The acquisition of reliable tissue-specific RNA sequencing data from human skin biopsy represents a major advance in research. However, the complexity of the process of isolation of specific layers from fresh-frozen human specimen by laser capture microdissection, the abundant presence of skin nucleases and RNA instability remain relevant methodological challenges. We developed and optimized a protocol to extract RNA from layers of human skin biopsies and to provide satisfactory quality and amount of mRNA sequencing data.® Solution, the control of specimen temperature, the use of RNase Inhibitors and the time reduction of the staining procedure. The quality of extracted RNA was measured using the percentage of fragments longer than 200 nucleotides (DV200), a more suitable measurement for successful library preparation than the RNA Integrity Number (RIN). RNA was then enriched using the TruSeq® RNA Access Library Prep Kit (Illumina®) and sequenced on HiSeq® 2500 platform (Illumina®). Quality control on RNA sequencing data was adequate to get reliable data for downstream analysis.The protocol includes steps of collection, embedding, freezing, histological coloration and relative optimization to preserve RNA extracted from specific components of fresh-frozen human skin biopsy of 14 subjects. Optimization of the protocol includes a preservation step in RNALaterThe described implemented and optimized protocol can be used for generating transcriptomics data on skin tissues, and it is potentially applicable to other tissues. It can be extended to multicenter studies, due to the introduction of an initial step of preservation of the specimen that allowed the shipment of biological samples.The online version of this article (10.1186/s12867-018-0108-5) contains supplementary material, which is available to authorized users. The ability to generate tissue-specific expression profiles can significantly expand our knowledge on the pathophysiological mechanisms of different diseases, providing information on functional elements and molecular constituents of the tissues and identifying new diagnostic and therapeutic biomarkers . High-thTo study tissue-specific expression profile, the first step is the isolation of the biological source. The laser capture microdissection (LMD) technology allows the isolation of cells of interest morphologically identified via microscopic visualization. It can minimize the hurdle of tissue heterogeneity observed in homogenates of whole tissues , 5, allo® preserving solution , making this protocol suitable also for multicenter studies in which biological specimens need to be collected by different centers and shipped for future processing in a central laboratory, allowing its use in clinical and research studies.In the present paper, we describe an optimized protocol starting from human skin biopsy collection at the distal leg up to the final step of RNA extraction from layers of fresh-frozen sections microdissected by LMD. We implemented several optimizations to overcome the problems related to the high instability of RNA molecule, the presence of high amounts of nucleases in the skin and the many steps necessary for biopsy processing, improving the quality of extracted RNA. Moreover, we introduced an initial step of immersion of the specimen into RNALater® solution at 4 °C for 1 week.Eight patients  affected by idiopathic sensory neuropathy (mean age ± standard deviation (SD): 63.4 ± 4.2 years), and six age-matched healthy controls  were recruited. Skin biopsies were collected from the 14 subjects and preserved in RNALaterWe included the biopsies in Killik cryostat embedding OCT medium and, after the storage in liquid nitrogen for at least 1 week, we proceeded with the cryosection facilitated by liquid nitrogen flow. H&E staining was then performed modifying the protocol published by Yee and colleagues  to accelProceeding with the LMD, two-dimensional measurements and numbers of collected areas were registered before cutting the selected area in order to calculate the volume of tissue from which RNA was extracted. The microdissected sections were the enriched layer of fibers (ELF), the glands (G), the dermis (D) and the whole section (WS). The epidermis was not separately dissected from the biopsy but it was part of the ELF and of the WS. A representative picture of the staining of the four tissue components are shown in Fig. 200 metric defined as the percentage of RNA fragments longer than 200 nucleotides, was used to evaluate sample quality for the libraries preparation according to Illumina technical note [200 ≥ 70% . Unexpectedly, for 7 of 24 samples that fulfilled the requested input of RNA it was not possible to reach 1 ng/µl of cDNA. On the contrary, for 24 out of 27 samples with a lower amount of input RNA, we obtained enough quantity of cDNA to proceed with the protocol. Hybridization occurred in multiplexed modality using 200–30 ng of cDNA from each sample depending on the reached cDNA amount. In detail, the amount for pools preparation was calculated according to the sample with the lowest concentration within each pool in order to obtain homogeneous enriched samples. Additional file 2: 0.52).As reported in Additional file A number of reads between 21 and 35 million/sample was obtained in 16 samples, between 35 and 90 million reads/sample in 22 samples, and less than 20 million reads (ranging from 13.2 and 17.8 millions of reads) in 3 samples  was evaluated and represented across tissue compartments  was performed to identify potential outliers. Additional file 200 was confirmed by Spearman’s correlation analysis, as shown in Additional file 2: 0.15).Aligning the sequenced reads to the human reference genome, we reached a mean percentage of uniquely mapped reads of 83.2% ± 1.4 (mean ± SD) across ELF, WS and D while for glands it was 82.2% ± 3.7 (mean ± SD) , a higher expression was detected in all the four skin components compared to non-skin tissue  (p < 0.001). A similar trend was obtained for other analyzed skin-related genes (data not shown).To further confirm the reliability of generated data, we evaluated the expression of skin-enriched genes emerged from a transcriptomic and proteomic profiling study performed in 2015 . As showCOL17A1, DMKN and KRT10) among skin compartments and whole blood as non-skin tissue, demonstrating a significant difference. To further explore our hypothesis, we considered the ELF in the protocol and in the analysis, because it is the component of greatest interest for detecting a transcriptional signature of peripheral neuropathy since it is enriched of intraepidermal nerve fibers selectively degenerated in the disease and which density is routinely counted in skin biopsy to diagnose the disease.The present study is aimed to implementing and optimizing a protocol for the extraction of RNA from different components of skin biopsy to generate transcriptomics data. The skin is innervated by sensory nerve fibers originating from the primary sensory neurons of dorsal root ganglia, and their degeneration inversely correlates with the severity of the peripheral neuropathy and the risk of developing neuropathic pain , 16. OurOur protocol was optimized to reduce the impact of several events that affect the quality and the yield of RNA such as the activation of nucleases induced by the excision of the tissue , the pre® Solution resulted in less degradation, although they preferred to avoid the use of the RNALater® Solution because it affects the immunolabeling downstream process. However, in our protocol the use of RNALater® Solution did not affect samples when they were examined for standard histological criteria [® Solution under refrigerated condition. Our protocol was set up on 7 days of RNALater® Solution incubation; however, few hours up to 1 month at 4 °C did not impact the RNA preservation.To preserve the RNA integrity, different solutions were evaluated. Brown et al.  suggestecriteria , therefoLMD allows isolating homogenous tissues from which nucleic acids and proteins can be extracted for downstream analyses . A good ® Plus RNase Inhibitor  in H&E solutions [H&E staining was performed following the protocol published by Yee and colleagues  with somolutions , 12, butThe hardness of the skin, mainly composed by keratinocytes and cornified layers, increases the risk of flaking of the outer epidermal layers during the laser capture microdissection. A specific balance between the cutting parameters of power, thickness, and velocity of the laser beam were adjusted to avoid the tissue break and to accelerate the cutting process limiting the RNA degradation due to the high temperature of laser.200 ≥ 70%, Additional file Despite the introduction of several optimization steps to reduce RNA degradation, some samples did not fulfill Illumina recommendations regarding the RNA input for the RNA sequencing profiling  and healthy controls. Lastly, the implemented protocol can be used also in other diseases that require to collect biopsies from skin or other tissues, as well as in collaborative projects that require sampling from different centers, offering the opportunity to enlarge sample size for transcriptomics studies.In the present manuscript we describe an implemented and optimized protocol that allows obtaining reliable RNA sequencing data from fresh-frozen skin human biopsy by combining the LMD technology to NGS technology. Crucial steps of optimization of the protocol were an initial treatment of the specimens with RNALaterSkin biopsies from 14 individuals  were collected at the IRCCS Foundation “Carlo Besta” Neurological Institute in Milan in agreement with the approval of the local Ethics Committee . Written consent was obtained from all recruited participants.® Solution  and stored for 1 week at 4 °C to allow the permeation of the solution into cells resulting in RNA stabilization .Skin biopsies were taken using a disposable 3-mm punch 10 cm above the lateral malleolus under sterile condition after topic anesthesia with spray ice , 25. Imm® Solution. Around 20–30 slices of 20 μm of thickness were transferred on Pen Membrane Glass Slides  previously activated by exposure to UV light for 20 min. Every two cryosected slides a little amount of liquid nitrogen was scrolled over the sample positioned on the object holder to harden it. The tissue slices on the glass were stored at − 80 °C before proceeding with the histological coloration.The frozen samples were cryosected using the Leica cryostat  set at − 20 °C. The blade was previously cleaned with RNaseZap® Plus RNase Inhibitor  shortly before the use [® Plus RNase Inhibitor  shortly before the use. Instead of staining the glass slides by immersion, they were horizontally positioned on the bench and drops of colored solutions were added over. Subsequently, after a 10 s wash in DEPC water and a 10 s wash in 70% ethanol, the tissues on glass slides were dried at room temperature for few minutes and then stored at − 80 °C before proceeding with the laser microdissection.In order to recognize the skin layers of the biopsy, a fast hematoxylin and eosin staining was performed on the slices with slight modifications of the protocol published by Yee and colleagues . Stainin the use , 12, wasThe colored slides were removed from − 80 °C and left few minutes at room temperature to dry before being placed on a Leica Microdissection system LMD6000 . To minimize the damage of the laser on the tissue, the focus and the energy of the laser were modified for each slide. Cut sections fell by gravity in the tube and were stored at − 80 °C before proceeding with the RNA extraction.From the 20–30 colored tissue slices of each subject, four different microdissected skin components were collected: (a) the enriched layer of fibers extending from the epidermis to 200–300 μm deep into the biopsy; (b) the glands; (c) the dermis, and (d) the whole section. No relative percentage of cell types was measured, and no specific recognition was performed to determine the differences between exocrine and endocrine glands. Moreover, fat cells were not removed from the glandular tissue during the microdissection.