Furthermore, we retrospectively evaluated the clinical features of 138 newly diagnosed CLL patients via chromosome banding analysis (CBA), FISH, and targeted next-generation sequencing. IGH-t was identified in 25 patients (18.1%). Patients with IGH-t CLL had lower flow scores than those with non-IGH-t CLL. The most frequent translocation was t (10 patients), followed by t (3 patients), and t, t, t (3 patients). The remaining nine patients included three with abnormal karyotypes without translocation involving 14q32, four with a normal karyotype, and two who failed CBA. The most frequently concomitant FISH-detected aberrations were 13q deletion, followed by +12 and TP53 deletion, while one case involved ATM deletion. Complex karyotypes were detected in five patients with IGH-t CLL, in whom all partner genes were non-BCL2. Available mutational information indicated that KMT2D mutation was the most frequent mutation among tested 70 patients, while TP53 mutation was the most frequent mutation in the IGH-t group. Moreover, the IGH-t group had higher FBXW7 (P=0.014) and ATM (P=0.004) mutations than the non-IGH-t group, and this difference was statistically significant. Our study demonstrates that IGH-t is not uncommon among Chinese CLL patients, and that its partner genes are multiple. The gene mutational profile of the IGH-t group was distinct from that of the non-IGH-t group, and the concomitant chromosomal abnormalities within the IGH-t CLL group differed. Thus, identification of IGH-t and its partner genes in CLL patients may help further refine risk stratification and strengthen the accurate management in CLL patients.Immunoglobulin heavy chain translocations (IGH-t) have occasionally been reported in Chinese patients with chronic lymphocytic leukemia (CLL). The objective of the present study was to identify the clinicopathologic features of patients with IGH-t CLL and compare them with those of patients with non-IGH-t CLL. We performed fluorescence  The s of CLL .via relocation close to active regulatory sequences, is considered a common cytogenetic marker of B-cell lymphoma and plasma cell myeloma. Therefore, we focused on CLL patients carrying the chromosome 14q32 rearrangement involving IGH, which may cause secondary pathogenesis by deregulating IGH partner genes.The use of immunostimulatory CpG-oligonucleotide DSP30 plus interleukin 2 (IL-2), a CLL B cell-specific mitogen in cultures , has impCurrently, large cohort studies pertaining to IGH-t CLL are few, and studies investigating this aberration in Chinese CLL patients are rare. Moreover, previous studies have shown that the clinical and molecular features harbored by Chinese CLL patients are different from those of the CLL patients in Western countries . In ordeA total of 138 patients who had been newly diagnosed with CLL, based on the iwCLL criteria  were cultured with CpG-oligonucleotide DSP30 (2 μmol/L) plus IL-2 (0.2 μg/mL) (Cells (102 μg/mL)  for 72 h2 μg/mL) .D13S19, +12-CEP12, del(11q22)-ATM, and del(17p13)-TP53. In addition to routine panel probes, an IGH break-apart translocation probe was used to identify IGH-t. Based on the presence or absence of IGH-t, the whole patient cohort was divided into two groups: the IGH-t group and the non-IGH-t group. Next, t , including del13q14)-q14-D13S1 Next, t , 13  and used for IGHV mutational status assessment and NGS gene panel analysis.http://imgt.org). The IGHV mutational status was designated as unmutated, if there were ≤ 2% mutations (> 98% identity), or mutated, if there were > 2% mutations (≤ 98% identity), compared to germline sequences.IGHV gene rearrangements were sequenced by Sanger sequencing using an automated genetic analyser . Obtained sequences were analyzed using the International ImMunoGeneTics (IMGT) information system and database tools ; +12 (21.0%), del(11q22)-ATM deletion (10.9%); and del(17p13)-TP53 deletion (8.7%). Based on the FISH risk classification, 66% of the patients were allocated to the favorable group, 21.0% to the intermediate group, and 19.6% to the unfavorable group  patients were positive for at least one of the four probes that were used, as follows: del(13q14)- Baseline clinical characteristics and groupwise comparison for those with and without IGH translocation are shown in via karyotyping was +12, which was found in 23 samples. Other recurrent aberrations involving chromosome loci hotspots were as follows: duplication of 1q21; deletions of 6q, 11q22, and 13q14; and translocation involving 12p13, and 20p13. Notably, 22 (17.6%) had a complex karyotype (CK) showing more than three chromosomal aberrations. In the IGH-t group, five samples (21.7%) exhibited CKs, including one case of low CK and four of high-CKs. In the non-IGH-t group, seventeen samples (16.7%) exhibited CKs, including five cases of low-CKs, one case of intermediate-CK, and eleven cases of high-CKs. There was no significant difference on aspect of CK in the two groups (P=0.540);  samples including 23 in the IGH-t group , 8, TP53 (12.9%), SF3B1 (8.6%), IGLL5 (8.6%), NOTCH1 (7.1%), MYD88 (7.1%), and FBXW7 (5.7%). 12 of the 25 IGH-t samples underwent NGS, and 11 (91.7%) showed positive results. The most frequent mutations in the IGH-t group involved TP53, KMT2D, NOTCH1, and IGLL5. Other relatively rare mutations were detected in the genes, CCND3 (2 cases), MYD88 (1 case), HIST1H1E (1 case), and CD79B (1 case). In total, 58 of the 113 non-IGH-t samples underwent NGS, and 33 (56.9%) were positive and the most frequent mutations in this group were found in KMT2D and TP53. However, no significant differences were found between the two genes in the IGH-t and non-IGH-t groups . The IGH-t group showed higher FBXW7 (P=0.014) and ATM (P=0.004) mutations than the non-IGH-t group, and the difference was statistically significant  carried at least one mutation and 30 (42.9%) had ≥2 mutations in the targeted NGS region. Detailed lists of mutation detected in 70 samples are shown in via the placement of a strong transcriptional enhancer from the IGH locus near a proto-oncogene normally located on the partner chromosome, thereby overriding normal gene transcriptional regulation  in CBA, with none showing CK. As previously reported by us, another three IGH-BCL3-positive patients showed t in CBA, with two showing a CK  hematopoietic cells with normal karyotypes were analyzed and it was found that leukemic cells were in the nondividing phase. Among three cases showing abnormal karyotypes regardless of IGH-t, case 18 had CK in CBA, and the remaining two patients (cases 17 and 19) already carried two chromosomal abnormalities combined with cryptic IGH translocation, which could be considered as a CK. Thus, the IGH probe further refined the prognostic stratification of these two patients. To our knowledge, a CK, defined as the presence of at least three chromosomal abnormalities in the same clone, is emerging as a new negative prognostic biomarker of an adverse outcome and worse response to chemoimmunotherapy and novel agents ;18)q32;q in CBA, l agents . There wts cases  and 19 aKMT2D was the most frequent mutation in CLL patients, which was substantiated by another study by Yi et al., which indicated that the frequency of mutations observed in MYD88 and KMT2D of Chinese CLL patients was higher compared to that observed with Western CLL patients -https://www.ncbi.nlm.nih.gov/sra; PRJNA669583.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and approved by the Ethics Committee of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.QL collected clinical sample and related data, designed the study and wrote the manuscript. YW reviewed all related literature, performed bioinformatic analysis, and revised the manuscript in this study. SX performed FISH experiment and gathered related data. HZ made chromosome banding analysis. XM performed the flow cytometry test. MX performed high throughput sequencing experiment. All authors contributed to the article and approved the submitted version.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.


