Navigating a dynamic world requires rapidly updating choices by integrating past experiences with new information. In hippocampus and prefrontal cortex, neural activity representing future goals is theorized to support planning. However, it remains unknown how prospective goal representations incorporate new, pivotal information. Accordingly, we designed a novel task that precisely introduces new information using virtual reality, and we recorded neural activity as mice flexibly adapted their planned destinations. We found that new information triggered increased hippocampal prospective representations of both possible goals; while in prefrontal cortex, new information caused prospective representations of choices to rapidly shift to the new choice. When mice did not flexibly adapt, prefrontal choice codes failed to switch, despite relatively intact hippocampal goal representations. Prospective code updating depended on the commitment to the initial choice and degree of adaptation needed. Thus, we show how prospective codes update with new information to flexibly adapt ongoing navigational plans. The ability to rapidly update our choices in response to new information is essential to navigating a dynamic world. During navigation, animals often hold an internal representation of their world via a cognitive map . When naHippocampus, which is essential for spatial navigation, is classically known to encode animals’ current position in an environment, but research has also found that non-local representations occur during active navigation. Prior studies have shown that sweeps of coordinated place cell activity tile the space behind and in front of animals during behavioral hallmarks of deliberation . During dynamic environment that requires flexible decision-making and reconsideration of potential choices? It is unclear how these hippocampal non-local codes and prefrontal cortex choice representations respond to new information to accurately update decisions when plans need to be changed.During goal-directed navigation, animals must not only encode spatial trajectories through their environment, but they must also select and maintain choice information as their trajectories unfold. Prefrontal cortex is thought to represent upcoming choice information and is required for flexible decision-making. In the context of spatial navigation, several studies have found evidence that prefrontal cortex acts in coordination with hippocampus to facilitate decision-making and planning for upcoming choices . PrefronTo address these open questions, we designed a novel memory-based decision-making task in virtual reality. In this task, we precisely controlled the timing of new information in the environment on a single-trial basis; animals were required to consider two possible paths and flexibly change course mid-trial when new information indicated a switch in the location of the rewarding outcome. We then recorded many units from hippocampus and prefrontal cortex during this behavior in order to evaluate how prospective codes respond to new information that demands flexible navigation. We assessed prospective codes for goal locations and upcoming choices using a Bayesian decoding approach, decoding the animal’s position and evolving choice from neural activity. In hippocampus we found that new, pivotal information causes non-local representations of both possible goal locations to rapidly increase. In prefrontal cortex we found that new, pivotal information causes choice codes to rapidly switch to represent the new choice. We then identified how prospective codes differed when animals failed to adapt; hippocampal position codes remained relatively intact but prefrontal choice codes did not switch to represent the new choice more strongly than the old choice. Finally, we assessed the neural responses as a function of how strongly animals were committed to their choice before new information was presented. We found that both hippocampal and prefrontal cortex prospective codes depended on the animals’ level of commitment to the initial choice and the degree of behavioral adaptation needed. These results reveal how prospective codes support flexible planning and accurate decision-making in the face of new information, and how prospective codes rapidly change to represent both possible options or upcoming choices depending on the degree of behavioral commitment.To investigate how animals update plans when presented with new, pivotal information, we designed a virtual reality spatial navigation task . Mice n. On a suGiven our central question about how prospective codes respond to flexible navigation demands, we were specifically interested in the behavior around the update cue on the switch trials, when animals had to update their behavioral trajectories in response to new information. The animals’ heading direction . These results show that when animals failed to switch goals, in hippocampus new goal arm codes did not increase as much in response to the update cue, and initial goal arm codes were elevated before the update cue compared to correct trials.After finding that new, pivotal information increased non-local coding for both goal locations in hippocampus and caused a rapid switch from old to new choice codes in the prefrontal cortex, we then asked if these coding adaptations fail when animals do not accurately update decisions. We hypothesized that if increases in non-local goal codes in hippocampus contribute to flexible adaptation of navigational plans, then failure to switch goals in response to new information would co-occur with a failure to increase both non-local goal representations after the update cue is presented. To test this, we compared our population decoding of position in hippocampus and choice in prefrontal cortex on correct versus incorrect switch trials. To account for differences in speed  as an explanatory variable for the amount of initial and new choice representations, we applied a general linear model (GLM). The residual goal codes from the model were then analyzed to assess changes in goal coding not explained by differences in speed and rotation . We founWe hypothesized that when animals failed to correctly respond to new information, the new and initial choice representations in prefrontal cortex would fail to flip from the initial choice being more strongly represented to the new choice being more represented. Indeed, we found new choice decoding did not overtake initial choice decoding on incorrect trials . This laTo test whether these observed codes were related to the animals’ behavioral performance, we then used the population measures of goal coding to predict behavioral outcomes on a trial-by-trial basis. We found that in both hippocampus and prefrontal cortex, the population representation of the initial and new goals after the update cue was presented predicted whether the animals would choose the correct or incorrect side  indicating strong commitment to the initial goal . In hippboth potential goal locations. Non-local goal representations in hippocampus increased more when animals needed to produce a larger trajectory change. In prefrontal cortex we found new, pivotal information causes choice codes to rapidly switch from representing old to new choices, prior to behavioral responses and prior to choice switching in hippocampus. On incorrect trials, hippocampal non-local goal codes remain relatively intact, but new prefrontal choice coding fails to increase. Together, these results show that non-local codes are generated in response to new, crucial information that requires flexible decision-making. New information triggers a rapid adaptation of ongoing plans, with hippocampus providing a swift increase in codes of possible goals and prefrontal cortex suppressing initial choices to select a new action plan. When more behavioral adaptation is needed, these prospective codes for new goals are stronger.Using a navigation task with precise timing of environmental changes, simultaneous hippocampal-prefrontal cortex recordings, and decoding of position and choice, we determined how prospective codes change in response to new information to guide flexible decision-making. In our paradigm, animals rapidly update their behavior in dynamic environments in response to new information. In hippocampus, our results show that when new, crucial information is presented that requires animals to update their goal destinations, hippocampus increases non-local coding of both goal locations increase similarly in response to new information on correct trials. We did not observe that the planned goal was more strongly decoded than the alternative goal on correct trials until the animals reached the goal arms, tying in with previous work that action plans in rodents are not necessarily predicted by hippocampal prospective codes , and one was excluded after electrophysiological recordings were started due to lack of performance during recording sessions, resulting in seven mice total in the final dataset.All animal work was approved by the Institutional Animal Care and Use Committee at the Georgia Institute of Technology. Eight-week-old male Animals were implanted with headplates at approximately 8 weeks of age. Mice were anesthetized with isoflurane before headplate implant surgery. A custom stainless steel headplate was fixed to the skull using dental cement , and the target craniotomy site for recordings was marked on the skull . Craniotomies were later performed before electrophysiology recording sessions in mice that reached the final phase of the task. These craniotomies (200-500um diameter) were made using a dental drill to thin the skull and then opening a small hole in the skull with a 27-gauge needle. Craniotomies were sealed with a sterile silicone elastomer (Kwik-Sil WPI) and only opened for recording experiments.The virtual reality environment was designed using ViRMEn  softwareThe behavioral task was a virtual y-maze in which animals had to choose to go to the left or right to receive a reward. The full length of the maze was approximately 3 m for the center arm. Trials were structured as follows: first, the screen displayed the central arm of the environment, and the mouse’s position was frozen in place for 3 seconds to allow the mouse time to adjust its running patterns before it began to move down the track. After this 3s period, the mouse could begin moving forward, but the view angle and x-position were restricted to 40 degrees and 0 virtual displacement for the first 0.05 fraction of the track. Then, once the mouse passed the initial zone of the track, the mouse could rotate and move freely. The visual cues turned on and off at different locations in the track as the animals passed. On correct trials, a reward was delivered at the end of the track, the VR screen froze for 3 seconds, and then the task shifted into an intertrial interval period of 6 seconds. On incorrect trials, the task immediately shifted into a 12-second intertrial interval, which was longer as a form of punishment.The animals underwent several phases of behavioral shaping and training to reach the final version of the task. In the first phase, animals ran down a linear track that was used to acclimate the mouse to the head-fixed VR setup. The linear track increased in length until the animals completed a certain number of trials for each track length. The second phase of the task was a short y-maze choice task where visual cues indicating the correct and incorrect locations were visible for the full length of the track. After the animals performed two sessions above 75 percent correct, they advanced to the