified by Sangeronly are shown in yellow dashed boxes.Additional file 6: SupplementaryFigure 5. Comparison of HPC-REDItools and the 3 methods used in our pipeline. Venn diagrams representingthe number of annotated (a) and unannotated (b) sites which were detected byone or more different methods in at least 2 samples. (c) Validation ratios(Y-axis) of the annotated (left) and unannotated (right) sites detected byREDItools only (grey), HPC-REDItools only (dark blue) and the pipeline (red).The values in the bars represent the total number of corresponding sites.Source data are provided as a Source data file.Additional file 7: SupplementaryFigure 6. Distribution of transcript abundance and editinglevel of all sites subjected to Sanger validation. (a) Read depths in the lnscale and (b) editing levels of sites that were positive (left) and negative(right) in the Sanger validation. The red dashed lines represent the numbers ofreads or editing levels for each quartile. The percentages on the right representthe validation ratios of sites which have read depths or editing levels thatare higher than those represented by the red dashed lines. Source data areprovided as a Source data file.


N6-methyladenosine (m6A) is an important epigenetic modification involved in RNA stability and translation regulation. Manipulating the expression of RNA m6A methyltransferases or demethylases makes it difficult to study the effect of specific RNA methylation.L. wadei) and human m6A demethylase ALKBH5 catalytic domain. PMEs specifically demethylates m6A of targeted mRNAs  to increase mRNAs stability. In addition, we discovered that a double ribozyme system can significantly improve the efficiency of RNA editing.In this study, we report the development of Plant m6A Editors (PMEs) using dLwaCas13a (from PMEs specifically demethylates m6A of targeted mRNAs to increase mRNAs stability, suggesting that this engineered tool is instrumental for biotechnological applications.The online version contains supplementary material available at 10.1186/s13007-023-01053-7. RNA methylation regulates gene expression at the post transcriptional level and is an important epigenetic regulatory mode . Over 20Due to the lack of effective means to detect m6A methylation, the research on m6A methylation had long been stagnant. Thus far, studies aiming to manipulate RNA m6A methylation have relied on modulating the expression of RNA methyltransferases or demethylases , which hL. wadei) [CRISPR/Cas system is a powerful tool for understanding biological function and dynamic variations of nucleic acids . Catalyt. wadei)  and the . wadei)  to develTo construct RNA editors, we synthesized human m6A demethylase ALKBH5 catalytic domain (66–292 aa) and fused it to the C-terminus of inactive dLwaCas13a (R474A and R1046A)-msfGFP structure using the unstructured 16-residue peptide XTEN as a linker , 12  regulators [STM and WUS determines shoot stem cell fate in plants. According to Shen et al. (2016) and Duan et al. (2017), crRNAs sites were designed near the m6A sites of the five genes [http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) , which changes the subcellular location of Cas13a-ALKBH fusion protein and enhances the editing efficiency , 12. MeaL. wadei) and human m6A demethylase ALKBH5 catalytic domain. We found that PMEs specifically demethylates m6A of targeted mRNAs to increase mRNAs stability. In addition, using the double ribozyme system could further improve the RNA editing efficiency of PMEs. The lack of effective tool makes it difficult to study the effect of specific RNA methylation and thus this tool has made a significant advance in the fields of RNA methylation.In summary, we developed a plant m6A editors (PMEs) using dLwaCas13a  ecotype Columbia (Col-0) used in this study was kindly provided by Dr. Shenxiu Du . The constructs were introduced into A. tumefaciens strain EHA105, and then transformed into Arabidopsis (Col-0) by the floral dip method. The seeds were collected from the T0 plants, screened on 1/2MS plates containing 25 mg/L hygromycin, and transplanted to soil. T1 plants were confirmed by PCR analysis of genomic DNA. DNA extraction was performed from young leaves of T1 plants using a CTAB protocol. PME-DEC-F and NOS-DEC-R designed according to the sequence of PME-MCS were used for detecting positive transgenic plants , and the mixture was adjusted to 5 µL, and then mixed with 5 µL of home-made Gibson Assembly mix. After incubation at 50 °C for 30 min, the product (1 µL) was transformed into commercial E. coli competent cells. The constructs PME-WS and PME-FSS were confirmed by PCR and DNA sequencing. For PME-WS-H and PME-FSS-H, crRNA expression cassettes, containing a snRNA promoter (AtU6) and four crRNAs flanked with double ribozyme system, were synthesized by GENEWIZ. Synthesized crRNA expression cassettes (15 ~ 20 ng) were inserted into PME-MCS vector (100 ng) linearized with AscI using home-made Gibson Assembly mix [The Arabidopsis codon optimized dLwaCas13a-msfGFP-XTEN-ALKBHmbly mix , 35. Formbly mix . Brieflytes 15 ~  ng each,WUS, STM, FT, SPL3 and SPL9) and leaves  were isolated using TRIzol reagent . Total RNA was used to synthesize cDNA from each sample using M-MLV Reverse Transcriptase  according to the manufacturer’s instructions. Specific primers for qPCR were designed according to the gene CDS sequence and listed in Additional file TUB2 (At5g62690) was used as an internal control to normalize target gene expression.For expression analysis, total RNA from Arabidopsis shoot apices  of SELECT-qPCR. Additional file 8. Phenotypes of T3 transgenic seedlings. Additional file 9. Primers used in this study.Additional file 1. Sequence of dlwCas13a-msfGFP-ALKBH cassette. Additional file 2. Sequence of crRNA expression cassette for PME-WS-H (Upper) and PME-FSS-H (Lower). Additional file 3. Secondary structure of crRNAs predicted by RNAfold. Additional file 4. PCR identification of T


The unscientific application of pesticides can easily cause a series of ecological environmental safety issues, which seriously restrict the sustainable development of modern agriculture. The great progress in nanotechnology has allowed the continuous development of plant protection strategies. The nanonization and delivery of pesticides offer many advantages, including their greater absorption and conduction by plants, improved efficacy, reduced dosage, delayed resistance, reduced residues, and protection from natural enemies and beneficial insects. In this review, we focus on the recent advances in multifunctional nanoparticles and nanopesticides. The definition of nanopesticides, the types of nanoparticles used in agriculture and their specific synergistic mechanisms are introduced, their safety is evaluated, and their future application prospects, about which the public is concerned, are examined. In recent years, crop diseases and insect pests have caused substantial economic losses to agricultural production throughout the world. Pesticides play a pivotal role in the prevention and control of pests and diseases ,2. HowevBacillus thuringiensis (Bt) proteins, with broad potential utility in pest control [Nanotechnology has a wide range of potential applications, which will revolutionize the fields of science and technology in the 21st century, including in the sectors of materials science, biomedicine, agriculture, and industry, and will play important roles in various changing fields . The app control ,15,16.Although the use of nanoparticles in agriculture has been growing exponentially, few reviews have examined all aspects of nanopesticides and nanoparticles. In particular, emerging nanoparticles and their synergistic mechanisms have been overlooked. In this paper, we discuss various aspects of the preparation of nanopesticides, their synergistic mechanisms and safety, and their frontier applications; summarize the research status of nanoparticles and nanopesticides in agricultural production; and clarify the important roles and potential utility of nanopesticides in the development of modern agriculture.Nanopesticides are compounds that kill insects, bacteria, and weeds, and are prepared at the nanometer scale with physical, physicochemical, and chemical methods. At present, the classification of nanopesticides is predominantly based on particle size, and there is no uniform globally accepted definition. Some studies have defined nanopesticides as having an upper size limit of 100 nm, which is too simple and excludes many nanopesticides . Kah andRalstonia solanacearum, the bacterial wilt pathogen of tomato, and reduced the disease index [Spodoptera littoralis [50) of copper oxide nanoparticles is 232.75 mg/L, and the LC50 of calcium oxide nanoparticles is 129.03 mg/L.Some nanomaterials have specific antibacterial or insecticidal properties, and can be used directly as the active components of pesticides . For insse index . Chitosase index ,25. Coppttoralis . The medNanomaterials also have many excellent physical and chemical properties, including their small size, large specific surface area, and strong adsorption capacity, which allow them to be used as efficient carriers of pesticides. On the one hand, nanocarriers can improve the chemical stability and water dispersibility of pesticides, and on the other hand, protect the effective ingredients from degradation by high temperature and ultraviolet radiation in nature, thus promoting their efficacy ,27. WithNanoparticles can not only load pesticides, but also ensure their biodegradation, environmental safety, processability, and chemical compatibility with the loaded pesticide . The devMSNs are characterized by a large surface area, tuning pores, modified interfaces, biocompatibility, environmental friendliness, and good adsorption performance. Since Kresge first reported MSNs, research into their preparation has advanced greatly . MSNs loPesticide-loaded MSNs can be prepared by the physical adsorption method, and these are most easily absorbed and transported by plants. The surface of the MSN contains hydroxyl and unsaturated keys, to which activated groups can be introduced through silicide coupling, branch aggregation, etc. to confer new performance capabilities on MSNs. Modified MSNs can be continuously