RNA editing by ADAR (adenosine deaminase acting on RNA) proteins is a form of transcriptional regulation that is widespread among humans and other primates. Based on high-throughput scans used to identify putative RNA editing sites, ADAR appears to catalyze a substantial number of adenosine to inosine transitions within repetitive regions of the primate transcriptome, thereby dramatically enhancing genetic variation beyond what is encoded in the genome.Here, we demonstrate the editing potential of the pig transcriptome by utilizing DNA and RNA sequence data from the same pig. We identified a total of 8550 mismatches between DNA and RNA sequences across three tissues, with 75% of these exhibiting an A-to-G (DNA to RNA) discrepancy, indicative of a canonical ADAR-catalyzed RNA editing event. When we consider only mismatches within repetitive regions of the genome, the A-to-G percentage increases to 94%, with the majority of these located within the swine specific SINE retrotransposon PRE-1. We also observe evidence of A-to-G editing within coding regions that were previously verified in primates.Thus, our high-throughput evidence suggests that pervasive RNA editing by ADAR can exist outside of the primate lineage to dramatically enhance genetic variation in pigs. Eukaryotes are known for relatively complex mechanisms used to regulate gene expression. One such mechanism, RNA editing, enables the cell to alter sequences of RNA transcripts  such thard highest copy number of any SINE cataloged on SINEBase [ADAR can only catalyze A-to-I editing within dsRNA. The high editibility of the primate specific Alu element is attributed to its capacity to induce dsRNA; these elements have a high copy number, are short, relatively undiverged from one another, and tend to cluster in gene rich regions of the genome . When apSINEBase .GluR-B in mice [ADAR-catalyzed editing has been reported for 12 coding genes in pig brain tissues during fetal development [Since Alu elements are generally found within and near genes, ADAR editing in humans preferentially targets non-coding regions of many genes such as introns, UTRs and upstream and downstream gene proximal regions. ADAR editing of these regions is thought to be a key component of RNA processing via mechanisms that include Alu exonization  and RNAi in mice . Also, selopment . Therefolongissimus dorsi muscle. Based on previous studies done in primates, a bioinformatic strategy was used to find A-to-I (observed as A-to-G) DNA to RNA mismatches that give evidence of ADAR catalyzed RNA editing events.Here, we demonstrate the pig’s capacity for RNA editing. By studying this process in a relatively distant species to human with a distinct repetitive element repertoire, we want to determine if RNA editing patterns seen in Alu bearing genomes can likewise be observed in pigs. RNA editing detection was done by analyzing a single pig using whole genome sequencing data and RNA sequencing data from liver, subcutaneous fat, and longissimus dorsi (LD) muscle were purified from samples obtained from a single animal, similar to another single-animal editome study [Sus scrofa reference genome sequence (Sus scrofa 10.2.69). An average of 106 M pass-filter strand specific cDNA reads were obtained from each tissue, with an average overall alignment rate of 76%.To provide the materials needed for a transcriptome-wide survey of RNA editing candidates, genomic DNA as well as total RNA from liver, subcutaneous fat, and me study . SequencSus scrofa 10.2.69 reference genome. Additional trimming 6 bp from the 5′ ends of cDNA reads was done to prevent misidentification of DNA-to-RNA mismatches due to artifacts associated with the use of random hexamers during cDNA library preparation [To scan the transcriptome for possible RNA editing sites, we utilized a custom pipeline influenced by previous studies done in human cell lines and primates , 17. Priparation . When coparation  was perfP-value of 20 or less. Specific thresholds for DNA and cDNA sequencing depths were determined according to a previous study that profiled the rhesus macaque editome from a single animal [Default editTools parameters were used, in which a mismatch was considered a candidate RNA editing site if at a particular locus 1) the genotype is homozygous according to 95% of the DNA reads, 2) at least 10 reads were used to determine the genotype, 3) neither genomic DNA nor cDNA samples are indels, 4) at least 5 cDNA reads from the same tissue differ from the genotype call, and 5) these cDNA reads must have a Phred-scaled strand-bias e animal . Using tTo understand differences in candidate RNA editing sites between tissues, canonical A-to-G mismatches were aligned across tissues if they were detected at the same physical position and on the same strand. The number of candidate RNA editing events was fewer in LD compared to liver or fat Fig. , consistp < 0.0003) or liver (p < 0.00001) tissues, suggesting that differential ADAR expression may contribute to differences in candidate RNA editing sites between tissues.One factor that may contribute to tissue specificity of RNA editing is differential expression of ADAR . Using Ri we computed the probability of observing at least 5 “edited” reads given the cDNA sequencing depth Ni and average sample mapping quality MQi. Among all 8550 repetitive and non-repetitive mismatch positions, the maximal probability of observing at least 5 “edited” reads was ~ 6.772e-15 for a site with N = 13 and average MQ = 29. If Bonferroni correction is used then 0.05 / 189,638 = 6.23e-07 can be used as a threshold for transcriptome-wide significance, where 189,638 was the total number of queried cDNA positions with a sequencing depth of at least 5 cDNA reads that were at the location of homozygous loci in the genomic sequence. From this evidence we conclude that our pipeline sufficiently minimizes artifacts associated with mapping quality when using the Sus scrofa 10.2.69 assembly.After imposing such strict restrictions as excluding genomic DNA and cDNA reads that had more than one recorded alignment and trimming the ends of reads pre-alignment, we wanted to assess how well such measures protect against mapping errors, which are among the leading causes of RNA editing misidentification when using short reads , 21. Mapnuclear prelamin A so that an Alu element is included in an exon [GRIA2 and BLCAP, both found to be edited in fat tissue, were previously