E. coli, K. aerogenes, Synechococcus PCC 7002 and Synechocystis PCC 6803 to provide evidence that transcriptional interference via the collision mechanism is likely a prevalent mechanism for bacterial gene regulation. Rifampicin time-series data can be used to globally monitor and quantify collision between sense and antisense transcription-complexes. Our findings also highlight that transcriptional events, such as differential RNA decay, partial termination, and internal transcriptional start sites often deviate from gene annotations. Consequently, within a single gene annotation, there exist transcript segments with varying half-lives and transcriptional properties. To address these complexities, we introduce ‘rifi’, an R-package that analyzes transcriptomic data from rifampicin time series. ‘rifi’ employs a dynamic programming-based segmentation approach to identify individual transcripts, enabling accurate assessment of RNA stability and detection of diverse transcriptional events.We used stochastic simulations and experimental data from  RNA time-series data using rifampicin (an inhibitor of transcription initiation) give insight into transcriptional interference, e.g. by sense/asRNA complex collision, and other features of RNA. The R-package rifi analyses these complex data. Internal transcription start sites (iTSS) and partial termination can divide the operon into distinct units2. Another mechanism is the differential operon decay caused by stabilization sites in the RNA structure3. Post-transcriptional gene regulation can also be achieved by the binding of trans-acting sRNAs4 or cis-antisense RNAs (asRNA) to the nascent transcripts7. An interesting mechanism is the steric interaction of RNA polymerases (RNAPs), a mechanism known as transcription interference (TI)8. TI has been well analyzed on a theoretical level and with synthetic constructs12. However, only few reports exist on naturally occurring TI17 and it is unknown how widespread TI actually is in bacteria. We employed stochastic simulations and developed strategies to detect these patterns in high-resolution rifampicin datasets from enterobacteria, E. coli18 and Klebsiella aerogenes KCTC 21903 and Cyanobacteria, Synechococcus PCC 700219 and Synechocystis PCC 6803. Our approach (‘rifi’) allows inference of the in vivo termination effect of TI and other factors influencing transcription termination, such as trans-acting sRNAs. We provide several examples of TI facilitated by asRNAs, including the nuo or mazEF operons from E. coli. Moreover, we demonstrate instances of TI between 3′−3′ overlapping genes and transcription termination mediated by sRNAs .Genes in prokaryotes are often organized into polycistronic operons, which combine several genes under the control of a single promoter. Overall, this arrangement harmonizes gene expression, but it lacks the needed fine-tuning for different RNA abundances of individual genes within operons21. Chen et al.22. observed that this property can lead to a delay of the exponential decay of RNA in which the RNA concentration remains constant. The delay depends on the time that the last elongating polymerase needs to pass the investigated transcript position and increases with the distance to the transcriptional start site (TSS). Furthermore, different modes of RNA decay have been hypothesized22. For the “co-transcriptional decay” it is assumed that the decay is already possible during RNA synthesis, e.g. by 5′ exo- or endonucleases. In contrast, “post-transcriptional decay” means that the decay is only possible from the 3′ end of the full-length transcript22. We show that the different decay modes lead to very different RNA abundance patterns that allow to differentiate between them.Rifampicin, an antibiotic commonly used to study RNA half-lives in bacteria, inhibits transcription initiation but does not affect elongating RNAPs23, because it is not relying on a given genome annotation for, e.g., the calculation of transcript half-lives. Instead, we used dynamic programming to combine bins (RNA-Seq) or probes (microarray) of similar behavior. That allows to detect transcript segments with different properties within one and the same annotated gene. Around 90 E. coli genes are associated with two or more transcripts with half-lives that differ by factors from 1.7 to 14. Using ‘rifi’, an R-package designed for platform- and organism-independent holistic analysis of high-resolution rifampicin time series data, we obtained the following findings: (1) We provide evidence for frequent gene regulation through transcriptional interference in bacterial model organisms. (2) The RNA decay can initiate already during transcription  in the investigated bacteria. (3) The mode of decay can be inferred from abundance patterns in standard transcriptomics data. (4) The transcriptome often deviates from genome annotation, highlighting the benefits of annotation-independent analysis for more accurate half-life calculations. (5) It is possible to extract transcriptional features such as termination sites and efficiency, elongation rates, TSS locations, and operon structures from rifampicin data.Our approach ‘rifi’ differs from previous studies analyzing rifampicin dataSynechocystis PCC 6803 by analyzing time-resolved microarray transcriptome data after rifampicin treatment. Surprisingly, soft-clustering of the time-series data from individual probes identified clusters with an initial abundance increase before onset of the exponential decay : A partial termination event occurs upstream of the investigated position, causing not all RNAPs that initiated at the TSS to reach this position. However, upon rifampicin addition, this termination is relieved, allowing elongating polymerases upstream of the termination site to reach the investigated position . Gundy et al. 7 demonstrated that absence of aMEF results in an increase in polycistronic mazEF transcript and a decrease in the shorter mazE transcript, aligning with the ~34% partial termination observed through RST analysis . Previously, Gundy et al.7 showed that this asRNA is hardly or not detectable in E. coli wildtype strains, but that it becomes visible in a RNase III mutant strain. This indicates that the asRNA is rapidly degraded by RNases and has a very low stability. Nevertheless, mutations in the asRNA promoter proved that the transcription of this asRNA has a regulatory effect7. There are strong indications for termination by TI exactly at the position of aMEF  is another valuable piece of information. Based on these data we combine bins that belong to distinct transcripts with unchanged transcription parameters by a dynamic programming algorithm. This segmentation is used by ‘rifi’ to identify transcriptional events such as iTSSs and  termination sites between consecutive transcript segments and to define continuous transcriptional units   and E. aerogenes (28.5 nt/s), E. coli MG1655 (27 nt/s) and E. coli BW25113 (33.2 nt/s) lie in between. The mean elongation rate of 27 nt/s from E. coli MG1655 is close to the previously reported 25 nt/s22.The delay is dependent on the distance of a given position from the TSS and the elongation rateipt Fig. . Deviatiipt Fig.  or velocipt Fig. . The higipt Fig. . Chen et38. This leads to overlapping transcription processes from transcripts initiated from different starting sites  or containing a riboswitch (btuB). Supplementary Figure RNA abundance changes can result from alterations in stability and/or synthesis rate. Assuming RNA abundance reached a steady state prior to rifampicin addition, synthesis rates can be calculated from fitted decay constants. An increase in the synthesis rate ratio between a 3′ fragment and its 5′ segment . The decay mode can be determined directly from steady-state RNA abundance patterns in standard transcriptomic data, without requiring rifampicin time series. A constant RNA abundance throughout the transcript indicates co-transcriptional decay, while post-transcriptional decay is indicated by a linear decrease towards the 3′ end. Stochastic simulation reveals that the co-transcriptional decay patterns can result from 5′ exoribonucleases or a combination of endo- and 3′ exoribonucleases. Notably, Synechocystis and Synechococcus possess both 5′ exo and endoribonucleases26, whereas E. coli is thought to primarily rely on endo- and 3′ exoribonucleases27. Co-transcriptional decay is consistent with coupled transcription-translation, which is common in many prokaryotes40. However, uncoupled transcription-translation is also frequent. In B. subtilis, transcription elongation outpaces translation, leading to uncoupling41, and there are instances of spatiotemporal separation of transcription and translation40. Nevertheless, the contradiction of co-transcriptional decay and uncoupled transcription-translation is not as substantial as anticipated. As previously mentioned22, co-transcriptional decay is a stochastic process and depending on the decay constant, transcript length and elongation rate, a given percentage of full-length transcript can be expected in the steady state. For a transcript length of 1000 nt  approximately 79% of transcripts are not degraded before termination  and terminators. Although the bin-based ‘rifi’ method lacks the resolution of RNA-Seq, it can integrate multiple data types to provide a more comprehensive understanding compared to sequencing-based methods. By analyzing synthesis rate changes ‘rifi’ distinguishes 3′ processing sites from termination sites and calculates actual termination rates.Extending on previous studiesThe delay of exponential decay serves as a measure to track uninterrupted transcription processes from the transcription start site (TSS) to the final termination site. The delay-based transcription unit (TU) definition remains unaffected by partial termination sites or internal processing sites and has the potential to detect iTSSs associated with parallel transcription processes. However, compared to the more accurate stability estimates, all delay-based estimates suffer due to the limited number of data points available for fitting the delay. A higher time resolution in sampling points following rifampicin addition would significantly improve the performance of velocity estimation, TU definition, and iTSS detection.trans-acting