Humans and other animals tend to repeat or alternate their previous choices, even when judging sensory stimuli presented in a random sequence. It is unclear if and how sensory, associative, and motor cortical circuits produce these idiosyncratic behavioral biases. Here, we combined behavioral modeling of a visual perceptual decision with magnetoencephalographic (MEG) analyses of neural dynamics, across multiple regions of the human cerebral cortex. We identified distinct history-dependent neural signals in motor and posterior parietal cortex. Gamma-band activity in parietal cortex tracked previous choices in a sustained fashion, and biased evidence accumulation toward choice repetition; sustained beta-band activity in motor cortex inversely reflected the previous motor action, and biased the accumulation starting point toward alternation. The parietal, not motor, signal mediated the impact of previous on current choice and reflected individual differences in choice repetition. In sum, parietal cortical signals seem to play a key role in shaping choice sequences. There is a well-known behavioral tendency to repeat past decisions, but the neural basis of this tendency remains unclear. Here the authors show that persistent activity in parietal cortex seems to play a key role in these idiosyncratic choice repetition biases. The terms intertrial dependence3, sequential choice bias4 and choice history bias8 more generally refer to both repetition and alternation tendencies in choice sequences. Such choice history biases are prevalent even when observers judge weak sensory stimuli presented in a random sequence11, and they generalize from humans12 to monkeys15 and rodents22. Choice history biases can be adapted to the correlation structure of stimulus sequences8, and they depend on the previous decision’s confidence22.The tendency to systematically repeat or alternate choices is ubiquitous in decision-making under uncertainty. Repetition effects in choice sequences have been called decision inertia23 and motor responses24 both tend to repel subsequent choices. Yet, reporting a choice with the same or different motor act seems to have little effect on choice history biases26. Behavioral analyses suggest that the biases are dominated by observers’ previous perceptual interpretation of the sensory input, rather than the sensory input or motor act per se25.Choice history biases seem to be shaped by sensory and motor, but in particular by central processing stages. Sensory stimuli27, although their causal role has been disputed28. The state of human motor cortex reflects previous motor acts30 and, in certain task protocols, predicts an individual’s tendency towards choice alternation24. In animals, associative areas of posterior parietal cortex31 and prefrontal cortex32 carry history information that seems to play a causal role in choice history biases. Critically, however, previous studies of the neural bases of choice history bias have commonly focused on a single brain region. Thus, it has remained unclear how neural history signals in different  cortical areas conspire to shape behavioral choice sequences.Neural signatures of previous choices have been identified in primate visual cortex12. Thus, we also asked whether any of the so-identified neural history signals mirrored these idiosyncratic behavioral patterns. We found that gamma-band activity in higher-tier areas of posterior parietal cortex mediated idiosyncratic biases towards choice repetition, but not alternation.Here we disentangled neural choice history signals across the visuo-motor cortical pathway, and linked them to distinct dynamic computations underlying the formation of a visual decision. We analyzed MEG and behavioral data from 60 observers who discriminated small changes in visual motion strength. We delineated multiple neural signals reflecting choice and action history, expressed in different cortical areas and frequency bands. Notably, choice history biases in the face of random stimulus sequences are highly idiosyncratic, leading some observers to systematically alternate, and others to systematically repeat their choicesn = 60) performed a two-interval motion coherence discrimination task  and “alternators” (N = 25), without implying a statistically significant deviation from 0.5 within each individual of these groups. Yet, repetition probabilities differed from 0.5 so consistently across blocks that they were statistically significant  in a substantial number (N = 12 each) of individuals from each group  = 5.664, p < 0.001; “Methods”). These streak effects were independent of trial outcomes , we observed occipital enhancement of high-frequency power , plus parietal and frontal regions carrying action-selective activity , and when tested only in the 12 repeaters whose repetition probabilities significantly differed from 0.5 .The stimulus category (stronger vs. weaker motion) modulated high gamma-band responses in all visual field maps up to IPS0/1 Fig. , top. CrLikewise, choice history affected alpha-band power in the same direction in neighboring area IPS0/1 . None of the cortical history signals reflected choices or actions beyond one past trial  and motor action (left vs. right button press) varied between participants, we could disentangle the described effects of choice history from effects of action history  in our group analysis. Signatures of action preparation in beta-band lateralization Fig.  were preIn sum, we identified three neural signals that encoded different aspects of the previous choice: the perceptual decision and the motor act used to report that decision. Following “stronger” compared to “weaker” choices, both (i) gamma-band activity in IPS2/3 during test and reference intervals, and (ii) alpha-band activity in IPS0/1 during the test stimulus interval, were enhanced. Additionally, (iii) beta-band activity in motor areas  was stronger contralateral than ipsilateral to the previous motor response, only during the reference interval. These three signals had dissociable anatomical, temporal and frequency profiles, and differed in their sensitivity to the outcome (correct or error) of previous choices. For brevity, we refer to these as IPS2/3-gamma, IPS0/1-alpha, and motor-beta , respectively. Note that the former two were extracted during the test stimulus interval, during which the decision was computed. The motor-beta signal was extracted from the reference interval, as its history modulation had vanished by the time of test stimulus viewing. Crucially, only the IPS2/3 gamma-band signal differed between subgroups of alternators and repeaters, suggesting it as a prime candidate for dominating behavioral choice history tendencies.t(59) = 3.971, p = 0.0002) but none of the other two signals  = 1.592, p = 0.1167; motor beta: t(59) = 1.178, p = 0.2434) mediated  the effect of previous choice on current choice  = 2.148, p = 0.0358; Fig. We then aimed to pinpoint the functional roles of these distinct neural history signals. We first ran a mediation analysis based on single-trial regressions Fig. . In our ice Fig. . While s7. We next tested this idea by means of computational modeling of reaction times and choices, using the different neural history signals as single-trial physiological regressors43.In our task, the parietal IPS2/3 gamma signal dominated overt choice history biases and its individual differences , and mediated choice sequences. This was not the case for the motor beta lateralization, contradicting previous results: Pape and Siegel ref. 47. When this decision variable reaches one of two bounds, a choice is made and the motor response is initiated. Different history-dependent neural signals may be the source of distinct biases in evidence accumulation that we have previously uncovered through behavioral modeling7. One possibility is that biased activity states in motor circuitry add to accumulated evidence, without interacting with the accumulation process per se. Alternatively, biases in neural circuitry upstream from the evidence accumulator48 may bias the evidence accumulation process directly. These scenarios can be disentangled behaviorally using accumulation-to-bound models, such as the widely used drift diffusion model (DDM). In this model, choice bias can arise from two different sources . On the other hand, the input to the evidence accumulator can be biased throughout decision formation, affecting the decision process just like a bias in the physical stimulus, and producing a stimulus-independent asymmetry in the rate of accumulation towards one versus the other bound (drift bias). These mechanisms can produce the same bias in choice fractions, but have distinct effects on the shape of reaction time distributions  encoded the decision-relevant sensory signal. The parietal signals also went in the direction of choice repetition . By contrast, the beta signal in action-related areas was expressed only before decision formation, and pointed toward choice alternation  during test interval may bias evidence accumulation toward choice repetition, while the motor beta signal  at the start of the trial  may bias the starting point towards choice alternation. The underlying rationale was that the parietal signals were present during decision formation, occurred in regions that were likely to be upstream from a putative evidence accumulator7, the data were best captured by a nonlinearly collapsing bound  at the group level, previous choices had a negative effect on starting point  and a positive effect on drift . In line with previous workp = 0.0426) , but had no effect on starting point  , but no effect on drift bias  or the previous choice per se modulated the bound height. There was no evidence for a trial-by-trial adjustment of decision bounds by previous choices  and anterior parietal and frontal areas involved in action preparation55. To constrain the functional interpretation of the thus identified neural history signals, we compared them with established MEG signatures of sensory encoding and action preparation in the above regions.Even in the face of random stimulus sequences, the tendency to systematically repeat or alternate previous choices is ubiquitous in decision-making24, we also found that the sustained lateralization of beta-band activity in motor cortical areas  encoded the motor act (left vs. right button press) used to report the perceptual judgment on the previous trial. However, this action history signal had a negligible effect of mediating choice history biases and did not differ between observers with repetitive vs. alternating behavior. Using neurally informed accumulation-to-bound models, we found that the motor-beta signal biased the starting point of evidence accumulation toward choice alternation, while the parietal-gamma signal biased the rate of evidence accumulation toward choice repetition. This starting point effect of the motor-beta signal extends a previous demonstration of this signal’s involvement in choice alternation24 by linking it to a specific computational parameter. Taken together, our results indicate that persistent activity in intraparietal cortex reflect action-independent choice history signals, which shape choice repetition behavior. Posterior parietal cortex may thus integrate and sustain