long y-maze choice task with the same visually guided trials as the previous phase but now with the central arm the same length as the final task. Animals were then advanced through phases with a delay at the end of the track. During the delay, the visual cues indicating the correct left or right side were no longer visible and the walls of the track were grey instead. This delay was moved gradually earlier in the track, making it longer in time, through three separate phases. After the animals had reached the learning criteria in the third delay phase, the update cue trials were introduced. In these trials, a second visual cue appeared after the first original visual cue, indicating either the same side as the initial cue or the opposite side. Once the animals showed signs of understanding these second cues, the delay location was moved earlier once more, so that the final update task trial structure had three different trial types. These trial types consisted of delay only trials , in which the initial cue was shown and there was a long delay until the end of the track, switch trials, in which there was an original cue, brief delay, and second cue on the opposite side of the track as the original cue  and finally stay trials, in which there was an original cue, brief delay, and second cue on the same side of the track as the original cue . To control for side biases, the animals’ preference for the left or ride side of the track, we applied a bias correction algorithm for selecting the next trial type. In short, the probability that the next trial would be on the left or right side was dependent on the history of left and right trial errors. If the animals preferred turning to the right arm at the choice point and had missed mainly left trials, then there would be a higher likelihood of the next trial being on the left, or non-preferred side . This alRecordings occurred during behavioral task performance when animals navigated through the virtual reality environment. Data were acquired using a SpikeGadgets acquisition system with a sampling rate of 30kHz and a ground pellet as reference. Animals were head-fixed on the treadmill for a maximum of one five-hour-long recording session per day  and the average of the sharp-wave ripple band envelope over time was calculated from each channel. The channel with the highest average sharp-wave ripple band power was used for all further LFP analyses, and this channel was predominately located in the middle of the depth-wise span of the NeuroNexus probe.The local field potential was obtained by downsampling raw traces to 2kHz and bandpass filtering between 1-300Hz. Outliers were eliminated by interpolating over outliers when the pre-filtered LFP signal was 15 standard deviations above the mean. All LFP analyses used the signal from a single channel that was putatively located in the Spike detection and sorting were performed using Kilosort 2.0 spike sorting algorithm  and thenTo quantify behavioral performance, we binned behavioral data into 40 trial windows for each trial type to calculate a rolling window of proportion correct throughout the behavioral sessions. We also obtained the spatial trajectories of the animals by calculating an average value for each position bin in the environment across individual trials, and then averaging the left and right trials separately to obtain a final average trajectory for each behavioral readout.For position representation analyses, the 2D position of the VR environment was converted into a 1D linearized position. Linearized position was calculated by generating a graph of the VR environment with 3 segments and 4 nodes: 1 node was at the start of the track, 1 node was at the choice point at the end of the central arm and between the two choice arms, and then the 2 final nodes were at the end of each choice arm. We used the track-linearization package to project each position in the environment using an HMM (hidden Markov map). Artificial gaps between the segments of the track were added for visualization purposes.Locomotion periods were defined as times when the animals’ movement was above a velocity threshold. To calculate this threshold, we plotted the distribution of optical mouse recorded velocities and observed a bimodal distribution. We selected a threshold that separated these two distributions and used that for subsequent decoding analyses. Multiple thresholds were tested and the results remained similar.th fold. The final prediction for each trial was the average of three repeats of this 6-fold cross validation. The training data was normalized and padded to the length of the longest trial, but all trials with a length greater than twice the average were excluded. The hyperparameters of the neural network were selected using a grid search on a subset of the recording sessions and were as follows: batch size = 32, epochs = 20, learning rate = 0.1. Finally, we took the prediction of the model and calculated the log likelihood with log base 2 to assess the overall performance. The prediction data was then used as a feature input to the Bayesian decoding model to assess how the neural activity represented evolving choice as the animals run through the environment.To calculate an estimate of choice as animals ran through the environment, we built an LSTM neural network using TensorFlow based on previous choice estimate values obtained in virtual reality two-choice maze tasks . The LSTTo identify a neural representation of different features of the task at a population level, we performed a Bayesian decoding analysis to estimate the probability of different positions or choices given the observed spiking activity at that period. For all decoding analyses, we analyzed hippocampal CA1 and medial prefrontal cortex separately. We first excluded any sessions in which there were less than 20 single units for the brain region of interest and there were less than 50 trials for the behavioral session. These thresholds were chosen by visually inspecting and quantifying the overall decoding accuracy of each session and determining a minimum criteria in which high-fidelity decoding could be obtained. These criteria were applied separately to each brain region . Brain region decoding outputs were also calculated separately. To build our encoding model, we used only periods within a behavioral trial  and only locomotion periods when the animal was moving. We used both correct and incorrect trials but only trials in which there was no update cue. Feature tuning curves were computed by calculating the number of spikes per feature bin  and normalizing for occupancy time. For decoding, we used 25ms when analyzing decoding by individual theta phase and 200ms windows otherwise. To perform the decoding calculations, we used the Pynapple  analysisTo calculate representational changes around the update cue, we integrated the posterior probability densities for the initial choice arm and new choice arm based on the boundaries defined by our track graph used for track linearization. We calculated decoding error as the difference between the predicted and actual position. For some analyses, we converted the decoding output into terms of probability density / chance , where cTo quantify the changes in prospective goal coding around the update cue, we calculated the average integrated decoding output value in the 0 to 1.5 second window after the update cue was presented to compare relative amounts of prospective coding between trial types. We also quantified the difference in initial and new goal representation on a trial-by-trial basis to compare relative amounts of initial and new goal representations between trial types. For analyses in which we quantified differences in goal coding preceding the update cue onset, we used the −1.5 to 0 second window as our baseline. When quantifying choice codes, we applied a 10% threshold to quantify strong choice representation of either the new or initial choice. We assessed other thresholds and found similar results.To assess how choice commitment affected neural representations around the update cue, we obtained the view angle at the time of the update cue onset for each trial. We then split this view angle data into quartiles and with these separately grouped trial types, used the same time windows and quantification analysis described above to calculate the average responses.For each session, we identified a channel as the putative pyramidal channel in CA1 using sharp-wave ripple power as described above. This channel was used as our LFP recording site. To isolate hippocampal theta oscillations, the LFP was bandpass filtered for theta (4-12 Hz) using an FIR (finite impulse response) equiripple filter. Peaks and troughs of the filtered LFP were detected and used to define the phase of the LFP. In order to use a common theta reference across recording sessions, we calculated a phase histogram of all putative pyramidal cell spiking activity in CA1 for a recording session using 30-degree bins. We then adjusted the theta phase measurement to be 0 degrees at the location of maximal CA1 firing.To calculate theta phase modulation of goal decoding, we calculated theta phase histograms by identifying the theta phase at the center time of each decoding window and then calculating the integrated posterior probability densities for each theta phase bin for each trial. To compare values across theta, we divided the theta oscillation into quarters and calculated the average decoding output value for each quarter of the theta oscillation.To account for the effects of speed and direction on the differences in neural codes between correct and incorrect trials, we applied a GLM with rotational and translational velocities as the explanatory variables or predictors and the goal decoding output as the response variable. A separate model was trained for the different goal representations, initial and new. To fit the model, we used the velocity and decoding output values in 200ms bins in the 2.5 seconds before and after the update cue onset across all trial types and animals. We then plotted the Pearson residuals as a measure of the decoding output not explained by speed and direction differences between trial types. Cox and Snell’s pseudo r-squared values were reported as an estimate of model fit.C and γ) using a random search method with leave one out cross-validation to prevent overfitting. After performing the random search over n=10 iterations to optimize classification accuracy, the best hyperparameters were selected. These hyperparameters were then used and a leave-one-out cross validation procedure was used to assess the overall accuracy of the classifier . This randomized hyperparameter search and final accuracy assessment were performed separately for each update trial type. As a control, the randomized search and cross validation were also performed with randomly shuffled target (choice) labels n=1000 times. The significance of the classifier was assessed by testing whether the classifier outperformed 95% of the distribution of accuracies from the shuffled classifier (permutation test).For prediction of animals’ final choice using neural activity, we performed a trial-by-trial classification analysis using support vector machines (SVMs). For each region and update trial type, we trained independent SVMs. For each trial, we used the quantification of goal representation difference between the initial and new goal as described above as a feature  to predict the animals’ final choice . Before training the classifier, we balanced the correct and incorrect trial classes using random resampling with replacement until both target classes had the same number of trials. We used a radial basis function (Gaussian) kernel for all SVMs, and we selected the hyperparameters (We used a linear mixed-effects models (LME) approach to assess the significance of differences while controlling for repeated measures from the same animals and sessions. In our model, our variables of interest such as decoding output or single unit firing rates were included as our fixed effects. We set our random effects as session-nested-in-animal random effects with random intercepts at both levels. Statistical significance was first estimated with an ANOVA with Kenward-Roger’s methods to determine whether the predictors had any significant effect. If the F-test was statistically significant, we then performed pairwise comparisons to assess significant differences using estimated marginal means and reported Tukey-adjusted p-values. For one-sample analyses, we used a Wilcoxon signed rank test. For correlation analyses, we calculated the Spearman rank-order correlation coefficient. All statistical tests were two-sided unless stated otherwise. Details on specific statistical parameters and the values of sample size n are described in the figure legends and statistical table .Analyses were performed using custom pipelines in Matlab, Python, and R with the following libraries: NumPy, SciPy, Matplotlib, Scikit-learn, Pandas, Tensorflow, Pynapple, nwbwidgets, pynwb, seaborn, track-linearization, pingouin, statannotations, statsmodels, lmer, lmerTest, emmeans, SHARP-Track, Kilosort, Phy.Supplement 1


