Ziziphus jujuba was purified using ion exchange, and gel filtration chromatography resulting in an 18.9-fold enhancement of activity with a recovery of 20%. The molecular weight of Z. jujuba peroxidase was 56 kDa, as estimated by Sephacryl S-200. The purity was evaluated by SDS, which showed a single prominent band. The optimal activity of the peroxidase was achieved at pH 7.5 and 50 °C. Z. jujuba peroxidase showed catalytic efficiency (Kcat/Km) values of 25 and 43 for guaiacol and H2O2, respectively. It was completely inactivated when incubated with β-mercaptoethanol for 15 min. Hg2+, Zn2+, Cd2+, and NaN3 (5 mM) were effective peroxidase inhibitors, whereas Cu2+ and Ca2+ enhanced the peroxidase activity. The activation energy (Ea) for substrate hydrolysis was 43.89 kJ mol−1, while the Z value and temperature quotient (Q10) were found to be 17.3 °C and 2, respectively. The half-life of the peroxidase was between 117.46 and 14.15 min. For denaturation of the peroxidase, the activation energy for irreversible inactivation Ea*(d) was 120.9 kJmol−1. Thermodynamic experiments suggested a non-spontaneous (∆G*d > 0) and endothermic reaction phase. Other thermodynamic parameters of the irreversible inactivation of the purified enzyme, such as ∆H* and ∆S*, were also studied. Based on these results, the purified peroxidase has a potential role in some industrial applications.In this study, peroxidase from  Class III plant peroxidase is a normal enzyme whose activity was already identified in 1855 and which was purified several decades later3. peroxidase is relatively stable at high temperatures, and its activity can be easily measured using simple chromogenic reactions. The oxidation of different phenolic and non-phenolic substrates that participate in the breakdown of H2O2, is catalysed by peroxidases, which are major antioxidant enzymes. Peroxidases play an important role in industrial applications, since peroxidases can catalyse the broad range of redox reactions in the presence of H2O25. Peroxidase enzymes have various important roles not only in the biomedical industry  but also in the agriculture industry and its allied sectors8. These also have important roles in plant physiology processes, such as cell safety against oxidative stress, the formation of lignin and suberin, crosslinking of cell wall components, wound healing, protection against pathogens or insects9, and promote plant darkening10. Peroxidase causing the oxidation crosslinking of pentosan in the dough. Oxidative enzymes such as peroxidase are added in bread dough to avoid stickiness. The positive effect of peroxidases on the breading process is due to the cross-linking of feruloylated arabinoxylans in larger aggregates. These have a better ability to hold water and enable water transfer in the dough. Additionally, peroxidase can influence the gluten network either by cross-linking the gluten proteins or by adding arabinoxylans to gluten proteins11. Peroxidase enzymes are also reported for bioremediation of various phenolic dyes and decolorizers12. The catalytic reaction of peroxidase occurs in three stages. The initial stage involves peroxidase oxidation to create an unstable intermediate compound called Cpd I (1). In the second stage, a corresponding electron donor reduces Cpd I to Cpd II and to a free radical (2). Then the second substrate further lowers Cpd II to restore the enzyme’s resting state and a radical one (3)13.Peroxidase (E.C.1.11.1.7) belongs to the class of oxidoreductases, containing iron (III) protoporphyrin IX as the prosthetic group, usually present in plants and responsible for the process of brewingZiziphus jujuba generally known as annep in Saudi Arabia, belongs to the Rhamnaceae family, which consists of 550 species and 45 genera that are commonly distributed in the tropical and subtropical regions around the world14. Ziziphus jujuba is a hardy tree present in arid regions and unfavourable growth conditions include saline soil in a hot and arid environment15. The chemical composition of jujubes has been studied by several researchers16. Jujuba has been consumed since ancient times and is still a popular and influential fruit in human diets. The recent pharmacological and phytochemical results have shown that triterpenic acid, flavonoids and polysaccharides are the main active components within jujubes20. Jujuba polysaccharides are suggested to be the primary active components due to their haematopoietic, immunomodulatory and haematopeutic roles22. Additionally, due to their anticancer and anti-inflammatory properties, triterpenic acids were considered the active ingredients of jujubes24. Moreover, jujuboside B and betulinic acid could be the active components, as they display beneficial effects on the cardiovascular system26. Various studies have supported the biological activities of jujube, which is considered medicinal herb as well as a food. In Chinese medicinal theory, jujube was considered a herb that can relieve mental stress and can calm the state of mind. In clinical practices, jujube is taken alone or combined with other herbal medicines to treat forgetfulness and insomnia. Recent reviews have summarized the composition of the jujube as well as its health benefits28.29, Kalipatti sapota peroxidase30, pearl millet grains peroxidase31, and green gram root peroxidase32. Nevertheless, to our full knowledge, there is no study on the purification and characterization of peroxidase from on Z. jujuba fruit.In the past, researchers have studied the characterization and purification of certain kinds of peroxidase enzymes, such as Arabian balsam peroxidaseZ. jujube and to investigate the potential contribution of Z. jujuba peroxidase in the production and storage of fresh Z. jujuba in order to select an appropriate method for mitigating peroxidase activity. The negative effect of peroxidase is causing unhealthy fruit browning and vegetable off-flavors33. Z. jujuba is seasonal fruit, and easily perishable. Thus, this research was therefore aimed purifying and characterizing peroxidase from Z. jujube and to investigate the physical, biochemical and thermodynamic characteristics of the peroxidase enzyme so that the conditions can be regulated for mitigating peroxidase activity that causes unhealthy fruit browning and then increases the fruit storage time.This research was therefore aimed purifying and characterizing peroxidase from Z. jujuba) fruit were collected from Al-Baha City, Saudi Arabia. DEAE-Sepharose, Sephacryl S-200, hydrogen peroxide, guaiacol and 4-aminoantipyrine were acquired from Sigma-Aldrich . All other chemicals used were of analytical grade.Jujube  and stacks (4%) was used to determine the purity and the subunit molecular weight of the purified enzyme. The Coomassie Blue staining method was used to detect the protein, and a prestained protein ladder (Thermo Scientific 26616) was used to determine the molecular massZ. jujuba peroxidase activity was determined in the range of pH 4.0–9.0 using the following buffers: 50 mM sodium acetate (pH 4.0–6.0) and Tris–HCl (pH 6.5–9.0). To evaluate the pH stability, the residual activity after incubation for 24 hours at 4 and 28 °C was assessed at different pH values (pH 6.0–9.0)37.Z. jujuba peroxidase was examined at different temperatures between 25 and 80 °C. Standard assay conditions were applied during testing. The mixture was then cooled. The highest activity was reported as 100%37. The Arrhenius plot was used to determine the activation energy of the peroxidase (Ea). The effect of temperature on the reaction rate was demonstrated in terms of Q10, which is a variable that increases due to an increase in temperature of 10 °C37.To determine the optimum temperature, the Z. jujuba peroxidase was evaluated for its preference for substrates, such as guaiacol, pyrogallol, 4-aminoantipyrine, ABTS, O-phenylenediamine and O-dianisidine. The enzyme activity was tested as outlined above.The Z. jujube peroxidase were determined for guaiacol and H2O2 substrates. The Km and Vmax values were calculated from Lineweaver-Burk plots. Then, the catalytic efficiency value (Kcat/Km) was calculated for each substrate38.Km, Kcat and Vmax values of Z. jujuba peroxidase was incubated with several compounds  for 15 min. the activity of the enzyme was measured as described above.Z. jujuba peroxidase activity, various metal ions, i.e., Fe2+, Ca2+, Cd2+, Ni2+, Cu2+, Hg2+ and Zn2+ , were applied individually to tubes containing an assay-buffered substrate solution, and the mixtures were tested for enzyme activity under normal activity analysis.To investigate the effect of metals on The thermal stability profile was investigated by heating the purified enzyme within the temperature range of 55 to 70 °C, and residual activity was calculated from sterile aliquots withdrawn at periodic intervals using the following equation:t and C0 describe the activities at time t (min) and time t = 0 min, respectively.where CThe enthalpy (ΔH*) was calculated using the relationship given in the following equation−1 mol−1 and is the universal gas constant and T indicates the absolute temperature (K).where R = 8.314 J K−34 J·s), k is the Boltzmann constant (1.381 × 10−23 J·K−1), and the inactivation rate constant (Kd) can be defined as the following equation:The free energy of activation (ΔG*) at varying temperatures was determined from the relation shown in the following equation:Activation entropy (ΔS*) was calculated using the formula shown below:1/2) was described as the time after which the enzyme activity was decreased to one-half of the original activity and was calculated, as outlined by Gohel and Singh39, according to the following formula:The enzyme half-life .The sensitivity factor , which is described as the temperature increase required to reduce 90% of the D-value by one logarithmic cycleZiziphus jujuba was isolated and purified through the successive steps of ion exchange and gel filtration chromatography. The results for Z. jujuba peroxidase purification are summarized in Table Z. jujuba peroxidases POD I, II, III and IV, respectively. The Z. jujuba peroxidase fraction POD II exhibited the highest activity and was separated on the Sephacryl S-200 column to obtain Z. jujuba peroxidase POD IIA , horseradish cv. Balady (56 kDa), broccoli (48 kDa), and palm leaf (48 kDa)45. The purified Z. jujuba peroxidase examined at distinct pH levels from pH 4 to 9, displayed optimum activity at pH 7.5  for peroxidase activity. As illustrated in Fig. peroxidase from IIA Fig. , which eGE Figs.  and 1S. GE Figs.  and 3S. 7.5 Fig. . The purZ. jujuba peroxidase was investigated experimentally in order to determine the optimum temperature for its activity  and activation energy (Ea*) are significant parameters that determine the enzyme stability and enzyme-substrate complex stability50. The activation energy of peroxidase was discovered to be 43.89 kJ mol−1 51. The temperature quotient for peroxidase was found to be 2.1, which is relatively greater than that reported for other peroxidases51.The purified ity Fig. . It was 2O2 substrate hydrolysis at 50 °C were 23.5 and 35.5 unit/ml and 0.23 and 0.91 mM, respectively  was 25 and 43 for guaiacol and H2O2, respectively  pace improved more than 8-fold. In addition, with increasing temperature, the half-life and decimal reduction time decreased as anticipated (Table Z. jujuba peroxidase showed good stability of a wide temperature range (55–65 °C), with a D-value of 388 to 230 min. However, the enzyme was found to be less stable at temperatures greater than 65 °C, as the D-values were discovered to be smaller. The deactivation energy Ea*(d) for Z. jujuba peroxidase calculated using an Arrhenius plot  are important parameters that provide precise proof of the unfolding of protein during inactivation53. Thermodynamic results for Z. jujuba peroxidase were calculated and are shown in Table Z. jujuba peroxidase at 55 °C were equal to 118.20 and 83.42 kJ mol−1, respectively. The ΔH* and ΔG* were reduced to 118.08 and 81.33 kJ mol-1, respectively at 70 °C, while ΔS* increased to 107 jmol−1. Relatively low enzyme enthalpy values reflect its resistant nature, while increased values represent response to protein denaturation54. An increase in temperature caused the free energy to decrease, while entropy slightly increased, but the change in entropy was not significant. Low entropy values are noted to be exceptional in biological systems. With increasing temperature, the Gibbs free energy decreased, indicating that the enzyme had shown less resistance to heat unfolding at greater temperatures. The purified Z. jujuba peroxidase exhibited high activities against guaiacol (100%), O-phenylenediamine (87%), and O-dianisidine (67%). Moderate and low enzyme activities were obtained with 4-aminoantipyrine (37%), pyrogallol (21%) and ABTS (14%) , Z (17.3 °C), Q10 (2.1) and the high activation energy values suggest that a high amount of energy is required to initiate peroxidase denaturation. The optimum pH and temperature conditions for Z. jujuba peroxidase activity were a pH of 7.5 and 50 °C, respectively. The vegetables can, therefore, be blanched at 70 °C to inactivate Z. Jujuba peroxidase, prevent browning. Also the processing pH should be lower than pH 7 when the inhibition of peroxidase is required for food products from Z. Jujuba. Guaiacol is a natural phenolic product of chemical substances in plants (vegetables and fruits) which play an important role in enzymatic browning, as they constitute substrates for browning–enzymes. The guaiacol Km value was 23.5 unit/ml, which suggests that the enzyme has a high affinity for guaiacol. At the stage, the aim is to purifying and characterizing peroxidase and establish the best conditions that would give the best physical, biochemical and thermodynamic characteristics of the peroxidase enzyme. The optimum conditions obtained in the present study would serve as a basis to employ the peroxidase in the process of manufacturing and storage to improve the nutritional value and exterior quality.Peroxidase from Supplementary figures.


