RNA interference (RNAi) technology has been widely used to knockdown target genes via post-transcriptional silencing. In plants, RNAi is used as an effective tool with diverse applications being developed such as resistance against insects, fungi, viruses, and metabolism manipulation. To develop genetically modified (GM) RNAi traits for insect control, a transgene is created and composed of an inversely-repeated sequence of the target gene with a spacer region inserted between the repeats. The transgene design is subject to form a self-complementary hairpin RNA (hpRNA) and the active molecules are > 60 bp doubled-stranded RNA (dsRNA) derived from the hpRNA. However, in some cases, an undesirable intermediate such as single-stranded RNA (ssRNA) may be formed, which is not an active molecule. The aforementioned characteristics of RNAi traits lead to increase the challenges for RNAi-derived dsRNA quantitation.f -qPCR) coupled with a ssRNA preferred endonuclease . This method enables the precise measurement of the active molecules  derived from RNAi traits of GM crops and separately quantifies the dsRNA from ssRNA. Notably, we also demonstrate that the RNase If -qPCR is comparable to a hybridization-based method (Quantigene Plex 2.0).To quantify the dsRNA and distinguish it from the ssRNA in transgenic maize, an analytical tool is required to be able to effectively quantify dsRNA which contains a strong secondary structure. Herein, we develop a modified qRT-PCR method , precision and accuracy. In this report, we demonstrated the deployment of this method to characterize the RNAi events carrying v-ATPase C in maize during trait development process. The method can be utilized in any application which requires the dsRNA quantification such as double-stranded RNA virus or sprayable dsRNA as herbicide.To our best knowledge, this is the first report of a method combining RNase IThe online version of this article (10.1186/s12896-018-0413-6) contains supplementary material, which is available to authorized users. Caenorhabditis elegans [Bt (Bacillus thuringiensis) traits (detailed reviews in [RNA interference (RNAi) mediates a posttranscriptional gene-silencing (PTGS) mechanism via the process of exogenous double-stranded RNA (dsRNA) silencing complementary to endogenous mRNA in the cell . Due to  elegans  and has views in , 9).Diabrotica virgifera) [Helicoverpa armigera) [The RNAi approach for insect control becomes a practical application in crop protection through the expression of hairpin RNAs (hpRNA) in GM crops to downregulate essential genes in target insects. Transgenic plants producing hpRNAs to control insect pests were first demonstrated in western corn rootworm (WCR) (rgifera)  and cottrmigera) . Both stCYP6AE14 gene showed effective larvae growth inhibition [Arabidopsis dicer mutants , it resulted in greater abundance of longer dsRNA, CYP6AE14 gene in the insect is repressed more effectively by transgenic plant materials. This result suggests that a minimal length of dsRNA is required to achieve the optimal efficiency of repression of targeted genes in pests. Moreover, two recent studies demonstrated the length of dsRNA is critical to achieve the desired efficacy. In the study described by Bolognesi et al., different sequence lengths of dsRNAs against Dv Snf7 were tested in bioassays, a size cut-off of approximately 60 bp dsRNA is required to display efficacy against corn rootworm [Dv v-ATPase C with at least 60 bp in length resulted in high levels of larval mortality [In the study by Mao et al. (2007) plants engineered to express dsRNAs against the bollworm hibition . More inrootworm . Anotherortality . In contInterestingly, the gene encoding RNA-dependent RNA polymerase (RdRP), required for the secondary siRNA amplification and transitive RNAi effects, is absent in insects  but is pSince the length and concentration of dsRNA are critical attributes to achieve the efficacy for controlling WCR, a reliable analytical tool to quantify dsRNA is required for RNAi trait development. The approach of detecting dsRNAs can be challenging due to its strong secondary structure derived from self-complementary inverted repeats (IRs). The secondary structure of the RNA molecules is often unpredicted and is determined by specific base pairing interactions within the encoded nucleotide sequence . In addif -treatment RNA prior to cDNA conversion (abbreviation: RNaseIf -qPCR) which has proven to be both cost effective and feasible. RNase If is an endonuclease which has preferential activity to digest ssRNA over dsRNA. RNaseIf -qPCR allows users to precisely distinguish dsRNA from ssRNA and effectively assay the dsRNA in a high-throughput manner. The reported method here may be easily adopted for any applications in the emerging field of RNAi-based insect pest control with the need for dsRNA quantitation.Studies in viral biology have developed some methods to characterize viral dsRNA such as immunofluorescence analysis or conventional RT-PCR. Using the monoclonal dsRNA-specific mouse antibody, the dsRNA may be detected via immunoblot or immunohistochemistry , 19. MorDiabrotica virgifera) for our testing. V-ATPase is a highly conserved enzyme complex that acidifies intracellular organelles through pumping protons across the plasma membranes and consists of catalytic V1 and membrane-bound V0 two subcomplexes [Dv v-ATPase C [Dv v-ATPase C open reading frame (ORF)) was selected for dsRNA synthesis [Dv v-ATPase C dsRNA was treated with DNase  to eliminate DNA contamination. After inactivation of the DNase, the dsRNA was then diluted to 100 pg/μL. The synthetic ssRNA, without further DNase treatment, was diluted to 100 pg/μL with RNase free H2O. The 10-point standard dilution and mixtures of dsRNA, ssRNA and wild-type maize B104 leaf RNA were created at the different ratios as specified in Tables In this study, we used RNAi carrying sequence targeting vacuolar H + -ATPase (V-ATPase) subunit C in western corn rootworm (WCR)  were created based on the vector previously described [Dv v-ATPase C open reading frame (ORF)) of Dv v-ATPase C sequence was constructed. The binary vector was designed to express a hpRNA with 149-bp invert-repeated sequence with a loop sequence in between the inverted repeats. Transcription of the v-ATPase C mRNA was driven by the maize uniqutin 1 promoter and terminated by a 3′ untranslated region (UTR) from a maize peroxidase 5 gene (ZmPer5 3’UTR). The binary vector was then transformed into Agrobacterium tumefaciens. The binary vector was transformed into maize via Agrobacterium-mediated transformation of immature embryos isolated from the inbred line, Zea mays c.v. B104 following the standard method [The transgenic maize events producing escribed . Brieflyd method .Plant RNA extractions were carried out using the Norgen Plant/Fungi RNA purification kit . Four leaf punches of maize plants at v4 stages were collected on ice into a 96-well collection plate. Samples were then ground in lysis buffer for 5 min using a 3.2 mm stainless steel bead. The supernatant was separated from the plant debris and precipitated with 100% ETOH. The subsequent binding and wash steps followed the manufacturer’s procedure with an on-column DNase I (Norgen) treatment for 15 min. Once eluted, the isolated RNA was then quantified on the NanoDrop-8000  and normalized to 50 ng/μL.f  per the manufacturer’s protocol at 37 °C for 10 min followed by heat inactivation at 70 °C for 20 min. The digested samples were purified using the RNA Clean-up and concentration kit (Norgen) following the manufacturer’s protocol. The RNase If treated and purified dsRNA were then used for cDNA synthesis.The isolated RNA was digested with RNase IHigh capacity cDNA RT kit  was used for qRT-PCR analysis with the following modifications. (1) The dsRNA is pre-incubated at 95 °C and 70 °C for 5 min in the presence of the random hexamers and flash frozen with ice. (2) Multiscribe™ reverse transcriptase was then added to the pre-incubated RNA:hexamers mixture, then follow the manufacture protocol for cDNA conversion.f to digest the ssRNA while another aliquot without RNase If treatment containing both the ssRNA and dsRNA was set aside. The same proportion of RNase If -treated and non-treated RNA was used for downstream cDNA conversion, respectively with the pre-incubation of RNA and random hexamers described above. For example, 1 μg of total RNA was digested via RNase If and eluted in 50 μL after clean-up. Five microliter (~ 100 ng) of RNaseIf-treated RNA was then used for cDNA conversion (both 95 °C and 70 °C); in a parallel set, 100 ng of untreated RNA was directly used for cDNA conversion (both 95 °C and 70 °C).For the synthetic dsRNA/ssRNA mixture or leaf total RNA from transgenic plants, an aliquot of RNA was treated by RNase IDv v-ATPase C in this study) and the Zea mays TIP41 as the reference gene. For cDNA converted from the RNA without being treated by RNase If, the assays were performed in biplex reaction . For cDNA from RNA treated with RNase If, only Dv v-ATPase C was assayed in the singleplex reaction. To normalize the dsRNA, a separate reaction using the same proportion of RNA without RNase If treatment was used, as the TIP41 signal from a separate non RNase If - treated RNA was used to normalize dsRNA. To prepare the assays, LightCycler®480 Probes Master (Roche) was prepared at 1× final concentration in a 10 μL volume containing 0.4 μM of each primer and 0.2 μM of each probe. TIP41: . Dv v-ATPase C .After the cDNA conversion using the RNA from different treatments, the ensuing cDNA was quantified via qRT-PCR using the LightCycler®480 system  with a hydrolysis probe. The assays were run for the target gene  where ∆Ct = Ct (target gene) - Ct (reference gene) to obtain the relative quantitation over the endogenous gene.A two-step qRT-PCR program was performed with 40 cycles of the denaturation step at 95 °C for 10 s and extension step at 58 °C for 40 s with fluorescence acquisition. Cp values, the point at which the florescence signal crosses the background threshold using the fit points algorithm (LightCycler® software release 1.5) and the relative transcript level (RTL) was calculated using 2^ f to remove the ssRNA and another aliquot was left un-treated. The samples were then precipitated and resuspended in 20 μL of RNA sample loading buffer . Five microliter (5 ng) of 0.3-1.5Kb DIG-labeled RNA molecular weight marker III (Roche) was aliquoted and added to 15 μL of RNA sample loading buffer. Samples and marker were denatured at 70 ͦ C for 15 min and immediately placed on ice.Synthetic ssRNA and dsRNA was diluted to 100 pg with 5 μg of wild-type maize B104 leaf total RNA. An aliquot of both was treated with RNase IA 1.5% denaturing gel was created using Roche Mp agarose (Roche) with the addition of 6.5% formaldehyde in 1× MESA buffer (MOPS-EDTA-sodium acetate). Gel was cast and run in electrophoresis equipment capable of running samples at 65 V/30 mA for 2 h and 30 min.Following electrophoresis, the gel was rinsed in 2X SSC for 15 min. The gel was passively transferred to a nylon membrane  overnight at RT, using 10X SSC as the transfer buffer. Following the transfer, the membrane was rinsed in 2X SSC for 5 min, subsequently crosslinked , and allowed to dry at RT for up to 2 days.The membrane was prehybridized in UltraHyb buffer (Ambion) for 1-2 h at 65 °C. Blots described were probed with a 149-nt antisense single-stranded RNA complementary to the sense-strand RNA of the v-ATPase C hpRNA labelled with DIG digoxigenin-11-UTP using the Roche DIG RNA labeling Kit (Roche) according to manufacturer’s protocols . Probes Dv v-ATPase C, Elf1α and TIP like-41 (TIP41). One hundred twenty-five nanogram of total RNA from the same RNA source that was used for RNase If -qPCR was analyzed as assay input for QGP assay. The QGP assay was performed following the standard protocol of QGP except with the additional step to incubate RNA with probe sets. Briefly, the purified RNA was incubated with the probe sets at 95 °C for 5 min prior to