Plasmodium parasites, a causative agent of malaria, requests two hosts and the completion of 11 different parasite stages during development. Therefore, an efficient and fast response of parasites to various complex environmental changes, such as ambient temperature, pH, ions, and nutrients, is essential for parasite development and survival. Among many of these environmental changes, temperature is a decisive factor for parasite development and pathogenesis, including the thermoregulation of rRNA expression, gametogenesis, and parasite sequestration in cerebral malaria. However, the exact mechanism of how Plasmodium parasites rapidly respond and adapt to temperature change remains elusive. As a fundamental and pervasive regulator of gene expression, RNA structure can be a specific mechanism for fine tuning various biological processes. For example, dynamic and temperature-dependent changes in RNA secondary structures can control the expression of different gene programs, as shown by RNA thermometers. In this study, we applied the in vitro and in vivo transcriptomic-wide secondary structurome approach icSHAPE to measure parasite RNA structure changes with temperature alteration at single-nucleotide resolution for ring and trophozoite stage parasites. Among 3,000 probed structures at different temperatures, our data showed structural changes in the global transcriptome, such as S-type rRNA, HRPII gene, and the erythrocyte membrane protein family. When the temperature drops from 37°C to 26°C, most of the genes in the trophozoite stage cause significantly more changes to the RNA structure than the genes in the ring stage. A multi-omics analysis of transcriptome data from RNA-seq and RNA structure data from icSHAPE reveals that the specific RNA secondary structure plays a significant role in the regulation of transcript expression for parasites in response to temperature changes. In addition, we identified several RNA thermometers (RNATs) that responded quickly to temperature changes. The possible thermo-responsive RNAs in Plasmodium falciparum were further mapped. To this end, we identified dynamic and temperature-dependent RNA structural changes in the P. falciparum transcriptome and performed a comprehensive characterization of RNA secondary structures over the course of temperature stress in blood stage development. These findings not only contribute to a better understanding of the function of the RNA secondary structure but may also provide novel targets for efficient vaccines or drugs.The development of  Plasmodium falciparum development and the pathogenesis of the disease. A previous study found that parasites can sense metabolism and the environment using a variety of regulatory reprogramming mechanisms; for example, parasites alter the expression of enzymes involved in glycolysis and gluconeogenesis gene to sensitize the glucose  and the other is primarily expressed in the mosquito host (S gene)  . PlasmodA copies .in vivo and in vitro RNA secondary structures. Low icSHAPE scores mean that the position is more structured or presents a protein-binding site of the structure. The flexibility of the RNA secondary structure measured by icSHAPE score indicates the probability that a nucleotide is present in unpaired or single-stranded forms. A combined analysis of these structural profiles and transcriptome data reveals a unique RNA secondary structure that responds to temperature changes and parasite stage development, including S-type rRNA, parasite-infected erythrocyte surface protein (PIESP2), and erythrocyte membrane protein.Despite the importance of the temperature response in parasite biology, the precise mechanism underlying this response is still exclusive; thus, the molecular mechanisms in the RNA structure responsible for these reactions are an important yet missing piece of the puzzle. The major strengths of this study are the discussion on parasite development by temperature through RNA secondary structure and our analysis profiles of the structure of transcripts in trophozoite and ring stages after cold shock (low temperature). Here, we applied icSHAPE to profile the transcriptomic-wide in vivo and indicate that the development of P. falciparum is closely connected to the RNA secondary structure even in P. falciparum after cold shock. However, the mechanism needs to be further explored. A greater understanding of the structure and function of RNAT response to changes in temperature in the 5′-UTR region could thus provide insights into the virulence and development of P. falciparum.Our results provide a framework for understanding how the malaria parasite responds to its environmental temperature through changes in RNA structure P. falciparum strain 3D7 parasites were propagated in human red blood cells under standard conditions  according to the manufacturer’s instructions and as described previously . These lescribed . Finally3 chemical reagent modification as described above. In total, 7 μg RNA without poly-A selected from the 37°C control group and the 26°C treated group in the trophozoite developmental stage was digested with RNase-free DNase to remove residual DNA. Then, these total RNAs were reverse transcribed by primer extension with a FAM-labelled oligonucleotide (5′- ACCCTAACATCAAAAGCTGATAGG -3′) in the 315–338-bp region of the 18S rRNA gene. The PCR program was as follows: 5 min at 65°C, then kept on ice for 2 min, followed by 1 h at 50°C and 5 min at 95°C, and finally, the sample was kept at 4°C when ending the reaction. The reverse-transcribed products were resolved by capillary electrophoresis (CE) using an ABI 3730XL DNA Sequencer, and the results were shown by GeneMarker.Total RNA was extracted from parasite lysates with NAI-NTotal RNA used for the qPCR analysis was isolated and purified as described above. Briefly, approximately 2 μg of the RNA sample was used for strand complementary DNA synthesized by either oligo dT primers or random primer mixes using a Superscript III Reverse Transcriptase kit. Real-time PCR amplification was carried out in the presence of 12.5 µl of SYBR master mixture (Takara Bio Inc.), 1 µl of cDNA template, and target gene-specific primers in a CFX96 System under the following conditions: 1) 1 min at 95°C; 2) 5 s at 95°C; 3) 30 s at 50°C; 4) 30 s at 72°C with a plate reader; and 5) repeat steps 2), 3), and 4) for another 39 cycles. The housekeeping gene, serine–tRNA ligase, putative (PF3D7_0717700), was used to normalize the gene transcriptional level of each sample. Primers for detecting different transcripts from the 37°C control group and the 26°C treatment group were designed in this study. The primer sequences for PCR are listed in P. falciparum using Bowtie , respectively, and the majority of transcripts were mRNAs  with poly-A-depleted RNA library of ring and trophozoite stages, resulting in an average of 46.1 million sequence reads that map to the escribed . Finallyre mRNAs . Additioectively . At lastld shock .P. falciparum 3D7 was propagated at 37°C to keep most of the parasites at a similar stage . As shown in To dissect the effect of cold shock on gene expression in P. falciparum gene expression and regulation in the ring stage and trophozoite stage were investigated by changing the culture temperature, and four samples from the parasite culture are illustrated in R ≥ 0.937, Plasmodium export protein, and rifin (RIF), and upregulation genes, such as ABC transporter G family member 2 and circumsporozoite (CS) protein (CSP). At the same time, when we compared parasites at the trophozoite stage with the 37°C control group parasites, 82 genes had higher expression and 105 genes had lower expression in the 26°C treatment group. A few examples of genes that were obviously expressed at lower level in the 26°C treatment group are erythrocyte membrane protein 1, early transcribed membrane protein, and nine genes encoding Plasmodium export protein  proteins (PF3D7_1407000) are expressed in the parasitophorous vacuole and are later associated with macrogametes  is translationally activated during ookinete formation and silenced in gametocytes . P. falcogametes . Using togametes . Altoget3 modifications from the icSHAPE experiment were similar to those obtained with CE-based in vivo structure probing. We combined icSHAPE scores (non-depleted RNA library) from the 37°C control group and 26°C treatment group in the trophozoite developmental stage with CE gel results, as shown in 3 modification were consistent with those for the region from 35–135 bp in the 18S rRNA  and S2-type 18S rRNA (PF3D7_1371000) . To accu18S rRNA . Finally18S rRNA . Meanwhiin vivo icSHAPE score of 18S rRNA on five different chromosomes to show the distribution of icSHAPE profiles  in the control and cold stress groups and found that the CDS region is actually predicted to be more accessible to chemical modification and opening of the double-strands for gene expression in the 26°C group than in the control group, which has also been reported for 5′-UTR and 3′-UTR regions; however, these regions are shown to be less accessible and have lower icSHAPE scores than the A2-type 18s rRNA (PF3D7_0531600). These results are consistent with our previous findings that after low-temperature treatment, the expression level of S-type rRNA in the 26°C treatment group was significantly increased compared with that in the 37°C control group, while the A-type rRNA showed little change. These observations also indicate that A-type rRNA is the predominant form in the blood stage development of parasites, and they are consistent with our speculation that transcripts with higher icSHAPE scores also have the most abundant level. After normalization for icSHAPE score differences between the two different temperatures, a significantly different (p < 0.01) average icSHAPE score at 26°C compared with 37°C was observed for three subregions in S2-type 18S rRNA (PF3D7_1371000) (p < 0.01) increase in average icSHAPE scores under cold shock compared with the CDS region and 3′-UTR region. Then, we mapped the 5′-UTR region of 18S A-type rRNA (PF3D7_0531600) and S-type RNA (PF3D7_1371000) secondary structures by our icSHAPE scores  , but not1371000) . The 5′-E scores  and compE scores , while tP. falciparum development by changing physiologically relevant temperatures and RNA secondary structures. We compared the average icSHAPE score from the CDS region of individual transcripts, and the results displayed a correlation that transcripts with higher icSHAPE scores also have most abundant level  measured at 26°C and 37°C (t-test was applied in icSHAPE scores of the 5′-UTR region (−200 to −1 from the start codon) from 37°C in vivo to 26°C in vivo, 37°C in vivo to 37°C in vitro, 37°C in vitro to 26°C in vitro, and 26°C in vivo to 26°C in vitro, respectively. Because crowded nucleotides or protein binding can affect icSHAPE scores, only a p value <0.05 of two-tailed paired Student’s t-test was considered a functional RNAT. Finally, these results suggested that at least 11 genes are the most potential RNAT in Plasmodium after cold shock. In future research, it would be interesting to confirm the mechanisms of the RNATs found by reporter gene fusions or single-nucleotide changes in the RNAT regions.Because in RNA structure some nucleotides that were crowded and 37°C . To furtP. falciparum. Additional studies are needed to confirm the RNATs found in our study by single-nucleotide changes in the RNAT regions.Overall, our study adds to the accumulating evidence that suggests that the special RNA structure, especially 5′-UTR regions, has dramatic effects on the complex biological program of P. falciparum in the host and mosquito  in the trophozoite stage and may assume multiple conformations that have regulatory purposes.This study identified some functional RNA structural elements, demonstrated that a dynamic RNA structurome responds to two different temperatures, and identified novel RNA thermometers. Our study adds to the accumulating evidence that febrile temperatures can suppress the growth and development of parasites with a maximal effect on trophozoites . We idenP. falciparum reported that the most abundant transcripts exhibited higher icSHAPE scores in human host body temperature (P. falciparum is connected to the RNA secondary structure, even in P. falciparum after cold shock. Cold-induced icSHAPE score changes were strongly correlated with cold-induced changes in transcript abundance. A directed analysis of the average icSHAPE score collected from the CDS regions and the UTR regions revealed that certain regions had significantly different icSHAPE scores after cold stress. Thus, at present, our experimental and computational conclusions suggest that these positions may provide candidate sites for the functional conformation of mRNA in response to low-temperature stress; however, their interaction with regulatory proteins needs to be tested further.A previous study in perature . We alsoin vivo at two different temperatures. These candidate 5′-UTRs of secondary structure probing maps at different temperature profiles can help reveal a large number of RNAT candidates in malaria parasites. These results imply that these features might favor changes in RNA structure in response to low-temperature stress conditions in parasites; however, the mechanism must be tested in the future. A possible RNAT was determined in P. falciparum, and our study provides the first structurome for post-transcriptome regulation in response to the temperature changes. Identifying cold response molecules will provide new clues for further understanding the virulence and development of P. falciparum.Based on a parallel analysis of RNA structures in our experimental and computational analysis, we mapped the 5′-UTR structures of 44 genes at two different temperatures. An interesting observation from our global map of the 5′-UTR of the RNA structure results is that the majority of mRNAs folded significantly differently


