Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector.Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought.Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model.Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis. Human resource management functions have evolved manyfold in the past few decades from a pure administrative job  to a strArtificial intelligence (AI) is transforming the way human resource management (HRM) is being carried out across various industries. The Indian pharmaceutical sector is no exception to this trend. AI-powered HRM practices can enhance the efficiency of HR processes, improve the quality of talent acquisition and retention, and contribute to better decision-making . SeveralDespite the potential benefits of AI-powered HRM practices, the adoption of AI in the Indian pharmaceutical sector is still at a nascent stage . FurtherTherefore, understanding the task-technology fit theory and its implications is crucial for organizations in the Indian pharmaceutical sector to effectively adopt AI-powered HRM practices. It can help organizations evaluate the fit between the technology and the HR tasks required in the sector, identify potential barriers to adoption, and design strategies to promote the adoption of AI-powered HRM practices. Moreover, this sector faces several challenges, such as a lack of awareness about AI technology, resistance from employees, and the cost of adoption . TherefoThus, this study aims to address this gap by exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector. The study is expected to provide insights into the organizational, technological, and individual factors that influence the adoption of AI-powered HRM practices. The findings of this study will contribute to the existing literature on AI adoption in HRM practices and also provide valuable recommendations for organizations in the sector to effectively adopt AI-powered HRM practices and achieve better outcomes.However, the Indian pharmaceutical industry operates under a complex and evolving regulatory environment . IntroduEffective implementation of AI in the pharmaceutical industry relies on access to vast amounts of high-quality patient data . HoweverThe technology acceptance model (TAM) proposes that perceived usefulness and the perceived ease of use are the main determinants of the user acceptance of technology . HoweverThe main rationale of the TTF  is to understand and predict users’ acceptance of new technology . DevelopIn other words, TTF focuses on the fit between the characteristics of the technology and the requirements of the task or job . TherefoThe TTF model has been widely researched to examine the adoption of technology in areas like healthcare, big data, banking and finance, and training management systems . FurtherOverall, the TTF theory provides a structured framework for researchers and practitioners to assess users’ attitudes and behaviors toward technology adoption, helping organizations make informed decisions and strategies to enhance technology acceptance and successful implementation .Based on the literature review, we discuss the hypothetical relationship and conceptual framework shown in Artificial intelligence will have an impact on human resources . When thH1Artificial intelligence adoption has a significant impact on effective HRM in the Indian pharmaceutical sector.Organizational preparedness comprises organizational structures, processes, and other resources that promote the use of technology . AccordiAI use in the workplace is still in its early stages, and larger enterprises currently have more resources than smaller firms; thus, we believe that larger companies will employ more HRM-related AI solutions. AI can give businesses a competitive advantage, compelling them to use it . An orgaOrganizational preparedness to adopt AI must be tailored to the unique characteristics of each company, such as its industry or organizational structure . FurtherH2Organizational preparedness has a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.H3Organizational preparedness has a significant impact on effective HRM in the Indian pharmaceutical sector.Perceived benefits are views about the benefits of a behavior in response to a real or perceived threat . The perH4Perceived benefits have a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.H5Perceived benefits have a significant impact on effective HRM in the Indian pharmaceutical sector.AI adoption is primarily concerned with the qualities of the current technology and the ease with which an organization might embrace new technology . TechnolH6Technological readiness has a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.H7Technological readiness has a significant impact on effective HRM in the Indian pharmaceutical sector.The pressure that a company believes its competitors are putting on it is known as competitive pressure . AccordiH8Competitive pressure has a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.H9Competitive pressure has a significant impact on effective HRM in the Indian pharmaceutical sector.Organizational culture refers to how the workplace influences how employees behave, think, and feel about their jobs . DespiteH10Organizational culture has a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.H11Organizational culture has a significant impact on effective HRM in the Indian pharmaceutical sector.This study delves into the mediating role of artificial intelligence on both independent and dependent variables. AI is widely recognized as a crucial growth driver that enhances business structures . Human iH12Adoption of artificial intelligence mediates the relationship between perceived benefits and effective HRM in the Indian pharmaceutical sector.H13Adoption of artificial intelligence mediates the relationship between competitive pressure and effective HRM in the Indian pharmaceutical sector.H14Adoption of artificial intelligence mediates the relationship between technological readiness and effective HRM in the Indian pharmaceutical sector.H15Adoption of artificial intelligence mediates the relationship between the organizational culture and effective HRM in the Indian pharmaceutical sector.H16Adoption of artificial intelligence mediates the relationship between organizational preparedness and effective HRM in the Indian pharmaceutical sector.The present study aims to address the gaps in the literature by examining the influence of key factors, such as organizational preparedness, perceived benefits, technological readiness, competitive pressure, and organizational culture, on the adoption of artificial intelligence in the Indian pharmaceutical sector. A conceptual framework  has beenThis descriptive research focused on understanding the impact of organizational preparedness (OP), perceived benefits (PBs), technological readiness (TR), competitive pressure (CP), and organizational culture (OC) on effective HRM directly and via the mediation of artificial intelligence adoption in pharmaceutical companies in India.An Indian pharmaceutical company’s employee working at a senior position, having an experience of 5 years and more in the sector, were considered as respondents for collecting primary data by the convenience sampling method . A self-This study has used the scale adapted by the earlier researcher’s research work on building effective HRM practices via the adoption of artificial intelligence . The fouThe content face validity was confirmed by two experts of the pharmaceutical sector. The suggestion provided by them was fully incorporated in the questionnaire, and for measuring the effectiveness of the questionnaire, a pilot study was conducted among pharmaceutical employees. The PLS-SEM statistical tool is used for analysis purpose, which even allows us to run the small sample size ; even noIn the demographic profile of the 88 respondents of the pharmaceutical sector, out of which only eight were female individuals and the remaining 80 were male individuals, there is a dearth of female workforce in India. The respondents fall in the age bracket of 30–39 years old and have received responses from all parts of the country, namely, Vadodara, Ahmedabad, Baddi, Sikkim, Kolkata, Visakhapatnam, Hyderabad, Bangalore, Chennai, Mumbai, and Pune.Using the variance inflation factor (VIF) to measure multicollinearity, the researchers found that none of the values in The measurement model presents the validity and reliability of the construct. Four measurements are addressed in this study: 1. indicator loading, 2. internal consistency reliability, 3. convergent validity, and 4. discriminant validity .PLS-SEM results were assessed for indicator loading in this study. Internal consistency should be presented through Cronbach's alpha and composite reliability . The valConvergent validity assessed the relationship among the similar and same construct, and the average variance extracted (AVE) to be reported; the minimum threshold value is 0.50 or higher . In thisDiscriminant validity explains the distinction among the constructs . By applFurthermore, the heterotrait–monotrait ratio (HTMT), shown in As per the techniques provided for structure model assessment , collineAfter establishing reliability and validity through the measurement model using PLS-SEM, the structural model is examined, which indicates the relationship between endogenous and exogenous variables. The path coefficients were studied by applying the bootstrapping process  .1) ; hence, H1 is supported. However, the relationship between competitive pressure and artificial intelligence (H2) could not be established in the study ; hence, it was found to be insignificant. However, competitive pressure had a strong negative relationship with effective HRM (H3) , which indicates that with the increase in competitive pressure, the effectiveness of HRM would decrease  ; thus, H4 is also supported. However, the relationship between organizational culture and effective HRM (H5)  could not be established in this study; similarly, organizational preparedness had an impact on artificial intelligence (H6) , which was found to be insignificant; thus, H5 and H6 are not supported. However, organizational preparedness proved to have a strong positive relationship with effective HRM (H7) . Similarly, the perceived benefit has a strong positive relation with artificial intelligence (H8) ; thus, H7 and H8 are supported. However, perceived benefits fail to establish a relationship with effective HRM in the study (H9) . Similarly, the relationship of technological readiness with artificial intelligence also could not be established (H10) . However, the relationship between technological readiness and effective HRM (H11)  was found to be significant; hence, H11 is supported.The structural model helps in verifying all the projected hypotheses. The results are shown in decrease ; hence, 2), values of 0.35, 0.15, and 0.02 are considered large, medium, and small for f2, as shown in 2) of artificial intelligence is found to be an important variable in assessing the effectiveness of HRM in the Indian pharmaceutical sector. Second, organizational culture was close to a medium impact in assessing artificial intelligence adoption in the Indian pharmaceutical sector. Furthermore, technological readiness is also identified in this research as an important variable to assess the effectiveness of HRM in the Indian pharmaceutical sector. Furthermore, it is observed that at places where the hypothesis has insignificant p-values, the effect size f2 of that same hypothesis is revealed to be very small, even less than the threshold value of 0.02 (Regarding effect sizes (f of 0.02 .PB ->AI ->EH perceived benefits have a significant relationship with effective HRM through the mediation of AI adoption (H12) , indicating full mediation as the perceived benefit was found to reject the direct relation with effective HRM (H9) (CP ->AI ->EH competitive pressure has a significant relationship with effective HRM through the mediation of AI adoption (H13) , indicating partial mediation, as competitive pressure was found to have a direct impact on effective HRM (H3)  , indicating partial mediation, as technological readiness was found to have a direct impact on effective HRM (H11)  , indicating full mediation, as organizational culture was found to reject the direct relationship with effective HRM (H5)   as organizational pressure is proven to have a direct impact on effective HRM (H7) (As per (H9) . Further (H3) . Similar(H11) . OC ->AI(H5) . Further(H16)  (b = 0.1 (H7) .R2) values indicate the ability of the exogenous latent variable to predict the endogenous latent variables  significantly explain 66% (R2 = 0.66) of variance in artificial intelligence adoption. Similarly, the six other latent variables  explain 73% (R2 = 0.73) of variance in developing effective HRM.The coefficient of determination (ariables ; R2 is 2, H5, H6, H9, and H10 are not supported directly from the perspective of the Indian pharmaceutical sector, but with the mediation of artificial intelligence, full mediation was shown to be established among the non-supported hypotheses.This study aimed at identifying the impact of factors like organization preparedness, perceived benefits, technological readiness, competitive pressure, and organizational culture on the adoption of AI and effective HRM. Additionally, the mediation of AI adoption is also measured in the study of pharmaceutical companies in India. The finding of the study reveals that organizational culture and perceived benefits are positively associated with AI adoption with competitive pressure, organizational preparedness, and technological readiness as an exception, which means organizational culture and perceived benefits support the adoption of AI in pharmaceutical companies, as the COVID 19 pandemic has made them realize the importance of an AI-enabled healthcare sector  to traceThus, these results indicate that artificial intelligence adoption plays a significant role to play in improving the effectiveness of HRM in pharmaceutical companies in India. However, several studies have proved the significance level of AI adoption in developing effective HRM, but exogenous variables considered in the current study have not been studied together in any previous research. Moreover, the current model of the study is applied in Indian pharmaceutical companies, which was not considered previously.Artificial intelligence has had a significant impact on HRM practices, but little research has been conducted to determine how well it works within organizations . SeveralAI, in my opinion, can improve the accuracy and effectiveness of the hiring process, but it all depends on the organization. Each organization will have unique efficiency goals and AI requirements. If organizations are to fully benefit