® RNA Isolation Kit . As first step, the addition of extraction buffer was performed with 100 μl for each tissue except for glands, for which 50 μl were added to avoid an excessive dilution of RNA due to the small amount of microdissected material. An on-column DNase digestion was performed treating the RNA bound to the column for 20 min with 5 μl of DNase I diluted in 35 μl of RDD buffer  to remove genomic DNA. 20 μl of RNase Nuclease free water for all tissues, except for glands for which 15 μl was used, were pipetted directly onto the membrane of the purification column to elute the RNA into an RNase free tube. RNA was then stored at − 80 °C.Extraction of total RNA was performed using the PicoPure® RNA HS Assay kit on Qubit® 2.0 Fluorometer .RNA quality was estimated by microfluidic capillary electrophoresis by running 1 μl of each sample on an Agilent 2100 Bioanalyzer Instrument  using the Agilent RNA 6000 Pico Kit. The RNA degradation was evaluated measuring the percentage of RNA fragments longer than 200 nucleotides and the RNA Integrity Number. The concentration of RNA was measured by fluorometric quantitation using the Qubit® RNA Access Library Preparation Kit  that allows generating libraries starting from degraded and low yield RNA. Briefly, the first and second cDNA strands were synthetized from input RNA in order to be adaptor-tagged, labeled and amplified. CDNA from each sample was then pooled. Pooling strategy was decided considering tissues and subjects uniformity. Each pool contained two to four samples of the same tissue and included both patients and controls. Pooled samples were enriched by a double step of probes hybridization. The enriched targets were captured by streptavidin labelled beads, cleaned up and amplified to obtain the final multiplexed libraries. Libraries quality was checked for fragments distribution using Agilent DNA High Sensitivity Kit on an Agilent 2100 Bioanalyzer Instrument  and quantified by Qubit® DNA HS Assay kit on a Qubit® 2.0 Fluorometer . The libraries were then sequenced on an Illumina HiSeq® 2500 platform (Illumina®) with a rapid paired-end sequencing (2 × 101 bp). The protocol for libraries generation was executed step by step except for the adjustment of cDNA amount used for hybridization (details shown in the results section).RNA was processed for library preparation using the TruSeqThe quality control check on raw sequenced data was assessed with FastQC (version 0.11.3) in a non-interactive mode . The obtStatistical data and graphs were performed in GraphPad Prism Software, version 5.04. Mean values and standard deviations are presented for different measures. Correlation analysis was carried out with the Spearman’s r coefficient calculation. All the other data were analyzed statistically by the Mann–Whitney nonparametric test. Differences were considered statistically significant when the p value was ≤ 0.05.Additional file 1: Table S1. Summary of characteristics of subjects and skin components. Quality parameters on extracted RNA (RIN and DV200), RNA input amount used for library preparation, RNA concentration of pre-pooled samples, amount of hybridized cDNA, final concentration of libraries and million of reads sequenced for sample are shown. Subjects are indicated by the sample ID. Samples from number 1 to 8 were patients and from number 9 to 14 were healthy controls. Samples pooled together are indicated by the same capital letter (from A to O) in the pool row. The samples that did not reach the requested amount for hybridization were excluded from the experiment (marked as “X”).Additional file 2: Figure S1. Base sequence quality score. The two graphs show the Phred quality score (Q score) on y axis across all sequenced based on x axis at each position in the FASTQ file for the different skin layers, flow cells (FC) and reads (1 and 2). All the values were reported as mean ± standard deviation. A) Q score distribution across different tissues ; B) Q score distribution across the different FC used for sequencing.Additional file 3: Figure S2. Density plot. Density plot of log10-transformed reads counts of protein coding genes is reported. The global trend shows a distribution close to a Gaussian distribution and a similarity across all samples. ELF: enriched layer of fibers, G: glands, D: dermis and WS: whole section.Additional file 4: Figure S3. Principal Component Analysis (PCA). PCA was performed on log10-transformed down-sampled reads counts. The first 3 principal components (PCs), explaining the 43% of the variance, are shown. For each sample, the color and the label indicate the tissue , while the number is related to the subject ID.Additional file 5: Figure S4. Percentage of uniquely mapped reads. A) Uniquely mapped reads reported in percentage for each tissue . Scatter dot plot shows the mean ± standard deviation and each dot represents the value of a single sample. Numeric values are reported for each tissue as mean ± standard deviation. B) Relationship between the uniquely mapped reads and RNA degradation expressed as DV200 . Each dot represents one sample.


Humans are often inconsistent  when choosing among simple bundles of goods, even without any particular changes to framing or context. However, the neural computations that give rise to such inconsistencies are still unknown. Similar to sensory perception and motor output, we propose that a substantial component of inconsistent behavior is due to variability in the neural computation of value. Here, we develop a novel index that measures the severity of inconsistency of each choice, enabling us to directly trace its neural correlates. We find that the BOLD signal in the vmPFC, ACC, and PCC is correlated with the severity of inconsistency on each trial and with the subjective value of the chosen alternative. This suggests that deviations from rational choice arise in the regions responsible for value computation. We offer a computational model of how variability in value computation is a source of inconsistent choices. Humans are often inconsistent when choosing between alternatives, but the neural basis of deviations from economic rationality is unclear. Here, the authors show that irrational choices arise in the same brain regions responsible for value computation, implying that brain ‘noise’ may underlie inconsistency. For example, if a decision-maker chooses some combination of milk and cookies (bundle A) over another combination of milk and cookies (bundle B) and also chooses bundle B when a third bundle C was available, then—if she is consistent in her choices—she should not strictly prefer bundle C over bundle A in any subsequent choice. Consistency is the fundamental axiom underlying rational behavior and the neoclassical construct of utility maximization2. Despite the centrality of rational behavior in neoclassical economics, since at least the 1950s studies have demonstrated that the choice behavior of humans violate consistency when choice sets are manipulated or framed6. Such results have given rise to a behavioral approach to decision-making, in which agents are not strictly treated as consistent or rational4. Explanations for such anomalies establish that the human decision process is limited by, or maladapted to, the particular choice context under the study. For example, agents might simplify the choice process by using various heuristics8, misunderstand the problem9, or in some cases, inconsistency might arise due to a limited cognitive capacity10.The study of consistency was formalized with the Generalized Axiom of Revealed Preference (GARP)15 even when choosing over simple bundles; for example, simply switching their choices when presented with the same lotteries17. Therefore, a degree of inconsistent behavior may be fundamental to the human decision-making process. Indeed, economic theorists have proposed that the valuations which underlie choice may themselves be stochastic19. By design, these theories place weak constraints on the pattern of choice behavior because utilities are typically assumed to be unobservable. Neuroscientific methods, on the other hand, suggest a stronger test of this hypothesis20. Many previous studies have identified several brain regions—primarily the ventral striatum (vStr), the ventromedial prefrontal cortex (vmPFC) and the posterior cingulate cortex (PCC)—that correlate with a utility function fit to choice behavior irrespective of the reward type23. Whether the value representations in these areas obey consistency, and how this network might give rise to inconsistent choice behavior, is unknown.However, more recently, lab experiments and real consumption data suggest that a degree of choice inconsistency might be present in human decision-making, even absent any particular framing or context induced by the experimenter. Subjects are often inconsistent and violate GARP26. The predominant explanation for this phenomenon is due to a fundamental property of the nervous system—the inherent variability in neural activity28. We propose that the same variability is responsible for inconsistency in choice behavior when the framing or the choice context is stable. Our modeling approach is an application of the Random Utility framework20, a parsimonious account of the aggregation of value signals and neural variability in the course of a decision32. In our model, valuations of choice options are inherently stochastic, and the skewed nature of neuronal activity33 implies that the severity of an inconsistent choice results from larger fluctuations in the computation of value. This prediction might seem surprising. After all, one might not expect that a brain region which computes the valuations of choice alternatives is more active when those valuations are contradicted in an inconsistent choice. To asses this hypothesis, we measure the severity of an inconsistent choice on a trial-by-trial basis, and identify the neural correlates of inconsistent choices.Similar to sensory perception and motor output, we propose that a substantial component of inconsistent behavior is due to variability in the neural computation of value. There is ample evidence demonstrating that both the behavioral and neural responses to the same sensory input are variable36; therefore, they all assign one inconsistency score per subject. This aggregation is severely limited because it yields only a simple between-subject analysis based on the average neural activity over all choices. By construction, it ignores the trial-by-trial variation in both behavior and neural activity. Such variation provides information not just about which subjects are more inconsistent, but also the level of activity in different brain areas during an inconsistent choice. To overcome this limitation, we develop a trial-specific inconsistency index, which measures the severity of inconsistency contributed by each choice. We apply our novel index to a well-established choice task, and use it to explore the neural computation underlying inconsistent choices on a trial-by-trial basis.Measuring the severity of inconsistent choice presents a challenge for the analysis of the neural data. Existing methods for measuring inconsistency either count the number of GARP violations or estimate the extent of  changes to the dataset required to make choices consistent38. GARP violations increase with aging and are negatively correlated with gray-matter volumes in the ventrolateral prefrontal cortex39. Neural correlates of intransitive lottery choices have been observed in BOLD signal from the vStr, anterior cingulate cortex (ACC) and the dorsolateral prefrontal cortex (dlPFC)40. Although these studies have identified which brain regions are involved in inconsistent choices across subjects, no study has yet examined how such behavior might arise in healthy human brains on a trial-by-trial basis.A few neuroscientific studies have previously examined consistency, albeit using only aggregate-level inconsistency indices. Patients with lesions in the medial prefrontal cortex (mPFC) violate GARP and transitivity more often than non-lesioned controls, suggesting this region is necessary for consistent behavior23, the vStr22, the PCC41, and