sRNAs. These phenomena are indicated through decay curves that exhibit increased RNA abundance after rifampicin addition.Stochastic simulations confirm that these distinctive patterns are fully explainable by the biological phenomenon of RST, i.e. through TI or sRNA interaction, or pre-steady-state expression.Technical noise and a systematic normalization bias have been excluded as explanations for these patterns.Cyanobacteria and Proteobacteria)  Fig. , Table 2E. coli, two instances of RST are supported by prior experimental findings. These include termination at the mazE/mazF junction likely due to TI by a cis-antisense RNA7 and termination at the galT/K junction by the trans-acting sRNA Spot4230.In E. coli strains and/or Klebsiella. This makes technical artifacts very unlikely, as the data are not only from different organisms but also from different research groups and partially based on different growing conditions.14 potential RST instances are conserved between different A surprising discovery of this study is that rifampicin time-series data offer a live view of TI by the collision of RNAPs and transcription termination by The identified potential cases of RST likely represent only a fraction of the overall occurrences. Our model demonstrates that a considerable number of TI events would not lead to a noticeable increase in RNA abundance after rifampicin treatment if the termination site is located too close to the TSS. We present evidence supporting the significant impact of asRNAs on the differential expression of operons, despite their limited detectability. This finding contributes to the growing body of evidence suggesting that many asRNAs are not merely products of futile pervasive transcription but actively participate in gene regulation.λ. The decay is then transformed into the half-life . The non-BG model is only used for probes where the RNA abundance at the last available time point is above a definable background threshold:For microarrays, we mostly use a variant of the co-transcriptional decay model that accounts for a constant background  as it is suitable for RNA-Seq and a model with BG for most probes from microarrays. Fitting with or without BG can make a considerable difference, especially when a stable transcript has not yet reached the baseline at the last measured point in time. In this case, a model with BG will misinterpret the RNA abundance at the final time as BG and the estimated half-life will be too low. An extreme case is L is relevant. The concentration at t = 0 is the steady-state concentration plus the number of transcripts that are currently actively transcribed and contain the respective position. The below model corrects the model presented by Chen et al.22, which models a constant position dependent RNA concentration in steady state before rifampicin addition instead of the actual position dependent abundance decline :If rifampicin is added in the pre-steady-state time window, meaning that the time of first transcription initiation β and the position of the rifampicin sensitive termination nterm. Here, n, i.e. the delay of the increase. The maximal time the RNA abundance can increase is In case of the rifampicin sensitive termination (RST), the concentration is dependent on the termination proportion α1 and the downstream promoter α2, we can model the transcript number with the following equations. For simplicity we assume similar elongation velocities v1 ≈ v2 ≈ v and degradation constants λ1 ≈ λ2 ≈ λ for both types of transcripts. The distance to the first promoter is given by n1 and the distance to the second promoter by n2:It is a frequent case that a position is passed by polymerases which have been started at two or more different TSSs. Here the measured RNA concentration/count/intensity is based on a mixture of transcripts of different lengths. Assuming a case with two promoters, were the more upstream promoter has the synthesis rate E. coli. 21. Assuming that all RNAPs are equally affected by the lag, this should result in an ubiquitous delay of some seconds for the 5′ ends of all transcripts. For hypothetical data with a much higher temporal resolution in the time frame <30 s this ubiquitous delay would be detectable by the delayed co-transcriptional model. In reality the temporal resolution is much lower and an experimental error of some seconds in the time taking for the sample time points can be assumed. In conclusion, the small lag of the rifampicin action should not seriously affect the results.The lag of the action of rifampicin after addition to the media is between 5 and 10 s in 43. Therefore, ‘rifi’ tries to minimize the residuals of the data points to a linear regression (delay) or the average  of the whole segment. In order to prevent excessive segmentation, each split adds a penalty P to the global segmentation score. Thus, the number of clustered fragments strongly depends on the penalties. Lower penalties lead to a stronger fragmentation and a higher sensitivity for event detection at the cost of a lower specificity. ‘rifi’ tries to automatically estimate the best penalties based on a sample of the given dataset. Different scoring functions are used to assess the similarity as described below. The optimal segmentation Sn,k that splits a sequence of n ordered elements in k non-empty segments is defined by the recursioni to n. Mostly we use the variant where the total score Sn,k is minimized but for the TU definition the score is maximized and the penalty P is negativ:First, all regions with continuous coverage, i.e. regions with no coverage gap >300nt, are taken as initial segments. The further segmentation is done in a hierarchical manner. Starting on the delay, then on the half-life and finally on the RNA abundance before rifampicin addition, i.e. the assumed steady-state expression level. The operon structure and transcriptional events (e.g. start sites or termination sites) are estimated based on this initial fragmentation as described in the following  or the RST model) and performs the general processing of the data structure. For the RNAseq data, bins with an average count below 10 were discarded and no further filtration was conducted. For the microarray dataset, probes were tagged for the BG model if the latest time point was below our BG threshold of 4000 A.U. Probes with the highest expression at the last time point or the lowest expression at the first time point were filtered. As the dynamic programming approach has a polynomial time complexity, it is paramount to divide the whole genome into smaller pieces by regions without expression. We segmented the genome at regions without expression over 300 nts (changeable parameter).t > 0  shows a 7.5% higher abundance than time point t = 0, are labeled with a value y of 1, while all other bins are labeled with y = −1. Dynamic programming is used to identify regions with predominantly probes that behave like they are under the influence of RST, to account for noise and irregularities. For this, a simple scoring function is used that calculates the sum of the absolute deviation from the mean of each fragment:A two-step process assigns the bins to the RST fitting model. Bins influenced by RST are likely to show an increase in relative RNA abundance. Bins or probes for which at least one time point P of 10 is applied in this segmentation. Segments with at least 75 % of such RST indicator bins are flagged as potential RST candidates and fitted with the RST model. Additionally, 1000 nucleotides before the potential RST event are likewise subjugated to the RST fit, to create a region of pre-RST. The fits are done using the R nls or nls2 functions with the appropriate models. The RST model introduces, compared to the simple co-transcriptional mode, 2 additional free parameters. For a termination proportion β = 0, the model boils down to the co-transcriptional model. The additional freedom in the RST model can favor a β > 0, also in the non-RST case, especially if the number of datapoints are low. On the other hand, a comparison of the RST with the non RST model by e.g. the Bayesian information criterion40 would be to conservative, too; again because usually only few data points can be used to fit the increase. Here we use the workaround that we collect all fits in a given residual range compared to the best fit (default: 20%). From these fits we choose the fit with the smallest termination probability. Reducing the range increases the sensitivity to detect regions with RST. All analyses were done with rifi R-package version 1.2.2 from Bioconductor version 3.16.A split penalty Penalties for delay, half-life, RNA abundance, and TI fragmentation are highly dependent on the number of datapoints per nucleotide and thus the bin size for RNA-seq data or probe size and probe distribution for microarray data. ‘rifi’ tries to automatically estimate the best penalties based on a sample of the coverage segments in the given dataset. For this sample a range of penalties is tried, then for all consecutive fragments, ‘rifi’ tests if they are statistically significantly different. Finally, the penalty with the highest difference between significant- and non-significant splits is selected. ANCOVA and t-test are used to evaluate the splits for delay and half-life, abundance, and TI respectively.The values extracted from the fitting  alongside the RNA abundance are all representative of a certain cellular behavior at the given position. By the various fragmentation steps, the bins are clustered into groups of similar behavior.The bins are fragmented by the described dynamic programming in a hierarchical manner  might interfere with the proper segmentation or force artificial splits. For that reason we use a scoring function that allows the exclusion of data points (outliers) from a segment. A maximal allowed number O of outliers can be defined, by default maximal 0.4*n but not more than 10 outliers are allowed. If a segment with n data points is scored, the number of values {1, …, n} considered in the fit will be reduced O times in a manner where the value corresponding to the highest residual is excluded each round, resulting in the vector o representing the indices of the elements that have the respective highest residuals.To fit the increase in delay, a scoring function