Goat farming is becoming more important in Germany and as dehorning is forbidden, it is necessary to facilitate animal welfare among horned and mixed-horned herds. In this study an optimized automatic concentrated feeding system was installed in a mixed-horned herd and lying behaviour was detected by accelerometer technology. Results show a seasonal progression of lying behaviour in dairy goats and an adjustment of behavioural differences between horned and hornless goats with the new feeding system.The aim of this study was to evaluate lying behaviour in dairy goats before and after installation of an optimized automatic concentrated feeding system (AFS). A mixed-horned herd of Bunte Deutsche Edelziege was used. As many agonistic interactions between goats happen at the feeding place, a new automated feeding system was installed to better fulfil the needs of horned goats. Lying behaviour is an indicator to ascertain animal welfare of ruminants. In order to measure lying behaviour accelerometer technology was used and verified by video analyses. The results show an agreement of 99.62–99.93% per lying time by comparing accelerometers to video data. Over all goats, a mean ± SD lying time (LT) of 11.78 ± 1.47 h/d, a mean ± SD lying bout duration (LBD) of 0.51 ± 0.10 h/bout and a mean ± SD frequency of lying bouts (FLB) of 24.35 ± 5.57 were found. Lying behaviour follows a seasonal progression with significant lowest LBD and highest FLB in summer. With the old AFS significant differences in LBD and FLB were detected between horned and hornless goats, but with the new AFS results were adjusted. Findings suggest that changes in feeding management do not affect the general seasonal progression of lying behaviour but can affect the behavioural differences between horned and hornless dairy goats. In Germany, particularly in federal state Baden-Württemberg, dairy goat farming is becoming increasingly important as herd sizes are rising ,2. The bDue to their species-specific behaviour, agonistic interactions and horn-induced injuries are often found, especially as a result of competition during feed intake. Limited resources such as those for feeding can be a reason for aggressive behaviour in goats . With thAdditionally, individual feed quantities adapted to a corresponding milk yield are of great interest in dairy goats. Individual feeding results in higher feed efficacy, due to individual preferred feeding patterns of goats . In calfFor the evaluation of a feeding system, besides functional tests, behavioural measurements can be used in order to evaluate the effect on animal health and welfare before and after installation. Several factors can be defined. One aspect especially important for ruminants is lying behaviour . TraditiThe present study evaluated a new feeding system, specially built for horned and hornless goats with respect to their natural behaviour patterns. With accelerometer technology, lying times were studied before and after installation of the new system. The aim of this study was to answer the question whether the new feeding system influences the lying time in dairy goats, with special attention to horned and hornless goats. A dairy goat herd of Bunte Deutsche Edelziege was used for this study. Goats were kept in a mixed group with horned and hornless dairy goats . The measurements were performed from April 2019 until October 2020 see . Meanwhi2 ground area. The herd was supplied with concentrated feed by an AFS and additionally during milking. This system consisted of three individual feeding stations, positioned around the concrete food container . The new AFS was adapted to goat-specific behaviour and was constructed as a walk-through, built on an elevated platform  in 2019 and 766.23 kg milk/goat (258.40 milking days) in 2020. Goats have an average life performance of over 3.000 kg milk (3.607 kg milk in 2019 and 3.654 kg milk in 2020). The farmer can program AFS in order to offer concentrated food according to the individual milk yield of each goat. In the present study, the farmer programmed a start with 0.1 kg/d in late lactation, followed by a slow increase until the highest needs after kidding (fixed at about 0.8 kg/d for this herd). AFS calculates the number of daily feedings individually according to the amount of concentrated feed and the number of visits of each goat. Each feeding contained a maximum of 0.2 kg. Hay was given four times a day, with additional grass and alfalfa in summer. In summer, depending on the weather conditions, goats had different grazing times on pasture. Access to water was given ad libitum.Measurements took place from April 2019 until October 2020, with 3 measurement periods per lactation period  to record animal behaviour during day and night. In order to validate accelerometer data, video data were analysed for one randomly chosen goat, which could be identified consistently. For this goat, times of lying down and getting up were analysed and compared with the accelerometer data of that day by manual detection using MxManagementCenter .Ethical approval for the study was obtained from the University of Applied Sciences Nürtingen-Geislingen Ethics Committee (Ref: 2019_01) and was in accordance with local and national guidelines ,21. Lying time (h/d per goat) (LT), representing means of all daily lying times of all goats.Lying bout duration (h/bout) (LBD), representing (the mean) daily lying bout duration with start and end point of all goats.Frequency of lying bouts (n/d and goat) (FLB), representing means of all daily lying bouts of all goats.All animals equipped with accelerometers were analysed statistically. For statistical analyses the following response variables were created:t-tests with the emmeans function. The three explanatory variables “month”, “year” and “horn status” were treated as fixed effects and interactions between all of them were assumed (two-way interactions and three-way interaction). Missing values have been removed for statistical analysis. The significance level was set at p < 0.05.The statistical software package R Studio  was used for a three-factor variance analysis (three-way-ANOVA) procedure using a linear model and performing paired Tukey’s Comparing lying times of the accelerometer measurements with video data (of one random goat) reached an agreement of 99.62–99.93% per lying time.Over all goats, a mean ± SD LT of 11.78 ± 1.47 h/d, a mean ± SD LBD of 0.51 ± 0.10 h/bout and a mean ± SD FLB of 24.35 ± 5.57 were found.Comparing data of April to June to October, goats showed the same tendency in lying bouts : (a) froAdditionally, comparing horned and hornless goats, significant differences were detected in 2019 see . Horned Comparing the seasonal progression divided for horned and hornless goats, similarities were observed . HornlesComparison of accelerometer and video data showed comparable results. Therefore, as shown in goats  and otheObservation of video recordings in the present study showed scratching behaviour with the hind legs for a duration between 5 and 8 s. Therefore, data were evaluated with a 10 s interval, although accelerometers were set to a 5 s interval for recording. The results of the performed random video analyses (99.62–99.93% agreement of lying) support the possibility of using accelerometers with a 10 s interval for observing lying behaviour in ruminants. This is confirmed by Robert et al. , who fouIn the present study, LBD was 0.51 ± 0.10 h/bout (mean ± SD) and FLB was 24.35 ± 5.57 bouts (mean ± SD); therefore, goats were more active than in the study of Zobel et al. , who docComparing lying parameters in two following years gives insight into the activity pattern of dairy goats. Significant differences were detected during the lactation cycle in both years. These differences were observed for the former and the newly installed optimized AFS. However, differences between horned and hornless goats were only evaluated with the former AFS. Comparing the different lying parameters  assessed from all dairy goats, no difference could be detected comparing 2019 and 2020; therefore, no differences were found which can be related back to the new AFS. Therefore, in the present study no direct conclusion can be drawn whether the new AFS influences lying parameters of dairy goats.In 2020, LT from June to October decreased, in contrast to 2019. This might be explained by the fact that in October 2019 the new AFS was installed, and with this lying behaviour should be interpreted carefully. Several reconstructions of the AFS happened at the time when the accelerometers were put on, such as the installation of the partition on the entrance-platform and the steepening of the exit-platform. These interruptions may have affected lying times in October 2019.Significant differences regarding lying behaviour (LBD and FLB) between horned and hornless goats were detected with the old AFS but not with the new AFS. If this adjustment in lying behaviour between horned and hornless goats in a mixed-horned herd is a reason for reduced stress in lactating goats, the changed lying times could have an effect on health, performance and welfare of the dairy goats, as has already been proven in other ruminants such as dairy cows . This muIn the present study, an overall LT of 11.78 ± 1.47 h/d (mean ± SD) was examined. This is lower than in studies of Zobel et al. ,22, who Focusing on the seasonal progression in a herd of horned and hornless dairy goats, lying periods follow a seasonal progression with significantly lowest LBD in summer (June) and simultaneous significantly higher FLB . These sAs the focus of the present study was to identify differences before and after installation of the AFS, this result cannot be explained in total by this study. However, as one example, grazing times were not documented during the study and may be different between both years. This general seasonal progression may not be influenced by small changes in management. Some studies discuss bedding material, such as that of Bøe et al. , with stThe study was conducted in a mixed-horned herd. Therefore, differences between horned and hornless goats should be analysed as well. The study design was set up in such a way that the ratio of horned and hornless goats equipped with an accelerometer was adjusted to the ratio in the total herd. Due to the changed ratio over the course of the studies, more horned goats were equipped with accelerometers than hornless goats, especially in 2020 see . This efSignificant differences were observed by comparing lying parameters of horned and hornless goats in the present mixed herd. In 2019, horned goats showed significantly higher FLB with simultaneous shorter LBD compared to hornless goats. These differences were only detected in April and June 2019 with the former AFS, but not in October 2019 and 2020. In 2020, no differences were observed between horned and hornless goats regarding LBD and FLB. There was no significant decrease in LBD and no significant increase in FLB of hornless goats from 2019 to 2020 and also no significant increase in LBD and no significant decrease in FLB of horned goats from 2019 to 2020. Nevertheless, the non-significant changes in lying behaviour of horned and hornless goats have converged these two parameters. This might show an adjustment in lying behaviour of horned and hornless goats after the new AFS was installed.As suggested by previous studies ,8,9, hor2 and 2 m2 or 3 m2. However, Andersen and Bøe [The new AFS was placed differently than the one before , and wit and Bøe  recommen and Bøe  showed t and Bøe , who com and Bøe , who fou and Bøe  showed t and Bøe ), which To detect lying behaviour in goats, accelerometer technology is a practicable method with precise results, but more analyses need to be conducted to ascertain the ideal measuring interval in dairy goats. No difference was detected in lying times comparing old and new AFS, which might be traced back to several other factors influencing lying behaviour in dairy goats. However, significant differences in lying behaviour (LBD and FLB) of horned and hornless goats were found with the old AFS but not with the new one. The optimized feeding system might be a reason for reduced stress, resulting in an adjustment of lying behaviour of horned and hornless goats. Further analyses are necessary in order to evaluate lying behaviour and interactions between goats in connection with the optimized feeding system.