released into the release medium, and prevent the degradation of the active ingredients by light ,32,33,34Graphene oxide is a two-dimensional nano-inorganic carbon material composed of single-layer carbon atoms. It has a large surface area and high thermal conductivity . GO combGO has been used to construct a nano-AVM-GO system with a high loading capacity, which has the release performance of pH response, which improves the anti-optical solution performance of AVM . The shaMetal–organic frameworks are porous crystalline materials with metal ions or clusters as the central atom and one or more organic ligands, constructed with the coordination mode, which form infinite periodic networks. The unique advantages of MOFs, such as their large surface areas, adjustable apertures, and diverse structures, have made them widely applicable in many fields ,42,43. TThe modification of MOFs can enhance their properties . The loaClay minerals are water-bearing silicates or aluminosilicates, mainly including kaolinite, montmorillonite, attapulgite, diatomite, hydrotalcite, etc. Most clay minerals are crystalline. They have a high specific surface area that can be modified with inorganic or organic cations. These modified derivatives are excellent adsorbents for many organic compounds, the release of which into the environment can be controlled by their adsorption of active components ,49. Li eThe porous structure, widespread sources, and easy availability of biochar have allowed its development as a nanoparticle for pesticides. Cai et al. prepared biochar and biosilicon from straw, and loaded it with chlorpyrifos, to improve the deposition and adhesion of the insecticide on leaves and to reduce the migration of the pesticide in the soil . When meAs a widely used nonsiliceous material, mesoporous alumina has received widespread attention and has been widely applied ,57. AlumDepending on their source, organic materials can be divided into natural polymers and synthetic organic materials. Natural polymer materials occur widely in nature, and have the advantages of excellent biodegradability, good environmental compatibility, and inexpensiveness. Synthetic organic materials have the advantages of stability and plasticity. After continuous exploration and optimization in recent years, a group of synthetic organic materials characterized by environmental friendliness and biocompatibility have also been developed as nanoparticles .2 and OH functional groups allow chitosan to react with glutaraldehyde, vanillin, and ginpinil in the preparation of microcapsules or hydrogels. These can then be combined with pesticides to prepare nanopesticides [Chitosan is a kind of natural polymer obtained by the deacetylation of chitin, which displays good biocompatibility and biodegradability. Its NHsticides . Chitosasticides . Chitosasticides . Drug-losticides .Lentinus edodes were prepared from sodium alginate. Release from the hydrogels showed good environmental sensitivity, and the release rate of L. edodes increased at elevated pH, temperatures, and Na+ concentrations. The hydrogel continuously induced plant resistance to the tobacco mosaic virus and promoted plant growth, while having no adverse effects on aquatic zebrafish [Sodium alginate is a natural anionic polymeric polysaccharide that can be cross-linked with polyvalent metal cations. A large number of amino and carboxyl groups on the surface of sodium alginate can also be modified to prepare drug carriers with different physicochemical properties. Calcium alginate hydrogels loaded with ebrafish .β-Cyclodextrin is characterized by its of biocompatibility and biodegradability. It has a hydrophilic external structure and a hydrophobic internal cavity . HydrophCompared with natural polymers, synthetic polymers have obvious advantages as pesticide carriers . First, Orius sauteri. SPc-loaded CNAP showed greater selective toxicity, which enhanced the negative effects of CNAP on predators [Many synthetic polymers used as pharmaceutical carriers have been reported. Yang et al. used a star cationic polymer (SPc) to load abamectin through hydrogen bonding and intermolecular van der Waals forces, forming nanoscale pesticide particles with enhanced virulence against green peach aphids . They alredators .Drosophila S2 cell line and Myzus persicae by about 20% [At present, most available pesticides are emulsifiable oils or wettable powders, with attendant problems, such as poor water dispersibility, low biological activity, and ineffective utilization. The poor water solubility of the effective components of pesticides is an important factor restricting the effective utilization of pesticides. Due to their small particle size, nanopesticides can improve the water dispersion of pesticides, expand the contact area between the pesticide and the target pest, and improve the bioavailability of the pesticide. Yan et al. used a star polycation (SPc) as a matrine nanocarrier. After complexation with matrine pellets, the particle size of matrine in an aqueous solution decreased from 858.38 nm to 9.12 nm, which increased its toxicity against the bout 20% . To confbout 20% . Wang etbout 20% .The small size and surface modifiability of nanopesticides can promote the absorption and transport of pesticides by plants, and thus enhance the plant uptake of pesticides, especially hydrophobic chemicals. Pesticides mainly enter the plants through either foliage spray or root application. Nanoparticles are deposited on or adsorbed by the leaves, stems, and roots of plants, penetrate the cuticle and epidermis, migrate to the vascular tissues through plastids or exoplasts, and are then transported to various parts of the plants through the vascular tissues ,98. DendB. thuringiensis is easily affected by rain, reducing the period of its activity. However, the application of magnesium hydroxide nanoparticles loaded with Cry1Ac insecticidal protein improved the adhesion of Cry1Ac to cotton leaves by 59%, increasing the death rate of pests by 75%. Cry1Ac is also degraded in acidic environments, after which it exerts no obvious toxicity against cotton and cotton bollworm [The fine structure of plant leaves confers a certain degree of hydrophobicity on the leaf surface, so it is difficult for pesticides to attach to them, causing a waste of pesticides . The surbollworm . Tong etbollworm . Zhao etbollworm .trans-cis and cis-trans isomers, in a photosensitive switch. This photosensitive slow-release herbicide is also attached firmly to the weed leaves.Nanocarriers can effectively improve the environmental stability of the effective components of pesticides, and can also be used to build a controlled release system that responds to the external pH, redox reactions, enzymes, light, temperature, or other factors. This can reduce the application dose and frequency of pesticides, thus improving their utilization rates ,106,107.The commercialization and popularization of nanopesticides are imperative, but the large-scale application of nanopesticides in the field of agriculture still faces many challenges. Large amounts of nanomaterials are released into the environment, and their presence in the food chain can pose risks to human health . For insAt present, the toxic mechanisms of nanomaterials in organisms are unclear. Studies have shown that the toxic effects of these materials mainly include the following four factors. First, particles of any nanomaterial can readily penetrate the cell membrane and enter cells, and therefore affect the normal physiological activities of the cell and inhibit photosynthesis ,111. SecThe particle size of traditional pesticides is large and their effective utilization rates are not high. Only a small proportion of the pesticide actually reaches the target, and most (>60%) is released into the environment during the application process. Nanopesticides have a higher probability of remaining in the environment, and with the increasing application of nanomaterials, they pose a greater risk than other pesticides . AlthougNanopesticides can be applied with conventional spraying, root irrigation, or seed coating, consistent with farmers’ traditional practices. In most cases, the application of nanopesticides does not require special spraying equipment. Based on the premise that the preparation of nanopesticides is stable and their quality is assured, there are not many technical barriers to their field application. The development prospects of nanopesticides are positive. In recent years, the following developmental trends have emerged.(1) RNA nanopesticides have developed rapidly and are expected to be officially registered and used in the next 1–2 years. RNA nanopesticides differ from traditional nanopesticides in that their effective component is double-stranded RNA or small interfering RNA, which targets the key genes of harmful organisms. The establishment and combination of nanodelivery systems and bacterial RNA synthesis technologies are expected to promote the production of RNA nanopesticides ,16,122.(2) Accelerated research and the development of intelligent nanocarriers will facilitate the development of intelligent agriculture. Intelligent/intelligent nanocarriers allow the precise targeting and efficient delivery of multiple exogenous plant-protection factors through directional transformation. They perform well in terms of manually controlled release, temperature sensitivity, light sensitivity, and magnetically controlled release, and are suitable for application in a variety of environmental scenarios. They are also suitable for future remote-control scenarios on smart farms ,114,115.(3) The technology of unmanned aerial vehicles (UAVs) for plant protection has become increasingly mature, and has promoted the application of nanopesticides in the field. At present, most plant-protection UAVs are equipped with small-caliber centrifugal sprinklers. Commonly used traditional pesticides have poor water solubility, which usually causes the blockage and wear of these sprinklers, thus reducing the efficiency of pesticide spraying and limiting the application and popularity of UAV technology for plant protection. The characteristics of nanopesticides make them suitable for plant-protection UAV operations.