the ACC42, which is also related to choice difficulty, foraging, control, and monitoring44. We find that the BOLD signal from these regions correlates with both inconsistency levels and utility. These findings are consistent with our computational model explaining how inconsistent choice behavior might arise in value-related regions.Therefore, we examine, using fMRI, the neural basis of choice inconsistency on each trial of a choice task. Importantly, in our task, subjects make choices over lotteries holding the framing fixed. Using our novel trial-specific inconsistency index, this design allows us to assess the degree of a violation of inconsistency on each trial. We then search for correlates to this index in the BOLD signal, with both a whole-brain analysis and a region of interest (ROI) analysis of brain regions known to participate in value-based choice: the vmPFC45. The MMI is a parametric measure of the extent of GARP violations in a dataset of choices from linear budget sets. It measures the minimal adjustments (in percentages) of the budget lines required to reconcile the decision-maker’s choices with the best-fitting parametric utility function  calculates the difference between the Aggregate MMI index  and the MMI index calculated over all observations less the given trial. Hence, our trial-specific MMI index measures the severity of inconsistency per trial within a subject (see the Methods section). We then use it as a regressor to track the neural correlates of choice inconsistency.For each trial, our index  assigned to the chosen bundle in each trial. We then use these SVs as another parametric regressor to identify the neural correlates of value modulation. Since SV does not depend on the specifics of the budget line (relative prices and endowment), while the trial-specific MMI depends on both, there is orthogonal information in the two regressors. Simultaneous identification of the severity of inconsistency on each trial, and the SV of that same trial, enables us to probe for the neural correlates of inconsistent choice behavior.13 inside the fMRI scanner. On each trial, subjects were presented with a set of 50/50 lotteries between two accounts, X and Y, and were asked to choose their preferred lottery (bundle). All possible lotteries in a given trial were represented along a budget line. The price ratios (slopes of the budget lines) and endowments were randomized across trials and subjects 13 study reveals that the distributions of the Afriat inconsistency index , suggesting that, although parametric, the Aggregate MMI is a good measure of inconsistency45. Compared with nonparametric indices, there was considerable variability in the trial-specific MMI, therefore it can be used as a trial-by-trial regressor for neural activity , we calculated the SV of the chosen bundle on each trial for each subject. The SV regressor was also positively correlated with activations in the mPFC and ACC  < 0.05, in the vmPFC and ACC, p < 0.0005, cluster-size corrected in PCC) and SV (p(Bonferroni) < 0.05, in the vmPFC and ACC, q(FDR) < 0.05 in the PCC), though we did not find any significant activation in the vStr. As expected, V1 was not correlated with trial-specific MMI  for SV and trial-specific MMI were the same in the vmPFC and dACC could not be rejected . However, for the PCC ROI, the difference was significant (p < 0.005). This suggests that the SV and trial-specific MMI predictors both have an important effect on the BOLD signal in the vmPFC and dACC  and the average change in the BOLD signal in any of the predefined ROIs (Supplementary Table ). At the behavioral level, we found a significant correlation with our parametric trial-specific MMI index, . However, the RFX-GLM analysis using the nonparametric trial-specific violations index as a regressor did not yield any significant voxels , Supplementary Note vi plus a random term ei.The distribution of 33 Therefore, we assume a skewed distribution for A skewed distribution of neural activity is observed in various contexts.v1 and v2.The difference between As the gap between valuations increases, the realization of the random component for the inconsistent alternative must be relatively larger for it to be chosen.Consider a decision-maker choosing between two alternatives {1, 2} with a valuation for each alternative given by The implications of this model for the neural data are the following: if the BOLD signal correlates with random utility, this signal will be higher when choices are more inconsistent, because the random component must make up the gap between the higher and lower valuation when the inconsistent option is chosen Fig. . BecauseThe proposed relation between the BOLD signal and choice inconsistency also holds as the number of alternatives increases beyond our binary choice example. Define the choice of alternatives with lower valuations as more inconsistent. When a low value alternative is added to a choice set, its corresponding random component must be larger to overcome the utilities of all higher-valued alternatives. Moreover, the largest realization of an error is required for the lowest value option to be chosen, so on average the largest BOLD signal will arise on a trial in which the lowest valuation alternative is chosen. As the number of low value alternatives increases, this will yield a corresponding increase in the BOLD signal on more inconsistent trials Fig. . This isTo demonstrate that the random fluctuations in value implied by NRUM are closely related to the observed neural activity, we assessed whether the same correlation pattern between the random utilities and inconsistency index could be observed. Based on the behavior of our subjects, we therefore simulated the valuation process implied by the NRUM and examined the correlation between the random utility valuations  and the inconsistency of their simulated choices. For all subjects, we found significant positive correlation Table , implyinβ = −0.00133, p < 0.001, clustered regression by subjects), even if we control for the expenditure and slope of the budget line. Also, as expected, less than 5% of the variance of the trial-specific MMI is explained by the SV regressor  analysis between our predefined ROIs  and other brain regions revealed that the activity in the seed regions and other brain areas interacted with the SV and trial-specific MMI regressors .In binary choice tasks, choice difficulty is usually considered to be the difference in the SVs between the two options (ΔSV)—the smaller ΔSV, the more similar the options are, and the higher the difficulty levelβ = −2.782, p < 0.0001, clustered regression by subjects). Trial-specific MMI is negatively correlated with our Choice Simplicity index , meaning, the more difficult is the choice problem, the higher is the corresponding inconsistency level. However, the index also explains little of the variance in trial-specific MMI scores (R2 = 0.0137), therefore, the relationship between choice difficulty and inconsistency is weak.In our Choice Simplicity index, the closer its value is to 0, the more difficult is the choice. As expected, difficult choices lead to longer RTs . It is precisely the variability that “connects” choice difficulty to inconsistency, with the obvious implication that more difficult problems are more likely to produce inconsistency. Therefore, a weak negative correlation between the Choice Simplicity index and the level of inconsistency is expected.49 , indicating subjects had the longest RTs in intermediate confidence-level choices.Another possible source for choice inconsistency is low levels of confidence in one’s own choice. Following Lebreton et al.β = −0.000027, p < 0.0001 in a clustered regression by subjects). Such result indicates that low levels of confidence correlate with high inconsistency scores; however, the R2 of the model is very low (0.0395). When we added the confidence predictor to the RFX GLM, our main results hold, suggesting that the BOLD activity in mPFC and ACC was larger on inconsistent trials, even after controlling for confidence .54. Moreover, Polania et al.54 find that choice behavior becomes less accurate when frontal–parietal coupling is disrupted by tACS. However, the results from these studies are compatible with our proposed computational model for choice behavior. When value-related regions are absent or disrupted, choices are highly inconsistent because of the limited ability of the brain to compute the valuations necessary for consistent choice behavior. When these regions are intact, value signals can be computed, but with a degree of variability inherent to neural computation. Thus, choices are largely consistent, but exhibit a pattern in which inconsistent choices correlate with an increase in aggregate activity. As further evidence, lesioned subjects in Camille et al.37 had an Afriat Index of 0.1 on average, compared with an average of 0.0623 in our sample, even though our subjects were facing a much more difficult task .At first glance, our empirical results might seem surprising given both lesion and stimulation studies which demonstrate that activity in value-related regions is necessary for consistent choice29. As such, the trial-by-trial variability we propose can arise from multiple sources, including higher order cognitive processes such as fluctuations in attention or heuristics or lower-level process like neuronal noise. At a computational level, a number of previous studies have explored the role of noisy computations in choice behavior, typically in the form of a bounded accumulation model, in which a noisy decision signal accumulates to some threshold 30. These models have found support in both single-neuron recordings30 and human imaging studies55, with the fMRI studies in particular observing value signals in the vmPFC. Since the NRUM is a general formulation of bounded accumulation models29, this large set of accumulation models can provide a computational account of the results we observe. Of primary importance, the distributions used in our examples  are skewed with a long right tail, a property consistent with neural data at both the level of single-neuron firing rates and aggregate network-level measures33. Therefore, our results are compatible with the evidence for a skewed distribution of neural activity in value-related regions. To demonstrate that the valuations implied by the NRUM are closely related to the observed neural activity, we simulated the valuation process of our subjects based on their observed behavior and demonstrated a correlation between their random utilities  and the inconsistency of their simulated choices .The neural random utility model represents a parsimonious account of the variability in value signals during a decision56, arising from thermodynamic noise at the cellular and synaptic level, and is present at all stages from primary sensory systems to motor execution57. Network computations can filter, or integrate, this noise, but the maximal signal–noise ratio is bounded due to physiological constraints59. Thus, some constrained optimal degree of noise persists at the network level across domains from perceptual30 to value-based choice32. In a seminal article, Fox et al.60 find that “inconsistency in perception or performance should not be automatically attributed to fluctuations in task-related cognitive processes such as attention, but could also be due to ongoing fluctuations in intrinsic neuronal activity”. Indeed, a recent study conducted to explicitly separate the sources of behavioral variability finds that 89% of deviations from optimal choice can be attributed to errors in value inference, rather than sensory processing or action selection61. This is consistent with our finding of a limited role for noise in motor output or numerical representation in generating inconsistent choices. Instead, the deviations from consistency were observed in valuation regions, suggesting that the value of choice options might be fluctuating on a trial-by-trial basis. This result is in line with non-human studies: In primates, variability in the firing rate of orbitofrontal cortex neurons predicted choices