Ribo-seq has revolutionized the study of genome-wide mRNA translation. High-quality Ribo-seq data display strong 3-nucleotide (nt) periodicity, which corresponds to translating ribosomes deciphering three nts at a time. While 3-nt periodicity has been widely used to study novel translation events such as upstream ORFs in 5′ untranslated regions and small ORFs in presumed non-coding RNAs, tools that allow the visualization of these events remain underdeveloped.RiboPlotR is a visualization package written in R that presents both RNA-seq coverage and Ribo-seq reads in genomic coordinates for all annotated transcript isoforms of a gene. Specifically, for individual isoform models, RiboPlotR plots Ribo-seq data in the context of gene structures, including 5′ and 3′ untranslated regions and introns, and it presents the reads for all three reading frames in three different colors. The inclusion of gene structures and color-coding the reading frames facilitate observing new translation events and identifying potential regulatory mechanisms.RiboPlotR is freely available (https://github.com/hsinyenwu/RiboPlotR and https://sourceforge.net/projects/riboplotr/) and allows the visualization of translated features identified in Ribo-seq data. Despite its importance in directly controlling protein production, translation is much less well understood than transcription. In the past decade, ribosome profiling, also known as Ribo-seq, has revolutionized the study of translation by enabling the mapping and quantification of ribosome footprints on individual transcripts genome-wide . High-quRiboPlotR package  we can detect novel translation events in the unannotated coding regions, such as those in the UTRs and introns; (2) by including all transcript isoform models in the plot, in most cases, we can visually determine which transcript isoform(s) is/are translated; (3) by comparing sequencing data and annotated gene models in parallel, we can identify discrepancies between the Ribo-seq data and the predicted CDSs, such as frameshifts and variations in coding regions; similarly, any discrepancies between the mRNA profile and the annotated transcript isoforms are also easily visualized; and (4) the relative Ribo-seq abundance in different transcript features, such as upstream ORFs (uORFs) and introns, can be visualized and thus used to infer potential regulatory mechanisms and generate new hypotheses. Below, we describe usages and examples with RiboPlotR to visualize translation events in a gene with a predicted uORF and in another gene with different transcript isoforms.Here, we describe the age Fig. B for visRiboPlotR uses base R commands for plotting. It requires the GenomicRanges, GenomicFeatures, GenomicAlignments, Rsamtools and rtracklayer packages from Bioconductor +) +/\1\t/' > name_output_file” to produce the P-site file.The Ribo-seq P-site coordinate file should be a tab-delimited file. From left to right, the Ribo-seq P-site coordinate file should contain “count”, “chromosome number”, “coordinate” and “strand” information, but without headers. For each chromosome coordinate, the column “count” should be the sum of the Ribo-seq P-site counts. The P-site information can be acquired from the RiboPlotR requires the following input files: (1) a gtf or gff3 file for transcriptome annotation, which should be recognizable with the GenomicFeatures package and the transcript name should be composed of a gene name, a period and a number (a widely used format for plant genome annotations); (2) mapped and coordinate-sorted bam file(s) for RNA-seq; and (3) tab-delimited file(s) for Ribo-seq P-site coordinates and counts. A gtf or gff3 file for uORF coordinates is optional. Moreover, users can read in one or two sets of bam and P-site files to compare translational profiles under two different conditions.RiboPlotR can be used to plot data from different organisms because it uses base R functions to input Ribo-seq P-site coordinates and uses widely adopted Bioconductor packages to input standard gtf/gff3 files and RNA-seq bam files. The RiboPlotR package contains a sample Ribo-seq dataset from Arabidopsis shoots and roots that was originally described in [ribed in . The sambasic workflow of RiboPlotR is:gene.structure.Load the transcriptome annotation gtf/gff3 file containing the gene, transcript, exon, CDS and UTR ranges using uorf.structure function. To plot a uORF for a transcript, load the uORF gtf/gff3 file using the rna_bam.ribo.Load the mapped and coordinate-sorted RNA-seq bam file and the Ribo-seq P-site position/count file by running Use one of the four plot functions below and enter the gene name and isoform number to plot the translation of the isoform.The Four plot styles are available:PLOTc: plots RNA-seq and Ribo-seq in one panel (plot compact).PLOTt: plots RNA-seq and Ribo-seq separately in two panels (plot two).PLOTc2: plots RNA-seq and Ribo-seq in one panel for two conditions.PLOTt2: plots RNA-seq and Ribo-seq separately in two panels for two conditions.RiboPlotR separately plots each transcript isoform of a given gene. Only one isoform is plotted at a time, and the default is to plot isoform 1. For each isoform, the same RNA-seq and Ribo-seq reads are used for plotting; the only difference is the expected coding region for the Ribo-seq reads, which is defined by a black dashed line (expected translation start) and a grey dashed line (expected translation stop). Inside the expected coding region, Ribo-seq P-sites that are mapped in the expected frame, the + 1 frame, and the + 2 frame are presented using red, blue and green lines, respectively. Ribo-seq P-sites that are outside the expected coding region are shown in grey. Thus, for high-quality datasets, most of the P-sites will be in red if the data agree with the annotation. The x-axis below the gene models indicates the genomic coordinates, whereas the primary y-axis (left) indicates the RNA-seq count, and the secondary y-axis (right) indicates the Ribo-seq P-site count. When an isoform is translated, the majority of P-sites should cover the expected CDSs and are shown in red. If two isoforms cover different coding frames at the 3′ ends, the two plots will have different color schemes at the 3′ end. This design allows users to quickly see if a plotted isoform is being actively translated (see examples below). Below, we provide two examples of how to interpret RiboPlotR plots. The code for examples below is available on github (https://github.com/hsinyenwu/RiboPlotR).AT3G02470, which encodes S-Adenosylmethionine decarboxylase (SAMDC), plotted using the PLOTc function. In addition to the abundant Ribo-seq reads in the expected CDS, many reads are present in the 5′ UTR  [AT3G02468 in TAIR10. We plotted AT3G02470 again with the 'uORF = "AT3G02468"' option. The default of uORF isoform is 1 and can be changed using the “uORFisoform” parameter. The Ribo-seq reads in the 5′ UTR are clearly highly enriched in the predicted CDS for CPuORF9 and are consistent with the expected reading frame  is/are expressed. For example, the three isoforms of CDS Fig. . HoweverAT4G21910, which encodes a multidrug and toxic compound extrusion (MATE) efflux family transporter, plotted using the PLOTc2 function and comparing data from the shoot and root. In this case, the first two isoforms are the major translated isoforms since the RNA-seq/Ribo-seq coverage and frame color are consistent with these two isoforms  [RiboPlotR package is primarily for visualizing read distribution.The reads plotted for the two conditions  are not normalized. If gene expression under the two conditions being studied is comparable, users can normalize the reads by randomly selecting a fixed total amount of reads from the RNA-seq bam file and Ribo-seq P-sites between the two conditions. However, normalization to the same RNA-seq and Ribo-seq read counts could be easily skewed by changes in the expression of highly expressed genes. For differential expression/translation analyses, users should use other software packages designed for those purposes  , 18. TheRiboPlotR. Similarly, if users wish to visualize the usage of non-AUG start sites, they can modify the CDS ranges of their genes of interest in the input annotation file. More examples of using RiboPlotR to explore unannotated translation events can be found in our recent publication on the Arabidopsis and tomato translatomes [Ribo-seq is particularly useful for discovering novel translational events, such as uORFs in 5′ UTRs and small ORFs translated within presumed non-coding RNAs. Since these novel translation events are not part of formal annotations, users must generate a customized gtf/gff3 file to include the coordinates of these novel ORFs for data visualization in slatomes , 17.RiboPlotR combines a transcriptome annotation file, a standard RNA-seq bam file, and a Ribo-seq P-site position/count file to plot the RNA-seq coverage and Ribo-seq P-site positions with genomic coordinates for each isoform considered. It can be used to investigate the translation of specific transcript isoforms, uORFs, and other novel translational events. This software has been tested in Arabidopsis and tomato, and we welcome users’ feedback to help us improve and update the software.In conclusion, RiboPlotR.Project name: https://github.com/hsinyenwu/RiboPlotR and https://sourceforge.net/projects/riboplotr/.Project home pages: Operating system(s): Platform independent.Programming language: R.GenomicRanges, GenomicFeatures, GenomicAlignments, Rsamtools and rtracklayer.Other requirements: R Bioconductor packages: License: GPL-3.Restrictions on use by non-academics: None.