Loss-of-function genetic tools are widely applied for validating therapeutic targets, but their utility remains limited by incomplete on- and uncontrolled off-target effects. We describe artificial RNA interference (ARTi) based on synthetic, ultra-potent, off-target-free shRNAs that enable efficient and inducible suppression of any gene upon introduction of a synthetic target sequence into non-coding transcript regions. ARTi establishes a scalable loss-of-function tool with full control over on- and off-target effects. Drug development is guided by genetic loss-of-function (LOF) experiments that validate a therapeutic target, study its general and disease-specific functions, and thereby model and benchmark expected activities of inhibitory molecules. Applied genetic tools include RNAi, CRISPR/Cas9, and site-specific recombination technologies such as the Cre-Lox or Flp-FRT systems . While tTo develop such an assay system, we reasoned that instead of using gene-specific LOF triggers for every new candidate gene, the expression of any gene could be efficiently suppressed by engineering the target site of a pre-validated, highly potent, synthetic short-hairpin RNA (shRNA) into its exonic sequence . BesidesWe tested these ARTi-shRNAmirs using an established knockdown reporter assay  for theidel19, we cloned ARTi-shRNAmir target sequences into an expression construct encoding EGFRdel19 fused to dsRed -induced expression of the ARTi-shRNAmir (ARTi.6570) strongly inhibited proliferation of PC-9 cells and triggered a near-complete suppression of the EGFRdel19::V5::dsRed::ARTi protein . In a last step, we further reduced possible off-target effects by eliminating all guides whose extended seed sequence (guide positions 2–14) had a perfect match in the human or mouse transcriptome and, finally, selected the following six top-scoring ARTi predictions for experimental validation:TCCGGATGAAGTTTATATCGAA/shRNAmir (97mer):ARTi.6588 – target site: TGCTGTTGACAGTGAGCGCCCGGATGAAGTTTATATCGAATAGTGAAGCCACAGATGTATTCGATATAAACTTCATCCGGATGCCTACTGCCTCGGATCCGGATGATATTGTTATCGAA/shRNAmir (97mer):ARTi.6570 – target site: TGCTGTTGACAGTGAGCGCCCGGATGATATTGTTATCGAATAGTGAAGCCACAGATGTATTCGATAACAATATCATCCGGATGCCTACTGCCTCGGATCCGGATGATGTTTTAATCGAA/shRNAmir (97mer):ARTi.6634 – target site: TGCTGTTGACAGTGAGCGCCCGGATGATGTTTTAATCGAATAGTGAAGCCACAGATGTATTCGATTAAAACATCATCCGGATGCCTACTGCCTCGGATCCGGATGATATTGTATACGAA/shRNAmir (97mer):ARTi.6786 – target site: TGCTGTTGACAGTGAGCGCCCGGATGATATTGTATACGAATAGTGAAGCCACAGATGTATTCGTATACAATATCATCCGGATGCCTACTGCCTCGGATCCGGATGATATTGCATACGAA/shRNAmir (97mer):ARTi.6834 – target site: TGCTGTTGACAGTGAGCGCCCGGATGATATTGCATACGAATAGTGAAGCCACAGATGTATTCGTATGCAATATCATCCGGATGCCTACTGCCTCGGATCCGGATGAAGTTTAATTCGAA/shRNAmir (97mer):ARTi.6516 – target site: TGCTGTTGACAGTGAGCGCCCGGATGAAGTTTAATTCGAATAGTGAAGCCACAGATGTATTCGAATTAAACTTCATCCGGATGCCTACTGCCTCGGAThe following ARTi target sequences were used for experimental validation studies:Insertion into the coding sequence before the STOP codon:ATCCGGATGATATTGTATACGAATCCGGATGATATTGTTATCGAA (with the first ‘A’ being inserted to retain the reading frame)Insertion after the STOP codon:TCCGGATGATATTGTATACGAATCCGGATGATGTTTTAATCGAATCCGGATGATATTGTTATCGAAshRNAs were ordered as single-stranded DNA Ultramer oligonucleotides (Integrated DNA Technologies), amplified by PCR and cloned into different retroviral or lentiviral miRE/miRF shRNAmir expression vectors LT3GFPIR  using Ec−1/100 μg ml−1, Sigma-Aldrich). HT-1080 (ATCC: CCL-121) and LentiX lentiviral packaging cells  were cultivated in DMEM (Thermo Fisher) with 10% FBS, 4 mM L-glutamine, 1 mM sodium pyruvate, and penicillin/streptomycin (100 U ml−1/100 μg ml−1). MV4-11 cells (ATCC: CRL-9591) were cultured in IMDM with 10% FBS, 4 mM L-glutamine, 1 mM sodium pyruvate, and penicillin/streptomycin (100 U ml−1/100 μg ml−1).Human HCT 116 cells (ATCC: CCL-247) were cultured in RPMI 1640 medium (Thermo Fisher) and PC-9 cells in McCoy’s 5A medium (Thermo Fisher), supplemented with 10% FBS and 1x GlutaMAX (Thermo Fisher). RKO (ATCC: CRL-2577) and MOLM-13 cells (DSMZ: ACC 584) were cultured in RPMI 1640, supplemented with 10% FBS (Sigma-Aldrich), 4 mM L-glutamine (Thermo Fisher), 1 mM sodium pyruvate (Sigma-Aldrich), and penicillin/streptomycin , and 50 μM β‐ME. G12DKras, Trp53-/-, OEMYC PDAC cells (EPP2), SV40 large T antigen immortalized mouse embryonic fibroblasts . MIA PaCa-2 (ATCC: CRL-1420) and GP2d (Ecacc: 95090714) cells were cultured in DMEM supplemented with 10% FBS. Ba/F3 (DSMZ: ACC300) cells were cultured in RPMI 1640 medium supplemented with 10% FBS, 10 ng ml-1 IL-3 (R&D Systems), and Ls513 cells were cultured in RPMI 1640 medium supplemented with 10% FBS. All cell lines were maintained at 37°C with 5% CO2, routinely tested for mycoplasma contamination, and authenticated by short tandem repeat analysis.Murine MLL-AF9lls RN2;  were culRRT-MEF; , and NIH+ (shRNA+) cells over mCherry- cells and normalized to Renilla luciferase specific neutral control shRNA (Ren.713).SFFV-GFP-P2A-Puro-ARTi-target sensor was cloned into pRSF91 retroviral plasmid  using Gi+ cells (GFP positive) was monitored by flow cytometry in regular intervals, and results were normalized to day 0.To investigate the effect of ARTi constructs in the absence of the endogenous target gene, competitive proliferation assays were performed in several human and murine cell lines. Human HT-1080, RKO, MOLM-13, and MV4-11 cell lines were lentivirally transduced with shRNAmir expression constructs cloned into pRRL-SFFV-GFP-miRF-PGK-Neo (SGFN) backbone at 20–60% efficiency. Initial infection efficiency was determined at day 4 post transfection (day 0) by measuring GFP expression as a readout using iQue Screener Plus flow cytometer (IntelliCyt). Percentage of shRNA-1 dox for 9–10 d and analyzed with Incucyte (Sartorius). Untreated cells served as reference.Human GP2d, Ls513, and MIA PaCa-2 cell lines were lentivirally transduced with shRNAmir constructs cloned into pRRL-TRE3G-GFP-miRE-PGK-Puro-IRES-rtTA3 backbone . 500 cells were seeded in duplicates in 96-well plates and treated with 1 µg mlMurine NIH-3T3, EPP2, and RN2 cells were retrovirally transduced with shRNAmir constructs cloned into pMSCV-miR-E-PGK-Neo-IRES-mCherry backbone , and initial infection levels were determined by flow cytometry based on mCherry expression 4 d post transduction (day 0).-1 dox. Medium was exchanged every 2–3 d. After 9 d, wells were washed with ice-cold PBS and subsequently stained with 1 ml of 2.3% crystal violet solution for 10 min. Subsequently, wells were washed with ultrapure water and dried overnight. Images were obtained with a scanner.To visualize ARTi-shRNAmir’s effect, crystal violet staining assays were performed. 25,000 HCT 116 cells and 15,000 PC-9 cells per well were seeded in a 6-well plate containing 2 ml tetracycline-free growth medium and 1 µg ml+ cells from the competition proliferative assay experiment were selected with Geneticin/G418 Sulfate (Gibco) for 7 d and checked for GFP expression. On day 7, one arm of the empty vector control group was treated with IC50 concentration of trametinib (MedChem Express: HY-10999) for 24 hr based on the data in the Genomics of Drug Sensitivity in Cancer database , and total RNA was extracted using the RNeasy Mini Kit (QIAGEN). NGS libraries were prepared as above. Samples were sequenced on an Illumina NextSeq 2000 platform with a 75 bp protocol.Engineered MIA PaCa-2 and PC-9 cells were cultured in the presence of 1 µg mlFor 3′ mRNA-seq reads derived from the human cell lines HT-1080 and RKO, the 6-nt-long 5′ UMIs were attached to each read name with umi-tools v1.0.0; . Subsequdel19::V5::dsRed::ARTi (RN_v_108) and the dsRed::Linker::KRASG12R-ARTi (RN_v_287) were also included. Mapping of the sequencing reads derived from the human cell lines was performed with STAR  as well as the EGFRv2.5.2b;  aligner v2.5.2b;  and featv2.5.2b; . Normaliv2.5.2b;  and voomv2.5.2b;  R packag-1 Polybrene (Merck Millipore). After 24 hr, stable transgenic cell pools were selected using 10 µg ml-1 Blasticidin (Sigma-Aldrich). Subsequently, cells were diluted to obtain single cell clones. After 14 d of culture, single cell-derived colonies were transferred to 6-well plates and analyzed by western blot. Identified homozygous PC-9_ EGFRdel19-ARTi clones were further engineered by cutting endogenous EGFR with a CRISPR all-in-one vector pX458_Exon20_gRNA TAGTCCAGGAGGCAGCCGAA (GenScript) using X-tremeGENE 9 DNA transfection reagent (Roche) according to the protocol supplied by the vendor. 48 hr after transfection, GFP-positive cells were sorted by FACS (SONY cell sorter S800Z) and diluted to obtain single-cell clones. Positive clones, which contained only the exogenous EGFRdel19-ARTi, but not the endogenous EGFR, were identified by western blot. Next, the selected EGFR clone was transduced with a pantropic LT3GEPIR_Puro_ARTi-shRNA TTCGATAACAATATCATCCGGA retrovirus cloned at GenScript, China, and produced via the Lenti-X Single Shot system (Clontech). 