We present a more efficient approach to define a variable safety margin based on the conditional probability density function of the effectively obtained pSAR10g value, given the estimated pSAR10g value.The introduction of a linear safety factor to address peak local specific absorption rate  using just the generic body model “Duke”; (2) using our model library to assess the maximum pSAR10g value over all models; (3) using the most representative “local SAR model”; (4) using the five most representative local SAR models; and (5) using a recently developed deep learning–based method.The conditional probability density function can be estimated from previously simulated data. A representative set of true and estimated pSARCompared with the more conventional safety factor, the conditional safety‐margin approach results in lower (up to 30%) mean overestimation for all investigated local SAR estimation methods.10g correction allows more accurate local SAR assessment with much lower overestimation, while a predefined level of underestimation is accepted .The proposed probabilistic approach for pSAR This is due to a variety of uncertainties in the actual examination setup. Indeed, even assuming that the reflected/lost power is properly monitored, and that there are no calibration errors, the used body model and its position within the MRI system could be very different compared with the patient under examination. To address this, a library of models can be used to cover a large patient population.Although the overestimation error results in unnecessarily long scan times and/or suboptimal image quality, only peak local SAR underestimation error poses a safety risk. For this reason, to diminish the probability of underestimation, a linear safety factor is usually applied to increase the estimated peak local SAR value to such an extent that underestimation will not occur.B1+ maps and the corresponding local SAR distribution. However, like the aforementioned methods, this method also suffers from residual underestimation errors that need to be addressed by applying a suitable safety factor.Recently, an alternative deep learning–based method for subject‐specific SAR estimation was presented.As this study will show, the conventionally applied linear safety factor often results in unnecessary very conservative estimation, in particular when high peak local SAR value is estimated. Therefore, we propose an alternative approach to diminish the probability of peak local SAR underestimation based on the conditional probability density function of the true peak local SAR value, given the estimated peak local SAR value. This approach allows us to define a variable safety margin for each possible estimated peak local SAR value.This approach is applied to five state‐of‐the‐art local SAR estimation methods: local SAR prediction based on (1) one generic model,In the present work, these methods are applied to assess the peak local SAR with a multitransmit array of eight fractionated dipole antennas for prostate imaging at 7 T.210g) underestimation bears one considerable drawback: If the estimated pSAR10g level is high, then the resulting corrected pSAR10g level shows severe overestimation of the true pSAR10g level. This is depicted in Figure 10g (pSARE) and truly obtained pSAR10g (pSART) are related. In particular, each point in the scatter plot represents one out of 5750 pSAR10g estimations by the deep learning method with 8 × 1W input power and random phase shimming. The diagonal red dotted line indicates where the estimated pSAR10g levels and true pSAR10g levels are equal. Points above the diagonal represent true pSAR10g levels larger than the corresponding estimate. The scatter plot clearly shows that there is a good correlation between estimated pSAR10g level and true pSAR10g level. However, the estimated pSAR10g level needs to be corrected to avoid underestimation, as there are many points above the dotted line. A linear safety factor of 1.62 is therefore determined to correct (increase) the estimated pSAR10g level, such that all of the potential true pSAR10g levels are lower than the corrected pSAR10g level.The introduction of a linear safety factor to avoid peak local SAR pSAR0g undere10g level on the horizontal axis results in a corrected pSAR10g  level. If the correction factor is chosen appropriately, all points will be below the dashed red line. As Figure The red dashed line shows how each estimated pSAR10g levels, the application of the linear safety factor results in corrected pSAR10g levels that have far too high overestimation. The overestimation could be reduced by determining the safety factor using the upper outer fence.10g of the one point that determines its slope. Then, using the upper outer fence definition,However, the figure also clearly illustrates that for large estimated pSAR10g levels, the corrected pSAR10g level is still much larger than what realistically could be expected. In fact, a previous study10g level that is not exceeded for 99.9% of the cases  is 3.8 W/kg. Therefore, a solution could be to combine these limits that use the corrected pSAR10g value if the value is lower than 3.8 W/kg, and set it equal to 3.8 W/kg otherwise . When a pSARE value is estimated, we would like to know the probability of pSAR10g underestimation: P(TpSAR > EpSAR). Thus, one needs to know the conditional probability density function T|Ef (T|pSAREpSAR), which describes the probability distribution of pSART for a given pSARE value. This function is calculated in Eq. Ef (EpSAR) is the marginal probability density function that describes the probability distribution of estimating the EpSAR value regardless of TpSAR fT|Ep10g underestimation (underestP)  is given asNow the probability of pSAREpSAR = E1 value, and the integral can be numerically evaluated to determinate a corrected E,CpSAR value with probability of underestimation equal to an arbitrary  ε:Therefore, the conditional probability density function can be calculated for the estimated FindpSARE,Csuchthat:E,C value effectively results in a CSM that depends on the estimated pSAR10g level and that can be determined for each possible estimated EpSAR value.This corrected pSARNote that, from the observed data, a variety of approaches can be used to estimate the underlying probability density function,310g estimation. In this study, the pSAR10g values were predicted using five state‐of‐the‐art local SAR estimation methods. Then, for each estimation method, the corrected pSAR10g values were obtained by applying the linear safety factor and the proposed approach based on probability density function.The exact extent of reduction of overestimation using the proposed approach depends on the method of pSART, pSARE) samples for the purpose of this study, our database of 23 subject‐specific models10g and the estimated pSAR10g by each method, when the transmit array is driven with different drive vectors.To build the required set of . This driving mode allows one to assess the performances during an arbitrary imaging examination in the lower abdomen region.Random phase settings: Drive vectors with uniform amplitude (8 × 1W input power) and random relative phase settings with respect to the first channel  and relative phase normally distributed around the average prostate phase shimming setU0,1) and random relative phase settings . This driving mode allows one to assess the performances even in the case of sophisticated RF pulse design strategies  validation sets are used to determine the required linear safety factor using the upper outer fence methodFor each model, 250 different drive vectors were used to calculate the true and the estimated pSAR10g values, particularly for high estimated pSAR10g levels. In our previous study,10g, we defined a 99.9% certain pSAR10g upper bounds of 3.8 W/kg and 3.2 W/kg for random phases and prostate shimmed phases, respectively. In the same way, we can define a 99.9% certain pSAR10g upper bound of 2.6 W/kg and 6.8 W/kg for random amplitudes and phase settings normalized to 1W maximum input power per channel and 8W total input power, respectively. Therefore, as explained in section 2, we can further reduce overestimation of the linear safety factor approach by combining it with these pSAR10g upper bounds, when higher corrected pSAR10g values are obtained  are samples from the joint probability density function E,Tf   and follow what appears to be a gamma distribution  can be determined using Equation Therefore, the conditional probability density function can be calculated, and the CSM with an arbitrary small probability of underestimationE,Tf  is discretized , ∆ (TpSAR) = 0.01 W/kg) and the estimated statistical models are used to obtain the 2D array of E,Tf  values and the 1‐dimensional array of E,Tf (EpSAR) values. Then, from the ratio between each column of the first array and the corresponding value of the second array, the 2D array of ,T|Ef (TpSAR|EpSAR) values are determined. Each column of the obtained array represents the conditional probability density function for the corresponding EpSAR value, and the CSM is determined by integrating numerically along the column until the required probability of underestimation is reached . Finally, the obtained CSMs for the discretized EpSAR values are interpolated on‐line to determine the CSM for any possible estimated EpSAR value.For the practical implementation of the proposed approach, the domain 3.210g value, for each model m and each drive setting s, the 10g‐averaged Q‐matrices (Q10 g)To obtain the true pSARatrices Q10 g20,r is the spatial location of each Q10 g matrix.where3.3m and each drive setting s, the corresponding pSAR10g values are estimated using five different methods.For each model 3.3.110g estimation based on the actual drive scheme can be performed.A very common approach consists of performing off‐line electromagnetic simulations using a generic body model. Then, assuming that the investigated model is representative for the current subject, on‐line pSAR3) is used as follows:To assess the performance of this method, the generic model “Duke” of the Virtual Family3.3.210g over all models.m, and each drive setting s, the maximum pSAR10g over the other models is determined as reported in Equation To cover an entire population of patients, a model library can be used to predict the maximum pSAR3.3.310g. Therefore, as presented in our preliminary study,m we consider the model n, which shows the most similar worst‐case local SAR distribution with uniform amplitudeSAR10gWoC ,SAR10gWoC distribution contains both patient anatomy information and electric‐field information (transmit‐array information). In particular, we select the model n, which presents theSAR10gWoC distribution that minimized the RMS error with respect to the registeredSAR10gWoC distribution of the model m . Then, for each drive setting s, the pSAR10g value is estimated using model n as follows: The estimation error might be reduced using a large database of models and selecting the most representative local SAR model for the patient under examination.3.3.4As results will show, the model library method results in large mean overestimation, while the model selection method has large underestimation errors. For these reasons, a good compromise could be the selection of multiple models.m, the group of five most representative local SAR models mA is identified 10g distribution and determine the peak value, as follows:where Note that the performance of the deep learning–based method is evaluated by performing a leave‐one‐out cross‐validation. This means that 23 separate times, the convolutional neural network is trained on all of the data samples from all models except for one model (22 × 250 = 5500 training samples with random phase shimming).3.410g overestimation error of all pSAR10g estimation methods with the linear safety factor and with the proposed CSM were evaluated and compared with each other for each driving mode.To assess the performance of all methods covered by this study, for each body model, 1000 test sets were generated for each driving mode. Then, using the obtained 23 000 (23 × 1000) test sets, the mean pSAR410g estimation method and for each driving mode, 5750 (23 × 250) validation sets were used to define the required linear safety factor and the CSM. For random phase and prostate shimmed‐phase drive modes, the required linear safety factors were similar and ranged from 1.36 for the deep learning method to 1.96 for the model selection method. For the amplitude and phase shimming driving mode, the obtained linear safety factors range was a bit wider, ranging from 1.22 to 2.09 for deep learning and model selection methods, respectively.For each pSAR10g estimation method, each scatter plot of true versus estimated pSAR10g was fitted with a Gaussian mixture distribution . Subsequently, the conditional probability density function was determined and the CSM was evaluated to have a probability of underestimation of 0.1% (ε=0.001). All probability density functions are reported in Supporting Information Figures To determine the joint and marginal probability density functions of each pSAR10g estimation method and each driving mode with 23 000 (23 × 1000) test sets.The performances of the defined safety factors were assessed for each pSAR10g considering the