from using AI in recruitment, they must have access to it and enough time to put it to use. This is because many organizations, whether they realize it or not, believe they want to deploy AI. They may not require this level of efficiency or quality or they may lack the technical expertise to implement it.Long-term organizational changes should be prioritized, and we should work to develop methods for teaching both humans and machines to recognize employee skills that will benefit the company in the long run. Recruiters used to perform repetitive tasks manually, but AI would make some of these tasks obsolete . RecruitAI assists recruiters in completing less time-consuming tasks in order to conduct direct applicant interviews. Application screening saves time during recruitment and AI can assist recruiters in identifying top candidates by generating short lists. When artificial intelligence is used in hiring, recruiters can access a larger prospect pool while doing less administrative work . One of This study provides corporate executives in the pharmaceutical sector and HR managers with recommendations for improving the effectiveness of their HR departments. HR leaders and upper management should be aware that integrating AI into the HR system may have benefits that extend beyond the HR department and to the entire company. Before implementing artificial intelligence, organizations must be prepared, understand the benefits, and have the necessary technical skills. HR managers and leaders should conduct research before developing a strategy for integrating AI into the current system. The first step can be to determine which tasks, procedures, and processes need to be modified. People may consider whether tasks can be automated in order to increase their productivity and efficiency at work. Furthermore, the value that people contribute can be quantified, considering how people analytics can help you improve your individual performance, education, and career development, as well as your pay and benefits. However, it is critical to determine whether a major overhaul is required. Once this preliminary research is completed, a strategy for automating AI use can be developed. AI will be used more and more in future HR departments, particularly when communicating with applicants and assisting them in preparing for interviews.This study provides important and novel information on the driving forces behind the use of AI in the HR department of pharmaceutical companies of India. However, the study has several limitations that must be addressed. First, we only looked at data from major cities. Second, we only considered the pharmaceutical industry as a whole. Third, we regard AI as a high-tech tool. However, AI’s cognitive capabilities differ significantly from those of traditional e-HRM tools. Many people believe that AI is more than just a technology . Future Artificial intelligence-based technologies are increasingly being used in industries such as education, healthcare, and the environment. AI-powered solutions enable people to make decisions based on how they interact with their surroundings. As a result, the factors influencing AI adoption must be comprehended. AI shows potential to significantly alter the workplace and provide businesses with a competitive advantage . As a re


Here we provide a comprehensive, stepwise mechanism of transcription initiation by solving high-resolution cryogenic electron microscopy (cryo-EM) structures of yeast mitochondrial RNAP and the transcription factor Mtf1 catalysing two- to eight-nucleotide RNA synthesis at single-nucleotide addition steps. The growing RNA–DNA is accommodated in the polymerase cleft by template scrunching and non-template reorganization, creating stressed intermediates. During early initiation, non-template strand scrunching and unscrunching destabilize the short two- and three-nucleotide RNAs, triggering abortive synthesis. Subsequently, the non-template reorganizes into a base-stacked staircase-like structure supporting processive five- to eight-nucleotide RNA synthesis. The expanded non-template staircase and highly scrunched template in IC8 destabilize the promoter interactions with Mtf1 to facilitate initiation bubble collapse and promoter escape for the transition from initiation to the elongation complex (EC). The series of transcription initiation steps, each guided by the interplay of multiple structural components, reveal a finely tuned mechanism for potential regulatory control.Transcription initiation is a key regulatory step in gene expression during which RNA polymerase (RNAP) initiates RNA synthesis de novo, and the synthesized RNA at a specific length triggers the transition to the elongation phase. Mitochondria recruit a single-subunit RNAP and one or two auxiliary factors to initiate transcription. Previous studies have revealed the molecular architectures of yeast A cryogenic electron microscopy study presents structures characterizing the initiation of RNA synthesis by yeast mitochondrial RNA polymerase at single-nucleotide addition steps. Initial RNA synthesis occurs by a conserved DNA scrunching mechanism10, which enables the RNA–DNA and transcription bubble to expand in the limited space of the polymerase cleft. The stressed IC intermediates trigger off-pathway backtracking and abortive RNA synthesis during early transcription initiation steps14. However, after 8- to 12-nucleotide (nt) RNA synthesis, the stressed IC undergoes conformational changes to switch into a stable EC. The dynamic IC intermediates are challenging to characterize structurally and biochemically; hence, such states from bubble opening to the EC transition have not been systematically captured in high-resolution structures.Transcription initiation is a multistep process catalysed by DNA-dependent RNAPs. The basic steps of transcription initiation are conserved in all domains of life; however, owing to the increasing complexity of the transcription machinery from bacteriophages to humans16. The mtRNAPs are closely related to the single-subunit bacteriophage T7 RNAP17. The yeast (Saccharomyces cerevisiae) mtRNAP  shares structural and functional similarities with the human mtRNAP . y-mtRNAP requires one factor, Mtf1, and h-mtRNAP requires two factors, TFAM and TFB2M20; T7 RNAP does not require any factor. We published high-resolution structures of y-mtRNAP–Mtf1 with a DNA promoter as a partially melted IC (PmIC) and a transcribing IC3 state1. These structures resolved the complete transcription bubble, showing base-specific interactions of Mtf1 with the conserved non-template (NT) sequence (−4) AAG (−2) in the PmIC; a complete bubble has not been traced in the IC structures of h-mtRNAP2 or T7 RNAP21. The y-mtRNAP IC3 structure showed a 2-nt RNA and an incoming NTP at the polymerase active site base-paired with template +1 to +3 nucleotides.Mitochondrial RNAP (mtRNAP) is essential for mitochondrial DNA transcription and replication in eukaryotes8. These structures enabled us to determine: the structural arrangements that help accommodate the growing RNA–DNA duplex and the transcription bubble in the polymerase cleft; the factors that govern abortive synthesis over RNA elongation, and why only short RNAs of specific length are aborted; and how the transition from initiation to elongation is triggered and orchestrated.Here we report single-particle cryo-EM structures of transcribing