decision-relevant information not only within45, but also across trials, providing a bridge between sensory responses and longer-lasting beliefs about the structure of the environment.Our approach uncovered a neural signature of choice repetition bias: persistent gamma-band activity in the intraparietal sulcus . This parietal gamma-band signal encoded previous choices (“stronger” vs. “weaker” motion) in a sustained fashion from the previous choice up into the subsequent decision interval, forming an active bridge between successive decisions. The signal was strongly related to idiosyncratic features of sequential behavior: it was only expressed in observers with a tendency to repeat their choices. Critically, in these repeaters, it mediated choice repetition bias. Replicating a previous study13, and are strongly idiosyncratic7. The ultimate source of these individual differences remains a target for speculation. Agents may differ in their representation of the stability of the environment, yielding distinct different history biases56. The IPS2/3 gamma signal identified here was specific to observers who tended to repeat their choices, pointing to a potential role in implementing an idiosyncratic assumption of environmental stability.Neural correlates of decision biases have commonly been studied in the context of explicit experimental manipulations of e.g., stimulus probabilities, rewards or single-trial cues. By contrast, the biases we describe here are intrinsic and highly idiosyncratic. They arise despite verbal instructions to focus only on sensory stimuli, cannot fully be eliminated with extensive training31 and rats20. History biases in rodents may specifically depend on PPC neurons that project to the striatum, rather than motor cortex31, highlighting how specific populations of neurons may be involved in distinct decision-making computations. Because of the diminished sensitivity of our MEG recordings57 for subcortical regions, we did not analyze subcortical areas in the present study. The correlative nature of our recordings precludes strong inferences about the causal role of human parietal history signals28, but optogenetic inactivation of rodent posterior parietal cortex reduces history dependencies in behavior20. Notably, this was observed only for inactivation prior to decision formation, and parietal inactivation during the next stimulus left choice history biases unaffected58. The latter observation, obtained in rodents and different tasks, seems to be at odds with our finding that the human IPS2/3-gamma signal during stimulus viewing was a significant mediator of choice history biases. This apparent inconsistency points to the need for combined inactivation and recording studies and more direct cross-species comparisons, in order to better understand the specific role of parietal cortical dynamics in shaping choice sequences.Our findings corroborate recent demonstrations of choice history signals in the posterior parietal cortex of mice34 as selective markers for the encoding of the near-threshold task-relevant sensory signals , instead of working memory representation that was newly formed on each trial. We reasoned that this might render choice history effects in our task less dependent on working memory of the reference. This assumption was supported by the persistent nature of choice history signals observed in IPS2/3, which were largely uninterrupted by the reference  may reflect a biased encoding of the sensory information50. Action-specific beta-power lateralization in the cortical motor system pushes the motor state away from the most recent response29. Combining physiology with single-trial behavioral modeling enabled us to show that this motor beta signal specifically biased the starting point of evidence accumulation toward response alternation. This beta signal is also found in pure motor tasks that do not entail decision-making, and it likely reflects idling in motor circuits41 that prevents action repetition. In our task, this signal did not have a detectable effect on overt choice sequences. This is largely in line with previous work: repetitive choice history biases persist even with variable stimulus response mapping26, and inactivation of PPC-M2 projection neurons does not abolish choice history bias in mice18. In our experiment, the choice-response mapping was fixed within individuals, different from the variable mapping used by Pape and Siegel24. This may have caused the motor history signal to decay as participants formed the next decision, reducing the impact of the beta rebound on subsequent choices. The dominant effect of accumulation biases (toward repetition) over starting point biases  on choices may arise from their temporal dynamics: the slower the decision, the more drift biases dominate over starting-point biases in their effect on choices50. So, we could only identify the effect of motor beta-rebound on starting point by jointly fitting choices and response time distributions.Previous behavioral work has pointed to a link between response preparation and the starting point of evidence accumulation44. For example, it is unlikely that integration is non-leaky and that drift biases are constant. We previously established that even in such cases, choice history most strongly affects the accumulation bias, rather than the starting point of evidence integration7. Future work could combine time-resolved sensory inputs with fitting of more complex decision-models62, to help refine the within-trial time course of choice history biases across cortical areas. Second, although history effects on behavior were long-lasting, we here only used the previous trial’s choice as a proxy. Various reinforcement-learning models have been used to account for choice history bias1, its dependence on confidence63 and its response to environmental statistics64. Another important avenue would be to relate model-derived single-trial estimates of behavioral history bias15 and/or drift65 to the rate of decision-related cortical build-up activity66.Our modeling approaches could be extended in several instructive ways. First, the DDM is a simplified model of the dynamics of evidence integrationIn conclusion, our results show that choice history is an important source of trial-to-trial variability in cortical dynamics, which in turn biases subsequent decision computations and choice behavior. These results contribute to our understanding of how decision processes arise from a rich interplay of sensory information and contextual factors across the cortical hierarchy.7, and were therefore not incorporated in the analyses presented here.Sixty-four participants  participated in the study after screening for psychiatric, neurological or medical conditions. All participants had normal or corrected to normal vision, were non-smokers, and gave their informed consent before the start of the study. The experiment was approved by the ethical review board of the University Medical Center Hamburg-Eppendorf (reference PV4648). Before each experimental session, participants were administered a pill containing donepezil (5 mg Aricept®), atomoxetine (40 mg Strattera®) or placebo (double-blind cross-over design). These pharmacological manipulations did not affect behavioral choice history biasesThree participants did not complete all sessions of the experiment and were thus excluded. After rejecting trials with excessive recording artifacts (see below), we discarded one additional participant with fewer than 100 trials per session remaining. In total, 60 participants were included in the analysis.67 was present in the center of the screen throughout the experiment. Each trial started with a baseline interval of 500–1000 ms of randomly moving dots (0% coherence). A beep  indicated the onset of the reference stimulus (70% coherence) that was shown for for 750 ms. The reference was followed by a variable (300–700 ms) delay (0% coherence). An identical beep indicated the onset of the test stimulus, whose motion coherence deviation from the 70% reference toward stronger or weaker coherence. The deviation was individually titrated prior to the main experiment (see below). A counterbalancing scheme ensured that each stimulus category (weaker or stronger) was followed by the same or the other category equally often68. During both non-zero coherence  intervals, dots moved in one of the four diagonal directions, counterbalanced across and constant within participants. After the offset of the test stimulus, observers reported their judgment (“stronger” vs. “weaker”) by pressing a button with their left or right index finger (response deadline of 3 s). The hands used to report “weaker” and “stronger” judgments were counterbalanced across participants. Feedback (correct/incorrect) was then indicated by a tone of 150 ms .Participants were asked to judge if the coherence of a random-dot motion stimulus in a so-called test interval was stronger or weaker than a preceding reference stimulus, which was shown afresh on each trial at 70% coherence Fig. . A red “2. Dots were white with 100% contrast from the black background and 0.2° in diameter. Signal dots were randomly selected on each frame, moved with 11.5°/second in one of four diagonal directions and had a limited “lifetime” of four consecutive frames, after which they were replotted in a random location. Signal dots that left the annulus wrapped around and reappeared on the other side. Noise dots were assigned a random location within the annulus on each frame, resulting in “random position” noise with a “different” rule69. Moreover, three independent motion sequences were interleaved on subsequent frames to prevent tracking of individual signal dots70.Random dot kinematograms were presented in a central annulus  around fixation. The annulus was defined by a field of dots with a density of 1.7 dots/degreesc, defined asδ is the guess rate (chance performance), γ is the lapse rate, and α and β are the threshold and slope of the psychometric Weibull function, respectively71. While keeping the guess rate δ bound at 50% correct, we fit the parameters α, β, and γ using a maximum likelihood procedure implemented by minimizing the logarithm of the likelihood function. This was done using a Nelder–Mead simplex optimization algorithm as implemented in Matlab’s fminsearch function. The individual threshold was taken as the stimulus difficulty corresponding to a 70% correct fit of the cumulative Weibull.Before their first MEG session, participants received instructions and then did one behavioral session to determine their 70% correct threshold for the main experiment. First, 600 trials with test stimuli containing 1.25, 2.5, 5, 10, 20, and 30% coherence difference (from the 70% coherence reference) were randomly interleaved. The inter-stimulus interval was 1 s, and participants took a short break after each set of 125 trials. They did not receive feedback. Stimuli were presented on an LCD screen at 1920 × 1080 resolution and 60 Hz refresh rate, 60 cm away from the participants’ eyes. To determine each individual’s psychometric threshold, we fit a cumulative Weibull as a function of absolute coherence difference Second, participants performed another 100 trials using a 2-up 1-down staircase procedure. This procedure accounted