identified as genes edited in fetal pig brain [COPA, Y/C in BLCAP, I/V in COG3, K/R in NEIL1, and Q/R in GRIA2. Interestingly, Y/C recoding of BLCAP via RNA editing has been associated with hepatocellular carcinoma (HCC) in humans as HCC samples were shown to express edited BLCAP in significantly higher amounts than non-HCC samples [NEIL1 by RNA editing was previously thought to be primate specific and attributed to the K/R site’s proximity to Alu dense regions [NEIL1 K/R recoding across genomes with entirely different SINE elements.Little is known about the average effect of RNA editing transcriptome wide. For humans, one prevailing hypothesis is that the exonization of Alu SINE elements is controlled in part by A-to-G editing. An instance of this mechanism has been demonstrated, where intronic A-to-G editing events contribute to alternative splicing of  an exon . To expl an exon , nearly  an exon , coding  an exon . From ouig brain . It can ig brain  can be f samples . Additio regions , howeverSince properties of the primate Alu element are suggested to influence RNA editing in both coding and non-coding regions, one of our primary interests was to determine which SINE elements in pigs are capable of attracting the majority of ADAR activity. Again using the functionality of editTools, we merged our mismatch data with data from RepeatMasker to determine which repetitive regions contain putative RNA editing sites. As mentioned previously, 5993 out of 6410 A-to-G mismatches are located within the body of a repetitive element. Upon closer inspection, 5715 of the 5993 are within pig SINE elements as opposed to LINE elements and others Fig. , althoug6, consensus length of 246 bp, and very little diversity among such members as Pre0_SS. These features influence the secondary structure of the transcriptome, which in turn affect ADAR editable targets. Surprisingly, conservation of specific editing sites such as those in NEIL1 and BLCAP appears evident between human and pigs. Therefore, we hypothesize that transcriptome secondary structure may be conserved among mammals enough to preserve particular RNA editing sites, and that SINE elements, regardless of origin, may conform to certain positions and orientations in order to allow conservation to occur.While Alu elements enable substantial RNA editing among primate genomes, we show that non-Alu bearing genomes can also utilize RNA editing as a means to achieve a similar result. Our high-throughput scan suggests that pig transcriptomes are highly editable among PRE-1 SINE retrotransposons. PRE-1, an element derived from an ancestral tRNA, has similar features to the primate Alu, derived from an ancestral 7SL RNA; a copy number of 1x10By demonstrating that pig transcriptomes have potential to be highly edited, we propose that pigs may be a valuable model to understand the patterns of ADAR controlled RNA editing. Additionally, by shedding light on the pig editome, we can begin to understand the extent to which this phenomenon enhances pig genetic variation. Such sources of variation may one day provide valuable explanatory power for a variety of traits of interest to both biomedical and agricultural communities.2 population resulting from crosses between 4 F0 Duroc sires and 15 F0 Pietrain dams [From Michigan State University’s pig resource population (MSUPRP), an Fain dams , a singlain dams .Sus Scrofa 10.2.69 using Bowtie v2.2.1 [Sus Scrofa 10.2.69 using TopHat v2.0.12 [DNA reads from whole genome sequencing were trimmed for quality at the 3′ end using Condetri v2.2  with pare v2.2.1  with par v2.0.12  with parhttps://github.com/funkhou9/editTools. By default, find_edits scans each variant site to search for candidate RNA editing sites according to the five criteria required for sufficient evidence . Inference of differential ADAR expression was calculated by one-way ANOVA (main effect of tissue on ADAR expression), and Tukey HSD (pairwise comparisons of tissue means).Total RNA was isolated from liver, LD skeletal muscle and subcutaneous fat tissues from 34 MSUPRP pigs, including the pig chosen for sequencing, using TRIzol reagent (Ambion) according to the manufacturer’s instructions. Concentrations were measured using a NanoDrop spectrophotometer (Thermo Scientific), and quality and integrity were determined using an Agilent 2100 Bioanalyzer . Total RNA was reverse transcribed using random primers with the High Capacity cDNA Reverse Transcription Kit with RNase Inhibiter (Applied Biosystems) according to the manufacturer’s instructions. A pig ADAR Custom TaqMan Gene Expression assay was designed using the online Custom TaqMan Assay Design Tool (ThermoFisher Scientific). The assay was designed to span exons 2–3 of the pig ADAR gene (Accession No. NC_010446.4). Assays were performed in triplicate using 50 ng cDNA and the TaqMan Gene Expression Master Mix  in a StepOnePlus Real-Time PCR System (Applied Biosystems). Cycling conditions were 52 °C for 2 min and 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Relative expression values were obtained using the 2MQ across all samples at mismatch site MQ we can compute the probability of mapping error p according to:The average phred-scaled mapping quality N assuming no RNA editing can be modeled using the binomial distribution, where:It follows that the probability of observing 5 “edited” reads at a homozygous site with a cDNA sequencing depth of http://www.repeatmasker.org/species/susScr.html. This function utilizes a binary search algorithm implemented in C++ to process large RepeatMasker files efficiently. The function write_vep was used to generate Variant Effect Predictor input from a mismatch data object. The output of Variant Effect Predictor was merged with the mismatch data object using add_vep. Additional documentation for find_edits, write_vep, add_vep, add_repeatmask is available within editTools v2.1.The editTools function add_repeatmask was used to merge a mismatch data object (generated with find_edits) with susScr3, a Repeatmasker dataset available for download at:


Scientific Reports6: Article number: 3637110.1038/srep36371; published online: 11042016; updated: 12012016In this Article, Haitang Yang is incorrectly listed as being affiliated with the ‘Department of Pathology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 230032, China’. The correct affiliation is listed below:Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 230032, China.