use of a single generic body model to evaluate the pSAR10g with random phases . The green line denotes the corrected pSAR10g using the CSM. Figure 10g correction method.Figure s Figure A, prostas Figure B and rans Figure C, and 8Ws Figure D. The or10g and the mean overestimation considering the model library pSAR10g estimation method. Figures 10g estimation error of each pSAR10g estimation method, each driving mode, and each pSAR10g correction method is presented in Supporting information Figures Figure 10g estimation methods covered by this study. It shows the mean overestimation after the correction for all considered pSAR10g estimation methods for each driving mode. The deep learning–based pSAR10g estimation method outperforms conventional methods. Compared with the pSAR10g estimation methods based on the selection of the most similar models from a database, after the application of the CSM, it achieves a mean overestimation reduction of 44%‐28% for phase shimming and 13%‐9% for amplitude and phase shimming. The multiple models selection and the model selection methods are the second and third best pSAR10g estimation methods, respectively.The bar diagram in Figure 510g) using a linear safety factor produces an overconservative pSAR10g prediction, even when the safety factor is defined with outlier rejection and a low underestimation probability is allowed.10g values, the corrected pSAR10g values are unreasonably high. If we consider the mean estimated pSAR10g value with almost all methods, it already produces corrected pSAR10g values higher than the 99.9% certain pSAR10g upper bound.The linear safety factor is a common approach to avoid potential underestimation due to errors in predicted peak local SAR levels.10g values to this upper bound. This produces a slightly higher probability of underestimation (< 0.2%) but severely reduces the mean overestimation. Such a combination of corrected pSAR10g values is in principle a first attempt at defining variable margin factors depending on the estimated SAR level.Therefore, it could be reasonable to limit the corrected pSAR10g values. Therefore, a new approach to define a variable safety margin based on the conditional probability distribution was presented. This approach allows one to define a CSM to obtain, for each estimated pSAR10g value, a corrected pSAR10g value that will allow for only a very small predefined probability of underestimation . The 0.1% probability pSAR10g threshold violations will be modest with rapidly decreasing likelihood for higher pSAR10g levels. Nevertheless, some users may prefer using the proposed approach with lower underestimation probabilities, such as 0.001% .However, a more appropriate way of tackling this problem is by making use of probability theory, to take into account the statistical distribution of realistically attainable pSAR10g estimation method. It was applied to five state‐of‐the‐art pSAR10g estimation methods for a multitransmit array of eight fractionated dipole antennas for prostate imaging at 7 TThis approach can be applied to any pSAR10g upper bound, the performance increase achievable with this method depends on the accuracy of the used pSAR10g estimation method. With pSAR10g estimation methods that show a good correlation between estimated and true pSAR10g values , the proposed approach allows a significant reduction of the mean pSAR10g overestimation (16%‐28%). For methods that have a poor performance in predicting the true peak local SAR level , the benefit of the proposed approach is much less pronounced. For these methods, the reduction in mean pSAR10g overestimation is less than 12%.Compared with the linear safety factor limited by the 99.9% certain pSAR10g values exhibit low correlation, or, in other words, are essentially independent; in this case, the joint probability is the product of their marginal probabilities P  = P (EpSAR)P(TpSAR). Then, the conditional probability coincides with the marginal probability of true value P(TpSAR|EpSAR) = P/P(EpSAR) =P(TpSAR). This means that from the knowledge of the estimated value, nothing can be inferred, and if the CSM is defined consideringε=0.001, for any estimated pSAR10g the CSM will determine a corrected pSAR10g value that will approximately coincide with the 99.9% certain pSAR10g upper bound. This is more or less the case for pSAR10g prediction using one generic model with random phase settings. As indicated in Figure 10g versus estimated pSAR10g. Therefore, the CSM results in an almost horizontal line following the 3.8 W/kg 99.9% confident upper limit.Note that a special case exists for extremely poor estimation methods. In that case, the estimated and true pSAR10g values, the linear safety factor produces lower corrected pSAR10g values than the conditional safety factor. Indeed, with the linear safety factor, the probability of underestimation is larger than 0.1% when a low pSAR10g value is estimated, whereas with the CSM it is 0.1% for any estimated pSAR10g value. Likewise, with the 99.9% certain pSAR10g upper bound, the probability of underestimation is larger than 0.1% when a high pSAR10g value is estimated. Moreover, because the histogram of estimated pSAR10g values follows a gamma distribution, close to the range boundaries, the number of estimated pSAR10g values is less and less. With a very small number of samples, the estimated conditional probability density function is larger (greater uncertainty) and less accurate . These are probably the causes of the observed large overestimation for low estimated pSAR10g values and the anomalies in the conditional probability density functions outside of the feasibly estimated pSAR10g ranges .E) to apply to the whole SAR distribution  for each time step . Subsequently, the SAR distributions are integrated over time, and then the global peak SAR value can be assessed.Furthermore, it must be considered that the time‐dependent drive vector of these custom RF pulses produces a time‐dependent SAR distribution with the peak value in a different location for each time step. The CSM, as well as the linear safety factor, is a global factor that does not take into account the spatial distribution of the SAR. Nevertheless, it can be used to define a correction factor .In addition, a comparison was made among the state‐of‐the‐art pSAR10g over all models. This pSAR10g estimation is obviously more conservative and requires smaller linear safety factors  from a database show better performance than the conventional estimation methods. If the most “similar” model is used, the pSAR10g estimation is more accurate but not very precise .The pSARe Figure . To mana10g variability, and allows us to reduce the required safety factors. This results in an even lower mean overestimation error . With the CSM, an overestimation reduction up to 15% is reached with these estimation methods.The use of multiple models reduces the probability of underestimation due to the intersubject pSAR10g correction method exploits the correlation between estimated and true pSAR10g levels. Therefore, with more accurate and precise pSAR10g estimation methods, the deep learning method provides the best performance . However, it should be noted that the neural network was trained with only phase‐shimmed training samples.It should also be noted that the deep learning–based method makes use of a network that was trained by penalizing the underestimation error more.610g) levels for multitransmit arrays, a safety factor is often applied to correct (increase) the predicted pSAR10g levels. This work has shown that this approach results in drastic and unnecessary overestimation of pSAR10g levels, particularly if the estimated pSAR10g level is relatively high.To avoid underestimation of predicted peak local SAR  distributions with uniform amplitude (8 × 1W input power)FIGURE S2 Root‐mean‐square error (RMSE) matrix. Each entry RMSE  represents the RMSE between the worst‐case pSAR10g distribution of the model n and the registered worst‐case pSAR10g distribution of the model mFIGURE S3 Worst‐case pSAR10g distribution of each model and registered worst‐case pSAR10g distribution of the most representative “local SAR model”FIGURE S4 Generic body model : scatter plot of the true pSART versus the estimated pSARE (first column); marginal probability density function E(pSARE)f of the estimated pSAR values (second column); joint probability density function E,TpSAR of the estimated and true pSAR values (third column); and 2D conditional probability density function T|E(pSART|pSARE)f obtained by combining the conditional probability density functions for each possible pSARE value (fourth column)FIGURE S5 Model library : scatter plot of the true pSART versus the estimated pSARE (first column); marginal probability density function E(pSARE)f of the estimated pSAR values (second column); joint probability density function E,TpSAR of the estimated and true pSAR values (third column); and 2D conditional probability density function T|E(pSART|pSARE)f obtained by combining the conditional probability density functions for each possible pSARE value (fourth column)FIGURE S6 Model selection : scatter plot of the true pSART versus the estimated pSARE (first column); marginal probability density function E(pSARE)f of the estimated pSAR values (second column); joint probability density function E,TpSAR of the estimated and true pSAR values (third column); and 2D conditional probability density function T|E(pSART|pSARE)f obtained by combining the conditional probability density functions for each possible pSARE value (fourth column)FIGURE S7 Multiple models selection : scatter plot of the true pSART versus the estimated pSARE (first column); marginal probability density function E(pSARE)f of the estimated pSAR values (second column); joint probability density function E,TpSAR of the estimated and true pSAR values (third column); and 2D conditional probability density function T|E(pSART|pSARE)f obtained by combining the conditional probability density functions for each possible pSARE value (fourth column)FIGURE S8 Deep learning : scatter plot of the true pSART versus the estimated pSARE (first column); marginal probability density E(pSARE)f of the estimated pSAR values (second column); joint probability density function E,TpSAR of the estimated and true pSAR values (third column); and 2D conditional probability density function T|E(pSART|pSARE)f obtained by combining the conditional probability density functions for each possible pSARE value (fourth column)FIGURE S9 Generic body model (test set). Scatter plot of true versus estimated pSAR10g and histogram of the pSAR10g estimation error for each driving mode and each pSAR10g correction method. The linear safety factor (LSF) and the conditional safety margin (CSM) were determined using the validation set (cyan dots)FIGURE S10 Model library (test set). Scatter plot of true versus estimated pSAR10g and histogram of the pSAR10g estimation error for each driving mode and each pSAR10g correction method. The LSF and the CSM were determined using the validation set (cyan dots)FIGURE S11 Model selection (test set). Scatter plot of true versus estimated pSAR10g and histogram of the pSAR10g estimation error for each driving mode and each pSAR10g correction method. The LSF and the CSM were determined using the validation set (cyan dots)FIGURE S12 Multiple models selection (test set). Scatter plot of true versus estimated pSAR10g and histogram of the pSAR10g estimation error for each driving mode and each pSAR10g correction method. The LSF and the CSM were determined using the validation set (cyan dots)FIGURE S13 Deep learning (test set). Scatter plot of true versus estimated pSAR10g and histogram of the pSAR10g estimation error for each driving mode and each pSAR10g correction method. The LSF and the CSM were determined using the validation set (cyan dots)FIGURE S14 A, Scatter plot of true versus estimated pSAR10g using the deep learning method with random phase settings . The orange line denotes the corrected pSAR10g by the LSF based on the worst‐case ratio of true and estimated pSAR10g. The green line denotes the corrected pSAR10g by the CSM with probability of underestimation of 0.001% (ε = 0.00001). B, Histogram of the pSAR10g estimation error for the corrected pSAR10g by the LSF. C, Histogram of the pSAR10g estimation error for the corrected pSAR10g by the CSM. The LSF and the CSM were determined using the validation setFIGURE S15 A, Scatter plot of true versus estimated pSAR10g using the deep learning method with random phase settings . The orange line denotes the corrected pSAR10g by the LSF determined with 23 × 1000 validation sets (LSF = 1.37). It practically coincides with the corrected pSAR10g by the LSF determined with 23 × 250 validation sets (LSF = 1.36). The solid and dotted green lines denote the corrected pSAR10g by the CSM determined with 23 × 1000 validation sets and with 23 × 250 validation sets, respectively (ε = 0.001). B, Histogram of the pSAR10g estimation error for the corrected pSAR10g by the LSF determined with 23 × 1000 validation sets. C, Histogram of the pSAR10g estimation error for the corrected pSAR10g by the CSM determined with 23 × 1000 validation setsTABLE S1 The five most representative local SAR models for each modelClick here for additional data file.