for any learning effects or strategy adjustments during thresholding. The coherence difference between the two stimuli started at their 70% correct threshold as obtained from the Weibull fit. It was increased by 0.1% coherence on making an error, and decreased by 0.1% on giving two consecutive correct answers. Thresholds from this staircase ranged from 3.3 to 13.4% (mean 6.9%) motion coherence difference.Participants then performed the task at their individual motion threshold for a total of 1.200 trials during two MEG sessions . Between these two MEG sessions, they performed three practice sessions  outside the MEG. In the behavioral practice sessions, we presented feedback immediately after the participants’ response. An ISI of 1 s was observed before continuing to the next trial. Participants completed training on 4500 trials, over 3 separate sessions, between the two MEG recordings. The training data are not used in our current analyses.12. This approach extends the psychometric function with a history-dependent bias term δhist (ht), reflecting was a linear combination of previous stimuli and choicesλ and γ were the probabilities of stimulus-independent errors (“lapses”), α was perceptual sensitivity. The bias term δ(ht) was the sum of the overall bias ωk were the weights assigned to each previous stimulus or choice. We set K = 7, and included both previous stimuli and previous choices. Each set of seven past trials was convolved three exponentially decaying basis functions3. Positive history weights ωk then indicated a tendency to repeat the previous choice, or to make a choice that matched the previous stimulus. Negative weights described a tendency to alternate the corresponding history feature. All parameters were fit using an expectation maximization algorithm3.We quantified individual choice history strategies by fitting an extended logistic regression model, as described int + 1, taking into account that trial’s stimulus category. To quantify a repeating bias irrespective of the choice identity, we afterwards sign-flipped those criterion values that followed a “weaker” choice. This resulted in a value that, at sequence length 1, reflected each individual’s repetition probability  were recorded simultaneously. All signals were low-pass filtered online (cut-off: 300 Hz) and recorded with a sampling rate of 1200 Hz. To minimize the displacement of the subject’s head with respect to the MEG sensors, we used online head-localizationStimuli were projected into the MEG chamber using a beamer with a resolution of 1024 × 768 pixels and a refresh rate of 60 Hz. The screen was positioned 65 cm away from participants’ eyes. Horizontal and vertical gaze position and pupil diameter were recorded at 1000 Hz using an MEG-compatible EyeLink 1000 on a long-range mount (SR Research) at 60 cm from the subject’s eye. The eye tracker was calibrated before each block of training.3 voxel resolution were obtained on a 3 T Siemens Magnetom Trio MRI scanner . Fiducials  were marked on the MRI.Structural T1-weighted magnetization prepared gradient-echo images  with 1 × 1 × 1 mm73 and custom scripts. MEG data were first resampled to 400 Hz and epoched into single trials from baseline to 2 s after feedback. We removed trials where the displacement of the head was more than 6 mm from the first trial of each recording. Trials with SQUID jumps were detected by fitting a line to each single-trial log-transformed Fourier spectrum, and rejecting trials where the intercept was detected as an outlier based on Grubb’s test. To remove the effect of line noise on the data, we computed the cross-spectrum of the data at 50 Hz, resulting in a complex matrix of size n-by-n, where n was the number of channels. We applied singular value decomposition to this cross-spectrum and took the first eigenvector  as the spatial topography reflecting line noise. The two-dimensional space spanned by the real and imaginary parts of this eigenvector was then projected out of the data, effectively suppressing any signal that co-varied with activity at 50 Hz. Line noise around 50, 100 and 150 Hz was then removed by a band-stop filter, and each trial was demeaned and detrended.MEG data were analyzed in Matlab using the Fieldtrip ToolboxWe also removed trials containing low-frequency artifacts from cars passing by the scanner building, muscle activity, eye blinks, or saccades. These were detected using FieldTrip’s automated artifact rejection routines, with rejected thresholds determined per recording session by visual inspection.74 a window length of 400 ms in steps of 50 ms, and 8 Hz frequency smoothing. For sensor-level analyses, the data from each axial gradiometer were decomposed into two planar gradients before estimating power and combined afterwards, to simplify the topographical representation of task-related power modulations.We computed time-frequency representations for each of the four epochs of interests, analyzing low and high frequency ranges separately. For the low frequencies (3–35 Hz in steps of 1 Hz), we used a Hanning window with a length of 400 ms in steps of 50 ms and a frequency smoothing of 2.5 Hz. For high frequencies (36–120 Hz in steps of 2 Hz), we used the multitaper technique with five discrete proloid slepian tapersThe time–frequency representations were converted into units of percent power change from the pre-trial baseline. The baseline power was computed as the across-trial mean from −300 to −200 ms before reference onset, separately for each sensor and frequency. The resulting time-frequency representations were further averaged across trials and sensors of interest.76. A source model with 4 mm resolution was created from each individual’s MRI, and warped to the Colin27 brain77 using a nonlinear transformation for group averaging.We estimated power modulation values for a set of cortical regions of interest  based on source-reconstructed voxel time courses from a sliding window DICS beamformerWithin the alpha (8–12 Hz), beta (12–36 Hz), and high-gamma (65–95 Hz) bands, we computed a common filter based on the cross-spectral density matrix estimated from the first 2 s of each trial, starting from the start of the baseline time window. For each grid point in the brain, we then applied the beamformer  in a sliding window of 250 ms, with steps of 50 ms. The resulting source estimates of band-limited power were again converted into units of percent power change from the trial-average baseline, as described above. Rare outliers with values larger than 500 were removed. All further analyses and modeling were applied to the resulting power modulation values.To select sensors for the unbiased quantification of visual responses, we computed power modulation in the gamma-range (65–95 Hz) from 250 to 750 ms after test stimulus onset, and contrasted trials with stronger vs. weaker visual motion. We then selected the 20 most active sensors at the group level, in the first and second session separately. This procedure yielded stable sensor selection across sessions Fig. , inset. 35, and used the same split-half approach to define the 20 most active sensors for the contrast left vs. right, as well as the 20 most active sensors the for opposite contrast, to extract left and right motor regions  in the 500 ms before button pressons Fig. , inset.For each session, we then extracted single-trial values from the sensors defined on the other session. Note that sensor selection was only used for visualizing TFRs, not for the beamformed signals that we later use for statistics and modeling (see below).38, we defined a set of ROIs spanning the visuo-motor cortical pathway from the sensory (V1) to the motor (M1) periphery. The exact delineation of ROIs was based on anatomical atlases from previous fMRI work, specifically: (i) retinotopically organized visual cortical field maps54 along the dorsal visual pathway up to IPS3; (ii) three regions exhibiting hand movement-specific lateralization of cortical activity: aIPS, IPS/PostCeS and the hand sub-region of M155; and (iii) a dorsal/ventral premotor cluster of regions from a whole-cortex atlas78. We grouped visual cortical field maps with a shared foveal representation into clusters79 (Table https://pysurfer.github.io/) was used to visualize each ROI on an inflated cortical surface.Following previous worksensory V to the mWe selected two time-windows for in-depth statistical modeling: (i) the test interval during which the decision was formed (0–750 ms after test stimulus onset); and (ii) for pre-decision state, the reference interval (0–750 ms after reference stimulus onset). For each trial, we averaged the power modulation values across all time bins within these two time-windows, and used the resulting scalar values for further analyses.The general linear modeling (see below) was applied to each ROI individually. For subsequent mediation and drift diffusion modeling, we further focused on three signals of interest: IPS2/3 gamma during the test stimulus (reflecting choice history), IPS0/1 alpha during the test stimulus , and motor beta  during the reference (reflecting action history). Choice-action mapping was counterbalanced across participants. We flipped motor lateralization signals for half of the participants, so that the lateralization was always computed with respect to the hand reporting “stronger” choices.82. We used this residual IPS0/1-alpha, unconfounded by the global signal, for all subsequent behavioral modeling.The choice history-dependent IPS0/1-alpha signal was superimposed by a spatially non-specific suppression of alpha power following error feedback, which was shared by all cortical ROIs but not related to specific choice history  we used cluster-based permutation testing across the group of participantsfitglme) to quantify the effect of choice history on single-trial power modulation values across all source-level ROIs, frequency ranges and the above-defined time windows. The model included a random intercept for each participant:n was the single-trial neural data , s indicated the stimulus category , c−1 indicated the behavioral choice  on the preceding trial, and p was a participant identifier.We used general linear mixed effects models  are available as a Supplementary File, see “Data availability.”We also tested if the effect of previous choices differed between participants with choice repetition probabilities larger (“repeaters”) or smaller  than 0.5. We first estimated effect sizes and confidence intervals separately for these two groups, using the model above. This was repeated after randomly sub-selecting 25 “repeaters,” to match the number of participants between the two groups. We also fitted the group interaction term explicitly:84. To additionally test for the between-subjects factor of subgroup, we used the mixed repeated-measures ANOVA in JASP85.We tested for the effect of sequence length (1–5) and sequence end  on behavioral and neural repetition biases. Within each subgroups this was done using pingouin’s repeated-measures analysis of variance (ANOVA)86. All statistical tests reported are two-sided.Throughout, error bars show 95% confidence intervals. These were obtained through bootstrapping  or from the GLME model fits . For the neural GLMEs, p-values were corrected for multiple comparison across