mixing with the capture beads for overnight hybridization [The QuantiGene Plex 2.0 assay (QGP) assay  used in this study was the custom triplex panel QGP Corn 41,669 3 Plx carrying three plex assay of dization . A typicDv v-ATPase C was calculated by dividing the Dv v-ATPase C (MFI ATPase C - MFI background) to TIP41 (MFI TIP41 - MFI background).The QGP signal is reported as median fluorescence intensity (MFI) and is proportional to the number of target RNA molecules extracted from the purified RNA of the samples. MFI was calculated by measuring signals on 35-100 beads/target. MFI values obtained from background blank wells with no target RNA were subtracted from MFI of each target reading. The relative quantification of f -qPCR assay. Figure f -qPCR assay (see protocol in methods). Briefly, RNA is first extracted from the plant samples. Then, the isolated RNA is aliquoted and treated with RNase If endonuclease to remove ssRNA, while an aliquot of RNA without RNase If was set aside. After the RNase If treatment, the remaining dsRNA  should yield no detection as all the ssRNA should have been digested and the 70 °C is insufficient to open the dsRNA duplex for annealing of the random hexamers inside the dsRNA region being assayed. In addition to the RNase If -treated RNA, an equal amount of the non- RNase If -treated RNA is used directly for cDNA conversion with random hexamers at the same 95 °C and 70 °C pre-incubation. The result from the 70 °C pre-incubated cDNA conversion detects and quantifies the ssRNA present in the samples (abbreviation 70 RTL), which resembles regular qRT-PCR protocol. The 70 RTL and R95 RTL result may serve as a direct comparison between RNase If -qPCR and regular qPCR. The result from the 95 °C pre-incubated cDNA conversion represents the total of both the dsRNA and ssRNA present in the sample (abbreviation 95 RTL). Both ssRNA and dsRNA RTL were calculated using the endogenous gene quantified by 70 RTL treatment.The RNAi transgene design is composed of inverted-repeat sequences of the target gene inserted into the genome with a spacer loop region connecting the repeats. The mRNA transcripts from such transgenes are subject to form a self-complementary hairpin RNA (hpRNA) structure with a based-paired stem encoded by the inverted repeats and single stranded loop sequence. However, in addition to the hpRNA, the mRNA transcripts may sometimes form an unpredicted intermediate such as partial single-stranded mRNA (ssRNA). Because of the strong secondary structure of the hpRNA and the potential for unexpected ssRNA formation, challenges for reliable RNAi analytics during the development of RNAi trait could be encountered. Since the dsRNA strands are the active molecules for efficacy , 12, an RNA Fig.  is furthols Fig. . The cDNDv v-ATPase C gene was selected for dsRNA synthesis. An ssRNA with overlapping region to the dsRNA was also synthesized . True to expectation, the dsRNA remained detected after the RNase If treatment with the RNA blot (lane3 and 4). This confirms the dsRNA duplex structure and validates the specificity of the RNase If treatment. The mixed dsRNA and ssRNA were still detected after the RNase If treatment, suggesting the dsRNA digestion is not affected in the presence of ssRNA (lane5 and 6).Since the whole procedure involves multiple steps, we first performed linearity testing to evaluate if the dsRNA or ssRNA analyzed by this method can display appropriate linearity. For this test, synthetic dsRNA and ssRNA were used for assay validation. A 149-bp sequence which targets the zed Fig. . To confzed Fig. . The synf -qPCR assay   Fig. .Fig. 3RNf -qPCR assay demonstrates dilutional linearity with acceptable accuracy over a range of concentration. Also, the method demonstrates exceptional sensitivity. The limit of quantitation (LOQ) for Dv v-ATPase C dsRNA was determined from the lowest amount from the 10-point dilution as ~ 0.001 pg/ μL.Using the 10-point dilution of synthetic dsRNA and ssRNA as outlined above, the post-extraction spike recovery was assessed, which may be inferred for the accuracy of the assay. The amount of 10-point dilution , the corresponding RTL  and % recovery are presented in Table To further assess the repeatability of the assay , we created 10-point dilution of dsRNA and repeated the assays three times; each by different analysts. The Cp values from qRT-PCR of each standard point was captured and the coefficient of variation (% CV) was calculated as inter-assay precision using the Cp values of each standard point observed from three assay runs , demonstrating that the assay is capable of accurately measuring and distinguishing the dsRNA from ssRNA.The RNase IDv v-ATPase C RNAi were produced using the method described . RNA was isolated from leaf tissues of distinct T0 transgenic events, and were analyzed via the RNase If -qPCR assay to quantify the dsRNA and ssRNA RTL, respectively  from ssRNA, .To demonstrate the practical applicability of this assay for deployment in trait development process, we tested transgenic maize containing an RNAi transgene. Transgenic events carrying ely Fig. . Within f -qPCR assay delivers reliable quantification to the active molecules of an RNAi transgene.To further investigate the dosage effects using this method, we deployed the assay in a segregating S1 population including homozygous and hemizygous transgenic events. Distinct transgenic events from different constructs were assayed. In each transgenic event, the homozygous plants showed ~ 1.6-2.0 fold increase in dsRNA expression as compared to the corresponding hemizygous plants, suggesting that the assay may quantify the dsRNA with an accurate dose response Fig. . Altogetf -qPCR, a custom QGP assay was designed based on the Dv v-ATPase C RNAi transgene in maize and the endogenous gene (e.g. TIP41). Purified leaf RNA from different transgenic maize events expressing Dv v-ATPase C RNAi transgene was used for cross comparison. RNase If -qPCR displays high concordance with QGP via the RTL measurements of the Dv v-ATPase C dsRNA expression among distinct transgenic events  and 1.07, respectively. Together, it suggests that the RNase If -qPCR assay can serve as an alternative analytical tool for dsRNA detection for RNAi trait in GM crops.The utility of QuantiGene Plex 2.0 (QGP) platform for dsRNA detection has been previously shown . The Quants Fig. . Regressf treatment. This new assay allows for the exclusive detection of the active RNAi molecules present in transgenes . Through the assay validation process, we demonstrated that the assay displays dilutional linearity, precision and accuracy. Importantly, it can accurately distinguish between dsRNA from ssRNA generating concordant dsRNA-to-ssRNA ratios. This allows for the accurate quantitation of the active molecules  vs. non-efficacious intermediate (ssRNA). Applied to transgenic maize plants, the assay identifies events containing a significant level of Dv v-ATPase C RNAs which are present in single-stranded form (ssRNA). Without the ability to differentiate dsRNA from ssRNA, results may be misleading with inaccurate levels of active RNAi molecules . Thus, the described method here may serve as an effective tool to quantify dsRNA for RNAi traits in GM crops. In addition to the utilization of this method to characterize RNAi traits in crops, the reported method may be used for potential GM detection of the active ingredient as RNA-based approaches.The application of RNAi in plants has been extensively explored in the past decades. Considering the application of RNAi for insect control was successfully demonstrated , this maf treatment and (2) 95 °C pre-incubation with random hexamers prior to cDNA conversion. Because it is important to ensure effective RNase If digestion, a QC step using 70 °C pre-incubation (R70 RTL) is important to evaluate if there are any undigested ssRNA remaining. Secondly, due to the strong intramolecular binding affinity of dsRNA, a 95 °C pre-incubation helps to ensure the dsRNA is open and accessible to the random hexamers prior to cDNA conversion. In addition to those two steps, because the RNase If -treated RNA lacks ssRNA, the RTL is normalized to the reference gene from a separate preparation produced without RNase If -treated RNA. Such cross normalization is not unprecedented used for qRT-PCR [f and non-RNase If cDNA conversions is particularly important in order to obtain reliable results due to the fact the RNA samples are treated separately. Overall, the method represents a cost-effective and feasible solution to quantify and differentiate dsRNA and may be leveraged into any application which requires for dsRNA quantitation.The success of this new dsRNA assay involves two critical steps: (1) Effective RNase I qRT-PCR . For traf -qPCR assay developed here provides an alternative method to effectively quantify the active molecules  of RNAi traits in GM crops. The modifications to conventional qPCR methods with the aid of a specific endonuclease and modified cDNA conversion protocol without additional equipment other than those used for conventional qPCR makes this method both feasible and cost-effective for all laboratories. This assay demonstrates equivalence to another hybridization-based method . Overall, the simplicity of this method makes it an alternative method for any application with the need of dsRNA quantitation such as the study of dsRNA virus in microbes.In conclusion, the RNase I


Plasticine is widely used in the analysis of metal forming processes, due to its excellent material flow ability. In this study, plasticine is used as the soft punch to fabricate array micro-channels on metal sheet in the micro sheet stamping process. This is because plasticine can produce a large material flow after being subjected to force and through the material flow, the plasticine can cause the sheet to fill into the micro-channels of the rigid die, leading to the generation of micro-channels in the sheet. The distribution of array micro-channels was investigated as well as the influence of load forces on the sheet deformations. It was found that the depth of micro-channels increases as the load force increases. When the load force reaches a certain level, a crack can be observed. The micro sheet stamping process was also investigated by the method of numerical simulation. The obtained experimental and numerical results for the stamping process showed that they were in good agreement. Additionally, from the simulation results, it can be seen that the corner region of the micro-channel-shape work piece has a risk to crack due to the existence of maximum von Mises stress and significant thinning. With the requirements of miniaturization in many industrial clusters, there is a growing demand for micro parts. Metallic micro-parts are becoming more and more important in practical applications due to their mechanical properties and thermal stability which are superior to those of polymeric micro-parts . As a prHowever, in such processes it would be hard and complex to design and manufacture the rigid die and punch because accurate assembly between the rigid punch and die should be guaranteed, as when traditional rigid punch forming technology is used to deform the metal sheet. Therefore, micro sheet stamping processes based on the soft punch are employed. It is easy to assemble the punch and rigid die precisely after using soft material as the punch in the process . The coset al. [The sheet soft punch stamping process has been a subject of investigation. Browne and Battikha  presenteet al.  optimizeet al.  investigIn order to overcome these drawbacks, plastic material may well be a potential choice as the soft punch. Plasticine, the most widely used soft modeling material, which shows elastic-plastic flow behavior and excellent material flow ability, has been successfully used as a convenient model material to simulate plastic deformation of metals in metal forming processes for the last 40–50 years ,13,14,15In this paper, plasticine is used as the soft punch