Some cis-acting RNAs have been shown to function through the formation of R-loops with the complementary sequence from their transcribed loci and affect local gene expression, as is the case with GATA3-AS1 and VIM-AS1 , as employed by MEG3 and KHPS1   on the structure of the chromatin itself, for example, by modifying its accessibility through the recruitment of structural protein factors or protein complexes that establish chromatin marks (Type I) . VariousWhile a growing number of RNAs acting on gene expression are being characterized, we still lack a bigger picture on how prevalent these interactions are, and on the importance of the proposed mechanisms. The first major obstacle in delineating which RNAs act on chromatin regulation resides in the immense number of existing transcripts. For example, estimates place the number of potentially functional lncRNAs in the tens of thousands    is not as well-explored in this context. Due to RNA's inherent ability to base pair and form complex higher order structures, it can simultaneously interact with DNA and multiple RNA and protein molecules (Lu and Chang, unctions  will onlTaken together, the methods described here represent useful tools for elucidating the role of RNA-DNA and RNA-RNA interactions in gene expression regulation. While various improvements are still needed, the existing datasets represent a comprehensive look of how a genome could be organized through RNA interactions. Several RNA-DNA interactions have now been cataloged either through these genome-wide techniques or through specific RNA directed techniques. Of these, lncRNAs seem to represent an important fraction of the factors that regulate gene expression, chromatin accessibility and genome organization. In addition, these RNAs are not limited only to chromatin binding but may act as conduits to bring in other types of interactors, such as other RNAs, RNA-binding proteins, and transcriptional complexes. All these elements combined together help forge the transcriptional landscape necessary to maintain and transition between defined cell states.GK and SH conceived and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a shared affiliation with the authors though no other collaboration at time of review.


Adad mutation on male germ cell RNA editing, CRISPR-induced alleles of each were generated in mouse. Mutation of either Adad resulted in complete male sterility with Adad1 mutants displaying severe teratospermia and Adad2 mutant germ cells unable to progress beyond round spermatid. However, mutation of neither Adad1 nor Adad2 impacted RNA editing efficiency or site selection. Taken together, these results demonstrate ADAD1 and ADAD2 are essential regulators of male germ cell differentiation with molecular functions unrelated to A-to-I RNA editing.Adenosine-to-inosine RNA editing, a fundamental RNA modification, is regulated by adenosine deaminase (AD) domain containing proteins. Within the testis, RNA editing is catalyzed by ADARB1 and is regulated in a cell-type dependent manner. This study examined the role of two testis-specific AD domain proteins, ADAD1 and ADAD2, on testis RNA editing and male germ cell differentiation. ADAD1, previously shown to localize to round spermatids, and ADAD2 had distinct localization patterns with ADAD2 expressed predominantly in mid- to late-pachytene spermatocytes suggesting a role for both in meiotic and post-meiotic germ cell RNA editing. AD domain analysis showed the AD domain of both ADADs was likely catalytically inactive, similar to known negative regulators of RNA editing. To assess the impact of  On a molecular level, RNA editing is the irreversible chemical modification of a nucleotide within an intact RNA. Two basic types of RNA editing are observed in mammals, adenosine to inosine and cytosine to uridine, of which adenosine to inosine (A-to-I) occurs much more frequently2. A-to-I RNA editing may occur at one or more sites in a given target RNA and across the entire population of a target RNA or a fraction thereof. To date, A-to-I RNA editing has been observed in a diverse range of RNAs including mRNAs, small RNAs, and long non-coding RNAs4. Functionally, inosine mimics the behavior of guanine and is read as such by the translational machinery5, thus A-to-I RNA editing events behave as A-to-G mutations on the RNA level. As a consequence, the outcome of A-to-I RNA editing varies widely based on the RNA target and the edited site or sites within the target. Reported impacts of RNA editing include altered protein coding potential6, splicing patterns7, and microRNA recognition (either from edits within miRNAs8 themselves or their targets2). The physiological relevance of RNA editing is clear as animals deficient for A-to-I RNA editing enzymes often show severe physiological defects11.RNA editing is a class of post-transcriptional modification that enhances the complexity of the transcriptome5. Within the AD domain, four amino acids coordinate a zinc in the active site of the domain and are presumably required for catalytic activity12. This presumption is further supported by the observation that all four amino acids are conserved in the single catalytically active RNA editing enzyme of C. elegans, cADR-212. AD domain activity also relies on binding of inositol hexakisphosphate which is coordinated by a large number of amino acids likewise largely conserved in cADR-2.In mammals, RNA editing is catalyzed by two adenosine deaminase (AD) domain-containing proteins: Adenosine Deaminase, RNA-specific 1 and 2 . Both enzymes contain at least one double-stranded RNA binding motif and an AD domain, which directly catalyzes the conversion of adenosine to inosineC. elegans. In humans ADAR3 negatively regulates ADAR1 and ADAR2-mediated RNA editing13 via competitive binding of target RNAs14 while in C. elegans, cADR-1 regulates the activity of cADR-2 by facilitating cADR-2′s interaction with target RNAs via a protein–protein interaction15. Although it is unclear why ADAR3 is catalytically inactive given its AD domain contains all of the identified amino acids necessary for editing activity, mutations at each of the zinc coordinating amino acids in cADR-1 suggests mutations at known cofactor binding sites may indicate AD domains that regulate, as oppose to catalyze, RNA editing.In mammals and worms, RNA editing is further regulated by catalytically inactive AD domain proteins: ADAR3 in humans (ADARB2 in mice) and cADR-1 in 16 suggesting tissue or cell-specific factors may play extensive roles in regulating tissue-specific levels of RNA editing. A large-scale analysis in humans identified two such tissue-specific regulatory systems13. In the brain, which displays unusually high levels of RNA editing, the expression of catalytically active ADARs (ADAR1 and ADAR2) was able to explain the majority of region-specific editing levels only when combined with expression of the negative RNA editing regulator ADAR3. Similarly, in skeletal muscle the expression of AIMP2, an aminoacyl-tRNA synthetase, was directly correlated with the unusually low levels of RNA editing detected therein. Other tissue-specific mechanisms of RNA editing regulation specifically by RNA binding remain to be identified.A-to-I RNA editing efficiency (the fraction of a given target RNA edited) and frequency (the number of editing sites within the transcriptome) is highly tissue and cell-type dependent. However, this variability cannot be explained by catalytically active RNA editing enzyme expressionAdad1) has previously been identified as tissue-specific, being expressed exclusively in the testis17 and required for normal male fertility18. As such, ADAD1 is a good candidate for tissue-specific regulation of RNA editing. Further, RNA editing in the testis is somewhat unusual in that, unlike many tissues where ADAR and ADARB1 act in concert to regulate RNA editing, testis RNA editing it is driven exclusively by ADARB1 19. Moreover, Adarb1 expression in specific cell populations of the testis does not correlate directly to the level of cell-specific RNA editing observed. The adult testis is composed of two cell populations: the developing germ cells and the somatic cells that support their differentiation. As germ cells mature (undergo spermatogenesis), they transition through three basic phases: mitosis, meiosis, and post-meiotic differentiation (referred to as spermiogenesis). These developmental stages are associated with dramatic changes in the transcriptome20 and RNA regulation21. In the case of RNA editing, Adarb1 has relatively high expression in the mitotic spermatogonia and post-meiotic spermatids and much lower expression in the meiotic spermatocytes and Sertoli cells, the dominant somatic cell population19. This is in contrast to the number of RNA editing sites detected in these populations where very limited sites are observed in spermatogonia and spermatids, a moderate number of sites detected in spermatocytes, and many sites detected in Sertoli cells. In sum, these observations suggest additional layers of RNA editing regulation within the testis19.One potentially fruitful approach to the identification of other RNA editing regulatory mechanisms is the identification of tissue-specific AD domain proteins. In the current mouse genome, a total of ten AD domain proteins that can act on RNA are annotated (USCS Genome Browser). Of these, one  and AD domain very similar to the ADARs  is a previously described RNA binding protein17 demonstrated testis-specific expression of Adad1, however no detailed in vivo analysis of Adad2 has been reported. To determine whether Adad2 was likewise expressed in a tissue-dependent manner, quantitative RT-PCR was performed across a panel of wildtype and mutant mouse tissues. This analysis demonstrated both Adad1 and Adad2 are expressed predominantly in the testis , generated via insertional mutagenesis that ablated the 5′ most translation start site, had been shown to be required for normal male fertility18. However since that time additional annotation evidence suggested an ADAD1 protein may also be generated from a more 3′ translation start site still intact in the Adad1tm1Reb allele. In order to ensure total protein ablation, targeting of Adad1 focused on the region around the more 3′ translation start site. No such constraints were required for Adad2 as it contains only a single translation start site. CRISPR-induced mutagenesis resulted in multiple alleles for both genes, and for each gene all three alleles displayed similar molecular and reproductive phenotypes. A single allele for each was selected for further study J (herein referred to as Adad1em2) was compared against wildtype and Adad1tm1Reb whole testes on the message level by quantitative RT-PCR and both alleles showed a similar level of message reduction relative to wildtype , this reduction is likely due to non-sense mediated decay of RNA generated from the mutant loci. As message was reduced but not lost entirely in both mutant models, further analysis on the protein level was conducted. Using a custom antibody derived from the C-terminal end, protein levels of ADAD1 were examined in Adad1tm1Reb and Adad1em2 homozygous mutants J(herein referred to as Adad2em3) demonstrated a significant reduction in message, likely driven by non-sense mediated decay of the premature stop containing mutant transcript, and a complete loss of ADAD2 in the CRISPR model, as determined by both Western blotting and immunofluorescence  was performed  were examined by immunofluorescence in wildtype and Adad2em3/em3 adult testis , a post-meiotic germ cell RNA-rich granule that serves as the site for a diverse range of RNA processes19 suggesting one potential mode of RNA editing regulation in the male germ cell is active suppression. Given the germ cell-specific expression of both ADAD1 and ADAD2, analysis of RNA editing in Adad mutants focused first on known germ cell RNA editing sites observed primarily in spermatocytes and spermatids and a site observed exclusively in spermatocytes19. If either ADAD1 or ADAD2 regulated RNA editing in a manner similar to that observed for cADR-1, dramatic changes in the efficiency of editing at a given site would be expected. In contrast to this expectation, Sanger sequencing of bulk amplified RNA editing sites demonstrated very similar levels in the ratio of edited to non-edited targets across four known germ-cell RNA editing sites . Based on these observations, genotype-dependent RNA editing events likely represent RNA editing identification artifacts stemming from low gene expression as opposed to genuine events. These data demonstrate Adad mutation does not appreciably impact either RNA editing efficiency or site selection in coding regions.RNA editing in the testis is rare and cell-type dependent, with fewer