In this review, we first describe the TME, cold and hot tumors, and m6A RNA modification. Then, we focus on the role of m6A RNA modification in cold tumor formation and regulation. Finally, we discuss the potential clinical implications and immunotherapeutic approaches of m6A RNA modification in cancer patients. In conclusion, m6A RNA modification is involved in cold tumor formation by regulating immunity, tumor-cell-intrinsic pathways, soluble inhibitory mediators in the TME, increasing metabolic competition, and affecting the tumor mutational burden. Furthermore, m6A RNA modification regulators may potentially be used as diagnostic and prognostic biomarkers for different types of cancer. In addition, targeting m6A RNA modification may sensitize cancers to immunotherapy, making it a promising immunotherapeutic approach for turning cold tumors into hot ones.Immunotherapy is a novel clinical approach that has shown clinical efficacy in multiple cancers. However, only a fraction of patients respond well to immunotherapy. Immuno-oncological studies have identified the type of tumors that are sensitive to immunotherapy, the so-called hot tumors, while unresponsive tumors, known as “cold tumors,” have the potential to turn into hot ones. Therefore, the mechanisms underlying cold tumor formation must be elucidated, and efforts should be made to turn cold tumors into hot tumors. N Severalls (DCs) . Neverth6-Methyladenosine (m6A) modification, which was first discovered in the 1970s, has gained increasing attention for its important role in eukaryotic epigenetic regulation (6A messenger RNA (mRNA) modification is intimately related with almost all cellular and biological processes  is an attractive combination immunotherapeutic agent for tumor treatment by targeting TAMs . Furtherenuation . As our enuation . An over6A, the methylation of adenosine (A) at the N6 position, was the first identified RNA modification and has been defined as the most widespread internal chemical modification in eukaryotic mRNA. Furthermore, m6A has also been identified in non-coding RNAs, such as ribosomal (rRNAs), small nuclear (snRNAs), small nucleolar (snoRNAs), micro- (microRNAs), long non-coding (lncRNAs), and circular (circRNAs) RNAs  and are highly enriched in the coding sequence, 3′-untranslated region (3′-UTR), and around stop codons  RNAs . Next-gep codons . Notablycontexts . In lineectively .6A writers install m6A through a methyltransferase complex (MTC) composed of several components. Methyltransferase-like 3 (METTL3), METTL14, and Wilms’ tumor 1-associated protein (WTAP) are core components of the m6A MTC , and transferring the methyl groups to adenine in the RNA molecule, producing S-adenosyl homocysteine (SAH). METTL3 and METTL14 are co-localized in nuclear speckles and form METTL3-METTL14 heterodimer complexes in a 1:1 ratio. METTL14 also contains the catalytic donor; however, METTL14 itself is not a catalytic subunit but maintains METTL3 conformation and identifies catalytic substrates , also called KIAA1429, mediates preferential m6A mRNA methylation in the 3′-UTR and near stop codon  was identified as the first m6A demethylase in 2011 (6A to form N6-hydroxymethyladenosine (hm6A). Then, hm6A is converted to N6-formyladenosine (f6A). Lastly, f6A is converted to adenosine, thus removing the m6A RNA modification in the nucleus  in snRNA and N1-methyladenosine (m1A) in tRNA in the nucleus  dependent, locates in the nucleus, and seems to be an m6A-specific demethylase involved in m6A RNA modification  3/4E/4G, poly(A) binding protein (PABP), and 40S ribosomal subunit, to magnify RNA translation  deadenylase complex through its N-terminal region (via synergy with YTHDF2 (6A-modified RNA through its K homology domains and promote the stability and translation of RNA (6A reader that can recognize m6A and bind to meiosis-specific coiled-coil domain (MEIOC) and 5′-3′exoribonuclease 1, further increasing m6A-modified RNA translation  or blocking serine- and arginine-rich splicing factor 10 (SRSF10) in the nucleus  mRNA, slowing down its degradation and ultimately attenuating toll-like receptor 4 (TLR4) signaling-mediated macrophage activation. Particularly, the authors suggested that METTL3 augments the tumoricidal ability of macrophages by promoting the polarization bias of TAMs toward the M1 macrophage phenotype and rescuing infiltrating CD4+ and CD8+ T cells  have illustrated that METTL3 has potential clinical implications in cancer. For instance, compared with adjacent non-tumor tissues, METTL3 expression is upregulated in hepatoblastomas. High METTL3 levels are associated with continual recurrence and poor prognosis of hepatoblastoma patients, suggesting that METTL3 could be used as a potential diagnostic and prognostic biomarker for hepatoblastoma patients  and progression-free survival (PFS) in luminal type A, luminal type B, human epidermal growth factor receptor 2 (HER2)-enriched type, and triple-negative-type breast cancer, indicating that the reduced expression of METTL14 and ZC3H13 leads to poor prognosis in breast cancer patients  have potential clinical applications as diagnostic and prognostic biomarkers for different types of cancer. In addition, targeting m6A RNA modification regulators could sensitize cancers to immunotherapy. Thus, targeting m6A RNA modification is a promising immunotherapeutic approach for turning cold tumors into hot ones.Within the past decade, m6A RNA modification and their role in diseases, a complete understanding of the mechanisms is far away, and especially, their implications in cancers is our concern. The present researches show that the abnormal level of m6A regulators are intimately associated with the prognosis of tumors, indicating their promising implications in improving cancer patient treatment outcomes, although it has been demonstrated that targeting RNA m6A modification could be the optional combination therapy in cancer immunotherapy, the limitation is that except for the role of RNA m6A modification in immune response, their functions in tumor development should be taken into consideration, which could be a cause of immunotherapeutic resistance or insensitivity. For example, PD-1/PD-L1 acts as a tumor suppressor and mediates resistance to PD-1 blockade therapy in tumor (6A RNA modification should focus on several aspects. First, some specific m6A RNA modification regulators play opposite roles in different cancers, indicating that the exact role of m6A RNA modification regulators is cell or tissue dependent (6A RNA modification regulators in cancers and their mechanisms will be crucial to direct specific m6A RNA modification regulator-based therapeutic interventions in the future. Second, we know that m6A RNA modification is found not only in mRNAs but also in non-coding RNAs, and that non-coding RNAs play critical roles in the immune response and immunotherapy in cancers (6A-related non-coding RNAs in cancer will contribute toward the development of more effective and novel cancer immunotherapies (6A RNA modification as cancer immunotherapy have mainly focused on regulating m6A RNA modification regulators through transfection experiments, which are difficult to translate to clinical trials or clinical practice; therefore, m6A RNA modification regulator agonists or antagonists should be searched in the future (6A modification RNA regulators that augment antitumor immune responses with reduced toxicity and side effects (Although tremendous progress has been achieved on understanding min tumor . Therefoependent . Consequ cancers ; therefoherapies . Third, e future . Lastly, effects .LZ and JuZ collected the data, finished the manuscript, and prepared the figures and tables. YC and BW gave constructive guidance. SZ, XL, JiZ, WW, YF, and SS participated in the design of this review. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Infection with influenza A and B viruses results in a mild to severe respiratory tract infection. It is widely accepted that many factors affect the severity of influenza disease, including viral replication, host adaptation, innate immune signalling, pre-existing immunity, and secondary infections. In this review, we will focus on the interplay between influenza virus RNA synthesis and the detection of influenza virus RNA by our innate immune system. Specifically, we will discuss the generation of various RNA species, host pathogen receptors, and host shut-off. In addition, we will also address outstanding questions that currently limit our knowledge of influenza virus replication and host adaption. Understanding the molecular mechanisms underlying these factors is essential for assessing the pandemic potential of future influenza virus outbreaks. The influenza viruses are negative-sense RNA viruses and members of the family of orthomyxoviruses. Within this family, four influenza virus types are recognized, identified by the letters A, B, C and D. All influenza types can infect vertebrates, including birds, pigs, and aquatic animals, but only the influenza A and B viruses  can cause morbidity and mortality in humans. Influenza C viruses (ICV) can infect humans as well, but they are rarely associated with symptomatic disease in humans, whereas influenza D viruses (IDV) have not caused infections in humans so far. Based on their impact on human health and the extensiveness with which they have been studied, we will here mainly discuss our understanding of IAV infections.IAV virions consist of a host cell-derived membrane and various viral proteins . On the The IAV RNA polymerase catalyses viral replication and transcription in the context of vRNPs ,7. ShortThe IAV RNA polymerase produces vRNA copies with a certain nucleotide misincorporation rate . IntroduThe mechanism underlying influenza disease and the factors that define low and highly pathogenic strains are not completely understood. Current research suggests that a dysregulation of the host immune response, reacting to the effects of the viral infection and releasing cytokines to achieve clearance of the viral infection, plays a key role in pulmonary tissue damage, systemic disease, and death in infections with highly pathogenic strains ,17,18. HThe RNA polymerase is a heterotrimeric enzyme consisting of three subunits: polymerase basic 1 (PB1), PB2 and polymerase acidic (PA). The complete structures of the IAV, IBV and ICV RNA polymerases have been solved through high-resolution X-ray crystallography and cryo-EM C 24,25,,25,26,27Several channels lead to and from the active site: the template entry channel, the NTP entry channel, the product exit channel, and the template exit channel D 6]. Th. Th6]. TThe RNA polymerase binds the 3′ and 5′ ends of the vRNA promoter using binding pockets on the surface of the PB1 and PA subunits . Upon biThe IAV genome segments are encapsidated in RNPs . In RNPsNP monomers consist of a head and body domain. In an RNP, the monomers are linked via a tail-loop structure that protrudes from the first NP and binds to the insertion groove of the second NP. vRNA or cRNA molecules are bound by a positively charged groove located between the NP head and body domains via the negatively charged RNA backbone . It is lThe transcription of the vRNA segments into viral mRNAs with a 5′ cap and a 3′ poly-A tail is performed by the resident viral RNA polymerase. Since the IAV polymerase lacks capping activity, the enzyme carries out a process called ‘cap-snatching’ to generate capped RNA primers that can prime viral mRNA synthesis . Cap-snaFollowing CTD binding, nascent host RNAs containing a cap-1 structure are bound by the PB2 cap binding domain and cleaved approximately 9–14 nucleotides downstream of the 5′ cap by the PA endonuclease, thus generating primers with a 3′-hydroxyl group ,49. InteTranscription elongation takes place until a sequence of 5–7 U-residues, located approximately 16–17 nucleotides from the 5′ end of the vRNA template, triggers transcription termination and polyadenylation ,10. The By contrast to IAV transcription, IAV replication is a two-stage process that is initiated in a primer-independent manner. During the first step of replication, cRNA synthesis takes place by de novo initiation on the 3′ terminus of a