to fabricate array micro-channels on metal sheet. The physics of the micro sheet stamping process using plasticine as soft punch is studied along with the influence of load forces on the deformations. The experimental results on the distribution of array micro-channels are also presented. The implicit procedure of ANSYS ﬁnite element code was employed to better understand the process as well as to evaluate the risk of cracks.In order to study the characteristics of the micro sheet stamping process using plasticine as soft punch, the experimental setup was prepared as shown in In this research, pure aluminum (10 μm in thickness), which has an excellent formability and wide applications in micro parts fabrication, was selected as the work piece. The red colored plasticine (made by CYMO) was chosen as the soft punch to transfer the forming force. Since commercially available plasticine contains a considerable amount of air pockets, these air pockets should be squeezed out of the material in order to establish a reasonable degree of homogeneity. After removing the air pockets by working the material through repeated rolling and folding operations, the plasticine material was cut into pieces, 40 mm in diameter and 10 mm in height by using a plexiglass “cookie cutter”. The cylindrical-shaped plasticine was placed in the rigid place, as shown in In order to investigate the influence of load forces on deformation behaviors, the maximum load forces were set as 1000 N, 2000 N, 3000 N, 5000 N, 8000 N and 9000 N. That is to say, in our experiments, the loading was stopped once the load force reached the set values described above. Since six array micro-channels were formed with the help of the rigid die after stamping, before going into a critical discussion of the influence of load forces on deformation behaviors, it is necessary to understand the data on the array micro-channel distribution. It is important to highlight the influence of load forces on the profile curves of the formed channels. In an attempt to simulate the micro sheet stamping process with different final load forces, the implicit procedure of ANSYS ﬁnite element code was employed.There are two different material models in the FEA model. One is the elastic-plastic model of metal sheets and the other is the elastic-linear strain hardening material model to describe the deformation of plasticine. In order to obtain accurate results, precise material models should be inputted into the numerical model.As mentioned above, 10 μm thick commercially supplied aluminum sheets were used in our experiments. The micro sheet stamping process is a quasi-static process with low value and small change of strain-rates, some assumptions are applied in the modelling such as: materials can be modelled as elastic/perfectly plastic, all plastic deformation occurs at the same low strain-rate, and a linear equation of state is valid . Therefon where K is the strength coefficient and n is the strain hardening exponent [Plasticine is a proprietary material, which has nonlinear stress-strain characteristics for relatively large deformations. The common use of plasticine does not demand a strict control of properties. As a consequence, the behavior of plasticine may vary from batch-to-batch, often in an unpredictable manner. The constitutive model selection has been a subject of investigation for many researchers in the past . There aexponent ,15,17. Iexponent ,19. In oThe compression tests were carried out using red plasticine (Cylinder: 12 mm in diameter and 30 mm in height) at the same constant crosshead speed of 5 mm/min in order to determine its mechanical properties. In these uniaxial compression experiments, data were taken in the form of compression load against the deformation of plasticine. Later on, these data were used to obtain the true stress-true strain curve of plasticine. Micro draw angle: α = 10°;R = 40 μm;Upper radius of the rigid die: r = 20 μm;Lower radius of the rigid die: w = 320 μm;Width of the micro groove: h = 500 μm.Depth of the micro groove: In this paper, numerical simulations and experiments were done to demonstrate the feasibility of the micro sheet stamping process using plasticine as the soft punch. It can be figured out that every micro-channel is independent under symmetrical loading during the stamping process. Therefore, one micro-channel was extracted to establish the FEA model to simulate the micro sheet stamping process. The elastic-linear strain hardening material is deﬁned for plasticine with size 0.8 mm × 0.72 mm , and the elastic/ perfectly plastic model is deﬁned for 10 μm thickness aluminum sheets with size 0.8 mm × 0.01 mm . The plasticine and the thin aluminum sheet were modeled by utilizing PLANE 182 element which is a two dimensional four-node element. The rigid punch and die are deﬁned with several lines and arc elements of type TARGE 169. The contacts between the surface of the plasticine and metal sheet and the surface of the rigid punch and die were modeled with the three-point element elements of CONTA 172. Symmetrical boundary conditions were deﬁned according to the actual bound conditions. Additionally, the rigid punch should be loaded with a constant speed of 5.0 mm/min and to meet the actual situation of the experiments, the friction coefficient was set to 0.18 since Vaseline was used in the stamping experiments as a lubricant.In order to find out the zone of crack, the birth-death element method was used. The elements which have stress values that exceed the failure stress would be removed in the simulation. (1)By experiment, the whole six array micro-channels can be obtained. It was observed that the surfaces of the micro-channels possess good surface quality. Since the plasticine for the soft punch is softer than the metal sheets and other rigid dies, the stamping process can be considered as a non-damage forming process.(2)The depths of the channels are increased with increase in the final load forces. When the final load force reaches a certain level, cracks can be observed. Additionally, according to the distribution of the array micro-channels as well as the depth changes, it can be assumed that the existence of friction between the container wall and the plasticine is responsible for the uneven distribution of the array micro-channel depths.(3)According to the simulation results, the micro sheet stamping process may be divided into two different stages: the ﬁrst is the plasticine self-deformation stage; the second is the metal sheets’ deformation under the pressure of plasticine to move downward with ﬁlling of the metal sheets until they reach the form limit.(4)Based on the contour of von Mises stress distribution, the maximum von Mises stress of the work piece occurred in the corner region of the work piece. During the process, the outside material of the sheet is bent, and then drawn into the die cavity. The inner material is pushed downwards and the tension force is applied at the corner region. The interfacial friction resists the material ﬂow and further accelerates the material thinning. The thin zone of the sheet is located in the corner region which is the same as the maximum von Mises stress zone. It is easy to generate a crack and have the risk of sheet cracks.In this paper, the micro sheet stamping process using plasticine as soft punch was studied with experiments and simulations along with the influence of load forces on the sheet deformations. The following conclusions can be made according to the experimental and simulated results:


Classical swine fever (CSF) is a highly contagious fatal infectious disease caused by classical swine fever virus (CSFV). A better understanding of CSFV replication is important for the study of pathogenic mechanism of CSF. With the development of novel RNA in situ Hybridization method, quantitatively localization and visualization of the virus RNA molecular in cultured cell or tissue section becomes very important tool to address these pivotal pathogenic questions. In this study, we established ViewRNA ISH method to reveal the dynamic distribution of CSFV RNA in PK15 cells.We designed several specific probes of CSFV RNA and reference gene β-actin for host PK15 cells to monitor the relative location of CSFV RNA and house-keeping gene in the infected cells. After determining the titer of reference strain CSFV (HeBHH1/95) with the 50% tissue culture infective dose (TCID50), we optimized the protease K concentration and formalin fixation time to analyze the hybridization efficiency, fluorescence intensity and repeatability. In order to measure the sensitivity of this assay, we compared it with the fluorescent antibody test (FAT) and immunohistochemical(IHC) method. Specificity of the ViewRNA ISH was tested by detecting several sub genotypes of CSFV  which are present in China and other normal pig infectious virus , porcine parvovirus (PPV), porcine pseudorabies virus (PRV) and porcine circovirusII(PCV-2).−8, while the sensitivity of FAT and IHC were 10−5 and 10−4, respectively. The ViewRNA ISH was specific for CSFV RNA including 1.1, 2.1, 2.2 and 2.3 subtypes, meanwhile, there was no cross-reaction with negative control and other viruses including BVDV, PPV, PRV and PCV-2. Our results showed that after infection at 0.5 hpi , the CSFV RNA can be detected in nucleus and cytoplasm; during 3–9 hpi, RNA was mainly distributed in nucleus and reached a maximum at 12hpi, then RNA copy number was gradually increased around the cell nucleus during 24–48 hpi and reached the peak at 72hpi.The lowest detection threshold of the ViewRNA ISH method was 10To our knowledge, this is the first to reveal the dynamic distribution of medium virulence CSFV RNA in PK15 cells using the ViewRNA ISH method. The sensitivity of the ViewRNA ISH was three to four orders of magnitude higher than that of FAT and IHC methods. The specificity experiment showed that the ViewRNA ISH was highly specific for CSFV and no cross-reaction occurred to negative control and other pig infectious virus. This assay is more suitable for studying the CSFV RNA life cycle in cell nucleus. The results proved that CSFV RNA enters into PK15 cells earlier than 0.5hpi, relative to the eclipse period of cytoplasm is 6–9 hpi and CSFV RNA has ever existed in nucleus. Classical swine fever (CSF) is a highly contagious fatal infectious disease that primarily affects pigs caused by classical swine fever virus (CSFV) –3. CSF iTherefore, it is important to study the replication circle which can pay the way to reveal the pathogenic mechanism of CSFV infection , 8. Howerns and E2 with the host cell membrane, and then enter the host cell by the receptor-mediated endocytosis [CSFV attaches the host cell mainly through fusion of its membrane glycoprotein Eocytosis . Cell moocytosis . Studiesocytosis . HoweverViewRNA ISH is a novel RNA ISH technology developed based on ISH, which has been widely used in cancer study , 13, andConsidering the fluorescence intensity and repetition are important for the optimum condition of ViewRNA ISH, the best working concentration of protease K and the optimum formalin fixed time were analyzed through orthogonal test. We found the best working concentration of protease K is one thousandth and the optimum formalin fixed time is 30 min. On this condition, it has the best S/N ratio and showed no impact to cellular morphology.