all ROIs, frequency bands and time windows by controlling the False Discovery Rate, the fraction of false positives, at 0.05lavaan package87. We fit the following regression equationsγ was the single-trial IPS2/3 gamma, α was the single-trial IPS/01 alpha residual, β was the single-trial pooled motor-beta, c was a vector of choices, and s was a vector with stimuli (−1 “weak” vs. 1 “strong”). We then defined our effects of interest as follows:To estimate the causal effect of trial-by-trial neural signals on choice behavior, we performed a mediation analysis using the pandas and pingouin84 and visualized with seaborn88.We fit the model separately for each participant using a WLSMV estimator, and then computed group-level statistics across the standardized individual coefficients. Data were analyzed with 43. In each model, we fit a stimulus-dependent drift rate v, starting point z, boundary separation a, and nondecision time t. First, we assessed if our data was best described by a static, linearly collapsing, or collapsing bound. The latter, best-fitting bound collapse was described by a Weibull functionTo fit a set of Hierarchical Drift Diffusion models with trial-by-trial MEG regressors, we used the HDDMnn packagev was the drift, z the starting point, s the stimulus category , and nx were vectors of single-trial neural signals of interest , each z-scored within participant. The model used “stimulus coding,” which fits response time distributions for “stronger” vs. “weaker” choices (rather than for correct vs. incorrect choices) in order to capture a selective choice bias.We then let both the starting point and drift bias of the DDM depend on single-trial neural data:For each model, we ran a Markov Chain Monte Carlo procedure with 10,000 samples, of which a tenth was discarded as burn-in. Five percent of trials were assumed to be outliers in the fitting procedure. Statistics were computed on the group-level posteriors.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


C9orf72 GGGGCC hexanucleotide repeat expansion include early dipeptide repeats, repeat RNA foci, and subsequent TDP-43 pathologies. Since the discovery of the repeat expansion, extensive studies have elucidated the disease mechanism of how the repeat causes neurodegeneration. In this review, we summarize our current understanding of abnormal repeat RNA metabolism and repeat-associated non-AUG translation in C9orf72 frontotemporal lobar degeneration/ALS. For repeat RNA metabolism, we specifically focus on the role of hnRNPA3, the repeat RNA-binding protein, and the EXOSC10/RNA exosome complex, an intracellular RNA-degrading enzyme. In addition, the mechanism of repeat-associated non-AUG translation inhibition via TMPyP4, a repeat RNA-binding compound, is discussed.Neuropathological features of frontotemporal dementia and amyotrophic lateral sclerosis (ALS) due to  Recently, the term “frontotemporal lobar degeneration” (FTLD) is often used to define these conditions from a neuropathological/mechanistic aspect. FTLD is heterogeneous in accumulating proteins and is subclassified into several subgroups according to the major contents of the accumulating protein. Among these, FTLD cases with prominent tau pathology are called FTLD-tau, and cases with abundant TDP-43 accumulation are called FTLD-TDP.Frontotemporal dementia (FTD) is a clinical concept of neurocognitive disorder that encompasses several groups of neurodegenerative conditions characterized by slowly progressive and characteristic alterations or deficit in behavior, executive function, and/or language These two categories account for roughly 90% of neuropathologically confirmed FTLD cases.C9orf72 (chromosome 9 open reading frame 72) gene. In contrast to 2-23 (or up to 30) GGGGCC repeats in non-carriers, hundreds to more than a thousand tandem expanded repeats can be found in the expansion carriers. This mutation has been reported at a low frequency in Japan, although there are some reported cases (4). How these extended repeats cause TDP-43 protein abnormalities, neurodegeneration, and ultimately neurocognitive and motor neuron diseases has been actively debated since the identification of repeat expansion.Although the majority of FTLD-TDP cases are sporadic, there are cases caused by genetic mutations. The most frequent genetic cause of FTLD-TDP is the hexanucleotide repeat expansion mutation in the intron of the C9orf72 mutation, neuronal TDP-43 typically forms aberrant intracytoplasmic inclusions with a concomitant absence of TDP-43 from the nucleus. The higher TDP-43-positive inclusion load correlates well with the severity of neurodegeneration. Thus, TDP-43 appears to be more akin to a downstream executor of neuronal cell death.TDP-43 is a ubiquitously expressed multifunctional RNA-binding protein and confers splicing, RNA transports, and metabolism. Physiologically, TDP-43 is predominantly present within the nucleus and shuttles between the nucleus and cytoplasm; however, in FTLD-TDP cases including the C9orf72 gene, the mature C9orf72 RNA transcript does not contain the repeat region and thus none of mature C9ORF72 protein contains the translated repeat sequence. Then, how does the repeat cause neurodegeneration? One hypothetical mechanism is the repeat RNA toxicity. The repeat-containing region in C9orf72 is bidirectionally (sense direction and antisense direction) transcribed; accordingly, the GGGGCC repeat RNA transcript and CCCCGG repeat RNA transcript are generated from the same DNA GGGGCC repeat expansion. These repeat RNA sequences can sequestrate RNA-binding proteins that preferentially bind to the repeated RNA sequence. These RNA/protein complexes may aggregate, and form intracellular structures called RNA foci. RNA foci are a neuropathological hallmark in C9orf72 repeat expansion carriers and can be found not only in neurons but also in non-neuronal cells . Although still controversial (5), several studies have revealed a correlation between RNA foci with neurodegeneration in C9orf72 FTLD/ALS cases (7). Moreover, a zebrafish model demonstrated clear repeat RNA toxicity (8).Aberrant DNA repeat expansion is a genetic cause and thus is unquestionably upstream of the pathological cascade. First, the DNA repeat is transcribed into an RNA repeat. As the expanded repeat is located in the intron (or promoter) of the C9orf72 FTLD/ALS cases is the accumulation of dipeptide repeat proteins (DPRs) (9). Transcribed intronic repeat RNA not only forms RNA foci but is also translated through repeat-associated non-AUG (RAN) translation (10). RAN translation can occur in all possible reading frames and thus results in the production of five different DPRs from the bidirectionally transcribed repeat RNA (14). Conventional translation requires a “start translation here” signal during ribosomal scanning from the 5'- to 3'-side of an RNA molecule. The start signal is usually an AUG (encoding methionine) initiation codon with a surrounding sequence that matches the Kozak rule well. Repeat-associated non-AUG translation (RAN translation) (10) is recognized as an unconventional translation event in which translation of the repetitive sequence occurs in the absence of an AUG initiation codon.The other disease hallmark of It is believed that there are at least two types of RAN translations: those initiated from near cognate codons  and those initiated from specific RNA structures such as RNA hairpins.C9orf72 repeat expansion has been reported as a CUG codon in the 5'-flanking region of the GGGGCC repeat (15). It has also been suggested that this CUG codon-dependent RAN translation of poly-GA is involved in the production of poly-Gly-Pro (GP) and poly-Gly-Arg (GR) through translational frameshift (17). Conversely, another group has ruled out the production of poly-GP and poly-GR by frameshifting (18). Additionally, the presence of an AUG-initiated upstream open reading frame (uORF) of 180 bases spanning exon 1 and intron 1 of the C9orf72 gene located in the poly-GP reading frame was reported (19). This uORF ends just before the GGGGCC repeat and translation of the uORF suppresses RAN translation in poly-GA and poly-GP frames, adding further complexity to the regulation of C9orf72 RAN translation.The start site of RAN translation in the poly-Gly-Ala (GA) reading frame of the (20). The RNA helicase DHX36 unwinds the G-quadruplex structure of GGGGCC repeats and promotes RAN translation, whereas DDX3X conversely represses the RAN translation of GGGGCC repeats in an RNA helicase activity-dependent manner (22). Cellular stress has also been reported to enhance RAN translation via the phosphorylation of eIF2α (23). Double-stranded RNA-dependent protein kinase (PKR), which is activated by repeat RNA, also enhances RAN translation via the phosphorylation of eIF2α. Conversely, the antidiabetic drug metformin, a PKR inhibitor, inhibits RAN translation (24). These RAN translation regulators are attracting attention as potential target molecules for disease-modifying drugs in C9orf72 FTLD/ALS via the inhibition of RAN translation.The molecules that mediate RAN translation are still little known. RPS25, a factor previously known to be associated with Internal Ribosome Entry Site-dependent translation, was reported to activate RAN translation (25). In contrast, poly-GP and poly-Pro-Ala (PA) have been shown to have no apparent toxicity (26).The neurotoxicity of DPR generated by RAN translation has been shown in multiple disease models. The most abundant DPR poly-GA aggregates adsorb large amounts of stacked proteasomes, which impairs intracellular proteostasis (28), chromatin (29), nuclear membrane pores, and nucleoli (31).Cellular organelles, such as the endoplasmic reticulum, lysosomes, mitochondria, and nucleus, are intracellularly compartmentalized by lipid bilayers, as described in textbooks. Unlike these classical organelles, intracellular structures, such as the nucleolus, centrosome, spliceosome, and stress granules, do not have such membrane partitions of lipid bilayers. Liquid-liquid phase separation is a mechanism commonly used in cells as a principle for ordering the formation and maintenance of such non-membranous structures. Poly-GR and poly-Pro-Arg (PR), that is, DPRs containing arginine residues within the repeat motif, preferentially disrupt the behavior of this liquid-liquid phase separation, leading to the impaired architecture and function of the membrane-less organelles, including stress granules (27) in part through the inhibition of small nucleolar RNA maturation (32). In addition, poly-GR and poly-PR directly inhibit translation through their interaction with ribosomes (35). Thus, each DPR is thought to impair cellular function through different mechanisms, eventually leading to neuronal death.A major physiological function of nucleoli is the production of cellular translational machinery, the ribosome. Poly-GR and poly-PR accumulate on nucleoli and inhibit ribosomal (r)RNA production (36). Even so, an association between RNA foci and the abnormal localization of TDP-43 has been noted in autopsy cases (7). We previously established an in vitro RNA-binding assay to purify RNA-binding proteins that selectively bind to GGGGCC repeat RNA, followed by their identification by mass spectrometry (37). We then performed secondary