is used that calculates the sum of the absolutes of the residuals of the linear fit through all delay values Outlier selection:N will be added to the score. The scoring function chooses the number of outliers j = {0, …, O} that lead to the minimal combined score. The minimal allowed length m for a delay fragment is three.For the addition of each outlier, an outlier-penalty Scoring function:m for a delay fragment is three:The fragmentations of the half-life, RNA abundance and TI fragments all use the same scoring function that, similarly to the scoring function for the assignment for the TI fit, calculates the sum of the deviations to the mean. In addition, outliers are penalized here in the same way as described for the delay fit, resulting in the following scoring and outlier detection functions. The abundance values are calculated as their log2 to normalize for higher spread in the data at higher intensities. The minimal allowed length P is −0.75.All delay fragments are combined into TUs by the distance between the delay at their last position and the delay of the first position of the consecutive fragment. A steep drop of the delay is more likely the initiation of a new TU, while a small drop implies an internal start (iTSS_I). If the delay does not revert back to ~0 min at the start of a new segment, in general overlapping transcription can be assumed. A delay fragment starting at zero minutes always initiates a new TU. In the current version ‘rifi’ defines TUs only based on the delay, which can be problematic if the delay difference between the final and the first segments of two TUs is too small for segmentation. This can happen for short TUs, or if the elongation rate is very high. In contrast to the other segmentations, here the score is maximized. The default split penalty n + 1 segments and n splits. We furthermore assume that fragment-starts with a low delay, below 0.6 min, are more likely to initiate a new TU than fragment-starts with higher delays. For that reason, each start gets a delay-based modifier based on a logarithmic function. The formula causes delay values at the start of the second fragment to be penalized exponentially harsher the closer the value is to 0, while values higher than 0.6 are rewarded but only lightly. The final scoring function isThe scoring function sums up the distance between the delay at the ending points e of each fragment except the last one to the delay at the starting point s of each corresponding consecutive fragment in potential TU with Escherichia coli K12 BW251133: 3 replicates, Klebsiella aerogenes KCTC 21903: 1 dataset no replicate, Synechococcus sp. strain PCC 7002:19 3 replicates, E. coli K12 MG165518: 2 replicates, Synechocystis PCC 6803: 3 replicates). All statistics are based on the distributions from the fitted values ) from individual segments. For the HL segments and the abundance segments the HL and abundance standard deviation and standard errors are calculated based on the distributions from the individual probes/bins belonging to the segments. The values are reported in the final ‘rifi’ results R-object. Consecutive HL and abundance segments are tested using a two-sided Student’s t test for a significant fold change. The log2 fold-changes and p-values are reported in the ‘rifi’ output as “Int_event” and “HL_event”. The log2 synthesis rate ratio Rsynt is calculated between all consecutive abundance fragments within the same TU, with the synthesis rates α, the RNA abundance before rifampicin addition int, the degradation constant λ and the half-life HL. Due to the hierarchical segmentation strategy an abundance segment will never overlap two half-life segments, but the matching half-life segment might be bigger than it. In those cases, the half-life segment is adapted to match the bins/probes of the abundance segment before the calculations:The statistics for decay constant, delay and RST termination rate from the nonlinear-least-square model fits to the individual bins/probes are not used for a probe filtering or as weights for the dynamic programming. Fits were done on all available replicates simultaneously . The drop is calculated from the respective fitted delays (linear regression) at a position between the two consecutive segments. In order to get the distributions for the t-test, the residuals from the segment-specific linear regressions are summed with the respective fitted delays at the comparison position. To check if two consecutive delay segments have a significantly different elongation rate (velocity event) we checked if the slopes of the respective linear regressions are significantly different with ANCOVA. In this case, segments are the categorical variables, the position is covariate and the delay is the response. We checked for a significant interaction of the categorical variables with the position. For the evaluation of the relation and events between fragments the following statistical tests and measures of differences are considered. For all t-tests and MANOVA we assumed a normal distribution of the values. The E. coli BW25113 7420/46803 probes are tagged as potential RST probes resulting in a background probability of 0.159. Based on this background probability we calculated the probability of getting n bins, i.e. the probability of getting n were compared with the actual observed numbers and frequencies  or and Gene Expression Omnibus (accession no: GSE109174), respectively. Standard quality control was performed, the reads were aligned to their respective reference genome  using the BWA alignment tool (0.7.17)44. Bedtools (2.30.0) genomecov was used to count the sequencing depth on every position45. Spike-in RNAs were used to calculate the normalization factor. Data from Synechococcus sp. strain PCC 7002 was readily available as normalized count data. The normalized counts for each base were then averaged into groups of 50 nt, creating bins to reduce the number of samples. The position of the bin refers to the genome position of the last nucleotide in the bin. Samples with an average count below ten were discarded.Rifampicin treatment sequencing data from Synechocystis PCC 6803 strain PCC-M were cultivated at 30 °C in standard BG11 medium with constant shaking and illuminated with white light of 50 μmol of photons m−2s−1. Triplicate liquid cultures with 250 mL volume were grown at standard conditions until an OD 750 nm of approximately 0.7. Samples for RNA extraction (25 mL each) were taken at time points 0 min (before the addition of rifampicin) and in a time series of 2 min, 4 min, 8 min, 16 min, 32 min and 64 min after the addition of 300 μg/mL rifampicin. For RNA extraction cells were harvested on Supor 800 membranes via vacuum filtration. The folded membrane was transferred into a Sarstedt tube with 1 mL PGTX solution46, snap frozen in liquid nitrogen and stored at −80 °C until extraction. The RNA extraction process started with 15 min incubation at 65 °C with vortexing several times in between. Then, 700 μL chloroform/IAA  24:1 mixture was added to the samples, they were mixed well and incubated for 10 min at room temperature with gentle mixing several times. Afterwards, the samples were centrifuged for 3 min at 6000g and the upper phase was transferred to a new tube. The next step was adding 1 volume phenol/chloroform/IAA mixture, centrifuging and transferring the upper phase to the new tube. Next, one volume chloroform/IAA was added, samples were centrifuged and the upper phase was transferred to a new 2 mL RNase free tube. One volume isopropanol was added and mixed to the samples and RNA was precipitated overnight at −20 oC. The next day samples were centrifuged at 4 °C at 12,000g for 30 min, the supernatant was removed and the transparent RNA pellet was washed with 200 μL 70% ethanol (without mixing). The samples were centrifuged for 10 min, 12,000g at 4 oC, the ethanol was removed, and the RNA pellet was air dried and dissolved in 20 μL RNase free H2O. RNA samples were stored at −80 oC. The RNA samples (10 μg) were DNase treated and precipitated by ethanol overnight, the quality was checked on an agarose gel and concentration was measured by Nanodrop. The RNA was directly labeled47 with the ULS labeling Kit (Kreatech). 2 μg of RNA was mixed with 0.8 μl Cy3-ULS solution per 1 μg of nucleic acid in a volume of 20 μl with 2 μL 10x labeling solution. The samples were incubated at 85 °C for 15 min in the dark and residual unused dye was removed with KREApure columns. The efficiency of labeling was controlled photometrically with the Nanodrop spectrophotometer (microarray mode), which measured simultaneously the absorption at 260 nm (nucleic acid) and 550 nm (Cy3). Agilent custom microarray 8X60k with one color technique were used. The hybridization, washing and scanning procedure was done according to the Agilent protocol for one-color microarray-based gene expression analysis. Shortly, labeled RNA samples were mixed with 10X blocking solution, RNase free water and 25X fragmentation buffer and incubated for 30 min at 60 °C in the dark. Subsequently, 2X Hybridization buffer was carefully mixed with the samples and after short centrifugation they were loaded on the microarray. Hybridization was performed for 17 hs at 65 °C with constant rotation. Subsequent washing was performed with washing buffer 1 and 2 and with acetonitrile for 1 min each. An Agilent Technologies Scanner G2505B was used for the detection of the signals. All array data can be downloaded from NCBI GEO (GSE209879). Signal intensities for probes were obtained from the scanned microarray image using Agilent Technologies’ Feature Extraction software version 10.5.1.1 (protocol GE1_105_Dec08). Rifampicin time series data have very asymmetric fold-changes because basically the expression of the whole transcriptome declines compared to the pre rifampicin time point. This is a problem for the common normalization methods such as quantile normalization. As we do not have spike-in RNA samples for an unbiased normalization we resorted to a cyclic loess (cLOESS) based on the least changing dataset, which was done in a similar way for diurnal time series data48. We selected genes showing no differential expression when comparing time 0 and after 64 min (log2FC ~ 0). In practice we used the 1% of probes with the lowest fold