−1 to 10−10) that were inoculated in three groups PK15 cells to test the sensitivity of ViewRNA ISH, FAT and IHC method, meanwhile measuring the respective TCID50 by FAT method. The lowest detection threshold of ViewRNA ISH was 10−8, also the TCID50 of diluted solution is 10−7.75/ml, while the sensitivity of the other two methods are 10−5 and 10−4, and the TCID50 of diluted solution are 10−4.25/ml and 10−4.6/ml, as shown in Fig. In order to detect the sensitivity of ViewRNA ISH, we compared it with the FAT and IHC method. We used 10-fold serial diluted HeBHH1/95 strain  presented in China and other pig infectious virus  were used to test the specificity by the ViewRNA ISH. Only SM strain(genotype 1.1), C-Strain (genotype 1.1), HeBHH1/95 , and HeNBY1/96(genotype 2.3) can be detected by ViewRNA ISH (red fluorescence) in the infected cells. Meanwhile, there is no positive signal in BVDV, PPV, PRV and PCV-2 infected cells  is a powerful technique for detecting nucleic acids in cells and tissues. Here we describe ViewRNA ISH that are optimized for detection of CSFV RNA in infected PK15 cell. The operational processes of ViewRNA ISH including The assay includes three steps of sample processing, probe hybridization and visualization.The relative position of viral RNA and nucleus can be observed under fluorescence confocal microscopy which has the function of obtaining different spectrum section fluorescence signals at the same time and processing obtained cascading effect photos with Volocity Demo software, overcoming the disadvantages of low signal group strength and worse sensitivity of traditional fluorescence in situ hybridization (FISH), and the sensitivity is obviously higher than FAT and IHC. ViewRNA ISH can detect CSFV RNA gradually transferring from karyon to cytoplasm at 6 ~ 9hpi, but FAT was unable to detect the viral E2 protein in cytoplasm until 16 hpi, indicating that ViewRNA ISH is better than FAT and IHC to study the dynamic distribution of CSFV RNA in the infected cell. The specific probe make this method very efficient and there were no cross reactions with BVDV, PPV, PRV and PCV-2 swine virus.As we know that CSFV entered cells by endocytosis, and replicated in the cytoplasm. The mature viruses were released from infected cells by budding or exocytosis. But viral RNA can also be detected in the karyon at the early infection stage in this experiment, suggesting it may generate some proteins which related with RNA self-duplication in cell nucleus. It was reported that heterogeneous nuclear ribonucleoprotein (hnRNPs) existing in nucleoplasm, taking part in the synthesis processing of viral RNA. For example, hnRNpAI participate in replication of hepatitis C virus. Knockout of the hnRNpAI, the efficiency of HCV RNA replication would be reduced . Some reIn addition, the envelope glycoproteins E2 is a non-essential protein of CSFV replication, which appeared behind the appearance of viral RNA. To study the dynamic distribution of CSFV RNA in infected cell, FAT were also used to test the E2 protein dynamic of HeBHH1/95 strain in PK15 cells, which can only detect weak fluorescence in the endochylema until 16hpi, however, ViewRNA ISH is a confocal ISH, which can detect positive fluorescenceat at 0.5hpi. As shown in Fig. RNA ISH has been applied to the studies on viral RNA localization of nuclear matrices, using ISH in combination with immunohistochemical method. However, this is the first report that ViewRNA ISH has been applied to the detection of the dynamic distribution of CSFV medium virulence strain (HeBHH1/95) RNA after infected the PK15 cells, which has exhibited some better features such as high sensitivity and specificity. The sensitivity of ViewRNA ISH was higher three and four order of magnitude than FAT and IHC methods, and ViewRNA ISH were highly specific for the detection of CSFV and no cross-reaction occurred in negative control and other pig infectious virus. ViewRNA ISH method seems to be very useful for the detection of the dynamic distribution of CSFV RNA in the early stage of viral infection, which may contribute to the study of CSFV replication and also will be helpful for designing and developing new vaccine. However, there are some drawbacks with this method, such as the ViewRNA ISH reagent are expensive and unmarketed. The results proved that CSFV RNA enters cell early than 0.5hpi, relative to the eclipse period of cytoplasm is 6–9 hpi and RNA has ever existed in nucleus.P1: GGACTAGCAAACGGAGGGACTAGCCGTAGTGGCGAGCTCCCTGGGTGGTCTAAGTCCTGAGTACAGGACAGTCGTCAATAGTTCGACGTGAGCAGGAGCP2: TATGATTTATTGCAAGCCCAGAGGTACGGTATAGAAGACGGGATAAATATCACCAAATCCTP3: AGGTGGTCAGACAACACTTCTAGTTACATGCCGGGGAGAAATACAACCACAATCCTAGCTAAAATGGCCACAAGGTTAGATTCCAGTGGTGAGAGGGGTACCATAGCATATGAGAAAGCAGTAGCATTCAGCTTCCTGCTGATGTACTACTB: TTCCTTCCTGGGTATGGAATCCTGTGGCATCCACGAAACTACCTTCAACTCAATCATGAASeveral specific CSFV probes (P1 is located at 5’-UTR 112 ~ 210 base sequence and P3 is located at NS5B 11426 ~ 11573 base sequence) and labeled probes (P2: label Cy3 red fluorescence) were designed based on CSFV (AF333000) sequence, and the designed probes should be avoid homology regions of bovine viral diarrhea virus (BVDV) (JQ799141) sequence and Border Disease Virus (KC963426) complete sequence. Specific pig house-keeping gene β-actin (ACTB) (AK237086) probe was designed as internal reference with labeled FITC green fluorescence. The design and synthesis of CSFV RNA ISH is completed by classical swine fever reference laboratory and NovoAT&M Biomaterials. The information of the probes sequences:CSFV shimen strain , hog cholera lapinizedvaccine (C-Strain), SX-DT/2001 strain, HeBHH1/95 strain and HeNBY1/96 strain are preserved at the China Institute of Veterinary Drugs Control; BVDV , porcine pseudorabies virus (PRV AV1211) and porcine parvovirus (PPV09/79 strain) was kindly provided by China Veterinary Culture Collection Center. Porcinecirco virus II (PCV-2) was kindly provided by Professor Zhou Jiyong of Zhejiang University. The information of these sample is listed in Table ViewRNA ISH reagent  is purchased from NovoAT & MBiomaterials Co, Ltd. The classical swine fever monoclonal antibody (WH303) was kindly provided by England AHVLA Trevor Drew. Lab-Tek II Chamber Slide is purchased from America Thermo Fisher Scientific Company.−1 to 10−6) were inoculated in PK15 cell in the 24 well plate, cultured in RPMI 1640 medium with 8% fetal bovine serum (FBS) under 37 °C in 5%CO2. After 72 h inoculation, the FAT method was used to measure the TCID50 titer.The diluted HeBHH1/95  of the strain HeBHH1/95 strain (10−4.5 TCID50) were inoculated in three groups PK15 cells. After inoculation for 72 h at 37 °C under 5%CO2, each group was used to detect the viral RNA by ViewRNA ISH,FAT and IHC, respectively, to test their sensitivity.200ul serial dilutions (from 102, two group inoculated cells was used to detect the specificity using ViewRNA ISH and FAT methods.Different kinds of CSFV, BVDV, PPV, PRV, PCV-2 and other related viral strain quality control sample were inoculated in two groups PK 15 cells. After inoculation for 72 h at 37 °C under 5% CO


Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5′ cap of 5′ and 3′ cap of 3′ untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA–protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org.As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients,  RNA plays pivotal roles in essentially every process in living cells, acting as gene regulators, molecular recognizers, temperature sensors, and reaction catalyzers . SimilarThe difficulty in RNA structure determination leads to development of chemical probes combined with next-generation sequencing for genome-wide measurement of RNA secondary structure and solvent accessibility of RNA bases in its tertiary structure . Such geTime-consuming and costly experimental studies make it necessary to predict RNA structures computationally. Progress has been made in predicting RNA tertiary structures directly from RNA sequences . It is sDe novo prediction of RNA secondary and tertiary structures is challenging. This is because RNA primary structures (sequences) have only four hydrophilic side chains in two sizes (purine and pyrimidine). RNA tertiary structural folding is further complicated by degenerate base-pairing and stacking and stable RNA duplexes that result in an ensemble of energetically similar structural states for most RNAs . FunctioStructural ensembles for isolated RNA chains raise a practically and fundamentally important question: Are functional tertiary structures predictable in the absence of their binding partners? To address this question, we focus on the solvent accessibility or the fraction of solvent accessible surface area (ASA) of nucleotides in the tertiary structure of an RNA chain. RNA solvent accessibility, reflecting the level of exposure of a base to solvent, is a one-dimensional structural property of tertiary structure that is more amenable for computational prediction than three-dimensional structure, as demonstrated in predicting the solvent accessibility of proteins . The abiHere, we built the first machine-learning model on protein-bound RNA structures for RNA solvent accessibility prediction (named as RNAsnap). We showed that the model yields a robust performance in cross-validation and independent tests. Its genome-scale application to mRNA indicates a positive correlation to the in vivo genome-scale measured RNA reactivity to dimethyl sulphate (DMS) . MoreoveP-value (<10−99).Using 89 high-resolution (<3.0 Å), nonredundant RNA structures in complex with proteins for training (TR89), we developed two separate support-vector-machine (SVM) models by inputting either the query RNA sequence alone or the sequence profile from multiple sequence alignment against the query sequence. These two models are called RNAsnap-seq and RNAsnap-prof, respectively. Supplemental Figure S1. The order of PCC values for four bases seems to be random, and follows neither the sizes of nucleotides where adenine (A) and guanine (G) are larger than cytosine (C) and uracil (U), nor the abundance of A, C, G, and U in our data sets . In addition, the averages and standard deviations of actual ASA values of A, C, G, and U are 137 ± 59, 140 ± 47, 144 ± 48, and 145 ± 56, respectively, indicating small differences among them.To examine whether the strong correlations (>0.6) found in the cross-validation and independent test set are biased toward specific nucleotides, we have calculated correlations for four bases, separately. For the fivefold cross-validation, the PCC values of RNAsnap-prof are 0.663 for A, 0.670 for C, 0.656 for G, and 0.626 for U, respectively. For the test set, the PCC values of RNAsnap-prof are 0.623 for A, 0.621 for C, 0.622 for G, and 0.656 for U, respectively. The results between predicted and actual ASAs for individual bases  for the test set are shown in We investigated if there is a difference between protein-binding and nonbinding nucleotides. In our training and independent test, these two types of bases were not distinguished from each other. We found that if we remove the bases involved in binding to proteins in evaluation of correlation coefficients, we obtained a higher PCC (0.687 vs. 0.655) between predicted and actual ASA for TR89 by RNAsnap-prof. Thus, RNAsnap-prof can recognize exposed, nonbinding RNA bases as well as binding RNA bases.P-value (<10−99) given the fact that a large number of mRNAs (>6000) is involved.To further test RNAsnap, we obtained genome-wide accessibility data of mRNAs generated by utilizing DMS to detect unpaired and exposed adenine and cytosine residues . We noteSupplemental Figure S2A,B, RNAsnap-seq provides a similar performance with PCC = 0.085 to in vitro and PCC = 0.324 to in vivo DMS accessibility, respectively. This indicates that the correlation obtained is robust and not solely caused by RNA conservation information contained in the sequence profile used by RNAsnap-prof. As a comparison, we found that predicted secondary structure (unpaired probability) by RNAplfold , highlighting the difference between predicted RNA secondary structure and predicted ASA in this study.As shown in NAplfold  does notTo further demonstrate that predicted ASA values reflect structural properties of functional conformations, we examined the relation between minor allele frequencies (MAF) of genetic variants and predicted ASA at mutation sites of the variants found in the 1000 Genomes Project based on healthy individuals . Allele Supplemental Figs. S4–S7). Unpaired probability in secondary structure at the mutation site shows positive correlations in all cases , consistent with the deleterious functional impact from disruption of