immunohistochemical screening for these candidate proteins using the hippocampal tissue of patients with C9orf72 FTLD/ALS and compared them with control tissue. From this, we noticed that hnRNPA3 is present in the cell nucleus in healthy tissues, whereas in tissues of patients with C9orf72 FTLD/ALS, nuclear staining was variably lost, and hnRNPA3-positive inclusion pathology was observed in the hippocampal dentate gyrus (37).There is debate about whether the repeat RNA itself is neurotoxic because of the difficulty in fully separating repeat RNA toxicity from DPR toxicity C9orf72 FTLD/ALS? To answer this question, we performed functional analysis of hnRNPA3 in the context of C9orf72 FTLD/ALS. siRNA-mediated knockdown of hnRNPA3 in cells exogenously expressing GGGGCC repeats increased the accumulation level of GGGGCC repeat RNA. An RNA-binding domain mutant of hnRNPA3 lacked the ability to rescue the phenotype. Conversely, overexpression of hnRNPA3 decreased the expression level of repeat RNA. Thus, hnRNPA3 repressively regulates the repeat RNA expression level and this inhibits the DPR expression level. These findings were confirmed from analyses using patient-derived fibroblasts and primary cultured rat neurons, as well as from analyses of patient brains. These results indicate that hnRNPA3 suppresses repeat RNA expression levels by promoting repeat RNA metabolism. When hnRNPA3 is lost and this repression is compromised, there is a marked accumulation of repeat RNA and an increased expression level of DPR (39).So, how does hnRNPA3 affect the pathogenesis of C9orf72 mRNA transcripts in cells derived from C9orf72 mutation carriers are only about half that of those without the repeat expansion. However, repeat RNA, derived from the same RNA transcript, accumulates as RNA foci. As an explanation for this seemingly contradictory phenomenon, we considered the possibility that the degradation of abnormally elongated repeat RNAs is impaired.As the expanded repeat hinders efficient transcription, the expression levels of mature (32). The RNA exosome complex is a multimeric protein complex that governs the metabolism of RNA and this EXOSC10 is a nucleolar enriched subunit of the RNA exosome complex. Interestingly, the genetic mutations of other components of the RNA exosome complex have been linked with neurodegenerative phenotypes (42).As there was no prior knowledge of how GGGGCC repeat RNA is degraded in the cell, we first knocked down molecules constituting several representative RNA-degrading enzyme systems and monitored the expression level of DPR as a readout in preliminary experiments. With this, we found that EXOSC10 plays an important role in the degradation of repeat RNA  C9orf72 FTLD/ALS resulted in intracellular repeat RNA accumulation and increased nuclear RNA foci, suggesting that the EXOSC10/RNA exosome complex is involved in the metabolism of endogenous repeat RNA in patient-derived cells. In cells that expressed poly-GR or poly-PR through RAN translation, EXOSC10 was redistributed diffusely into the nucleus instead of being confined to the nucleolus.The knockdown of EXOSC10 in fibroblasts derived from patients with(32).Moreover, these arginine-rich DPRs inhibit endogenous EXOSC10 activity, which leads to the additional accumulation of GGGGCC repeat RNA. These results suggest that poly-GR and poly-PR inhibit the metabolism of repeat RNA by inhibiting the EXOSC10/RNA exosome complex. The accumulation of repeat RNA accelerates poly-GR and poly-PR production through RAN translation, thus further aggravating the pathological processes (45). GGGGCC repeat RNA is known to adopt a strong tertiary structure called the G-quadruplex in the presence of potassium ions. TMPyP4 porphine) is a type of porphyrin that has been reported to bind to the G-quadruplex of GGGGCC repeat RNA (46). We therefore investigated the effect of TMPyP4 on RAN translation of C9orf72 GGGGCC repeats. In a cellular model, TMPyP4 inhibited DPR production by RAN translation, while sparing the repeat RNA expression level, nucleocytoplasmic distribution of repeat RNA, and global translational activity. Though an artificial insertion of the AUG initiation codon just before the repeat strongly enhanced repeat translation through conventional initiation, TMPyP4 strongly inhibited repeat translation even in the presence of the AUG initiation codon, suggesting that TMPyP4 does not specifically inhibit the non-AUG-dependent initiation of RAN translation. This finding led us to the hypothesis that TMPyP4 may inhibit the elongation step of RAN translation rather than non-AUG initiation. If elongation is inhibited, a large number of ribosomes stop on a single repeat RNA, and the complex of repeat RNA and ribosomes can be recovered in the higher-density fraction of sucrose density-gradient centrifugation of cytoplasmic cell lysate. Indeed, in cells treated with TMPyP4, more repeat RNA was found in the poly-ribosomal (highest density) fractions than in untreated cells. Furthermore, TMPyP4 and repeat RNA showed a strong interaction that was resistant to denaturing urea. The tight interaction between TMPyP4 and repeat RNA would physically inhibit ribosomal translocation. These results suggest that TMPyP4 binds tightly to GGGGCC repeat RNA and inhibits RAN translation by blocking the RAN translation elongation (47).The selective inhibition of RAN translation and DPR production may lead to the development of a novel therapeutic strategy (52). In particular, in a mouse model expressing 200 repeated poly-GR, poly-GR was found to cause TDP-43 aggregation in the cytoplasm via the mislocalization of nucleocytoplasmic transport factors and nuclear pore component proteins, pointing to a link between DPR and TDP-43 pathology (53).It has been pointed out that DPR and repeat RNA, as well as TDP-43 aggregates themselves, disrupt nucleocytoplasmic transport mechanisms and nuclear membrane pore function in multiple disease models (56). In addition, a small number of cases have been reported with clinical FTD and abundant DPR pathology at autopsy, but little or no TDP-43 pathology (59).The relationship between DPR pathology and clinical phenotypes in human patients remains unclear and requires further investigation. Although TDP-43 inclusion pathology correlates well with neurodegeneration, it has been noted that DPR inclusion pathology does not usually co-localize with TDP-43 inclusion. However, recent detailed reports have pointed to an association between poly-GR load and neurodegeneration C9orf72 open reading frame was initially unknown, but its physiological function and role in C9orf72 FTLD/ALS pathogenesis have gradually become clear. The C9ORF72 protein forms a heterotrimer complex with SMCR8 and WDR41 and functions in the autophagy/lysosome system. As abnormally expanded GGGGCC repeats reduce the efficiency of transcription by RNA polymerase, the expression of C9ORF72 protein is decreased (61). Importantly, no neurodegeneration is observed in C9orf72 knockout mice (62), but systemic inflammation, including in the brain, lymph node enlargement, splenomegaly, and shortened lifespan due to autoimmune reactions have been observed (63). Such lysosomal system dysfunction and systemic inflammation are considered to exacerbate repeat-mediated gain of toxicity (65).The function of the C9ORF72 protein encoded by the C9orf72 FTLD/ALS. One such approach is an antisense oligonucleotide (ASO), which effectively reduces repeat-containing transcripts (66). Another approach is to identify the RAN translation inhibitor that can selectively inhibit RAN translation, while sparing conventional translation as mentioned in the above section. The other approach includes repeat transcription inhibitors (67).Several attempts are being made to develop a treatment for patients with C9orf72 mutations. Especially, poly-GP, which is relatively abundant and has less aggregation potency, could be detected in cases with C9orf72 repeat expansion specific manner and is currently used as a target engagement biomarker to demonstrate proof of concept in drug discovery. Poly-GP levels are rather consistent in each patient in repeated measurements (72). The frequently used assay platforms are based on Meso Scale Discovery ELISA or single-molecule array (68).Biomarkers reflecting disease status are important for diagnosis, disease progression monitoring, and assessment of response to potential treatment. Although RAN translation is considered a rather inefficient event, DPRs can be detected with state-of-the-art highly sensitive assays in the cerebrospinal fluid (CSF) of patients or carriers with C9orf72(69). CSF poly-GA and poly-GR levels did not correlate with clinical phenotypes, but a patient with C9orf72-ALS treated with an ASO targeting the repeat-containing C9orf72 transcript showed decreased CSF poly-GA, poly-GP, and poly-GR levels.Recent highly sensitive assays also enabled poly-GA and poly-GR measurements from the CSF of patients with C9orf72 FTLD/ALS have so far suggested that autophagic dysfunction due to haploinsufficiency of the C9ORF72 protein enhances primary gain of toxicities from repeat RNA and DPR. Interestingly, this mutation is known to cause psychiatric symptoms more frequently than FTD because of other genetic causes or sporadic FTD (75). By analyzing the disease mechanism of this particular mutation in detail, we hope to contribute to develop more accurate and early diagnosis and disease-modifying therapy. More specifically, we hope to extend the knowledge gained from the analysis of hereditary FTD to the pathophysiology of more frequent sporadic FTD.Extensive efforts to elucidate the pathogenesis of This article is based on the study, which received the Medical Research Encouragement Prize of The Japan Medical Association in 2021.NoneK.M. is supported by the JSPS KAKENHI grant numbers JP20H03602, JP20H05927, and JP22K19492; JST FOREST Program under grant number JPMJFR200Z; and SENSHIN Medical Research Foundation and Takeda Science Foundation. S.G. is supported by a JSPS Research Fellowship for Young Scientists JP22J12248. S.A. is supported by KAKENHI JP21K15682. S.T. is supported by KAKENHI JP22K07559. M.I. is supported by AMED grant number JP21ek0109510h0001.We apologize that we could not cite all relevant original papers due to space limitations.