vRNA segment . The RdRThe cRNA 5′ and 3′ ends are thought to bind to the same binding sites as the vRNA terminal sequences ,59, althIn addition to full-length vRNA and cRNA molecules, the IAV RNA polymerase can produce RNA products that are shorter than the vRNA or cRNA template from which they derive. These aberrant RNA species include small viral RNAs (svRNA) , defectiIt is currently not fully understood what triggers IAV aberrant synthesis. In tissue culture experiments, IFN-inducing mutations have been linked to the accumulation of high numbers of DI genomes. For instance, Perez-Cidoncha et al. performed selection experiments in non-IFN-responsive cells to isolate virus mutants that were unable to prevent IFN induction. The identified mutants were shown to be stronger IFN inducers and to generate higher numbers of DI genomes than wild-type viruses . SimilarAnother trigger for aberrant RNA synthesis and, in particular, mvRNA synthesis appears to be limiting NP levels, as shown by experiments that either overexpressed the viral RNA polymerase subunits or knocked down NP expression ,72,73. TIt is currently not fully understood how IAV aberrant RNAs are synthesized, though it has been ruled out that splicing or ligation mechanisms are involved . Deep-seThe first line of defence during an infection relies on the recognition of conserved structures exhibited by pathogens, known as pathogen-associated molecular patterns (PAMPs). These PAMPs can be sensed by host proteins known as pattern-recognition receptors (PRRs). IAV is recognized by at least three distinct families of PRRs, including toll-like receptors (TLRs), the nucleotide oligomerization domain (NOD)-like receptors (NLRs) and RIG-I-like receptors (RLRs) . These PRIG-I is an RNA helicase that provides viral sensing in the host cell cytosol and nucleus ,84. RIG-MAVS comprises an N-terminal CARD domain, a proline-rich region and a C-terminal transmembrane (TM) domain. With its TM segment, MAVS is targeted to the outer mitochondrial membrane, a location that is crucial for competent signal transduction , while tMAVS proteins have been shown to self-associate via their TM domains to create a platform for the interaction with downstream signalling proteins . FurtherIn addition to RIG-I sensing, recent research has shown that Z-DNA binding domain protein 1 (ZBP1 or DAI) is able to bind IAV RNAs ,101. AftVarious in vitro studies have been performed to identify the RNA species that bind and activate RIG-I. Most of these RIG-I agonists contain a short double-stranded RNA sequence and a 5′ terminal triphosphate (ppp) group , but othThe IAV 5′ and 3′ termini form a panhandle structure in solution that consists of partially double-stranded RNA and a 5′ ppp end. This panhandle can act as RIG-I inducer, since in vitro transcripts of influenza virus segments are capable of inducing IFN production when transfected into cells , althougWhile the above studies demonstrate that RIG-I can bind viral RNAs, they do not provide information on the timing and location of RIG-I binding during an infection. Interestingly, no RIG-I ligands could be isolated in infected cells treated with a translation inhibitor . BlockinvRNPs and cRNPs protect the viral RNA from RIG-I, in part because the 5′ and 3′ termini are tightly bound by the viral RNA polymerase . If RIG-IAV infections lead to the suppression of host transcription and translation, a process called host shut-off. It has been proposed that host shut-off favours translation of viral mRNAs over host mRNAs, and that it reduces the expression of innate immune response genes. Various mechanisms have been identified that contribute to host shut-off during IAV infections , includiIn addition to detecting viral RNA by distinguishing it from host RNA, RIG-I can also bind cellular 5S ribosomal RNA pseudogene 141 (RNA5SP141) transcripts during viral infections, including infections with an influenza virus . RNA5SP1The pathogenesis of IAV infections is a complex process that involves multiple cellular, viral and, occasionally, bacterial factors. Exposure to IAV can lead to a wide range of disease outcomes, and the host innate immune response is here a mixed blessing: it is indispensable for restricting viral replication and spread, but an overstimulation of the inflammatory response may also lead to severe disease. In efforts to explain pathogenic differences between IAV strains, mutations in IAV proteins have been identified that alter the receptor binding specificity, pH sensitivity, or function of IAV accessory proteins, such as NS1, PA-X and PB1-F2. These accessory proteins play a role in modulating the host response upon IAV infection. Mutants derived from avian-adapted IAV strains may be less efficient at modulating the innate immune response in humans than mammalian-adapted IAV strains. IAV proteins, or IAV infections, have also been found to induce alterations in the infected host cell, such as H3K79 acetylation, the release of nuclear RNAs, or Pol II read-through. How all these factors and modulations come together and add up to differences in virulence or pathogenicity is not fully understood. In addition to mutations in the viral proteins, it has been discovered that aberrant IAV RNAs produced by erroneous polymerase activity can induce RIG-I activation and the expression of pro-inflammatory cytokines ,113. ManIn conclusion, the induction of a cytokine storm that underlies the pathogenesis of some human IAV infections may be the result of a combination of a strong RIG-I activation and the inability of the virus to inhibit or evade key players of the host defence system. Indeed, passaging of wild-type IAV in non-IFN-responsive cells results in IAV mutants containing IFN-inducing mutations at conserved positions in the IAV genome, and not just in known IFN-linked sites in, for example, the NS1 gene. This view suggests that the entire IAV genome is optimized for both viral replication as well as counteracting or evading the host response. From that assumption it follows that due to suboptimal interactions with mammalian host factors, avian-adapted IAV proteins may be more likely to generate aberrant RNA products and be less efficient at suppressing the innate immune response. Future studies will be needed to better understand these interactions and their contribution to IAV virulence and pathogenicity.


RBFOX1, its target genes, and genes in the autism-associated Prader–Willi locus (15q11). The abundance of SNORD115/116 genes from locus 15q11 was positively associated with editing activity across the transcriptome. We contend that insufficient editing of autism-related genes in inhibitory neurons may contribute to the specific perturbation of those cells in autism.Conversion of adenosine to inosine in RNA by ADAR enzymes, termed “RNA editing,” is essential for healthy brain development. Editing is dysregulated in neuropsychiatric diseases, but has not yet been investigated at scale at the level of individual neurons. We quantified RNA editing sites in nuclear transcriptomes of 3055 neurons from six cortical regions of a neurotypical female donor, and found 41,930 sites present in at least ten nuclei. Most sites were located within Alu repeats in introns or 3′ UTRs, and approximately 80% were cataloged in public RNA editing databases. We identified 9285 putative novel editing sites, 29% of which were also detectable in unrelated donors. Intersection with results from bulk RNA-seq studies provided cell-type and spatial context for 1730 sites that are differentially edited in schizophrenic brain donors, and 910 such sites in autistic donors. Autism-related genes were also enriched with editing sites predicted to modify RNA structure. Inhibitory neurons showed higher overall transcriptome editing than excitatory neurons, and the highest editing rates were observed in the frontal cortex. We used generalized linear models to identify differentially edited sites and genes between cell types. Twenty nine genes were preferentially edited in excitatory neurons, and 43 genes were edited more heavily in inhibitory neurons, including  The extraordinary structural and functional complexity of the human brain arises via multiple mechanisms of genetic regulation. The conversion of adenosine to inosine (A > I) in nascent RNA transcripts in the nucleolus by ADAR1 and ADAR2 enzymes, known as “RNA editing,” is the most abundant RNA modification in the primate central nervous system, and confers transcriptomic diversity beyond that encoded in the genome . RNA ediDysregulated editing is implicated in epilepsy , glioblaGLUR2, which produces a Q > R amino acid substitution. This site is edited in nearly 100% of human GLUR2 transcripts, and limits the calcium permeability of the resulting ion channel, which is thought to dampen neuronal excitability  analyses between cortical regions, cell types, and genes therein. The final section reports correlation analyses between gene abundance and editing rates that identify potential modulators of neuronal RNA editing.Supplemental Fig. S1). We discarded 72 nuclei due to either high mitochondrial reads (n = 42), or low library complexity (n = 30) . We therefore quantified RNA editing signals in 3055 high-quality neuronal nuclear transcriptomes, supported by 9.17bn uniquely mapped reads. Excitatory neurons from the superior temporal cortex (Brodmann area 41) were the most abundantly represented cell type in this data set . The mean ratio of inhibitory to excitatory neurons across each cortical region was 0.285, or approximately 1 inhibitory to every 3 excitatory neurons.Our independent read processing and unsupervised clustering of 3127 neuronal nuclei from a single donor, originally assayed by Supplemental Fig. S3). By filtering on-site coverage, prevalence, genomic context, and previous evidence, we detected 41,930 editing sites. An average of 1850 candidate editing sites were transcribed per cell , of which 290 (0.7%) were edited . When averaged across cells, the minor  allele frequency distribution was positively skewed, and previously uncataloged “novel” sites showed lower overall editing frequencies . Approximately 80% of editing sites were located within nonoverlapping regions of protein-coding genes. The majority of these  were located in intronic Alu repeats documented in the REDIportal database of human RNA editing sites  were detected in at least one bulk brain sample, of which 30.8%  were detected in the frontal cortex. A further 3638 sites were cataloged in other human tissues . As expected, the distance between neighboring nonrepetitive sites was greater (median neighbor distance = 30 nt) than for Alu and non-Alu repetitive sites, which mostly occurred in clusters . We next intersected our data with dEd sites reported from analyses of bulk brain RNA sequencing of neuropsychiatric patient cohorts, and could provide single-cell level context for 1730 of 18,071 sites edited in the frontal cortex of schizophrenic (SCZ) patients    ; and 910d sites) . Taken td sites) A.Supplemental Table S3). Importantly, 26% of newly detected sites in the Lake et al. data, were also detectable in this independent sNuc-seq data set , we sought to validate these findings in the only other publicly available sNuc-seq data from healthy human brain, containing 116 neuronal nuclei from six unrelated donors, published by data set A. This iSupplemental Table S3). Genes containing at least three such sites were enriched for associations with autism spectrum disorder (q < 6 × 10−3), and epilepsy syndrome (q = 0.026), via DOSE disease enrichment analysis  showed identical missense mutations to those previously reported . All but 12 of the 316 detected missense sites were novel, in nonrepetitive sequence regions of 223 genes that are not currently established as ADAR recoding targets. Glutamate- and aspartate-to-glycine substitutions were the most common predicted protein recoding consequences of editing at these sites . We searched for enriched gene ontology molecular function terms among the 232 putative target genes and found both “RNA binding,” and “adenyl ribonucleotide binding” among the top 20 most enriched terms—the latter result deriving from missense sites in DEAD box helicases, HSP90 