RNA secondary structure ; and transition between pyrimidines (T↔C) or purines (A↔G). We found that the correlations found above are slightly stronger for transition SNVs than for transversion SNVs. For example, for SNVs in 5′ UTR, PCC values between predicted ASA at the mutation site and the allele frequency of the mutation are 0.72 for 304 T→A,G and C→A,G transversions, 0.71 for 306 A→T,C and G→T,C transversions, and 0.83 for 983 T↔C and A↔G transitions. Thus, strong correlations are observed for all possible mutation types, indicating the consistent functional impact of mutating inaccessible RNA bases.To further illustrate the utility of predicted RNA accessibility, we performed genome-wide prediction of 15,642 human gene transcripts containing 5′ UTR, CDS, introns, and 3′ UTR of 100 bases or more. Supplemental Figure S8. Ideally, the window size should cover the entire sequence of an RNA chain so that a machine-learning method can learn potential interactions between all nucleotides . However, if the window size is too large, the large number of features will increase the risk of overtraining. This is why the window size needs to be optimized to maximize the performance while reducing the risk of overtraining by cross-validation and independent test. Our optimized window size is 40 because there are seven times more nucleotides  in large (>80) RNAs than those in small RNAs (3288). Covering the entire sequence for small RNAs by an optimized window size of 40 ensures the best learning for small RNAs while optimizing the performance for large RNAs.This paper establishes the first machine-learning model trained on protein-bound RNA structures for predicting solvent accessibility. We found that a window size of 40 bases provided the best performance, as shown in This window size of 40 is much larger than a typical window size (8–10) of a protein used for predicting protein secondary structure and ASA . This isThe resulting sequence-profile-based model (RNAsnap-prof) has achieved a correlation coefficient of 0.655 for fivefold cross-validation using TR89 and 0.630 for an independent test using TS44. The slightly poorer performance on the independent test set is somewhat expected because TS44 is made of lower resolution protein-bound structures (3.0 Å–4.0 Å), as TR89 has covered all nonredundant high-resolution structures (0–3.0 Å) in the Protein Data Bank. The consistent performance in TR89 and TS44 suggests that the solvent accessibility predictor developed here is robust.Interestingly, both sequence- and profile-based models do not perform well on protein-free RNA structures (CN48), with overall PCC values between predicted and actual ASAs for the entire data set at about 0.2, compared with 0.5–0.6 for training and test sets of protein-bound structures. The reason for the failure of RNAsnap for protein-free structures is not clear. One possibility is that protein-free structures have a sequence–structure relation different from protein-bound structures. We attempted to develop a method specifically for protein-free RNA structures using CN48. We failed to produce a method that can achieve a reasonably accurate cross-validation result. This failure may be caused by the fact that CN48 has only 3858 bases, compared with >20,000 bases in TR89. More protein-free structures are needed to understand if solvent accessibility of protein-free RNA structures is as predictable as protein-bound structures from RNA sequences by machine-learning models. The inherently different structures between small and large RNAs are consistent with a recent finding that large-size RNA structures (>110 nt) behave in a manner more akin to unorganized collapse, whereas small RNAs and proteins fold with better packing .P-value ∼ 0) suggests that the SVM model in RNAsnap is able to capture the sequence–structure relation of RNAs beyond protein-bound RNAs.The robust prediction of protein-bound tertiary structural accessibility indicates for the first time that functional structures of RNA sequences are predictable in the absence of their binding partners. This is further supported by positive correlation between predicted ASA and measured in vivo DMS accessibility (PCC = 0.37) but not between predicted ASA and in vitro DMS accessibility (PCC = 0.07) for 6178 mRNAs. Such a positive correlation is obtained although there are only two mRNA fragments  in our training set and the DMS reacts with both unpaired and exposed adenine/cytosine residues, rather than exposed residues only (solvent accessibility). The statistically significant association between predicted and measured mRNA ASA values , and stronger correlations for more accurate RNAsnap-prof, suggest that the association of MAF with ASA is robust. The lack of correlation in ncRNA regions by RNAsnap-seq suggests that the sequences in noncoding regions have weaker structural signal than in coding, 3′ UTR, and 5′ UTR regions. Moreover, we found that predicted secondary structure has a weaker correlation to MAF than predicted ASA. This finding suggests that mutations in buried RNA regions are more disruptive than mutations in secondary structure regions, confirming the important functional role of RNA tertiary structures.The above results further illustrate that functional conformations are encoded in RNA sequences even if unbound structures are flexible with dynamic transitions between different structural conformations. In other words, functional structures are within the dynamic structural ensemble and bound structures are likely formed through conformational selection upon binding. This mechanism is increasingly supported by experimental studies .Success in correlating to in vivo DMS accessibility and allele frequencies of genetic variants in the 1000 Genomes Project encouraged us to make genome-scale application of RNAsnap to 15,642 gene transcripts. We found that introns are the most solvent-exposed whereas exons are the least exposed . More exWhile genome-wide DMS accessibility studies of pre-mRNA are not yet available, genome-wide mapping of secondary structures across 5′ UTR, code regions, and 3′ UTR has been made . It was P-value ∼ 0) to in vivo DMS accessibility, although DMS accessibility is not the same as ASA for which the method was trained. This correlation is not achieved by accident because only very weak correlation (<0.01) was found between predicted secondary structure and DMA accessibility and only weak correlation (<0.1) between RNAsnap and in vitro DMS accessibility. The ability of our methods to predict the “never seen” data confirms the generality of the method. In addition, although our data set is small in terms of number of RNA structures, it is not small in terms of the number of nucleotides (27216 for TR89 and 8392 for TS44).The above multiple independent tests adequately addressed the potential issue of overfitting and the concern of small training and testing data. This is reflected from the fact that the Pearson correlation coefficients for fivefold cross-validation are nearly the same as those for independent tests (0.60 for TR89 and 0.54 for TS44 by RNAsnap-seq and 0.66 for TR89 and 0.63 for TS44 by RNAsnap-prof), although TS44 are made of lower resolution RNA structures with less accurate calculation of experimental accessible surface areas. These correlations found here are considered as moderate (0.4–0.6) or strong (>0.6). Moreover, application of our method to experimentally measured DMS accessibility of >6000 mRNAs reveals weak (PCC = 0.37) but statistically significant correlations . The answer is affirmative because RNAsnap-seq can make a reasonably accurate prediction of ASA for protein-bound structures with PCC = 0.54 for the independent test set TS44. RNAsnap-prof is used to illustrate if sequence conservation information contained in multiple sequence alignment is useful for more accurate determination of tertiary structural properties. A significant increase in the PCC values between predicted and actual ASAs from 0.54 to 0.63 by using the sequence profile confirmed that an RNA base can be conserved for its tertiary structural role . SequencIt should be noted, however, that if a nucleotide is highly conserved, it could be conserved for its functional or structural role. The use of RNAsnap-seq without sequence conservation information allows us to be certain that the disruption of RNA structure plays a significant role in disease-causing genetic variants based on the correlation between average predicted ASAs and average minor allele frequencies. RNAsnap-prof, on the other hand, can capture both structural and functional disruption caused by disease-causing genetic variants. This explains the small performance difference between RNAsnap-prof and RNAsnap-seq in ASA prediction, but the large difference in function-disruption prediction due to mutation.http://sparks-lab.org/server/RNAsnap.We noted that unlike RNA, solvent accessible surface areas of amino acid residues in proteins have long been predicted with high accuracy , utilizeSupplemental Table S1.We built our training (protein-bound) sets based on high-resolution RNA structures. We downloaded 1093 RNA-containing structures with resolution <3.0 Å and RNA chains longer than 32 bases from the Protein Data Bank (March 2015). After removing redundant chains by cd-hit-est , with thE-value < 0.001 and a maximum of 50,000 homologous sequences in the nonredundant nucleotide (NT) database. The probability of j base  in multiple aligned homologous sequences at a given sequence position i, Pi,j was calculated by using Pij, = − log[/∑j], where Ni,j is the observed number of base type j at position i. To avoid zero occurrences, a small number correction i)ss before training or test.Homologous sequences for each query sequence were searched by BLASTN  with E-v2 because there is no significant difference between the largest accessible areas for different base types. Two separate prediction methods were developed. In the single-sequence-based method (RNAsnap-seq), a sequence of length L was defined by a 4 × L matrix where each sequence position was represented by a four-dimensional vector with 1 for the query base type at the position and −1 for other base types. In the sequence-profile-based technique (RNAsnap-prof), this four-dimensional vector was replaced by the profile generated from multiple sequence alignment described above. These sequence-based features within a neighboring window w around the query sequence position were utilized as input; that is, the number of features is 4×(2 × w + 1). A vector of  was used for missing neighboring bases at the terminal ends. We used support vector machines (SVMs) implemented in libsvm version 3.12 (w = 40). The fivefold cross-validation was done by randomly dividing RNA chains in TR89 into five parts and each part (fold) was used in turn as a test by using the remaining four folds for training. The dependence of Pearson's correlation coefficient on the window size is shown in Supplemental Figure S8. Only sequence-based features were used here to minimize the possibility of overtraining. The final method was tested by the protein-free control (CN48) and the independent test set (TS44).Residue-specific ASA for each RNA chain was calculated with a solvent probe diameter of 1.5 Å by the TINKER package . All ASAion 3.12  for predDMS reactivity data for all mRNAs of human bone marrow K562 cells was downloaded from NCBI (geo: GSE45803). We obtained 6178 mRNA chains with at least 4000 reacted bases at four experimental conditions: denature, in vitro, vivo300 (in vivo treated with 300 µL DMS), and vivo400 (in vivo treated with 400 µL DMS) . In thisWe downloaded the 1000 Genomes Phase 3 VCF file from Ensembl annotated by ANNOVAR . We obtaWe downloaded all 21,983 protein-coding gene sequences in the human genome from ENSEMBL version 83 . After rFor genome-scale studies, we compared our RNAsnap to secondary-structure prediction software RNAplfold from the package Vienna RNA 2.1.9 . DefaultSupplemental material is available for this article.