72 hr later, stable transgenic cell pools were selected using 0.5 µg ml-1 Puromycin (Sigma-Aldrich). Following the selection, cells were diluted to obtain single cell clones. After 14 d of culture, single cell-derived colonies were transferred to 6-well plates and induced via 1 µg ml-1 dox. Positive clones were characterized by a strong GFP-induction that was identified by flow cytometry.Genome engineering of PC-9 cells was done as previously described . In brie-1 Polybrene. After 72 hr, stable transgenic cells were selected by using 50 µg ml-1 Blasticidin, without adding IL-3.Ba/F3 cells were transduced with an ecotropic pMSCV-EGFRdel19_V5_dsRed_ARTi-PGK-Blasticidin retrovirus cloned at GenScript and produced in Platinum E cells in the presence of 4 μg mlG12R-ARTi-PGK-Blasticidin retrovirus cloned at GenScript, China, and produced in Platinum E cells in the presence of 8 μg ml-1 Polybrene. After 24 hr, stable transgenic cell pools were selected using 10 µg ml-1 Blasticidin. Subsequently, endogenous KRAS was knocked out by transient transfection of three gRNAs targeting exon 2, and the region containing the G12C variant (present in MIA PaCa-2 cells) was used in a co-transfection . Knockout clones were identified by the absence of the endogenous KRAS protein using western blot.MIA PaCa-2 ARTi-shRNAmir-expressing cells were transduced with an ecotropic pMSCV-dsRed::KRASTTCTTCAGACTTCAGAACAT or CTGAAGAAAATTTACAAATC were cloned into the pSpCas9(BB)–2A-GFP plasmid  and used in a co-transfection. Simultaneously, a STAG1_AID_V5_P2A_Blasti_STOP_ARTi repair template with 800 bp of left and right homologous arms of the STAG1 genomic locus (in pUC57-Simple backbone) was transfected into HCT 116 cells using Lipofectamine 3000 transfection reagent (Thermo Fisher) according to the manufacturer’s instructions. A stable transgenic cell pool was selected 48 hr after transfection using 5 µg ml-1 Blasticidin and diluted to obtain single-cell clones. Positive clones were identified by western blot.HCT 116 cell line was engineered by cutting STAG1 with gRNAs targeting the region close to the STOP codon. Guide RNAs Identified homozygous HCT 116_STAG1_ARTi clones were further engineered by disrupting STAG2 gene with a CRISPR all-in-one vector Hs0000077505_U6gRNA-Cas9-2A-GFP and Hs0000077502_U6gRNA-Cas9-2A-GFP, respectively. Cells were transfected using X-tremeGENE 9 DNA transfection reagent according to the manufacturer’s instructions, sorted 48 hr post transfection for GFP-positive cells, and diluted to obtain single-cell clones. Positive clones were identified by western blot.TTCGATAACAATATCATCCGGAretrovirus cloned at GenScript, China, and produced via the Lenti-X Single Shot system (Clontech). 72 hr later, stable transgenic cell pools were selected using 2 µg ml-1 Puromycin . Following the selection, cells were diluted to obtain single-cell clones. After 14 d of culture, single cell-derived colonies were transferred to 6-well plates and induced via 1 µg ml-1 dox. Positive clones were characterized by a strong GFP-induction that was identified by flow cytometry.Next, the selected HCT 116 clone was transduced with a pantropic LT3GEPIR_Puro_ARTi-shRNA The following primary antibodies were used for immunoblot analyses: EGFR ; STAG1 ; STAG2 ; b-actin ; KRAS ; and V5 . PC-9, MIA PaCa-2, and HCT 116 cell pellets harboring EGFR, KRAS, and STAG1/STAG constructs respectively were lysed in Triton X-100 lysis buffer, sonicated, and stored at –80°C. For protein detection, the pellets were thawed on ice, followed by 15 min centrifugation at 13,000 rpm and 4°C. Furthermore, cell lysates were loaded onto a pre-casted SDS–polyacrylamide gel (4–12%) and proteins were transferred onto a nitrocellulose or PVDF membrane. Membranes were probed with the respective primary antibodies overnight. The next day, secondary antibodies conjugated with fluorescent dye were added and the proteins were detected by the Odyssey detection system.2 for 5 hr, followed by the compound addition. Cells were treated with seven different concentrations of inhibitors in a serial eightfold dilutions starting with the highest concentration of 3 µM. For comparability, DMSO normalization to the highest added volume was performed. Subsequently, cells were cultivated for 96 hr at 37°C and 5% CO2. 50 µl of Cell Titer Glo reagent was added to each well, incubated for 10 min in the dark, and luminescence was measured using the multilabel Plate Reader VICTOR X4. The measurement time was set to 0.2 s. Luminescence values relative to DMSO-treated cells were plotted in GraphPad Prism and fitted using nonlinear regression with a variable slope to calculate IC50 values at 50% inhibition. MIA PaCa-2 KRAS G12C inhibitor (AMG-510) . For this purpose, 10 mM stock solutions in DMSO of afatinib , osimertAMG-510)  treatmendel19::ARTi study was performed at the AAALAC-accredited animal facility of CrownBio Leicestershire, UK. Female NSG (NOD.Cg-PrkdcscidII2rgtm1wjl/SzJ) Crl mice were obtained from Charles River. The age of animals at study initiation was 7–8 wk and had an acclimatization period of ≥14 d. Mice were group-housed in IVCs. The study complies with the UK Animals Scientific Procedures Act 1986 (ASPA) in line with Directive 2010/63/EU of the European Parliament and the Council of September 22, 2010, on the protection of animals used for scientific purposes.The PC-9 EGFR k.o., EGFRnuFoxn1 mice were obtained from Taconic Denmark at 6–8 wk of age. After the arrival, mice were allowed to adjust to the housing conditions at least for 5 d before the start of the experiment. Mice were housed in pathogen-free and controlled environmental conditions (open-cage housing), and handled according to the institutional, governmental, and European Union guidelines .All other in vivo experiments were performed at the AAALAC-accredited animal facility of Boehringer Ingelheim RCV GmbH & CoKG. Female BomTac:NMRI-Studies were approved by the internal ethics committee  of Boehringer Ingelheim RCV GmbH & Co KG in the Department of Cancer Pharmacology and Disease Positioning. Furthermore, all protocols were approved by the Austrian governmental committee .6 HCT 116 in PBS, 5 × 106 MIA PaCa-2 in 1:2 Matrigel:PBS with 5% FBS or with 1 × 107 PC-9 cells in PBS with 5% FBS.To establish subcutaneous tumors, mice were injected with 2 × 103) was calculated according to the formula ‘tumor volume = length * diameter2 * π/6.’ Mice were randomized into the treatment groups when tumor size reached between ~130 and 190 mm3. Group sizes were calculated for each tumor model based on tumor growth during model establishment experiments. A power analysis was performed using a sample size calculator (https://www.stat.ubc.ca/~rollin/stats/ssize/n2.html). For all models used in the studies, 10 mice per group were used. 2 mg ml-1 doxycycline hyclate (Sigma) and 5 mg ml-1 sucrose were added to the drinking water of the treatment groups, the control group received water with 5 mg ml-1 sucrose only. Osimertinib  was dosed per os daily at a dose of 25 mg kg-1 in Natrosol, and control mice were dosed per os daily with Natrosol. To monitor side effects of treatment, mice were inspected daily for abnormalities and body weight was determined three times per week. Animals were sacrificed when the tumors reached a size of 1500 mm3. Food and water were provided ad libitum. In vivo experiments were not repeated.Tumor diameters were measured with a caliper three times a week. The volume of each tumor  according to the manufacturer’s instructions using a recombinant rat monoclonal STAG1 antibody  after cooking 10 min at 110°C in antigen unmasking solution (Vector #H3301). After staining, the slides were digitalized (scanner: Leica Aperio AT2). All slides were reviewed and evaluated for quality by a board-certified MD pathologist. Imaging analysis was performed using the digital pathology platform HALO (Indica Labs). A tissue-classifying algorithm was trained to selectively recognize viable tumor tissue against stroma, necrosis, and skin. The tissue classification output for each scan was reviewed and manually edited as necessary. A cell detection and scoring algorithm was trained to measure DAB optical density (OD) in the nuclei of tumor cells. A positivity threshold for DAB OD was determined by normalization with respect to the DAB OD as calculated from bona fide negative tissue . The object data for background-normalized nuclear ODs for each tumor cell were exported from three control and three doxycycline-treated cases . The cumulative distribution of the background-normalized DAB OD for each tumor cell nucleus was then plotted for control and doxycycline-treated cases, separately .All newly generated ARTi cell lines are available upon request. The following parental cell lines were used: valuable method to study the mechanism of action of essential genes and novel putative drug targets. Evidence for the effectiveness of the system, which is based on engineering pre-validated targets for RNA-mediated knockdown into genes of interest, is compelling, and the method should find use as an orthogonal method for generating gene-specific knockdowns.This manuscript describes a  In this manuscript the authors proposed a novel system by which they can suppress the expression of any gene of interest precisely and efficiently with a pre-validated, highly specific and efficient synthetic short-hairpin RNA. The idea of identifying potent artificial RNAi (ARTi) triggers is intriguing, and the authors successfully identify six ARTi with robust knockdown efficiency and limited to no off-target effects. As a proof-of-concept, the authors examined three oncology targets for validation, including EGFRdel19 , KRASG12R (for which there are no in vivo compatible inhibitors yet) and STAG1 . The authors demonstrated significant suppression of colony formation and in vivo tumor growth for all three oncology targets.This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:• The authors claim in the introduction that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the methods does not, as the authors acknowledge, eliminate the possibility of off-target effects.• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins. This is not, as the authors point out, a replacement for other LOF methods, and there are other ways to achieve gene-specific regulation via, for example, degrons. However it is an effective orthogonal approach that many users may find compelling for their applications. In this manuscript, Hoffmann et al. introduce a novel and innovative method to validate and study the mechanism of action of essential genes and novel putative drug targets. In the wake of many functional genomics approaches geared towards identifying novel drug targets or synthetic lethal interactions, there is a dire need for methods that allow scientists to ablate a gene of interest and study its immediate effect in culture or in xenograft models. In general, these genes are lethal, rendering conventional genetic tools such as CRISPR or RNAi inept.The ARTi system is based on expression of a transgene with an artificial RNAi target site in the 3'-UTR as well as a TET-inducible miR-E-based shRNAi. Using this system, the authors convincingly show that they can target strong oncogenes such as EGFRdel19 or KRasG12 as well as synthetic lethal interactions (STAG1/2) in various human cancer cell lines in vivo and in vitro.The system is very innovative, likely easy to be established and used by the scientific community and thus very meaningful. The following is the authors' response to the original reviews.Reviewer #1 (Public Review):[…] This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).We thank the referee for highlighting the novelty and potential of the ARTi system.ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:• The authors claimed in the introduction sections that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the advantages of ARTi over CRISPR are likely less than claimed.We thank the reviewer for raising these very valid concerns about potential off-target effects related to the CRISPR/Cas9-based gene knockout or engineering of endogenous ARTi target sites. In contrast to conventional RNAi- and CRISPR-based approaches, such off-target effects can be investigated prior to loss-of-function experiments through comparison between parental and engineered cells, which in the absence of CRISPR-induced off-target events are expected to be identical. Subsequent ARTi experiments provide full control over RNAi-induced off-target activities through comparison of target-site engineered and parental cells. However, we agree that undetected CRISPR/Cas9-induced off-target events cannot be ruled out in a definitive way, which we have pointed out in our revised manuscript.• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins.We fully agree that the required design of gene-specific sgRNAs and subsequent CRISPR-engineering steps are by no means trivial. However, we believe that decisive advantages of the method, in particular the robustness of LOF perturbations and additional means for controlling off-target activities, can make ARTi an investment that pays off. In our experience, much time can be lost in the search for effective LOF reagents, and even when these are found, questions about off-target activity remain. While ARTi overcomes many of these challenges by providing a standardized experimental workflow, we do not propose to replace all other LOF approaches by this method. Instead, we would position ARTi as a unique orthogonal approach for the stringent validation and in-depth characterization of candidate target genes, as we have highlighted in our revised discussion.• The approach of knocking-out an endogenous gene followed by replacement of a regulatable gene can also be achieved using regulated degrons, and by tet-regulated promoters included in the gene replacement cassette. The authors should include a discussion of the merits of these approaches compared with ARTi.We thank the reviewer for pointing out these alternative LOF methods. We had already included a brief discussion of chemical-genetic LOF methods based on degron tags. While we certainly share the current excitement about degron technologies, they inevitably require changes to the coding sequence of target proteins, which can alter their regulation and function in ways that are hard to control for. In our revised discussion, we have added a brief comparison to conventional tet-regulatable expression systems, which unlike ARTi require the use of ectopic tet-responsive promoters. Overall, we would position ARTi as an orthogonal tool that enables inducible and reversible LOF perturbations without changing the coding sequence and the endogenous transcriptional control of candidate target genes.Reviewer #2 (Public Review):[…] The ARTi system is based on expression of a transgene with an artificial RNAi target site in the 3'-UTR as well as a TET-inducible miR-E-based shRNAi. Using this system, the authors convincingly show that they can target strong oncogenes such as EGFRdel19 or KRasG12 as well as synthetic lethal interactions (STAG1/2) in various human cancer cell lines in vivo and in vitro.The system is very innovative, likely easy to be established and used by the scientific community and thus very meaningful.We thank the reviewer for her/his enthusiasm about ARTi.Reviewer #1 (Recommendations For The Authors):• The authors claimed that ARTi enables specific, efficient, inducible, and reversible suppression of any gene of interest. However, there are no experiments supporting the reversible suppression of their gene of interest. Data are required to support this statement.We thank the reviewer for pointing this out. The statement about the reversibility ARTi-mediated effects was based on a rich body of literature that has demonstrated the reversibility of Tet-shRNAmir-induced LOF perturbations and associated phenotypes. As ARTi employs the same Tet-shRNAmir expression vectors, we have no reason to believe that this feature would be lost. However, since we have not demonstrated this in our study, we have removed this statement in our revised manuscript.• In Figure 1E, the authors did make the point by including trametinib treated samples as positive controls. However, the trametinib treated samples also made the transcriptome changes in the ARTi groups hard to read. I wonder what the PCA analysis will look like if the authors exclude the trametinib treated groups.In Figure 1E, we used PCA as a common and easy-to-digest visualization tool to showcase the neutrality of ARTi shRNAmirs. Given the complete absence of significantly deregulated genes for all three ARTi shRNAmirs , we believe that a PCA analysis of just these samples would merely represent experimental noise and not yield additional insights.• This universal and potent ARTi should ensure standardized and controllable knockdown efficiency, however, the knockdown efficiency for KRASG12R is not as potent as that for EGFRdel19. The authors should discuss the differences.We thank the reviewer for pointing this out. The exact level of knockdown on the protein level is hard to determine due to detection limits of the used method. The differences in the extent to mRNA knockdown could be attributable to cleavage efficiencies due to potential secondary structures in the respective mRNAs. We suspect that the KRASG12R transgene expresses at higher levels, compared to EGFRdel19. We might therefore still be looking at the same overall magnitude of knockdown. As we did not perform a detailed analysis of the respective knockdown levels, we do not feel comfortable in stating differences in knockdown levels and therefore do not think that addressing potential differences are justified.• It is interesting to see that, unlike other cancer types, tumor burdens did not decrease after inducing knockdown of STAG1 in STAG2 knockout HCT116 lines in Figure 2L. Have the authors examined senescence markers in this set of mice?We have not investigated these markers and thank the reviewer for this suggestion. More detailed analyses of the phenotype are planned.