complexes of y-mtRNAP–Mtf1, each with a fully resolved transcription bubble and an RNA–DNA hybrid. The PmIC to IC8 structures visualize the entire transcription initiation process from promoter opening to the EC transition at single-nucleotide addition steps 24 containing a 9-base-pair (bp) RNA–DNA, and these nine structural states are sequentially morphed into videos showing the dynamic process of transcription initiation and transition to elongation (Supplementary Videos We used three pre-melted −4 to +2) DNA scaffolds of a consensus yeast mitochondrial DNA promoter with slightly different coding sequences to generate the IC2–IC8 states  is a unique insertion in y-mtRNAP that is absent in T7 RNAP and h-mtRNAP. Owing to the dynamic behaviour of the downstream DNA and its surroundings, y-ins has a lower resolution than the core. The AlphaFold2+ ion chelates the phosphates of the +2 GTP, and the second ion is not resolved. Considering the abortive 2-nt synthesis cycle  is about 1.6× the average (80 Å2) for the structure; IC3 has a B-factor of about 0.9× the average for the region. The NT strand in IC4 carries less stress energy and favours RNA extension over abortive synthesis. Notably, the 4-bp RNA–DNA duplex in IC4 is in a half-translocated state; that is, the 3′ end of the nascent RNA has moved by about 3.5 Å at the C1′ position from the N site but has not reached the P site  has a poor density and no noteworthy interactions with protein atoms. The B-factor of the NT region G base in all IC states. The NT staircase is supported by backbone interactions with the F16/Y18 groove in the Mtf1 N-terminal domain G base Fig. . The Y10sis Fig. ; that issis Fig. .We optimized the promoter sequence to successfully produce the structures of IC7 and IC8 states . The IC states of y-mtRNAP are captured in various conformational states of the active site  and eukaryotes. The RNAP and transcription initiation factor(s) remain bound to the promoter throughout initiation and dissociate only following transition to the EC. The y-mtRNAP IC structures in this study show how DNA scrunching driven by the growing RNA–DNA reorganizes the template strand, NT strand and the flexible C tail of Mtf1 to support RNA synthesis during initial transcription and later promoter escape for the EC transition 2. This difference suggests that the homologous factors in y-mtRNAP and h-mtRNAP ICs may use distinct promoter melting mechanisms. Although y- and h-mtRNAPs are expected to follow a similar initiation path, the involvement of TFAM versus y-ins might create noticeable structural differences between the two in initial RNA synthesis and transition into elongation. The y-ins domain essential for transcription initiation by y-mtRNAP is absent in h-mtRNAP, which recruits TFAM for initiation. Some of the tasks of y-ins are delegated to TFAM as both stabilize the promoter. It also remains to be seen whether the NT scrunching and progressive rearrangements of the transcription bubble in y-mtRNAP are followed in h-mtRNAP. The findings of the current study can guide in designing experiments to obtain the structures of h-mtRNAP IC intermediates. Such insights will enhance our understanding of the mechanisms involved in mitochondrial DNA transcription initiation in the human system.The transcription initiation mechanism of y-mtRNAP is most similar to that of h-mtRNAP. A major difference is that h-mtRNAP requires TFAM to position and engage the h-mtRNAP–TFB2M at the promoter start siteS. cerevisiae Mtf1 was transformed into Escherichia coli BL21 competent cells and expressed and purified as described previously41. Cells were grown at 37 °C in LB medium supplemented with 100 µg ml−1 ampicillin to an optical density of 0.8 at 600 nm before induction with 1 mM isopropyl β-d-1-thiogalactopyranoside. After 16 h at 16 °C, cells were collected and the cell pellet was lysed by sonication in the presence of protease inhibitor and lysozyme in lysis buffer ), and subsequently subjected to polyethylenimine (10%) and ammonium sulfate (55%) precipitation. The dissolved ammonium sulfate pellet was loaded on a 5-ml DEAE Sepharose and 5 ml Ni-Sepharose cartridge  connected in tandem. After sample loading, the DEAE Sepharose cartridge was detached and the Ni-Sepharose cartridge was washed with 50 ml wash buffer . The Mtf1 protein was eluted with a 70-ml gradient of 20 mM to 500 mM imidazole in wash buffer. The Mtf1 peak eluent was collected and loaded on two 1-ml heparin-Sepharose columns  connected in tandem. Subsequently, the columns were washed with 20 ml heparin wash buffer , 1 mM PMSF) before elution with a 50-ml gradient of 150 mM to 1 M NaCl in heparin wash buffer. The eluted Mtf1 protein was collected and concentrated using a 10-kDa-cutoff Amicon ultra-centrifugal filter (Merck Millipore) and stored at −80 °C. The Mtf1 mutant proteins were purified using the same protocol  ΔN100 y-mtRNAP to TEV protease for 16 h at 4 °C. The cleaved protein was loaded on a 5 ml Ni-Sepharose cartridge and the flow-through was collected and concentrated using a 10-kDa-cutoff Amicon ultra-centrifugal filter (Merck Millipore) and stored at −80 °C. The y-ins-deleted y-mtRNAP was purified using the same protocol  were carried out using y-mtRNAP (1 µM) and Mtf1 (2 µM) in reaction buffer  and the indicated combinations of 100 µM of ATP, GTP and UTP, and 200 µM of 3′-dCTP spiked with [α-32P]GTP. The RNA synthesis reaction was conducted at 4 °C for 2 h or at 25 °C for 15 min and terminated using 0.5 M EDTA and formamide dye mixture . RNA products were resolved on a 24% polyacrylamide, 4 M urea gel. Visualization, runoff RNA product quantification and analysis of the gel were accomplished with ImageQuant software. The quantified runoff products were graphed using GraphPad Prism 9 software.In vitro transcription initiation assays were carried out as discussed previously32P]ATP in reaction buffer  at 25 °C for 15 min. Transcription reactions on nicked and nucleotide-deleted promoters were carried out similarly but were monitored using [α-32P]GTP. Reactions were terminated using 0.5 M EDTA and formamide dye mixture ; uncropped gels are in Supplementary Fig. Transcription reactions on duplex promoters (2 µM) were carried out with y-mtRNAP or mutant 1 µM) and Mtf1 or mutants (2 µM) and 100 µM of ATP, GTP and UTP, and 200 µM of 3′ dCTP spiked with [γ- µM and M22. Three y-mtRNAP PmICs were prepared by incubating ΔN100 y-mtRNAP, Mtf1 and a promoter DNA , was purified by size-exclusion chromatography using a Superdex 200 Increase 10/300 GL column connected to an AKTA PURE 25 FPLC system  maintained at 6 °C. The column was in-line with a multi-angle light-scattering device (mini-DAWN), a differential refractive index measuring device (Optilab) and a dynamic light-scattering device (DynaPro Nanostar) from Wyatt Technology for measuring the molecular mass and hydrodynamic radius of the eluted samples from the size-exclusion column. After purification, each PmIC was concentrated to about 3 mg ml−1 (about 15 µM), aliquoted and stored at −80 °C before further use. The concentrations of the PmICs were determined through NanoDrop One UV-VIS Spectrophotometer (Thermo Fisher) measurements with buffer A as the blank.Complex assembly and characterization were carried out as described previouslyThe PmIC on the 33-bp bubble promoter was incubated with GTP at a molar ratio of 1:50 to generate IC2, and with pppGpG (TriLink BioTechnologies), UTP and ATP at a molar ratio of 1:3:25:25 to prepare IC4. The PmIC on the 36-bp bubble promoter was incubated with pppGpG and ATP at a molar ratio of 1:3:50 to generate IC5, and pppGpG, ATP, UTP and GTP at a molar ratio of 1:3:50:20:20 to generate IC6. The 37-bp bubble promoter PmIC was incubating with pppGpG, ATP and UTP at a molar ratio of 1:10:50:40 for IC7; and with GTP, ATP and UTP at a molar ratio of 1:40:40:30 for IC8. All of the ICs were incubated for 2 h at 4 °C before grid preparation.