Saccharomyces cerevisiae isolates. Ty1 retrotransposons contribute to the genetic diversity of host cells, but they can also act as an insertional mutagen and cause genetic instability. Interestingly, retrotransposition occurs at a low level despite a high level of Ty1 RNA, even though S. cerevisiae lacks the intrinsic defense mechanisms that other eukaryotes use to prevent transposon movement. p22 is a recently discovered Ty1 protein that inhibits retrotransposition in a dose-dependent manner. p22 is a truncated form of Gag encoded by internally initiated Ty1i RNA that contains two closely-spaced AUG codons. Mutations of either AUG codon compromise p22 translation. We found that both AUG codons were utilized and that translation efficiency depended on the Ty1i RNA structure. Structural features that stimulated p22 translation were context dependent and present only in Ty1i RNA. Destabilization of the 5′ untranslated region (5′ UTR) of Ty1i RNA decreased the p22 level, both in vitro and in vivo. Our data suggest that protein factors such as Gag could contribute to the stability and translational activity of Ty1i RNA through specific interactions with structural motifs in the RNA.The long-terminal repeat retrotransposon Ty1 is the most abundant mobile genetic element in many  Pseudoviridae family and the most abundant mobile genetic element in the Saccharomyces cerevisiae reference strain -labelled at their 3′-ends with T4 RNA ligase (ThermoFisher) according to standard procedures.All DNA templates for secondary structure probing experiments and in vitro translation were amplified from plasmid pBDG433, which contains transcribed sequences of Ty1-H3 subcloned into the riboprobe vector pSP64 . Forward and reverse primers are listed in 2) was added and samples were incubated for 30 min at 37 °C. Folded RNA was separated equally into two reactions and mixed with the 20 mM N-methylisatoic anhydride (NMIA) in dimethyl sulfoxide (DMSO)  or DMSO alone. Both reactions were incubated for 45 min at 37 °C followed by purification of RNA using Direct-zol RNA MiniPrep Kit.The reaction mixture (100 μL) containing 20 pmol of RNA in SHAPE renaturation buffer , pH 8.0) was heated at 95 °C for 3 min and placed on ice for 5 min. Fifty microliters of 3× SHAPE folding buffer  was refolded using the same conditions as those employed in the SHAPE experiments, then divided equally into two 24 μL reactions. Refolded RNA samples were mixed with 1 μL of dimethyl sulphate (DMS) in ethanol  or ethanol alone. Both reactions were incubated 1 min at room temperature and mixed with 475 μL of stop solution . RNA was purified using Direct-zol RNA MiniPrep Kit immediately after stopping the reaction.4)Fe(SO4)2, 50 mM sodium ascorbate, 1.5% H2O2 and 2.75 mM EDTA were applied separately to the wall of the tube followed by centrifugation. Six microliters of water were added to the control reaction. Reactions were incubated for 10 s at room temperature, then quenched by the addition of thiourea and EDTA to final concentrations of 20 mM and 40 mM, respectively. RNA was recovered using Direct-zol RNA MiniPrep Kit.RNA samples (10 pmol) were refolded by heating at 95 °C for 2 min in water followed by incubation at 25 °C for 5 min. Next, 3× SHAPE folding buffer was added and the reaction was incubated for 25 min at 37 °C, then diluted 20× with 20 mM Tris-HCl pH 8.0. To initiate the production of hydroxyl radicals, 1.5 μL of 2.5 mM  , 79 mM potassium acetate, 20 units of ribonuclease inhibitor (ThermoFisher) and 1 pmol of refolded capped or uncapped RNA in the final volume of 25 μL was incubated for 1 hour at 25 °C. Translation products were resolved on sodium dodecyl sulphate (SDS)-polyacrylamide gels followed by radioisotope imaging using a FLA 5100 image analyser . Bands intensities were analysed using MultiGauge software (Fuji). At least three repetitions were obtained for each in vitro translation reaction.In vitro translation experiments were carried out using wheat germ extract (WGE) as recommended by the manufacturer (Promega). The reaction mixture containing 12.5 μL of WGE lysate, 80 μM amino acid mixture minus methionine, 1.25 μL of [32P]-labelled RNA was refolded in SHAPE renaturation buffer by heating at 95 °C for 5 min and 4 °C for 5 min. SHAPE folding buffer contained increasing MgCl2 concentrations ranging from 0.1–10 mM. The reaction mixture (15 μL) was incubated at 37 °C for 25 min following the addition of 1.5 μL of 25% ficoll. Samples were analysed by native polyacrylamide gel electrophoresis using 12% gels in 0.5× TB at 4 °C. Electrophoresis was carried out at a gel temperature of 4 °C  -labeled domain I of Ty1i RNA (0.2 nM) was incubated for 4 min at 95 °C without magnesium ions and Triton X-100, and slowly cooled to 37 °C. MgCl2 and Triton X-100 were added following incubation for 10 min at 37 °C. Ty1 Gag protein solutions were prepared by sequential two-fold dilution of Gag in binding buffer. The binding reaction was initiated by mixing equal volumes of RNA and Gag protein in a microplate . The reactions were incubated for 15 min at 24 °C, filtered and washed with 2 × 200 μL binding buffer containing 50 mM NaCl. A 96-well dot-blot  was used with nitrocellulose  on top and charged nylon  membranes on the bottom. Prior to use, both membranes were soaked in binding buffer containing 50 mM NaCl. After filtration, membranes were dried and exposed to a phosphoimager screen. Data were fitted to the Hill equation using Origin 8.5 software . Reactions were performed in binding buffer  -labeled riboprobes corresponding to Ty1 nucleotides 1266–1601 and ACT1, followed by phosphorimaging using a STORM 840 phosphorimager and ImageQuant software  [Yeast cultures for total cellular RNA and protein extraction were grown in SC-Ura + 2% glucose medium at 22 °C for 24 h. RNA was extracted using the MasterPure Yeast RNA purification kit  . For eaclthcare) . Proteinlthcare) . A rabbihis3-AI mobility frequencies were determined as described previously [his3-AI mobility was calculated by the number of His+ Ura+ colonies/the number of Ura+ colonies per mL of culture.Ty1eviously ,33. BrieStructure prediction experiments were performed by RNAComposer  webserveOur previous results demonstrated that p22 translation can be initiated from AUG1 and AUG2 codons and is strictly cap-dependent. Also, either AUG1 or AUG2 can function to initiate translation when the other is mutated . HoweverfrsAUG2 RNA  in comparison to GCG1AUG2 RNA mutant where only AUG2 is present [frsAUG2 RNAs was significantly lower when compared to wild-type AUG1AUG2 RNA. These results raise the possibility that AUG1AUG2* and AUG1frsAUG2 RNAs affect the structure of the 5′ UTR of Ty1i RNA, leading to translation inhibition, and that the 5′ UTR may also regulate the production of p22.Taken together, the results of in vitro translation show that both AUG codons present in Ty1i RNA can be actively translated and AUG1 is preferentially utilized to initiate p22 synthesis. Our results also suggest that leaky scanning is the most likely mechanism for p22 translation from AUG2. Experimental support for leaky scanning is illustrated by the decrease of AUG2 translation levels from AUG1AUG2* and AUG1 present . MoreovefrsAUG2 RNAs suggests that the structure of the region containing AUG1 and AUG2 is important for p22 translation. Therefore, we performed selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) [Significant loss of translational activity from AUG1 in AUG1GCG2  Figure , AUG1AUG (SHAPE)  on the 5RNAstructure software [software ,39 whichOur results suggest that Ty1i RNA folds into two major domains. The smaller domain I (G1000–1083) and larger domain II (A1096–U1501) were connected by a 12nt-long single-stranded region (A1084–G1095). Interestingly, domain I included the Ty1i 5′ UTR and p22 coding sequence, and contained both p22 initiation codons . This stThe data from SHAPE probing support the predicted structure of domain I. Nucleotides within single-stranded regions were reactive towards the SHAPE reagent, including apical loops of both hairpins, internal loops, bulges and mismatches. The presented structure was also supported by dimethyl sulfate (DMS) probing. DMS methylates N1 of adenosines and N3 of cytidines that have an accessible Watson–Crick edge of the base rings . In our Interestingly, domain I contained both p22 initiation codons localized in different structural contexts . AUG1 coDomain II folded into a large multibranched structure  organizeWe reported that the combined level of p22 synthesized from AUG1GCG2 and GCG1AUG2 RNA constitutes only 30% of that obtained from wild-type AUG1AUG2 RNA . SecondaTo determine if the GCG mutation altered the structure of domain I, we performed secondary structure probing of AUG1GCG2 RNA using SHAPE. Although the overall reactivity pattern of the AUG1GCG2 RNA was preserved A, the reGCG2) along with the wild-type domain I to native gel electrophoresis  increased in 241-Gag RNA while the proximal part of the single-stranded region connecting domains I and II (A1084–G1089) lost accessibility to NMIA modification. The observed alterations suggest that domain I and the neighboring regions fold differently when the 5′-terminal sequence of genomic RNA is present in the transcript. The secondary structure of the full-length Ty1 RNA has been determined inside virus-like particles (VLPs) by in virio SHAPE analysis . In the Importantly, comparing the reactivity profiles of 241-Gag and full-length Ty1 RNA  revealedThe distinct structure of the region encompassing domain I in the full-length Ty1 RNA raised a question concerning how domain I might influence p22 translation. The initiation of p22 synthesis from the 241-Gag RNA is unlikely to occur, which raises the possibility that p22 synthesis requires a specific structure of domain I in Ty1i RNA . The preTo further understand the role of the Ty1i 5′ UTR in p22 translation, we analyzed in vitro several mutant RNA constructs . In AUG1We observed significant inhibition of p22 translation from all three RNA constructs A. DeletiTranslation initiation can be regulated not only by RNA structure but also by protein factors that interact with structural elements in mRNAs . Since t1000-1095) to encompass both Gag binding sites. The calculated dissociation constant (Kd ~3 nM) suggests that there is a high affinity binding site for Gag in domain I. To examine whether Gag binding is specific, we determined the Kd with increasing concentrations of NaCl, which is often used to compete out non-specific RNA/protein interactions [To further investigate the interaction between Gag and domain I, we calculated dissociation constants of RNA/protein complex formation using a double filter binding assay C. We useractions . The GagGAG that is driven by the GAL1 promoter [GAL1 promoted transcription of Ty1 is repressed. However, Ty1i RNA and p22 are still expressed from pGPOLΔ under glucose repression since Ty1i RNA is transcribed from internal initiation sites. To investigate the effects of the H1 hairpin on Ty1i RNA and p22 expression in vivo as well as on Ty1 transposition, a mutated pGPOLΔ plasmid was constructed  that expresses Ty1i RNA lacking the H1 sequence U1015–A1035) from the 5′ UTR . Wild ty–A1035 frS. paradoxus strain with 1 chromosomal Ty1 element  and the isogenic Ty1-less parent  that contain WT pGPOLΔ or pH1Δ plasmids  will complement the HIS3 deletion mutation present in the strain. Therefore, the number of His+ colonies generally reflect the level of Ty1 mobility. As expected for cells undergoing Ty1 CNC, the level of Ty1his3-AI mobility decreased about 15-fold from plasmid-based expression of p22 [Finally, we asked if deleting the H1 hairpin from the Ty1i RNA affected Ty1 mobility C. A quanhis3-AI . A Ty1HIn of p22 ,14. HoweOur Ty1i RNA structural and functional studies indicate that the 3D structure of domain I is important for efficient p22 translation. However, determining the 3D structure of RNA in solution is challenging. Therefore, we combined chemical probing experiments to map RNA secondary  and tertTranslation initiation is the rate-limiting step of protein synthesis and is highly regulated by RNA binding factors and structural properties of the messenger RNA. This coordinated action allows cells to rapidly adapt to their environment without the need of de novo mRNA synthesis and transport from the nucleus to the cytoplasm . In addi