We are remarkably adept at inferring the consequences of our actions, yet the neuronal mechanisms that allow us to plan a sequence of novel choices remain unclear. We used functional magnetic resonance imaging (fMRI) to investigate how the human brain plans the shortest path to a goal in novel mazes with one  or two (deep maze) choice points. We observed two distinct anterior prefrontal responses to demanding choices at the second choice point: one in rostrodorsal medial prefrontal cortex (rd-mPFC)/superior frontal gyrus (SFG) that was also sensitive to (deactivated by) demanding initial choices and another in lateral frontopolar cortex (lFPC), which was only engaged by demanding choices at the second choice point. Furthermore, we identified hippocampal responses during planning that correlated with subsequent choice accuracy and response time, particularly in mazes affording sequential choices. Psychophysiological interaction (PPI) analyses showed that coupling between the hippocampus and rd-mPFC increases during sequential  planning and is higher before correct versus incorrect choices. In short, using a naturalistic spatial planning paradigm, we reveal how the human brain represents sequential choices during planning without extensive training. Our data highlight a network centred on the cortical midline and hippocampus that allows us to make prospective choices while maintaining initial choices during planning in novel environments. Using neuroimaging and computational modelling, this study explains how the human brain represents initial versus subsequent choices during spatial planning in novel environments. We are remarkably adept at inferring the consequences of our actions, even in novel situations. However, the neuronal mechanisms that allow us to plan a sequence of novel choices remain a mystery. One hypothesis is that anterior prefrontal brain regions can jump ahead from an initial decision to evaluate subsequent choices. Here, we examine how the brain represents initial versus subsequent choices of varying difficulty during spatial planning in novel environments. Specifically, participants visually searched for the shortest path to a goal in pictures of novel mazes that contained one or two path junctions. We monitored the participants’ brain activity during the task with functional magnetic resonance imaging (fMRI). We observed, in the anterior prefrontal brain, two distinct responses to demanding choices at the second junction: one in the rostrodorsal medial prefrontal cortex (rd-mPFC), which also signalled less demanding initial choices, and another one in the lateral frontopolar cortex (lFPC), which was only engaged by demanding choices at the second junction. Notably, interactions of the rd-mPFC with the hippocampus, a region associated with memory, increased when planning required extensive deliberation and particularly when planning led to accurate choices. Our findings show how humans can rapidly formulate a plan in novel environments. More broadly, these data uncover potential neural mechanisms underlying how we make inferences about states beyond a current subjective state. Goal-directed behaviour rests on being able to rapidly evaluate the potential consequences of future actions. For example, consider the neuronal processing required for planning a new route home when a road you normally take is closed. Although previous studies have implicated anterior prefrontal regions in planning –5, it haHere, we ascertained whether different anterior PFC regions signal uncertainty about novel sequential choices in a distinct manner during plan formation. Specifically, we were interested whether rostrodorsal medial PFC (rd-mPFC), a brain region associated with imagining/simulating potential choices –10, mighWe created a spatial planning task that would require little to no learning in which participants could call on an internal model of space deployed during exploration of the physical world . Our tasn = 29) during the fMRI experiment, with an average RT of 492 ms (SD = 150 ms). In deep mazes, when participants were prompted with choices that were at junctions deeper in the maze , they made correct choices 84.9% (SD = 9.89%) of the time, with an average RT of 671 ms (SD = 172 ms). There was no significant difference (t(28) = 1.84; p = 0.077; SD = 6.62%) in behavioural accuracy (percentage of correct choices) between deep  and shallow trials . In contrast, there was a significant difference in RT (t(28) = 14.3; p < 0.001; SD = 39.3 ms), with greater RTs for deep  versus shallow trials . Notably, mean RTs were not correlated with accuracy across participants .Participants made correct choices 84.0% of the time  but no similar linear trend for RT . Notably, we observed positive and negative significant linear trends with prospective path length differences for accuracy  and RT , respectively.Investigating the effect of path length differences on participant choice accuracy and RT in deep mazes, we observed a significant interaction between initial  and prospective  path length differences for both accuracy : 25.6; p < 0.001; p < 0.001; p < 0.001) with larger path length differences, whereas there was a significant negative trend with RT : 173.1; p < 0.05) main effect of the prospective choice path length difference : 6.57; p = 0.005; p = 0.007). However, we found no significant difference in prospective choice accuracy when split by the initial path length difference  = 0.887; p = 0.424; p = 0.005; p = 0.009; p = 0.021). However, we did not observe a significant linear decrease in RT when we split prospective choice RTs by the path length difference of the initial choice/starting point  = 3.1; p = 0.089). Taken together, these results suggest that the path length difference of the initial choice did not affect performance on prospective choices but did influence deliberation time .We then investigated the influence of path length differences at the initial choice point on prospective choice behaviour. Unsurprisingly, when participants were prompted with the prospective choice point, we observed a significant  at the statistical threshold of p < 0.005 uncorrected. The only exception is in the hippocampus, where all reported activations contain a peak-voxel that survives (p < 0.05) small-volume correction (SVC) for the bilateral hippocampus.We only report clusters that survive family-wise error (FWE) correction for multiple comparisons  changes related to choosing between the two paths at the second choice point that were not fully explained by path length differences at the initial choice point. We observed a very large cluster peaking in dorsal anterior cingulate cortex/pre-supplementary motor area  based on path length differences did not distinguish whether these fMRI results were due to the number of paths or the depth of planning. Indeed, when using a Shannon entropy model that compared RT-fitted uncertainty for prospective path length differences versus the absolute value of the difference between all four available paths lengths see .In the reverse contrast, larger path length differences at the prospective choice point elicited responses in pregenual anterior cingulate cortex/ventromedial PFC  but not lFPC (t(28): −0.468; p = 0.644; t-values related to other parametric regressor of interest). It is important to note that this vmPFC cluster only responding to large initial path length differences . We found that larger unchosen path length differences engaged the right angular gyrus/TPJ  nor lFPC (t(28): 0.399; p = 0.693) significantly encoded the unchosen path, further suggesting that these regions corresponded to rapid sequential inference but not necessarily an exhaustive search of all possible paths.In contrast, smaller unchosen path