While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub ( RNA molecules play important roles in various biological processes, ranging from carrying genetic information, participating in protein synthesis, catalyzing biochemical reactions, and regulating gene expressions, to acting as a structural molecule in cellular organelles . GeneralIn the last decade, there are some computational models have been developed for predicting RNA 3D structures, among which the knowledge-based fragment assembly methods  and the Furthermore, the existing knowledge-based methods usually produce an ensemble of candidate structures, which should be further evaluated to recognize the best candidates as close to native structures as possible . To addrObviously, all these advances on RNA structure modeling indicate that to gather various statistics of RNA 3D structures is generally essential to predict RNA tertiary structures. However, there are few tools or web servers that can be used to make comprehensive statistical analysis for RNA 3D structures . RecentlHere, we present a novel tool, named as RNAStat, special for the statistical analysis of RNA 3D structures. It can be used to calculate structural information of RNA 3D structure(s) at different levels: global 3D structural level, secondary structure level, and atomic level. We first introduced the function and principle of the RNAStat. Afterward, based on a non-redundant RNA structure dataset established by us, we utilized the RNAStat to perform statistical analysis for RNA 3D structures, and provided various statistical data of RNA structural properties . Throughout the article, we also discussed the potential value of these statistics on RNA 3D structure prediction and evaluation.The RNAStat provided in this work can be used to make calculation (or statistics) for given RNA structure(s) in the following aspects: 1) the radius of gyration : and shape; 2) the secondary structure motifs; 3) the geometry of base-pairing and base-stacking; 4) the distances between atoms; see N is the number of heavy atoms  in the RNA molecule, ith atom. The The mean radius of gyration  protein , since ilated by 〈Rg2〉=1Nith atom. Due to the Since the shape of RNAs is rather important in determining the overall motion of RNA and their interaction with other biomolecules, two rotationally invariant quantities, the asphericity parameter al shape  2(trT)2where λTo obtain the secondary structure motifs for an RNA PDB structure, the RNAStat can directly call the DSSR through the corresponding python command ; The DSSR is an integrated and automated command-line tool for analysis and annotation of RNA tertiary structures, and it can characterize nucleotides, base pairs, pseudoknots, loops, stems, and coaxially stacked helices ; see an j in the coordinate system constructed on base i is described by the vector rij, which can be conveniently expressed in cylindrical coordinates   . And theθ planes .i and j with types of a and b, respectively, can be simply calculated in Cartesian coordinate by:ith atom with type of a . In the RNAStat, there are two modes for users to choose: 1) calculating distances between atoms specified by the user; 2) calculating all distances between any two types of atoms. In addition to the calculation of distance, the RNAStat can automatically output the distribution of the distance between two atom types, and which could be directly used to construct distance-based statistical potential , including PDB IDs, and PDB CIF files.To test the RNAStat, we established a non-redundant dataset based on the RNA 3D Hub set (Release nrlist_3.157_4.0 Å), in which the sequence identity between any two chains in the set is less than 95% . Firstly program . Finallyhttps://github.com/RNA-folding-lab/RNAStat. In the following, we will give a brief introduction of the usage method of the tool.In this work, we present the RNAStat, an integrated tool for making comprehensive statistics on RNA 3D structures. As shown in The input to RNAStat is the coordinate file(s) of RNA 3D structure(s) in CIF format. Based on the needs of users, the input can be a single PDB file of an RNA structure or the PDB files for a given RNA structure set. For each PDB file, the RNAStat can calculate the size and shape of the RNA through More importantly, for RNA structure set, the RNAstat can provide statistical information for all the above structural properties as well as the frequency distribution of various base pairs, which could be directly used to build statistical potentials for RNA structure evaluation or refinement . The detTo show the applicability of the RNAStat tool, we established a non-redundant RNA 3D structure dataset , and took it as an example for RNA 3D structure analysis and statistic. Simultaneously, based on the RNA structure set, we also provided various statistical results of RNA structures, and which could contribute to building RNA statistical potentials or energy function of RNA CG models.L; seen in We calculated the radius of gyration ling law . Althoug.5L1/3) , the par.1L0.37 , suggest.1L0.37 , which iSince RNA structure formation is generally hierarchical , the infAs shown in ab initio models  could be helpful in the evaluation of structures predicted by o models . Figure o models . Althougo models  , which can be directly employed by the user to establish base-pairing/stacking potentials for RNA 3D structure prediction or evaluation.In view of the fact that most of the knowledge-based statistical potentials for RNA structure evaluation are based on the distances between atoms . The RNAIn summary, RNAStat is an integrated computational tool to perform comprehensive statistical analysis for the RNA 3D structures given by the users. The tool cannot only automatically calculate RNA global structural properties such as size and shape, but also analyze atom-atom distance distributions at atomic level. Furthermore, the tool can provide statistics of RNA secondary structure elements  and geometric properties of base-pairing and base-stacking. In this work, we have established and utilized a non-redundant RNA 3D structure dataset to test the usability of the tool, and the statistical data could be directly used to build statistical potentials or energy functions for RNA 3D structure evaluation and prediction.Still and all, further improvements need to be made on the tool to perform more detailed statistical analysis and to make it easier to use. For example, most of the available RNA statistical potentials generally adopt a distance-dependent scheme, however for proteins, the orientation-dependent statistical potentials, which consider the many-body interactions by statistically describing both distance and relative orientation between interacting atom groups, and have been proved to have better performance than the traditional distance-dependent potentials . Thus, i


Immune checkpoint inhibitors (ICIs) have revolutionized metastatic melanoma (MM) treatment in just a few years. Ultraviolet (UV) in sunlight is the most significant environmental cause of melanoma, which is considered to be the main reason for tumor mutation burden (TMB) increase in melanoma. High TMB usually predicts that PD-1 inhibitors are effective. The sunlight exposure pattern of MM might be a clinical feature that matches TMB. The relationship between sunlight exposure patterns and immunotherapy response in MM is unclear. This study aims to investigate the correlation between sunlight exposure patterns and immunotherapy response in MM and establish nomograms that predict 3- and 5-year overall survival (OS) rate.We searched the Surveillance, Epidemiology, and End Results (SEER) database and enrolled MM cases from 2005-2016. According to the advent of ICIs in 2011, the era was divided into the non-ICIs era (2005-2010) and the ICIs era (2011-2016). Patients were divided into three cohorts according to the primary site sunlight exposure patterns: head and neck in the first cohort, trunk arms and legs in the second cohort, and acral sites in the third cohort. We compared survival differences for each cohort between the two eras, performed stratified analysis, established nomograms for predicting 3- and 5-year OS rate, and performed internal validation.Comparing the survival difference between the ICIs and non-ICIs era, head and neck melanoma showed the greatest improvement in survival, with 3- and 5-year OS rate increasing by 10.2% and 9.1%, respectively (P=0.00011). In trunk arms and legs melanoma, the 3- and 5-year OS rate increased by 4.6% and 3.9%, respectively (P<0.0001). There is no improvement in survival in acral melanoma (AM) between the two eras (P=0.78). The receiver operating characteristic (ROC) curve, area under the ROC curve (AUC) and calibration graphs show good discrimination and accuracy of nomograms. Decision curve analysis (DCA) suggests good clinical utility of nomograms.Based on the classification of sunlight exposure patterns, there is a gradient difference in immunotherapy efficacy for MM. The degree of sunlight exposure is positively correlated with immunotherapy response. The nomograms are sufficiently accurate to predict 3- and 5-year OS rate for MM, allowing for individualized clinical decisions for future clinical work. Since ipilimumab was approved by the FDA (U.S. Food and Drug Administration) in 2011, metastatic melanoma (MM) treatments have revolutionized in just a few years . So far,Ultraviolet (UV) in sunlight is the most significant environmental cause of melanoma , 6. The There are significant differences in sunlight exposure between different areas on the human body’s surface. Head and neck generally considered as chronic sunlight exposure areas, trunk arms and legs are intermittent sunlight exposure areas, and acral sites are lesser sunlight exposure areas –14. ThisTumor mutation burden (TMB) has been identified as a promising predictive biomarker for immunotherapy in various cancers . High TMHowever, systematic studies on the correlation between sunlight exposure and immunotherapy response in MM are lacking. We hypothesized that immunotherapy efficacy for MM may vary depending on the sunlight exposure pattern at primary site. According to the advent of ICIs in 2011, we divided the era into the non-ICIs era (2005-2010) and the ICIs era (2011-2016). We analyzed immunotherapy efficacy by comparing the survival differences between the two eras and exploring the differences in immunotherapy efficacy between different sunlight exposure pattern cohorts. We also created nomograms to predict 3-and 5-years OS rate in MM patients.The study is based on the Surveillance, Epidemiology, and End Results (SEER) database. The SEER database has collected survival data of about 34.6% from the United States population cancer registry. It is one of the most authoritative sources of tumor information in the United States. According to the exemption regulations released by the SEER database, the use of the data does not require informed patient consent. SEER database does not contain personally-identifying information.https://seer.cancer.gov/data). Data was downloaded by using the SEER*Stat software (Version 8.4.0.1) and the SEER-18 registries dataset (November 2019 submission). During 