−1 was loaded into disposable cuvettes (Wyatt Technologies) and placed in the sample chamber maintained at 4 °C. DLS experiments consisted of 30 acquisitions for each sample, and data were analysed using Dynamics software .The complexes were characterized by stand-alone cuvette mode DLS experiments using a DynaPro Nanostar (Wyatt Technology) to confirm similar hydrodynamic radius and polydispersity values for all IC samples. For the DLS experiments, 8 µl of IC samples at a concentration of 0.5 mg ml−1 (4 μM) and 0.9 mg ml−1 (4.5 μM), respectively, on Cu300 grids. The sample  was incubated on the grids for 30 s before back-blotting for 12 s using two pieces of Whatman Grade 1 filter paper, and the plunge-freezing was carried out by dipping the grids in liquid ethane at a temperature of −172 °C. Optimized grids for IC4, IC6, IC7 and IC8 were obtained by spotting 3 µl of the sample at 0.7 (3.5 µM of IC4 and IC7), 0.8 (4 µM of IC6) and 0.5 mg ml−1 (2.5 µM of IC8) on Au300 grids, before back-blotting for 12 s.The workflows for IC2–IC8 structure determination are shown in Supplementary Fig. The grids were clipped and mounted on a 200-keV Glacios cryo-transmission electron microscope (Thermo Fisher) with an autoloader and Falcon 3 direct electron detector as installed in our laboratory. High-resolution datasets for y-mtRNAP IC2 and y-mtRNAP IC4 to IC8 were collected on the Glacios using EPU software version 2.9.0 (Thermo Fisher).Electron microscopy data were recorded as movies in counting mode at a nominal magnification of ×190.000 for IC2 yielding a pixel size of 0.76 Å, and at a nominal magnification of ×150.000 for IC4 to IC8, yielding a pixel size of 0.97 Å. The total exposure time was 25.15 s for IC2, 38.41 s for IC4 to IC6, and 37.06 s for IC7 and IC8 with a total dose of 40 electrons per square angstrom for all datasets. All videos were recorded as gain-corrected MRC files. The data collection parameters for all structures are listed in Extended Data Tables 42) as implemented in the Relion 3.1 package43 and the contrast transfer function parameters were estimated by CTFFIND-4 (ref. 44). The particles were automatically picked using the reference-free Laplacian-of-Gaussian routine in Relion 3.1. The picked particles were cleaned by cycles of two-dimensional (2D) and 3D classifications. The previously obtained density map for IC3  ID: 6YMW) was blurred to 40 Å resolution and used as the template for initial 3D classifications. The 3D classifications eliminated partially disordered and incomplete particles. The particles forming lower-resolution density maps .For each dataset, individual video frames were motion-corrected and aligned using MotionCor2 (ref. 6YMV) and IC3 (PDB ID: 6YMW) structures were used as references for building the models of the y-mtRNAP IC2 and IC4-IC8 structures. Furthermore, the AlphaFold prediction of y-mtRNAP (UniProt ID: P13433) was used for modelling the insertion region . In addition, the y-ins region of previously published PmIC and IC3 structures have been corrected on the basis of the AlphaFold model. All model building were carried out manually using COOT45. Model building was coupled with iterative rounds of real-space structure refinement using Phenix 1.19.2 (ref. 46). In the IC2 final structure, Mtf1 is traced from S2 to S340 and y-mtRNAP is traced from I386 to the end residue S1351 . In the IC4 structure, Mtf1 is traced from S2 to G341 (end residue) and y-mtRNAP is traced from A407 to S1351 (with the stretches of residues 559–588 and 1311–1319 missing). In the IC5 structure, Mtf1 is traced from S2 to Y335 and y-mtRNAP is traced from I386 to S1351 (with the stretches of residues 556–588 and 1310–1319 missing). In the IC6 structure, Mtf1 is traced from S2 to T337 and y-mtRNAP is traced from I386 to S1351 (with the stretches of residues 554–588 and 1311–1319 missing). In the final IC7 structure, Mtf1 is resolved from residues S2 to T337 and y-mtRNAP is traced from I386 to S1351 (with the stretches of residues 554–588 and 1312–1318 missing). In the IC8 structure, Mtf1 is traced from S2 to D330 and y-mtRNAP is traced from I386 to S1351 . The DNA and RNA chains are different for each structure and were modelled independently into the experimental density maps. In IC2, the base of template nucleotide 22 has been removed owing to weak density. In IC4 the bases of NT nucleotide 123 and template nucleotides 22 and 24 were removed, and template nucleotide 23 was omitted. All structure figures were generated using PyMol (https://pymol.org/2/), Chimera47 and ChimeraX48. Videos were generated using Chimera.Data processing yielded 3.47-Å, 3.44-Å, 3.39-Å, 3.62-Å, 3.75-Å and 3.62-Å density maps for IC2, IC4, IC5, IC6, IC7 and IC8, respectively, and these were used to fit the atomic models for the respective structures. Previously published PmIC (PDB ID: Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06643-y.Supplementary InformationSupplementary Figs. 1–3 and legends for Supplementary Videos 1 and 2.Reporting SummaryPeer Review FileSupplementary Video 1Structural changes in the transcription bubble after each nucleotide addition from PmIC to IC8 and EC.Supplementary Video 2Structural changes in y-mtRNAP and Mtf1 after each nucleotide addition from PmIC to IC8 and EC.Source Data Fig. 2Source Data Extended Data Figs. 1 and 6


N6-methyladenosine (m6A) has boosted the recent emergence of RNA epigenetics, due to its high abundance and a transcriptome-wide widespread distribution in mammalian cells, proven to impact antiviral innate immunity. However, the other types of RNA modifications are also involved in regulating antiviral responses, and the functional roles of these non-m6A RNA modifications have not been comprehensively summarized. In this Review, we conclude the regulatory roles of 2’-O-methylation (Nm), 5-methylcytidine (m5C), adenosine-inosine editing (A-to-I editing), pseudouridine (Ψ), N1-methyladenosine (m1A), N7-methylguanosine (m7G), N6,2’-O-dimethyladenosine (m6Am), and N4-acetylcytidine (ac4C) in antiviral innate immunity. We provide a systematic introduction to the biogenesis and functions of these non-m6A RNA modifications in viral RNA, host RNA, and during virus-host interactions, emphasizing the biological functions of RNA modification regulators in antiviral responses. Furthermore, we discussed the recent research progress in the development of antiviral drugs through non-m6A RNA modifications. Collectively, this Review conveys knowledge and inspiration to researchers in multiple disciplines, highlighting the challenges and future directions in RNA epitranscriptome, immunology, and virology.The post-transcriptional RNA modifications impact the dynamic regulation of gene expression in diverse biological and physiological processes. Host RNA modifications play an indispensable role in regulating innate immune responses against virus infection in mammals. Meanwhile, the viral RNAs can be