detectible RNA editing sites observed in germ cells as compared to the somatic cells of the testistes Fig. A. Howeveied Fig. B, C. As Adad1 or Adad2 resulted in no impact on RNA editing efficiency at known germ cell RNA editing sites. Additionally, no changes in testicular RNA editing efficiency or editing frequency were observed when assessed transcriptome-wide. Taken together, these data demonstrate neither ADAD1 nor ADAD2 regulate RNA editing in the testis suggesting other factors may be involved. Further, this work demonstrates both ADAD1 and ADAD2 play essential, and likely distinct, roles in germ cell biology.Adenosine deaminase domain proteins are canonically associated with A-to-I RNA editing, whether it be as direct catalyzing enzymes or indirect regulators. The testis, and especially the male germ cell, is a site of relatively low RNA editing, leaving open the question of whether RNA editing is specifically repressed in this tissue. The aim of this work was to test whether two male germ cell-specific AD domain proteins, ADAD1 and ADAD2, regulate RNA editing and whether one, ADAD2, is required for male germ cell differentiation. Both ADAD1 and ADAD2 were shown to have amino acid mutations similar to other AD domain proteins that indirectly regulate RNA editing. However, mutation of either 28, RNA splicing29, miRNA processing30, and RNA storage31. In each case, these impacts were shown to be independent of ADAR1′s RNA editing capacity but generally involved direct RNA binding by ADAR1 suggesting AD-domain containing proteins generally function by regulating cellular RNAs. In the context of the testis, RNA regulation is especially important and it is reasonable to propose both ADAD1 and ADAD2 are required for germ cell differentiation due to their regulation of some other aspect of germ cell RNA processing.As AD-domain proteins, both ADAD1 and ADAD2 represented reasonable targets for regulators of germ cell RNA editing. Yet, in both cases mutation resulted in minimal impacts on editing suggesting both proteins impact germ cell biology through some other mechanism. For a discussion of potential RNA editing considerations in the testis, please see the accompanying Supplementary Discussion. Non-RNA editing functions for AD-domain containing proteins is not without precedence. ADAR1, in particular, appears to impact the transcriptome via a number of mechanisms. As a result of interactions with other RNA bindings proteins ADAR1 has been shown to alter RNA stabilityPrm1) mRNA17. Prm1 is among the classically defined translationally regulated transcripts in the testis32 and proteins that bind to its 3′ UTR are often associated with translational regulation or RNA storage. However, mutation of Adad1 did not impact Prm1 translation or the translation of several other translationally regulated transcripts as reported by Connolly et al.18 suggesting ADAD1 may post-transcriptionally regulate a different set of transcripts.For both ADAD1 and ADAD2, loss results in relatively minor gene expression changes in spite of pronounced phenotypes suggesting they both function post-transcriptionally. For ADAD1, this conclusion is buoyed by previous observations demonstrating it binds to the 3′ UTR of the protamine 1 In total, this report demonstrates ADAD1 and ADAD2 to be AD-domain containing proteins that do not regulate RNA editing but are required for normal male germ cell differentiation. These analyses also suggest ADAD1 and ADAD2 regulate post-transcriptional events but play distinct roles in germ cell differentiation, with ADAD2 functioning primarily in mid- to late-stage spermatocytes and ADAD1 likely mediating events in post-meiotic round or elongating spermatids. Future work using the alleles generated will aim towards fully characterizing the molecular roles of ADAD1 and ADAD2 in meiotic and post-meiotic germ cell RNA biology.www.uniprot.org) for each protein of interest and aligned via ClustalW, using a Gonnet matrix, open gap penalty = 10, extend gap penalty = 0.2, and delay divergent = 30%.For adenosine deaminase domain comparisons across species, A to I editase domain sequence was extracted from Uniprot  as per manufacturer’s recommended methods. Extracted RNA was DNase I (Qiagen) treated, purified by RNeasy mini column purification (Qiagen), and quality and quantity assessed by Nanodrop prior to cDNA synthesis by Superscript III RT (Life Technologies) with random hexamer priming per manufacturer’s suggestions. SYBR Green  quantitative RT-PCR utilized the primers indicated in Supp. Table 38 using Rps2 as the endogenous control.Tissues were collected from wildtype and mutant adult and embryonic tissues and flash frozen prior to RNA extraction. For Antibodies against ADAD1 and ADAD2 were generated commercially by ThermoFisher. For ADAD1, the standard 80-day guinea pig protocol with a synthetic peptide comprised of amino acids 583–605 was used. Two anti-ADAD2 antibodies were generated through the standard 90-day rabbit protocol utilizing a synthetic peptide from amino acid 543 to the C-terminus. A portion of the terminal bleed serum for each antibody was affinity purified commercially. For each immunization, two animals were used . Antibody usage for images in this manuscript was as follows: ADAD1 western: anti-ADAD1 affinity purified, ADAD2 immunofluorescence: Anti-ADAD2 terminal bleed animal #1, and ADAD2 Western blot: anti-ADAD2 affinity purified animal #2. In all cases, immufluorescent signal and western blotting signal were consistent across all antibodies against a given target.Adad1tm1REB, Adad1em2, Adad2em3, and wildtype male mice. Total protein was extracted by RIPA buffer  with protease inhibitors  and colorimetrically quantified using the DC protein assay (BioRad). One hundred μg protein per sample was electrophoresed on 10% acrylamide gels. Following wet transfer of proteins to PVDF membrane (BioRad) the proteins were detected by western blotting as per39 as follows: anti-ADAD1 1:500 (guinea pig), anti-ADAD2 1:2000 (rabbit), and GAPDH 1:4,000 . Secondary antibodies: Goat-anti-guinea pig 1:2000 (Invitrogen A18775) and goat-anti-rabbit 1:2000 . Images were developed with ECL reagent (Thermo Scientific) and visualized using an Azure Biosystems C600 imager.Testes were collected from adult 20. Testes were dissected from adult mice and fixed overnight in 4% PFA before embedding in paraffin and sectioned to 4 μm. Sections were deparaffinized by xylenes and rehydrated. Antigen retrieval was performed by boiling slides in citrate solution pH 5.95 for 7 min. Primary antibodies were incubated overnight at room temperature and secondaries for one hour at room temperature. Antibody concentrations were as follows: ADAD2 1:200. Secondary antibody: Alexafluor 488 goat-anti-rabbit 1:1,000 (Invitrogen A-11008). Samples were visualized on a custom-built microscope (Zeiss) with fluorescent and brightfield capabilities.Immunofluorescence was performed as inhttps://crispr.mit.edu/. This platform was used to obtain candidate sgRNA sequences with low offtarget specificity. Guide efficiency scores were calculated using https://www.broadinstitute.org/rnai/public/analysis-tools/sgrna-design and guides were selected based on the least number of off-target genes and highest efficiency scores. Preparation of sgRNA was as previously described40. Briefly, sgRNA templates were amplified with T7 promoter sequence-conjugated primers and transcribed with MEGAshortscript T7 Kit (Life Technologies) and purified by MEGAclear Kit (Life Technologies). The concentration of RNA was measured by a NanoDrop instrument (Thermo Scientific) and Agilent Bioanalyzer  and stored at − 80 °C until used. C57BL/6 J and CB6F1/J mouse strains were used as embryo donors and pseudopregnant recipient dams, respectively. Cas9 or Cas9-D10A nickase RNA , sgRNA (50 ng/μl) were injected into the pronuclei of zygotes. Oviduct transfers were performed on the same day into pseudopregnant dams. The resulting offspring were screened for mutations in the targeted regions. In each case, three alleles were obtained and each founder was used to generate homozygous male carriers. Each was analyzed for molecular ablation on the protein level by western blotting of testis tissue and a single allele selected for each gene.All animal use protocols were approved by both the Jackson Laboratories and the Rutgers University animal care and use committees. Mouse procedures were conducted according to relevant national and international guidelines (AALAC and IACUC). CRISPR-guide design utilized the guide design resources available from Adad1em2 were as follows: a 30 s primer melting step at 95 °C, 15 s anneal at 56 °C and a 60 s elongation at 72 °C, repeated 37 times with a final 5-min elongation at 72 °C. Adad1tm1REB genotyping PCR consisted of separate reactions for wildtype and mutant alleles. Wildtype conditions were a 30 s primer melting step at 95 °C, a 15 s anneal at 58 °C and a 2 min elongation at 72 °C, repeated 40 times with a final 5-min elongation at 72 °C. Mutant reaction conditions consisted of a 30 s primer melting step at 95 °C, a 15 s anneal at 56 °C and a 60 s elongation at 72 °C, repeated 37 times with a final 5-min elongation at 72 °C. Adad2em3 genotyping conditions consisted of a 30 s primer melting step at 95 °C, 30 s anneal at 67.4 °C and a 30 s elongation at 72 °C, repeated 35 times with a final 5-min elongation at 72 °C.Genotyping of generated alleles utilized primers described in Supp. Table Adad1 wildtype and mutant mice and fixed overnight in Bouin’s fixative. Sections were stained with periodic acid–Schiff’s reagent. Three cross-sections at least 100 μm apart were scored per animal. Stages were determined based on the criteria set out in41 and retained spermatids were counted if morphologically mature spermatozoa were detected in stages IX or X. Sperm concentration was determined by mincing epidydimal tissue in 1 ml of phosphate-buffered saline (PBS) and incubating for an hour at 37 °C. Samples were diluted 1:10 in PBS and sperm quantified as previously39. The remaining sample was centrifuged at 100×g at room temperature for 5 min to remove debris and applied to charged slides for sperm morphology analysis. Slides were incubated overnight in a humid chamber, air-dried, and stained by PAS. Sperm morphology parameters were applies as in18. To quantify tubules with round spermatids, testes from Adad2 males were fixed overnight in 4% PFA as described above, deparaffinized, rehydrated, and stained with DAPI Fluoromount-G (Southern Biotech). Three cross-sections at least 100 μm apart were scored per animal. All images were captured on a custom-built microscope (Zeiss) with fluorescent and brightfield capabilities.For basic histological analyses, testes and epididymides from adult animals of the indicated genotype were collected, fixed overnight, and embedded in paraffin. Four μm sections were used for all analyses. For analysis of retained spermatids, testes from adult Adad1tm1REB, Adad1em2, and wildtype males was assessed by mating adult mutant males and littermate control males with fertile C57BL/6 J females (6–8 weeks old). Litter number and size was recorded for a period of 4 months.Fertility of Adad1em2, Adad1tm1REB, Adad2em3, and wildtype testes as described above and reverse transcribed into cDNA using the SuperScript III kit (Invitrogen). Standard PCR was utilized for site-specific amplification using the primers indicated in Supp. Table RNA was isolated from adult 42. Briefly, individual samples were collected into RNAlater and send for paired-end RNA sequencing on an Illumina HiSeq 2000 at The Jackson Laboratory . Total RNA extraction via the mirVana RNA isolation kit  was performed per manufacturer’s recommendation, including DNase treatment. RNA sequencing libraries for 100 bp paired-end sequencing were produced using the TruSeq RNA Sample prep Kit v2 Set A and B . For Adad2 mutant gene expression, gene and isoform abundance were calculated as a function of an extended testis transcriptome20 within individual samples by RSEM43. Significant differences were calculated by EBSeq and plotted using ggplots2. Adad2 mutant RNA-sequencing data has been deposited in GEO under the accession GSE150755. For cell-type expression profiles of differentially expressed genes, single-end 76 bp RNA-seq strand-specific reads derived from isolated testicular cell types were obtained from the SRA database . Individual samples were aligned to the expanded transcriptome and expression estimated via RSEM. Expression estimates were extracted for differentially expressed genes and the values plotted by heatmap using ggplots2. Assessment of RNA editing was performed as previously described by Snyder et al.19.RNA sequencing was conducted as outlined previously by Snyder et al.Supplementary information.