length differences engaged dACC , peaking in the right hippocampus  influenced neural responses during planning, we investigated whether any fMRI signals during planning correlated with subsequent RT during the choice phase. During planning, fMRI signals in an extremely large portion of cortex—peaking in visual cortex—positively correlated with subsequent RT . The larp < 0.05) positive correlations with increased subsequent RT in right angular gyrus/TPJ (t(28) = 3.53; p = 0.002) and PCC (t(28) = 3.01; p = 0.006) regions relating to larger unchosen path length differences, which provides additional evidence that these regions prune unviable paths during deep planning trials. The only negative correlation with subsequent RT was in the insula extending into a large portion of white matter .We also observed similar significant responses in smaller clusters in middle temporal gyrus and dACC see . Likewisp < 0.001; We investigated which regions responded to the distance of the shortest available path length . We observed responses in inferior occipital cortex extending into right posterior hippocampus  and dlPFC . We did Next, we investigated whether during the planning phase there were any regions outside of the hippocampus whose responses correlated with subsequent RT for deep versus shallow planning trials differently. We found that visual cortex and right PMC correlated with increasing RT more strongly during shallow planning trials  but did p = 0.012; p < 0.05 at the whole-brain level). We did not observe any other significant clusters that coupled with the hippocampus anywhere else in the brain for deep versus shallow planning.We conducted a psychophysiological interaction (PPI)  analysisp = 0.005; To assess the functional relevance of hippocampal coupling with rd-mPFC during deep planning, we conducted a separate GLM analysis splitting deep planning trials based on whether the subsequent choice trial was answered correctly or not  during planning. Distinguishing the role of these two regions in planning prospective choices, we found that rd-mPFC most strongly responded to deep mazes with larger path length differences at initial/starting choice points and smaller path length differences at prospective choice points . In contHighlighting distinguishable contributions to prospective planning for medial versus lateral anterior prefrontal regions, we find that rd-mPFC responds to difficult prospective choices while maintaining easier initial choices, whereas lFPC responds to prospective path length differences without being significantly modulated by initial path length differences. These findings are in line with the perceived capacity of anterior PFC to exploit recent reward trends during value-guided choice  and spatThis lateral versus medial distinction parallels previous research on anterior PFC, where lateral areas are believed to process stimulus-independent  information, whereas medial areas are engaged by stimulus-oriented information . FurtherOur result showing increased lateral FPC responses to prospective path length differences might relate to the perceived function of FPC as a simultaneous evaluator of multiple options, perhaps due to a higher sampling capacity  than rd-mPFC. Simultaneous evaluation of multiple options is necessary whether a decision is a sequential choice problem or not and is supported by the putative role of FPC in the rapid learning of novel abstract rules  and counDecisions often rely on prospection during multi-step events in order to anticipate a potential outcome, which is a process commonly linked with hippocampal-based memory , with additional responses related to increased deliberation, longer distance between starting and goal locations, and, most prominently, subsequently incorrect choices. Taking into account the importance of the dACC in model updating –61, it iIn contrast with dACC, vmPFC responses did not relate to comparisons of all available path lengths. Although subgenual portions of vmPFC responded to larger path length differences at both initial and prospective choice points, we did not observe any vmPFC signals that correlated with subsequently correct choices or quicker subsequent RT. A potential explanation for this result could be that vmPFC initially helps locate task-relevant sub-goals and signals an update of the current state ,66.Our findings also uncovered parietal responses that parallel activations observed in dACC and vmPFC. Smaller path length differences at both initial and prospective choice points engaged structures like PPC that have previously been implicated in value-guided decision-making when there is surprise and/or time pressure ,67. NotaNotably, vmPFC, TPJ, and PCC responses to larger initial path length differences  overlap with a brain network commonly observed during value-guided choice ,73. SpecWe observed increased coupling between the hippocampus and rd-mPFC during sequential plan formation that also predicted subsequent performance. Notably, resting-state fluctuations in these same regions—along with angular gyrus and PCC—are also correlated and form the default network –78. PromWe present a task adapted from rodent spatial navigation that enabled us to elucidate core neural computations underlying our ability to make fast and robust multi-step inferences in the absence of prior learning –87. Our Thirty-four healthy adult participants performing the fMRI experiment gave informed written consent and were studied and compensated . Due to poor participant performance  in the fMRI experiment, we removed five participants, leaving a final sample of 29 participants . All participants were right-handed had normal or corrected-to-normal vision and reported good health with no prior history of neurological disease.http://www.vislab.ucl.ac.uk/cogent.php) toolbox running in MATLAB . Over the course of 220 trials, participants viewed 220 different mazes from a slightly tilted (overhead) viewpoint and later chose from first-person viewpoints within mazes generated using Blender (http://www.blender.org). All mazes had a starting location (a red square) towards the bottom of the maze and a goal location (a green square) further into the maze. Mazes differed by hierarchical depth : there were 110 mazes with two possible routes  and 110 mazes with four possible routes (deep mazes).Stimuli were presented using the Cogent  and then asked to determine the shortest path from a starting location (a red square) at the bottom of the screen to the goal location (a green square). The overhead view appeared on the screen for 3.25 s, after which a location (choice point) along the path was highlighted briefly for 250 ms with an orange circle. The choice point location could either be the starting location or, if there were four paths to the goal location, a second choice point. Crucially, participants would only have to make a decision about one choice point for each trial. At any choice point, it was necessary to choose between two different directions, which could be left, forward, or right, with an additional option to select equal, if both routes were the same distance. No second choice points with two incorrect choices were ever chosen, only a second choice point along the optimal path after the starting location could be chosen . After the choice point was highlighted, a “zoomed in” viewpoint of this location  was presented. Depending on the possible direction at the location, participants had less than 1,500 ms to decide whether to go left, forward, right, or occasionally either direction. If no button press was made within 1,500 ms, the trial counted as an incorrect trial and the experiment moved on to the 1500-ms intertrial interval (ITI) phase. Participants never received any feedback or reward for