of near indifferent alternatives62, while stability in neural populations in the medial frontal cortex accounted for variability in choice in rats.63 Furthermore, neural variability can be influenced by varying levels of attention66. These results point to a speed-accuracy tradeoff in decision-making governed by metabolic costs67.What might be the source of this variability in value computation, and why should it persist in decision-making? Variability is inherent to neural computationTaken together, these results question whether the form of inconsistency we observe should be considered sub-optimal. Our results are consistent with a degree of constrained-optimal variability around the normatively defined benchmark of a utility representation. In fact, our results may be interpreted as implying that inconsistent choice behavior is an integral feature of human decision-making.Thirty-eight subjects participated in the study . Subjects gave informed written consent before participating in the study, which was approved by the local ethics committee at Tel Aviv University and by the Helsinki Committee of Sheba Medical Center. Three subjects were dropped due to sharp head movements (> 3 mm). Another subject opted out from the experiment before completing the scan, and another subject was dropped due to anatomical abnormalities. We therefore report the data for the remaining 33 subjects.13. On each trial, subjects faced a visualization of a budget line  point on the budget line corresponds to a lottery with a 50% chance of winning the tokens allocated to account X (the x-axis coordinate) and a 50% chance of winning the tokens allocated to account Y (the y-axis coordinate). Thus, the budget line describes the possible allocations to accounts X and Y on a two-dimensional graph. On each trial, the subject was asked to choose the desired bundle (of X and Y tokens) from the budget line, knowing that only one of the accounts will be realized. At the end of the experiment, one of the trials was randomly selected for monetary payment (to satisfy incentive compatibility). The subject won the monetary value of the tokens allocated to the winning account on the trial drawn for the payment. Each token was worth 5 NIS ($1 ≅3.5 NIS).We used a modification of the task presented by Choi et al.ine Fig. . Each diX relative to one unit of account Y. We varied and randomized the budget lines (slopes and endowments) across trials and subjects. The x-axis and y-axis were scaled from 0 to 100 tokens, and the resolution of the budget line was 0.1 tokens. Subjects could not choose inside the budget line. In trials where subjects did not make any choice in the allotted time, a text reading “No choice was made” appeared on the screen. These trials were excluded from the analysis . The average prize was 191.6 NIS + 100 NIS show up fee.In this task, the slope of the budget line determines the price of one unit of account Subjects performed the experimental task using an fMRI compatible trackball to choose their preferred bundle. On each trial, subjects had a maximum of 12 s to make their choices, followed by a 9 s variable inter-trial-interval . If subjects made their choice before the end of the maximal 12 s, the remaining time was added to the ITI. There were 27 trials in each block, and each subject completed four blocks, for a total of 108 trials.After completing the main task, we obtained an anatomical scan and two functional localizers, one numerical and one motor , aimed to control for alternative sources of choice inconsistency .x, y) coordinate. After the pre-scan questionnaire, the experimenter went over their answers. In case the subject made a mistake, the experimenter explained the instructions orally, and then repeated the question in the questionnaire until the subject answered correctly. See an English translation of the instructions and pre-scan questionnaire in Before the scan, subjects read an instruction sheet and completed a pre-scan questionnaire to verify the task is clear. The instructions included many examples and were written in simple terms to avoid confusion. In the pre-scan questionnaire, subjects were given several decision problems  and were asked to identify intersections with the axes, identify the cheaper account, and calculate the possible winning prize (in terms of both tokens and NIS) for a specific . Fifty-two slices with no inter-slice gap were acquired in ascending interleaved order, and aligned 30° to the AC–PC plane to reduce signal dropout in the orbitofrontal area. Anatomical images were acquired using 1-mm isotropic MPRAGE scan, which was comprised from 208 axial slices without gaps at an orientation of −30° to the AC–PC plane.Scanning was performed at the Strauss Neuroimaging Center at Tel Aviv University, using a 3 T Siemens Prisma scanner with a 64-channel Siemens head coil. To measure blood oxygen level-dependent (BOLD) changes in brain activity during the experimental task, a T2*-weighted functional multi-band EPI pulse sequence was used  = 215 mm; slice thickness = 2.5 mm; band factor = 2BrainVoyager QX (Brain Innovation) was used for image analysis, with additional analyses performed in MATLAB (MathWorks). Functional images were sinc-interpolated in time to adjust for staggered slice acquisition, corrected for any head movement by realigning all volumes to the first volume of the scanning session using six-parameter rigid body transformations. Spatial smoothing with a 6-mm FWHM Gaussian kernel was applied to the fMRI images. Images were then co-registered with each subject’s high-resolution anatomical scan and normalized using the Montreal Neurological Institute (MNI) template. All spatial transformations of the functional data used trilinear interpolation.xi isx, denoted xiR0x, if pixi ≥ pix.Directly revealed preferred to another bundle x, denoted xiP0x, if pixi > pix.Strictly directly revealed preferred to bundle x, denoted xiRx, if there exists a sequence of observed bundles , that are directly revealed preferred to one another, xiR0xj, xjR0xk,…, xmR0x. Relation R is therefore the transitive closure of the directly revealed preferred relation.Revealed preferred to bundle Consider a finite dataset D satisfies the General Axiom of Revealed Preference (GARP), if every pair of observed bundles, xiRxj, implies ¬(xjP0xi). We say a subject is consistent iff she satisfies GARP. We say that a utility function u(x) rationalizes D if xiR0x implies u(xi) ≥ u(x). According to the Afriat theorem2, there exists a well-behaved utility function  that rationalizes the data iff the subject satisfies GARP . In the current study, we also compute a parametric index for inconsistency—the Aggregate MMI.As subjects often violate GARP, and therefore are inconsistent45 consider the continuous and non-satiated utility function u(∙) as representing the preferences of the subject. u(∙) induces a complete ranking on the bundles such that if u(x) > u(y), then bundle x is preferred to bundle y. In addition, each actual choice induces a partial order on the bundles since when a subject chooses bundle xi, she, by the principle of revealed preference, ranks this bundle over all other feasible bundles. If these two rankings are compatible for every choice made by the subject, u(∙) rationalizes D. Otherwise, if these two rankings are incompatible for some choice according to u(∙), some feasible bundles are ranked higher by u(∙) than the chosen Bundle xi. For every observation, the incompatibility between the two rankings can be measured by the minimal expenditure , such that the adjusted budget set does not include any bundle that is strictly preferred over xi according to u(∙). Halevy et al.45 show that this measure is exactly the well-known money metric68. Formally, given the prices pi, the money metric m for observation i is the minimal expenditure required for the dataset to include a bundle y such that u(y) ≥ u(xi):i is u(∙) and the dataset D, given the aggregator f. Finally, we iterate over all utility functions in the set of utility functions D. The MMI, denoted D, as the incompatibility between the set of utility functions D given the aggregator f.45 prove that had we examined the set of all continuous non-satiated utility functions 35. As it is not feasible to examine all utility functions in this set, they propose restricting to a specific functional form. The MMI thus includes a misspecification element. Fortunately, the MMI is separable additive in Varian inconsistency index and the misspecification:; (b) elicitation of subject-specific utility function parameters. The subject-specific utility function is the function for which aggregate MMI is minimal, and hence constitutes the best fit for the subject’s choices among the investigated family of utility functions Following Halevy et al.ixi Fig. . Hence, εD be an aggregate inconsistency index of dataset D. Let Di− be a subset of D that includes all n−1 trials but the ith observation. Let εD−i be the aggregate inconsistency index of Di−, and let i.A Leave-One-Out procedure. Let εD, as it is the nonparametric index with the highest number of degrees of freedom. However, it is not possible to compute Varian index for datasets with 108 observations in feasible time. Therefore, we had to choose between two other alternatives. The first was to use computational-convenient nonparametric indices (Afriat index and GARP violations). Nevertheless, when using those nonparametric indices, the trial-specific index εD, and refer to https://github.com/persitzd/RP-Toolkit.The best practice would be to use Varian index as 69, as it includes many well-known types of preferences in the context of risk45 . Formally,γ is the weight of the better outcome, and ω is a CRRA utility index with a relative risk aversion parameter ρ. When β = 0, this is the common Expected Utility function with parameter ρ . When β > 0 the individual over-weights the probability that the lottery will yield the lower prize (“disappointment aversion”). When β → ∞, the subject cares only about the element with the lower quantity and therefore her optimal behavior would be to always choose the safe bundle, so that the lottery is meaningless . When β < 0, the individual overweights the probability that the lottery will yield the higher prize (“elation seeking”). When β = −1, the subject cares only about the element with the larger quantity  in each trial i, using the subject’s recovered parameters (using the MMI), β and ρ.To use SV as a parametric regressor in our analysis, we calculated the value of the Disappointment Aversion model with CRRA functional form at the chosen bundle  for the neural noise vis and the calibrated distributions for ei, we calculated the random utility values , and the endowment measured by the safe portfolio on the 45 degrees line from the origin.To identify the neural correlates of choice inconsistency, we estimated a general linear model (GLM) with 11 predictors. The trial-by-trial inconsistency index trial-specific MMI and the trial-by-trial SV, entered for the total trial duration up until the subject made a choice, normalized and convolved with the canonical hemodynamic response function (HRF). We modeled RT using a boxcar epoch function, whose duration was equal to the RT of the trialAll these predictors were entered for the trial duration, normalized and convolved with the HRF. In addition, six motion-correction parameters and the constant were included as regressors of no interest to account for motion-related artifacts.21. For the dACC, we drew a 12 -mm sphere around the peak voxel that Kolling et al.43 reported. For the PCC and V1 ROIs, we used neurosynth.org meta-analyses masks. We then conducted the same RFX GLM reported above and correlated trial-specific MMI and SV with BOLD activity extracted from each of the ROIs.We also conducted a region of interest (ROI) analysis, in order to increase the power of the statistical test. We defined the vmPFC and vStr ROIs based on the masks provided by Bartra et al.71. The psychological regressors were normalized and convolved with the canonical HRF and entered into the regression model. An additional regressor represented the