2005-2016, MM patients were selected based on the following criteria: Select melanoma  codes and CM codes in the international classification of diseases for oncology, third edition (ICD-O-3) site/histology. This article only included microscopically confirmed cases, first primary or first only, patients older than or equal to 18 years and surviving more than 0 days. The appropriate codes for MM (stage III and IV) were selected according to Derived AJCC Stage Group 6th Ed (2004-2015), Derived AJCC Stage 7th Ed (2010-2015) and Derived SEER Cmb Stg Grp (2016+).Research data use agreement was obtained at the SEER program , type of reporting source coming from “Autopsy only” and “Death certificate only”, and T0 stage (no evidence of primary tumor).We collected a large dataset concerning demographic, clinical characteristics and curative treatments. The following variables were evaluated: primary site ; age ; sex ; race (white or others); marital status (married or others); T stage ; N stage ; M stage (M0 or M1); ulceration (yes or no); distant organ metastasis (yes or no); surgical margin of primary lesion ; local lymph node resection ; metastasectomy (yes or no); radiotherapy (yes or no); chemotherapy (yes or no); vital status ; patient ID.Distant organ metastasis only included bone, brain, liver and lung. Among the surgical margin of primary lesion variables, margins less than or equal to 1 cm were considered narrow margins, and vice versa. Without conventional surgery  and without surgery were defined as others. Among the local lymph node resection variables, needle aspirate biopsy of regional lymph nodes was performed but not known to have been removed was defined as others.We took 2011 as the dividing line, 2005-2010 as the non-ICIs era, and 2011-2016 as the ICIs era. MM patients were divided into three cohorts based on the sunlight exposure patterns of primary site, patients with primary site of head and neck in the first cohort, trunk arms and legs in the second cohort, and acral sites in the third cohort Figure 1We screened 9806 MM patients, 4264 cases of which were recorded in the non-ICIs era, and 5542 cases were recorded in the ICIs era. Comparing the differences in the variables between the two eras, there were statistically significant differences in surgical margin of primary lesion  and local lymph node resection in the three cohorts . The detailed results are shown in Propensity score matching (PSM) analysis was performed to reduce the bias. Except for distant organ metastasis, as it only contains data from the ICIs era. The patients were matched 1:1 by estimated propensity scores with a caliper width of 0.02. After matching, 4069 matched pairs (8138 cases) were included in the analysis Figure 1Univariable and multivariable Cox analyses were used to screen independent prognostic factors in the training cohorts at P<0.05 level. Independent prognostic factors as variables were used to create nomograms. Nomograms were used to predict 3- and 5-year OS rate. Evaluate nomograms performance by receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), calibration graph and decision curve analysis (DCA). The discriminative power of nomograms was assessed by using the ROC curve and AUC. The accuracy of nomograms was evaluated by using calibration graph. The ROC curve reflects the specificity and sensitivity of the prediction model . The clo2 test or Fisher’s exact test was used for categorical variable comparison. 3- and 5-year OS rate were compared by KM survival curves and log-rank test. Univariable and multivariable Cox analyses were used to screen for independent prognostic factors. Nomograms were created by using the rms package in R Studio (http://www.r-project.org/).All statistical analyses were performed by R studio version 4.1.3. Statistical significance was set at P < 0.05 level. Continuous variables were expressed as mean ± standard deviation, Mann-Whitney U test was used for comparison. χSurvival was significantly improved in the ICIs era (There was a graded difference in ICIs efficacy when comparing 3-year and 5-year OS rate in three cohorts between the two eras. The first cohort benefited the most from ICIs (P=0.00011), with 3-year and 5-year OS rate increasing by 10.2% and 9.1%, respectively  and second  cohorts. Age, sex, ulceration, and surgical margin of primary lesion were independent prognostic factors in non-acral CM (the first and second cohorts). M stage and radiotherapy were independent prognostic factors in AM (the third cohort) Table 4.We include data from two eras to create nomograms and set the era as a new variable. The ICIs era as yes (assuming to receive ICIs therapy) and the non-ICIs era as no (not receiving ICIs therapy). All used a 2-to-1 ratio to randomly assign cases to the training set for nomogram construction and the validation set used to perform internal validation. We use data from the training set to select variables by univariable and multivariable Cox regression analysis at P<0.05 level Table 5.The nomogram of the first cohort is constructed by age, marital status, M stage, ulceration, distant organ metastasis, surgical margin of primary lesion and ICIs therapy  and ipilimumab (a CTLA-4 checkpoint inhibitor) have complementary activities in MM, compared with ipilimumab monotherapy, two-drug treatment significantly improved the PFS of patients , 52. If In summary, based on the classification of sunlight exposure patterns, there is a gradient difference in immunotherapy efficacy for MM. The degree of sunlight exposure is positively correlated with immunotherapy response. Head and neck melanoma has the best curative effect, trunk, arms and legs melanoma is the second and AM is the last. In the ICIs era, age, sex, ulceration, and surgical margin of primary lesion are independent prognostic factors for non-acral CM. M stage and radiotherapy are independent prognostic factors for AM during the ICIs era. The AUC and calibration graphs of the nomogram perform well. The nomograms are of good clinical utility and are internally validated to provide accurate predictions of 3- and 5- year OS rate.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.ML and WL conceptualized the study. XM, YC and BW contributed to process data. MC and LZ contributed to validation. SZ, AC, and YP contributed to visualization. JG and JZ administered the project. All authors contributed to the article and approved the submitted version.


Bombus terrestris) to discriminate between differently angled edges. In a multiple-choice, “meadow-like” scenario, bumblebees successfully discriminated between angled bars with 7° differences, significantly exceeding the previously reported performance of eastern honeybees . Neither the rate at which bees learned, nor their final discrimination performance were affected by the angular orientation of the training bars, indicating a uniform performance across the visual field. Previous work has found that, in dual-choice tests, eastern honeybees cannot reliably discriminate between angles with less than 25° difference, suggesting that performance in discrimination tasks is affected by the training regime, and doesn’t simply reflect the perceptual limitations of the visual system. We used high resolution LCD monitors to investigate bumblebees’ angular resolution in a dual-choice experiment. Bumblebees could still discriminate 7° angle differences under such conditions . Bees eventually reached similar levels of accuracy in the dual-choice experiment as they did under multiple-choice conditions but required longer learning periods. Bumblebees show impressive abilities to discriminate between angled edges, performing better than two previously tested species of honeybee. This high performance may, in turn, support complex visual processing in the bumblebee brain.Simple feature detectors in the visual system, such as edge-detectors, are likely to underlie even the most complex visual processing, so understanding the limits of these systems is crucial for a fuller understanding of visual processing. We investigated the ability of bumblebees ( Low-level feature detectors –5 such aApis mellifera) to collect food from feeders bearing either vertical, horizontal or 45° black bars. In tests, bees could discriminate the training pattern from one with only a 10° difference in orientation; at 8°, bees’ performance deteriorated; and they failed completely at 5°. Chandra et al. [Apis cerana) to feeders with black stripes of various angular orientations, presented on a white disk. Two tests were used: in one, the bees chose between two cues, one with stripes matching the trained orientation and one in a different orientation; the other test employed a multiple-choice paradigm, in which bees chose between the trained orientation and 11 deviations from it. Honeybees performed better in the multiple-choice situation, successfully choosing the trained orientation over alternatives rotated by 15°, whereas under the dual-choice conditions they only succeeded in differentiating between stripes that differed by 25°. This result gives a clear demonstration of the risks of inferring the limits of discrimination from behavioural performance, since such performance depends not just on what the bee sees, but also on the training procedure, as well as individual differences in motivation, cognition and, potentially, even “personality” [Wehner and Lindauer , 15 traia et al.  trained onality” . There ionality” –20. Componality” , 21, andBombus terrestris audax) to discriminate between cues with differently oriented edges, in both a multiple-choice, meadow-like environment and in a dual-choice setup, to determine whether their performance differs from that of honeybees and how it is affected by the behavioural context.We investigated the ability of bumblebee workers . Each colony was kept in a custom-built wooden nest box (280 × 160 × 110 mm high) and connected via a Perspex tunnel to a foraging arena (700 × 700 × 400 mm high) with white painted walls and green laminated paper floor . High frad libitum sucrose solution (20% w/w) from ten feeders placed on the arena floor. Each feeder consisted of a sucrose-solution-filled feeding tube (Ø 7mm) placed horizontally at the centre of a vertically aligned, circular disk (Ø 110mm), made of laminated white paper. Each feeder was supported by an attached block weight (15 × 25 × 45 mm high) that allowed the feeder disk to be aligned perpendicular to the floor arena ; the remaining five feeders had the bar oriented to a different angle, B, and were non-rewarding, filled with a 10μl water droplet (CS-). Feeding tubes were refilled from the rear, preventing sucrose or water from being deposited at the entrance of the tube.During the training phase, bees were allowed to forage individually in the arena. Ten feeders  were arrA and B, although only 12 were assigned to bees in practice. In a pilot experiment, three bees very quickly learned to discriminate bars whose orientations differed by 15°, so we investigated whether they could discriminate a smaller difference. In experiment 1, we trained 25 bees to bar orientations differing by 7°. A range of bar orientations was used in order to examine discrimination