C9orf72 gene. This mutation in C9orf72 leads to nuclear depletion and cytoplasmic aggregation of Tar DNA-RNA binding protein 43 (TDP-43). TDP-43 pathology is characteristic of the majority of ALS cases, irrespective of disease causation, and is present in ~50% of FTD cases. Defects in nucleocytoplasmic transport involving the nuclear pore complex, the Ran-GTPase cycle, and nuclear transport factors have been linked with the mislocalization of TDP-43. Here, we will explore and discuss the implications of these system abnormalities of nucleocytoplasmic transport in C9orf72-ALS/FTD, as well as in other forms of familial and sporadic ALS.Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two adult-onset neurodegenerative diseases that are part of a common disease spectrum due to clinical, genetic, and pathological overlap. A prominent genetic factor contributing to both diseases is a hexanucleotide repeat expansion in a non-coding region of the  Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by the degeneration of motor neurons in the motor cortex and spinal cord. It is a fatal condition that ultimately leads to paralysis and death through the denervation of skeletal muscles. Approximately 35–40% of ALS patients exhibit cognitive impairment caused by degeneration of the frontal temporal lobes    RNA toxicity through abnormal sequestration of RNA binding proteins to nuclear RNA foci generated from bidirectionally transcribed repeat-containing RNA; and (2) toxic effects of dipeptide repeat proteins (DPRs) generated by repeat-associated non-AUG (RAN) translation in the sense  and antisense  directions. Loss-of-function is caused by transcriptional repression of the C9orf72 mRNA and reduced C9orf72 protein. There is evidence for all three mechanisms, and a combination of these mechanisms likely contributes to the overall disease pathogenesis in individuals with the C9orf72 mutation . TDP-43 is a primarily nuclear DNA/RNA binding protein that has pleiotropic roles encompassing diverse cellular processes involved in gene expression and RNA regulation  and is studded with ribosomes. The inner nuclear membrane is lined with the nuclear lamina, a fibrous meshwork of lamin filaments that provides structural support to the nucleus. Nuclear-cytoskeletal coupling is facilitated by the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex, which spans the nuclear envelope and comprises SUN (Sad1p/Unc84) domain proteins in the inner membrane that interact with the nuclear lamina and KASH (Klarsicht/ANC-1/Syne-1 homology) domain proteins, which are localized to the outer membrane and interact with actin filaments, intermediate filaments, and microtubules. Thus, the LINC complex is crucial for maintaining nuclear stability, nuclear positioning, and mechanotransduction , which are macromolecular structures embedded within the nuclear envelope, and serve as channels that facilitate the transport of molecules between the nucleus and cytoplasm. NPCs are complex macromolecular structures comprised of varying stoichiometries of approximately 30 nucleoporins (Nups). Nups are categorized into different subgroups based on their structural locations within the NPCs, ranging from the cytoplasmic face to the nuclear face. These include -III pathway, which involves vacuolar protein sorting 4 (VPS4) and charged multivesicular body protein 7 (CHMP7)  they recognize. In the classical NLS pathway, Importin α/β complexes recognize classical NLSs, which are short stretches of positively charged amino acids, typically rich in lysine and arginine residues. There are also non-classical NLSs that are recognized by specific import receptors. For instance, the PY-NLS found in FUS is mediated by TNPO1 (Transportin-1).The nuclear export of proteins that contain nuclear export signals (NES) is typically mediated by exportin-1 , and other members of the exportin family. The nuclear export of mRNA also requires NTF-mediated transport, and this involves proteins such as ALYREF, GLE1, and SRSF1  RNA inherently induces both pathomechanisms. To circumvent this, some studies described herein use non-GC-rich alternate codons to drive the expression of individual DPRs to specifically attribute phenotypes to DPRs. Although studies using relatively short (G4C2) repeat expansions  find little-to-no DPR expression, it does not rule out low levels of DPRs that are below the detection limit. Thus, although we have largely categorized studies using (G4C2) expression as being related to RNA toxicity, there may be some DPR contributions to the phenotypes described. Moreover, when considering human iPSC-derived neurons and post-mortem tissue, careful analysis and interpretation are necessary, as without strong experimental support, it is unclear which pathomechanism(s) are responsible for any observed phenotypes.C9orf72 are bidirectionally transcribed in sense (G4C2) and antisense (C4G2) directions to form repeat expansion-containing nuclear RNA foci that abnormally sequester RNA binding proteins. RNA toxicity arising from the C9orf72 repeat expansions has been shown to disrupt multiple components of the NPC and the nuclear envelope. In vitro assays have shown that G4C2 RNA directly interacts with lamin proteins of the nuclear envelope  in a neuronal-specific manner without affecting transcript levels 30 -RNA. Using structured illumination microscopy (SIM), it was shown that nuclei from C9orf72-iPSC neurons had reduced levels of various Nups  -RNA toxicity has been shown to age-dependently dysregulate Nup nuclear localization ; and poly PA, poly PG, and poly PR (antisense transcript). It is generally considered that expression is greatest from the sense transcript in the first reading frame and decreases thereafter, with a lower expression from the antisense transcript, leading to the relative expression of GA > GP > GR > PA/PR , GR and PR, are the most toxic species , and drives cytoplasmic TDP-43 accumulation, which was accompanied by RNA metabolism alterations evident of a loss of nuclear TDP-43  have been shown to have multiple deleterious effects on the NPC and nuclear envelope integrity , a rare ALS-causing gene  30 identified a dominant gain-of-function allele of RanGAP (similar to RanGAP overexpression) as a strong suppressor of (G4C2)30 toxicity  repeat RNA S. cerevisae and Drosophila have identified members of the Ran-GTPase cycle as modifiers of DPR toxicity. Loss of RCC1  enhanced toxicity of PR50 in S.cerevisae , and expression of one of the variants, LSM12V135I, in SH-SY5Y cells and iPSC-derived neurons led to impaired NCT and disrupted Ran-GTPase gradient  has been shown to localize to the nuclear envelope of postmortem spinal motor neurons, with this labeling lost in sALS and C9orf72 ALS cases. This loss of nuclear membrane labeling correlated with the presence of cytoplasmic TDP-43 pathology, as well as a reduction in Ran-GTPase and Importin β-1  demonstrated that C9orf72 interacts with Ran-GTPase  could impact Ran-GTPase function  dysfunction, there is less evidence for perturbations of the Ran-GTPase cycle in non-Grn−/−) mice, in which nuclear depletion of TDP-43, in the absence of cytoplasmic aggregation, precedes neurodegeneration of retinal neurons , which causes rare juvenile ALS, exhibit reduced nuclear import in primary neurons from both transgenic lines and abnormal Ran-GTPase and RanGAP labeling of the nuclear envelope in spinal motor neurons of SETXL389S mice , and knockdown of Sig-1R disrupts the Ran-GTPase gradient  RNA can interact with KPNA2 30  RNA foci in spinal motor neurons of C9orf72-ALS cases  RNA toxicity in These interactions suggest that the effects of RNA toxicity could be reliant upon nuclear shuttling systems for both protein and RNA, however, in the context of SRSF1 and ALYREF involvement, it is also likely that the RNA export pathway is associated with DPR toxicity, as DPR production initially requires nuclear export of DPR-encoding transcripts (described in Section 4.2).Drosophila screen identified exportin as an enhancer of (G4C2)58 toxicity  toxicity, and rescued downstream effects on the autophagolysosomal system  demonstrated that knockdown of Importin β-1, IPO11, KPNA3, TNPO1, TNPO3, or XPO1 enhanced toxicity, whereas knockdown of IPO4, IPO5, IPO7, KPNA2, or XPO5 suppressed toxicity  for the influence of R-DPRs on nuclear import receptors. Proteomic studies have identified direct interactions between R-DPRs and nuclear import receptors, such as Importin β-1, TNPO1, and IPO7 , is also implicated in R-DPR toxicity 36-expressing Drosophila  was overexpressed, and knockdown of Importin β-1, IPO7, TNPO3, KPNA1, and KPNA3 resulted in greater cytoplasmic TDP-43 mislocalization, whereas overexpression of Importin β-1, KPNA3, or Importin β-1-KPNA3 gave nuclear localization  contribute to neurodegeneration, the overlap among their effects on NPC, Ran-GTPase, and NTFs suggests a convergence on disruption of NCT as a key underlying disease mechanism.There are many routes to NPC, Ran-GTPase, and NTF dysfunction in The factors underlying nuclear depletion and cytoplasmic accumulation of TDP-43 in disease are complex, and although some models disrupting NCT cause TDP-43 mislocalization, many do not. It is also not well-understood if other high-importance cargoes are also mislocalized. These potential cargoes are important to identify as they may help elucidate the underlying mechanisms causing TDP-43 mislocalization. Moreover, as aging is also a large determinant of the efficiency of NCT  exhibit abnormalities of multiple Nups, including Nup62, as well as RanGAP (Anderson et al., Drosophila can be rescued by treatment with nuclear export inhibitor KPT-350 (Anderson et al., Several important considerations of ALS-associated pathology could influence NPC, Ran-GTPase, and NTFs. First, it has been demonstrated that cytoplasmic protein aggregation disrupts NCT, which could create a feed-forward mechanism enhancing NCT impairments (Woerner et al., PM and JR performed literature review and wrote manuscript. All authors contributed to the article and approved the submitted version.