Small extracellular vesicles (EVs) play a significant role in intercellular communication through their non-coding RNA (ncRNA) cargo. While the initial examination of EV cargo identified both mRNA and miRNA, later studies revealed a wealth of other types of EV-related non-randomly packed ncRNAs, including tRNA and tRNA fragments, Y RNA, piRNA, rRNA, and lncRNA. A number of potential roles for these ncRNA species were suggested, with strong evidence provided in some cases, whereas the role for other ncRNA is more speculative. For example, long non-coding RNA might be used as a potential diagnostic tool but might also mediate resistance to certain cancer-specific chemotherapy agents. piRNAs, on the other hand, have a significant role in genome integrity, however, no role has yet been defined for the piRNAs found in EVs. While our knowledgebase for the function of ncRNA-containing EVs is still modest, the potential role that these EV-ensconced ncRNA might play is promising. This review summarizes the ncRNA content of EVs and describes the function where known, or the potential utility of EVs that harbor specific types of ncRNA. Small extracellular vesicles (EVs) have been extensively studied for about two decades because of the interest in their possible biological roles. Initially, EVs were simply perceived as recycling bins , until iTo date, a major limitation in resolving the biological role of extracellular vesicles was the lack of a method of isolation that would allow for the effective separation of individual groups of EVs based on their origin, instead of their estimated size. Indeed, according to the recent recommendations of the International Society for Extracellular Vesicles , a more Exosomes are small vesicles with a diameter of around 100 nm that is released into the extracellular space by the fusion of multivesicular bodies with the plasma membrane . The tisThe non-random composition of EVs and the potential biological consequences of this cargo has made decoding their cargo a research priority. While the initial attention for EV cargo was primarily focused on proteins, the discovery of biologically active RNA particles in small EVs by Valadi et al.  in 2007 The most detailed knowledge about the presence of various species of RNA in small EVs comes from next generation sequencing studies. The available data are rather consistent in that EVs are enriched in different types of non-coding RNA (ncRNA) species. The most frequently reported molecules belong to the rRNA, tRNA, and miRNA families, however, Y RNA, piRNA, snRNA, snoRNA, and lncRNA are also quite common ,27,28,29Some studies suggest that the most represented RNAs are microRNA molecules (about 50% to 70%) ,24,28,298 of small EVs that resulted in the isolation of 10–15 ng of total RNA [Besides preparation methodologies, one of the more serious challenges in studies of RNAs in EVs is the low content of isolated nucleic acids. Studies of Eldh et al.  and Prenotal RNA . Furtherotal RNA  reportedotal RNA .A separate issue is a reliable quantitative assessment of the concentrations of small RNA molecules. A comparison of quantities isolated by few more popular techniques of RNA quantification obtained for miRNA isolated from serum samples revealed significant differences among these methods . In geneMicroRNAs (miRNA) are small non-coding RNAs composed of 21–25 nucleotides that are responsible for the regulation of gene expression. Maturation of the molecule includes several stages, like transcription into hairpin-structured pri-miRNa and further processing into pre-miRNA hairpins consisting of 70 nucleotides ,37. In tAs important components of the cargo of EVs, miRNA are also considered as promising targets for translational research. Recently, several reports described the use of EV-related miRNA as biomarkers for different diseases. For example, based on the comparison of the profiles of EV-associated miRNA isolated from the serum of patients with recognized relapsing–remitting multiple sclerosis (RRMS) treated with the immunomodulatory drug INF-β, or not, a panel of 16 miRNas were identified with potential prognostic value for therapeutic response in patients with multiple sclerosis . SimilarCircular RNA (circRNA) is a single strand RNA formed as the closed continuous loop with covalently linked ends . Based oTransfer ribonucleic acid (tRNA) are small molecules consisting of 76–93 nucleotides that form a characteristic cloverleaf structure terminated by the CCA trinucleotide at the 3′-end with an acceptor stem, a dihydrouridine (D) stem-loop, an anticodon stem-loop, and a TψC stem-loop . tRNA istiRNA molecules are created by the cleavage of the tRNA molecule in the anticodon loop, performed by angiogenin, during stress conditions . The 3′ All three classes of tRNA were identified in small EVs. Shurtleff et al.  performeY RNAs are small non-coding RNAs represented in humans by four types of particles—hY1, hY3, and hY4 that are found predominantly in the cytoplasm, although also in the nucleus ; and hY5Deep sequencing of EV RNA content confirms the presence of Y RNA in small vesicles with the percentage of Y RNA particles being relatively high when compared to all non-coding RNAs. In small EVs isolated from the serum of lung adenocarcinoma and lung squamous cell carcinoma patients, over 50% of sequence reads were annotated to Y RNA . Y RNAs Interestingly, Y RNA content in EVs from human semen suggests that each type of Y RNA is loaded selectively . The majD. melanogaster, C. elegans, and Mus musculus. piRNA are single-stranded, 21–30 nucleotide molecules transcribed from characteristic piRNA clusters located in euchromatic (mice) or heterochromatic regions (Drosophila), which in this case were not transcriptionally silent [Drosophila [Piwi-interacting RNA (piRNA) is an abundant class of small non-coding RNAs with little conservation of sequence between organisms . What isy silent ,94,95. Ty silent . This apy silent ,94. piRNy silent . Dependiy silent . The funosophila , and canosophila . Althougosophila  and mousosophila  and are osophila . In addiosophila  or gastrosophila , and areosophila  and Ng eosophila ). Piwi-iosophila  and HeLaosophila  cell linosophila , or humaosophila ,106. Usuosophila  reportedosophila , where tRibosomal nucleic acid (rRNA) is a major component of the ribosomes–molecular machines responsible for protein synthesis (translation). Human ribosomes have a molecular weight of 4.3 MDa and consist of two subunits—large (60S), which includes three types of rRNA particles; (i) 28S (5064 nt), (ii) 5S (120 nt), and (iii) 5.8S (157 nt) rRNA; and small (40S), which includes a single 18S (1869 nt) rRNA molecule ,109. RibLong non-coding RNAs (lncRNA) are traditionally defined as transcripts longer than 200 nucleotides with no protein-coding function. However, at least one exception was noted . This brAlthough the functions of some lncRNA are quite well-understood, the general knowledge of lncRNA regulation and function is still immature. The known roles for lncRNA include a crucial role in many cellular and developmental processes via chromatin remodeling, transcription regulation, and intercellular trafficking . For exaBased on what is known about EV-derived lncRNA, lncRNAs, like EV-derived miRNAs, might be a key component of the EV cargo, which act as intercellular mediators . The selWhile miRNA is the most extensively studied component of small EV cargo, recent studies show that it is not the only representative of the non-coding transcriptome present in small EVs. The fact that the composition of individual molecules of RNA with very diverse functionalities is dependent on the origin and physiological state of parental cells and that these might be influenced by various environmental factors suggests that there is a distinct need to understand the biological function of the ncRNA cargo of EVs. What is known of the ncRNA species in EVs appears to be particularly promising . The preOther molecules with regulatory function include tRNA fragments that are significantly enriched in EVs from some types of cells. Given their role in the context of cellular stress , tRNA fragments appear to be particularly interesting as prognostic or predictive molecules of basic biology or therapeutic response. Similarly, lncRNAs might be important in at least two areas. The first, the EV-mediated resistance to chemotherapeutic agents like lncRNAs delivered by EVs, are able to reduce sensitivity to a number of chemotherapeutic agents like erlotnib and sorafenib. Second, these act as a biomarker for the presence of prostate cancer. Although there is very little knowledge about Y RNA in and of themselves, the level of these molecules in EVs might vary significantly, depending on their origin and physiological state, which makes them a very promising candidate for molecular biomarkers.Both piRNA and rRNA are families of non-coding RNA that were not necessarily expected to be common components of EVs. While the primary function of piRNAs is genome integrity and were mainly identified in germinal cells, their presence was confirmed in EVs from different sources, although no biological function for piRNA found in EVs has been described. At the same time, rRNA molecules are one of the most controversial components of small vesicle content. Not only is their function unknown, even their presence in EVs is still a subject of discussion.Last, determining the function of, or consequences of, non-coding small and long RNA being found in extracellular vesicles remains a challenge. Furthermore, it would appear that there might be RNA-binding proteins that might form stable complexes with small RNAs within exosomes, which are responsible for maintaining small RNAs in exosomes as well as shuttling small RNAs between exosomes and recipient cells . With ti