• Have the authors carefully examined the transcriptome changes induced or if not across all targets at least in the case of ARTi knock into the 3'UTR of STAG1?We thank the reviewer for this suggestion. This would indeed be interesting to conduct for STAG1/2, especially for genes with an integration of the ARTi into the 3’UTR. The reason why we did not perform this analysis with our cell lines is that we used a construct that also adds an AID tag to STAG1 (STAG1_AID_V5_P2A_Blasti_STOP_ARTi), as outlined in the methods section. After the engineering, STAG1 carries the ARTi sequence in the 3’UTR but is also fused to AID::V5. In addition a P2A::Blasticidin_resistance Protein is made from the same transcript. We chose to use this complex strategy with the aim of comparing AID mediated degradation with ARTi-mediated knockdown. Unfortunately, the AID-based approach did not work, and we were not able to observe a reduction in protein levels. We however observed lower expression of STAG1 in the engineered versus the parental cells, likely caused by the tag, and consequently did not conduct gene expression analyses, as we would not be able to assess if transcriptome changes could be solely ascribed to the changes in the 3’UTR. The knockdown levels are hence only analyzed on the protein level.Reviewer #2 (Recommendations For The Authors):This is a fantastic paper, easy to read and provides a very meaningful new and innovative approach for drug target validation. I think the manuscript could be further improved by adding a section to the discussion outlining other approaches that could be used to solve the same problem. For example, Bill Kaelin came up with a strategy of expressing shRNA- or sgRNA-resistant and rtTA- or tTA-regulated cDNAs of essential gene-of-interest followed by sh/sgRNA-mediated ablation of the endogenous gene (e.g.PMID: 28082722), which is conceptually quite similar to the ARTi approach. Similarly, people have used conditional degron tags such as AID tags, dTags, HALOTags, IHZF3 degrons or SMASh either knocked into the endogenous locus or as rescue transgene. Comparing and contrasting the pros and cons of these methods to the ARTi-based approach would be certainly beneficial to the readers.We thank the referee for pointing out these alternative LOF methods. We certainly share the current excitement about various degron tags and are applying them in our own research. In our view, a major downside of these strategies is that they inevitably require changes to the coding sequence of target proteins, which can alter their regulation and function in ways that are hard to predict and control for. We had briefly mentioned this distinguishing feature in our discussion. The strategy proposed by Bill Kaelin, i.e. rescue of the the endogenous gene through Tet-regulated expression of sh/sgRNA-resistant cDNAs, indeed shares many features of the ARTi system, but requires expression of the candidate target from an ectopic promoter element. In contrast, ARTi enables similar perturbations of candidate genes without altering their endogenous transcriptional regulations – a feature that we have highlighted in our revised discussion.All my other comments outlined below should be considered minor and are not essential.1, Suppl Fig.1 C: Please explain what the red star means. How can the knock-out be more than 100%. Please specify what the controls are. Why does shRNA660 exhibit no knockdown at all?The red star indicates ARTi-shRNAmirs that were selected for further characterization. Depicted GFP knockdown levels are normalized to the performance of shRen.713, a well-characterized potent control shRNA targeting Renilla Luciferase. Values of more than 100% mean that the respective shRNA exceeded effects of shRNA.713. shRNA.660 served as a neutral control – its target site was not included in the reporter construct. We thank the reviewer for bringing up these points, which we have clarified in the legend.2, x-axis label in Suppl Fig. 1D is missingWe thank the referee for spotting this and have added this information to the figure and its legend.3, I would argue that ARTi6634 also has a slight effect in MV4-11 similar to its effect to RN2. Maybe add that to the text.We thank the reviewer and have added this observation to our revised text.4, Suppl. Figure Legend 1F - specify which cell line was used (HT-1080 presumably)We apologize for this mistake and now have indicated the cell line in the legend.5, Fig. 2A and E, it might be nice to add the dsRED fusion to the schematics so that the reader sees the difference between the endogenous and the endogenous. One could then also change the color to red instead of blue.We thank the reviewer for this suggestion and adapted the figure accordingly.6, Fig. 2B - In the third lane, there appears to be a residual band of the endogenous EGFR despite the fact that it should be KO. Is this a EGFR wt lysate with EGFR::dsRED::ARTi overexpression and as such a type in the legend or is this a non-complete KO? It might be beneficial to label the legend with EGFR::dsRED::ARTi instead of EGFR::ARTi have one arrow depicting EGFR and one additional arrow showing the EGFR::dsRED fusion .We thank the reviewer for this insightful comment. We interpret the WB signal in lane three as potential cleavage/degradation products of the transgene as all signal disappears upon ARTi-mediated knockdown. Due to space reasons, we would prefer to keep the label as it is. The exact nature of the transgene is stated in the text and in the methods section.7, Suppl Fig. 2d: It is interesting that there is such a huge upregulation of DUSP6 in cells that express EGFR::ARTi compared to parental? The figure legend states: expression levels of DUSP6 in parental and engineered PC-9 cells. I assume the first box (EGFR::ARTi -/ dox -) is the parental line? Is there really a 5x upregulation of DUSP6 upon overexpression of EGFR::ARTi compared to parental (despite the fact that the endogenous EGFR::ARTi is expressed to similar levels compared to the endogenous EGFR)? Please clarify a little better which of the cells are parental and which are EGFR KO and which are transduced with EGFR::ARTi. Might suffice to just explain in the supplmental figure legend that expression of the exogenous EGFR::ARTi in EGFR KO cells leads to increased expression of ERK targets such as DUSP6 and EPHA2 etc.EGFR genes in PC-9 cells is mutated and the rest is wild-type. Therefore, the net-output of EGFR signaling would be higher, even if the EGFR protein levels were exactly the same, as the EGFR gene is only present in the oncogenic form in the engineered cells but a mixture of mutant and wild-type proteins would make up the EGFR pool in the parental cells. The figure legend was changed accordingly, highlighting that DUSP6 is a MAPK downstream gene.We thank the reviewer for this comment. We ascribe the increased expression of DUSP6 to the forced expression of the oncogenic variant of EGFR (EGFRdel19) while only a subset of 8, Suppl Fig. 2e: Similar to my comment #7. Expression of endogenous EGFR is lost upon KO of EGFR, but cylcinD1 expression as well as expression of other ERK target genes increases upon loss of the endogenous EGFR gene with concomitant expression of EGFR::ARTi . It is nice to see that most of those genes are down-regulated upon DOX treatment. However, CyclinD1 is strongly up-regulated - any idea why? Might be nice to comment on this in the supplemental material to make it easy for the reader to interpret the data.We agree with the reviewer that the direct MAPK target genes follow the expected pattern of strong downregulation. We have not studied the expression of CCND1 in detail and therefore cannot comment on the mechanistic basis of this observation.9, Fig. 2F - might be nice to provide some densitometry data to quantify the effect of ARTi-mediated KRasG12R knock-down.We thank the reviewer for this suggestion and apologize that this data is not available for this study. We will include densitometry data in upcoming studies involving ARTi. As the observed knockdown was almost complete and hence readily observable by eye, we did not measure the effects using densitometry. In addition, we would like to mention that the sensor assay contains a quantitative analysis of the knockdown levels.10, Fig. 2I, it might be nice to add the V5 tag to the schematic and mention the V5 tag in the text: ... and homozygously inserted ARTi target sites into the 3'-UTR as well as a V5 tag to the endogenous STAG1 alleles We thank the reviewer for the suggestion and explained the exact makeup of the construct better in the main text. We would however like to keep the figure as simple as possible and put the focus on the endogenous engineering here.11, Fig. 2J - might be nice to provide some densitometry data to quantify the effect of ARTi-mediated STAT1::V5 knock-down.We thank the reviewer for this suggestion and apologize that this data is not available for this study. We will include densitometry data in upcoming studies involving ARTi. As the observed knockdown was almost complete and hence readily observable by eye, we did not measure the effects using densitometry. In addition, we would like to mention that the sensor assay contains a quantitative analysis of the knockdown levels.12, Suppl. Fig 4B: the authors write: 'Western blotting confirmed ... the homozygous insertion of the targeting cassette into the STAG1 locus, ...' . I think the WB nicely shows insertion of the V5 tag into the STAG1 locus, but it I think WB cannot show homozygous insertion. The fact that in Suppl Fig 1B STAG1 expression is  completely ablated, is a good indication, but in Fig. 2J, there is still about 50% expression. As such, proofing homozygous insertion by PCR/Sanger sequencing or densitometry over several experiments or just rephrasing the text a little might be beneficial.We agree with the reviewer and have adapted the respective passage in the main text.Competing interests statement: A patent application related to the design and use of the ARTi system entitled ‘Methods and molecules for RNA interference (RNAi)’ has been submitted by T.H., M.H., J.Z. and R.N. to the European Patent Office (application EP21217407.2).