Tomato chlorosis virus (ToCV) is a typical member of the genus Crinivirus, which severely threatens Solanaceae crops worldwide. The CPm protein encoded by ToCV has been reported to be associated with virus transmission by vectors and is involved in RNA silencing suppression, while the mechanisms remain ambiguous.CPm was ectopically expressed by a Potato virus X (PVX) vector and infiltrated into Nicotiana benthamiana wild-type and GFP-transgenic16c plants.Here, ToCV CPm using a PVX vector resulted in severe mosaic symptoms followed by a hypersensitive-like response in N. benthamiana. Furthermore, agroinfiltration assays in N. benthamiana wilt type or GFP-transgenic 16c indicated that ToCV CPm protein effectively suppressed local RNA silencing induced by single-stranded but not double-stranded RNA, which probably resulted from the activity of binding double-stranded but not single-stranded RNA by ToCV CPm protein.The phylogenetic analysis showed that the CPm proteins encoded by criniviruses were distinctly divergent in amino acid sequences and predicted conserved domains, and the ToCV CPm protein possesses a conserved domain homologous to the TIGR02569 family protein, which does not occur in other criniviruses. Ectopic expression of ToCV Taken together, the results of this study suggest that the ToCV CPm protein possesses the dual activities of pathogenicity and RNA silencing, which might inhibit host post-transcriptional gene silencing (PTGS)-mediated resistance and is pivotal in the primary process of ToCV infecting hosts. Crinivismission . Tomato t manner . ToCV wabiotype) . The genbiotype) , 2007. Rbiotype) . Most prCrinivirus, Lettuce infectious yellows virus (LIYV), constitutes an integral part of the tail protein complex, whereby CPm binds to the anterior foregut of whitefly epithelial cells for plant-to-plant transmission (Beet yellows closterovirus (BYV) is a prerequisite for virus cell-to-cell movement in the host (Potato virus X (PVX)-infected host and plausibly possessed silencing suppression activity  proteins . RNA siln plants . The RSSn plants . Therefon plants .It is well documented that the RSS proteins of plant viruses also exhibit properties such as the enhancement of viral accumulation and pathogenicity . This duThe CPm protein of ToCV was previously verified to be activated to accentuate PVX infection and to plausibly suppress host RNA silencing, reminisced of the dual activity of RSS proteins of plant viruses. However, the molecular mechanisms by which the CPm protein of ToCV activates this dual activity are largely unknown. In this study, a detailed characterization of the CPm protein encoded by the ToCV Shangdong isolate  in terms of its dual activities of pathogenicity and suppression of PTGS was deciphered. The results described in this paper provide novel insight to better understand the interaction between criniviruses and their host plants.2.2.1.Nicotiana benthamiana were grown in pots with peat and vermiculite  inside a culture room with 25 ± 2°C and 60% relative humidity with 16-h light/8-h dark illumination. The third or fourth leaves of 4- to 6-week-old N. benthamiana plants were used for infiltration with an Agrobacterium culture.Wild-type and transgenic GFP 16c of 2.2.The amino acid sequences of ToCV and 11 other crinivirus CPm proteins were retrieved from GenBank.2.3.CPm was cloned using specific primers containing the adapter of homologous recombination  for pathogenicity experiments, into pBIN-3HA for RNA silencing analysis, and into pET28α from prokaryotes expressing and purified the recombinant CPm protein. The vectors expressing the βC1 gene of Tomato yellow leaf curl China virus beta-satellite (TYLCCNB) and the P19 gene of Tomato bushy stunt virus (TBSV) were kindly gifted by Prof Zhenghe Li  and described previously ]. Ten micrograms of total protein was separated by 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and transferred to a polyvinylidene difluoride (PVDF) membrane. The PVDF membrane was blocked with either 5% nonfat dry milk in TBS with 0.1% Tween-20 for 1 h at room temperature and then incubated for 1 h at room temperature with primary mouse monoclonal antibody , followed by goat anti-mouse secondary antibody conjugated to horseradish peroxidase (Bio-Rad). Blotted membranes were washed thoroughly and visualized by chemiluminescence mode .2.5.NbActin gene (GenBank: JQ256516.1) was selected as an internal control for the assays. The primers for qPCR are listed in −ΔΔCt method for analysis.Total RNA was extracted from the inoculated or systemic leaves using TRIzol reagent . The concentration and purity of extracted RNA were assayed by a NanoDrop 2000 , and 1,000 ng of RNA was used to synthesize the first strand of cDNA for qRT–PCR using a TransScript All-in-One First-Strand cDNA synthesis SuperMix for qPCR  kit  following the manufacturer’s instructions. qPCR was conducted using the TransStart Green qPCR SuperMix UDG kit  on the qTOWER3G qPCR system . The 2.6.Escherichia coli co-incubated with the 5′-biotinylated double-stranded GFP RNA probe was set as positive control  and is listed in  control . Signals control .3.3.1.Lettuce chlorosis virus (LCV), Bean yellow disorder virus (BYDiV), Cucurbit yellow stunting disorder virus (CYSDV), and Sweet potato chlorotic stunt virus (SPCSV)  encodes a protein of 669 amino acids (aa). To examine the evolutionary relationships of CPm sequences from different criniviruses, we first examined the phylogenetic dendrogram of the complete CPm amino acid sequences of 11 different criniviruses. As shown in  (SPCSV) . Otherwi (SPCSV) . Figure  (SPCSV) , but lac (SPCSV) . The dis3.2.Solenaceae crops -based vector, for overexpression. A. tumefaciens carrying PVX-CPm was inoculated into young N. benthamiana plants at the four-week stage by agroinfiltration. During the first 14 dpi, the phenotype of N. benthamiana inoculated with PVX-CPm was similar to that of the plants inoculated with PVX-GFP. And PVX-GFP -infected plants showed signs of recovery, with a loss of veinal chlorosis and mosaic symptoms by 30 dpi. Otherwise, PVX-CPm-infected plants and systematic leaves showed more veinal chlorosis and mosaic symptoms than PVX-GFP (control group) at 14 and 30 dpi  uptake method. In the presence of H2O2, DAB polymerizes to produce a deep brown color that can be visualized after ethanol clearing of the tissues , or PVX-CK (PVX-GFP as a negative control). As shown in the 1st and 2nd lines of N. benthamiana 16c plants similar to PVX-CPm infected wild-type N. benthamiana plants , heat shock 90–2 (HSP90-2), and basic leucine zipper 60 (bZIP60)  of the virions based on verification from the LIYV CPm protein . The P225.The results presented here elucidated that the CPm protein encoded by ToCV is a pathogenicity determinant that suppresses host RNA silencing-based antiviral defenses induced by ssRNA but not by dsRNA. The pathogenicity activity of the CPm protein was probably correlated with its activities of accentuating virus replication and triggering the hypersensitive-like response and ER stress in the host. ToCV CPm only suppresses local RNA silencing triggered by single-stranded GFP but not by double-stranded GFP. The activity of blocking host RNA silencing of CPm protein likely resulted in its property of binding dsRNA. The results of this study suggest that blocking host local RNA silencing induced by ssRNA is critical for virus primary infection because the main RNA silencing suppressor of the P22 protein was not translated, which blocks RNA silencing triggered by ss- and dsRNA and induces systematic RNA silencing.The original contributions presented in the study are included in the article/SZ, DZ, and YL contributed to the conception, design of the study, and wrote the manuscript. XY, XWL, YZ, and XYL conducted the study, measured the plant phenotypes, and contributed to laboratory analyses. ZZ, XO, XS, and FL provided the statistical analyses for the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China , China Agricultural Research System (CARS-23-D-02), Talent of Hunan Province (2020TJ-Y05), Innovation Project of Hunan Academy of Agricultural Science (2021CX07).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.


SRSF3 (SRp20) is the smallest member of the serine/arginine (SR)-rich protein family. We found the annotated human SRSF3 and mouse Srsf3 RefSeq sequences are much larger than the detected SRSF3/Srsf3 RNA size by Northern blot. Mapping of RNA-seq reads from various human and mouse cell lines to the annotated SRSF3/Srsf3 gene illustrated only a partial coverage of its terminal exon 7. By 5ʹ RACE and 3ʹ RACE, we determined that SRSF3 gene spanning over 8422 bases and Srsf3 gene spanning over 9423 bases. SRSF3/Srsf3 gene has seven exons with exon 7 bearing two alternative polyadenylation signals (PAS). Through alternative PAS selection and exon 4 exclusion/inclusion by alternative RNA splicing, SRSF3/Srsf3 gene expresses four RNA isoforms. The major SRSF3 mRNA isoform with exon 4 exclusion by using a favorable distal PAS to encode a full-length protein is 1411 nt long (not annotated 4228 nt) and the same major mouse Srsf3 mRNA isoform is only 1295 nt (not annotated 2585 nt). The difference from the redefined RNA size of SRSF3/Srsf3 to the corresponding RefSeq sequence is at the 3’ UTR region. Collectively, the redefined SRSF3/Srsf3 gene structure and expression will allow better understanding of SRSF3 functions and its regulations in health and diseases.  •Human SRSF3 and mouse Srsf3 are oversized in the annotated NCBI RefSeq sequences.•SRSF3 and Srsf3 RNA are primarily ∼1.5 kb in size, ∼2.8 kb and ∼1.3 kb shorter than the respective RefSeq sequence.•The redefined 3′ UTR contributes to the observed difference from the RefSeq sequence.•Four RNA isoforms of SRSF3/Srsf3 are expressed by alternative RNA splicing and polyadenylation. In human cells, the SRSF3 probes detected a major SRSF3 mRNA band slightly bigger than 1.5 kb and two other weak bands, one just above 1 kb and the other above 2 kb  of human SRSF3 and mouse Srsf3. Total cell RNA extracted, separately, from three human cancer cell lines, HeLa, CaSki and SiHa (for human SRSF3) and mouse epithelial keratinocytes (MEK) (for mouse Srsf3) was used for 5′ RACE in the presence of a human SRSF3-specific antisense primer, oVM238 or a mouse Srsf3-specific antisense primer, oMA28. The 5′ RACE products were visualized by agarose gel electrophoresis D.Fig. 2MAs shown in Sequencing of the major band from the 5ʹ RACE products derived from MEK D showed Analyses of the region upstream of human SRSF3 C and mouSRSF3 protein sequence is well conserved almost in all animal species. Translation of both human SRSF3 and mouse Srsf3 proteins is initiated from the first AUG in their RNA exon 2. This translation initiation codon bears the Kozak content with a purine G at −3 position C and F , 1999. A2.3By searching RNA polyadenylation signals in the 3ʹ UTR regions within exon 7 of NCBI annotated SRSF3/Srsf3 RNAs (NM_003017.5 and NM_013663.5), we found several consensus polyadenylation signals (PAS) and wished to determine why these consensus PAS could not be used for RNA polyadenylation. We first mapped the RNA-seq reads from different human cell lines including HeLa, CaSki, SiHa, BCBL-1 and HEK293T to the annotated human SRSF3 gene in the human reference genome hg38 and visualized distribution of the SRSF3 RNA-reads along with the annotated SRSF3 gene by the Integrative Genomics Viewer . We discovered that the RNA-reads from all cell lines were consistently mapped only to the first one-third of the exon 7. The last two-third of exon 7 annotated by the NCBI NM_003017.5 exhibited no mapped