interaction between the psychological and physiological factors, and indicated in which areas there were significant differential functional connectivity with each seed ROI. We modeled RT similarly to the whole-brain analysis, and used the price ratio of the budget set and the endowment as control predictors. These predictors were entered for the trial duration, normalized and convolved with the HRF as well. In addition, six motion-correction parameters and the constant were included as regressors of no interest to account for motion-related artifacts.The time series of the BOLD signal in each ROI was z-scored to generate the time series of the neuronal signal for each source region as the physiological variable in the PPI. We tested (separately) each parametric regressor, SV and trial-specific MMI, as the psychological variable, suggesting that a given region can be connected to distinct regions/networks depending on task contextp < 0.0005 with cluster-size correction, similarly to the main GLM analysis. We hence ran six models in total .We used RFX for the group-level analysis, and set the threshold to To examine the neural footprints of the non-correlated part of the SV and trial-specific MMI predictors, we used an orthogonality analysis. We ran a clustered regression of trial-specific MMI on SV and obtained residuals Since each decision problem in our task is continuous, we cannot simply use ΔSV as a choice difficulty index. Moreover, choice difficulty in our task is determined by the subject’s own preferences and not only by the slopes of the budget sets. For example, a risk-averse subject might struggle with steep slopes, as the temptation for allocating all tokens to one account rises. A risk-seeking subject, on the other hand, will have difficulties in moderate slopes. Therefore, we derive a novel measure for choice difficulty, which considers both subject’s elicited parameters and the continuum of the budget set.vi,b,s of each bundle i along each budget set b for each subject s using the parameters elicited by the MMI. Next, we subtracted vi,b,s from the maximal subjective value of that budget line to obtain a ΔVi,b,s measure for each bundle along the budget set . We then averaged all the ΔVi,b,s across the 1000 bundles, and normalized the result by the endowment of the given budget set to be able to compare across trials. Formally, denote i∈  is the bundle (N = 1000); b∈  is the decision problems and s∈  is the subject.We discretized each budget line into 1000 possible bundles. We then calculated the subjective value Vb,s), making the options along the line relatively similar, which results in an overall low index. Hence, the higher is our index, the easier it is to make a choice, suggesting that trials with index values closer to 0, are the more difficult choices. Hence, we refer to this index as a choice simplicity index. The normalization with Endowmentb,s of the chosen bundle is aimed to minimize the problem of over-scoring trials with higher endowments. In such cases, the budget line is longer (further away from the origin), and therefore ΔVi,b,s is bigger for the mere fact that the distance between bundles is bigger.Difficult choices occur when the bundles along the budget line have similar values to the maximum value . We ran the same RFX-GLM, and used trial-specific MMI-blocks and SV-blocks as our inconsistency and value modulation regressors, respectively.Moreover, we classified subjects’ choice behavior in each block, and identified eight subjects (out of 33) who changed behavior across blocks  utility index with an absolute risk aversion parameter A (rather than CRRA utility index):In order to rule out the possibility that our results reflect the MMI’s misspecification element, we repeated our main analysis, using a different functional form. Halevy et al.β over the course of the entire experiment, to account for the average change in BOLD signal. We then correlated the average change in BOLD signal in each ROI with aggregate-level inconsistency indices—aggregate MMI, Afriat index, and number of GARP violations (see Aggregate inconsistency indices section for details). We corrected our analysis for multiple comparisons, using Bonferroni correction, and set pi ≤ 0.05/(18 comparisons) = 0.0028 as our statistical threshold , as well as the mPFC/ACC cluster that was correlated with the trial-specific MMI (MMI ROI). For each subject, in each ROI, we extracted the average GLM-coefficient In the motor imprecision functional localizer, subjects were presented with linear graphs, and had to reach a black target using a trackball. It resembled the main task, but excluded any numerical or value representation . In both localizers, the graphs were varied and randomized across trials and subjects. We calculated motor and numerical imprecisions as the Euclidean distance between the cursor position at the moment the subject clicked the trackball, and the predefined target. In order to identify the neural correlates of the motor/numerical imprecision, we used the trial-by-trial motor/numerical imprecision as a predictor in an RFX GLM. Other predictors included a boxcar epoch function for the trial duration to model RT, as well as the graph’s slope. All these predictors were entered for the trial duration, normalized and convolved with the HRF. In addition, six motion-correction parameters and the constant were included as regressors of no interest to account for motion-related artifacts.Similarly, in the numerical execution imprecision localizer, subjects were presented with linear graphs, and had to reach a target {Supplementary InformationTransparent Peer Review File


In situ hybridization (ISH) is a technique to determine potential correlations between viruses and lesions. The aim of the study was to compare ISH techniques for the detection of various viruses in different tissues. Tested RNA viruses include atypical porcine pestivirus (APPV) in the cerebellum of pigs, equine and bovine hepacivirus  in the liver of horses and cattle, respectively, and Schmallenberg virus (SBV) in the cerebrum of goats. Examined DNA viruses comprise canine bocavirus 2 (CBoV-2) in the intestine of dogs, porcine bocavirus (PBoV) in the spinal cord of pigs and porcine circovirus 2 (PCV-2) in cerebrum, lymph node, and lung of pigs. ISH with self-designed digoxigenin-labelled RNA probes revealed a positive signal for SBV, CBoV-2, and PCV-2, whereas it was lacking for APPV, BovHepV, EqHV, and PBoV. Commercially produced digoxigenin-labelled DNA probes detected CBoV-2 and PCV-2, but failed to detect PBoV. ISH with a commercially available fluorescent ISH (FISH)-RNA probe mix identified nucleic acids of all tested viruses. The detection rate and the cell-associated positive area using the FISH-RNA probe mix was highest compared to the results using other probes and protocols, representing a major benefit of this method. Nevertheless, there are differences in costs and procedure time. In situ hybridization (ISH) represents a useful tool for the in situ visualization of nucleic acids within cytological preparations and histological sections, as well as whole organisms ,7,96,7,9For time-saving aspects, methodological establishment should be avoided in cases of virus discovery. Thus, our standard in-house ISH protocol was used for self-designed and commercially produced probes, several of them already established for detection of some of the aforementioned viruses ,8,14,15.https://imagej.nih.gov/ij/download.html) was used for pixel measurements of total tissue areas and positive, cell-associated regions of the whole respective tissue present on the slide.Total tissue area of sections was screened for positive signals following ISH targeting the different viruses using a fluorescence microscope . For prediction of detection rate in diagnostic approaches, tissues were treated with the different probes and screened for a positive signal. Detection rate was assessed for each technique for the groups RNA viruses, DNA viruses and viruses in total. Animals with a positive PCR result were assumed to be infected. As negative controls tissue of the same origin of non-infected animals of the same species as well as non-probe incubations were used. Detection rate of the different methods was calculated using Fisher’s exact test in GraphPad Prism . Furthermore, the percentage of the cell-associated virus positive areas were determined by measuring the total as well as the cell-associated virus positive region in organs treated with different probes. ImageJ  and the estimated hours for the whole procedure including incubation but not ordering times  are presented in In total, seven different viruses were screened with different ISH techniques . For theFor APPV, the FISH-RNA probe mix showed a diffuse positive signal within the inner granular cell layer of the cerebellum measuring 7.77% of total cerebellar area. The self-designed DIG-labelled RNA probes (sense and anti-sense) failed to detect the virus . The negin situ (The self-designed DIG-labelled RNA probes (sense and anti-sense) as well as the commercially produced DIG-labelled RNA probes failed to detect EqHV in situ a–d, wherin situ e. Tissuein situ .The self-designed sense and anti-sense DIG-labelled RNA probes for SBV showed a strong positive signal in scattered neurons of the cerebrum of the infected goat  and the commercially produced DIG-labelled DNA probe (sense and anti-sense) in the tested animal . In the For all probes, no specific signal was present in the virus-negative animals and the non-probe incubations. In addition, none of the negative animals revealed a false positive signal in any tested tissue. Furthermore, as an additional negative control, the EqHV specific FISH-RNA probe mix was used as an unrelated negative control probe on cerebral tissue of the SBV positive goat and the SBV specific FISH-RNA probe mix was used on hepatic tissue of the EqHV positive horse, lacking a specific signal . The selThe positive cell-associated area was detected by measurements of total tissue area and the positive cell-associated region in cases of viruses that were detected by all compared methods. Regarding the positive area, the FISH-RNA probe mix again showed satisfying results. It was able to detect the largest cell-associated positive area for PCV-2 in all three examined tissues and for CBoV-2. SBV showed the largest area using the RNA anti-sense probe, followed by the FISH-RNA probe mix and the RNA sense probe.Regarding the duration of the procedure, which is an important factor in virus discovery and additionally a cost-saving aspect, there were also differences between the three methods. The pure working time including the generation of self-designed DIG-labelled RNA probes and the subsequent ISH took a total of about 15 h. The commercially-produced DIG-labelled RNA and DNA probes only had to be ordered. Thus, the time for selection of the correct probe and the ISH itself takes about 7 h. The ISH with the commercially available kit and the probe order took only 3 h. The working time, including the incubation times, which represents the time that a person is really occupied by the method, showed a similar distribution. The generation of a DIG-labelled RNA probe, including the subsequent ISH, took approximately 186 h; the ISH with an ordered probe took 62 h, and the ISH with the ordered FISH-RNA probe mix took approximately 13 h. Nevertheless, differences in ordering times that may vary between laboratories and countries have to be considered. Furthermore, material costs per slide may differ substantially. An overview of the results is presented in Virus discovery pipelines using next generation sequencing are important tools for the early detection of new and emerging viruses and provide a basis for the development of intervention strategies . As a fiISH with a self-designed DIG-labelled RNA probe was able to detect three out of the seven viruses (43%), one RNA (25%), and two DNA (66.67%) viruses. The ordered DIG-labelled DNA probes were also able to detect two DNA viruses out of three (66.67%), whereas the ordered DIG-labelled RNA probe failed to detect EqHV. These results