deposited with RNA modifications to interfere with the host immune responses. The  As thAs the first line of defense against invaders, the innate immune system protects the body from the harmful effects of viral infections. A typical antiviral response can be succinctly outlined as follows: first and foremost, the innate immune system recognizes virus pathogen-associated molecular patterns (PAMPs) and triggers downstream signaling pathways. PAMPs typically include viral single-stranded (ss) or double-stranded (ds) RNAs, dsDNAs, and viral proteins , 6. The N6-methyladenosine (m6A) stands out as the most abundant RNA modification in mammalian mRNA. The advancement of the base-resolution mapping tools targeting m6A, which are continually being refined, has facilitated the ongoing identification of its functional and mechanistic roles in diverse regulatory networks , 5-methylcytidine (m5C), adenosine-inosine editing (A-to-I editing), pseudouridine (Ψ), N1-methyladenosine (m1A), N7-methylguanosine (m7G), N6,2’-O-dimethyladenosine (m6Am), and N4-acetylcytidine (ac4C). These non-m6A RNA modifications manifest diverse and intricate regulatory roles in controlling gene expression, modulating metabolic networks, and impacting the development of diseases  modification is a highly conserved modification in which the 2’-hydroxyl group of nucleotides is methylated by 2’-O-methyltransferase (2’-O-MTase) co-transcriptionally or post-transcriptionally. Discovered initially to exist in ribosomal RNA (rRNA) and transfer RNA (tRNA), Nm has since been found to exhibit varying levels of abundance in diverse RNA species  exhibit high sensitivity to IFIT1 , an antiviral protein . IFIT1 ito IFIT1 . Note thto IFIT1 , 72, 73.to IFIT1 .Despite the incomplete comprehension of internal Nm modifications in viral RNA, recent studies have partially illuminated their regulatory role in innate immunity. Like internal Nm modifications in host mRNA, internal Nm modifications in viral RNA of various viruses can impair replication by affecting the elongation of the RNA polymerase . InteresIn addition to the essential role in the development of immune cells such as macrophages and the expression of immune-related genes, host RNA Nm modification can also dynamically regulate the immune response through changes in Nm stoichiometry . The hume.g., HIV), “cap snatching” from host mRNA , and encoding specialized enzymes and active sites for capping   , 45, 87.S-COV-2) . Structuelopment . Numerouelopment –92. Simielopment –95. In aelopment .2.25C is a modification that occurs at the 5th position of cytosine residues and is widely distributed in eukaryotes RNA, with high enrichment in tRNA and other non-coding RNAs  and the NOL1/NOP2/SUN domain (NSUN) family  family . Notablynd NSUN4 . This coogenesis , 37, 101isorders , 101.5C writer. However, they can utilize host writers to aid in their invasion  that target DNA methylation also exhibit effects on RNA m5C   . Among te genome . This moe genome . In hoste genome –131. Wite genome . In ncRNe genome . In addie genome . Patholoe genome , 135.In host cells, ADAR1, especially its IFN-responsive isoform ADAR1 p150, is a critical IFN inhibitory factor in antiviral innate immunity, and its negative regulation is essential for suppressing abnormal antiviral responses and maintaining immune homeostasis . EndogenThrough editing the viral RNA, transcripts, template strands, and immune-responsive RNA, ADAR’s impact on viruses can be either proviral, antiviral, or even have no effect. This complexity primarily depends on the combination of the virus and host and the specificity of the editing sites. Like Nm, viruses can utilize/recruit host ADAR to edit viral RNA, effectively disguising it as “self” to evade detection by RNA-sensing . NotableADAR can achieve its proviral effects through other mechanisms as well. Viruses can exploit ADAR and influence viral RNA’s replication, transcription, and protein production efficiency through editing . A typicAs an immune regulator, ADAR has been shown to have antiviral activity. Cells lacking ADAR exhibit increased susceptibility to various viruses, while overexpression of ADAR1 can inhibit viral replication . During Alu RNA caused by SARS-CoV-2  transcripts and is distributed internally across mRNA, tRNA, and rRNA  and rRNA (maintaining ribosome function), m1A has also been found to be distributed in mRNA  protein as an eraser  converts Am to m6Am  to promote viral replication  and transmembrane serine protease 2 (TMPRSS2) mRNA by installing m6Am, promoting their expression and facilitating viral infection  region using antibody-based sequencing methods  to add ac4C to viral RNA, increasing its stability and promoting replication   . Howevere (m5Cm) . Uridylae (m5Cm) , 251. Loe (m5Cm) . Anothere (m5Cm) . This vie (m5Cm) , 254. Moe (m5Cm) –257. Man3Review, we summarized the regulatory roles of eight typical non-m6A RNA modifications in antiviral innate immunity within the current scope of knowledge, including 2’-O-methylation (Nm), 5-methylcytidine (m5C), adenosine-inosine editing (A-to-I editing), pseudouridine (Ψ), N1-methyladenosine (m1A), N7-methylguanosine (m7G), N6,2’-O-dimethyladenosine (m6Am), and N4-acetylcytidine (ac4C). It is evident that compared to the extensively studied m6A modification, there is still a significant gap regarding the immune regulatory functions of non-m6A RNA modifications. The lack of clarity regarding the regulators of non-m6A RNA modifications has caused gaps in understanding the mechanisms underlying innate immune responses, particularly the utilization and induction patterns of viruses. In addition, low abundance, complexity of distribution, and diversity of responsive effects are all challenges for the study of non-m6A RNA modifications in antiviral immunity.In this 6A RNA modifications, such as Ψ, Nm, m7G, m5C, m1A, and ac4C, has been enabled to resolve the challenges in studying the location and stoichiometry of these modifications on viral and host RNA, as well as the regulatory modes of RNA modification landscapes during host-virus interactions. These recent advances in base-resolution sequencing technology offer the super-resolution profiles of non-m6A RNA modifications transcriptome-wide; meanwhile, the breakthroughs in quantitative mapping tools could aid the comprehensive investigation of non-m6A RNA modifications in antiviral innate immunity through monitoring the dynamics of these RNA modifications.Considering the recently emerging base-resolution sequencing methods, the quantitative analysis of non-m6A RNA modifications have been demonstrated to facilitate the immune escape and replication of certain viruses; however, these non-m6A RNA modifications are also essential for the host to enhance the antiviral immune response. Thus, the manipulation of RNA modifications at specific sites within host RNA or viral RNA may have an impact on the development of antiviral drugs with therapeutic potential. Overall, the functional and mechanistic study of RNA modifications in antiviral innate immunity has led to entirely new perspectives, yielding in-depth insights into translational medicine and potentially benefiting related research in biological sciences, biomedical engineering, clinics, and the pharmaceutical industry.Besides, some non-mSS: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing. LZ: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing.