RNA-reads A, indicaTo determine the real SRSF3 cleavage sites (CS) for SRSF3 RNA polyadenylation in the human genome, total RNA isolated from HeLa, CaSki, SiHa, and W12 cells was analyzed by 3′ RACE using a SRSF3-specific primer oJR56 from the beginning of exon 7 A. FollowSimilar to human SRSF3, RNA-seq reads from mouse skin tissues were mapped to the annotated Srsf3 gene  in the mouse reference genome mm10 and visualized their distribution along with the annotated Srsf3 gene by IGV. We discovered again the similar discrepancy of RNA-seq reads distribution in the Srsf3 exon 7 to that of human SRSF3, with Srsf3 RNA-seq reads only being mapped to the two-third of Srsf3 exon 7 C.To determine the authentic 3ʹ end of mouse Srsf3 RNA transcripts, total RNA from MEK cells was subjected to 3ʹ RACE using a Srsf3-specific primer, oLLY531 C. FollowRNA polyadenylation is essential for gene expression, which is initiated by cleavage of the nascent transcripts and followed by addition of ∼150 adenosine residues at the RNA 3′ end. RNA transcripts without a poly(A) tail are degraded or not efficiently exported to the cytoplasm. Efficient RNA polyadenylation requires four core cis elements: an UGAU cis-element for a downstream PAS definition, a cleavage site generally positioned 10–30 nt downstream of PAS, and a U/GU-rich motif downstream . As show2.4As described above, the mapped 3ʹ UTRs from human SRSF3 and mouse Srsf3 RNAs are much shorter than the annotated 3ʹ UTRs from the NCBI RefSeq SRSF3/Srsf3 sequences. The major isoform of human SRSF3 RNA we identified has a 3ʹ UTR only in size of 794 nt, not 3611 nt as annotated from the NCBI NM_003017.5 A. The maThe 3ʹ UTR has been a major focus in functional miRNA studies  and the 2.5https://www.genecards.org/cgi-bin/carddisp.pl?gene=SRSF3). We mapped SRSF3 TSS at nt 36,594,362 on chr6 and its primary RNA transcripts are cleaved at nt 36,602,783 mainly using a distal PAS at nt 36,602,754 to produce the SRSF3 pre-mRNAs in size of 8422 nt, but uncommonly cleaved at nt 36,602,293 by using a proximal PAS at nt 36,602,272. This alternative polyadenylation of human SRSF3 pre-mRNAs produces a minor isoform in size of 7932 nt, 490 nt shorter than the major longer isoform  and is normally excluded from a mature mRNA during pre-mRNA splicing. Given alternative RNA polyadenylation, therefore, four mRNA isoforms are produced from human SRSF3 or mouse Srsf3 pre-mRNA processing and illustrated, respectively, in 3SRSF3 is one of most important splicing factors involving in many cellular functions  and SRSFThe RNA 3ʹ UTR has been extensively explored for its regulatory functions of RNA polyadenylation, RNA stability, RNA-protein and -miRNA interactions, subcellular mRNA location and protein translation . It has A widespread mechanism to generate mRNA isoforms with alternative length of the 3ʹ UTRs is by RNA alternative cleavage and polyadenylation (APA) which has emerged as a major regulator of mRNA fate and functions of protein synthesis . We disc44.12 atmosphere.CaSki, SiHa, HeLa, C33A and HEK293T cells were obtained from ATCC  and were maintained in Dulbecco's modified Eagle's medium (DMEM)  with 10% fetal bovine serum . BCBL-1 cells  were gro4.2GEO database under each accession number: GSE158033, GSE179727, GSE179728, and GSE136647. The read were aligned to the reference genomes hg38 (human) and mm10 (mouse) using STAR aligner and the reads mapped to SRSF3/Srsf3 were visualized by the Integrative Genomics Viewer . The 5ʹ and 3ʹ UTR were aligned using EMBOSS Stretcher (https://www.ebi.ac.uk/Tools/psa/emboss_stretcher/). The protein sequences were aligned using Clustal Omega .The total RNA extracted by TriPure reagent  was used for construction of RNA-seq libraries followed by Illumina sequencing. Sequencing libraries were constructed following Illumina Stranded Total RNA protocol . Paired-end 150-bp read length sequencing with a depth of 100 million reads per sample was performed on the HiSeq 2500 sequencer according to the manufacturer's instructions (Illumina) for CaSki cells. For SiHa cells, the length of the paired-end read is 75-bp with a depth of 75 million reads per sample. For HeLa cells, the length of the paired-end read is 50-bp with a depth of 25 million reads per sample. For BCBL-1 cells, the length of the paired-end read is 150-bp with a depth of 40 million reads per sample. For mouse skin tissues, the length of the paired-end read is 126-bp with a depth of 50 million reads per sample. Raw data and analyzed RNA-seq data supporting the findings in this study have been deposited in the NCBI 4.3Total RNA was extracted using the TriPure reagent . The 5′ and 3ʹ RACE assays were conducted with a SMARTer RACE cDNA amplification kit  as recommended with 1 μg/reaction of total RNA as a template. The following primers were used: human SRSF3 primer oJR56 was used for 3ʹ RACE PCR amplification, while primer oVM238 was used for 5ʹ RACE PCR amplification; mouse Srsf3 primer oLLY531 was used for 3ʹ RACE PCR amplification, and primer oMA28 was used for 5ʹ RACE PCR amplification. See 4.432P-labeled oligo probes antisense to exon 3 of human SRSF3 or exons 1–3 of mouse Srsf3  and separated in 1% formaldehyde-denaturing agarose gel together with the RNA Millennium Marker (ThermoFisher Scientific). The ribosomal RNAs visualized by ethidium bromide staining (ETBR) were used as loading control. After transfer, the membrane was hybridized with 4.5https://www.targetscan.org/vert_80/) using default setting (The miRNA-binding sites in the 3ʹUTR of human SRSF3 and mouse Srsf3 mRNAs were predicted by Targetscan Release 8.0 ( setting .L.Y., V.M., and Z.M.Z. designed the study. L.Y. and V.M. performed the experiments, bioinformatic analysis, and composed figures. L.Y., V.M., R.J. and Z.M.Z. contributed to data analyses and manuscript writing, editing, and proofreading. All authors have read and agreed to the final version of the submitted manuscript.The authors declare no conflict of interest.


Staphylococcus aureus is a major human pathogen that utilises many surface-associated and secreted proteins to form biofilms and cause disease. However, our understanding of these processes is limited by challenges of using fluorescent protein reporters in their native environment, because they must be exported and fold correctly to become fluorescent. Here, we demonstrate the feasibility of using the monomeric superfolder GFP (msfGFP) exported from S. aureus. By fusing msfGFP to signal peptides for the Secretory (Sec) and Twin Arginine Translocation (Tat) pathways, the two major secretion pathways in S. aureus, we quantified msfGFP fluorescence in bacterial cultures and cell-free supernatant from the cultures. When fused to a Tat signal peptide, we detected msfGFP fluorescence inside but not outside bacterial cells, indicating a failure to export msfGFP. However, when fused to a Sec signal peptide, msfGFP fluorescence was present outside cells, indicating successful export of the msfGFP in the unfolded state, followed by extracellular folding and maturation to the photoactive state. We applied this strategy to study coagulase (Coa), a secreted protein and a major contributor to the formation of a fibrin network in S. aureus biofilms that protects bacteria from the host immune system and increases attachment to host surfaces. We confirmed that a genomically integrated C-terminal fusion of Coa to msfGFP does not impair the activity of Coa or its localisation within the biofilm matrix. Our findings demonstrate that msfGFP is a good candidate fluorescent reporter to consider when studying proteins secreted by the Sec pathway in S. aureus. Green fluorescent protein (GFP) has been used for decades as an intracellular reporter for gene expression and as a fluorescent tag to visualise single proteins in the cytoplasm of bacteria . An advaCorynebacterium glutamicum [Bacillus subtilis [Streptococcus mutans [Mycobacterium smegmatis [Staphylococcus epidermidis [B. subtilis [While GFP fusion proteins have taught us much about intracellular proteins, little research has been done on extracellular proteins, such as surface-bound proteins or other secreted proteins. Some GFP variants have been successfully secreted to the periplasm and outer membrane of Gram-negative bacteria –5, howevutamicum , Bacillusubtilis , 8, Stres mutans , Mycobacmegmatis , and Stadermidis . Split Gsubtilis . There isubtilis , 13. AddEscherichia coli cells [Most extracellular proteins are secreted in an unfolded state via the Secretory (Sec) pathway, where they are exported across the cytosolic membrane into the periplasm in Gram-negative bacteria or outside the cell in Gram-positive bacteria . It is ali cells . Folded li cells .Staphylococcus aureus, and to determine if msfGFP can be secreted by either of the two secretion pathways Sec and Tat. S. aureus is a Gram-positive coccus which has both the Sec and Tat secretion pathways [S. aureus utilises many surface-associated and secreted proteins to interact with host tissue, to establish infections, and evade the immune system [S. aureus escape immune cells [S. aureus also secretes a family of proteins called secretable expanded repertoire adhesive molecules (SERAMs). These include extracellular adherence protein (Eap), extracellular matrix protein-binding protein (Emp), extracellular fibrinogen binding protein (Efp), coagulase (Coa), and von Willebrand factor binding protein (vWbp). Eap inhibits neutrophils and therefore inhibits the immune response [S. aureus would benefit from a reliable system with which to label and visualise proteins such as these that are important to its virulence and pathogenicity, especially in complex environments such as biofilms where traditional antibody labelling methods may fail. Antibodies are approximately 10 nm in size [The aims of our present study were to investigate whether monomeric superfolder GFP (msfGFP) is a good candidate for extracellular fusion proteins in pathways , 18. It pathways . S. auree system . These pe system . Examplee system , fibronee system , and colne cells . S. aureresponse , Emp binresponse , which aresponse , and Efpresponse  and decrresponse . Coa andresponse , in two response , enhanceresponse , and incresponse . S. aure in size , which iS. aureus, which is predicted to have a Sec-type signal peptide [S. aureus biofilms. Coa is responsible for producing a fibrin pseudocapsule and has previously been located within the pseudocapsule [We chose msfGFP as our model fluorescent protein due to its brightness and enhanced folding properties , and it  peptide . We demoocapsule , 31. We S. aureus. We generated four strains of S. aureus that carried different variants of the overexpression pRMC2 plasmid . Strain 1 contained an empty pRMC2 vector, which served as negative control, strain 2 contained pRMC2 encoding msfGFP without a signal peptide and was used as a positive control to verify msfGFP expression, strain 3 contained pRMC2 encoding Tat:msfGFP for secretion of GFP through the Tat pathway, and strain 4 contained pRMC2 encoding Sec:msfGFP for secretion through the Sec pathway. The presence of functional msfGFP was then measured as the appearance of green fluorescence of cultures and cell-free supernatants using a fluorescence plate reader after inducing expression of msfGFP from the plasmid.The fluorescent protein msfGFP is a good candidate for tagging extracellular proteins in Gram-positive bacteria, but its implementation depends on whether it can be secreted and fold properly in the extracellular space. We therefore tested the ability of msfGFP to become fluorescent after secretion via the Tat and Sec pathways in Figure 2A). The fluorescence intensity from the