Cryphonectria parasitica, and encode an RNA silencing suppressor. CHV4 is another hypovirus asymptomatically that infects the same host fungus. This study shows that the N-terminal portion of the CHV4 polyprotein, termed p24, is a protease that autocatalytically cleaves itself from the rest of the viral polyprotein, and functions as an antiviral RNA silencing suppressor.Host antiviral defense/viral counter-defense is an interesting topic in modern virology. RNA silencing is the primary antiviral mechanism in insects, plants, and fungi, while viruses encode and utilize RNA silencing suppressors against the host defense. Hypoviruses are positive-sense single-stranded RNA viruses with phylogenetic affinity to the picorna-like supergroup, including animal poliovirus and plant potyvirus. The prototype hypovirus Cryphonectria hypovirus 1, CHV1, is one of the best-studied fungal viruses. It is known to induce hypovirulence in the chestnut blight fungus, Cryphonectria parasitica suppresses the induction of one of the key genes of the antiviral RNA silencing, dicer-like 2, and stabilizes the infection of RNA silencing-susceptible virus MyRV2. This study shows functional similarity between CHV4 p24 and its homolog p29, encoded by the symptomatic prototype hypovirus CHV1.Previously, we have reported the ability of a symptomless hypovirus Cryphonectria hypovirus 4 (CHV4) of the chestnut blight fungus to facilitate stable infection by a co-infecting mycoreovirus 2 (MyRV2)—likely through the inhibitory effect of CHV4 on RNA silencing . In this study, the N-terminal portion of the CHV4 polyprotein, termed p24, is identified as an autocatalytic protease capable of suppressing host antiviral RNA silencing. Using a bacterial expression system, CHV4 p24 is shown to cleave autocatalytically at the di-glycine peptide (Gly214-Gly215) of the polyprotein through its protease activity. Transgenic expression of CHV4 p24 in  Cryphonectria parasitica [Fusarium graminearum [Magnaporthe oryzae [Colletotrichum higginsianum [Neurospora crassa [Sclerotinia sclerotiorum [C. parasitica (chestnut blight fungus), only one dicer-like (dcl2) and one argonaute-like (agl2) gene play central roles [RNA-dependent RNA polymerase (rdr) genes are required for antiviral RNA silencing [dicer and agl genes also contribute to antiviral RNA silencing [Although RNA silencing is increasingly believed to be the primary host defense against viruses in fungi, it has only been shown in a few fungi . Trotiorum , while trotiorum ,12. Specal roles , and no ilencing , unlike ilencing ,16,17. Iilencing ,7,8.Hypoviridae) that confers hypovirulence to the host fungus. The functional roles attributed to this protein include roles as a papain-like cysteine protease [dcl2 and agl2 upon virus infection. A few other RNA silencing suppressors have previously been identified from fungal viruses, including the S10 gene of a multi-segmented dsRNA virus, Rosellinia necatrix mycoreovirus 3  [Fusariviridae”) [dcl) and FgAGO1 (an agl), of F. graminearum. Although the MyRV3 S10-coded suppressor activity has been demonstrated in planta using an agroinfiltration assay, no information about how the S10 (VP10) suppressor functions in fungal cells is available.Plant viruses encode RNA silencing suppressors, as exemplified by potyvirus HC-Pro, tombusvirus P19, and luteovirus P0, which bind RNA silencing key players to impair their functional roles or to lead their proteolytic degradation ,19,20. Aprotease ,22, sympprotease ,24, partprotease , as wellprotease . CHV1 p2viridae) , and theC. parasitica, in which a symptomless hypovirus, CHV4-C18 , a member of the family Hypoviridae, facilitates the stable infection of a mycoreovirus, MyRV2 (mycoreovirus 2), which is susceptible to antiviral RNA silencing, and enhances its replication and its vertical transmission through asexual spores [In a previous study, we described an interesting virus/virus interplay in l spores . This syC. parasitica doubly infected by CHV4-C18 and MyRV2 and derivative isogenic isolates that were either virus-free or singly infected with CHV4-C18 or MyRV2 were described [C. parasitica [dcl2 [Viral and fungal strains used in this study are listed in escribed ,29,30 earasitica , and itsca [dcl2 ,33. FungC. parasitica strain C18 as described previously [dcl2pro::egfp) or CHV4 p24 expressing (pCPX-CHV4-p24) constructs. The eGFP reporter strain containing an egfp gene under the control of the dcl2 promoter (~2 kb) of strain C18, termed C18 dcl2pro::egfp, was constructed as described earlier [Spheroplasts were prepared from eviously , and tra earlier . pCPX-CHEscherichia coli strain BL21 (DE3)  was transformed with this construct. Induction of the recombinant protein with IPTG  at 15 °C for 4 h was according to the manufacturer’s instructions . The affinity-purified recombinant protein was examined by SDS-polyacrylamide gel electrophoresis (PAGE).The N-terminal region of the CHV4 polyprotein  was amplified by RT-PCR and cloned into the expression vector pCold I  that had been modified to include GST (glutathione S transferase) in the expression cassette. The CHV4-derived 52kDa coding region was fused in frame with a His (hexa histidine)-tag with GST at the N-terminus and an HA (hemagglutinin)-tag at the C-terminus (pCold-His/GST-CHV4 52kDa-HA). Proteins in purified preparations separated on an SDS-PAGE gel were transferred onto polyvinylidene difluoride (PVDF) membrane using 10 mM CAPS buffer containing 10% methanol and followed with amido black staining . The specific band obtained from amido black staining was then excised from the membrane and processed for amino acid sequences on a gas-phase protein sequencer Shimadzu Model PPSQ-31A  at the Department of Instrumental Analysis and Cryogenics, Advanced Science Research Center, Okayama University .dcl2 mRNA, MyRV2-S10 segment, and CHV4-C18. The accumulation levels of targeted RNAs were quantified by ImageJ software  and normalized to those of ribosomal RNA (28S rRNA). To confirm the virus infection, conventional RT-PCR and one-step colony RT-PCR were carried out as described earlier using QuickTaq  or PrimeScript® OneStep RT-PCR Ver.2 (Takara Bio), respectively [dcl2 mRNA and MyRV2 S4 mRNA. Primer pairs, double-stranded RNAs were isolated by the method of Eusebio-Cope and Suzuki [Total RNA extraction and Northern blotting were performed as described previously . Digoxigomal RNA S rRNA. Tectively . Table Sectively  to quantd Suzuki .C. parasitica C18 strain was subjected to small RNA sequencing analysis. Small RNA cDNA library preparation and subsequent deep sequencing with Illumina platform   were conducted by Macrogen Inc. . Raw reads  were trimmed by removing the adapter with low-quality base and size filtering (15 to 32 nt in length), the retained reads in each library were mapped into each virus genome (CHV1-EP713 and CHV4-C18) or respective genomic segments (MyRV2) using CLC Genomic Workbench . The mapped virus-derived small RNA reads were used for in-depth analysis with the program MISIS-2 [Total RNA extracted from virus-infected  MISIS-2 .A micro cover glass   was placed on the top of cellophane-overlaid PDA. A small plug of the fungal culture of the GFP reporter strain infecting each of the tested viruses  was placed at the edge of micro cover glass, and allowed to grow for three days. After the mycelia grew between the micro cover glass and cellophane, the clover glass with mycelia was detached and placed on top of a micro slide glass (Matsunami). GFP expression was observed using Olympus Fluoview FV1000 confocal laser scanning microscope .dcl2 induced by co-infecting MyRV2 in C. parasitica strain C18 [dcl2 transcript levels was developed in the genetic background of standard C. parasitica strain EP155, in which the dcl2 promoter was linked to an egfp gene (pCPXHY-dcl2pro::egfp) [dcl2 promoter region of strain C18 was used to replace the corresponding region of pCPXHY-dcl2pro::egfp (namely pCPXHY-C18-dcl2pro::egfp). Fungal transformants expressing this construct in the virus-free C18 genetic background were prepared and fused with C18 isolates singly infected by CHV4-C18, MyRV2 and CHV1-p69, respectively, or doubly infected by CHV4-C18 and either MyRV2 or CHV1-p69  . The 2-kCHV1-p69 . First, brightly B. Similabrightly . The redblotting C, in whidcl2 triggered by infection with heterologous viruses (MyRV2 or CHV1-p69) that otherwise induce antiviral RNA silencing.These results confirm that CHV4 is able to impair the otherwise strong transcriptional induction of dcl2 in C. parasitica affects the small RNA profile of CHV1-∆p69 [C. parasitica strain C18 infected with CHV1-EP713, a hypovirus with a well-characterized RNA silencing suppressor, was used as a reference. CHV1 derived small RNAs showed lesser accumulation in their negative-strands (27%) than the positive-strands (73%), both having a peak of 21 nt as reported by Zhang et al. [Previous results have shown that the disruption of HV1-∆p69 . To inveg et al.   [dcl2 induction in the fungal hosts [dcl2 by MyRV2 compared to single infection by CHV4 . The relative accumulation of virus-derived small RNAs and dcl2 was inversely correlated, and yet was non-proportional, as reported earlier by Andika et al. [Interestingly, single infection by CHV4-C18 showed a unique small RNA profile that has a size distribution without a sharp 21-nt peak in the positive-strand small RNAs, no preferential nucleotide at their 5′ termini, and a high preponderance of the small RNAs being of positive-strand polarity (94.5%) . This isvirus 1) ,41. Thisal hosts ,42. Co-i by CHV4 C. An alm by CHV4 A,C. MyRV by CHV4 C. Co-inf by CHV4 A. This ia et al. .Hypoviridae contains four species whose exemplar strains were derived from C. parasitica: CHV1, CHV2, CHV3, and CHV4 [The family and CHV4 . CHV2 anand CHV4  characteand CHV4 . To examand CHV4 ,22, whicand CHV4 , a symptand CHV4 ,24,44, aand CHV4 .E. coli expression construct was prepared, as shown in E. coli cells. The expected total molecular mass of the recombinant protein (His/GST-CHV4 52kDa-HA) is ~84 kDa.To test the hypothesis that CHV4-C18 p24 is a papain-like cysteine protease, an E. coli cells transformed by the pCold-construct were examined by SDS-PAGE E. coli cells upon induction by cold shock and IPTG treatment. From the predicted cleavage site of p24, the two products were assumed likely to be a fusion of His-GST-CHV4 p24 (~53kDa) and CHV4-remaining polypeptides (amino acids 214–473) with HA-tag (~28kDa), respectively. These two protein bands were observed neither in cells transformed with the expression construct, pCold-His/GST-CHV4 52kDa-HA, under non-induced conditions, nor in cells transformed cells with the empty vector regardless of whether under induced or non-induced conditions. The N-terminal sequence of the 25 kDa-product was determined to be G-R-E-S-D-A-D-S-H-P, identical to the CHV4-C18 polyprotein sequence at 215 to 224 and in agreement with the prediction for the CHV4-SR2 strain by Linder-Basso et al. [To test whether CHV4 p24 had auto-proteolytic activity, total protein fractions of o et al.   likely in a co-translational manner as in the case for the peptidase C7 superfamily homologs of other oviruses ,45.dcl2 and agl2. To investigate whether CHV4-C18 p24, a putative homolog of CHV1 p29, exhibits RNA silencing suppressor activities, the corresponding p24 ORF region was expressed in a virus-free isogenic isolate of C. parasitica C18 strain. Five transformants (designated as C18p24-1 to -5) were tested for the suppression of the dcl2 transcriptional upregulation following infection by MyRV2 as an RNA silencing trigger [dcl2 transcription was observed in transformants expressing CHV4-C18 p24 (29% for C18p24/MyRV2) relative to non-transformants (expressed as 