Drosophila sensory and motor neurons. Furthermore, caper dysfunction results in larval and adult locomotor deficits. Nonetheless, little is known about which proteins interact with Caper, and which RNAs are regulated by Caper. Here we identify proteins that interact with Caper in both neural and muscle tissue, along with neural specific Caper target RNAs. Furthermore, we show that a subset of these Caper-interacting proteins and RNAs genetically interact with caper to regulate Drosophila gravitaxis behavior.Post-transcriptional gene regulation is a fundamental mechanism that helps regulate the development and healthy aging of the nervous system. Mutations that disrupt the function of RNA-binding proteins (RBPs), which regulate post-transcriptional gene regulation, have increasingly been implicated in neurological disorders including amyotrophic lateral sclerosis, Fragile X Syndrome, and spinal muscular atrophy. Interestingly, although the majority of RBPs are expressed widely within diverse tissue types, the nervous system is often particularly sensitive to their dysfunction. It is therefore critical to elucidate how aberrant RNA regulation that results from the dysfunction of ubiquitously expressed RBPs leads to tissue specific pathologies that underlie neurological diseases. The highly conserved RBP and alternative splicing factor Caper is widely expressed throughout development and is required for the development of  Alternative splicing is a fundamental gene regulatory mechanism in eukaryotic organisms that increases both transcriptomic and proteomic complexity. Alternative splicing is a highly regulated process that occurs in the majority of the genome. It is estimated that 92–97% of multi-exon encoding genes in humans undergo alternative splicing . Thus, nAlthough aberrant splicing can be the result of a variety of mutations in both cis- and trans-acting elements, there is particular interest in the role that RNA-binding proteins (RBPs), specifically alternative splicing factors, play in the regulation of alternative splicing. Aberrant splicing, due to mutations in RBPs, has been associated with neurological disorders, including the neuromuscular degenerative disease amyotrophic lateral sclerosis (ALS) and autism spectrum disorder . For exaA significant portion of mRNAs that are alternatively spliced undergo developmental-and tissue-specific splicing events . HoweverDrosophila melanogaster and C. elegans  in both neural and muscle-enriched tissues suggest that Caper may also have a role in translational regulation. This aligns with the recent discovery that Caper is expressed in the cytoplasm in Drosophila larval brains, and that murine RBM39 is found within the cytoplasm  extraction. Samples were DNAse I treated and then RNA was isolated using phenol:chloroform:isoamyl and ethanol/sodium acetate precipitation. Samples were sent to University of Colorado Anschutz Medical Campus Genomics Core Facility for library preparation and RNA sequencing.Drosophila probes was used to generate RNA-Seq libraries. Paired-end sequencing reads of 150 nt was generated on NovaSeq 6000  sequencer at a target depth of 40 million paired-end reads per sample. Raw sequencing reads were de-multiplexed using bcl2fastq.RNA purity, quantity and integrity was determined with NanoDrop (ThermoFisher Scientific) and TapeStation 4200  analysis prior to RNAseq library preparation. The Tecan Universal Plus Total RNA-Seq with the D. melanogaster reference genome (Flybase version dmel_r6.36). We observed 53–57% uniquely mapped reads for input samples and 79–89% uniquely mapped reads for pulldown samples. Final read alignments were quality checked and further processed. We furthermore extracted coverage signals from read alignments for manual data inspection in the IGV genome browser. For differential expression analysis, we downloaded gene annotations from flybase and calculated strand-specific count tables with featureCounts (subread v2.0.1) with the following configuration:Analysis for RNA-sequencing was performed on 20 million to 43 million 150 base-pair paired-end reads. Sequencing adapters were removed, and the reads were quality trimmed with fastp (v0.20.1) and quality was checked with fastqc (v0.11.8). Processed reads were mapped with STAR (v2.7.6a) against the featureCounts -T 8 -s 1 -p -Q 20 -t exon -g gene_id -F GTF -a ${gtf} -o feature_counts.tsv ${bams}.p-value < 0.05 and log-fold change > 0 from the IP with Caper antibody compared to the input.Further data processing was conducted in RStudio (v1.2.5001) using DEseq2 (v1.22.2). From the resulting data, putative interactors were selected by filtering for adjusted Drosophila heads that expressed FLAG-tagged Caper driven by heat-shock. Lysis produced from the heads of yw flies were used as a control. RNA was purified from the immunoprecipitation samples and their respective inputs using TRIzol® Reagent (Invitrogen) extraction. Samples were DNAse I treated and then RNA was isolated using phenol:chloroform:isoamyl and ethanol/sodium acetate precipitation. cDNA libraries were created from isolated RNA using a High Capacity RNA-to-cDNA kit  according to manufacturer protocol. PCR was performed on the cDNA products using OneTaq® DNA polymerase  according to manufacturer protocol. Primers were used to identify the presence of RNA for the following genes: quaking related 58E-1 (qkr58E-1), chickadee (chic), uncoordinated 115a (unc-115a), prospero (pros), longitudinals lacking (lola), zn finger homeodomain 1 (zfh1), acinus (acn), uncoordinated 115b (unc-115b), discs large 1 (dlg1), turtle (tutl), bruchpilot (brp), and chronologically inappropriate morphogenesis (chinmo)  on tissue extracted from adult (chinmo) . Electrop-values are listed in The RT-PCR products were quantified using FIJI gel analysis tool to measure the intensity of the bands. The intensity of the bands was normalized to their respective inputs and then a T-test was performed on the normalized intensities. Caper affinity purifications were denatured, reduced and alkylated using 5% (w/v) sodium dodecyl sulfate (SDS), 10 mM tris(2-carboxyethylphosphine) (TCEP), 40 mM 2-chloroacetamide, 50 mM Tris–HCl, pH 8.5 with boiling 10 min, then incubated shaking at 1000 rpm at 37°C for 30 min. Proteins were digested using the SP3 method . Brieflyp-value < 0.05 and log-fold change > 0 from the IP with Caper antibody compared to the control IP with Rabbit Serum.Tryptic peptides were suspended in 3% (v/v) can, 0.1% (v/v) trifluoroacetic acid (TFA) and directly injected onto a reversed-phase C18 1.7 μm, 130 Å, 75 mm X 250 mm M-class column (Waters), using an Ultimate 3000 nanoUPLC (Thermos Scientific). Peptides were eluted at 300 nL/min with a gradient from 2 to 20% ACN in 40 min then to 40% ACN in 5 min and detected using a Q-Exactive HF-X mass spectrometer (Thermo Scientific). Precursor mass spectra (MS1) were acquired at a resolution of 120,000 from 350 to 1,550 m/z with an automatic gain control (AGC) target of 3E6 and a maximum injection time of 50 milliseconds. Precursor peptide ion isolation width for MS2 fragment scans was 1.4 m/z, and the top 12 most intense ions were sequenced. All MS2 spectra were acquired at a resolution of 15,000 with higher energy collision dissociation (HCD) at 27% normalized collision energy. An AGC target of 1E5 and 100 milliseconds maximum injection time was used. Dynamic exclusion was set for 5 s with a mass tolerance of ±10 ppm. Rawfiles were searched against the Uniprot Human database UP000005640 downloaded 11/2/2020 using MaxQuant v.1.6.14.0. Cysteine carbamidomethylation was considered a fixed modification, while methionine oxidation and protein N-terminal acetylation were searched as variable modifications. All peptide and protein identifications were thresholded at a 1% false discovery rate (FDR). Statistical analysis was performed on log2 transformed iBAQ and LFQ intensities usingp values were adjusted to control the false-discovery rate (FDR) using the Benjamini-Hochberg procedure (cutoff significance was 0.05).GO term enrichment analysis and pathway annotation network analysis was performed using the ClueGO plugin for Cytoscape . Genes wcaper adult negative geotactic behavior. Fifty-one genes encoding protein interactors and RNA targets of Caper were tested with the gravitaxis assay .Protein interactors