culture (bacteria and supernatant) was at a similar level to the supernatant alone, indicating that msfGFP was primarily present in the supernatant. In cultures expressing Tat:msfGFP or msfGFP without a signal peptide, fluorescence was detected in bacterial cultures but not the supernatants , indicating that msfGFP could fold correctly within cells, but was not secreted via the Tat pathway. Although Tat:msfGFP was not successfully secreted, the fluorescence intensity from Tat:msfGFP cell cultures was higher than the fluorescence intensity from Sec:msfGFP cell cultures, which may reflect differences in the activity of the two different pathways, different rates of msfGFP transcription, translation, or protein folding when fused to a particular signal peptide.Only the culture expressing Sec:msfGFP produced fluorescence in the cell-free supernatant, which indicated that msfGFP can secrete and fold correctly when exported by the Sec-pathway . There was a weak fluorescence in S. aureus expressing Sec:msfGFP, which reflects that there was a small fraction of GFP that was not secreted from the bacteria or that remained linked to the cell wall. Furthermore, in-gel fluorescence showed that only the Sec:msfGFP strain secreted a functional msfGFP . However, the secreted msfGFP was found in two distinct sizes in the supernatant of the Sec:msfGFP cultures, which indicates that the signal peptide was not always removed from some of the msfGFP during secretion. There is an additional band underneath the GFP control , which is likely GFP that lacks a His-tag. Although we have confirmed msfGFP is found in the supernatant by bulk measurements using a plate reader , the fluorescence could not be seen in the supernatant using CLSM because the fluorescent protein was too diluted to be visualised. There is also a weak surface-associated fluorescent signal seen with CLSM on S. aureus containing an empty vector without msfGFP, which is due to autofluorescence from ATc [The presence of fluorescent msfGFP in the intracellular and extracellular environment was verified by CLSM imaging of cell cultures expressing Tat:msfGFP, Sec:msfFP, msfGFP, and cells containing the empty vector. As expected, msfGFP fluorescence was detected inside cells expressing Tat:msfGFP and msfGFP  [msfGFP was successfully secreted via the Sec pathway, so to demonstrate its suitability to tag extracellular proteins, it was fused to Coa by insertion into the nfection , 30. In n (vWbp) . Loss ofS. aureus and the fusion protein did not get cleaved, demonstrated by in-gel fluorescence analysis . Fluorescence from GFP was present in the supernatant of bacterial cultures expressing Coa:msfGFP and not in cultures without Coa:msfGFP, demonstrating that the fusion protein was secreted extracellularly . Coa:msfGFP from the supernatant of bacterial cultures did not travel as far through the gel as GFP alone, or msfGFP fused to a Sec signal peptide. Therefore, the weight of the fusion protein was much larger, demonstrating that the protein is intact and contains both Coa and msfGFP . Coa was also functional, as S. aureus with chromosome-integrated coa:msfGFP coagulated plasma similarly to the parental strains , and biofilms formed similar fibrin structures as the parental strains, i.e. fibrin was visible as pseudocapsules surrounding clusters of bacteria and as an extended fibrous network between clusters of bacteria . This was true for both the wildtype and the mutant lacking vWbp, thus the fusion to msfGFP did not inhibit the function of Coa. We confirmed that coagulation occurred due to Coa and vWbp alone by including a control mutant of S. aureus that lacks both coa and vwbp, which did not coagulate plasma  nor produce fibrin fibers in the biofilm matrix . Fibrin was visualised by the addition of fluorescently labelled fibrinogen to the biofilm growth medium, which is converted into fibrin fibers by an activated complex formed by Coa and vWbp binding to host prothrombin. The small amount of red fluorescence seen in Figure 4C are aggregates of fluorescent fibrinogen that have not been converted into fibrin because of the absence of both Coa and vWbp.The fusion protein Cos:msfGFP was secreted successfully from S. aureus biofilms, we visualised the bacterial cells, fibrin, and Coa:msfGFP by CLSM. Coa:msfGFP localised to the surface of the bacteria, where we predicted that Coa catalyses the formation of a fibrin pseudocapsule . This finding corroborates previous studies, which also detected Coa in the fibrin pseudocapsule by immunolabelling [Figure 4B). We have thus demonstrated the use of msfGFP for labelling a protein secreted by the Sec pathway in S. aureus. The signal from msfGFP appears brighter in the mutant lacking vWbp, and this is most likely caused by a less dense extracellular matrix in this strain which lacks one of the coagulases, and the signal from msfGFP is therefore attenuated less.To assess the location of Coa in abelling . BiofilmS. aureus, but that the application is limited to proteins that are secreted through the Sec pathway. When fused to Coa, msfGFP did not hinder the biological function of Coa, and the fusion protein localised to the fibrin pseudocapsule surrounding clusters of S. aureus cells. This result is in agreement with previous studies [S. aureus proteins exported by the Sec pathway, and the majority of extracellular proteins are indeed secreted by this pathway [We show that msfGFP can be used to generate extracellular fluorescent fusion proteins in  studies , 31 and  pathway .S. aureus, and this approach now opens possibilities of studying the location of secreted proteins that remain associated with the extracellular matrix of staphylococcal biofilms. The fluorescence signal was fairly dim when imaging Coa:msfGFP, however, it is not known how much Coa is produced and therefore the concentration could be low. Additionally, we imaged the fusion protein in the complex environment of a biofilm. Biofilms are thick, heterogeneous samples that are autofluorescent and attenuate and distort both the excitation and emission from fluorescent molecules, which makes them a challenging environment to image in. However, the fact that Coa:msfGFP could be visualised by standard CLSM imaging is encouraging, and advanced microscopes with more sensitive detection will facilitate more detailed analyses. For example, single-molecule microscopy on live bacteria has revealed important details of Tat-mediated transport in Gram-negative E. coli, and similar investigations could be pursued for Sec-secreted proteins in Gram-positive bacteria using msfGFP fusions [msfGFP has a superfolding mutation that makes it fold quickly and without chaperones, even when fused to another protein, and it exhibits a high level of brightness  that mak fusions . In part fusions , 39, or  fusions –42.S. aureus is a major biofilm-forming human pathogen that establishes infections, causes disease, and evades the immune system through a number of secreted and cell surface associated proteins, many of which contain a Sec-type signal peptide [Staphylococci that are compatible with this vector, and the Tat and Sec signal peptide constructs can be cloned into different vectors for studies in other Gram-positive bacteria. peptide . Fusions peptide . The autS. aureus. We fused msfGFP to coa in the S. aureus chromosome and demonstrated that fusion to msfGFP did not prevent Coa from functioning correctly, that msfGFP could fold correctly and fluoresce in the extracellular environment, and that the fusion protein localised as expected in the extracellular environment. S. aureus utilises a myriad of surface associated and secreted proteins to establish infections and cause disease, and our work opens the door for developing fusion proteins to investigate these and progress our understanding of S. aureus infection.We have confirmed that msfGFP is a good candidate for labelling proteins secreted by the Sec pathway in Table 1. For long-term storage, bacteria were stored in 25% glycerol at -80°C. E. coli and S. aureus were cultured in Luria Broth  and Brain Heart Infusion , respectively, at 37°C with 180 rpm shaking. When grown on agar, 15 g/L agar  was added to the media. For plasmid selection, the media was supplemented with 25 µg/ml or 10 µg/ml chloramphenicol , or 100 µg/ml ampicillin . Biofilms were grown in modified BHI (mBHI) supplemented with 50% heparin stabilised human plasma to mimic physiological conditions. mBHI is BHI supplemented with 2.1 mM CaCl2  and 0.4 mM MgCl2 . When low autofluorescence conditions were required, bacteria were suspended in mM9 medium. mM9 is a minimal medium comprising of M9 salts  supplemented with 2 mM MgSO4 , 0.1 mM CaCl2 , 1% glucose , 1% casamino acids , 1 mM Thiamine-HCl , and 0.05 mM nicotinamide  [g for 15 minutes at 4°C and stored in aliquots at -80 °C. Before use, frozen plasma was immediately thawed in a water bath at 37°C. Tat and Sec signal peptide fusion protein expression was induced with the addition of 340 ng/ml anhydrotetracycline .All bacterial strains, plasmids, and primers used are listed in Aldrich) . Plasma msfGFP to create tat:msfGFP and sec:msfGFP in the vector pRMC2 , a plasmid with an inducible Pxyl/tetO promoter and origin of replication for E. coli and Staphylococci (Table 1) [Figure 1). Note that the Shine-Dalgarno sequences were added later as described in the following section.Tat and Sec signal peptide sequences were fused to Table 1) . A positS. aureus USA300 codon usage table (see Supplementary Table S1 for sequences). The RNA polymerase α and β subunits are highly conserved, and their nucleotide sequences were used to predict codon usage in S. aureus USA300 and S. aureus 29213, and an S. aureus USA300 codon usage table was deemed suitable. Signal peptide sequences were ordered as oligos (Thermo Fisher Scientific) and msfGFP with a linker at its N-terminal was ordered on a high copy plasmid . The signal peptide sequences and msfGFP were amplified by PCR with Phusion polymerase  according to the manufacturer's instructions. The primers (Invitrogen), listed in Table 2, contained overhangs intended to join fragments and add KpnI and EcoRI restriction sites at the 5' and 3' ends. Primers 2Ftg/2Rb and 2Fsg/2Ra were used to amplify Tat and Sec signal peptide sequences, respectively, and msfGFP was amplified with 1Fa/1Rsg. The signal peptide sequences were joined to msfGFP via SOE-PCR to create tat:msfGFP (primers 1Fa/Rtg) and sec:msfGFP (primers 1Fa/2Ra). msfGFP was also amplified alone with no signal peptide sequence to be used as a control. PCR products were analysed by gel electrophoresis and purified with the GenElute Gel Extraction Kit . All PCR products and pRMC2 were digested by KpnI  and EcoRI , and PCR products were ligated into pRMC2 with T4 DNA ligase  according to the manufacturer's protocols.Sequences for msfGFP , Tat 1818, and SmsfGFP possible, the Shine-Dalgarno sequence was inserted upstream of the signal peptide and msfGFP sequences via site directed mutagenesis [tat:msfGFP, sec:msfGFP, and msfGFP to ensure maximum translation efficiency [tat:msfGFP, sec:msfGFP and msfGFP and simultaneously insert the sequence at the desired place. A unique reverse primer was designed for each construct, while the mutagenic primer MutF remained the same . The primers were phosphorylated using T4 Polynucleotide Kinase  according to the manufacturer's instructions. The constructs were then amplified using the phosphorylated primers and Phusion polymerase  according to the manufacturer's instructions. The new PCR products were digested with DpnI to remove methylated template DNA, after which the mutated plasmids were ligated back into a whole plasmid according to the manufacturer's instructions .In order to make the translation of agenesis . The conagenesis  and inseficiency . To do tE. coli IM08B in order to gain a methylation profile mimicking S. aureus [E. coli IM08B was diluted to OD600 0.02 and grown to OD600 0.3, then chilled on ice for 10 minutes. Cells were harvested by centrifugation at 4000 x g for 10 minutes at 4°C and resuspended in 5 ml ice cold 0.5 M CaCl2. The centrifugation was repeated, and the cells resuspended in 1.2 ml 0.5 M CaCl2 before incubating on ice for 30 minutes. For transformation, 1-3 µl of each pRMC2 construct was incubated for 30 minutes on ice with 50 µl of competent cells. A heat shock was applied at 4 °C for 90 s, and cells were then transferred to ice for 2 minutes. 