E. coli for combined expression and packaging of non-viral RNAs into Qβ VLPs, we investigated the extent of chemical protection conferred by packaging of RNA in VLPs. We also probed relationships between packaging efficiency and RNA size, sequence and intrinsic compaction. We observe that VLP packaging protects RNA against assault by small diffusible damaging agents such as hydroxyl radicals and divalent cations. By contrast, the extent of unmediated cleavage, in the absence of reactive species, is the same for RNA that is free or packaged within VLPs, and is very slow. In vivo packaging of RNA within VLPs appears to be more efficient for intrinsically compact RNAs, such as rRNA, and less efficient for unstructured, elongated RNA such as mRNA. Packaging efficiency is reduced by addition of the ribosome binding site to a target RNA. The Qβ hairpin is necessary but not sufficient for efficient packaging.Virus Like Particles (VLPs) are devices for RNA packaging, protection and delivery, with utility in fundamental research, drug discovery, and disease treatment. Using  VLP packaging is most efficient for compact RNA, and protects RNA against assault by small diffusible damaging agents. Small interfering RNAs (siRNAs) and microRNAs (miRNAs) modulate gene expression by inhibiting translation or by stimulating degradation of mRNAs.4,5 RNA can be active as antibacterial or antiviral agents.6,7RNA-based tools have utility in fundamental research, drug discovery, and disease treatment8 Chemical and biological stability of RNA is low.9,10 RNA production on large scales by in vitro transcription11 or solid-phase synthesis12 is problematic.13,14Impediments to broadly applicable RNA-based therapeutics remain. Cellular uptake is inhibited by the high anionic charge of RNA.in vivo, in coupled RNA production and packaging systems, may be effective for overcoming at least a subset of these challenges.15 RNA expression in vivo16–18 can employ recombinant nucleic acids that encode polymerase promoters, affinity tags, functional ‘target’ sequences and genes for packaging proteins.The packaging of RNA by protein(s) T = 3 icosahedral nanoparticles with diameters of ∼30 nm, formed of 180 copies of the coat protein (CP).19–21 Qβ VLPs are hollow with pores 1.4 nm in diameter.20Virus Like Particles (VLPs) are packaging systems that can be used to deliver RNA and other cargo. Qβ and MS2 VLPs are derived from single-strand RNA bacteriophages. Their genomes are small and their protein capsids are et al.22 and Fang et al.15 previously demonstrated packaging of non-viral RNA in Qβ VLPs by co-expressing target RNA and CP from distinct plasmids in E. coli. In vivo packaging of target RNA in VLPs is promoted by an RNA hairpin (hp), derived from Qβ RNA, which has high affinity for CP , and sequence-specific interactions.25 RNA secondary structure also promotes viral assembly; RNA stem-loops are thought to promote capsid formation, while CP interactions with RNA promote RNA collapse.26,27Lau y for CP .23,24 CP28–33 tRNA fusions were produced and packaged in MS2 VLPs within E. coli by Ponchon et al.14 We recently demonstrated simultaneous production and packaging of active RNAi in Qβ VLPs using a novel scaffold.15Biological stability of RNA can be increased by fusing target RNA to stable folds such as 5S rRNA or tRNA. These fusions allow simultaneous production and purification of active RNA in fermentation-based systems.in vivo. We report that packaging in VLPs chemically protects RNA from small diffusible chemicals that can readily penetrate the VLP. It appears that packaging chemically stabilizes RNA by mechanisms beyond direct exclusion of reactive species from proximity to the RNA. We show that intrinsic compaction of RNA may increase the efficiency of assembly. Intrinsically compact RNA, here derived from rRNA, appears to package with high efficiency in vivo.Here, we explore factors that influence the stability and quantity of target RNA that is packaged in Qβ VLPs E. coli  transformants were selected based on streptomycin resistance (50 μg mL−1) and screened for inserts by colony PCR.DNA encoding Qβ CP (NCBI reference sequence: NC_001890.1) with flanking restriction sites Nco I and Avr II was synthesized by recursive-PCR (R-PCR) Table S1 and clon Table S1. E. coli34 (Table S2E. coli BL21(DE3) pCDF-CP/pET-RNA transformants were selected based on LB streptomycin/kanamycin resistance (50 μg mL−1).Template DNAs flanked by XbaI and BlpI restriction sites were synthesized by R-PCR Table S2 and clon Table S2. E. coliE. coli BL21(DE3) containing pCDF-CP vector was inoculated in NZY medium (1.0% select peptone (NZ amine), 0.5% sodium chloride, 0.5% yeast extract) containing streptomycin (50 μg mL−1) and incubated overnight at 37 °C. Qβ VLP production was initiated with the addition of 1% overnight culture to ZYM-5052 auto-induction medium.35 Production culture was incubated for 24 h at 37 °C. Cells were collected by centrifugation at 4 °C and 6500 × g for 30 min. Cells were resuspended in an equal volume of Qβ buffer  and lysed by sonication at 40 watts for 3 min with 10 s on/off intervals. The lysate was centrifuged for 30 minutes at 23 400 × g. Ammonium sulfate precipitation (2 M) was followed by 30 minute centrifugation at 23 400 × g to obtain crude VLPs, which were suspended in 1 mL Qβ buffer and extracted three times with 1 : 1 n-butanol : chloroform. VLPs in the aqueous layer were purified by step sucrose gradient ultracentrifugation (10–40% w/v) at 40 000 rpm for 2 hours. Qβ VLPs were precipitated with 20% w/v PEG8000. Resuspended Qβ VLPs were dialyzed against dialysis buffer  for 2 h. Qβ VLPs were characterized by SDS polyacrylamide gel electrophoresis. Qβ VLP–RNA complexes were obtained as described above but by co-expression of pCDF-CP/pET-RNA selected with streptomycin/kanamycin (50 μg mL−1).g. The RNA retained in the aqueous phase was precipitated in ammonium acetate at −20 °C for 24 h followed by 15 minute centrifugation at 16 300 × g. The RNA pellet was washed three times with 80% ethanol. RNA was dissolved in nuclease free water and stored at −80 °C. In this extraction method, RNA yield is about 4–5 μg for every 50 μg VLP or VLP–RNA. RNA samples were analyzed by denaturing urea polyacrylamide gel electrophoresis or Agilent 2100 Bioanalyzer.To extract RNA from purified VLPs, 50 μg of VLP was added to extraction buffer  to 250 μL. The solution was incubated at room temperature for 20 minutes. An equivalent volume of low pH phenol–chloroform was then added to the solution. RNA extracts were centrifuged for 15 minutes at 16 300 × −1). A sample volume of 15 μL was deposited on a copper grid and incubated for 5 min. Excess sample was wicked off with filter paper. The grid was then placed face down on a droplet of 2% ammonium molybdate stain for 2 min. Excess stain was wicked off with filter paper and the grid was dried for 2 h before imaging.TEM images were obtained with a Hitachi H-7500 electron microscope. Samples were prepared by fixing with 250 μL of glutaraldehyde (8%) per 500 μL of sample (1 μg mL36 VLP–RNA assemblies (250 μg) or unpackaged RNA (25 μg) were added to pre-cleavage solution  to a volume of 50 μL. Cleavage reactions were initiated by addition of 10 μL of freshly prepared 200 μM iron(ii) EDTA with a 2 : 1 ratio of (NH4)2Fe(SO4)2·6H2O to EDTA. Reactions were quenched at various timepoints (0–180 minutes) by addition of 20 μL of 0.1 M thiourea. For reactions with varying concentrations of hydroxyl radical, 10 μL of iron(ii) EDTA (0–1500 μM) was prepared and added to solutions of VLP–RNA complexes or unpackaged RNA in solution as described above. RNA extraction was performed according to the procedures above.Following established methods,37 VLP–RNA assemblies (250 μg) or unpackaged RNA (25 μg) were added to Mg2+ cleavage solution  to a final volume of 60 μL. Samples were incubated at 37 °C for various time periods (0–120 hours). VLP–RNA complexes or unpackaged RNA were added to solutions of varying concentration of MgCl2 (0–300 mM). Samples were incubated at 37 °C for 15 minutes. Reactions were quenched by freezing at −80 °C. RNA extraction was as described above.Following established methods,To investigate chemical stability of the RNA in the absence of reactive species, VLP–RNA complexes or unpackaged RNAs were incubated in a solution lacking divalent cations . These samples were incubated at room temperature for various time periods.Reaction products were run on denaturing urea polyacrylamide gels and stained with SYBR® Green II. Band intensities were integrated with AlphaView® software for FluorChem systems. Packaging efficiencies were determined by normalization of packaged target RNA to co-packaged CP mRNA from the same co-expression/purification.in vivo packaging efficiency. A series of RNAs were co-expressed with Qβ VLP CP in E. coli (Here we assessed: (i) the chemical stability conferred to RNA by packaging in Qβ VLPs, and (ii) the effect of specific RNA characteristics on  E. coli . The RNA38 were constructed and treated with reactive chemicals. a-rRNA is a well-characterized RNA derived from the ancestral core of the Thermus thermophilus large ribosomal subunit rRNA.38 Here, a-rRNA was fused to the Qβ hairpin  and 23S rRNA-hp (3051 nt) are packaged with similar efficiencies . This increase in mobility is consistent with a greater compaction of a-rRNA-hp over mRNAGFP-hp in the native state.44We used gel mobility to qualitatively characterize the extent of compaction of a-rRNA-hp compared to mRNAGFP-hp fused to the ribosomal binding site (RBS)45 . This result suggests competition between ribosomal binding and Qβ assembly in vivo.Quantitative comparison of packaged RBS-mRNAThe Qβ hp is necessary for efficient packaging of essentially any RNA within a Qβ VLP. a-rRNA fused to the Qβ hp (a-rRNA-hp) shows the highest packaging efficiency of any RNA tested here. We investigated the importance of the Qβ hp to packaging of a highly structured RNA by removing Qβ hp from a-rRNA-hp a-rRNA, . Removalin vivo may depend upon the compactness of the target RNA structure, the absence of competing binding motifs in the RNA, and the presence of the Qβ hp.Together, these results suggest that efficient packaging of RNA within VLPs E. coli for combined expression and packaging of RNA into Qβ VLPs, we probed relationships between packaging efficiency and RNA size and intrinsic compaction. We characterized the degree of RNA protection from small diffusible agents such as hydroxyl radical and magnesium cations conferred by packaging in VLPs.The potency and efficacy VLP–packaged RNA is related to the functionality, purity and quantity of the packaged RNA, and to the chemical stability of the RNA within the VLP. Using 39 The mechanism of this protection is steric; the enzymes are significantly larger than the pores in the VLP capsid. Here we establish that packaging in VLPs protects RNA against small, diffusible damaging agents that are smaller than the capsid pores.RNA packaged within MS2 VLPs is completely protected from digestion by plasma ribonucleases.20 that are significantly larger than the metal ions and their hydrates. Mg2+ has an ionic diameter of 0.13 nm and a fully hydrated diameter of just less than 1 nm.46 Pores in VLPs allow entry and exit of metal ions and species such as hydroxyl radical. Diffusion of metal ions into Qβ VLPs has been demonstrated by Finn, who showed that RNA packaged in VLPs is degraded by lead(ii) acetate.47 It has been shown that RNA within MS2 VLPs is degraded by high pH.48We determine the level of protection afforded against metal ion mediated strand cleavage by packaging in Qβ VLPs. Mechanisms and levels of protection against small reactive molecules such as metal ions are expected to be distinct from those of nucleases because Qβ VLPs contain pores2+ and Fe2+ enter VLPs and degrade RNA. The results are consistent with expectations that rates of RNA cleavage caused by these metals are highly attenuated for compact RNA packaged in the interior of VLPs compared to rates of cleavage of free RNA in solution. RNA is substantially protected against hydroxyl radical and magnesium-mediated in-line cleavage by packaging in VLPs , the electrophile (the P) and the leaving group (the 5′ oxygen). The reaction