change as reference for the cLOESS normalization with limma49.Liquid cultures of 50 using 20 clusters and a fuzzification parameter of 1.25 on standardized expression data.Soft-clustering of the time-series data of individual bins or probes was done with Mfuzzr = {1,2,3,4,5}. The integers resemble the positions that are existing in the respective transcript. Growing transcripts and terminated full-length transcripts are stored in separate lists. Potential asRNAs are also stored in dedicated lists. All actions are simulated in 1 s steps, for a predefined number of steps as described in the following.initiations/s) at selectable starting positions (≥1). A random number between 0 and 1 is generated. If the number is smaller or equal to the respective given initiation rate at the respective position a new vector containing the respective start site as an integer is added to the growing transcript list. Given the starting positions 1 and 100, the respective RNA vectors would be r = {1} and r = {100}.Rifampicin addition time: If the predefined rifampicin addition time step is reached no further transcription initiations are triggered. All other steps, e.g. elongation or decay continue until the predefined maximum time step is reached.ras = {1000,999,998}).Initiation of an asRNA: The asRNA is initiated as described for the sense RNA, but written in a dedicated growing asRNA list. In contrast to sense RNAs only one antisense TSS is possible. The start position of the asRNA needs to be given relative to 1, i.e. an asRNA start site of 1000 means that the 5′ end has position 1000 and that the 3′ end extends towards 1 (Change of initiation rate: It is possible to set a time point and a set of secondary initiation rates. If the respective time step is reached the secondary rates are taken (only sense transcripts).De novo transcription initiation: At each timestep one or more overlapping sense transcripts are initiated with a selectable initiation rate . Given the RNA vector r = {1,2,3,4} and an elongation rate of 5 nt/s, the RNA vector after one elongation step is r = {1,2,3,4,5,6,7,8,9}.Elongation: Each growing RNA vector is extended by n consecutive ascending integers depending on the defined elongation rate : If the defined pause start probability is greater than 0, all growing transcripts for which the 3′ end overlaps with the defined pausing site are identified. For each of those RNAs a random number between 0 and 1 is sampled. If the number is smaller or equal to the pause start probability (default: 1) the transcript is moved from the growing RNA list into a pause list. Paused RNAs can still undergo decay but they are excluded from the elongation.s−1) the respective transcripts are subjected to the decay function. If transcripts are selected for decay the actual degradation is considered to appear instantaneous within the respective time step.Post-transcriptional mode: Only full-length RNAs are considered for decay. Selected transcripts are completely deleted.r = {5,6,7,8,9,10} would become r = {10} after the co-transcriptional decay.Co-transcriptional mode: Both growing and full-length transcripts are considered. Selected full-length transcripts are completely deleted. For growing transcripts all positions but the most 3′ prime positions are deleted. The cut transcript can still undergo further rounds of elongation and decay. An example RNA vector Endo_exo mode: This mode simulates endonucleolytic cuts at defined positions and a rapid 3′ to 5′ exonucleolytic decay of the new 3′ ends. For all RNA vectors that contain the predefined endo-site-positions a random number between 0 and 1 is sampled. If this number is smaller or equal to the input decay constant, the rightmost endo site becomes the new 5′ end of the respective RNAs.Decay: For each existing transcript a random number between 0 and 1 is sampled. If this number is smaller or equal to the input decay constant  a random number between 0 and 1 is sampled. If this number is equal to or smaller than the given termination probability, the respective transcript is moved to the full-length RNA list.Termination by collision: For this mode, the transcription of an cis-asRNA is simulated in parallel. In a first step sense/anti transcript pairs are selected if their 3′ positions are in the same window (default: 30nt). For all selected pairs a random number between 0 and 1 is sampled. If the number is smaller or equal to the given sense collision termination probability (ti_prob_sense) the sense transcript is moved to the full-length sense RNA list. Otherwise the growing asRNA is moved to the full-length asRNA list. A ti_prob_sense <0.5 means that the sense RNA is less often terminated than the asRNA. Collisions without termination are not implemented.Termination: If the integer at the 3′ position of a growing RNA vector is equal to or bigger than the selected input total RNA length, the transcript is deleted from the growing RNA list and added to the full-length RNA list. The following additional non-standard termination options exist:Transcript number counting (position dependent): Finally, for a predefined set of positions, the number of transcripts containing the respective position is counted at each time step. Here, all lists  are considered. Potential asRNAs are counted independently.The simulation was used for an unbiased confirmation of the models for post-transcriptional decay, rifampicin sensitive termination by a cis-asRNA or a fixed termination factor and position as well as the multiple TSS model. Furthermore, it was used to generate an unbiased dataset with known truth for the evaluation of the delay-based ‘rifi’ estimates . For the simulation, RNAs are represented as vectors of integers. An RNA ranging e.g. from position 1 to 5 is represented as To test the accuracy of the velocity estimate a 1000nt transcript with constant elongation rate was simulated. In total 36 parameter combinations  including 4 different elongation rates  were used. The simulations for each parameter set were done in triplicates. Poisson noise was added to somewhat resemble technical sequencing noise before fitting and segmenting the data with the penalties used for the Dar & Sorek data . This procedure was repeated 50 times for each parameter set. The velocities of the resulting segments were compared with the input velocities.Further information on research design is available in the Peer Review FileSupplemental MaterialDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


If DNA is the playbook of life, then RNA, in all its forms, is the company of actors that brings life into being. RNA is likely the original organic molecule that gave rise to DNA as well as enzymes and other proteins that create the flow of genetic information known as the central dogma (DNA -> RNA -> protein). Although the central dogma still holds true generally, our understanding has expanded the concept to encompass reverse transcription of RNA to DNA, regulatory functions of noncoding RNAs, and epigenetic modifications. In this focus issue, we highlight many of the forms and functions of RNA in plants.The issue includes 10 reviews and 7 research articles. In a vignette-style review by Cuscuta campestris, showing that they have a U6-like snRNA promoter that produces miRNAs with a mechanism distinct from canonical miRNAs, which they speculate may allow these miRNAs to be exported to host plants. In a breakthrough report, The Plant Cell. Articles published in this area within ~12 mo of this focus issue will be added to an online collection on plant RNA biology, building on the articles presented here.We encourage authors to continue to submit their best work on plant RNA biology to


Microbial infection is accompanied by remodeling of the host transcriptome. Involvement of A-to-I RNA editing has been reported during viral infection but remains to be elucidated during intracellular bacterial infections.Herein we analyzed A-to-I RNA editing during intracellular bacterial infections based on 18 RNA-Seq datasets of 210 mouse samples involving 7 tissue types and 8 intracellular bacterial pathogens (IBPs), and identified a consensus signature of RNA editing for IBP infections, mainly involving neutrophil-mediated innate immunity and lipid metabolism. Further comparison of host RNA editing patterns revealed remarkable similarities between pneumonia caused by IBPs and single-strand RNA (ssRNA) viruses, such as altered editing enzyme expression, editing site numbers, and levels. In addition, functional enrichment analysis of genes with RNA editing highlighted that the Rab GTPase family played a common and vital role in the host immune response to IBP and ssRNA viral infections, which was indicated by the consistent up-regulated RNA editing of Ras-related protein Rab27a. Nevertheless, dramatic differences between IBP and viral infections were also observed, and clearly distinguished the two types of intracellular infections.Our study showed transcriptome-wide host A-to-I RNA editing alteration during IBP and ssRNA viral infections. By identifying and comparing consensus signatures of host A-to-I RNA editing, our analysis implicates the importance of host A-to-I RNA editing during these infections and provides new insights into the diagnosis and treatment of infectious diseases. The world has recently witnessed the threat that infectious diseases pose to public health, particularly the COVID-19 pandemic caused by SARS-COV-2 . In the Streptococcus pneumonia triggers histone H3 dephosphorylation (Adars) family  family , has bee) family . Notably) family , 16. RNA) family , and inf) family . Up-regufection. –22, its Herein we conducted a transcriptome-wide analysis of RNA editing profiles of intracellular bacterial pathogen (IBP) infections in various mouse tissues and organs to characterize the consensus signature of host RNA editing. In particular, we compared the host RNA editing patterns between pneumonia caused by IBPs and single-strand RNA (ssRNA) viruses, highlighting both similarities and differences between the two types of infections. Our findings could provide insights into the epigenetic underpinnings of these infectious diseases.18 Datasets of IBP infections in mice were analyzed including the lung, liver, right femur, brain, bone-marrow-derived macrophages (BMDM), bone-marrow-derived neutrophils (BMDN), and macrophage cell line raw264.7. All samples from the datasets were firstly combined into an uninfected group and an infected group  and Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Gamma (Ywhag: chr5: 135909342) were shared by 12 infection datasets, which were also predicted to exert a cis-regulatory effect on the gene expression (A-to-I RNA editing is mediated by RNA editing enzymes Adar and Adarb1 . Our resBhhe40)  and Protein Phosphatase 1 Regulatory Subunit 15B (Ppp1r15b) . Two cis-regulatory DRE sites with down-regulated RNA editing levels were Sideroflexin 2 (Sfxn2)  and Nuclear Factor I A (Nfia)    and Terminal Nucleotidyltransferase 5C (Tent5c) . Two cis-regulatory DRE sites with down-regulated RNA editing levels were also observed in Fas Associated Via Death Domain (Fadd)  and Lysine Methyltransferase 2D   contained six A-to-I RNA editing sites . The shared GO and KEGG pathways enriched by DRE genes between IBP and viral infections were mainly related to myelocyte-mediated immunity, autophagy, apoptosis, lysosomes, and small GTPases.   , 58. Rec3097111) –62.Furthermore, the pathways of DRE genes identified in IBP and viral infections were mostly related to neutrophils , 64. Recvia RNA editing , a key factor for virus replication . The details of all bacterial and viral infection datasets can be accessed in Raw data of RNA-Seq were downloaded from the European Nucleotide Archive (ENA) of the European Molecular Biology Laboratory (The process of RNA sequencing reads was conducted as previously described . In brieSingle nucleotide variants (SNV) were called by using VarScan (version 2.4.4) . The varPseudo-counts of gene expression were calculated from the RNA-Seq alignment files using FeatureCounts , and traPrincipal component analysis (PCA) was performed using the function prcomp in R (version 4.2.1) and visualized using the ggplot2 package (version 2.2.1). Heatmaps were plotted using the Pheatmap package in R (version 4.2.1).Random Forest  was usedThe enrichment analysis of genes with RNA editing including gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathways were conducted using Enrichr .https://www.gsea-msigdb.org/gsea/msigdb/mouse_geneset_resources.jsp) |> 1 were considered significant. NES indicated the analysis results across gene sets. Pairwise P-values were calculated using the non-parametric Kruskal-Wilcoxon test followed by the Tukey post-hoc test.The GSEA version 4.2.3 software and dataset were used to function of genes based on the GSEA website MSIGDB database (ces.jsp) , using aStudent’s t-test was used to compare gene expression levels. The Spearman correlation was used to analyze the correlation between RNA editing and gene expression, and correlation coefficients (r) and P-values were calculated. The statistical significance level was set at P < 0.05.The generalized linear model (GLM) method and likelihood ratio test were used to compare the intergroup RNA editing levels. The The original contributions presented in the study are included in the article/Conceptualization: Z-YW, Z-XW, J-HC, Y-YJ, Y-SL, Data curation: Z-YW, Z-XW. Funding acquisition: J-HC, Y-YJ. Investigation: Z-YW, Z-XW, Y-SW, DG, S-YX, J-HL. Methodology: Z-YW, Z-XW, Y-SW, DG, Y-NL, J-HL. Supervision: J-HC, Y-YJ, Y-SL, X-BP. Writing – original draft: Z-YW, Z-XW. Writing – review & editing: Z-YW, Z-XW, J-HC, Y-YJ. All authors contributed to the article and approved the submitted version.


About 10% of all home peritoneal dialysis regimens in children with chronic kidney disease stage 5 are reported to involve some form of a tidal peritoneal dialysis (TPD) prescription. Despite this, there remain several gaps in how pediatric nephrologists approach the use of TPD. This stems from a combination of factors such as the confusing technical terminology pertaining to TPD, seemingly conflicting data on the risks, benefits, and indications for TPD, and lastly, limited published guidelines on the practical aspects of how to write a TPD prescription, based on the indication, in children. Our educational review, using evidence-based data, attempts to bridge this gap and provide an easy-to-use guide on the key practical aspects of TPD in children. The 2021 United States Renal Data System annual data report indicates that 7.7% of the prevalent US population on dialysis received peritoneal dialysis (PD) as the primary dialysis therapy in 2019, a modest increase from 5.3% in 2009; in patients 0–17 years of age who received dialysis during 2019, 44% were started on PD at dialysis initiation [TPD was originally developed, in part, to increase the contact of the peritoneal membrane with dialysate, and thereby increase solute clearance, based on animal studies first done in the 1960s and 1970s . HoweverThere are several reasons why TPD might afford a significant advantage compared to other forms of automated PD (APD). With TPD, the residual volume theoretically maintains continuous contact of dialysate with the peritoneal membrane, thus maximizing actual time when dialysis is occurring, improving solute clearance. In addition, dialysate outflow times in TPD are regulated differently compared to other forms of APD, as discussed below .The outflow of drained fluid during PD is biphasic; after an initial fast phase with constant rapid outflow, a slow phase abruptly ensues. This transition or ‘break point’ usually occurs after around 5 min after a 2-L dwell and adds to the total time on dialysis, without contributing to any substantive clearance . This isAnother potential advantage of TPD is in settings where patients experience significant pain during the drain phase of CCPD. Mechanistically, the residual volume (recommended to be about 200 to 300 mL in adults) in the abdomen theoretically allows the dialysis catheter to remain ‘afloat,’ minimizing irritation of the peritoneal lining and visceral organs caused by abutment of the catheter tip against those structures in a dry abdomen. This has been demonstrated to be effective in children , in adulThe keys to successful TPD, therefore, are to ensure that there is a sufficient residual volume, to reduce drain pain, and adequate mixing of dialysate to optimize solute clearance. This was supported by data showing higher solute clearances with TPD in animal studies compared to continuous ambulatory PD .Human data are not supportive of the finding in animal studies that TPD inherently leads to higher solute clearances. While clearances in many instances are improved with TPD, this is a result of higher dialysate flow rates (from the frequent exchanges that are sometimes used in TPD). When dialysis duration and total volume are kept constant, TPD does not result in an improvement in clearances (as measured by creatinine clearances and Kt/V urea) compared to conventional APD, at least with dialysate volumes up to 24 L/day .p < 0.05); Kt/V urea was no different between the 50% TPD and the non-tidal APD groups. Creatinine clearances were the same across all modalities and prescriptions. Similarly, Piraino et al. [In 2000, Juergensen et al.  studied o et al.  found thBalaskas et al. compared the effect of non-tidal APD with TPD in 12 patients, assessing differences in serum chemistries, blood pressure, body weight, and measures of patient well-being, including appetite and sleep. Again, using similar volumes of dialysis solutions per exchange and session and the same duration of dialysis, no differences were seen between the 2 groups in any of the studied parameters . A more Possible explanations for why TPD, per se, does not improve clearances, despite its theoretical benefits, include incomplete mixing of the residual volume with fresh dialysate and/or the creation of stagnant fluid layers in the peritoneum. This inadequate mixing of the tidal with the residual volumes may be the result of the types of dialysate inflow in different dialysis modalities. TPD with a pulsatile inflow, as opposed to a gravity-dependent inflow, increases mixing and may result in better clearances. However, there are currently no commercially available cyclers with pulsatile flow, to make this a reality.Nevertheless, the benefit that TPD continues to afford is that it may allow greater options in modifying different aspects of the PD prescription, such as the fill volume, the percent TV, and the frequency of cycles, without necessarily prolonging the total time that a patient has to remain on dialysis.Many small studies have been published on the use of TPD in children, who may have more to benefit from a tidal prescription, both for comfort, and to maintain solute clearance and ultrafiltration without increasing time on dialysis inordinately such that quality of life for the patient and family are adversely affected. The use of TPD in infants  and olde2 (maximum 2000 mL) and four to five exchanges for 8 to 10 h per night. The transition to TPD involved the same fill volume and dialysis duration, but with many more cycles (28 to 35) with a TV that was 50% of the fill volume; to prevent overfilling, patients underwent complete emptying of the abdomen every 6–7 tidal exchanges. The total dialysate volume in TPD, as expected, was about four times higher; it resulted in statistically significant increases (1.5 times higher) in the Kt/V urea. In the 9 patients who used a dialysate with the same glucose concentration for both dialysis prescriptions, the nightly fluid removal amount per hour with TPD was two times higher (p = 0.0039). Both the clearance of β2-microglobulin and serum β2-microglobulin were not significantly different, though the clearance of creatinine and phosphorus achieved with TPD improved. Based on their experience, the investigators proposed the use of a TV of 50% as being ideal, when increases in solute clearance are desired and large-volume TPD is being considered. While alternatives, such as increasing the number of cycles with standard APD prescriptions or the use of icodextrin for the day dwell, can be viable alternatives to TPD, especially in children with a high peritoneal membrane transport status [Hibino et al.  evaluatet status , both ma maximum 00 mL and maximum 00 mL and2 and 8 to 10 exchanges  or to TPD with the same fill volume but many more exchanges . At the end of the 6-month study period, the investigators