100% for C18/MyRV2), and this suppression was slightly smaller than that in CHV4 co-infection (18% for C18/MyRV2+CHV4)  , top rowV2+CHV4) . Note thnificant .MyRV2 accumulation in CHV4-C18 p24 transformants, represented by the S10 segment, compared with that in non-transformant C18 in the absence or presence of CHV4-C18. Interestingly, transgenic expression of CHV4-C18 p24 enhanced MyRV2 accumulation, though not as greatly as CHV4-C18 co-infection, , in the C. parasitica host strain C18, CHV4-C18 co-infection enhances the stability of MyRV2, which is otherwise susceptible to antiviral RNA silencing and is eliminated [C. parasitica wild-type strain C18 [It has previously been shown that during repeated subcultures of iminated . We testrain C18 , MyRV2 wrain C18 . MyRV2 wrain C18 , while nWe have noted that MyRV2 could rarely be moved laterally by anastomosis to wild type C18 in the absence of CHV4-C18. We attempted to transfer MyRV2 to virus-free C18 (C18-VF) using three plates in each of three independent co-culturing assays. We could move MyRV2 only to two recipient colonies out of nine co-cultures. To examine whether p24 alone could also facilitate the horizontal transmission of MyRV2, CHV4-C18 p24 transformants (C18p24), and non-transgenic controls were co-cultured with MyRV2-infected C18 strain (C18/MyRV2) . EfficieThe combined results, shown in C. parasitica strain C18, in which the former virus facilitates efficient vertical transmission and stable infection of the co-infecting second virus during subculture [We have previously shown a commensal interaction between a symptomless hypovirus, CHV4-C18, and a hypovirulence-conferring mycoreovirus, MyRV2, in the bculture . In the C. parasitica hypoviruses [Rosellinia and Fusarium fungi [As predicted for another CHV4 strain, SR2, by Linder-Basso et al. , the putoviruses ,49, putaum fungi .dcl and agl) are transcriptionally upregulated upon virus infection. Here, a recently developed reporter assay [dcl2 transcription induction was monitored by GFP fluorescence  complex [Robust systems for examining RNA silencing suppression mechanisms in virus-infected plants are available, and many have been well studied ,51. Planiviridae , functioiviridae . In the  complex ,40. It rHypoviridae, they show different and similar molecular and biological properties. CHV1 has two ORFs, A and B, each encoding a papain-like cysteine protease, p29 (peptidase C7 superfamily) and p48 (peptidase C8 superfamily). CHV4 has only one ORF and encodes one single protease, p24 (peptidase C7 superfamily). CHV1 has been reported from Asia, Europe, and North America and induces hypovirulence in C. parasitica together with associated phenotypic alterations that vary in degree depending on the fungal strains. CHV4 is generally asymptomatic and has been only reported from the USA and it is pervasive in the country [dcl2. Additional roles assigned to CHV1 p29 include its action as a symptom determinant [While CHV1 and CHV4 are members of the family  country . Despiteerminant ,24,44, aerminant . It willdcl2, upon infection by a trigger virus, MyRV2, and enhanced levels of MyRV2 content. Furthermore, CHV4 p24 enhanced the maintenance of MyRV2 during subculturing and horizontal transfer during co-culturing. These combined results allowed us to identify CHV4 p24 as an RNA silencing suppressor.In conclusion, we have described a functional role of CHV4 p24 in RNA silencing suppression. CHV4 p24, encoded by the 5′-terminal portion of the single large open reading frame, is cleaved from the large polyprotein by its autocatalytic protease activity between the two glycine residues at Gly214 and Gly215. Transgenic expression of CHV4 p24 led to reduced levels of the upregulation of the antiviral RNAi key genes,


Transcriptomics is a developing field with new methods of analysis being produced which may hold advantages in price, accuracy, or information output. QuantSeq is a form of 3′ sequencing produced by Lexogen which aims to obtain similar gene-expression information to RNA-seq with significantly fewer reads, and therefore at a lower cost. QuantSeq is also able to provide information on differential polyadenylation. We applied both QuantSeq at low read depth and total RNA-seq to the same two sets of mouse spinal cord RNAs, each comprised by four controls and four mutants related to the neurodegenerative disease amyotrophic lateral sclerosis. We found substantial differences in which genes were found to be significantly differentially expressed by the two methods. Some of this difference likely due to the difference in number of reads between our QuantSeq and RNA-seq data. Other sources of difference can be explained by the differences in the way the two methods handle genes with different primary transcript lengths and how likely each method is to find a gene to be differentially expressed at different levels of overall gene expression. This work highlights how different methods aiming to assess expression difference can lead to different results. Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with multiple causes. Only 10% of cases have a family history  with theFus gene to avoid overexpression artifacts, allowing us to more accurately see any true effects of mutations and separate them from noise caused by FUS overexpression. These mutant mice carry a mutation inducing the skipping of the penultimate exon 14, and will henceforth be referred to as d14 mice  littermate controls. We observed significant differences between the genes that are found to be differentially expressed using the two methods. While some of the differences will have come from the fact that QuantSeq uses only mRNA and total RNA-seq uses all RNA-present in the cell, we have identified some other possible causes of the differences in which genes are recognized as differentially expressed, including differences in how the two methods handle genes based on the number of reads they have and the length of their primary transcript.Fus d14 models were created as previously described . Fus knoFor RNA sequencing experiments Fus d14 or KO heterozygous and homozygous mice were compared to their respective WT littermates. Spinal cords were collected from E17.5 mouse embryos. Tissues were snap frozen, genotyped and total RNA was extracted from the appropriate samples using Qiazol followed by the mini RNAeasy kit (Qiagen). RNA samples used for sequencing all had RIN values of 9.9 or 10. The same samples were used for total and QuantSeq sequencing. For total RNA-seq, cDNA libraries were made at the Oxford Genomics facility using a TruSeq stranded total RNA RiboZero protocol (Illumina). Libraries were sequenced on an Illumina HiSeq to generate paired end 150 bp reads. For QuantSeq libraries the 3′ mRNA-seq library prep kit REV for Illumina (Lexogen) was used QuantSeq and samples were sequenced by the Lexogen facility (Austria). In QuantSeq the average number of reads was 933,955 in d14 and 1,453,108 in KO. In RNA-seq the average number of reads was 35,678,902 in d14 and 39,159,292 in KO.Alignment of the RNA-seq samples was performed using an in house pipeline. It trims the samples using trim galore , sequencZ-scores were calculated as a derivative of the p-value. Some analysis was done using the R packages Enrichment Browser  four Fus KO mice and WT littermate controls, and (b) four Fus d14 mice with their own littermate WT controls. The same RNA samples were then sequenced either using standard total RNA-seq for library preparation or QuantSeq kits produced by Lexogen. QuantSeq selectively amplifies regions of RNA close to a polyA signal, whilst total RNA-seq sequences all pieces of RNA present within the cell regardless of a presence of a polyA signal. The core differences between the two methods are illustrated by p < 0.0005 in the d14 datasets, and cor = 0.3586, p < 0.0005 in the KO datasets). This means that the two methods see roughly the same number of reads after normalization overall in each gene and that any differences observed are due to other factors.We investigated how expression of single genes  comparedZ-scores do show that changes do occur, with rare exceptions, in the same direction, and show a weak correlation in both datasets    showed t < 0.05) , only 2 ap in KO  is even p-values in one dataset to adjusted p-values in the other  based on the number of reads, as shown in p-values, the correlation in QuantSeq remains, but and RNA-seq is still no more likely to find a gene to be significant over a certain number of reads , but there is no longer obvious correlation in RNA-seq  have been linked to neurodegeneration , or in tIt is possible that other methods are better optimized for use with QuantSeq. This study does not demonstrate improved performance in measuring differential expression for either method, but highlights substantial differences between the two methods that should be taken into consideration when designing experiments. QuantSeq finds a lot more genes to be differentially expressed with smaller library sizes than RNA-seq does. The fact that it does seem to provide useful information at lower read depth may mean QuantSeq can act as a cost-effective method to sequence large numbers of samples at low read depth to find genes for further investigation. It is especially worthwhile in this situation if differential polyadenylation is an area of interest.However, it must be borne in mind that most of these genes are not seen to be differentially expressed by RNA-seq, so some of them may be called into question. RNA-seq is a more widely used library preparation method and, while it is possible that the differences between the two methods are either false positives or false negatives on the part of QuantSeq, more standard library preparation may be preferable for general standardization of results since they have a proven track record of finding true differential expression in genes. The low read depth of our QuantSeq data is likely responsible for a large portion of the difference between the datasets, but we have highlighted some of how the two methods do differ in their handling of data.The goal of this article is not to disprove QuantSeq’s capability of producing useful results. A demonstration of its utility can be found in PRJNA668024. The total RNA-seq data was uploaded to the Gene Expression Omnibus, accession number GSE147288.The QuantSeq data has been uploaded to the Sequence Read Archive, accession number The animal study was reviewed and approved by the UCL Institutional Ethical Review Committee.SJ performed the data analysis. NB generated the data. SJ, PF, and VP designed the study. PF and MS assisted with revisions and improving the final manuscript. PF, MS, and VP supervised the project. SJ wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.