alpha AA1 and AB1, and several calcium transporting ATPases . Together these results indicate that RNA editing preferentially affects the RNA structure of ASD and epilepsy-related genes, which as a group are less tolerant of missense genetic mutations. In contrast, editing introduces low-prevalence coding variation in genes involved in RNA processing.To investigate the relationship between RNA editing, RNA structure and genetic sequence conservation, we first investigated the predicted effect of editing sites on local RNA secondary structure. Approximately 7% of edited sites were predicted to perturb RNA structure . Although there was no significant difference in the number of reads mapped to grouped excitatory or inhibitory neurons, a modest effect of library size on GEI was detected, and controlled for in subsequent statistical testing. Inhibitory neurons displayed significantly greater editing than excitatory neurons (P < 1 × 10−30), which was largely attributable to greater editing in the In6, and lower editing in the Ex3 neuronal subgroups identified by Lake and colleagues . Differences in mean GEI between cortical regions were related to different proportions of neuronal subtypes . Similarly, the superior temporal cortex (BA41) was enriched for group Ex1 neurons, and showed a lower mean GEI than the frontal cortex (BA8 and BA10) in which inhibitory neurons  were more abundant.To compare editing rates between neuronal type and cortical region, we calculated a “global editing index” (GEI), taken as the mean minor allele (G) frequency across the sites transcribed in each nucleus . Among 29 genes preferentially edited in excitatory neurons were the putative glutamate transporter gene SLC38A6, and the neuronal growth and migration factors PKN2 and FAM19A2. Editing in transcriptional regulators was particularly notable, in RFX3, POU2F2, CHD6, CDK17, SSBP2, HAT1, and SLC44A5 , cell cycle and division proteins , and nervous system development proteins .To further interrogate RNA editing at the level of individual sites and genes, we aggregated single-cell editing signals into “pseudobulk” RNA samples and applied a differential editing framework which has been previously used to quantify differential DNA methylation . Briefly SLC44A5 A. LastlyRBFOX1, encoding a splicing regulator, and eight of its binding target genes , which oligomerizes with GRIA2, was more prevalent in inhibitory neurons. The excitatory neuronal marker gene CDKK2 also showed a strong editing bias in these cells. Lastly, preferential editing in inhibitory neurons was identified in genes involved in mitochondrial metabolism: carnitine palmitoyltransferase 1C (CPT1C), and metallochaperone CMC2; as well as genes related to neurogenesis and synaptic function .The remaining 43 dEd genes were more edited in inhibitory than excitatory neurons. Included in this group was et genes . The smaRBFOX1 and RIMS2 respectively, with two sites each in DNAJC11, EPN2, PEAK1, and TPST1 . Taken together these results suggest that major editing differences generally reflect neuronal phenotype rather than cortical region of origin.A similar analysis of editing within each cortical region compared to the mean of the others, correcting for cell type, identified 99 dEd sites across 90 genes. Whereas no gene was significantly enriched with edited sites in any region, the majority of dEd sites were identified in the frontal cortex , with 14 dEd sites identified in the temporal cortex (BA22), and fewer than ten in other cortical areas. Three individually significant dEd sites were identified in TRIM9 and SNHG14 genes. Conversely, only seven dEd sites in ASD and one in schizophrenia were identified among genes displaying increased editing in excitatory neurons (P = 0.16).Hundreds of RNA sites are reported as differentially edited in the brains of individuals with schizophrenia  and auti neurons A. AlthouSupplemental Fig. S7). To identify potential novel modulators of editing, we then modeled GEI as a function of gene abundance, controlling for neuronal type. A total of 170 genes were significantly associated with GEI, with absolute effect sizes greater than 0.01 (FDR < 0.05) . Because these significant genes were among those most consistently expressed across cells , to allow a fairer comparison we down-sampled the data to a maximum of 1500 cells per gene. All results remained significant.Abundance of ADAR family enzymes is at best a modest predictor of editing activity across human tissues , which hSNHG14 (SNORD3 cluster on chromosome 17 were detected together with SNORD114 (chromosome 14) and H/ACA snoRNAs transcribed from chromosomes 1, 3, and 20. Other noncoding RNAs including three small nuclear RNAs and eight ribosomal RNAs were also positively associated with GEI. Only two protein-coding transcripts in this group were identified: the mitochondrial carnitine transporter CACT, and the lipid transport protein APOA1. We curated the predicted interacting partners of editing-associated snoRNAs as detailed in Supplemental Methods. When collapsed across all nuclei, the mean editing rate of 35 snoRNA target genes was nonsignificantly higher than for nontarget genes (29% vs. 27%) .Consistent with previous reports , abundanSNHG14 B. Anothetrans-synaptic signaling , and metallopeptidase protein-coding genes —some with roles in cellular differentiation . Greater abundance of coagulation factor X (F10) and leucine-rich repeat protein 15 (LRRC15) was also negatively associated with GEI, as was the abundance of seven long noncoding RNAs. Also in this group were the Alu repeat-containing transcripts 7SL and olfactory receptor OR52A5, and genes related to neurogenesis (chimerin 1 and olfactomedin). Transcripts encoding proteins with transcription factor activity  and several related to neurotransmission , were also negatively associated with GEI. Lastly, RNA levels of heat shock protein 90 and mitochondrial NADH oxidase showed a negative relationship with editing. When the effect-size threshold was omitted, we observed enrichment of nuclear-encoded mitochondrial genes among the negative transcriptional correlates of editing , and were significantly segregated from positively associated genes -targeted data generated on the 10× platform, the abundance of nuclear/nucleolar pre-mRNA species, and ncRNA species in this data set profiled with full-length capture technology, remains a rare and valuable resource. Our analysis of sNuc-seq data therefore facilitated deep insight into editing activity across the nuclear transcriptome occurring in physical proximity to brain-specific ADAR enzymes . By applThis work was made possible by publicly available sequencing data and curated databases. The novel sites identified here require further validation and functional studies, and will constitute the first publicly available cell-type specific editing reference database. The main limitations of this study lie in the lack of RNA stranding information in the sNuc-seq data, and the lack of reference genomes for brain donors—both of which hamper our ability to disambiguate editing sites from genomic SNPs. To address these concerns, we took stringent measures including: (i) removing common genomic SNPs from the results, (ii) removing sites derived from transcripts that overlap on opposing strands; and retaining only those sites (iii) detected within genes on the cognate strand (iv) covered by at least five reads, and (vi) detected in at least ten nuclei. The latter filter should eliminate PCR and other random artifacts because SMART-seq library preparation is independent for each nucleus. We note that these thresholds are quite conservative, and therefore detection of the RNA editing signal is likely not saturated in the filtered data set, meaning further independent investigations in this type of data will likely further increase the catalog of known editing sites.The proportionate distribution of edited sites across RNA motifs (Alu and non-Alu repeats) and genic features (mostly intronic or 3′-UTR) broadly agrees with the original characterization of editing in the human transcriptome . ExcitinSupplemental Fig. S4). Accordingly, a fundamental reevaluation of our understanding of this process is warranted as more sNuc-seq data at higher coverage becomes publicly available. Routine integration of editing and expression data in single-cell studies has great potential to inform the structural and functional heterogeneity of the brain. As the present data is enriched for nuclear RNA, it likely predominantly reflects editing activity of ADAR2, which is spatially restricted to the nucleus and predominantly expressed in neurons. Conversely, highly prevalent but low-penetrance editing, and sites edited in cytosolic RNA, will be underrepresented in the current data.Whereas previous studies of bulk RNA portray editing as a pervasive low-frequency phenomenon , our resOur study was able to survey editing sites across cortical regions and neuronal subtypes with high resolution, as well as investigate the transcriptomic underpinnings of neurological and psychiatric illness in the aggregated data, and in the context of cell type and cortical region. Most editing in neuronal nuclear pre-mRNA occurs in intronic regions where the inference of functional effects is difficult. It was therefore surprising to find enrichment of ASD and epilepsy disease associations among the pre-mRNAs that were predicted to undergo multiple structural conformation changes upon editing. In noncoding RNA, this process modulates intronic retention, nuclear export and protein translation rates, all of which likely depend on the steric accessibility of protein-binding motifs. Although these heavily edited disease gene sets act in many different biological pathways, this finding suggests that susceptibility to editing-related structural changes may unify neuropsychiatric disease genes. We hypothesize that subtle editing-related structural effects on multiple transcripts may mediate the pervasive psychological effects associated with ASD and epilepsy, which often co-occur. We also report a relative abundance of genes with low amino acid substitution tolerance among the putative structurally affected transcripts. Together these findings appear to support constraints on both the coding sequence and allowable RNA structural conformations of these uniquely important genes.Conversely, we found that a number of transcripts encoding putative editing-dependent missense sites, are involved in adenosine binding. Editing-based feedback loops in editing-related enzymes are reported in model systems , and mayComparison of the global editing index (“GEI”) between cell types and cortical regions was facilitated by averaging the editing frequencies of transcribed sites in each cell. In the frontal cortex, both major cell types exhibited a higher GEI than other regions. Interestingly, Brodmann areas 17 and 41 house the primary visual and auditory cortices, respectively, and exhibited lower global editing rates. If higher editing rates are assumed to dampen neuronal activity in both inhibitory and excitatory cell types, then these regional differences may support the prevailing understanding of the functional organization of the cortex, wherein sensory regions provide strong excitatory input (corresponding to reduced editing); and the frontal cortex is primarily inhibitory, acting to increase the salience of selected perceptual inputs .SLC38A6, which is notable given the high requirement in these cells for glutamate—the primary excitatory neurotransmitter. Nearly a quarter of genes preferentially edited in excitatory neurons encode transcription factors or histone modification enzymes, pointing to potential feedback between editing and transcription. In addition, heightened editing in excitatory neurons of transcripts involved in self-renewal of neural progenitors (LATS1) and prosurvival proteins , suggests that a primary role of editing in these cells could be to contribute a homeostatic signal. This is supported by negative associations between editing and abundance of both nuclear mitochondrial genes, and four cell-type markers of the Ex1 subgroup. It may be that prosurvival transcripts act as sensors of excessive mitochondrial