in this study found that patients and parents felt safe with TPD (within a few weeks of starting TPD) and no patients reported dialysis-induced pain during TPD compared to three patients (23%) during CCPD. The total dialysis time was somewhat shorter on TPD, but this was not significantly different. In this study, Kt/V urea and creatinine clearance were higher during TPD in patients with high peritoneal membrane permeability, but similar in other patients; albumin and phosphate losses were comparable between the two groups.Family perceptions were also explored in one of the largest prospective pediatric studies on TPD . SeventeIn summary, while TPD is not necessarily a more effective modality in all children, it allows higher flow rates/volumes in shorter times and so may be of benefit in settings when suboptimal clearances and UF rates are noted such as in infants and older children who are high transporters. It may also be of value in patients with mechanical outflow problems in whom a larger fraction of the time on dialysis may get ‘wasted’ due to prolonged outflow times.Other indications for TPD include patients with ascites, in whom a tightly controlled dialysate outflow is preferable , and thoP = 0.001) and dialysis modality  were associated significantly with mortality, with a lower odds of death with the use of TPD. This study, albeit small in study sample size, and involving a cohort that was not as sick as typically seen in critically ill patients with AKI, does provide an alternative to hemofiltration in patients with AKI in limited-resource settings and in instances where hemofiltration may not be feasible [Additionally, TPD may have a valuable role in the management of patients with acute kidney injury (AKI), especially when high dialysate flow rates are used. While data are limited, theoretically TPD may have a benefit in critically ill patients; since there is more consistent fluid in contact with the peritoneum, inflammatory mediators may get cleared more effectively. In practice, there is insufficient data from randomized controlled trials to definitely support this hypothesis . In a smfeasible .Among the things to consider with the use of TPD is cost. Since more dialysate bags are needed to perform large-dose TPD, costs invariably increase, too. Increasing the number of bags from 2 to 3, increases costs by 50% and increasing the number of bags from 2 to 4 doubles the cost for the dialysis fluids. Considering that the retail price of common PD solutions is approximately $50–75 per 6-L bag, this would increase the cost between US$18,250 and US$54,750 per year. If patients needed an extra manifold for their dialysis at night to place 5 or more bags, this would incur an additional cost. Also, in such a scenario, patients would require more time to set up and connect their PD circuit nightly, adding to their home care time. It would also require a larger storage area for supplies in their homes. In general, if prescribers are utilizing an alteration in prescription to use TPD to alleviate outflow symptoms during CCPD, the change in costs would be minimal, as the total volume of dialysis is usually similar to their original CCPD prescription.Other potential issues with TPD are related to the need to predict and account for the volume that will be ultrafiltered during each cycle. It may be more difficult to predict initial UF precisely with TPD. If UF were excessive, there could be a negative impact on dialysis clearance from an excessive intraabdominal dialysate volume. It might also exacerbate existing hernias, create new abdominal wall defects, or cause a dialysis catheter fluid leak from the exit site. This is the rationale why early studies and long-term prescribing practices have led prescribers to completely drain the abdomen periodically during TPD sessions. However, most published studies on TPD have not demonstrated a higher incidence of such complications . FurtherHowever, increased intraperitoneal volume (IIPV) events were seen more frequently in TPD and small fill volume therapies . SpecifiAs emphasized in the preceding sections, abdominal pain during dialysis remains the main indication for TPD. Other less common indications for TPD are conditions in which higher solute clearances or UF are desired without increasing dialysis duration.The ideal TPD prescription, therefore, is one that is targeted to the indication for TPD, and in general is one that would achieve the goal of pain- and alarm-free therapy with optimal clearances and UF, while delivering a safe treatment that avoids overfilling.The percentage of the initial fill volume that should be exchanged with each cycle (the TV) and amount that should stay in the peritoneal cavity ;Frequency of tidal full drain, i.e., how often to fully drain the peritoneal cavity; andThe net UF goal for the day.When contemplating a TPD prescription, the prescriber must decide on the following parameters, as mandated by the Baxter Home Choice® software (the most commonly used home cycler for pediatric patients in the USA), to ensure a safe and effective treatment:There is no absolute recommended TV percentage necessary to keep the catheter afloat and decrease pain and drain alarms, and there is great variation in the literature on what has been used. In a cross-sectional survey of 6 adult dialysis units in Ontario, Canada, the TV percentage most frequently used varied between 75 and 80%; the most common indication for TPD in this cohort was drain pain . As a geIn the few instances when TPD is considered for solute clearance, a TV of at least 50% has been proposed to achieve comparable clearances to standard APD prescriptions , 14. ThiAfter determining the TV percentage, the prescriber must decide on the frequency of complete drainage of the peritoneal cavity. The main goal of complete drainage is to avoid overfilling (increased IIPV) and increased intraperitoneal pressure that can have grave consequences including death . A commoThe final step is to enter the total anticipated  UF for the therapy. This step is necessary to ensure that the volume that is ultrafiltered is removed along with the TV, and not retained until therapy is completed since that would lead to an overfilled state and its associated risks. The expected UF must be estimated carefully as adverse events may occur if the UF entered in the cycler program is set too low or too high. If the expected UF is set too low, progressive overfilling of the abdomen can occur from retention of the UF in the abdomen increasing the residual volume, which may lead to increased intraperitoneal pressure causing patient discomfort and as mentioned above, other serious complications. If the anticipated UF is set too high, the residual volume will be progressively depleted. This may lead to drain pain and would defeat the very purpose of initiating TPD. One way of estimating a patient’s UF is to review the previous actual UF achieved in the patient, the week prior to initiating TPD, and using 70% of that as the anticipated UF on TPD. Once tidal therapy is started, re-evaluating and re-assessing the actual UF is crucial to prevent overfilling or depleting the residual volume. In the new model of Baxter Home Choice® cycler, the minimum anticipated UF value defaults to 1000 mL per day. The prescriber can modify this number; the value entered has be greater than zero.In summary, while TPD has not been demonstrated in studies to inherently afford a benefit in solute clearance or UF compared to standard APD prescriptions, it does allow manipulation of the dialysis prescription to optimize dialysis, without inordinately prolonging the duration of dialysis. Such large-volume TPD can be considered in infants, high transporters, and in settings where the dialysis catheter is malfunctioning, and no other alternatives are available. The main role for TPD in today’s era is in the context of drain pain; by potentially floating the catheter in a residual volume, patient comfort and quality of life can be improved. There are limited data on whether TPD affords a survival benefit in critically ill patients with AKI. Lastly, close attention must be given to the TPD prescription — the choice of the TV, the frequency of complete drains, and the estimated UF can all impact patient safety and comfort, and affect outcomes.Key summary pointsTPD entails always leaving a reservoir of fluid in the abdomen  and refilling the abdomen with fresh dialysate  with each cycleTidal prescriptions have their greatest utility in reducing drain pain by allowing the dialysis catheter to remain afloat in the residual volume and reduce irritation of the peritoneumOn occasion, large-volume TPD can be used to optimize clearances and ultrafiltration when standard APD is not achieving desired outcomesInstances when large-volume TPD may be considered include infants, high transporters, and those with malfunctioning cathetersA TPD prescription requires choosing and programming a tidal volume , the frequency of complete drains, and the net UF estimated to be achievedAnswers can be found after the reference listA)Drain painB)Abdominal herniasC)Low average transportersD)AKI from sepsisWhich of the following is the most common indication for the use of TPD in the current era?A)The total nightly fill volumeB)The volume of dialysate that is always left in the abdomenC)The volume of dialysate that is refilled into the abdomen after the first cycleD)The net UF expected with each cycle in TPDThe tidal volume refers to which of the following:A)Overfilling of the abdomenB)Suboptimal clearanceC)Lack of resolution of drain painD)All of the aboveWhich of the following may be encountered as a complication of TPD if the expected UF is not accurately estimated?A)Human studies have demonstrated an inherent benefit of TPD in improving solute clearanceB)TPD is superior to CKRT in critically ill patients with AKIC)Patient comfort and machine alarms can be improved with a tidal prescriptionD)Ascites is a contraindication to the use of TPDWhich of the following statements is true about TPD?A)To maximize solute removalB)To prevent overfilling of the abdomenC)To reduce drain painD)To increase ultrafiltrationWhich of the following is the true reason as to why programming periodic complete drains in a TPD prescription is important?