To identify the most significantly differentially expressed circular RNAs (circRNAs) in colorectal cancer (CRC) tissues in terms of their expression levels and circularity, and to analyze the relationship between their expression levels and the clinical characteristics of patients.circRNA RNA-seq technology was used to screen differentially expressed circRNAs in CRC. Sanger sequencing was used to identify circRNA back-splice junction sites. The relative expression levels of hsa_circ_0003761 (circMSH3) in CRC tissues and cell lines were detected by quantitative real-time fluorescence PCR technology. An RNA-protein pull-down assay was used to detect protein binding to circRNAs. Dual-luciferase reporter gene vectors were constructed to verify that circRNAs bind to microRNAs.P = 0.043); the area under the curve (AUC) of circMSH3 for CRC diagnosis was 0.75, with a sensitivity and specificity of 70.9% and 66.4%, respectively. circMSH3 could bind to a variety of proteins, mainly those involved in RNA transcription, splicing, cell cycle, and cell junctions. Furthermore, circMSH3 could bind to miR-1276, miR-942-5p, and miR-409-3p.Four hundred twenty circRNAs were found to be upregulated, and 616 circRNAs were downregulated. circMSH3 was derived from the MutS homolog 3 (MSH3) gene and was formed by a loop of exons 9, 10, 11, and 12. In 110 pairs of CRC and adjacent tissues, circMSH3 expression was 4.487-fold higher in CRC tissues. circMSH3 was also highly expressed in the HT-29 and LOVO CRC cell lines. The expression level of circMSH3 was associated with distant metastasis in CRC patients (circMSH3 is a potential biomarker for the diagnosis of CRC and affects the distant metastasis of CRC. Multiple RNA-binding protein binds to circMSH3, and circMSH3 binds to miR-1276, miR-942-5p, and miR-409-3p, thereby affecting the expression of circMSH3. Circular RNAs (circRNAs) are a newly discovered class of endogenous non-coding RNAs that form special covalently closed circular structures without 5′–3′ polarity and a polyadenylation tail. circRNAs are involved in almost all cellular physiological processes, and the abnormal expression of circRNAs is closely related to tumor invasion and metastasis. circRNAs are widely expressed in tissues and blood, are highly stable, and have high specificity . TherefoColorectal cancer (CRC) is one of the most common malignant tumors of the digestive tract worldwide and is a serious threat to human life and health . The occTumor and paracancerous tissues were collected from 110 patients with colon cancer who underwent surgical resection in the Central Hospital of Panyu District, Guangzhou. Prior to this, none of the patients had received neoadjuvant chemotherapy, radiotherapy, or other antitumor therapy, and all tissue samples were pathologically confirmed. All patients or their families signed an informed consent form to participate. The study was approved by the Ethics Committee of the Guangzhou Panyu District Central Hospital, approval number PYRC-2023-017.2. The medium was changed every other day. The cells were digested with 0.25% trypsin and subcultured, and cells with logarithmic growth and 95% viability were selected for further experiments  were purchased from the Shanghai Institute of Cell Biology . The cell lines were grown in Dulbecco’s modified Eagle’s medium/F12 medium containing 10% fetal bovine serum and maintained at 37 °C in a humidified incubator with 5% COeriments .P-value of 0.05 and a fold change of ≥—2—. Differentially expressed circRNAs were visualized using differential distribution volcano plots and differential circRNA cluster analysis.Selected 10 pairs of colorectal cancer patients’ cancer tissue and paired paracancerous tissue samples, among which five patients had tumor metastasis, and the other 5 patients had no tumor metastasis. Total RNA was isolated using a HiPure Total RNA Mini Kit  following the manufacturer’s protocol. RNase R  was used to digest the total RNA for 1 h after DNase I processing at 37 °C. After quantifying the sample RNA, the RNA library was prepared using a KAPA RNA Hyperprep Kit with Riborase (HMR) . Sequencing was performed using a Hiseq X10 system (Illumina) in PE150 mode. For analysis of circRNA-seq, sequencing readings were mapped to the Hg19 reference genome using STAR software , and cirCT−△△ formula.Total RNA was extracted from tissues and cells using TRIzol. RNA was reverse transcribed into cDNA using an EasyScript First-Strand cDNA Synthesis SuperMix Kit. PCR was performed with a SYBR Green qPCR SuperMix Kit using the primers shown in g for 15 min, and the supernatant was collected and mixed with the probe-bead mixture. An RNase inhibitor (5 µL) and poly(dI⋅ dC) (5 µL) were added, followed by incubation at 25 °C for 2 h and elution at 37 °C for 2 h. The supernatant was transferred into a new tube and used for protein mass spectrometry and western blotting. circMSH3 probe sequence were designed and generated by Ribo Bio .An Pierce™ Magnetic RNA-Protein Pull-Down Kit  was used to detect interactions between circMSH3 and proteins. The circMSH3 probe solution (1 µg) was denatured at 90 °C for 2 min and incubated with pre-cooled RNA structure buffer to form RNA secondary structures. Next, streptavidin magnetic beads were incubated with the mixture at 25 °C for 30 min. The cells were lysed at 4 °C and centrifuged at 15,000× circMSH3: 5′-Biotin-UCCUUUCGACUCGAAUUCUG-3′;Negative Control (NC): 5′-Biotin-UUCUCCGAACGUGUCACGUTT-3′.Wild-type or mutant circMSH3 fragments were constructed and inserted downstream of the luciferase reporter gene in the pmirGLO plasmid . We transfected reporter plasmids into 293T cells using Lipofectamine 3000, followed by hsa-miR-409-3p, hsa-miR-942-5p, hsa-miR-1276, hsa-miR-513a-5p, and hsa-miR. The -7151-5p mimic was co-transfected with the reporter gene into 293T cells and the luciferase activity was measured 48 h later using a dual-luciferase reporter kit . The relative luciferase activity was normalized to the Renilla luciferase internal control.t-test. The relationship between the expression levels of circMSH3 in CRC tissue and the clinicopathological characteristics of the patients was compared between two groups using an independent samples t-test, and the comparison between multiple groups was performed using one-way analysis of variance. P-values <0.05 were considered statistically significant. The diagnostic performance of circMSH3 was analyzed using receiver operating characteristic (ROC) curves.IBM SPSS statistics 21.0 software  was used for statistical analysis, GraphPad Prism 8.0 software  was used for graphing, and values were expressed as the mean ±standard deviation. The expression levels of circMSH3 in CRC and adjacent tissues were compared by a paired In order to explore the expression characteristics of circRNAs in colorectal cancer tissues, RNA-seq results showed that most circRNAs originated from the exon region, chromosome 1-3 formed a large number of circRNAs, and the length of circRNA sequences was mainly distributed in 200 bp–500 bp –1C. circcircMSH3 was derived from exons 9, 10, 11, and 12 of the MSH3 gene with a length of 423 bp. To verify the circularity of circMSH3, we performed reverse Sanger sequencing of the PCR product. The sequencing results were consistent with the sequence of the circMSH3 circularization site in the CircBase database , 2C.P < 0.001)  . circMSH< 0.001) . circMSH< 0.001) . MSH3 ge< 0.001) –3D. Rece< 0.001) . These rP = 0.043) but not with sex (P = 0.105), age (P = 0.526), tumor size (P = 0.953), degree of invasion (P = 0.509), lymph node metastasis (P = 0.596), TMN stage (P = 0.416), or degree of differentiation (P = 0.492) , lymph node metastasis (N), distant metastasis (M), TNM stage, and differentiation. The results showed that circMSH3 expression was significantly correlated with distant