950 µl of preheated LB media (37°C) was added and then cells incubated with 180 rpm shaking for 1 hour at 37°C. Cells were finally plated on agar with Amp and incubated at 37°C overnight. Plasmids were extracted from positive transformants with the GeneJET Plasmid Miniprep Kit  and sent for sequencing with Macrogen Europe with primers FwdRMC2/RevRMC2.The pRMC2 constructs expressing Tat:msfGFP, Sec:msfGFP, or msfGFP, and empty pRMC2, were first transformed via heat shock into . aureus . To prepS. aureus 29213 by electroporation. To prepare electrocompetent cells, an overnight culture was diluted to OD600 0.5 and grown to OD600 0.6. Cells were harvested by centrifugation at 4000 x g for 10 minutes at 4°C and washed in 50 ml ice cold MilliQ water three times. Cells were then centrifuged and resuspended in 50 ml, then 5 ml, 2 ml, and finally 0.25 ml ice cold 0.5 M sucrose. Up to 1 µg plasmid DNA was incubated on ice with 50 µl fresh competent cells for 10 minutes before being transferred to a chilled 1 mm electroporation cuvette and electroporated at 2.1 kV, 200 Ω, and 25 µF in an ECM 630 BTXTM (Harvard Apparatus). Immediately afterwards, 1 ml preheated BHI supplemented with 0.5 M sucrose (37°C) was added to the cells, which were then incubated at 37°C with 150 rpm shaking for 2 hours. Cells were finally plated on agar containing Cm and incubated overnight at 37°C. Positive transformants were confirmed by sequencing as described in the prior section.Plasmids with the correct sequence were transformed into coa and vwbp genes were achieved through splicing by overlap extension PCR according to Monk and colleagues [et al. 2022 [coa plasmid into the Δvwbp mutant and deleting the coa gene in the Δvwbp mutant.In-frame single deletions of the lleagues  and perfal. 2022 . The douet al. [coa:msfGFP from a pUC57-msfgfp and add overhangs for Gibson Assembly. pIMAY was digested using restriction enzyme KpnI  and then ligated to coa:msfGFP via Gibson Assembly [E. coli IM08B to gain a methylation profile mimicking that of S. aureus [S. aureus 29213 wildtype and Δvwbp, a mutant lacking vWbp, the other coagulase that S. aureus produces [S. aureus, the plasmid was then integrated into the chromosome and finally the backbone was excised using the protocol from Monk et al. [600 0.5 in 1 ml of 1:6 heparin-stabilised human plasma in 0.8 % NaCl (w/v)  in sterile glass tubes and incubated for 24 hours at 37°C with no shaking. Coagulation was assessed by tilting and observing the tubes after 24 hours incubation. A negative control without bacteria was also included in addition to the mutant lacking both Coa and vWbp, which should not coagulate plasma.A C-terminal, chromosome-integrated fusion Coa:msfGFP was created by allelic replacement using the protocol from Monk et al. . PrimersAssembly  using a . aureus , and wasproduces . After tk et al. . The genS. aureus 29213 expressing Tat:msfGFP, Sec:msfGFP, msfGFP, or no msfGFP from the overexpression vector pRMC2 were grown overnight in BHI and diluted to OD600 0.1 in mM9 medium and incubated at 37 °C with 180 rpm shaking until OD600 0.5. mM9 was used in place of BHI because it is less autofluorescent. ATc (340 ng/ml) was added to the cultures, after which they were further incubated for 60 minutes to induce the Pxyl/tetO promoter and msfGFP expression. Final OD600 was recorded, and 2 ml of each sample taken. For the fluorescence bulk measurements, 200 µl was added directly into a 96-well plate  and the remaining 1.8 ml was centrifuged at 14104 x g for 10 minutes. The supernatant was removed, sterile filtered with a 0.2 µm filter , and 200 µl was added to the 96-well plate. Three biological replicates (from independently grown cultures) and three technical replicates  were tested per construct. Fluorescence was measured at 488 nm wavelength excitation, 510 nm wavelength emission, and 1000 ms exposure time in a Varioskan Lux Flash Plate Reader (Thermo Fisher Scientific). Median fluorescence values were calculated and normalised to the optical density. The values were tested for normality with a Shapiro-Wilk test, after which they were compared using a one-way ANOVA followed by a Tukey's test with a p < 0.05 significance level.To verify if msfGFP was successfully secreted by the Tat and Sec pathways, the supernatants of bacteria expressing the signal peptide and msfGFP fusions were investigated for fluorescence by bulk and in-gel measurements. Bacterial cultures and supernatants from To verify whether Sec:msfGFP and Tat:msfGFP were secreted and folded correctly, and to confirm whether Coa:msfGFP was secreted as an intact, functionally fluorescent protein, we separated the supernatant on a native PAGE gel. Cultures that expressed either Coa:msfGFP, Tat:msfGFP, msfGFP, an empty vector were grown to the exponential phase in mM9, and the chromosome-integrated Coa:msfGFP cultures were grown to the stationary phase in BHI, after which the supernatant was collected and either stored at –80°C or used right away. The supernatants were mixed 1:1 with a native sample buffer  and separated on a 4–15% precast polyacrylamide protein gel . Fluorescence was detected in the Amersham Typhoon Scanner  with a 488 nm excitation and 510 nm emission. A His-tagged GFP  was also loaded on the gel and used as a positive control for GFP fluorescence as well as a size marker.600 0.1 in mM9 medium and were grown to OD600 0.5, then incubated for a further 2 hours with 340 ng/ml ATc and imaged with the LSM700 confocal microscope (Zeiss) with a 10 mW 488nm wavelength laser at 2% power and a Plan-Apochromat 63x/1.40 NA oil immersion objective lens. Images were captured with the Axiocam HR camera (Zeiss) and using the Zen Black software (Zeiss).To visualise whether msfGFP was retained within cells, all overexpression constructs were also imaged with confocal laser scanning microscopy (CLSM). Overnight cultures were diluted to ODS. aureus expressing either the chromosome-integrated fusion Coa:msfGFP or unmodified Coa were grown overnight in BHI and then diluted to OD600 5. Microwells  were preconditioned with 180 µl BHI supplemented with 50% plasma, 10 µM Syto41 , and 0.4 µg/ml Alexa Fluor 647-conjugated fibrinogen  by incubating at 37°C for 30 minutes. Then 20 µl OD600 5 cultures were added and incubated for a further 2 hours. The biofilms were imaged with 405 nm, 488 nm, and 639 nm wavelength excitation and a Plan-Apochromat 63x/1.40 NA oil immersion objective in the LSM700 confocal microscope (Zeiss). Images were captured with the Axiocam HR camera (Zeiss) and using the Zen Black software (Zeiss). GFP fluorescence was detected with 488 nm wavelength excitation and 490-600 nm wavelength emission, and Alexa 647-conjugated fibrinogen was detected with 639 nm wavelength excitation and 640-750 nm emission.Click here for supplemental data file.www.microbialcell.com/researcharticles/2023a-evans-microbial-cell/.All supplemental data for this article are available online at


In response to a recent article, this Formal Comment discusses nonreplicative joining of fragments of viral RNAs, a class of reactions which might be widespread in nature, contributing to conservation and evolution not only of viruses but of cellular organisms as well. Recombination between genomes of RNA-viruses is a key actor in their micro- and macroevolution as well as in repair after genetic injuries ,2. RepliOriginally, we demonstrated the existence of nonreplicative recombination in animal RNA viruses by using different pairs of fragments of the poliovirus genome . The 5′-However, in the original  and a suIn these experiments , none ofThese recombinants were most likely formed by different mechanisms. Several pairs of the partners were represented by the full poliovirus RNA molecules with single nicks between neighboring nucleotides in different parts of the RdRp-encoding sequence. When such 5′- and 3′-partners contained 3′-phosphate and 5′-OH termini, respectively, efficient precise ligation was observed, yielding viable viruses. The partners with overlapping (>170 nt-long) sequences within the RdRp gene also generated homologous recombinants. Depending on the forms of their to-be-joined nucleotides , the chimeric genomes contained either one of the partners in its entirety or fragments of both partners. The exact reconstitution of the viral genome in the overwhelming majority of these recombinants most likely resulted from their selective survival because nonhomologous joining  would predominantly generate nonviable genomes. The location of the crossover sites could be mapped only in a few cases due to the nearly complete identity of the overlapping sequences. However, by using fragments of heterotypic poliovirus RNA , we demonstrated that the crossovers could occur also in many places and again with a few hot spots [It could not be ruled out that some homologous recombinants were secondary recombinants arising through additional RdRp-dependent steps but the primary covalent joining of the partners unambiguously occurred without the participation of this enzyme, that is, nonreplicatively.Some similarities between the effects of the structures of terminal nucleotides of recombinational partners on the efficiency and character of crossovers in our experiments targeted to the 5′ UTR  and RdRpThe existence of nonreplicative recombination in different RNA viruses was reported by several other labs  direct ligation of the 3′- and 5′-terminal nucleotides of the partner fragments; (2) invasion of the 3′-end of one partner into the internal sequence of the other one; (3) similar invasion of the 5′-end; (4) recombination between internal nucleotides of the 2 partners. The feasibility and efficiency of such reactions depend on some structural features such as the nature of the nucleotides to-be-joined and of the secondary and tertiary structure around the recombination site. These requirements, although far from being well characterized, do not appear to be extremely strong, suggesting that nonreplicative recombination could be relatively common.The reactions involved in nonreplicative RNA recombination are catalyzed by cellular enzymes that remain unidentified. Among potential candidates are ligases, endo- and exonucleases and enzymes catalyzing transesterification. Some other actors such as ribozyme-like structures, cannot be excluded either. An important requirement is the timely co-localization of participants of the process.The nonreplicative RNA recombination most likely generates a variety of nonfunctional “dead” products. The viable chimeric viral genomes should be infectious, replicable (by exploiting the newly formed or helper’s RdRp), and able to withstand the competition with other co-replicating viruses (including their own parents). Putative recombinants between cellular RNAs, in order to persist through generations, should be reverse-transcribed and integrated into the genomic DNA. Due to all of the aforementioned complications, nonreplicative RNA recombination generates functional viral genomes far less frequently than replicative recombination. However, the promiscuity of nonreplicative recombination implies that it could be a widespread contributor to rare, but important, biological events –8. In pa