indicate that the different DIG labelling efficiency of the ordered probes compared to the self-produced probes, as well as differences in length, ranging from 41 to 155 nucleotides, had only a minor impact. In contrast, the FISH-RNA probe mix was able to detect seven out of 7 investigated viruses (100%), supporting the assumption that the FISH-RNA probe mix seems to have an enormous potential for the detection of viruses. For the EqHV probe, five mismatches were displayed in For the detection of APPV and CBoV-2, slight inconsistencies can be observed in the regions covered by the different probes. Nevertheless, CBoV-2 was detected by all tested different probes. Due to the only partial overlap of the regions covered by the self-designed APPV specific probe and the FISH-RNA probe mix, it cannot be ruled out completely that this was the cause for the lacking reactivity of self-designed RNA probes.in situ hybridization methods in combination with immunohistochemistry for the detection of feline panleukopenia virus (FPV) [Possible discrepancies between PCR and ISH, as seen for the negatively tested viruses using ISH, despite previously confirmed virus infection by PCR, could be due to variable virus nucleic acid amounts and inter- and intracellular distributions. Single cells with a high viral load might be tested positive by ISH methods with low and high sensitivities, whereas a high number of cells with a very low virus load might be negative in low sensitivity ISH techniques, although both samples were positive in PCR and highly sensitive ISH techniques . This dius (FPV) . A DIG-lus (FPV) . Nonetheus (FPV) . Interesus (FPV) .These findings also underline the high detection rate of FISH-RNA probe mix systems. The high detection rate of the FISH-RNA probe mix system for the demonstration of viruses might be caused by a higher sensitivity of this method due to a higher amplification of the signal and the use of multiple small probe pairs which might not always have to bind together to result in a signal, thus making it possible to detect single RNA molecules. The FISH-RNA probe mix revealed a detection rate of 100% for both RNA and DNA viruses. The FISH-RNA probe mix is a very effective method, compared to the self-designed DIG-labelled RNA probe system that only detected one out of four tested RNA viruses (detection rate 25%) and two out of three DNA viruses (detection rate 66.67%). In contrast, for SBV, the positive area using the self-designed RNA anti-sense probe was larger compared to the FISH-RNA probe mix and the sense probe, probably due to the negative sense genome of this pathogen .For the detection of DNA viruses, the use of commercially produced DNA- or self-designed DIG-labelled RNA probes shows a detection rate of 66.67% which is inferior compared to the results for the FISH-RNA probe mix. Nevertheless, the detection rate for DNA viruses using the DIG-labelled probes was higher compared to the detection rate of RNA viruses. Similarly, the beneficial use of DIG-labelled probes has been reported for the detection of canine parvovirus and porcine circovirus 2 using canine parvovirus RNA and porcine circovirus DNA probes ,34. HoweA previous comparative study of CISH and FISH reported similar results for the detection of Epstein-Barr virus and cytomegalovirus in human FFPE samples, as observed between CISH and FISH in the present study. FISH showed a marked higher detection rate for both viruses compared to other ISH methods and immunohistochemistry . Howeverin situ detection of viruses. Nevertheless, differences in order times and material costs that are laboratory- and country-dependent have to be considered.Regarding the time evaluation, the ISH with the FISH-RNA probe mix is very time saving with only 3 h total working time. Thus, and due to the discouraging results of the self-designed DIG-labelled RNA probes and ordered DIG-labelled RNA and DNA probes, the FISH-RNA probe mix should be considered for the None of the negative cases tested positive using the different methods. Thus, false positive results seemed not to have a major impact on ISH for virus detection. Furthermore, adaptations of the ISH protocol for the use of self-designed RNA and ordered DNA probes may help to improve results for the detection of viral nucleic acids. In contrast, these modifications can be performed in a limited manner using the standardized, commercially available FISH-RNA probe mix only. Nevertheless, such modifications are time-consuming, representing a problem in virus discovery. However, the FISH-RNA probe mix retrieved a positive signal in all tested cases, avoiding the need for further adaptations. Additionally, there are several other advantages using the FISH-RNA probe mix that have to be mentioned. There is no need to strictly work RNAse-free as was essential for the other ISH protocols applied . Moreove


Fine-needle aspiration biopsy (FNAB), an important diagnostic tool given its simplicity, safety, and cost-effectiveness, is fast becoming a popular procedure in the diagnosis of thyroid diseases. Generally, cells isolated from biopsies are transferred directly to microscope slides to prepare smears for cytopathological examination; however, the technical difficulties of this procedure often cause poor reproducibility, which limits the accuracy of diagnostic results. Liquid-based cytology (LBC), in which isolated cells are collected in a fixative solution, is advantageous in that it facilitates the preparation of homogenous cytological specimens. However, LBC has not been applied to molecular diagnoses, such as RNA expression-based diagnosis, mainly because of difficulties in cell recovery and RNA isolation. This study was aimed to improve RNA extraction from papillary cancer-derived K1 cells and thyroid FNAB specimens suspended in LBC solutions.U6 small nuclear RNA was detected in these RNA samples by reverse transcription-polymerase chain reaction.K1 cells suspended in CytoRich-Red and CytoRich-Blue, fixatives for LBC, were efficiently recovered by trapping to glass-fiber filters. Importantly, subsequent Proteinase K treatment was essential for efficient RNA extraction from the fixed cells. This finding was also applicable to RNA extraction from CytoRich-Red-fixed thyroid FNAB specimens processed in the same way. Consistently, The online version of this article (10.1186/s13104-018-3914-4) contains supplementary material, which is available to authorized users. The number of new cases of this cancer in 2017 is estimated to be 56,870 worldwide, ranking eleventh among all cancer types (https://seer.cancer.gov/statfacts/html/thyro.html).Thyroid cancer is a malignant endocrine gland neoplasm accounting for approximately 3.4% of total cancer diseases (http://www.pathologyoutlines.com/topic/thyroidwho.html). Thyroid nodules are observed in over 5% of the adult population and possibly give rise to both benign adenomas and malignant lesions; thus their occurrence is not a diagnostic sign of thyroid cancer (https://www.endocrineweb.com/conditions/thyroid/fine-needle-biopsy-thyroid-nodules). Hence, it is necessary to differentiate between benign nodules and cancerous solitary thyroid nodules at any rates. The thyroid has a great advantage over other tissues in that it is easily accessible to needles. Rather than removing a small piece of the tissue by operation , thyroid cells can be removed by inserting a thin needle into the tissue and subjected to microscopic examination, a process called fine needle aspiration biopsy (FNAB). The cells collected for FNAB are sometimes treated with a liquid fixative containing alcohols and/or formaldehyde and subjected to cytological tests . In general, different types of thyroid tumors can be easily differentiated by histopathological diagnosis. However, there are exceptions; for example, distinguishing between metastatic and non-metastatic minimally invasive (MI)-follicular thyroid carcinomas (FTCs) is currently difficult by any pathological modalities. Thus, it is necessary to establish a method for the molecular diagnosis of thyroid tumor to substitute or complement the histopathological method.The pathological diagnosis and classification of thyroid tumors are defined based on the recent World Health Organization classification system , have attracted wide attention in the medical sciences. MicroRNAs (miRNAs) are representative short (of approximately 22 nucleotides in length) ncRNAs involved in the post-transcriptional regulation of gene expression. To date, numerous studies have been conducted to identify dysregulated miRNAs, also called ‘onco-miRs’, in many types of cancer –8, incluf MI-FTC . Howeverf MI-FTC .In this study, our aim was to improve RNA recovery from human thyroid cancer cells processed with a fixative for LBC.2 and 95% air. These cells were suspended in 5 ml CytoRich-Red and/or CytoRich-Blue fixative solutions , commercial products developed for LBC, and stored at 4 °C for 3 days.Human thyroid papillary carcinoma-derived K1 cells were obtained from DS Pharma Biomedical Co. Ltd. , and maintained according to the manufacturer’s instructions. Briefly, the cells were cultured in a mixture of Dulbecco’s Modified Eagle’s Medium , Ham’s F12 nutrient mixture (Wako), and MCDB105 medium  (2:1:1) supplemented with 10% fetal bovine serum at 37 °C in an atmosphere of 5% COFive papillary thyroid carcinoma cases were randomly selected for this study. We obtained written informed consent and approval for this study from the Ethics Committee of Kuma Hospital . The aspirated thyroid cells in the needles were transferred to CytoRich-Red or CytoRich-Blue fixative solutions.K1 cells and the thyroid FNAB specimens suspended in CytoRich fixative solutions were obtained by trapping using Whatman GF/C glass-fiber filters   in combination with the RNA 6000 Nano LabChip kit (Agilent), and its quality was assessed by RNA integrity number (RIN).U6 small nuclear RNA (RNU6) levels, TaqMan MicroRNA Assays  were used. Monitoring of RNU6-derived PCR products was performed on a DICE Real-Time PCR System (Takara-bio).For the quantitative reverse-transcription (RT)-PCR analysis of Application of LBC specimens to molecular diagnosis has been attempted in previous studies. Most of these studies, however, focus on genomic mutations, and studies on RNA analysis are limited. To our knowledge, the only study to evaluate RNA expression using FNAB specimens was conducted in medullary thyroid carcinomas . In thatTo overcome the first problem, we tried to use a glass-fiber filter for cell tapping. The device for trapping cells in a glass-fiber filter is illustrated in Additional file CytoRich-Red is more generally used than CytoRich-Blue for LBC of thyroid FNAB specimens, because CytoRich-Red efficiently lyses contaminated blood cells. Thus, only CytoRich-Red was used for the following experiments. To verify the importance of Proteinase K in RNA extraction, we extracted RNA from CytoRich-Red-fixed K1 cells incubated in both Proteinase K-containing and Proteinase K-free lysis buffers. As shown in Fig. RNU6 RNA, a ubiquitously expressed small nuclear RNA. As expected, RNU6 RNA was detected in RNA extracted from the Proteinase K-treated materials  Scheme of cell trapping in a glass-fiber filter and RNA isolation. Cell suspension in a fixative was passed through the glass-fiber filter to trap the cells. The filter with trapped cells was subjected to RNA isolation. (B) Verification of cell trapping. Microscopic images of the cell suspension before and after filtration. After the K1 cell suspension was passed through the glass-fiber filter, only few cells were observed in the filtrate, indicating that the cells were efficiently trapped in the grass-fiber filter. Figure S2. Detection of RNU6 RNA in RNA samples isolated from materials fixed with LBC solution. The total RNA (10 ng per reaction) isolated from the CytoRich-fixed K1 cells and thyroid FNAB specimens that were treated with Proteinase K was subjected to TaqMan-based quantitative RT-PCR to detect RNU6 RNA. Assays were performed in triplicates. No amplification was observed when a cDNA template was omitted from the reaction (negative control).