is faster in the presence of magnesium because it stabilizes the transition state by accepting a proton from the 2′ oxygen and/or by withdrawing electron density from the electrophilic phosphorous. Flexible regions of RNA are most labile to in-line cleavage because they more frequently occupy transient conformational states similar to the transition state.37,41 As expected, in the presence of magnesium the rate of cleavage decreases when RNA is packaged in the VLP. The reaction rate decreases because the dynamics of RNA within the VLP are suppressed by confinement and association with CP. Packaging of RNA in Qβ VLPs protects RNA not only against nucleases51 but against assault by small diffusible species.The RNA backbone is cleaved spontaneously in both the presence and in absence of magnesium.in vivo RNA packaging of RNA within VLPs may be most efficient for intrinsically compact RNA such as rRNA and less efficient for dynamic, elongated RNA such as mRNA. The Qβ hp is necessary but not sufficient for efficient packaging.The results indicate that 52–54 and tentatively identified 60 RNA stem-loops in the MS2 genome.26 RNA collapse during assembly in vitro depends initially on RNA–protein interactions, followed by CP–CP interactions.55 Packaging involves multiple weak RNA–CP interactions along the length of the viral RNA and a single strong interaction with the MS2 hp.27 MS2 and Qβ CP share about 25% amino acid sequence identity,56–58 with highly similar three-dimensional structures and RNA binding sites.Stockley previously suggested that for viral RNA, stem-loops dispersed through the MS2 viral genome interact with CP during assemblyin vivo packaging efficiency of non-viral RNA within Qβ VLPs may be high when the RNA (i) is intrinsically compact, (ii) includes the Qβ hp, (iii) lacks binding sites for assemblies that compete with CP , and (vi) is as large as the 23S rRNA. We prepared a series of RNAs (E. coli. The RNAs expressed here contain (i) a-rRNA, a ∼650 nt intrinsically compact RNA stabilized by 11 GNRA tetraloops,38 (ii) 23S rRNA, a ∼2970 nt intrinsically compact RNA containing over forty stem-loops, or (iii) mRNAGFP, an unstructured ∼780 nt RNA. The expressed RNAs either contained or lacked the Qβ hp, and/or a RBS. The results show that both a-rRNA-hp and 23S rRNA-hp are efficiently packaged within Qβ VLP in vivo  with affinity for competing species or lacks the Qβ hp, then the amount of contaminating RNA is increased. In total, it appears that sufficient RNA is packaged in the VLP to fully compensate for the cationic charge of Qβ CP.64 It is possible that RNA is required within the VLP for neutralization of positive charge.65 In our two-plasmid expression system, T7 RNA polymerase was used to induce high-level expression of both Qβ CP and target RNA. Without the simultaneous expression of RNA in vivo, high concentrations of positively charged viral CP may form non-specific RNA–CP interactions that serve to stabilize the protein structure and neutralize RNA charge.66We and others observe that non-target RNAs, including Qβ CP mRNA, tRNAs, 67 VLPs have been shown to enhance the immunogenicity of some peptides through surface display.68 Expression of Qβ VLPs in E. coli packages host RNAs capable of inducing immune response in animal models, an incidental adjuvant.67,69 Several VLP-based therapies have already shown “good safety and tolerability” in clinical trials.67Qβ VLPs are non-infectious and non-replicative platforms for delivery of various therapies with high stability and biocompatibility.70 and to decrease immune response in rats administered adenovirus-based therapy targeting metastatic colonies.71 PEG is already clinically approved and is in use in pharmaceuticals.The immunogenicity of VLPs presents a challenge for their use in systematic delivery. However, efforts to attenuate immunogenicity are in progress in several laboratories, for example by “PEGylation”. This approach has been shown to limit the primary immune response of mice to cowpea mosaic virus,E. coli.15 The current study explored the role of RNA sequence and structure in efficient packaging by co-expression in E. coli, and the stability of RNA within VLPs once purified. Though these specific RNAs are not intended for use in VLP-based therapies, they represent a range of properties relevant to the development of RNA-based VLP therapeutics.We previously demonstrated delivery of functional RNAi to cancer cell lines using a highly structured RNAi scaffold co-expressed with VLPs in E. coli for combined expression and packaging of non-viral RNA in Qβ VLPs, we probed relationships between in vivo packaging efficiency and RNA size, sequence, and intrinsic compaction. We purified assembled VLPs and evaluated the extent of resistance to metal ion mediated degradation conferred on RNA by packaging in VLPs. We conclude that in vivo packaging in Qβ VLPs (i) protects target RNA against cleavage by hydroxyl radical and magnesium, and (ii) is efficient when the target RNA is intrinsically compact, includes the Qβ hairpin, and lacks the RBS.Using There are no conflicts to declare.RA-008-C8RA02084A-s001


New RNA-binding small-molecule scaffolds are needed to unleash the pharmacological potential of RNA targets. Here we have applied a pharmacophore-based virtual screening approach, seldom used in the RNA recognition field, to identify novel conformational inhibitors of the hepatitis C virus internal ribosome entry site. The conformational effect of the screening hits was assessed with a fluorescence resonance energy transfer assay, and the affinity, specificity, and binding site of the ligands were determined using a combination of fluorescence intensity and NMR spectroscopy experiments. The results indicate that this strategy can be successfully applied to discover RNA conformational inhibitors bearing substantially less positive charge than the reference ligands. This methodology can potentially be accommodated to other RNA motifs of pharmacological interest, facilitating the discovery of novel RNA-targeted molecules. In fact, a number of marketed small-molecule drugs, including natural antibiotics and oxazolidinone agents, exert their action by binding to ribosomal RNA. However, the discovery of new small-molecule agents binding to less complex RNA structures is hindered by a poor understanding of the principles governing RNA recognition [Many human, bacterial, and viral RNA molecules are potential therapeutic targets, but RNA remains relatively unexploited from a pharmacological perspective due to difficulties associated with the low physicochemical diversity of this biomolecule, a polyanion mostly composed of four different nucleotides (nt) . In addiognition ,3,4,5. Iognition ,7.o bend in helix II so that apical subdomain IIb is correctly oriented for interacting with the 40S ribosomal subunit [Pharmacophore matching has seldom been used in the RNA recognition field. This ligand-based computational technique defines a pharmacophore, or three-dimensional arrangement of functional groups considered to drive a biological response, and uses it to screen a virtual collection of compounds, bypassing or reducing the need to apply structure-based docking methods, which usually perform better from protein targets than RNA . Here we subunit ,10 , and an out-of-plane cationic donor (defined by the dimethyl-amino propyl chain attached to N1) (The pharmacophore was defined based on the available structure–activity relationship (SAR) data for the interaction between benzimidazole ligands and HCV IRES subdomain IIa , together groove   B. After d to N1)  to facild to N1) .d duplex containing Cy3 and Cy5 fluorescent probes at the 5′ termini , followed by gn1 , in agreement with the results of the fluorescence experiments.We also used  nt only . In cont binding . Compounqn1, qn2, and gn1 interactions was assessed by duplicating the FRET experiments in the presence of a 100-fold molar excess of tRNA. gn1 and, to a lesser extent, qn1, exhibited good specificity ratios , in agreement with the NMR and fluorescence intensity experiments, which detected binding of this compound outside the IIa bulge , sufinition B, contrign1 and qn1 are screening hits. The Kd value of the best compound, gn1, is in the low μM range  to select a smaller subset of 791,776 small molecules fulfilling the pharmacophore requirements. The “Conformation Import” module of MOE was subsequently employed to generate representative three-dimensional conformations for these compounds . The “Pharmacophore” module of MOE was then utilized to screen these conformations using a pharmacophore comprising two adjacent aromatic centers, two hydrogen-bond donor groups located in the plane of the aromatic system, and an out-of-plane cationic group  or Chembridge Corp. (bz1) and dissolved in DMSO or DMSO-d6 (for NMR spectroscopy experiments) in a 5 mM concentration. The reference benzimidazole compound Isis-11 was prepared as described [2O in a 5 mM concentration. The protonation and tautomeric states at pH 7.0 of the selected molecules and the reference compound Isis-11 were predicted with MOE , as well as negative  and positive (a mixture of IIad and the reference inhibitor Isis-11 at 200 μM concentration) inhibition controls. The specificity of the most active compounds was evaluated by duplicating the experiments in the presence of a 100-fold molar excess (10 μM) of tRNALys. The FRET experiments were repeated at least three times for each compound and condition, correction for absorbance was taken into account according to the method described previously [50 or IC50 values were calculated with GraphPad Prism using three-parameter sigmoidal models.These experiments were conducted at 25 °C essentially as described by Zhou et al. , using 92 . The IIaeviously , and EC5d bending activity of Mg2+ and Na+, which led to FRET increments associated with EC50 values of 286 μM and 214 mM, respectively  was snap-cooled in a buffer containing 10 mM HEPES (pH 7.6) with either 0 or 2 mM MgCl2 and incubated with increasing amounts of compound. In all cases, the equilibrium dissociation constants Kd were determined by fitting the fluorescence intensity curves to a two-state binding model [These experiments measured association to a fluorescein-labeled IIa hairpin C and werng model  with Grah-55F and Isis-11, for which we obtained a Kd of 11.5 μM in the absence of magnesium  and NMRFAM-Sparky 3.115 [h RNA samples were previously microdialyzed in an aqueous solution containing 10 mM sodium phosphate (pH 6.0) and 0.1 mM EDTA. The interaction of 63 μM (8 ODs) IIah, samples with each compound was monitored at 27 °C using the aromatic RNA resonances of one-dimensional and two-dimensional total correlation (TOCSY) spectra acquired at increasing RNA:ligand molar ratios, typically 1:0, 1:1, 1:2, 1:4, and 1:6. We also titrated IIah with gn1 in the same buffer, additionally including 2 mM MgCl2. In addition, a sample containing 152 μM of isolated IIah RNA hairpin was separately examined with NOESY (120 and 250 ms mixing times) and TOCSY experiments at three different temperatures and in the absence and presence of 2 mM MgCl2. The assignment of IIah was based on the analysis of these spectra and confirmed with previous IIah NMR studies [ky 3.115 . The IIa studies .