making the correct choice. As soon as participants chose a direction, the ITI phase of a trial began. Participants repeated this trial sequence 110 times per session, for a total of two sessions. Sessions lasted approximately 10–15 min. Session order was counterbalanced between participants.All participants completed a brief practice session consisting of 40 mazes/trials before the experiment (on a laptop outside of the scanner). Deep mazes contained another branch/choice between routes further in the maze, and the path length to reach the two choice points further in the maze was always equal. Mazes had square tiled floors and were 8 x 8, 9 x 9, or 10 x 10 squares in total area. In shallow mazes, path length differences were split between 2, 4, and 6, with one catch trial per session having equal path lengths. In deep mazes, path length differences were split between 2 , 4 (medium difference), or 6 (large difference) squares  was acquired to co-register and display the functional data.Functional images were acquired on a 3T Siemens Trio scanner. BOLD T2*-weighted functional images were acquired using a gradient-echo EPI pulse sequence acquired obliquely at 45° with the following parameters: repetition time, 3,360 ms; echo time, 30 ms; slice thickness, 2 mm; inter-slice gap, 1 mm; in-plane resolution, 3 × 3 mm; field of view, 64 × 72 mmired EPI . After twww.fil.ion.ucl.uk/spm). The first five volumes were discarded to allow for T1 equilibration. Standard preprocessing included correction for differences in slice acquisition timing, realignment/unwarping to correct for inter-scan movement, and normalization of the images to an EPI template (specific to our sequence and scanner) that was aligned to the T1 Montreal Neurological Institute (MNI) template. Finally, the normalized functional images were spatially smoothed with an isotropic 8-mm full-width half maximum Gaussian kernel. For the model described below, all regressors, with the exception of six movement parameters of no interest, were convolved with the SPM hemodynamic response function. Data were also high-pass filtered (cut-off period = 128 s). Statistical analyses were performed using a univariate GLM with a rapid event-related experimental design.Functional images were processed and analysed using SPM8 : for the two shortest paths present at the starting point , the path length difference between the shortest path and the longest unchosen path length that was not available at the second choice point (Unchosen Path Length Difference), the path length difference at the second choice point (Prospective Path Length Difference), log RT for the subsequent decision phase (Log RT), length of the shortest available path (Length of the Shortest Path), whether the participant made a correct choice during the subsequent choice phase (Performance), and whether the first or second choice point was prompted for deep maze trials  based on a bilateral ROI of the hippocampus constructed using the SPM Anatomy toolbox [p = 0.005 and cluster-level correction for multiple comparisons (FWE p < 0.05), unless indicated otherwise. We also mention whether any significant clusters had a very large cluster extent , and the cluster extent for every significant effect is reported in Given the previously hypothesized role of the hippocampus in spatial planning, we report whether hippocampal peak-voxels survive SVC for multiple comparisons ( toolbox –92. For S1 Text(DOCX)Click here for additional data file.S1 Fig(A) Accuracy when participants are prompted on the prospective/second choice point further in the maze. Left: Fraction of correct prospective choices split by whether there was a small, medium, or large path length difference at the prospective choice point. Right: Fraction of correct prospective choices split by whether there was a small, medium, or large path length difference between the two shortest options available at the first initial choice point. (B) Log RT when participants are prompted on a choice point further in the maze. Left: Log RT for prospective choices split by whether there was a small, medium, or large path length differences at the second choice points. Right: Log RT for prospective trials split by whether there was a small, medium, or large path length difference between the two shortest options available at the first choice point.(PDF)Click here for additional data file.S2 Figp < 0.05 for multiple comparisons and is shown at p < 0.005 uncorrected.Anterior prefrontal peak for initial  versus prospective path length differences. Images showing dlPFC peak of cluster (circled in blue) also containing rd-mPFC sub-peak that significantly responded to small prospective versus small path length differences in shallow mazes see . dlPFC c(PDF)Click here for additional data file.S3 Figp < 0.05.Top: Sagittal image centred on vmPFC activation that responded to large initial path length differences. Regions responding to large initial path length differences shown in orange and regions responding to small initial path length differences shown in blue. Bottom: Effect size for 8-mm sphere around the vmPFC peak for initial, prospective, and unchosen path length differences. Asterisk signifies (PDF)Click here for additional data file.S4 FigImages centred on rd-mPFC that correlated with smaller distance between the starting and goal location .(PDF)Click here for additional data file.S5 Figp < 0.05) in the correlation with subsequently incorrect choices for deep versus shallow planning trials (mean ± SEM). Asterisks indicate a significant correlation (p < 0.05) with path length differences. A negative effect size represents a correlation with incorrect trials, whereas a positive effect size represents a correlation with correct trials. (B) Evoked dACC/pSMA BOLD response during planning separated by subsequently correct and incorrect choice trials (mean ± SEM).(A) Left: Sagittal image showing dACC/pSMA activity during planning that was higher prior to incorrect versus correct choice trials. Right: Effect size for an 8-mm sphere around the dACC/pSMA peak voxel showing that there is no significant difference ((PDF)Click here for additional data file.S6 Fig(A) Images centred on PPC region that significantly responded to smaller initial path length differences in shallow versus deep mazes. (B) Images centred on left PPC region that significantly responded to subsequently incorrect choices in shallow versus deep mazes. (C) Images centred on right inferior temporal cortex region that responded to increasing distance between the starting and goal location in deep versus shallow mazes.(PDF)Click here for additional data file.S1 TableThe first and second columns report the model variant and the corresponding Bayesian information criterion . A small BIC reflects greater (log) model evidence.(DOCX)Click here for additional data file.S2 TableSeveral models of RT were compared using the BIC after penalizing for the number of parameters. A smaller BIC value indicates that a model has higher evidence, after penalizing its accuracy for its complexity or number of parameters.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableModel-free regressors.(DOCX)Click here for additional data file.S5 TableShannon entropy model-based regressors.(DOCX)Click here for additional data file.S6 TablePPI correct or incorrect choice regressors.