metastasis in CRC patients (= 0.492) . The resMany studies have shown that circRNAs can act by binding to proteins or miRNAs . To explvia cell cycle progression and apoptosis. The downregulation of circRNA-FOXK2 can significantly inhibit the migration, invasion, liver metastasis, and tumor growth of pancreatic cancer cells (circRNAs have been one of the most popular research topics in the RNA field in recent years. At the molecular level, circRNAs exert their biological functions mainly by acting as binding proteins for the “sponge adsorption” of miRNAs or proteins . For exaer cells . Recentler cells . To dateer cells .We screened for circRNAs that were differentially expressed between five pairs of metastatic and non-metastatic CRC groups by high-throughput sequencing technology in the early stage. We selected the circRNA circMSH3 for further verification using qRT-PCR and expanded the clinical sample to 110 CRC samples, which showed that circMSH3 was significantly highly expressed in CRC tissues and cells. ROC curve analysis demonstrated that when circMSH3 was used for the diagnosis of CRC, its AUC was 0.75, with a sensitivity and specificity of 70.9% and 66.4%, respectively. Correlation analysis between circMSH3 and the clinical characteristics of CRC tissue samples showed that it was significantly associated with distant metastasis, indicating that circMSH3 can be used as a potential biomarker for the diagnosis of CRC and prediction of distant metastasis.In terms of mechanism, we initially explored the proteins and miRNAs that circMSH3 can bind to. According to an RNA–protein pull-down assay combined with mass spectrometry, we found that circMSH3 can bind to a variety of proteins involved in splicing, methylation, RNA transport, and biological processes related to the cell cycle and cell junctions . These rin vivo and in vitro, which will be of value in elucidating the specific mechanism of circMSH3 in colorectal cancer.It should be recognized that this study also has the following limitations. First of all, limited by the number of collected colorectal cancer clinical samples, we will collect more samples for the development of circMSH3 as a diagnostic marker in the future. Secondly, the results of luciferase reporter gene experiments and RNA pulldown experiments in the previous period showed that circMSH3 can bind multiple miRNAs and proteins. These clues suggest that circMSH3 may play an important role in colorectal cancer and is an important potential therapeutic target. It will be promising to further explore the molecular mechanism and cellular function of circMSH3 In conclusion, the findings of this article show that circMSH3 is a potential marker for the diagnosis of CRC, may have important molecular functions in the occurrence and development of CRC, and can be used as a potential therapeutic target.10.7717/peerj.16297/supp-1Supplemental Information 110 pairs of colorectal cancer patients compared between groups, differentially expressed circRNAs.Click here for additional data file.10.7717/peerj.16297/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.16297/supp-3Supplemental Information 3The photo of the original film corresponding to the WB electrophoresis results of the positive control HuR in the pulldown experimentClick here for additional data file.10.7717/peerj.16297/supp-4Supplemental Information 4The original result files including the circMSH3 binding miRNAs site prediction, dual fluorescent reporter gene carrier sequencing results and signal detection.Click here for additional data file.


CRLF2) gene has been observed in patients with acute lymphoblastic leukemia BCR-ABL1-like subtype. Currently, there is no commercial system available for the direct detection of the IGH::CRLF2 fusion by fluorescent in situ hybridization (FISH), as there are for many other leukemia-related gene fusions. In an effort to verify the IGH::CRLF2 fusion, some researchers prepare home-grown FISH probes from bacterial artificial chromosome clones flanking the IGH and CRLF2 genes, which is the best alternative to confirm the fusion, however difficult to reproduce in most cytogenetic laboratories.High expression of the Cytokine Receptor-Like Factor 2 (IGH::CRLF2 gene fusion we designed a methodological approach requiring the two commercially available IGH and CRLF2 break-apart probes.For the direct observation of the IGH::CRLF2 gene fusion and has the potential to be used for identification of other gene fusions.Our methodological approach allows direct visualization of the  CRLF2) gene has been observed in subsets of patients with T or B-cell acute lymphoblastic leukemia (ALL), especially in those with BCR-ABL1-like subtype of B-cell precursor ALL. The main molecular mechanisms leading to high CRLF2 expression are either an intra-chromosomal deletion causing the P2RY8::CRLF2 fusion or one of the chromosomal translocations t and t, which produce the IGH::CRLF2 gene fusion [High expression of the Cytokine Receptor-Like Factor 2 , as there are for many other leukemia-related gene fusions. Most cytogenetic laboratories perform one or two independent FISH studies with CRLF2 break-apart or IGH break-apart probes (CRLF2-BA or IGH-BA) to establish the presence of the IGH::CRLF2 fusion [IGH::CRLF2 fusion, some researchers carry out their FISH studies through break-apart probes on metaphase cells processed either by the trypsin-Giemsa method or by additional FISH studies with centromeric or painting probes, in order to identify the chromosome acceptor of the translocated signal (i.e. the der(14) for FISH with the CRLF2-BA probe, or the der(X)/der(Y) for FISH with the IGH-BA probe) [IGH::CRLF2 fusion is to do a FISH with a single-fusion extra-signal probe mixture prepared by themselves from bacterial artificial chromosome (BAC) clones flanking the CRLF2 and covering the IGH genes [Since its role in lymphoid transformation in B-cell precursor ALL was described , 2, dete2 fusion –15; howeA probe) , 10, 11.GH genes , 12, 13.CRLF2 signals observed in cells from Fig. IGH signals revealed with the FISH study using the CRLF2-BA/IGH-BA probe mix  the informative cells.With the methodological approach here presented we made the following observations. The separated mix Fig. B and D. CRLF2 gene by flow cytometry or RT-PCR [IGH::CRLF2 fusion as the cause for such overexpression.Currently, the strategy preferred by some researchers for the study of ALL is, firstly, to carry out studies focused on the detection of overexpression of the r RT-PCR –8, 13–15IGH::BCL6, IGH::TRA, IGH::TRD, IGH::MYC, IGK::MYC, IGK::BCL2, IGK::BCL6, IGL::BCL2, IGL::BCL6, IGL::MYC, TRA::MYC, RUNX1::MECOM, FUS::ERG, FUS::DDIT3, and EWSR1::DDIT3, among others.Moreover, other neoplasia-associated gene fusions, for which there is no commercially available two-gene dual-color probe, could be addressed with the approach here presented by us to confirm their existence. Some of these gene fusions are IGH gene), but where the partner gene from the other derivative chromosome is rarely affected , or even unknown. For these instances, the combination of a commercial red/green probe (as IGH-BA probe) with another probe prepared from BAC clones and labeled in blue/aqua spectrum, could be an excellent approach to clearly identify such fusions.In addition, our method could be applied in those chromosomal rearrangements (translocations or inversions) where a known gene from a derivative chromosome is broken , which is described in the following steps.IGH::CRLF2 fusion should be studied by FISH with the CRLF2-BA probe  only IGH is the reported partner  are the same as those performed in the first FISH experiment, i.e., following the supplier's recommendations.Locate the previously recorded microscopic fields and reanalyze them with emphasis on the informative cells.For the confirmation of the