performance across the full range of possible orientations. We started with four base angles at -60°, 0°, 45° and 90°. These were paired with an angle that differed by 7°, either clockwise or anticlockwise (e.g. 45° could be paired with 38° or 52°), giving 8 pairs of angles. We assigned pair of angles to each bee at random and further randomised which of the two angles in the pair was associated with the sucrose reward and which with water. There were thus 16 possible combinations of angles Before training, all bees were removed from the arena and the setup was cleaned with 70% ethanol. The focal bee was allowed repeated access to the foraging arena until a total of 100 consecutive feeder choices were recorded. A choice was defined as a bee landing on a feeding tube, and for each choice we recorded whether the feeder was rewarding or unrewarding. After each foraging bout , all feeders were cleaned and the positions of rewarding and unrewarding were randomized before the bee was allowed to re-enter the arena. Rewarding feeders were refilled with 10μl sucrose solution each time a bee had drunk and departed from the feeder. Bees did not consume water from the unrewarding feeders, so they did not require refilling after each choice, but unrewarding feeders were cleaned and refilled after every foraging bout.We tested bumblebee workers from a third colony  on dual-choice tests, presenting the stimuli on two high-resolution, high-refresh-rate LCD computer monitors . ThA larger, wooden flight arena (1150 × 1300 × 500 mm high) was used to accommodate the monitors. The flight arena was covered with a red Gaussian random dot pattern , printed onto white laser copy paper and laminated. At the top of the arena a fine fabric net was attached, this extended to the laboratory ceiling. High frequency fluorescent lighting  were used to illuminate the apparatus.Two monitor screens were positioned at the rear wall of the flight arena and aligned at 60° angle from each other, allowing the bee to see both screens from the entrance of the arena (980 mm in front of the monitors). A transparent Plexiglas sheet was placed 15 mm from each monitor screen with a small hole (Ø 10mm) at the centre, leading to a feeding tube . As the tube was behind the Plexiglas from a bee’s point of view, it did not block any of her flight movements in front of presented stimuli and allowed us an unobstructed view of the bee’s movements.Stimuli were created and displayed on the monitors using custom MATLAB code and the PsychToolbox . Each moA) were assigned to 6 bees at random; the bar for the unrewarding feeder was rotated either +7° or -7°, relative to the rewarding stimulus  and released again from the entrance of the arena to make another choice. Each foraging bout consisted of approximately four feeder visits, and lasted until the bee’s crop was full and it returned to the nest (mean crop capacity: 80 to 120μL ). BetweeThe experimental training phase followed the same procedure as above. During each foraging bout, each of the two monitors showed an oriented bar . The location of the rewarding stimulus was pseudo-randomized for each foraging bout, and the feeders and surrounding Plexiglas were cleaned with 70% ethanol between bouts. Each bee was trained for approximately 130–190 choices, continuing until the bee achieved at least 80% correct choices over two consecutive 10-choice batches. If the bee did not achieve this 80% accuracy after 190 choices, the training was abandoned.Once the bee achieved ≥80% correct choices in two consecutive batches of 10 choices, or failed to do so over 190 choices, it was subjected to a test bout. During tests, both feeding tubes contained a 20μL drop of water regardless of the stimulus. After each batch of 10 choices between unrewarding stimuli, the bee was given 10 choices under training conditions, with the trained stimulus again rewarded with sucrose, to maintain its motivation to visit the feeders. The number of choices was recorded for each bar during unrewarded test trials. The test bout ended when the bee no longer attempted to visit either feeding tube.signrank function).We investigated whether bees in each experiment learned to discriminate between angled bars by testing whether the proportion of correct choices over the last 20 feeder visits by each bee, during training, was significantly greater than chance. We used a one-sample Wilcoxon signed-rank test for each experiment to compare the median proportion of choices for the CS+ to a chance level of 0.5  and bars ±7° from each of those angles . Only onkruskalwallis function).To test whether experienced bees from each group differed in their ability to discriminate between angled bars, we used a Kruskal-Wallis test, in which the dependent variable was the proportion of choices for the CS+ over the last 20 feeder visits made by bees in each orientation group .Finally, we investigated whether there were differences between bees trained to discriminate 7° orientation differences under dual- and multiple-choice conditions in either final performance or rate of learning. We tested for an effect of experimental paradigm on the discrimination performance of experienced bees, by comparing the proportion of choices for the CS+ over the final 20 feeder visits made by bees in experiment 1 to those in experiment 2. Because there was a large difference in sample sizes between experiments , we used an exact permutation test .Bees trained to discriminate between angled bars whose orientation differed by 7° in a multiple-choice setting, learned to do so with high accuracy . The mea22,21 = 4.98, P = 0.083; Although four groups of bees with different base bar orientations were used in the experiment, one group (0°±7°) contained only one bee, so we investigated whether bar orientation affected discrimination performance using the remaining three groups, only . There were no significant differences between the groups of bees trained on each of these three different base bar orientations in the number of correct choices over their final 20 choices .There was no significant difference in discrimination performance between bees in the two experiments over their final 20 choices .1,255 = 73.19, P < 0.0001; 1,255 = 26.49, P < 0.0001), demonstrating that bees in experiment 2 learned at a slower rate than those in experiment 1 . By contrast, only four of six bees ever reached this level of performance in experiment 2, despite those that never reached the threshold experiencing nearly twice as many trials as those in experiment 1. The four that did reach 80% performance did so after 147.50 ± 8.54 choices (range: 130–170).Bombus terrestris) are able to discriminate between oriented bars with an angle difference of just 7°, in both a meadow-like multiple-choice, and a dual-choice scenario, and regardless of bar orientation. Similar levels of accuracy were eventually reached under both scenarios. This performance slightly exceeds that reported for European honeybees (Apis mellifera), which discriminated 10° differences with certainty and still showed some evidence of discrimination at 8°, in dual choice tests [Apis cerana) could not be trained to discriminate angle differences below 15° in a multiple-choice setup, and below 30° under dual-choice conditions [In this study, we demonstrate that bumblebees , clearly demonstrating that they could perceive differences of at least that magnitude, and that this ability was not dependent on the specific conditions of one particular experimental paradigm.Why might bees learn to discriminate angles more quickly in a multiple-choice experiment? One important factor may be the frequency with which bees were able to get feedback on their choices. In the meadow-like, multiple-choice experiment, bees visited several feeders during every foraging bout (round-trip from the nest to the flight arena and back), but in the dual-choice experiment they could make only one choice before being returned to the arena entrance. The comparative difference in how rapidly they can sample different options and rewards may account for the difference in learning speed. Alternatively, the lower rate of reward might have reduced bees’ motivation in the dual-choice experiment.Wolf et al.  found thThere were a number of methodological differences between our experiments, so it is not possible to determine conclusively which affected the rate of learning. For example, the pre-training stage used different visual stimuli; the training stimuli were changed from black to magenta in the dual-choice experiment (to help observers track the bee’s position more effectively); and the volume and concentration of the sucrose reward was increased (to maintain bees’ motivation under conditions in which their rate of intake slowed); any of which may have affected motivation.A. cerana under dual-choice conditions: there was a 0.5 chance of reward from each feeder sampled, so it may have been efficient to sample all stimuli at random, rather than investing time and computation in discriminating between them. By contrast, in their multiple-choice experiment, the odds of success from random sampling were just 0.0833, so bees may have been motivated to invest in learning to provide a greater medium- or long-term rate of energy gain. The odds of success from random sampling were 0.5 in both of our experiments, however, so differences in expected reward from random sampling cannot account for the difference between paradigms.Chandra et al.  suggesteThe stimuli for our multiple-choice test were printed on paper, while the dual-choice test was presented on computer monitors, which have previously been found to make fine discriminations more difficult for bees . HoweverB. terrestris [We also cannot rule out a role for stochastic differences between the bees used in each experiment. Different colonies of bumblebees were used for the two experiments and intercolony variation in learning performance has been demonstrated in rrestris . Future rrestris  similarlWe did not measure any aspect of body size in our bees, so cannot investigate whether physiological differences may explain some of our observed variation in performance. Differences in body size are related to differences in eye size, number of ommatidia and facet size , 35, andBody size in bumblebees has also been found to affect investment in learning , and howApis mellifera), in several behavioural contexts [Even in a multiple-choice setup, the limit of angular discrimination previously reported for eastern honeybees  is doublcontexts , 34, 41.A. mellifera eyes lies between that of A. cerana and B. terrestris in many respects , yeOur results and those of Chandra et al. , show th