NRAS mRNA. In vitro experiments reveal that ZnAPC binds to a G-quadruplex–forming oligonucleotide derived from the 5′-untranslated region of NRAS mRNA even in the presence of excess double-stranded RNA, which is abundant in cells, resulting in selective cleavage of the target RNA’s G-quadruplex upon photo-irradiation. In line with these results, upon photo-irradiation, ZnAPC decreases NRAS mRNA and NRAS expression and thus viability of cancer cells. These results indicate that ZnAPC may be a prominent photosensitiser for a molecularly targeted photodynamic therapy for cancer.Aberrant activation of RAS signalling pathways contributes to aggressive phenotypes of cancer cells. The RAS-targeted therapies for cancer, therefore, have been recognised to be effective; however, current developments on targeting RAS have not advanced due to structural features of the RAS protein. Here, we show that expression of NRAS, a major isoform of RAS, can be controlled by photo-irradiation with an anionic phthalocyanine, ZnAPC, targeting  NRAS contributes to the proliferation and metastatic nature of many types of cancer cells. Here, the authors show that NRAS can be controlled by an anionic phthalocyanine coordinating Zn2+ in combination with photo-irradiation.Hyperactivity of the gene  The photosensitisers used for PDT mostly consist of porphyrins and their analogues such as phthalocyanines, which possess low cytotoxicity in the dark and preferentially accumulate in tumour tissue6. Moreover, PDT typically uses light in a wavelength range of 600–800 nm to avoid interference by endogenous chromophores9. A photosensitiser absorbs light and subsequently relaxes to the first excited singlet state. Then, the singlet state undergoes conversion to the triplet state when it does not go back to the ground state. An electronic energy of the photosensitiser in the triplet state transfers to oxygen, resulting in formation of cytotoxic reactive oxygen species (ROS) such as singlet oxygen (1O2) and superoxide (O2−). Thus, cancer cells are killed by these photosensitisers in response to light exposure. Recently, in PDT, several photosensitisers targeting key molecules such as KRAS and Ki-67, which are associated with aggressive cancer cells, have been explored13.Photodynamic therapy (PDT) is being widely recognised as a minimally invasive cancer treatmentHRAS, KRAS and NRAS) result in their constitutive activation. In addition to these mutations, the overexpression and aberrant activation of receptor tyrosine kinases, such as epidermal growth factor receptor (EGFR) and hepatocyte growth factor receptor (HGFR/c-Met), lead to RAS hyperactivation. Because RAS hyperactivation promotes abnormal cell proliferation and metastasis, cancer therapies that involve drugs that target RAS are widely believed to be effective. Nonetheless, the drugs that directly target a RAS protein could not be identified for a long time because of the structural features of the RAS proteins14. It is known that in cancer cells, NRAS and KRAS are often hyperactivated in comparison with HRAS and therefore NRAS and KRAS are much more important therapeutic targets in cancer when compared to HRAS15. Although several potential small molecules that target the mutant KRAS G12 protein have been reported18, effective inhibitors of NRAS have yet to be identified.In cancer cells, commonly occurring missense mutations in three members of the RAS family genes  is guanine and N (loop) may be any nucleotide, can form G-quadruplexes20. G-quadruplex–forming sequences are found in telomeres and are enriched in the promoters and untranslated regions (UTRs) of genes, especially cancer-related genes such as NRAS, VEGF and BCL227. Because DNA G-quadruplexes in the promoter often suppress gene expression through impairment of the initiation of transcription28, Balasubramanian’s and other groups have demonstrated that formation of G-quadruplexes in mRNAs regulates gene expression31. It has also been reported that RNA G-quadruplexes in the 5′ UTR repress translation by interfering with the recruitment of the pre-initiation complex32. The gene expression regulated by formation of G-quadruplexes is assumed to be associated with cancer development and progression27. This notion has been supported by a growing body of evidence on G-quadruplex ligands as potential anticancer drugs35. Yet, there are no approved anticancer drugs targeting DNA or RNA G-quadruplexes. This is at least partly because of non-specific binding of known ligands of G-quadruplexes to DNA and RNA duplexes, which are the most abundant structures of genomic DNA and coding and non-coding transcripts, respectively36. Moreover, almost all known ligands bind to a target G-quadruplex in a reversible manner, leading to only a transient effect of the ligand on the respective gene expression. The expression of cancer-related proteins regulated by formation of a G-quadruplex, including NRAS, can be effectively controlled if a ligand can not only bind reversibly but also attack irreversibly DNA and RNA G-quadruplexes.G-quadruplexes are non-canonical structures of nucleic acids formed by guanine-rich oligonucleotides with four Hoogsteen-paired coplanar guanines, called a G-quartet2+ (ZnAPC) not only binds but also cleaves a G-quadruplex-forming 5′ UTR of the NRAS mRNA G-quadruplex after photo-irradiation and induces cell death. It is next shown that ZnAPC is a capable photosensitiser for direct transfer of energy to NRAS mRNA and induces its breakdown upon photo-irradiation even under low-oxygen conditions, which are defining feature of solid tumours. The approach in this study holds promise for a molecularly targeted PDT for cancer.Here, we demonstrate that an anionic phthalocyanines with Zn39. Among these molecules, anionic phthalocyanines (APCs) coordinating Ni2+ (NiAPC) or Cu2+ (CuAPC) bind to DNA G-quadruplexes derived from human telomeric DNA41. According to these results, we hypothesised that anionic phthalocyanine derivatives can control NRAS expression by photo-irradiation. Because a phthalocyanine coordinating Zn2+ has a high photosensitising ability among phthalocyanine derivatives3, we used anionic phthalocyanines, ZnAPC with zinc as a coordinated metal in addition to NiAPC, CuAPC and FeAPC with iron as a coordinated metal.Some small molecules have been found to bind to a G-quadruplex in a selective mannerNRAS mRNA (the nucleotide sequence and circular dichroism (CD) spectrum are shown in Supplementary Table 42. Hence, the spectral change of ZnAPC induced by the addition of NRAS RNA shows that the oligomer of ZnAPC dissociates into the monomeric form by the binding to NRAS RNA. Figure Kd) of ZnAPC for binding to NRAS RNA was estimated to be 3.1 μM at 25 °C   Fig. . On the re) Fig. , implyin °C Fig. , indicat40, the APCs is assumed not to bind to DNA and RNA duplexes. It was next shown that the binding of 2 μM ZnAPC to NRAS RNA was almost the same even in the presence of 100 μM dsRNA . We also checked the binding of APCs to a dsRNA , which acts as a sensitive fluorescent probe for the detection of the most active ROS: the hydroxyl radical48. Fluorescence intensity of HPF at 515 nm increased after photo-irradiation of ZnAPC and CuAPC, although the increment of the fluorescence intensity yielded by ZnAPC was significantly higher than that of CuAPC  after photo-irradiation, depending on the coordinated metal at the centreAPC Fig. . ContrarNRAS amplification has been reported49. Figure We next examined the effects of ZnAPC, as well as FeAPC as a negative control APC, on the ROS formation in human breast cancer MCF-7 cells, in which From this point of view, we tested whether, upon photo-irradiation, ZnAPC degraded the NRAS RNA under the experimental conditions. Figure NRAS mRNA harbouring the G-quadruplex motif (NRAS F-RNA) , influences the photo-cleavage by NRAS RNA. Figure NRAS mRNA and NRAS protein expression by ZnAPC , was estimated to be 2.97 ns. The τ1 values in the presence of NRAS RNA or NRAS DNA were the same as those without the G-quadruplex and consistent with the steady-state fluorescence spectra  reflect the photo-induced electron transfer that directly proceeds between ZnAPC and a G-quadruplex.It has been reported that fluorescence decay of a porphyrin–DNA G-quadruplex complex has two lifetime components, whereas the porphyrin has a single lifetime component in its free state60. It is therefore possible that τ1 of NRAS RNA, smaller than that of NRAS DNA  abstraction from an RNA strand by the hydroxyl radical is much faster than the reaction with a hydroxyl radical scavenger, and consequently such a scavenger cannot inhibit the H abstraction from RNADNA Fig. , c, suppDNA Fig. , the dirNRAS mRNA by ZnAPC. On the other hand, it was also clearly shown that not all RNA G-quadruplexes are targeted by ZnAPC. To rationalise the selectivity of ZnAPC, we studied ZnAPC binding with various RNA G-quadruplexes. First, we evaluated the binding of ZnAPC to other naturally occurring G-quadruplex-forming RNA oligonucleotides (VEGF RNA and BCL2 RNA) derived from the 5′ UTR of mRNA encoding vascular endothelial growth factor (VEGF) and B-cell lymphoma 2 (Bcl2), respectively. Nucleotide sequences and CD spectra of these RNAs are given in Supplementary Table Kd were estimated to be 48 μM for VEGF RNA and 36 μM for BCL2 RNA at 25 °C. These results mean that the binding of ZnAPC to VEGF RNA or BCL2 RNA is weaker than the binding to NRAS RNA ger Fig. . It was ger Fig. . BecauseNRAS mRNA by ZnAPC. Studies using both melanoma cells and melanoma tumour-bearing mice by the group of Luigi Xodo have successfully shown that porphyrin C14H28-alkyl derivative C14, a photosensitiser identified as a G-quadruplex binding compound, induces growth arrest of