were identified through liquid chromatography-mass spectrometry (LC–MS) of the IPs and RNA targets were identified through RNA sequencing of RNA isolated from the IPs. In combination, 386 proteins were identified as potential interactors of Caper between the FLAG IP and Caper IP, with 192 proteins unique to the FLAG IP, 94 proteins unique to the Caper IP, and 100 proteins overlapping in both IPs . Furtherd chinmo .This furDrosophila larval neurons and interacts with the translational regulator Fragile X Messenger Ribonucleoprotein (FMRP)  .Drosophila Caper and its human and mouse ortholog RBM39 in alternative splicing  that either result in nonsense mediated decay or the translation of a truncated protein. Furthermore, manual investigation of sequencing read counts reveals that PTC-containing exons of caper mRNA have higher reads in the immunoprecipitation samples than input samples, suggesting Caper might specifically regulate its own mRNA by regulating poison exon inclusion. Therefore, we suggest three different models by which Caper regulates its own mRNA. The first is that Caper regulates the splicing of its RNA to select for the PTC-containing exons as a negative feedback loop , syncrip (syp), and purine-rich binding protein-alpha . Finally, FMRP, which was identified as an interactor of Caper in previous publications is also identified as a conserved interactor in humans , cardinal (cd), suppressor of ER stress-induced death (superdeath), Collapsin Response Mediator Protein (CRMP) (both mutant lines tested), disabled (dab) (one of two mutant lines tested), reversed polarity (repo) (one of two mutant lines tested), discs overgrown (dco) (one of two mutant lines tested), quaking related 58E-1 (qkr58E-1), tan (t) (one of two mutant lines tested), and syp (one of four mutant lines tested). Six of these genes had stronger effects in females than males, as revealed by significant genotype x sex interactions  granules that include the association of RNAs and RNA-binding proteins through liquid–liquid phase separation . Some ofP-body protein components have been identified in HEK293 human epithelial cell lines, and importantly several are orthologous to proteins identified as interactors of Caper including DEAD box helicase 6 (DDX6) / maternal expression at 31B (Me31b), Insulin Like Growth Factor 2 mRNA binding protein 2 (IGF2BP2) /Imp, Argonaute RISC component 1 and 2 (AGO1 and AGO2) /Argonaute 2 (AGO2), and UPF1 RNA Helicase and ATPase (UPF1) /Upf1 RNA helicase (Upf1). Since many Caper interacting proteins are known granule components, Caper may be a component of P-bodies and Staufen-containing granules. Furthermore, all of these RBPs are recognized as being involved in translational repression, mRNA decay, or the miRNA pathway . This sumyc mRNA was pulled down with Caper in the FLAG IP suggesting that myc is also an RNA target of Caper, and that Caper may also function to promote the stability of myc and other mRNA targets.Furthermore, several components of the coding region determinant (CRD) mediated complex were also identified in P-bodies from HEK293 cells and the fly orthologous proteins co- immunoprecipitate with Caper. These proteins include Heterogenous Nuclear Ribonucleoprotein U/CG30122, Synaptotagmin Binding Cytoplasmic RNA Interacting Protein (SYNCRIP)/Syp, Insulin Like Growth Factor 2 mRNA binding protein 1 (IGF2BP1)/Imp, and DExH-box Helicase 9 (DHX9)/maleless (mle) . These cIt is also likely that Caper associates with Staufen-containing RNP granules, which have been demonstrated to mediate translational repression and mRNA transportation . Althougcaper in the regulation of the development of several neural subtypes  which causes reduced production of the neurotransmitter acetylcholine  neurons in Drosophila larvae  was identified as one of the top regulators for the module enriched in GO terms for organ development, locomotion, and neuron differentiation , male-specific lethal 1 (msl-1), male-specific lethal 3 (msl-3), males absent on the first (mof), over compensating males (ocm), and the long non-coding RNA on the X 1 (lncRNA:roX1). Here we show that Caper also targets several RNAs in the sex-lethal (sxl) specific cascade that contributes to sexual development including transformer (tra), transformer 2 (tra2), fruitless (fru), doublesex (dsx), and sister-of-Sex-lethal (ssx). Thus, Caper’s regulation of mRNAs associated with sexual development could explain the observed sex-bias in various neurological phenotypes.We previously demonstrated that the dysfunction of rphology . HoweverSxl utilizes the alternative splicing of genes such as tra and msl-2 as part of an alternative splicing cascade that impacts sex determination and sexual differentiation  inhibits the positive feedback loop of sxl, favoring the inclusion of the poison exon that results in the male variant of sxl  oviduct motor neurons in females. The female-specific splicing of fru and dsx in males results in increased persistence of FS-Ilp7. However, the male-specific splicing of tra results in the loss of FS-Ilp7 in females . Thus, it is possible that Caper regulates only specific aspects of sex determination and development, for example, in a tissue specific manner.ntiation . Howeverin males . Expressin males . Interest of sxl . Since seviously . Three awith sxl . Dysfuncwith sxl . Further females . Thus, Ccaper mutant animals may be aberrant dosage compensation, since one of the GO Terms enriched from Caper RNA targets is for dosage compensation. The proteins Msl-1, Msl-2, Msl-3, Mle, and Mof have been demonstrated to form complexes that are important for binding the X-chromosome in males  encodes an mRNA targeted by Caper that has also been implicated in dosage compensation. Ocm appears to work antagonistically to the dosage compensation complex in Drosophila and reducing the expression of ocm results in rescue of lethality observed in flies with reduced function of msl-1. Conversely, reduction of ocm results in female sterility  asymmetric division and in the chordotonal (ch) cell lineage . There are five members of the CRMP family of proteins in humans and their phosphorylation influences their biological function (Drosophila embryonic axonogenesis (Additionally, interactors of function . For exafunction . In addiogenesis .caper dysfunction results in aberrant NMJ morphology, it is particularly interesting that several genetic interactors of caper are also involved in the development of the NMJ. Syp, a protein interactor of Caper regulates synaptic growth and plasticity in the Drosophila larval NMJ by regulating the translation of key mRNAs with important synaptic functions by associating with RNA encoding key proteins involved in synaptic function, such as futsch, discs large, alpha-spectrin, msp-300, syd-1, highwire, and neurexin-1 (caper mutant NMJs (Acn also regulates NMJ morphology (caper mutants exhibit aberrant NMJ morphology (syp and Acn can also modify caper NMJ phenotypes.Since urexin-1 . Additioant NMJs . Acn alsrphology . Since crphology , it willcaper plays in the development, maintenance and function of the nervous system. Furthermore, we find that Caper interacts with distinct proteins in neuronal tissue compared to muscles, which begins to illuminate why the nervous system may be more sensitive to the dysfunction of ubiquitously expressed RBPs.In sum, our identification of Caper interacting proteins and RNAs in combination with a genetic modifier screen of a subset of these interactors provides better context of the role https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE221381 accession number GSE221381.The original contributions presented in the study have been deposited in the GEO database. This data can be found at: MT and AC performed the experiments and wrote the manuscript. CE performed the mass spectrometry. NP performed the RNA sequencing analysis. JB performed the statistical analysis. EO conceptualized and designed experiments, obtained funding and resources, and supervised the project. All authors contributed to the article and approved the submitted version.This work was supported by the National Institutes of Health 1R15NS104976 to EO and NINDS-NS080685 to MT. The proteomics works was supported by the National Institutes of Health S10-OD025267 to CE.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.