Editing events from RNA-editinghigh pattern were significantly associated with glutamate receptors and regulated synaptic transmission. Interestingly, most non-synonymous high-editing sites were mapped to ion channel genes of glutamatergic synapse which were associated with neurotransmission by controlling ion channel permeability and affecting exocytosis. Meanwhile, these non-synonymous editing sites were evolutionarily conserved and exhibited a consistently increasing editing levels between mouse and human retinal development. Single-cell RNA-seq data analysis revealed that RNA editing events prefer to occur in two main cell types including bipolar and amacrine cells. Genes with non-synonymous high-editing sites were enriched in both bipolar cells and retina ganglion cells, which may mediate retina ganglion cell differentiation by altering channel ion permeability. Together, our results provide novel insights into mechanism of post-transcriptional regulation during retinal development and help to develop novel RNA editing-guided therapeutic strategies for retinal disorders.Adenosine-to-inosine (A-to-I) RNA editing leads to functional change of neurotransmitter receptor which is essential for neurotransmission and normal neuronal development. As a highly accessible part of central nervous system, retina has been extensively studied, however, it remains largely unknown how RNA editing regulates its development. Here, a genome-wide screening of high-confidence RNA editing events were performed to decipher the dynamic transcriptome regulation by RNA editing during mouse retinal development. 2000 high-confidence editing sites across eight developmental stages of retina were called. Three unique patterns (RNA-editing Due to structural similarities, inosines (I) are read by the cellular machinery as guanosine (G) Transcriptional profiling of genome-wide RNA sequencing confirmed a large number of RNA editing sites in many important neuronally expressed genes As the most accessible part of the central nerve system(CNS), neural retina is an excellent model system to analyze key aspects of neurogenesis As the most accessible part of CNS, the mechanisms underlying retinal development are highly conserved across vertebrates, making it an excellent model system for studying nerves. Although RNA editing is essential for the functions of nervous system, their dynamic changes and specific roles in retinal development and mechanisms are unclear.high pattern, RNA-editingmedium pattern and RNA-editinglow pattern were identified. Among them, we found that RNA-editinghigh pattern showed a clearly increasing trend during retinal development. The associated genes are significantly enriched in glutamatergic genes, which play a crucial role in neurotransmission by controlling ion channel permeability and affecting exocytosis. Moreover, non-synonymous RNA editing sites in glutamate ion receptors are associated with bipolar cells and retinal ganglion cells. All in all, our study provided a genome-wide map of RNA editing events and characterized different RNA-editing-mediated post-transcriptional modification patterns during retinal development. The novel high-confidence retina-specific editing sites expanded the known RNA editing resource.In this study, we developed a framework to comprehensively characterize dynamic RNA editing events during mouse retinal development. Three distinct patterns including RNA-editing22.1https://www.ncbi.nlm.nih.gov/sra, accession number SRP090043) The bulk RNA-seq data for mouse retina development were downloaded from the NCBI Sequence Read Archive (2.2We used a pipeline adapted from a previously reported method 2.3https://genome.ucsc.edu/).To further analyze the function of RNA editing sites, we used ANNOVAR 2.4https://github.com/YuLab-SMU/clusterProfiler). We used all mouse genes as a reference gene set to compare with edited genes for functional enrichment analysis. Only the terms of "biological process" were selected for GO enrichment analysis. Enrichment with FDR was adjusted by Benjamini-Hochberg multiple comparisons. The selected KEGG pathways related to RNA editing were visualized using the Pathview package We performed KEGG pathway and Gene ontology (GO) enrichment analyses by the enrichGO and enrichKEGG function in the R package clusterProfiler 2.5https://meme-suite.org/tools/meme) Motif prediction was performed using MEME-ChIP in the MEME suit  deletion upstream of the editing site and some G enrichment downstream A. Moreovi.e. in most species, a large proportion of editing events occur in repeating elements may form long double-stranded RNA structures that are more favorable for enzyme binding To examine the genome distribution of the RNA editing sites, we annotated the sites by ANOVAR tool To explore possible functional significance of these RNA editing events across retinal development, we performed GO functional enrichment analysis. The edited genes  containing at least one high-confidence RNA editing site were found to be significantly enriched in retinal development in the camera-type eye, dendrite development and glutamate receptor signaling pathways (p.adjust < 0.05) F. FunctiTo sum up, we identified 2000 high-confidence RNA editing sites across eight retinal development stages. More than third of them were retina-specific, which may have some characteristics different from that of brain although they were all enriched in neurons.3.2To further characterize these editing sites, we examined the number of high-confidence editing sites located in major repeat families in mouse. On the one hand, consistent with human beings, high-confidence RNA editing sites were primarily located in short interspersed element (SINE) (75.9%) A that weTo further dissect RNA editing events in the B1 element, editing levels between repeat family regions and non-repeat regions were compared. Result demonstrated that RNA editing level of B1 elements was significantly higher  than other repeat elements and non-repeat regions during retinal development, while there was no editing level difference between other repeat elements and non-repeat regions B.Altogether, during mouse retinal development, high RNA editing events prefer to occur at B1 repeat elements, which may form dsRNA structure that allows ADAR enzymes to bind easily to mRNA sequences 3.3To explore changes of RNA editing during retinal development, we quantified genome-wide editing degree by calculating A-to-I RNA editing level across all stages. For high-confidence RNA editing sites, editing rate was defined as the ratio of reads number that supported adenosine (A) to guanosine (G) to all reads number covered the sites(G/(G + A)). We observed that the RNA editing level gradually increased during retinal development A, which high; green, RNA-editingmedium; blue, RNA-editinglow pattern)  A. We chopattern)  B as thehigh pattern sites were highly enriched in glutamate receptor and regulated synaptic transmission. In contrast, RNA-editinglow pattern editing sites were significantly enriched in essential functions, such as mRNA splicing and binding-related genes  and with highest proportion of non-synonymous editing sites among all patterns during retinal development (high pattern and RNA-editingmedium pattern) had a higher proportion of editing sites in the repeat region than the RNA-editinglow pattern  were mapped to ion channel proteins (Cacna1d) and glutamate ionotropic receptors   A. Among high pattern also showed increasing trend with higher editing levels compared with common sites (high pattern during retinal development.Interestingly, six of the eight non-synonymous editing sites were evolutionarily conserved between mouse and humans B. Duringon sites C. Moreovon sites . These r3.5The major cell types of retina has been well-characterized and showed a stereotyped birth order during development To further explore the contribution of each retinal cell type to RNA editing events during retinal development, gene expression and RNA editing profiles of different retinal major cell types were analyzed. The enrichment scores of edited genes were calculated to in bulk-seq for each individual cell using the Addmodulesocre function of Seurat package. As indicated by the boxplot, amacrine cells possessed highest proportion of edited genes A and B. To investigate the specificity of these non-synonymous high-editing sites in various cell types, enrichment scores of these non-synonymous edited genes for each cell were calculated. We found that these non-synonymous edited genes were enriched in amacrine bipolar cells, retinal ganglion cells, HC cells and amacrine cells D, in whi4high pattern were significantly enriched in glutamatergic genes and ion channel genes, which is consistent with previous findings that high editing sites were mainly detected in neurons low patterns were observed in genes involved in RNA binding and splicing. As reported, RNA binding proteins such as FMRP and ILF3 could directly regulate RNA editing by affecting the interaction between ADAR enzyme and RNA sequence cis-acting signals that regulate splicing activity and destabilize double-stranded structures To our best knowledge, this is the first study with comprehensive characterization of A-to-I RNA editing events in mouse retina. In addition to identification of novel high confidence retina-specific editing sites, we featured distinct dynamic post-transcriptional regulation patterns during retinal development. Edited genes from RNA-editingIn humans, most RNA editing events were found in transcribed repeat elements high patterns in retinal development, play a crucial role in neurotransmission by controlling ion channel permeability and affecting exocytosis Non-synonymous mutations were largely considered deleterious because they resulted in the change of amino acid sequence. However, these non-synonymous editing sites, from RNA-editingComparative analysis of vertebrate and invertebrate genomes suggests that a substantial proportion of vertebrate genes had no homologous genes in the invertebrate genome During our investigation, we suffered from short reads, low coverage and easy mismatches when using single-cell data for RNA editing analysis. To address these issues, we merged reads from the same cell types to increase coverage and used RNA editing profiles from bulk-seq as background to avoid false sites generated by mismatches. With the development of single-cell sequencing technology, we may solve these problems possibly at the sequencing level and identify more cell-specific editing sites. Single-cell analysis of RNA editing found that non-synonymous editing was associated with bipolar cells and retinal ganglion cells. Consistent with previous reports, our results revealed that non-synonymous RNA editing could modulate the electrical signaling pathway between bipolar cells and retinal ganglion cells by affecting glutamate ion receptor permeability during stimulus transmission.