cancer cells through downregulation of KRAS by targeting the G-quadruplex in its mRNA11. In these articles, it was shown that C14 inhibits translation of KRAS mRNA even without photo-irradiation and induces light-mediated cleavage of KRAS mRNA, which is thought to be associated with its ability to generate ROS. By combining these results on KRAS and our results on NRAS in this study, we can propose that the specific binding and photo-cleavage of G-quadruplex structures of RAS mRNAs by RNA G-quadruplex ligands are promising as photosensitisers in a molecularly targeted PDT for downregulation of a RAS signalling pathway.In conclusion, we show here that NRAS expression can be downregulated through targeting the G-quadruplex in 63. ZnAPC  was purchased from Frontier Scientific, FeAPC , NiAPC [nickel(II) phthalocyanine-tetrasulfonic acid tetrasodium salt] and CuAPC  were purchased from Sigma-Aldrich. Actinomycin D and NAC were bought from WAKO Pure Chemical Industries, Ltd. Hydroxyphenyl fluorescein (HPF) was purchased from Goryo Kayaku Co., Ltd. Anti-N-Ras mouse monoclonal  and anti-β-actin  mouse monoclonal antibodies served as a primary antibody. A horseradish peroxidase (HRP)-conjugated sheep anti-mouse IgG antibody  served as a secondary antibody.All the high-pressure liquid chromatography (HPLC)-grade DNA and RNA oligonucleotides analysed in this study were acquired from Hokkaido System Science Co., Ltd. and Sigma-Aldrich Japan and used without further purification. Single strand concentrations of the oligonucleotides were determined by measuring the absorbance at 260 nm and high temperature using a UV-1800 spectrometer . Single-strand extinction coefficients were calculated from mononucleotide and dinucleotide data using the nearest-neighbor approximationN-morpholino) ethanesulfonate [MES]-LiOH (pH 7) and 100 mM KCl at 25 °C. Before measurement, each sample was heated to 90 °C for 5 min and gently cooled to 25 °C at 0.5 °C min–1.Visible (Vis) absorption spectra for ZnAPC (2 μM) and FeAPC (2 μM) were recorded on a spectrophotometer  with a quartz cell with 1 cm path length. All the measurements were carried out in a buffer consisting of 50 mM 2-(–1.Fluorescence spectra for 2 μM ZnAPC in the presence of 0–20 μM RNA in a buffer consisting of 50 mM MES-LiOH (pH 7) and 100 mM KCl  were recorded on a FP-8200 spectrofluorometer  equipped a temperature controller with a 0.3 cm × 0.3 cm quartz cell at 25 °C. Before measurement, each sample was heated to 90 °C for 5 min and gently cooled to 25 °C at 0.5 °C min−2, CCS Inc., Kyoto, Japan). For photo-irradiation of phthalocyanines in the cell, cells were seeded at 5 × 104 cells cm−2 and cultured overnight. The medium was then replaced with a CO2-independent medium (Gibco) supplemented with 10% of foetal bovine serum and 1% of the penicillin/streptomycin solution. The cells were pre-treated with ZnAPC or FeAPC for 30 min or 1 h at 37 °C and next irradiated with LED light with peak emission wavelength of 630 nm .For photo-irradiation of phthalocyanines in vitro, ZnAPC, FeAPC, NiAPC or CuAPC in a buffer consisting of 50 mM MES-LiOH (pH 7) and 100 mM KCl was irradiated with a light-emitting diode (LED) light with peak emission wavelength of 615 nm  were pre-treated with ZnAPC or FeAPC for 30 min, followed by 10 μM HPF for 30 min. After photo-irradiation for 60 min, the cells were washed with PBS. A 488 nm laser was employed for imaging. Images were acquired by means of a confocal microscope . The mean fluorescence intensity of HPF was quantified in the ImageJ software.An in vitro assay of ROS formation after the photo-irradiation of the APCs, fluorescence for 10 μM HPF in the presence of 2 μM ZnAPC, FeAPC, NiAPC or CuAPC in a buffer consisting of 50 mM MES-LiOH (pH 7) and 100 mM KCl was recorded at 25 °C ; an FP-8200 spectrofluorometer  connected to a temperature controller was used with a a quartz cell with 0.3 cm path length. Before measurement, each sample was heated to 90 °C for 5 min and gently cooled to 25 °C at 0.5 °C min–1. After the photo-irradiation of the mixtures in a quartz cell with 0.1 cm path length, a 1.5-fold volume of a stop solution  was added. The samples containing 0.6 pmol RNA or DNA were then analysed by electrophoresis in a 10 or 15% polyacrylamide gel with 7 M urea at 70 °C. After that, the gels were stained with SYBR® Gold (Invitrogen), and the signals were quantified with a fluorescent imager, Typhoon FLA-9500 . When the efficiency of RNA cleavage was compared with that under aerobic and anaerobic conditions, the mixture of 2 μM ZnAPC and 0.1 μM NRAS RNA was dried before we dissolved it in the buffer.The mixtures of 2 μM ZnAPC or FeAPC and 0.1 μM RNA or DNA in a buffer consisting of 50 mM MES-LiOH (pH 7) and 100 mM KCl were heated to 90 °C for 5 min and gently cooled to 25 °C at 0.5 °C min2 followed by decompressing and purging with Ar gas three times. After thawing, the mixtures were dissolved in the buffer in the argon-equilibrated glove box. Uncropped scans of gels are shown in Supplementary Figure To conduct the reaction under anaerobic conditions, the buffer was purged with argon for 5 min and then frozen by means of liquid NNRAS mRNA following T7-promoter was constructed (GenScript Japan Inc.). The vector was digested with EcoRV, and the resultant DNA fragments were next separated by electrophoresis in a 1% agarose gel. The fragment containing DNA encoding the 5′ UTR of NRAS mRNA was purified by ethanol precipitation after it was extracted from the agarose gel with the GenEluteTM Gel Extraction Kit (Sigma-Aldrich). This DNA served as a template for in vitro RNA transcription. RNA was labelled with DIG and detected using the DIG RNA Labelling Kit . Briefly, the transcription reaction driven by T7 RNA polymerase in a reaction mixture containing DIG-11-UTG and an RNase inhibitor was carried out at 42 °C for 1 h. After treatment with DNase I at 37 °C for 15 min, RNA was purified by ethanol precipitation after phenol/chloroform extraction. A 1.5-fold volume of a stop solution was added to the RNA solution, and the mixture was subjected to electrophoresis in a 5% polyacrylamide gel with 7 M urea at 70 °C. The band of ~260 bases corresponding to NRAS F-RNA was cut out, and the RNA was purified by ethanol precipitation after extraction in TE buffer by rotation at 4 °C for 2 h. The mixtures of 2 μM ZnAPC and 0.1 μM RNA in a buffer consisting of 50 mM MES-LiOH (pH 7) and 100 mM KCl were heated to 90 °C for 5 min and gently cooled to 25 °C at 0.5 °C min–1. After photo-irradiation of the mixtures in a quartz cell with 0.1 cm path length, a 1.5-fold volume of a stop solution was added. The samples containing 0.6 pmol RNA were then analysed by electrophoresis in a 15% polyacrylamide gel with 7 M urea at 70 °C. After that, RNAs in the gel were transferred for 1 h at 100 mA in a cold room to a Hybond N+ membrane (Amersham Biosciences) using 0.5 × TBE (Tris-borate-EDTA) and fixed by UV irradiation for 3 min. The membrane was washed with washing buffer and then blocked with blocking buffer for 30 min. After incubation with anti-DIG-AP  for 30 min at room temperature, the membrane was washed with washing buffer and incubated with detection buffer for 30 min. The bound antibodies were visualised with a chemiluminescent substrate, CDP-Star  and subsequently detected by exposing blots to X-ray film (Fujifilm).The pUC57 NRAS F-RNA vector containing the sequence encoding the 5′ UTR of –1.Absorption spectra for 2 μM ZnAPC or 2 μM FeAPC at various concentrations of RNA or DNA in a buffer consisting of 50 mM MES-LiOH (pH 7) and 100 mM KCl were recorded from 550 to 750 nm. A Shimadzu UV-1800 spectrophotometer connected to a Shimadzu TMSPC-8 thermoprogrammer  was used with a quartz cell with 1 cm path length. Before measurement, each sample was heated to 90 °C for 5 min and gently cooled to 25 °C at 0.5 °C minKd) of ZnAPC with RNA or DNA oligonucleotides were evaluated by a curve-fitting procedure for the plot of ΔAbs [=(absorbance at 680 nm in the presence of the oligonucleotide) minus (absorbance at 680 nm in the absence of the oligonucleotide)] versus concentration of the oligonucleotide by means of the following equation at 25 °C:max is the maximum difference in ΔAbs, and [oligonucleotide] is concentration of the added oligonucleotide. Note that the values of ΔAbsmax for BCL2 RNA and VEGF RNA were adjusted to the same value as that for NRAS RNA to evaluate Kd values of ZnAPC because the ΔAbsmax values for these RNAs were too small to fit the data under our experimental conditions.The dissociation constants  equipped with a photon counting unit . The decay curve measurements were carried out in a buffer consisting of 50 mM MES-LiOH (pH 7) and 100 mM KCl with or without 30 wt% DMF at room temperature. The fluorescence lifetime was evaluated by a curve-fitting system .MCF-7 human breast cancer cells provided by the American Type Culture Collection (ATCC) were cultured in Dulbecco’s modified Eagle’s medium  supplemented with 10% of foetal bovine serum (Sigma-Aldrich) and 1% of a penicillin/streptomycin solution .3VO4, 10 mM NaF, and a protease inhibitor cocktail ) and then centrifuged at 20,000×g for 10 min after sonication. The supernatants served as total cell extracts and were subjected to sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). Proteins were transferred to PVDF membranes and blocked for 30 min with 5% low-fat milk (Sigma-Aldrich) in TBS-T before the addition of primary antibodies. After incubation overnight at 4 °C or one hour at room temperature, the membrane was washed with TBS-T and incubated with an HRP-conjugated secondary antibody for 30 min. Bound antibodies were visualised with the HRP Chemiluminescent Reagent (Perkin Elmer) and subsequently detected by exposing blots to X-ray film (Fujifilm). Uncropped scans of immunoblots are shown in Supplementary Figure Cells cultured on 60 mm dishes were disrupted with lysis buffer , and cDNA was prepared with the PrimeScript 1st strand cDNA Synthesis Kit (Takara Bio Inc.). Real-time PCR analysis was performed on a StepOnePlus Real-Time PCR System . Conditions for each transcript were as follows: 1 min at 95 °C, then 40 cycles at 95 °C for 15 s and 55 °C for 1 min. The following primers were used: human –1.CD spectra of oligonucleotides were measured for 20 uM oligonucleotide in the buffer consisting of 50 mM MES-LiOH (pH 7) and 100 mM KCl using a Jasco J-820 specroplarimeter  with a quartz cell with 0.1 cm path length. Before measurement, each sample was heated to 90 °C for 5 min and gently cooled to 25 °C at 0.5 °C minCells were cultured on a 35 mm glass bottom dish . ZnAPC incorporated into the cells was visualised because of its autofluorescence (ex: 488 nm and em: >630 nm). Images were acquired using a confocal microscope  and then analysed in the ImageJ software (NIH).t-test.Statistical analysis of data was performed by unpaired Student’s two-sided The cell lines and all data supporting the findings of this study are available from the authors upon reasonable request.Supplementary InformationPeer Review File