Circular RNAs (circRNAs) are a class of covalently closed, endogenous ncRNAs. Most circRNAs are derived from exonic or intronic sequences by precursor RNA back-splicing. Advanced high-throughput RNA sequencing and experimental technologies have enabled the extensive identification and characterization of circRNAs, such as novel types of biogenesis, tissue-specific and cell-specific expression patterns, epigenetic regulation, translation potential, localization and metabolism. Increasing evidence has revealed that circRNAs participate in diverse cellular processes, and their dysregulation is involved in the pathogenesis of various diseases, particularly cancer. In this review, we systematically discuss the characterization of circRNAs, databases, challenges for circRNA discovery, new insight into strategies used in circRNA studies and biomedical applications. Although recent studies have advanced the understanding of circRNAs, advanced knowledge and approaches for circRNA annotation, functional characterization and biomedical applications are continuously needed to provide new insights into circRNAs. The emergence of circRNA-based protein translation strategy will be a promising direction in the field of biomedicine. CircRNA was originally regarded as incorrect RNA cleavage products in viroids . With thIn recent years, several key characterizations of circRNAs have been identified , 9. AlthIn the past few years, circRNAs have been regarded as competing endogenous RNAs that sponge miRNAs that silence their target genes , 21, 22.Given that circRNAs have structural stability advantages and that the negative effect of intron-derived circRNAs on triggering the immune response is smaller than that of other RNAs, the development of RNA drugs based on circRNAs has important application prospects , 9, 36. In this review, we collected the recent progress in the biogenesis, degradation and biology of circRNAs and describe novel technologies for the identification, accurate quantification, and functional characterization of circRNAs. Based upon our findings, we also discuss the current challenges of circRNA analysis and new insight into strategies to determine circRNA functions and the biomedical implications of circRNA.In general, circular RNA is usually derived from back-splicing of pre-mRNA to form a closed RNA transcript , 10, 11.To date, studies using the application of emerging approaches have elucidated various regulatory mechanisms of circRNAs, which highlight many aspects of gene expression, DNA damage, RNA editing and immunity. We will focus on the representative epigenetic regulation of circRNAs Fig. b–g, inclA majority of studies have shown that circRNA can act as miRNA sponges in a manner similar to that of mRNA . CirculaIn recent years, some emerging epigenetic regulatory mechanisms of circRNAs have been illuminated. DNA: RNA immunoprecipitation sequencing (DRIP-seq) data have also shown that circRNAs frequently form R-loop structures and tend to regulate DNA damage and genome instability , 59 Fige. Some c6A motifs with translational potential and that high m6A levels in circRNAs have the ability to improve the efficiency of translation [As mentioned above, circRNAs are a class of noncoding RNAs, but recent scientific research has shown that some circRNAs also have certain coding capabilities . The 5' nslation . Interesnslation , 63, 67.ankrd52 pre-mRNA from R-loops by ci-ankrd52 replacement and subsequent ciRNA removal via RNase H1-mediated degradation [Due to the special structural characteristics of circRNA, it cannot be degraded by RNase H, which is conventionally used to eliminate linear RNA . The speradation . This RNradation . Upon viradation  , which are functional in gene expression regulation and implicated in malignant transformation –90. CurrThe circRNA discovery tools above are mostly compatible with the reads of next-generation RNA-seq , 100. DuOf note, due to circNick-LRS and circPanel-LRS eliminating the need for prior circRNA enrichment, a large number of nonconical splicing events in the global genome have been found to produce various types of circRNAs, including novel exons, intron retention and microexons. Both circFL-seq  and isoCBecause the above methods always require RNA-seq data as input, circRNA signals with low abundance are usually missed , 100. ItWith the development of bioinformatic tools for circRNAs, an increasing number of public circRNA databases have emerged , 112–114In recent years, the clinical significance of circRNAs has gained substantial attention, with increasing evidence showing their potential as clinical biomarkers and therapeutic targets , 118. SpDespite progress in circRNA detection and annotation, the lack of standardized naming conventions remains a pressing issue in this field. The diverse naming methods used across different databases and articles have created a significant barrier for research, leading to information duplication and errors. Some databases use a 'circ_' prefix followed by a numeric ID or the parental gene symbol to name circRNAs , 113. HoSeveral methods have been developed to study the functions of circRNAs , 46. We CircRNA sequencing of rRNA-depleted and RNase R-treated cells is the method used to discover novel circRNAs and was also used in all early circRNA profiling studies , 82, 133Northern blotting is the gold standard method for validating all kinds of RNAs, including circRNAs , 128. AnInterestingly, droplet digital PCR (ddPCR) can overcome this shortcoming brought by RT‒qPCR , 33. ddPDownregulating the expression of circRNAs is a popular strategy to explore their cellular functions , 5, 9. MIntroducing siRNA corresponding to circRNA specifically targeting BSJ into transfected cells is a convenient and effective method to inhibit the expression of circRNA in cancer cells , 125. ThALU element can prevent circHIPK3 formation [In recent years, CRISPR/Cas9, which is a highly specific and efficient tool to edit the genome, has also been used in circRNA knockout , 142. Inormation . Howeverormation .Therefore, it is still necessary to gain insight into circRNA knockdown-based strategies, which should be considered with many different factors involved in circRNA production.Several methods based on chemical synthesis and enzymatic ligation have been used to generate circRNAs in vitro; however, circRNA production in vivo has only recently been delineated , 148. ThConsidering the complexity of circRNA biogenesis, suitable strategies are needed for studying the different structural and functional features of circular RNA occurring in cells .The replacement of stronger enhancers including ICSs, Alu elements, other RNA pairing structures and adding BSJ associated RBPs may be strategies to improve circRNA overexpression , 12, 150When circRNAs enter the cytoplasm, some of them become competitive endogenous RNAs (ceRNAs) , 100. CiAlthough circRNA-miRNA sponging is the most well-known function, increasing evidence has also shown that circRNAs can interact with RBPs to exert widespread regulatory effects , 132. FoTo date, experimental research on circRNA-RBP interactions has mainly been conducted through RNA pulldown assays or RNA immunoprecipitation (RIP) for experimental analysis , 155 (FiMLL recombinome in mouse leukemia xenograft models [R-loops are widespread structures that are often formed co-transcriptionally , 157–159t models . These st models . They obt models .cis manner [trans. There is still an interesting question that whether circR-loops interact with special RBPs to mediate chromatin marks, chromatin accessibility or active chromatin landscape.Recent studies may suggest that many circRNAs in circR-loops regulate the cognate DNA locus or mRNA transcription in a s manner , 17. It Because circRNA has tissue- and cancer-specific expression and stability in body fluids, it can be used as a rapid, accurate, and noninvasive biomarker for early diagnosis and prognosis , 164. SeCancer cells present aberrant expression of circRNAs, which are usually related to some clinical characteristics, such as tumor type, tumor size, histological grade, tumor invasion and metastasis Table . For exaCircRNAs can not only distinguish different tumor subtypes but also indicate different prognostic levels in the body , 175. FoIn recent years, numerous dysregulated circRNAs have been found to affect the proliferation, apoptosis, metastasis, DNA damage and other life activities of cancer cells , 99, 130In the current clinical treatment of cancer, various chemotherapeutic drugs have been developed to inhibit the growth of cancer cells and have achieved good clinical effects –187. HowCircRNAs can also interact with oncoproteins to help cancer cells establish drug resistance , 194. FoDrug resistance is an urgent problem to be solved in current tumor therapy treatments. Recent studies have shown that circRNAs can regulate drug tolerance pathways by interacting with miRNAs, proteins and translated proteins in tumor cells , 189. TaAlu sequence, which is important for circRNA formation [Although recent studies have suggested that circRNAs are promising therapeutic targets in many diseases, there are still some challenges , 99, 130ormation , 15, 143ormation , 195–197ormation , 141. HoRecent studies have found that some circRNAs can also be directly translated into small peptides and play a role in cells , 65, 198Given that circRNAs have the perfect characteristics of stable conformation, high stability, and special immunogenicity, RNA circle-based technologies were developed , 18, 67.With advances in bioinformatics and biotechnologies, circRNA research has become an increasingly popular and important field , 99, 130The sequence overlaps of circRNAs with their cognate linear RNA sequences usually restrict the determination of circRNA functions , 11. AltConsidering the structural stability advantages, cancer-specific expression, and drug resistance exhibited by circRNAs, they hold significant promise as noninvasive biomarkers for cancer and as targets in cancer treatment , 99, 130The discovery of circRNA translation not only brings exciting new perspectives for translation machines but also brings novel design concepts for the treatment of major diseases based on circRNA translation , 62, 206