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Phytophthora castaneae, an oomycete pathogen causing root and trunk rot of different tree species in Asia, was shown to harbor a rich diversity of novel viruses from different families. Four P. castaneae isolates collected from Chamaecyparis hodginsii in a semi-natural montane forest site in Vietnam were investigated for viral presence by traditional and next-generation sequencing (NGS) techniques, i.e., double-stranded RNA (dsRNA) extraction and high-throughput sequencing (HTS) of small RNAs (sRNAs) and total RNA. Genome organization, sequence similarity, and phylogenetic analyses indicated that the viruses were related to members of the order Bunyavirales and families Endornaviridae, Megabirnaviridae, Narnaviridae, Totiviridae, and the proposed family “Fusagraviridae.” The study describes six novel viruses: Phytophthora castaneae RNA virus 1–5 (PcaRV1-5) and Phytophthora castaneae negative-stranded RNA virus 1 (PcaNSRV1). All six viruses were detected by sRNA sequencing, which demonstrates an active RNA interference (RNAi) system targeting viruses in P. castaneae. To our knowledge, this is the first report of viruses in P. castaneae and the whole Phytophthora major Clade 5, as well as of the activity of an RNAi mechanism targeting viral genomes among Clade 5 species. PcaRV1 is the first megabirnavirus described in oomycetes and the genus Phytophthora. Phytophthora spp. are oomycetes taxonomically grouped under the kingdom Straminipila  is a plant pathogen mostly known for causing trunk rot on Japanese chestnut (Castanea crenata Sieb. et Zucc.). It is a homothallic species native to Southeast and East Asia  Berl. & De Toni-associated lesions  Berl. & De Toni and Sclerophthora macrospora (Sacc.) Thirum., C.G. Shaw & Naras  Uzuhashi, Tojo & Kakish., formerly classified as Py. nunn Lifsh., Stangh. & R.E.D. Baker, Pythium nunn virus 1 (PnV1), a distinct member of the viral genus Gammapartitivirus in the family Partitiviridae, was described (G. ultimum (Trow) Uzuhashi, Tojo & Kakish.  and Pythium polare bunya-like RNA virus 1  Uzuhashi, Tojo & Kakish., formerly Py. splendens Hans Braun, Pythium splendens RNA virus 1  have been discovered in many major taxa of phytopathogenic fungi . In oomyophthora , a primaophthora . A metag lesions ; a (+)ss & Naras . In the  Buisman . In an iescribed . Viruses Kakish. . A toti- PpBRV1; . Another1 PsRV1; .Phytophthora, mainly from the notoriously devastating potato blight pathogen P. infestans (Mont.) de Bary ssRNA virus families Narnaviridae and Botourmiaviridae, detected by HTS of total RNA depleted of rRNA, but not discovered earlier by extensive dsRNA screening (Armillaria (Fr.) Staude, the presence of several ssRNA viruses has been revealed by HTS of total RNA, while no dsRNA elements were detected  is one of several pathways grouped under RNA silencing phenomena, defined as sequence-specific RNA degradation activated by the presence of long dsRNAs . RNAi isO. Dodge , which tO. Dodge .P. castaneae combining traditional and next-generation sequencing (NGS) techniques, and, in addition, to examine whether P. castaneae has an active RNAi mechanism and processes viral RNAs into siRNAs as an act of defense. To the best of our knowledge, this is the first virome investigation of the tree pathogen P. castaneae or any species of Phytophthora phylogenetic Clade 5.The objectives of this study were primarily to identify and characterize viruses in P. castaneae, i.e., VN999, VN1004, VN1008, and VN1012, from the culture collection at Phytophthora Research Centre (PRC), Mendel University in Brno, originally isolated in 2017 from rhizosphere soil of four Chamaecyparis hodginsii (Dunn) Rushforth trees growing in a montane Chamaecyparis-Quercus forest stand in northern Vietnam close to Sapa , 16 g/l of agar (Sigma-Aldrich), 2 g/l of CaCO3, and 900 ml/l of distilled water] covered with cellophane membrane . For dsRNA extraction, the isolates were grown in 100 ml broth containing 0.2 g of CaCO3, 10 ml of V8 juice, and 90 ml of distilled water. The Erlenmeyer flasks with the liquid cultures were incubated at 20°C and 120 RPMs for 7–10 days in a Benchtop Incubated Shaker .Four isolates of  to Sapa , were seP. castaneae isolates were first examined for putative virus presence using a modified dsRNA extraction protocol . The tubes were then submerged in liquid nitrogen and vortexed using the Vortex-Genie 2 and high-speed bead tube holder until the mycelium was ground to a fine powder. Total RNA was isolated using the SPLIT RNA extraction kit  following the protocol provided by the manufacturer, eluted in 50 μl of SB buffer (Lexogen), and stored at −80°C.™ Single Tube Turner BioSystems device . The integrity of total RNA was visually assessed by running it on a 1.2% agarose gel stained with SYBR™ Green I Nucleic Acid Gel Stain . The libraries were generated directly from total RNA using the TruSeq small RNA library preparation kit  according to the manufacturer’s instructions. After reverse transcription and library amplification, libraries were loaded onto an Agilent High Sensitivity DNA chip  according to the manufacturer’s instructions and run on an Agilent – 2100 Bioanalyzer, Automated Electrophoresis Instrument (Agilent Technologies). cDNA constructs were purified and loaded into a Novex 6% TBE polyacrylamide 15-well gel  in 5X Novex® TBE Running Buffer  and ran on the XCell SureLock® Mini-Cell electrophoresis unit . After the run, the gel was stained with GelRed® Nucleic Acid Gel Stain  and viewed under UV light. The bands that correspond to the adapter-ligated constructs derived from the 22-nucleotides (nts) and 30-nts sRNA fragments [the region between 145 base pairs (bp) and 160 bp amplicons] were cut out using a sterile razor blade. Gel slices were placed into 0.5 ml gel breaker tubes. The target products were purified from PAGE using glycogen , 3 M NaOAc (Sigma-Aldrich), and ethanol. The final product was checked for size, purity, and concentration and the pellet resuspended (to reach a concentration of 2 nm) in 15 μl of Tris–HCl 10 mm, pH 8.5.Prior to library construction, the quantity of extracted RNA was measured by a ModulusThe libraries were sequenced on an Illumina MiniSeq sequencer using a MiniSeq High Output Reagent kit . Constructed and normalized libraries were diluted to 1 nm and denatured. The 500 μl of prepared 1.8 pM library was then loaded to the reagent cartridge’s MiniSeq High Output Reagent (75 cycles). Six-base indexes were used as they significantly reduce ligation bias and ensure accurate measurement of miRNA expression. The libraries were sequenced in a single-read mode, 1 × 36 bp for VN999 and VN1008, and 1 × 50 bp for VN1004 and VN1012.P. castaneae isolate VN999 and P. condilina BD661 . RNA quantity and quality were measured by the Qubit® 2.0 Fluorometer  and Tape Station 4200 (Agilent Technologies). Total RNA was sent to Macrogen Inc., the Republic of Korea, for RNA library construction and deep sequencing. Prior to the library preparation, ribosomal RNA (rRNA) was depleted using the Ribo-Zero rRNA Removal Kit (Human/Mouse/Rat). The library was prepared using an Illumina TruSeq® Stranded Total RNA Sample Preparation Kit (Illumina) and sequenced in paired-end (2 × 101 nts) mode on a NovaSeq6000 (Illumina).Total RNA of na BD661  was pool® 2019.0.4 bioinformatics software for molecular biology and sequence analysis.The raw data quality was checked using the FastQC-0.10.1 program . AdapterP. castaneae genome, the reads were assembled to the genome of the most similar species from the same clade, P. agathidicida GCA_001314435.1_NZFS3772v2_genomic.fna.gz  and, subsequently, the contigs having no hits were examined further with blast searches and Geneious software.For the data processing of stranded total RNA-Seq reads, two approaches were used. For both, quality control of raw sequencing data was performed using the FastQC-0.10.1 program . In the c.fna.gz , using tc.fna.gz . In the y v2.6.5 , and ally v2.6.5  with an te BD661  as well ® 2020.2.3. For calculation of the coverage depth, the following formula was used as:In order to calculate coverage depth for each virus, and to get information on the number of reads representing individual viruses, raw reads were mapped to the final contigs of each virus. The number of total RNA reads for each virus was calculated by Bowtie 2 v2.3.0 . The num*Total number of reads mapped to the final virus sequence.® RT  following the manufacturer’s recommendations with some modifications. The samples were homogenized in 1 ml of RNAzol® RT (Sigma-Aldrich) using a Mixer Mill MM 400  for 2 min at 30 Hz. After the isopropanol precipitation step, the samples were loaded on RNA extraction columns  and centrifuged at high speed for 30 s (RT). This step was repeated until the whole sample was loaded. The columns were then washed twice with 600 μl 75% ethanol and spin-dried after the last wash for at least 1 min at high speed. The columns were then transferred to sterile nuclease-free 1.5 ml tubes. The RNA was eluted by adding 50 μl Elution buffer SPLIT RNA extraction kit (Lexogen) preheated to 70°C, incubated for 1 min, and then centrifuged for 1 min. The quality of total RNA was assessed by visualization under UV light in a 1.2% agarose gel (visible presence of 18 S and 28 S rRNA bands). A Qubit® 2.0 Fluorometer  was used to estimate RNA quantity. The final product was stored at −80°C. Prior to cDNA synthesis, total RNA was incubated for 10 min at 65°C. cDNA was synthesized using a High-Capacity cDNA Reverse Transcription Kit . To confirm the successful synthesis of cDNA, the actin housekeeping gene was amplified with primers MIDFWACT and MIDREVACT  designed for universal primer annealing, 1 μl of each 10 mm primer, 3–5 μl of cDNA depending on the virus and its abundance and PCR grade water in a total volume of 50 μl. Cycling conditions followed the manufacturer’s recommendations, and the annealing temperature of 60°C was universal for all primers. Amplification was checked in 1.2% agarose gels stained with DNA Stain G  after separation by electrophoresis . All amplicons of the appropriate size were sent to Eurofins Genomics  to be sequenced in both directions.After 7–15 days of growth, approximately 100 mg of mycelium was collected and frozen in liquid nitrogen. Total RNA was isolated by RNAzolIDREVACT . Every cde novo assembly of total RNA reads and for determination of the exact nucleotide sequence of virus variants among studied isolates, virus-specific primers (® RT (Sigma-Aldrich) and transcribed to cDNA as described above. RT-PCR was conducted as described previously in 25 μl reaction volumes using 1.5–2.5 μl of cDNA. Obtained amplicons were sent to Eurofins Genomic to be Sanger sequenced in both directions. Pairwise sequence alignments were done by MAFFT v7.450  sequences of RdRp regions of each virus were included in phylogenetic analyses. Sequences were aligned in Geneious 2020.2.3 by MUSCLE 3.8.425  togetherP. castaneae  revealed different dsRNA banding patterns . The putative virus shows similarities to the large (L) segment of megabirnaviruses and has a 60.2% GC content, two overlapping ORFs likely coding for the CP  and the RdRp . The putative dsRNA1 of PcaRV1 shows a 29.15% identity  to the Rosellinia necatrix megabirnavirus 2 (RnMBV1). With Rhizoctonia solani RNA virus HN008 (RsRV-HN008), PcaRV1 shares 26.60% identity in its RdRp region . The second segment of PcaRV1 was not detected based on the initial BlastX search against the virus protein database. Thus, the dsRNA2 was searched among Trinity contigs encoding for proteins having no detectable homology with proteins of the NCBI nr database  was detected. Besides, in the interval from 1,547 to 3,028 nts of the ORF1 with unknown function, the search revealed a significant record  with the provisional large tegument protein UL36 , which is the largest protein of herpesviruses. Multiple alignment of the RdRp aa sequence of the PcaRV1 with those of other related viruses enabled detection of conserved sequence motifs  is 2,891 nts long, coding a single protein, putatively RdRp . The seqiviridae . The ML iviridae . Accordiiviridae .Totiviridae, a family of linear uncapped dsRNA viruses approximately 4.6 to 7.0 kb in size  has a length of 5,470 nts and 59.3% GC content. It has two overlapping ORFs, ORF1  putatively encoding the CP and ORF2  corresponding to the RdRp. Conserved motifs of the RdRp (I-VIII) have been detected by the CDD search . The ORF in size . Accordi in size  but it a in size , 33.62%  in size , 35.01%  in size , and 32. in size  and to o in size .