Tight regulation of cellular processes is key to the development of complex organisms but also vital for simpler ones. During evolution, different regulatory systems have emerged, among them RNA-based regulation that is carried out mainly by intramolecular and intermolecular RNA–RNA interactions. However, methods for the transcriptome-wide detection of these interactions were long unavailable. Recently, three publications described high-throughput methods to directly detect RNA duplexes in living cells. This promises to enable in-depth studies of RNA-based regulation and will narrow the gaps in our understanding of RNA structure and function. In this review, we highlight the benefits of these methods and their commonalities and differences and, in particular, point to methodological shortcomings that hamper their wider application. We conclude by presenting ideas for how to overcome these problems and commenting on the prospects we see in this area of research. They interact with proteins, RNA, and DNA and thereby interconnect different regulatory systems. The elucidation of the underlying networks is thus of central importance for a holistic understanding of cellular regulation. Computational prediction based on sequence alone turns out to be unsatisfactory, and, with the availability of second- and third-generation sequencing technologies, data-based inference of regulatory networks involving ncRNAs is the more promising approach. RNA–protein interactions are more or less routinely studied using crosslinking immunoprecipitation-sequencing (CLIP-seq)6, represent an important first step toward this goal. In the following, we will discuss their pros and cons and present our view on what future improvements may look like and what their impact might be.A large fraction of ncRNAs carry out their function through complementary base pairing to other RNAs, often mRNAs. This may involve proteins, as in the case of the RISC complex for microRNA (miRNA)- or small interfering RNA (siRNA)-mediated post-transcriptional gene silencing, but the ncRNA is the main factor that specifies the target RNAs via base complementarity. Thus, unbiased methods for the transcriptome-wide discovery of direct RNA–RNA interactions are needed to decipher RNA regulatory networks. Approaches developed in 2016, described as Direct Duplex Detection (DDD) methodsin vitro. Second, there is no technology available that can sequence interacting RNA strands and maintain their relationship. The first approach to tackle these problems (in a rather coarse way) was described by Ramaniet al.7. They presented RNA proximity ligation (RPL), in which interacting RNA strands are ligated to each other in a standard ligation reaction using RNase inhibitor-treated crude cell extracts followed by RNA-seq. The resulting sequencing reads contained chimeric reads but to a rather low fraction (0.28%). Nevertheless, the proximity ligation approach presents a solution to the abovementioned second problem because it allows one to trace back RNA–RNA interactions in data sets obtained by next-generation sequencing technologies.RNA–RNA interactions pose two basic problems when analyzing them. First, they are commonly disrupted during the process of RNA extraction and cannot be reconstituted to their native statein vivo crosslinking of RNAs has been known since the 1970s and successfully used to interrogate the structure of ribosomal RNAs, interactions between small nuclear RNAs, and several other RNA structures and interactions20.The remaining problem of losing RNA–RNA interactions due to the harsh RNA extraction protocols is as long-standing as its solution: direct RNA crosslinking via psoralen derivatives. Following irradiation, psoralens covalently and reversibly connect two pairing stretches of RNA. Thus, duplexes are maintained during RNA extraction, which enables their enrichment, for example, by nucleases that digest single-stranded RNAs. The psoralen-mediated21, psoralen analysis of RNA interactions and structures (PARIS)22, and sequencing of psoralen-crosslinked, ligated, and selected hybrids (SPLASH)23. Although they differ in several steps of their experimental protocols, they all share the same principal design, which isin vivo RNA crosslinking followed by RNA extraction, enrichment of crosslinked RNAs, proximity ligation, and sequencing via RNA-seq. A comparison of the individual methods is shown inThe combination of the two aforementioned strategies is the hallmark of the currently available DDD methods, namely ligation of interacting RNA followed by high-throughput sequencing (LIGR-seq)24, meaning that interactions in GC-rich regions may be under-represented. Nevertheless, the chance that uracil residues are neighboring another base pair is 25%, assuming a uniform distribution of all possible pairs of bases.A common bias that occurs when psoralen is used to crosslink double-stranded RNA (dsRNA) is its preference to intercalate into adjacent opposite pyrimidine bases, preferably uracilThe major problem of all of the abovementioned methods is that the proximity ligation step is highly inefficient and that it is not possible to perform an RNA-seq library preparation that enriches for or specifically targets the ligated RNAs. Furthermore, the proximity ligation step does not exclusively ligate interacting RNA strands but also all kinds of single-stranded RNAs in a random fashion. As a result, the yield of RNA–RNA interaction informative reads is very low, as shown in21. Data from PARIS support the results of Linet al.25, who discovered that the interactions within the long ncRNA NEAT1 have an important architectural function in paraspeckle formation and thus impact the regulation of gene expression. In addition, PARIS could detect inter-repeat duplexes in the long ncRNA XIST, which is essential for X chromosome silencing22. Furthermore, the use of SPLASH has uncovered the RNA–RNA interactome of influenza A viruses required for virus growth26; this might facilitate the prediction of the emergence of new pandemic influenza strains.Nevertheless, the presented approaches provided interesting and deep insights into the RNA–RNA interaction networks of the studied organisms and the respective growth conditions. With the help of these DDD methods, already experimentally verified RNA structures could be accurately recapitulated; in addition, novel RNA structures as well as new RNA–RNA interactions were revealed. For instance, LIGR-seq identified novel interactions between small nucleolar RNAs (snoRNAs) and mRNAs, including the snoRNA SNORD83B that controls the steady-state levels of its target mRNAsin vivo. Nevertheless, there is still room for improvement. A stricter enrichment of crosslinked RNAs by combining enzymatic digestions (LIGR-seq) with two-dimensional gel-electrophoretic separation (PARIS) or biotinylated-psoralen selection (SPLASH) or both could eliminate the undesired fortuitous ligation of single-stranded or non-crosslinked RNA duplexes.Even based on the highly inefficient proximity ligation, the currently available DDD methods have been able to capture and analyze crucial RNA–RNA interactions in a transcriptome-wide fashion andThe efficiency of the proximity ligation could be enhanced with non-complementary overhangs. These may be either left over by a limited nuclease digestion or introduced synthetically before ligation, for example, by polyA tailing or by using different terminal transferases. Such duplex overhangs are more likely to be ligated and could lead to improved yields of interaction-informative sequencing reads.27. However, to the best of our knowledge, the results have never been reproduced, nor was the method used in further studies. In addition, future discoveries might provide engineered DNA ligases with substrate promiscuity to increase the dsRNA ligation efficiency. Eventually, a direct ligation of dsRNA adapters would pave the way for novel and hopefully more-efficient DDD protocols.The most straightforward approach to turn RNA duplexes into sequencing templates would be a direct dsRNA ligation method. So far, only one report has proposed using RNA and DNA ligases for the enzymatic joining of two or more dsRNA moleculesLikewise, the bioinformatics pipelines for basic data analysis and statistical inference of interactions need to be improved. Currently, none of the existing software packages considers the actual complementarity of the putative interaction partners but relies only on statistical measures. Furthermore, RNA–RNA interactions are driven by base-pair formation, for which thermodynamic parameters and kinetic models exist. Integrating these aspects into the analyses may additionally improve the reliability and robustness of data analysis.28, schizophrenia29, and various cancer types30 such as leukemia31, breast cancer32, or lung cancer33). Thus, there is and will be a wide application range for first- and next-generation DDD methods that have the potential to provide important contributions in areas of basic and applied sciences.In conclusion, the currently available DDD methods pave the way to a deeper understanding of the so-far little-studied RNA–RNA interactome. Crucial for their future acceptance and success will be to increase the yield of informative reads, which would allow RNA regulatory networks to be deciphered in greater depth. Results from such studies could have a great impact on many research areas. For example, a lot of human diseases are associated with ncRNAs (for example, Alzheimer’s disease