Introduction: This study aimed to determine the incidence of offset stem usage in revision total knee arthroplasty (rTKA), and to assess the necessity for their use with the femoral and tibial components. Methods: This retrospective radiological study included 862 patients who underwent rTKA between 2010 and 2022. Patients were divided into a non-stem group (group NS), offset stem group (group OS), and straight stem group (group SS). Two senior orthopedic surgeons evaluated all the post-operative radiographs of the group OS to assess the necessity of offset use. Results: In total, 789 patients met all eligibility inclusion criteria and were reviewed ) with a mean age of 72.7 ± 10.2 years old . Eighty-eight (11.1%) patients had undergone rTKA with offset stems  and 609 (70.2%) with straight stems. The tibial and femoral stems were diaphyseal of over 75 mm in 83 revisions (94.3%) for group OS and 444 revisions (72.9%) for group SS (p < 0.001). Offset in the tibial component was located medially in 50% of rTKA, while the offset in the femoral component was placed anteriorly in 47.3% of the rTKA. Assessment by the two independent senior surgeons found stems were only necessary in 3.4% of cases. Offset stems were only required for the tibial implant. Discussion: Offset stems were used in 11.1% of revision total knee replacements, however, they were deemed necessary in 3.4% and for the tibial component only. While the goals of revision total knee arthroplasty (rTKA) are the same as primary total knee arthroplasty (TKA), it remains a challenging and more complex procedure due to the potential of bone defects and soft tissue insufficiency , 2, whicA valuable option to avoid the possible negative consequences of straight stems is using stems with an offset. Offset stems improve the ability to achieve maximal anatomical coverage without overhang or cortical impingement. In addition, offset stems can assist implant alignment on the metaphysis in the coronal and sagittal plane and balance the flexion and extension gaps by effectively moving the implants , 3, 7. HThe primary objective of this study was to provide an estimate of the incidence of the use of offset stems in rTKA performed in a high-volume arthroplasty referral institution. The secondary objective was to retrospectively assess the necessity for an offset stem in the same series.This was a retrospective radiological study of prospectively collected data in a high-volume arthroplasty referral institution. Inclusion criteria were rTKA using tricompartimentale prosthesis, regardless of the etiology and constraint required at revision. Three groups were identified based on the use and type of stem. In group NS (no stem), no stem was used for either the femoral or tibial implants. In group OS (offset stem), a stem with offset was used for at least one component. In group SS (straight stem), a straight stem was used for at least one component. Between January 2010 and May 2022, 862 patients underwent rTKA. Of these patients, 789 patients , with a mean age of 72.7 ± 10.2 years old  matched the eligibility criteria and were included in the analysis. Overall, the most common cause of revision TKR was infection (31.8%), followed by aseptic loosening (28.6%) and malposition of the implants (13.2%) .Figure 1All surgeries were performed by four senior orthopedic surgeons specializing in adult reconstructive knee surgery. To determine the appropriate component size and positioning, all patients underwent pre-operative radiographic evaluation with templating. Patient information was abstracted from a prospective registry and is summarized in ® software  femoral and tibial components were templated for size and positioning, and stems were templated for length, diameter, and need for offset. An offset stem was deemed necessary retrospectively when it was not possible to use a straight stem while respecting the following criteria: (1) adequate coverage of the epiphysis (the largest possible implant without overhang); (2) the shortest stem possible ; (3) an absence of malalignment due to the stem positioning  was utilized for the comparison of categorical variables. The level of significance was set at p < 0.05.Collected data were analyzed with SPSS (Version 24.0). Continues variables were expressed as mean ± sd [range]; categorical variables as percentages. The Kolmogorov–Smirnov test was utilized for normality analysis. Mann–Whitney n = 88). The distribution of offset stem over time is displayed in p < 0.001). The distribution pattern for constrained knees was varus-valgus constrained (VVC) TKA in 89.8% (n = 79) and hinge TKA in 1.1% (n = 1) for group OS compared to 28.5% VVC (n = 174) and 44.8% hinged (n = 273) for group SS (p < 0.001) (n = 5) and 27.1% (n = 165) for group OS and group SS, respectively, while stems were diaphyseal (length > 75 mm) in 94.3% (n = 83) for group OS and 72.9% (n = 444) for group SS (The incidence rate for offset stem use was 11.1% (< 0.001) . A tibiagroup SS .Figure 3n = 26, 47.3%), while most of the offset for the tibial stem was placed in a medial position   out of 88 cases and tibial stems with offset were used in 34 (38.6%) out of 88 cases, while both combinations were chosen in 24 (27.3%). Most of the offset for the femoral stem was placed in an anterior position  .Figure 4n = 3) and 5.7% (n = 5) of offset stems were necessary (n = 3) of offset stems were necessary. Offset stems were only required for the tibial implant when the tibial plateau was translated laterally compared to the diaphysis, secondary to tibial malunion or previous high tibial osteotomy. A femoral offset was never necessary for this series. A shorter stem or improved femoral component positioning could avoid an offset in every case.Initial assessment of the radiographs by two independent senior surgeons found that only 3.4% (ecessary . After tThe main finding of this study was that the stems with offset were used in revision TKA in 11.1% of procedures over 12 years but were only deemed necessary in 3.4% after a new assessment by experienced surgeons on postoperative radiographs.During rTKA, using more constrained implants, such as VVC and hinged prostheses, require femoral and tibial stems , 9. StemHowever, there are possible disadvantages to the use of long stems, which can be avoided with metaphyseal fixation . These iIn some cases, an offset stem was still indicated. For all these cases, the deformity needing an offset was on the tibial side, with a lateral displacement of the tibial plateau compared to the diaphysis. This deformity was due to malunion or high tibial osteotomy by a lateral closing wedge. In a cadaveric study, Hicks et al. tried to determine the relationship between the tibial plateau and the intramedullary canal of the tibia. They noted that the center of the intramedullary canal was relatively anterior and medial to the tibial plateau, indicating the need for pre-operative templating in revision cases using long stems . In the Our study should be interpreted in light of some limitations. Firstly, due to the study design’s descriptive nature, it cannot imply causality or provide explanations for unexpected findings. Furthermore, no clinical outcomes or survival were assessed. However, this study aimed to analyze and improve our preoperative planning for rTKA. Finally, the patient population at this academic orthopedic institution may not accurately represent the general worldwide population, and caution should be taken in extrapolating these results.Stems with offset were used in 11.1% of rTKA but were deemed necessary in only 3.4% after a retrospective radiographic assessment considering the current approach of RTKA with a zonal fixation concept. With metaphyseal fixation, the requirement for offset stems is very low, except in some post-traumatic or post-osteotomy cases on the tibial side.