−30; Luteovirus, Totivirus, and Rotavirus and it is a member of the superfamily cl02808. According to the BlastX search, PcaRV4 is associated with proteins of viruses putatively belonging to the newly proposed family “Fusagraviridae.” The ORF1 of PcaRV4 is most similar to Bremia lactucae associated fusagravirus 1 ORF2 (QIP68010.1) with 28.15% identity and 59% coverage , hypothetical protein 1 of Wuhan insect virus 28 (YP_009342429.1) with which it shares 22.48% similarity  and to hypothetical protein EXH54_gp1 of Phytophthora infestans RNA virus 3 (YP_009551327.1), with 29.96% but less coverage (23%). ORF1 is also similar to Phytophthora infestans RNA virus 3 (YP_009551328.1), RdRp of Bremia lactucae associated fusagravirus 2 (QIP68015.1), hypothetical protein of Macrophomina phaseolina fusagravirus 1 (QDM35289.1), and other viruses. In its RdRp region, PcaRV4 is most similar to RdRp of Bremia lactucae associated fusagravirus 1 (QIP68009.1), with which it shares 29.96%  but also with RdRp of Phytophthora infestans RNA virus 3 (YP_009551328.1), RdRp of Spissistilus festinus virus 1 (YP_003800001.1), and other viruses. The whole sequence is most similar to ORF2 of Bremia lactucae associated fusagravirus 1  is 6,884 nts long. PcaRV4 has two large discontinuous ORFs , ORF1 p−30; . Pfam021viridae”  revealedBunyavirales. The phylogenetic analysis based on the partial RdRp (aa) sequence of PcaNSRV1 and those of related viruses revealed that PcaNSRV1 clusters with unclassified bunyaviruses of other Phytophthora species, i.e., P. cactorum . Many bunyavirus L proteins have this domain at the N-terminus (The nearly complete (−)ssRNA genome of a virus designated Phytophthora castaneae negative-stranded RNA virus 1 (PcaNSRV1) was 8,345 nts long, with one single 8,271 nts long ORF , putatively coding for a polyprotein/RdRp  and L secactorum , P. condondilina , and thoophthora . Accordiophthora . PcaNSRVora −07; . In additerminus .Endornaviridae were not detected in VN999 de novo assembled total RNA reads, contigs 139, 274, and 295 (Endornaviridae. Virus-specific primers (−119), 48.92% to the polyprotein of Chalara endornavirus CeEV1 , and 46.14% to the polyprotein of Phytophthora cactorum alphaendornavirus 3 . Conserved domain of pfam00978 RdRP_2 super family  was detected at interval from 1,544 to 2,014 bp of nucleotide sequence. A longer sequence of PcaRV5 was not determined and the putative virus was not further characterized.As the RNA of isolate VN1008 was sequenced only by HTS of sRNAs, and contigs of viruses similar to  and 295  were use primers  were des primers . It is 3P. castaneae isolates was examined using Sanger sequencing. The intraspecific genetic variability among virus strains present in the four isolates of P. castaneae appeared to be very low and homogeneous. Thus, the overall pairwise (pw) sequence comparison (PASC) percentage between PcaRV1 nucleotide sequences (strains) was 99.8% for an alignment of 5,985 bp; in PcaRV2 strains alignment , it was 98.8%; for PcaRV3 strains, it was 98.9% ; for PcaRV4, it was 98.5% ; and for PcaNSRV1 strains, PASC had the lowest value, 95.6% . Within PcaNSRV1, the highest pw variability was shown between the strain VN1008 and the rest of the strains (PASC 91.21–91.23%), while the pw similarity between VN999, VN1004, and VN1012 ranged 99.89–99.97%.The intraspecific genetic variability of PcaRV1-4 and PcaNSRV1 hosted by the four The high number of reads and depth of coverage show that the five virus genomic sequences obtained from Trinity assembly are properly supported. PcaRV1 had the highest number of reads mapped to its almost complete genome in all four sRNA datasets, as well as the deepest coverage in all sRNA libraries, but one . OverallP. castaneae.A three-method approach including two HTS methods, small and stranded total RNA sequencing, and the conventional dsRNA extraction method was employed in order to characterize and have a better understanding of the viral community hosted by the oomycete pathogen In the current study, the dsRNA detection technique provided first insights into the potential occurrence of viruses and, based on the presence of dsRNA banding patterns, isolates were chosen for further sequencing analyses. Linking sizes of dsRNA molecules with the described viruses was attempted, but the patterns were not consistent through all performed extractions . FurtherP. castaneae. While sRNA sequencing was found not to be the best method for that purpose, this study demonstrates that P. castaneae hosts a diverse virome processed into sRNAs, suggesting that P. castaneae has an active RNAi mechanism. To our knowledge, P. castaneae is the first Phytophthora species reported with such a diverse virome and high number of viruses from different viral families co-occurring in a single isolate, although an abundant occurrence of viral co-infections and extreme virus prevalence among host isolates was also found in P. cactorum  appear to host viruses belonging to the families Megabirnaviridae, Narnaviridae, Totiviridae, “Fusagraviridae,” and the order Bunyavirales. However, in isolate VN1008, in addition to the above-mentioned viruses, the occurrence of a putative endornavirus was also detected by sRNA sequencing. It is common for mycoviruses to co-infect and interact in a variety of ways, forming interactions from synergistic to neutral and antagonistic  and (−)ssRNA (bunya) viruses (Pl. viticola-associated lessions in which 283 novel viruses resembling genomes of (+)ssRNA, (−) ssRNA, and dsRNA from different virus families and ORFan RdRp viral segments were identified using a metagenomics approach. Interestingly, members of the families Narnaviridae [(+)ssRNA], Totiviridae (dsRNA), and the order Bunyavirales [(−)ssRNA] have been described in the majority of oomycete species screened for viruses so far, suggesting a strong coevolution history of these virus clades with oomycetes. Multiple viral infections are also reported from various fungal plant pathogens  and dsRNA 2 , each with two ORFs. The only known member of the family Megabirnaviridae and its single genus Megabirnavirus is Rosellinia necatrix megabirnavirus 1  of its larger segment (dsRNA1). However, as the 3′- and 5′- termini were not confirmed, we cannot rule out the possibility of the larger segment being longer. The putative smaller segment (dsRNA 2) of PcaRV1 was not detected in the total RNA seq data. Similarly, the second segment has not been reported from some other megabirnaviruses. For example, RsRV-HN008, characterized from Rhizoctonia solani J.G. Kühn, has a genome of 7,596 nts organized on one segment containing two non-overlapping coding regions  2021 report,Giardiavirus, Leishmaniavirus, and Trichomonasvirus, all associated with infections of protozoa, and Totivirus and Victorivirus infecting fungi. However, many viruses with similarities to totiviruses are unclassified and the host range is much wider than previously assumed  and smaller virus particles of ca. 40 nm diameter  than other viruses putatively grouped in the proposed “Fusagraviridae” family, but the full length of its genome is not confirmed. Genome organization and phylogenetic placement of PcaRV4, as well as the occurrence of programmed −1 ribosomal frameshifting and presence of putative shifty heptamer motif located immediately upstream of the stop codon of ORF1, are in accordance with other “Fusagraviridae” members . PcaRV4 Bunyavirales is an order of (−)ssRNA viruses accommodating 12 families infecting a broad range of hosts including plants, vertebrates, and invertebrates, and includes the genus Coguvirus unassigned to any viral family with a single species Citrus coguvirus associated with citrus disease (ca. 1–2 kb), medium , and the large (L) segment  de Bary , the family thropods , algae ssRNA viruses with genome sizes ranging from 9.7 to 17.6 kb. There are two genera within this family, to which endornaviruses are assigned based on the size of their genome, presence of distinct domains, and their host association  and Phytophthora endornavirus 3 (PEV3) were found to inhibit hyphal growth and stimulate production of zoosporangia. In addition, PEV2 and PEV3 reportedly had a possible effect on both reduced as well as increased fungicide sensitivity of their host isolates . However, due to the high viral load, no virus-free isolates have been obtained so far. Such rich mixed viral infections as observed in the studied isolates of these P. castaneae might have an effect on the overall energy consumption and vitality of the oomycete, which could be related to our observation that compared to other isolates these P. castaneae isolates have reduced survival time in storage and need more frequent transfers when compared to other isolates of PRC collection. Similar behavior has been previously observed by P. infestans infected by PiRV-1. The latter authors also reported difficulties in curing PiRV-1 from host isolates, as well as the inability of transferring this virus via somatic fusion to a virus-free isolate.P. castaneae are targeted by the P. castaneae RNAi machinery, and the most abundant size of sRNA reads in all four isolates was 21 nts, consistent with other Phytophthora species  Göker, Riethm., Voglmayr, Weiß & Oberw., a natural, obligate biotrophic downy mildew pathogen of Arabidopsis thaliana (L.) Heynh.  Heynh. . In P. cf 25-nts . In P. pdominant . 21-nts dominant . Althougnsu lato , and in oviruses , HTS of ondilina  and the ondilina  when mapd before .Chamaecyparis-Quercus forest stand on Sau-Chua mountain in northern Vietnam, where P. castaneae was the only Phytophthora species recovered . The sequences of virus genomes are available in the NCBI GenBank database under the accession numbers listed in The RNA-Seq raw datasets presented in this study can be found in the online repository Sequence Read Archive (SRA), and they are available at this link P. castaneae isolates. MR prepared the original draft and wrote the manuscript. LB contributed to the writing. All authors have read, critically reviewed, and edited the manuscript and agreed to the published version of the manuscript.MR, LB, and TJ: original idea and funding. AE, EH, EV, MR, LB, and SS: formal analysis and investigation. AE, EV, and MR: bioinformatics sRNA. MR and SS: bioinformatics of total RNA. TJ provided This research was supported by the European Regional Development Fund, project “Phytophthora Research Centre,” Reg. No. CZ.02.1.01/0.0/0.0/15_003/0000453 and Specific University Research Fund of the Faculty of Forestry and Wood Technology, Mendel University in Brno LDF_VP_2019016.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.