ARGONAUTE1 (AGO1) to suppress RNA silencing. Although P0 harbors an F‐box‐like motif reported to be essential for interaction with S phase kinase‐associated protein 1 (SKP1) and RNA silencing suppression, it is the autophagy pathway that was shown to contribute to AGO1 degradation. Therefore, the role of P0–SKP1 interaction in silencing suppression remains unclear.P0 protein of some polerovirus members can target Br).We conducted global mutagenesis and comparative functional analysis of P0 encoded by Brassica yellows virus (BrYV) (P0Br are required for local and systemic silencing suppression activities. Remarkably, the F‐box‐like motif mutant of P0Br, which failed to interact with SKP1, is destabilized in vivo. Both the 26S proteasome system and autophagy pathway play a role in destabilization of the mutant protein. Furthermore, silencing of a SKP1Nicotiana benthamiana  ortholog leads to the destabilization of P0Br. Genetic analyses indicated that the P0Br–SKP1 interaction is not directly required for silencing suppression activity of P0Br, but it facilitates stability of P0Br to ensure efficient RNA silencing suppression. Consistent with these findings, efficient systemic infection of BrYV requires P0Br.We found that several residues within P0Our results reveal a novel strategy used by BrYV for facilitating viral suppressors of RNA silencing stability against degradation by plant cells. The synthesized dsRNAs are processed sequentially to produce ‘secondary’ vsiRNAs  forms homodimers that bind AGO1 and possibly AGO2, and compromises AGO1 loading with siRNAs  interacts with SGS3 and RDR6 to coaggregate and enwrap the SGS3‐RDR6 consortium (siRNA bodies)  potyvirus mediates degradation of SGS3 via ubiquitination and autophagy pathways . Various strategies are used by VSRs, including sequestration of double‐stranded vsiRNAs, inhibition of vsiRNA stabilization, and inactivation of the silencing effector proteins or competition with them , also known as Beet western yellows virus isolate FL1 (BWYV‐FL1), is the first VSR reported in the genus polerovirus  and the Phe/Trp (FW) residues within the C‐terminal consensus sequence ((K/R) IYGEDGX3FWR)  Inner Mongolian isolate (P0PL‐IM) triggers AGO1 degradation and suppresses RNA silencing without interaction with SKP1  suppresses RNA silencing in Nicotiana benthamiana  further demonstrates that knockdown of an N. benthamiana SKP1 ortholog (NbSKP1), the host factor interacting with P0 via its F‐box‐like motif, destabilizes P0 protein. Genetic evidence also shows that, rather than directly playing a role in silencing suppression, the interaction between P0Br and SKP1 seems to ensure silencing suppression activity through facilitating the stability of P0Br. In addition, our results indicate an important role for P0Br in BrYV systemic infection.Brassica yellows virus (BrYV) is a newly identified polerovirus infecting crucifer crops in China  transgenic N. benthamiana 16c line, and a P0Br‐6Myc transgenic N. benthamiana line were germinated from seeds and maintained at 24°C with a 13 h (c. 75 μmol m−2 s) daylight regimen.All the primers used in this study are listed in Supporting Information Br‐3Flag was described previously  or pGD‐3FLAG to produce pGD‐NbSKP1‐GFP or pGD‐GUS‐3FLAG. Full‐length NbSKP1 without start or stop codon was cloned into pTRV2 to produce pTRV2‐NbSKP1  and photographed using a digital camera  with a yellow filter  at 2 and 14 d post‐infiltration (dpi), respectively. The number of systemically silenced 16c plants was measured for each treatment, and the silencing ratio was calculated from plants tested in four different experiments. All experiments were repeated three times.et al., Plant total RNA used for RNA and sRNA gel blots was extracted using Trizol Reagent (Invitrogen) according to the manufacturer's protocol. Isolated RNA was reverse transcribed using oligo (dT) primer HC51118TR and Moloney murine leukemia virus (M‐MLV) reverse transcriptase (Promega). Semiquantitative reverse transcriptase PCR (RT‐PCR) was performed as described previously (Wang Y. 32P] dCTP using the Prime‐a‐Gene Labeling System (Promega) in hybridization buffer (Sigma‐Aldrich) at 65°C for 16 h. The signals were detected by exposing the membrane to X‐ray film with the chemical fluorescent substrate. Signal intensity was quantified using imagequant tl software .Total RNA was separated in a 1.5% denaturing gel and then blotted to Hybond‐N+ membrane (Amersham Pharmacia Biotech). Templates of probes were amplified by PCR with respective primers and labeled with [α‐et al., N. benthamiana leaves using ×2 sodium dodecyl sulfate (SDS) sample buffer (100 mM Tris (pH 6.8), 4% SDS, 20% glycerol and 0.2% bromophenol blue) containing 10% β‐mercaptoethanol. Total yeast proteins were extracted as described  experiments were performed with the Matchmaker GAL4 Two‐Hybrid System 3  as previously described , 1 mM EDTA, 150 mM NaCl, 2% (w/v) polyvinylpolypyrrolidone, 10 mM dithiothreitol, ×1 protease inhibitor cocktail (Sigma‐Aldrich), 0.1% Triton X‐100 (Sigma‐Aldrich)). After centrifugation at 3000 g for 10 min at 4°C and filtration with a 0.45 mm filter, the clarified lysate was incubated with 4% BSA preblocked anti‐FLAG M2 agarose beads (Sigma‐Aldrich) for 3 h and the complex was washed five times with immunoprecipitation buffer , 1 mM EDTA, 150 mM NaCl, 0.1% Triton X‐100). The immunoprecipitates were denatured and subjected to immunoblotting using corresponding antibodies.Co‐immunoprecipitation (co‐IP) was performed as previously reported with minor modifications  was used as a solvent control.MG132  was used at the final concentration of 100 μM. E‐64d  was used at a final concentration of 50 μM. For inhibitor treatment, MG132 or E‐64d was infiltrated into RZ‐NOS were transformed into A. tumefaciens strain C58CI. The Agrobacterium harboring cDNA clones were infiltrated into 4‐ to 5‐wk‐old N. benthamiana plants.Constructed cDNA clones of BrYV and its derivative mutants based on pCB301‐2x35S‐MCS‐HDVAgrobacterium‐mediated transient coexpression assay in N. benthamiana , an Agrobacterium‐mediated coexpression assay was performed in the GFP transgenic N. benthamiana line 16c   or Ala substitution of Gln2 (Q2A), Try61 (Y61A), Try96 (Y96A) or Try211 (Y211A) abolished RNA silencing suppression and cell death induction  or Ala substitution of Leu184 (L184A) or His206 (H206A) abolished systemic RNA silencing suppression and cell death induction without affecting local RNA silencing suppression  are important for both cell death and RNA silencing suppression function of P0Br, but the C‐terminus residues 225–249 and two residues (Leu184 and His206) are important for systemic RNA silencing suppression and cell death induction but not for local RNA silencing suppression activities.To determine the regions of P0ted Fig. d. In addBr: 63‐LPLLLGDHVHDDVRKSILVP‐82  gene using a TRV‐based VIGS system  were fused to the GAL4 DNA binding domain (BD) as bait and N. benthamiana SKP1  ortholog was fused to the GAL4 activation domain (AD) as prey. P0Br and Q2A interacted with NbSKP1 as indicated by yeast growth and the presence of a blue color (LacZ activity) on SD media lacking Ade, His, Trp and Leu (SD/−AHLW) with X‐gal   in LP  rescued the protein accumulation  to target AGO1 for degradation, 6Myc‐tagged Arabidopsis AGO1 (6Myc‐AtAGO1) and 3FLAG‐tagged P0Br or its mutants were coexpressed in the leaves of N. benthamiana as previously described , 6Myc‐AtAGO1 accumulated to a similar level as that of the empty vector (pGD)    had no effect on the viral RNA level of BrYVFS  Fig. . By contVFS Fig. . These ret al., N. benthamiana promotes resistance to TuYV. Thus, it was proposed that the P0–SKP1 interaction is essential for RNA silencing suppression  suppresses RNA silencing more efficiently than P0 of cereal yellow dwarf virus RPV (P0RPV), but P0PRS binds Arabidopsis SKP1‐like 2 (ASK2) less efficiently than P0RPV ) within P0 proteins has long been suggested to be required for RNA silencing suppression, because the mutation of this motif compromises P0's RNA silencing suppression activity . Furthermore, alanine substitution of Leu184 also had the same effect (data not shown). We speculate that alanine substitution of lysine or leucine residues alters the attackable conformation or post‐translational modification status of LP to a stable one with similar features to that of P0Br in the context of P0–SKP1 interaction. Notably, as shown in Fig. Tu and AtCUL1 requires SKP1 as a bridging component  through the autophagy pathway in a ubiquitylation‐dependent manner to suppress antiviral RNA silencing. Moreover, the mutant LPK and the wild‐type P0 encoded by PLRV (P0PLRV) may represent an alternative evolutionary trend of polerovirus P0 to stabilize itself via an unknown strategy, indicating that silencing suppression is independent of P0–SKP1 interaction.In conclusion, we propose a model in which the BrYV VSR protein P0 maintains its own stability and then exerts RNA silencing suppression activity for efficient systemic infection Fig. . In the CH, YL, QS and HX designed the research; YL, QS, HX, ZW, TZ, Xiaoyan Zhang, Xin Zhang and CZ performed the experiments; YL, QS, HX, ZW, TZ, CZ and CH analyzed the data; YL, YW, XW, SPD‐K and CH wrote the paper; DL, JY, XW, YZ and YW contributed through discussions; YL and QS contributed equally to this work.New Phytologist Central Office.Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries  should be directed to the Fig. S1 The Phe219 residue in P0Br is essential for local and systemic silencing suppression.Fig. S2 Symptoms of Nicotiana benthamiana plants infected with TRV:00 or TRV:NbATG5.Fig. S3 P0Br‐mediated degradation of AGO1 is blocked by E‐64d inhibitor.Fig. S4 Detection of Myc‐tagged P0Br and its mutants in yeast. Total proteins were extracted from yeasts.Fig. S5 Detection of P0Br‐6Myc in XVE:P0Br‐6Myc transgenic Nicotiana benthamiana plants.Fig. S6 Mutation of the tyrosine‐61 residue in P0Br abolished suppression of RNA silencing.Fig. S7 Complementation analysis of P0Br and its mutants.Table S1 Sequences of primers and probes.Click here for additional data file.