respiration, which impinges on RNA editing and viability in excitatory neurons. We stress however that the negative association between mitochondrial gene abundance and editing is also seen in bulk tissue sequencing , DLGAP1 and DTNA.Several transcripts assayed in inhibitory neurons exhibited high-density editing, namely RBFOX1 . Raw counts for the universal human reference RNA and human brain reference RNA data sets assayed using TGIRT technology (SRP066009)  sequencing of single brain cell nuclei were downloaded from dbGaP  . The neuP066009)  were dowP066009)  and convP066009) , which iP066009)  and GTExP066009) , was dowRNA sequencing reads were mapped to the human reference genome (GRCh38.91) using the STAR aligner  in two-pWe called single nucleotide polymorphisms (SNPs) in the nuclear transcriptomes using the GATK best-practices pipeline  for RNA-To assess the replication of editing sites detected in this and other studies, candidate sites that survived filtering were intersected with sites reported in bulk RNA sequencing data sets and single neuronal nuclei   was calculated and displayed as a density function.Q-value <0.1 were considered significant.Edited sites were grouped by sequence context, and genes in which at least three sites of a single context were detected, were interrogated for disease associations using the R disease ontology enrichment analysis package DOSE . The setP-value distributions calculated by P-values <0.1 were considered significant.The effect of A > I (G) editing on local RNA structure for nonoverlapping sites was estimated using RNAsnp software  based onDifferential editing was assessed at the level of whole nuclei, aggregated over all transcribed candidate editing sites; and between genes, using a pseudobulk framework. A nucleus-level “global editing index” (GEI) was quantified as the mean minor allele (G) frequency for candidate editing sites in each cell. This metric was compared with an “Alu editing index” (AEI), composed of Alu sites only; and a more stringent AEI comprising Alu sites transcribed in at least 100 cells. Edited sites were subsequently grouped by transcriptomic context  to determine the contribution of each to global editing.Supplemental Table S1). This allowed testing differential site-wise editing between gross neuronal subtypes while controlling for cortical region, using a negative binomial model of gene expression in the limma R package, as has been successfully applied for differential methylation analysis  and alternate (edited) alleles at sites with count variances >0.2, across neurons in phenotypically similar subgroups as defined by analysis . In contanalysis .Relationships between length-normalized log-transformed neuronal transcript abundance (transcripts per million) and global editing index were assessed with linear models, controlling for gross neuronal phenotype (excitatory or inhibitory) and library size unless otherwise stated. Gene set Gene Ontology enrichment analysis was performed on transcripts significantly associated with editing using the limma goana module , after eData transformation and analysis was performed in R , using tSupplemental material is available for this article.


RNA is critical to a broad spectrum of biological and viral processes. This functional diversity is a result of their dynamic nature; the variety of three-dimensional structures that they can fold into; and a host of post-transcriptional chemical modifications. While there are many experimental techniques to study the structural dynamics of biomolecules, molecular dynamics simulations (MDS) play a significant role in complementing experimental data and providing mechanistic insights. The accuracy of the results obtained from MDS is determined by the underlying physical models i.e., the force-fields, that steer the simulations. Though RNA force-fields have received a lot of attention in the last decade, they still lag compared to their protein counterparts. The chemical diversity imparted by the RNA modifications adds another layer of complexity to an already challenging problem. Insight into the effect of RNA modifications upon RNA folding and dynamics is lacking due to the insufficiency or absence of relevant experimental data. This review provides an overview of the state of MDS of modified RNA, focusing on the challenges in parameterization of RNA modifications as well as insights into relevant reference experiments necessary for their calibration. The canonical four-letter code that comprises RNA is no longer sufficient to capture the abundance of information that RNA can convey. Over 140 naturally occurring modifications of adenosine (A), guanosine (G), cytosine (C), and uracil (U) have been discovered to date , messengSaccharomyces cerevisiae in the 1950s [The first modified nucleotide, pseudouridine, was discovered in he 1950s , but barhe 1950s . Therefohe 1950s ,17,18. HNot surprisingly, researchers are still scratching the surface when it comes to discerning the biological relevance of RNA modifications. Several modifications have now been linked to disease pathologies , stress Before delving into the methods used to study modified RNA structure and dynamics, it is important to appreciate the chemical and structural diversity of the RNA modifications. From a structural perspective, the two main factors that drive RNA folding pathways are base stacking and base pairing, which are achieved by hydrogen bonding (h-bond) interactions ,25. BaseOut of the 143 naturally occurring RNA modifications that are currently listed in the Modomics database , 57% conAmong the purines (A and G), the hydrogens in the amine group are most frequently substituted by one or two methyl groups. Since the amine group is on the W-C-F edge b, methylModifications on C comprise only 13% of the total RNA modifications discovered thus far. In the current data set, there are more cases where positions 4 and 5 on the pyrimidine ring in C are modified, followed by positions 2 and 3. While the modified groups at positions 2, 3, and 4 change the base-pairing face of C, the 5th position modifications have shown to improve duplex stability .U is the most commonly modified nucleotide in RNA, and many of these modifications are found at position 5 of the pyrimidine ring. Unmodified, U forms a canonical and a wobble base pair with A and G respectively. Modifications that occur at position 5 can shift the base-pairing preference ,42. FurtThe ribose is a moiety found in every nucleotide, modified or unmodified. An unmodified ribose can have two configurations: C2′-endo sugar puckering or C3′-endo sugar puckering . TypicalThe ever-expanding universe of known RNA modifications also displays remarkable chemical diversity c. The moIt is no surprise that methylation is the most common modification found in all four nucleotides at various positions including the sugar, considering the framework to introduce methylations is well established and methylations are common epigenetic markers and post-translational modifications ,22,48. I2) are the next most common type of RNA modification, depending on the original canonical nucleotide. These modification groups can add hydrogen donors and acceptors to the base pairing edges [2U and s4U) have been shown to base pair with a wide range of partners as well as affect thermostability by enhanced stacking interactions [After methylations, groups such as hydroxyls (OH), thiols (S), and amines  to second (s) timescales. MDS are an extremely useful computational tool to elucidate how the “wiggling and giggling” of atoms gives rise to the folding pathways, three-dimensional (3D) structure, and interactions of biomolecules. The two key components needed for reliable MDS are accurate initial 3D coordinates and robust “force-field” parameters, which steer the simulation over time to explore energetically favorable conformations. Force-fields are a collection of analytical functions and their associated model parameters that estimate the intra and intermolecular forces between atoms and molecules in MDS. It is standard practice for MDS to use experimentally solved 3D structures from databases like the Protein Data Bank (PDB)  to studynucleotides) encounter several challenges as has been pointed out in recent reviews [However, obtaining a reasonable initial 3D structure is often a bottleneck, as only 7% of total structures deposited in the PDB contain RNA and only a small fraction of those contain any RNA modifications . While strategic modeling and advanced sampling techniques can somewhat alleviate the shortage of acceptable initial RNA structures, they cannot overcome the inability of current force-field parameters to depict inter and intramolecular interactions of RNA accurately. This obstacle is enough to prevent the achievable folding of RNA sequences into their characteristic 3D structures in silico. Fortunately, there is rapid ongoing progress in the development of improved force-field parameters for unmodified RNA, which have been recently updated with RNA-specific improvements in several popular force fields ,62,63. H reviews ,65. Due  reviews  and Adur reviews , for exa reviews  or publi reviews ,69. ThesMDS incorporate two major types of molecular interactions as depicted in The fitting of nonbonded interactions, on the other hand, are much more varied between different force fields. The philosophy behind each parameterization strategy will have a greater impact on how the modified RNA nucleotide will behave in MDS. Classical, all-atom simulations of biomolecules typically include two separate nonbonded forces: Coulombic interactions between charged atomic sites and Lennard-Jones forces to account for electron overlap and correlational effects. Coulombic interactions are described by static, atom-centered point charges, while Lennard-Jones forces are described by a pairwise additive inverse power law consisting of attractive 6th power and repulsive 12th power components see . These nIt should be emphasized that all these functional forms were historically chosen purely based on computational convenience and less because of any deep connection to the underlying quantum-mechanical phenomena they are intended to represent. Electrons are not well-described as static point charges embedded in the center of atoms (even to a first approximation), and dispersion forces are inherently multi-body, environment-specific interactions that are not well described by static, spherically symmetric pairwise additive functions. Together, however, these functional forms provide an ample parameter-space for creating simple atom-centered classical models that can reproduce a wide range of physical phenomena with a small number of carefully calibrated parameters  h5U34 and ms2t6A37 are both necessary for the ASL of tRNALysUUU to successfully recognize the AAG codon. MDS showed that the methyl-thio group enhances the stability of the codon:anticodon minihelix by additional stacking interactions while the threonyl group shields the codon and the anticodon from the solvent, thus stabilizing the wobble G:U base pair in the AAG bound conformation of the tRNA [ILe, where Lysidine and t6A are at the 34th and 37th positions respectively, MDS show that the Lysidine preferentially pairs with AUA instead of AUG [2U) in the 34th position were shown to recognize only G-ending codons. MDS of the mRNA bound tRNALys ASL showed that the loss of a proton donor due to geranylation in uridine prevents the A:ges2U pair from forming. However, the G:ges2U pair interacts with stable hydrogen bonds in the presence of the modification, and the bulky geranyl group does not disrupt any ribosomal interactions of the tRNA or mRNA [Another common modified RNA system that is explored using MDS is the tRNA: mRNA minihelix in the context of the ribosome and the effects of modified nucleotides on codon-bias, and frame-shifting. Experimental observations provide evidence of the significance of the modification status of anticodon stem-loop (ASL) of tRNAs during translation and MDS studies alongside available experimentally derived structures, furnishing mechanistic insights for such observations. For example, codons NNA or NNG can be identified by the same tRNA with