Fusarium graminearum, F. verticillioides, Neurospora crassa, Sordaria macrospora, and Pyronema confluens (Pezizomycotina) [Schizosaccharomyces pombe (Taphrinomycotina) [Ganoderma lucidum, a mushroom-forming species of Polyporales, also during fruiting body formation [G. lucidum and the five Polyporales fungi were not only different from the typical A-to-I editing but also showed that transitions  were preferred over transversions, leading mainly to synonymous changes in contrast to missense changes found in filamentous ascomycetes [In fungi, adenosine-to-inosine (A-to-I) RNA-editing was shown to occur during sexual development and fruiting body formation in filamentous ascomycetes ycotina) , 2, but ormation . Afterwaormation , 5. It womycetes –5.Wu et al. predicted RNA-editing sites by aligning transcriptomic reads to the reference genome assembly. However, that approach assumes that the reference assembly is complete, which is rarely the case. The majority of available fungal genome assemblies contain gaps and errors. Even in haploid genomes, multiple nearly-identical DNA regions, for example, transposons or tandem or segmental duplications, can be erroneously collapsed into single fragments, or misassembled. Therefore, RNA reads mapped to such collapsed regions would correspond to distinct genomic regions with 100% coverage and identity and thus would not represent transcripts of the same genes. To avoid this issue, gDNA reads can be aligned and analyzed simultaneously with RNA reads. When a potential RNA-editing site has the same single nucleotide variants as in the gDNA reads alignment, the site should be excluded from candidate RNA-editing sites.Antrodia sinuosa, DNA and RNA were extracted from different strains [We used this approach to reassess RNA-editing previously reported by Wu et al. for five species of Polyporales , 5. When strains , which eSome of these remaining candidate sites did not show the single-nucleotide variations when the later version of HISAT alignment was used with intron length restriction 2000 bp). That is because the reads having supported the variations were decoupled and mapped to different regions or were not mapped due to the intron length limitation. In addition, when we searched reads pooled rather than aligned reads, we observed distinct groups of gDNA reads matching the polymorphic RNA reads with 100% identify of the entire length and thus corresponding to different parts of the genome  for which transcriptomic data were collected across several developmental stages as reported previously [C. cinerea during sexual development.Since the RNA of all five Polyporales species was extracted from mycelium, we extended our analysis to the homokaryon of eviously . The sameviously , 8. ThusGanoderma lucidum [G. lucidum using analysis of both gDNA and RNA reads, with 94 sites verified with Sanger sequencing [G. lucidum is a heterokaryotic species, making the analysis more challenging because of the ploidy combined with segmental duplications. In order to confirm RNA editing in this species, two haplotypes need to be separated and analyzed independently with proper DNA and RNA reads mapped to each of them to identify haplotype-specific polymorphisms indicating possible mis-assemblies that need to be filtered out in analysis of RNA polymorphisms. This may be quite challenging, but the editing pattern reported in G. lucidum was similar to those we corrected in the haploid genomes of Polyporales in that transitions dominate transversions leading to mostly synonymous amino acid changes.Similar transitions-over-transversions nucleotide patterns of RNA-editing were reported in  lucidum . The repquencing . While tG. lucidum by separate analysis of its two haplotypes. We were also unable to detect RNA-editing patterns in any transcript profiles during development of C. cinerea, including sexual development. In short, after reevaluation of available genomes and transcriptomes we did not find evidence of RNA-editing in Basidiomycota.To conclude, we revisited the previously reported RNA-editing in five basidiomycetes, and extended analysis to one more species with transcriptionally profiled developmental stages. The previously reported sites of RNA-editing were artifacts caused by genome misassemblies or variations in the genomes of different isolates from which DNA and RNA were extracted. Furthermore, we suggest reevaluating the RNA-editing in the dikaryotic https://sourceforge.net/projects/bbmap/). Subsequently, the reads were quality controlled using TrimGalore  by filtering out the reads with bad base quality (<20) and short length (<40). We aligned filtered genomic and transcriptome reads against the corresponding genome assemblies using Bowtie v2.3.5.1 (http://bowtie-bio.sourceforge.net) and HISAT v2.1.0 (http://www.ccb.jhu.edu/software/hisat/), respectively. The candidate RNA-editing sites were detected using JACUSA v1.3.0 [http://cab.spbu.ru/software/spades) (--careful --cov-cutoff auto) to reassemble D. quercina genomic reads. The original and newly assembled genomes were aligned using NUCmer 4.0.0beta2 (http://mummer.sourceforge.net) to find corresponding sites. The RNA reads from the same isolates that DNA reads were generated were downloaded from MycoCosm (https://mycocosm.jgi.doe.gov/) [We downloaded genomic and transcriptomic read data used in this study from the NCBI database Table . The genA v1.3.0   .


In our study, we included a total of 100 RNA modification regulators of eight types of cancer-related RNA modifications  to systematically profile their specific roles in LUAD. By gene mutation and expression analysis, we identified extensive dysregulations and complicated interactions of 100 RNA modification regulators in LUAD. Based on unsupervised clustering analysis, gene set variation analysis (GSVA), and single-sample gene-set enrichment analysis (ssGSEA), two RNA modification patterns in LUAD were defined to show distinct biological characteristics. The favorable prognostic pattern was enriched with infiltrated immune cells, including activated B cells, CD8 T cells, eosinophil cells, dendritic cells, and natural killer cells, while the unfavorable prognostic pattern was enriched with cancer hallmarks, including hypoxia, epithelial-mesenchymal transition (EMT), angiogenesis, PI3K-AKT-mTOR pathway, MYC pathway, and glycolysis pathway. We also constructed an RNA modification score (RMScore) based on five critical genes  to evaluate the RNA modification status of individual LUAD patients. RMScore was revealed to be related to the infiltrated immune cells and cancer hallmarks and was an independent prognostic factor in the TCGA-LUAD cohort and two external GEO-LUAD cohorts. Our study was the first to comprehensively investigate the dysregulations, crosstalk, and potential prognostic value of eight types of RNA modifications in LUAD. Our results highlighted the significance of eight types of RNA modifications in tumor microenvironments and cancer hallmarks and provided novel prognostic biomarkers and potential therapeutic targets in the management of LUAD patients in the future.RNA modification has become an exciting underexplored field in recent years. In lung adenocarcinoma (LUAD), m Lung cancer, one of the most prevalent cancers, is the leading killer and represents one of the most challenging health problems worldwide , 2. Lung6A (N6-methyladenosine) is the best characterized one. For example, the m6A modification of circIGF2BP3 mediated by METTL3 could increase the level of PD-L1 in NSCLC and facilitate the immune escape of NSCLC by blocking the CD8+ T cell response [6A-modified long noncoding RNA (lncRNA) LCAT3 promoted LUAD tumor growth and metastasis by activating the c-MYC pathway [6A eraser ALKBH5 functioned as an antitumor effector in LUAD by reducing the m6A modification and downregulating the expression of YAP [6A modification, m5C (5-methylcytosin), m1A (N1-methyladenosine), Nm (2′-O-methylation), and other types of RNA modifications have also been documented to have critical roles in lung cancer or other cancers [6A regulators to predict the survival of LUAD patients [6A-related lncRNA and built a risk model that was an independent prognostic factor and could predict the response of immunotherapy in LUAD [5C modification patterns in LUAD patients and calculated the m5C score, which was related to the tumor microenvironments and LUAD patient outcomes [RNA modification is an important post-transcriptional program, which has become an exciting underexplored field in recent years because of the development of novel technologies. There are over 170 kinds of RNA modifications found in eukaryotes [ pathway . M6A eran of YAP . Except  cancers –13. Howepatients . Xu et a in LUAD . Chen aloutcomes . HoweverThus, in our research, we systematically profiled a total of 100 RNA regulators of eight types of RNA modifications in LUAD for the first time. We revealed the extensive dysregulations and comprehensive interactions among eight types of RNA modifications and found two LUAD-related RNA modification patterns with different cancer hallmarks, infiltrated immune cells, and different prognoses. We also constructed an RNA modification score (RMScore) based on five RNA modification-related genes, which could predict the survival of LUAD patients, and it was validated to be an independent prognostic factor in different LUAD cohorts. Our study underlined the importance of eight types of RNA modifications in LUAD for the first time and provided potential therapeutic targets for the management of LUAD patients in the future.2 transformed to represent the mRNA expression levels of genes. Raw count data was used for differential expression gene analysis. Patients lacking age, gender, TNM stage, and survival information were excluded from our study. Genes with FPKM value below 1 in more than half of patients were also excluded from our study.Level-three RNA-seq data, raw count data, gene mutation data, copy number variation (CNV) data, and clinical data of the LUAD cohort were downloaded from the TCGA database. Fragments per kilobase of transcript per million fragments mapped (FPKM) values were log2 transformation followed by quantile normalization was used to process microarray data. A total of three GEO-LUAD datasets  were included in this study, with GSE40419 containing 87 LUAD tumors and 77 adjacent normal tissues, GSE41271 containing 178 tumor samples, and GSE50081 containing 127 tumor samples. Detailed information about TCGA-LUAD and three GEO-LUAD datasets were summarized in The microarray datasets of LUAD patients were obtained from the gene expression omnibus (GEO). Log6A, m1A, m5C, Nm, m7G (7-methylguanosine), Ψ (uridine-to-pseudouridine), A-to-I (adenosine-to-inosine transition), and mcm5s2U (5-methoxycarbonylmethyl-2-thiouridine)) were included in our study. The 100 RNA regulators were listed in Because of the huge number of RNA regulators and their physiological effects in eukaryotes, we limited our study to cancer-related RNA regulators that have been widely documented to show critical roles in LUAD or other cancers –13. Finat-test and unpaired t-test were used in the TCGA-LUAD cohort  and GSE40419 dataset , respectively, to find dysregulated RNA regulators. Detailed information about the 56 tumor samples and matched normal tissues of the TCGA-LUAD cohort was summarized in p value < 0.05 and log2 (fold change) ≥ 1 or log2 (fold change) ≤ −1 were identified as differentially expressed genes.The mutation pattern of 100 RNA regulators in 8 types of RNA modifications was analyzed by the Maftools R package. Paired p value < 0.05 in univariate cox regression analysis together with clinical characteristics  were included in multivariate cox regression analysis to find independent prognostic regulators in LUAD patients. Forest plot and Kaplan–Meier curves were used to show the prognostic value of