(DOCX)Click here for additional data file.S7 Tablep < 0.005 activation threshold, cluster-based threshold p < 0.05), many of the activations are very large  and span multiple brain regions. Consequently, the labels assigned to each cluster should be interpreted with caution.List of peak voxels for clusters found in the initial path length difference contrast. Please note that despite our stringent threshold ((DOCX)Click here for additional data file.S8 Tablep < 0.005 activation threshold, cluster-based threshold p < 0.05), some activations are very large  and span multiple brain regions. Consequently, the labels assigned to each cluster should be interpreted with caution.List of peak voxels for clusters found in the prospective path length difference contrast. Please note that despite our stringent threshold ((DOCX)Click here for additional data file.S9 TableList of peak voxels for clusters found in the Unchosen Path Length difference contrast.(DOCX)Click here for additional data file.S10 Tablep < 0.005 activation threshold, cluster-based threshold p < 0.05), some activations are very large  and span multiple brain regions. Consequently, the labels assigned to each cluster should be interpreted with caution.List of peak voxels for clusters found in subsequent choice RT contrast. Please note that despite our stringent threshold ((DOCX)Click here for additional data file.S11 Tablep < 0.005 activation threshold, cluster-based threshold p < 0.05), some activations are very large  and span multiple brain regions. Consequently, the labels assigned to each cluster should be interpreted with caution.List of peak voxels for clusters found in the length of shortest available path contrast. Please note that despite our stringent threshold ((DOCX)Click here for additional data file.S12 TableList of peak voxels for clusters found in the accuracy contrast.(DOCX)Click here for additional data file.S13 Tablep < 0.005 activation threshold, cluster-based threshold p < 0.05), some of the activations are very large  and span multiple brain regions. Consequently, the labels assigned to each cluster should be interpreted with caution.List of peak voxels for clusters found in contrasts for deep versus shallow interactions related to different parametric regressors. Please note that despite our stringent threshold ((DOCX)Click here for additional data file.S1 DataData underlying plots in (XLS)Click here for additional data file.S2 DataData underlying plots in (XLS)Click here for additional data file.S3 DataData underlying plots in (XLS)Click here for additional data file.S4 DataData underlying plots in (XLS)Click here for additional data file.


We examined how and to what extent structural intervention measures that support choice-disabled individuals can reduce HIV transmission and prevalence.Despite massive investment in HIV control programs, HIV incidence rates in countries with generalized epidemics have not fallen for most of the last decade. It appears that those at risk are not adopting effective prevention strategies. Those who are unable to implement their prevention preferences are referred to as A mathematical model was developed to describe HIV transmission among and between choice-disabled and choice-enabled individuals. Data were available from field trials identifying factors and effects of choice disability. The model was used to estimate the potential impact of an intervention strategy in which choice-disabled individuals are enabled to make prevention choices. Several scenarios were considered and compared: supporting only one or both genders; supporting only HIV– individuals or also HIV+ choice-disabled individuals.Substantial declines in HIV incidence and prevalence are observed when supportive interventions are included in the model. The magnitude of these declines depends on the scope of the intervention program. The largest positive effect occurs when the support program is offered regardless of HIV status.Addressing the effects of choice disability in any HIV intervention program could be crucial to the program’s success. Structural intervention programs to support choice-disabled individuals in implementing prevention strategies greatly reduce HIV incidence and prevalence in mathematical models. The successes in countering the generalised HIV epidemics in countries with the highest HIV rates like Botswana and Swaziland seem to have bottomed out . For moschoice disabled describes the inability of some people to implement prevention measures that might otherwise be very logical [Efforts to interpret these trends—excessive risk for young women and bottoming out of incidence despite massive investment in conventional HIV control programmes—suggest that those at risk are not adopting effective prevention strategies. The term  logical . Severel logical . In the How much choice disability matters depends on the prevalence of choice disability and the relevance of HIV preventive strategies in this group. The iconic view of HIV prevention however, implies that people have the choice to abstain, chosen rather than forced sexual debuts, options to choose the number of partners, and the ability to insist on use of condoms. These strategies are much less relevant to the choice disabled than to their choice-enabled peers.Observing the failure of conventional prevention strategies like promoting condoms to halt the epidemic, recent years have seen a surge of interest and investment in combination and structural interventions that change the objective position of those who are most at risk , 5. ManyIn the current study, a mathematical model is employed to describe heterosexual HIV transmission in a generalized HIV epidemic. The model is based on a highly successful class of disease transmission models that have been applied to a wide variety of infectious diseases, including many aspects of HIV. Recently such models have been used to consider the effects of condom use in HIV control , treatmeWe use a mathematical model to investigate the potential results of a structural intervention aimed at individuals who are not in a position to implement their prevention choices, i.e. those who are choice disabled.The model focuses on heterosexual transmission of HIV between sexually active 15 to 29 year olds in Africa. The model identifies each individual in three categories: sex , disease status (susceptible/infectious), and choice status (enabled/disabled), producing eight subgroups. Mathematically, the model consists of a system of differential equations that track the number of individuals in each subgroup over time as individuals enter or leave the population, susceptible individuals become infectious, and individuals become choice-disabled or regain choice-enabled status through structural interventions. Each of these processes is described below and the model is summarized by Demographic Processes: Individuals enter one of the eight subgroups when they become sexually active and leave the population when they turn 30 years old or cease being sexually active.Disease Transmission: A susceptible woman can become infectious through sexual activity with an infectious man, choice enabled or disabled, and similarly, susceptible men can become infectious through sexual activity with infectious women of either choice class.activity patterns that describe the number of sexual partners that an individual has from within each of these communities. We consider several possible activity patterns.As choice status is often associated with social or economic class we do not expect that individuals choose sexual partners independent of choice status. Instead, individuals of different choice classes participate in overlapping communities or social groups, such as those based on social and educational background. To describe the resulting mixing of individuals mathematically, we define The probability of disease transmission in the event of sexual contact between an infectious and susceptible individual depends on gender and choice status. Risk can be greatly reduced through condom use, or increased by engaging in dry or anal sex –18. ThosAcquiring Choice Disability: While some individuals are already