The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.  RNA molecules play many key functions within cells. Perhaps the most striking example is in translation, where it has been shown that the ability to build proteins is orchestrated by ribosomal particles, with the crucial catalytic step being performed by the ribosomal RNA itself, with amino acid residues delivered specifically by transfer RNAs. Untranslated regions of mRNAs and viruses harbor numerous regulatory elements. There are also a large number of noncoding RNAs (ncRNA) for which, despite decades of research, we have only a scant understanding of their functions. An example is the large class of long noncoding RNAs in animal genomes. These RNA genes are numerous, perhaps exceeding the number of protein-coding genes and seem to play a range of subtle regulatory roles . Many ncRNA building blocks, nucleotides, are chemically complex with aromatic nitrogenous bases, chiral ribose sugar rings and phosphate groups. The bases are able to stack on each other by van der Waals interactions, but they also carry large electrical moments and can form strong hydrogen bonds. Ribose rings strongly constrain backbone geometries by their puckers; the C3’-endo pucker prevails in RNA, but a ribose can also locally adopt the C2’-endo pucker, thus radically changing the backbone geometry. The phosphate groups are perhaps structurally the most complex parts of the RNA molecules due to d-orbitals in phosphorous atoms. Both torsion angles describing the conformations around the phosphodiester bonds O3’-P and P-O5’ called ζ and α prefer -gauche orientations, but the torsions can adopt any other combinations of gauche, trans and -gauche  conformations. Phosphates in nucleic acids under normal conditions are charged and render whole RNA or DNA molecules strongly negative, which needs to be neutralized by interacting positive ions. The single negative charge of each phosphate is distributed between its unbound oxygen atoms that are highly polarizable and capable of forming hydrogen bonds to other RNA atoms, proteins and water, but also of forming charge-charge interactions to amino acids, other cellular components such as amines and prominently also to metals. All intra- and inter-molecular interactions in which RNA molecules are involved determine their structures. Figure in silico the 3D structures of RNA molecules based on sequence homology  data that arise from the lack of community-accepted quality standards. They are related to base pairing, valence geometry and backbone geometry; their combination can lead to a flood of imprecisely and unreliably refined structures.A formal description of base pairing is essential to build reliable 3D models. However, base pairing in public archives is not described reliably; it is often incomplete or incorrect. The programs used to assign base pair topology to 3D structures, such as MC-Annotate , RNAviewPerhaps of lesser but existing importance for the prediction of large RNA structures is the inconsistency of targets used in the refinement of bond distances and angles. These valence geometry targets differ in various refinement programs, validation packages and the PDB, leading to confusion in the community. Therefore, an ELIXIR-led effort was undertaken by the Nucleic Acid Valence Geometry Working Group  to formuA significant source of errors in the structural description of RNA (and DNA) is the misconception about the geometry of the nucleic acid backbone. The structural complexity of the backbone was understood early on , but theThe efficiency of 3D RNA structure prediction is likely to be improved using information from multiple sequence alignments (MSA). MSA has already been incorporated into several expert-based modeling methods in the human categories of RNA-Puzzles and CASP-RNA . Such a To illustrate the difference in quantity, we can compare two resources, Pfam and Rfam. Pfam and Rfam are collections of protein/RNA alignments and models annotate them in genomes. Rfam is the oldest and largest source of alignments for ncRNAs. Although there are other resources that collect similar data, for example, miRBase  or MirGeFirst, while Rfam is similar to Pfam in spirit and goals, it contains far less data than Pfam. At the time of writing this paper, the current version of Rfam, 14.9, contains 4108 alignments, while the current release of Pfam, 35.0, contains 19 632. The difference in resource size is due to historical bias towards RNA gene discovery, the difficulty in identifying homology between related RNAs, and the difficulty in building new alignments for Rfam. Constructing Rfam alignments requires using covariance models, which are much more computationally expensive compared to the hidden Markov models applied to build Pfam alignments.Second, RNA alignments are on average smaller than protein alignments. This relationship relates to the number of sequences, with seed alignments containing an average of 5 sequences in Rfam versus 23 in Pfam Figure , as wellThird, Rfam alignments have several global biases that make working with them difficult. One is that the most common alignments are for simple molecules. Taking into account the type of RNA, most alignments concern miRNA precursors (35%) followed by snoRNA (19%) Figure . miRNA pAnother global bias is observed in the number of seed or full sequences, Rfam has the most data for bacterial small RNA (sRNA) sequences. However, there are few structures of these molecules with <50 in PDB at the time of writing. In terms of full alignments, tRNAs constitute the largest group (45%), and rRNA subunits are the third largest, accounting for another 8% Figure . These fIn addition to the global bias in the RNA data, there are specific issues with Rfam alignments that must be considered in machine learning. For example, not all non-Watson–Crick base pairs are aligned in Rfam, and the aligned ones have not been handled in a consistent manner. Moreover, Rfam consensus secondary structures can represent parts of the structure as unfolded. However, looking at the 3D structure, when available, in that region often shows a clear secondary structure. These regions include places known to have species-specific structure or their unstructured form results from Rfam limitations. Rfam families are intended to cover a wide phylogenetic range. For example, the eukaryotic large subunit ribosomal RNA family (RF02543) represents all large rRNA subunits in all eukaryotes. However, rRNA is well known to vary considerably within the kingdom, or even within a species, with important functional consequences . Since tIn summary, there are several issues with the RNA alignment dataset that will pose problems for deep learning. The data set is small compared to proteins, is highly biased in several ways, and the existing alignments have some shortcomings. While work is ongoing to fix all these issues, it will be challenging to use these data to successfully predict 3D structures. One key issue will be creating a test/train dataset that represents the observed complexity, while not being overly biased.Given the history of protein fold prediction, can we anticipate when the RNA realm will see similar results? AlphaFold’s success came 50 years after the first work on computer-based protein structure prediction. This period of time was necessary to accumulate a sufficient volume of high-quality, reliable data on protein sequences and structures. At the same time, information and computer technology were developed, enabling efficient applications of artificial intelligence models to solve problems that traditional computational methods could not deal with. Artificial neural networks as an idea are already 80 years old , but it A simple way to estimate when AlphaFold for RNA will be created is to consider when the number of RNA structures or sequence alignments are comparable to the currently available protein data. As mentioned above Pfam contains 19 632 protein sequence alignments. Historically, the growth of Rfam has been linear due to the requirement for manual work to build each alignment and we observe that on average Rfam adds approximately 205 alignments per year. Thus, we estimate Rfam will contain 19 000 alignments in approximately 70 years. This is undoubtedly a vast overestimate as we expect the RNA 3D structure prediction problem to be solved by then. One technique which may help is automatic family building. While this is still unsolved for RNA, there has been recent work on this issue which may be promising . AutomatWe believe that there are several viable approaches to enable the prediction of the 3D RNA structure in the near future. First, the RNA community can improve knowledge of RNA structure through more data, second, we can diversify the data used in prediction, and finally, we can improve the machine learning methods used.What data is missing that would improve predictions? We do not seem to know enough about RNA motifs to predict their global structures. We may provide an educated guess, at least for the small structural motifs, of which the most important are base-pair topologies. Concerning the latter, it is very likely that they exist in known structures of reasonably high resolution and can provide reliable geometries. There are also strong reasons to believe that the CANA alphabet describes more than 90% of the existing dinucleotide conformers; only a few of them may be missing . In our Another approach is to improve the size and scope of multiple sequence alignments of RNA. Alignments of four-letter RNA sequences are more challenging than those of 20-letter protein sequences. Some classes of RNA, such as ribosomes, have a large number of sequences and we know how to align them. However, more well-aligned sequences of underrepresented RNA classes are needed. Perhaps the Tree-of-Life projects ,95 will Consistently annotating RNA families across all genomes will be useful and may increase the diversity of RNA sequences available; however, it seems that a prediction method would benefit from a wider range of RNA families. As discussed above, many Rfam families are structurally similar. We believe that providing a more diverse training set would be useful. While Rfam is the global repository of RNA families, not all known families can be found there. Correcting this and working to create new families that are different from existing ones should be a focus of the RNA community. Additionally, creating high-quality alignments remains a challenge .If the current amount and growth rate of currently available sequence and structure data are not sufficient, can they be supplemented with other sources of data? We think so. In particular, RNA biochemistry has a rich history and has developed many methods to rapidly probe 3D structures ,98. A suFinally, the rapid and hard-to-predict development of ML methods may potentially change our pessimistic predictions about the ability to accurately predict 3D RNA structures. Development of methods that are less data hungry, e.g. transfer learning, may allow successful prediction sooner. We believe that RNA structure prediction is an excellent test case for researchers interested in machine learning in the face of limited data. At the moment, we do not believe that reliable 3D RNA prediction will be available in the 2020s, but we challenge the community to prove us wrong.