anticodon UNN, where N can be A, C, G, or U. Such systems have been shown to rely on modifications at the 34th and 37th positions in the tRNA to introduce codon bias. In eukaryotes, mcmthe tRNA ,90. In cd of AUG . In bact or mRNA . A 2′O m or mRNA . It is important to point out that although the MDS studies listed above are in qualitative agreement with experimental observations, they are somewhat speculative when providing mechanistic insights into the effects of the modifications on RNA structure and function. The force-field parameters used in these studies are acquired using the AMBER or CHARMM methodology and are only as good as those for the canonical RNAs. There is no intrinsic reason why either the AMBER or CHARMM methodology should be better suited for simulating modified RNAs. Although the two parameterization strategies are quite distinct, there is no unique mapping of the inherently many-body quantum interaction energies into a classical, pairwise additive effective potential. Both strategies have evolved to incorporate calibrations or assessments against experimental data when available but must resort to fitting against gas-phase QM interaction energies in the absence of such data. Both Xu et al.  and AdurThe degree to which the parameterization strategy may matter scales directly with the chemical nature of the modification. If the modification mostly involves space-filling or nonpolar additions such as methylations or other simple hydrocarbon groups , the simulator must rely on their chemical intuition for how strong or weak bonding should occur for each modification relative to their canonical interactions. In such a case, ab initio interaction energies can still be a useful gauge, however, unless some sort of scheme is used to account for how the fragments interact in a solvated environment via a thermodynamic free energy calculation, these calculations should only be considered as a qualitative and not a quantitative measure of accuracy given the potential complexity of the interaction.Lastly, some modification groups may introduce a net charge or additional rotatable bonds, and these necessitate additional considerations. Net charged moieties would be expected to interact strongly with counter-ions, depending on how solvent-exposed and localized the charge is distributed. RNA itself is famously a polyelectrolyte whose behavior is altered significantly depending on both the identity and the concentration of the counter-ions present ; therefore, modifications that affect the overall charge of the nucleic acid may also exhibit ion-dependent behavior requiring additional calibration. In terms of rotatable bonds, simple modifications resembling the set of organic compounds used in the genesis of early force fields ,95 likelMany different analytical techniques are available to study modified RNAs. However, not all techniques afford data that is directly comparable to simulation results for parameter calibration or overall assessment of simulation accuracy. Below, several experimental techniques that are commonly used to investigate modified RNA systems are discussed. First, detection methods are addressed, as identifying a possibly modified position in the sequence of a RNA is critical as well as chemically identifying the modified nucleotide. Then, the focus turns to structure analysis methods, as ultimately, evaluating the structural effects of RNA modifications on a biological RNA will answer inquiries into its function. Each technique is outlined, and the advantages and disadvantages of using the technique when investigating modified RNA systems and how the experimental data generated may translate to a computational study are discussed. A summary of this section is outlined in MS can be used to chemically identify RNA modifications as well as sequence modified RNAs. MS requires a very little amount of sample (as low as attomolar concentrations), making the technique attractive to troublesome biological targets with low yields . Yet, thThere are three main methods one can use to analyze modified RNA by MS: top-down, bottom-up, and nucleoside MS . Top-dowBottom-up analysis typically uses partially hydrolyzed RNAs for mass mapping and to provide some sequence context, though this method does require a sequence to compare back to . These pNucleoside MS of a fully hydrolyzed RNA can give chemical identities of modifications even at extremely low abundances ,107. HydMS experiments are sensitive to RNA modifications and can provide the chemical identity, sequence position, and the number of RNA modifications. Through -omics methodology, MS can provide these data via high throughput technologies for larger RNAs as well as heterogeneous biological samples  ,113,114.RNA sequencing techniques are a rapidly developing field. The field is currently split into two generations: next generation and third generation. Both generations provide single nucleotide resolution, allowing the position of RNA modifications to be uncovered. However, these techniques may not provide the identification of the exact modification. Next generation sequencing (NGS) techniques rely upon various chemical treatments that affect particular RNA modifications, allowing them to be detected as either: a reverse transcription (RT)-stop , as a misincorporation of nucleotides into the cDNA, through chemically-induced cleavage of the backbone, or through antibody-based enrichment methods  ,120. NatThird generation sequencing (TGS) takes advantage of single-molecule analysis. NGS involves amplification steps and provides only an average picture of the possible modified positions in a RNA sequence, whereas TGS techniques can provide the exact combination of modified sites for a given single RNA molecule. Two TGS techniques have been developed recently: PacBio SMRT technology  and OxfoBoth NGS and TGS techniques can detect some RNA modifications and can provide a sequence position for RNA modifications within a modified RNA. TGS shows the most promise as both PacBio SMRT technology and Oxford nanopores analyze a single molecule and can passively detect a modification without interpreting an RT stop or misincorporation. NGS techniques are still under development regarding the detection of RNA modifications, however, there have been some recent successes combining CLIP with RNA-modifying enzymes to identify dihydrouridine positions across the transcriptome .Optical melting experiments using UV spectroscopy have been used for decades to determine thermodynamic data for RNA. The principle depends on a two-state model, where a double-stranded RNA is subjected to increasing temperatures that break the hydrogen bonds between base pairs, resulting in a single-stranded/unstructured RNA . RelativOptical melting experiments can provide melting temperature, enthalpy, entropy, and free energy changes for state changes including duplex formation . These d13C, 15N) to be detected, which requires some preparation and quite a bit of care must be taken to maintain the purity of both sample and structure to use NMR successfully [J-couplings, residual dipolar couplings, and cross-correlated relaxation rates [NMR is a powerful tool when investigating RNA structure and is particularly sensitive to the effects of RNA modifications on a modified RNA structure. The RNA of interest must be labeled  and assignment of distance restraints (NOESY) . NOEs caX-ray crystallography is a well-known structural technique for biomolecules. However, the crystallization of RNA molecules is often more challenging than proteins. Analyzing modified RNA by X-ray crystallography is hampered by two major limiting factors: crystallization and phase problem . RNA’s hExperimentalists have developed a methodology to convert naturally occurring RNAs into sequences/structures that can crystallize ,163,164.X-ray crystallography data can provide the three-dimensional coordinates of a biomolecule within a crystal. This is particularly helpful in determining the position and orientation of RNA modifications within a structure. However, as discussed above, very rarely are RNA modifications within a solved RNA crystal structure (which themselves are rare), and even so, the structure may not be considered ‘native’ due to the crystallization process. Therefore, the 3D coordinates provided by X-ray crystallography would not give insight into the modifications’ structural effect on the native, biologically relevant RNA. Cryo-EM gathered attention as an alternative to X-ray crystallography and NMR for biomolecule structural determination as it removed the need for crystallization . When a Cryo-EM data includes images collected on direct electron detectors that have several frames per image, increasing sensitivity and allowing for conformationally heterogeneous samples to be separated . Cryo-EMHowever, the data provided by cryo-EM experiments directly translates to a 3D model. Additionally, if the signal to noise ratio is good and the refinement is of high quality, one can obtain several 3D models representative of different structural populations of RNA . Since RComputational investigations into modified RNAs require experimental data to give context to the modifications’ effects on structure and dynamics. However, no one experimental technique can provide enough context to parameterize these modified nucleotides to effectively simulate a modified RNA . TherefoIn the past decade, interest in RNA modifications has skyrocketed due to improvements in detection and identification methods that have revealed them to play a much larger role in biology than previously assumed. However, a detailed understanding of their function remains elusive. In many cases, the presence and identity of specific modifications can be detected, but their actual effect on RNA folding and function is still poorly understood. Techniques such as X-ray crystallography, NMR, and Cryo-EM, which are all well-suited for obtaining high-resolution 3D structures of proteins, face challenges for characterizing RNA, whose structures are often unresolvable due to RNA’s inherent flexibility and dynamics. This creates a pressing need for accurate physics-based computer simulations of modified RNAs that could potentially provide atomistic insight into how modifications affect RNA structure and function. In this review, we highlighted some of the challenges faced by both experimental and computational approaches with a focus on how that affects our ability to model and simulate modified RNAs. In a more mature field, a review article such as this would be expected to give an overview of notable past successes as well as detailed established best practices in the field. However, when it comes to atomistic simulations of modified RNAs, the field is still very much in its infancy. At the current time, even unmodified RNAs of >20 nucleotides cannot be accurately folded de-novo using unbiased all-atom molecular dynamics simulations, and these are systems where incredibly abundant detailed biophysical information on their conformational thermodynamics is readily available. With so little that is experimentally known about how modifications affect RNA folding, dynamics, and molecular recognition, it is extremely difficult to ascertain if the behavior of a simulated modified RNA is realistic or not. This leads to an inherent chicken-and-egg problem , that on one hand simulations are needed to provide mechanistic insight into the behavior of modified nucleotides that are difficult to measure experimentally, and on the other hand, with absent detailed experimental measurements, we cannot meaningfully assess how accurate these simulations predictions are, much less calibrate them to improve their performance.That said, MDS can still be utilized to provide useful insights in cases where the modifications occur in a well-defined structural context such as in a synthetic double-helix  or the So where do we go from here? There are lessons that can be learned from the successes in protein simulations. The systematic improvement in protein force fields required widely agreed-upon test systems that were both computationally tractable and experimentally well-characterized, as detailed in Lindorff-Larson et al.’s review . The fre