choice disabled at the age of 15 when they enter the sexually active population in our model, others will acquire choice disability sometime between the ages of 15 and 29. In this model, a fraction of the choice enabled population will acquire choice disability every year.Structural Intervention: The structural interventions of the Botswana INSTRUCT program aim to reduce the impact of choice disability by putting choice-disabled individuals in a position where they share the benefits of the choice-enabled.We implement such interventions in the model by moving a fraction of individuals from the choice-disabled to the choice-enabled group every year.SEF), the number of enabled susceptible women, the equation readsThe equations track how the number of individuals in each of the eight subgroups changes over time. These expressions relate the instantaneous rate of change of the size of each subgroup on the left hand side to the different processes producing this change. For (N0) multiplied by the fraction (eSEF) of individuals who enter the group SEF. The second term describes the number of individuals leaving as a leaving rate (lSEF) multiplied by the number of SEF. The third term is the infection term that reduces the number of SEF by the contact rate (cEF) multiplied by the total number of SEF and the probability that a single contact leads to infection (Pr(SEF → IEF). The last term accounts for SDF who become enabled by the rate of support (sSF) that they receive.In this equation, the first term describes the number of individuals entering the sexually active group as the total number of individuals entering . In the first activity pattern, a fraction tEM→EF.To interpret this expression, notice that a member of The fraction of contacts of an enabled female with an enabled male in the first activity pattern according to proportionate mixing  is the nTo simulate the outcome of the model, we need to specify parameter values for all of these processes. Some of these values are reported in the literature, for others, only ranges are available.Of the approximately 50,000 fifteen year olds in Botswana, roughly 80% have debuted sexually, so that about 40,000 individuals enter the population in our model each year. We assume a 50:50 sex ratio. According to a recent study from Botswana , half ofAs the model considers a 15-year age range, about 1/15 = 6.67% of the population leaves each year.A typical rate for sexual contacts in the literature is approximately 80 per year. For example, Gray et al.  found thBoily et al.  conducteHigher risk sexual behaviour increases transmission probabilities. For example, dry sex is very common in South Africa, especially in younger individuals with low education , and it For sexual contacts between choice enabled women and choice disabled men, we assume condom use 65% of the time, but half of the non-condom contacts will be high-risk activities such as anal or dry sex. Then the transmission probabilities are 0.0014 from women to the men and 0.0041 from men to the women.For contacts between choice enabled men and choice disabled women, we assume that all of the non-condom sexual activity is high risk. This results in a transmission probability of 0.0068 from men to women and 0.0017 from women to men.When both partners are choice disabled, we assume that condoms are used half as often as in the previous cases, and that 35% of sex acts are high risk. Then the transmission probabilities are 0.0079 from men to women and 0.0026 from women to men.To be consistent with our choice of model parameters, initial conditions are also taken from data collected in Botswana , and theWe now define the activity patterns that model how individuals in the various choice classes interact. We consider a scenario where people interact within two overlapping communities: a high-status community dominated by the choice enabled and a low-status community dominated by the choice disabled. We describe two different scenarios.In the first scenario individuals mostly interact with those of similar backgrounds. Choice enabled individuals make 80% of their sexual contacts within the high status community and the remaining 20% in the low status community. Meanwhile, choice disabled individuals show the reverse pattern making only 20% of their sexual contacts in the high-status community and 80% of their contacts low-status community.In the second scenario, transactional sex is included by considering an activity pattern where choice-enabled individuals more often seek sexual encounters with those who are choice disabled. The choice enabled make only 60% of their contacts within the high-status community and the remaining 40% within the low-status community.The model is used to consider a structural intervention that moves a constant fraction of the choice-disabled population into the choice-enabled population every year. In the model, this fraction can range between 0 and 13%, depending on the strength of the intervention program. The corresponding rate parameter in the differential equation model ranges from 0 to 2 per year since exp(−2) ≈ 13%. Interpreted differently, these figures imply that individuals are on average choice enabled for at least 6 months before they benefit from intervention.All parameters, together with their meaning and base numerical value as used in model simulations are listed in atlab). Various scenarios involving contact rates, activity patterns, and level of intervention support are considered.The model is used to estimate HIV prevalence and annual incidence rate over a 15 year period. Standard numerical software is used to solve the differential equations  is estimated at 34.0% (19.3%) with no intervention. When an intervention targeting HIV-negative individuals is implemented, this estimate drops to 19.3% (11.9%) with an intervention where individuals remain choice disabled for 1 year on average. When both HIV positive and HIV negative individuals are included in the intervention, the estimated HIV prevalence is reduced further to 15.0% (9.6%)When the overall contact rate is higher in choice disabled individuals then concentrating intervention on non-infections individuals only has a smaller effect than including all disease classes. In fact, even very strong support programs that target only the HIV-negative population lead to a higher prevalence after 15 years than moderate programs that include HIV-positive individuals.More insight about the influence that each mechanism and intervention has on HIV prevalence after a 15 year period is found in the sensitivity analysis summarized in In general, the results of the sensitivity analysis are as expected. Increasing the number of contacts made by individuals in any of the choice and sex classes increases HIV prevalence. Similarly, increasing any of the transmission coefficients increases HIV prevalence. Increasing the intervention support level for any population results in a decrease in HIV prevalence. Due to their large correlations, the support levels are highlighted as most important for HIV prevalence while several of the contact rates are also important.For a more detailed view of the changes in prevalence over the 15 year time period that we have been considering, and to see the effects of different activity patterns, see The four plots of panel B show the estimated prevalence in each choice and sex class when an intervention providing support for the choice disabled is implemented. HIV prevalence in the choice-enabled classes is initially seen to increase rapidly as choice disabled people with high HIV prevalence are moved into the choice-enabled classes. Meanwhile, HIV prevalence in the choice-disabled classes drops. Very little difference is seen between the two activity patterns in