RNA regulators. The crosstalk and correlation within one kind of RNA regulator and among different kinds of RNA regulators were analyzed using the Spearman correlation algorithm. Correlations with adjusted p value < 0.05 and |correlation coefficient > 0.5 were shown using Cytoscape software 3.8.0.Univariate cox regression analysis was performed to find survival-associated RNA regulators in 8 types of RNA modifications. RNA regulators with adjusted The Consensus-Clusterplus R package was utilized to conduct an unsupervised clustering analysis in LUAD patients based on the expression of 100 RNA regulators. To enhance the stability of clusters, they were repeated a total of 1000 times.To clarify the biological significance of different clusters determined by 100 RNA regulators, the gene set variation analysis (GSVA) was performed using the GSVA R package. The hallmark gene set “h.all.v7.4.symbols.gmt” was downloaded from MSigDB datasets and was used for GSVA analysis. The single-sample gene-set enrichment analysis (ssGSEA) method was used to calculate the quantity of infiltrated immune cells in different clusters of LUAD patients. The immune scores and stromal scores of LUAD patients in different clusters were compared by the estimate R package.p value < 0.05 and log2(fold change) ≥ 1 or log2(fold change) ≤ −1 were identified as DEGs.Differentially expressed genes (DEGs) between different RNA modification clusters were identified using the DESeq2 R package. Raw count data was used for DEG analysis. Genes with adjusted The univariate cox regression analysis was performed to find survival-associated DEGs.Rbsurv R package was utilized to perform robust likelihood-based survival analysis and further select survival-associated DEGs, which made the selected target genes more reliable.To evaluate the RNA modification status for an individual patient, we construct the RMScore following the steps below.p value < 0.05 in multivariate cox regression analysis were used to construct the RMScore using the following formula: RMScore = ∑coefi ∗ genei.Multivariate cox regression analysis was conducted to construct the final prognostic model. Genes with a ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001; ns, not significant.To illuminate the clinical significance of RMScore constructed in our study, we divide LUAD patients into two groups based on the median value of RMScore. GSVA and ssGSEA methods were used to compare tumor-specific hallmark pathways and infiltrated immune cells in the RMScore-high group and RMScore-low group. Spearman correlation analysis was performed to reveal the correlations between the individual RNA modification regulator and the RMScore. Kaplan–Meier curves, together with ROC curves, were plotted to show the prognostic value in the training cohort (TCGA dataset) and the validation cohorts (GEO datasets). All analyses were performed on RStudio 1.4.1106. The protocol of this study was approved by the ethics committee of our institute and the study was conducted in accordance with the Declaration of Helsinki. Considering that only publicly available data were used in this study, the requirement for informed consent from patients was waived by the ethics committee.6A, 16 regulators in m5C, 15 regulators in Nm, 11 regulators in Ψ, 9 regulators in m1A, 6 regulators in m7G, 5 regulators in A-to-I, and 5 regulators in mcm5s2U   . M6A wripatients . Geneticectively , which up value < 0.05 and log2(fold change) ≥ 1 or log2 (fold change) ≤ −1 were considered differentially expressed genes. As a result, IGF2BP1 and IGF2BP3 of m6A, DNMT3B, and TET1 of m5C, METTL1 of m7G, SNORD48 and MRM1 of Nm, and PUS7 and PUS7L of Ψ were found to be significantly upregulated in TCGA-LUAD tumors, while ADARB1 and ADARB2 of A-to-I were found to be significantly downregulated . We can see that the RNA regulators of m6A and m5C showed positive correlations with each other. The RNA regulators of m6A also showed significant correlations with the RNA regulators of Ψ, m1A, m7G, and Nm . As shown in nificant . GSVA anβsignaling pathway, KRAS signaling pathway, hypoxia, EMT, and androgen response pathway, were enriched in BRCA patients in Cluster B  in the TCGA dataset. A total of 1163 BRCA patients were divided into two clusters based on the 100 RNA regulators and patients in Cluster A, which showed a better prognosis than Cluster B . GSVA anluster B , while mluster B . Howeverluster B , the proluster B . All of To further evaluate the RNA modification pattern and predict the prognosis of an individual patient with LUAD, we constructed the RNA modification score (RMScore) using differential expression analysis followed by a series of survival analyses in the TCGA-LUAD cohort. The differential expression analysis between RNA modification Cluster A and Cluster B identified a total of 1348 differentially expressed genes (DEGs), and 229 DEGs were proved to be associated with the OS of LUAD patients in a univariate Cox regression analysis . To inve1 ∗ CYP17A1 + coef2  ∗  NTSR1  +  coef3  ∗  PITX3  +  coef4  ∗  KRT6A  +  coef5 ∗ ANLN)  . Among t ∗ ANLN) –S8E. Kap ∗ ANLN) –S8J.Then, we divided LUAD patients into high and low RMScore groups based on the median of RMScore. The clinical characteristics and expressions of five RMScore genes were shown in p value < 0.05 and r ≥ 0.3 or r ≤ −0.3 were shown in 6A, m5C, m1A, Nm, m7G, and Ψ were revealed to be closely related to the RMScore, among which all regulators in m6A, Nm, and m7G were positively associated with the RMScore, while TRDMT1 and NSUN7 of m5C, TRMB10 B and RRP8 of m1A, and PUS10 of Ψ were negatively associated with the RMScore. The RMScore gene ANLN was also positively associated with almost all RNA regulators, except for TRDMT1 and NSUN7 of m5C, TRMB10 B and RRP8 of m1A, and PUS10 of Ψ, while the RMScore gene CYP17A1 was negatively associated with most RNA regulators.To better understand the crosstalk between RNA regulators and RMScore, we conducted the Spearman correlation analysis between 100 RNA regulators and the RMScore together with five RMScore genes. Correlations with adjusted To further verify the clinical significance of the RMScore, we used the two GEO cohorts of LUAD patients as validation datasets. The RMScore was calculated based on the expression of five RMScore genes in two validation cohorts. Kaplan–Meier curves demonstrated that patients in the RMScore-low group also had a longer OS than patients in the RMScore-high group Figures . Receive6A-related regulators, with the roles of other kinds of RNA modifications, and their crosstalk with each other remains unknown.RNA modification is a novel frontier of RNA epitranscriptomics, which is characterized by abundant chemical modifications in different kinds of RNAs. Recently, increasing evidence has suggested that RNA modifications play a critical role in cancer and might serve as novel drug targets in the management of cancer patients. However, studies about RNA modifications in LUAD are mainly limited to a few RNA modification regulators, especially m6A, m5C, Nm, Ψ, m1A, m7G, A-to-I, and mcm5s2U) were included because of their potential cancer-related functions, which were reported by other pieces of research before. Among all cancer-related RNA modifications, m6A is the most important one. m6A is methylation at the N6 position of the adenine of RNA and is widely documented to regulate the expressions of many oncogenes or tumor suppressor genes to affect the pathological characteristics of various tumor cells or tumor microenvironments [5C is methylation at position 5 of cytidine residues, which are abundant on DNA, mRNA, and ncRNAs [5C were revealed to promote the progression of lung cancer, prostate cancer, and oesophageal squamous cell carcinoma by targeting rRNA or ncRNAs [1A is the methylation of adenosine at position 1, m7G is the methylation of guanosine at position 7 [5s2U is an important post-transcriptional modification of U34 on specific tRNAs [1A, m7G, A-to-I, and mcm5s2U have also been reported to be dysregulated to function as oncogenes or tumor suppressors in various tumors, including lung cancer, prostate cancer, hepatocellular cancer, breast cancer, melanoma, and glioblastoma [In our study, a total of 100 RNA modification regulators of eight types of RNA modifications , followed by ZC3H13 of mα pathway, PI3K-AKT-mTOR pathway, MYC pathway, and glycolysis pathway were enriched in Cluster B and RMScore-high group, while more infiltrated immune cells were enriched in Cluster A and RMScore-low group, which might account for the different prognosis. Previous studies have also discovered different RNA modification patterns in cancer, including LUAD. For example, Huang et al. identified three m6A modification patterns with different tumor microenvironments and biological behaviors and constructed the m6A scoring system to predict the prognosis of patients with hepatocellular carcinoma [6A-related modification pattern and the scoring system have also been constructed in gastric cancer [6A modification subgroups with different clinical outcomes were also identified based on 13 m6A regulators [6A scoring system was also constructed to predict the survival and the response of immunotherapy [6A modification, validated the existence of different m6A modification patterns, and highlighted the significance of m6A in cancer while ignoring the functions of other kinds of RNA modifications.We also identified two RNA modification patterns in LUAD and constructed the RMScore to evaluate the modification status and prognosis of individual patients. Patients in Cluster A or RMScore-low group showed a significantly longer overall survival than those in Cluster B or RMScore-high group. Most cancer hallmarks, such as hypoxia, EMT, angiogenesis, TNFarcinoma . Similarc cancer , pancreac cancer , prostatc cancer , kidney c cancer , and glic cancer . In LUADgulators . The m6Aotherapy , 34. The6A, and focused on the interactions with each other. For example, Chen et al. explored four major RNA adenosine modifications  and determined two distinct RNA modification patterns in colorectal cancer, which were characterized by infiltrated inhibitory immune cells or survival favorable signaling pathways and showed significantly different survival [6A, m1A, APA, and A-I, and constructed a risk prediction model to predict the survival of patients with ovarian carcinoma [6A and m5C, identified m6A- and m5C-related lncRNAs, and revealed their importance in accessing the prognosis and immunotherapy response of colorectal cancer patients [6A, m1A, or m5C and provided clues for the investigations of other kinds of RNA modifications in the future. Moreover, based on the eight types of cancer-related RNA modifications, we discovered two distinct modification patterns in LUAD. The differences between the two subgroups revealed that cancer hallmarks and tumor microenvironments were extensively affected by these regulators, which highlighted the significance of other types of RNA modifications in LUAD. The RMScore constructed based on RNA modification patterns was determined to be the independent prognostic factor to predict the overall survival of LUAD patients, which underlined the prognostic value of RNA modifications in LUAD.Recently, some researchers have paid attention to other kinds of RNA modifications, except for msurvival . Ye et aarcinoma . Song etpatients . In our Taken together, our study was the first to explore the complicated roles and comprehensive interactions of eight types of cancer-related RNA modifications in LUAD. We revealed two distinct RNA modification patterns and their underlying biological characteristics and constructed a novel scoring system to predict the survival of LUAD patients. This study highlighted the significance of eight types of RNA modifications in LUAD and provided a novel horizon for the investigations of LUAD in the future.