this case.The model in the current study is novel in that distinguishes between the choice disabled and the choice enabled, and it includes decreased ability of the choice disabled to make decisions to lower their risk of HIV. The model allows testing potential impacts and estimating effects of structural intervention programs for the choice disabled on HIV incidence and prevalence.This mathematical model is the first to include choice disability in a disease transmission model. It is a high-level model to show proof of concept, independent of the particular causes and effects of choice disability and disregarding different levels of choice disability. The model illustrates how choice disability might explain the excessive risk among young women and how choice disability contributes to the limited impact of HIV prevention programs in sub-Saharan Africa.The model results suggest a promising role for structural programs that ameliorate the effects of choice disability in HIV transmission prevention. It seems likely that differently implemented intervention programs will have different results. The modelled simulations suggest that the best results are achieved when those who are HIV positive benefit along with their HIV negative peers. The benefits of these types of programs may be particularly pronounced when the choice disabled have more sexual contacts than the choice enabled.An examination of the sensitivity results for the model confirms that structural intervention programs could play an important role in HIV prevalence reduction, but that other factors, particularly the number of sexual contacts made by each group, should not be ignored in designing intervention programs.Structural programs to support the choice disabled may also neutralize some of the HIV transmission effects of transactional sex. Including transactional sex into the model without an intervention program increased the overall HIV prevalence in the choice enabled population. However, when an intervention program is included the estimated prevalences were very similar with and without transactional sex.There are several limitations to the current model. For simplicity, only two choice classes were considered: enabled and disabled. However, the literature shows that there are multiple levels of severity of choice disability that increase HIV risk farther. Two other factors that were also excluded to simplify the model are homosexual sexual activity and intravenous drug use. The extent to which these two factors contribute to transmission varies considerably with geographic region. Our simplification is consistent with a recent review study that showed that heterosexual transmission dominates in sub-Saharan Africa, whereas drug use and sex work contribute significantly in North Africa and the Middle East, and homosexual sexual activity is the greatest contributor in Latin America . FinallyTo parameterize the model, data was pooled from different countries and demographics, but very few of these studies address choice disability directly. The model uses the Botswana population as illustrative and would need adjustment for other contexts.Future work will be needed to deal with these complexities. Models can be developed that take choice disability into account along with other factors such as multiple transmission pathways, treatment programs, or more detailed effects of choice disability and HIV infection. Such models could provide a more nuanced picture of the causes of choice disability and its effects on HIV transmission.The model was also developed for a high transmission/heterosexual behavior setting at the epicentre of the HIV pandemic. Therefore, careful consideration is needed to apply the model to lower transmission setting such as East Africa or to the concentrated epidemics of North America and Europe, where, in addition, homosexual transmission is often the driver of the epidemic .


Escherichia coli on a global scale. UV crosslinking immunoprecipitation and sequencing (CLIP-seq) identify RNAs that interact directly with CsrA in vivo, while ribosome profiling and RNA-seq uncover the impact of CsrA on translation, RNA abundance, and RNA stability. This combination of approaches reveals unprecedented detail about the regulatory role of CsrA, including novel binding targets and physiological roles, such as in envelope function and iron homeostasis. Our findings highlight the integration of CsrA throughout the E. coli regulatory network, where it orchestrates vast effects on gene expression.CsrA is a post-transcriptional regulatory protein that is widely distributed among bacteria. This protein influences bacterial lifestyle decisions by binding to the 5′ untranslated and/or early coding regions of mRNA targets, causing changes in translation initiation, RNA stability, and/or transcription elongation. Here, we assess the contribution of CsrA to gene expression in  E. coli, revealing new binding targets and physiological roles such as in envelope function and iron homeostasis.The RNA-binding protein CsrA regulates the expression of hundreds of bacterial genes. Here, Potts et al. use several approaches to assess the contribution of CsrA to global gene expression in  Although the Escherichia coli transcriptional regulatory networks have been thoroughly studied4, the contribution of post-transcriptional regulation to global gene expression patterns even in this model species remains to be clearly understood. Integrated and overlapping transcriptional and post-transcriptional networks mediate complex responses to environmental cues and signals5, which are difficult or impossible to predict without detailed knowledge of the circuits. Thus, the relative dearth of information about post-transcriptional regulatory circuitry has hampered the predictive value of known networks. E. coli possesses many post-transcriptional regulators, including small RNAs (sRNAs)6, ribonucleases7, RNA helicases8, as well as CsrA9, Hfq10, and other RNA-binding proteins11.Free-living bacteria recognize and adapt to changes in their environment through complex interconnecting regulatory networks12, carbon metabolism14, and virulence of bacterial pathogens15. In general, CsrA activates processes that support exponential growth while it represses stationary phase and stress responses, such as biofilm formation and the stringent response17. CsrA offers an ideal model for investigating the global influence of bacterial post-transcriptional regulators due to its conserved binding motif, detailed understanding of its regulatory mechanisms, and its broad phylogenetic distribution18. CsrA typically binds to sites containing critical GGA motif(s) in the 5′ untranslated region (5′-UTR) and/or the early coding region of its messenger RNA (mRNA) targets, causing changes in RNA structure, translation, stability, and/or transcription elongation9. Regulation of CsrA activity is multifactorial, but is mediated largely through molecular mimicry by inhibitory sRNAs, CsrB, and CsrC16. Using numerous high-affinity CsrA binding sites, these sRNAs sequester CsrA from its lower affinity mRNA targets. Thus, CsrB/C synthesis and turnover govern expression of the Csr regulon19.CsrA (RsmA) is the key component of the Csr (carbon storage regulator) system, which coordinates numerous complex physiological processes, including motilityE. coli during mid-exponential phase growth in LB using next-generation sequencing approaches. UV crosslinking immunoprecipitation and sequencing (CLIP-seq) are used to identify RNAs that interact directly with CsrA in vivo. Ribosome profiling and RNA-seq are used to gage the impact of CsrA on translation, RNA abundance, and RNA stability. For the gene expression studies, we compare a csrA wild-type (WT) and csrA::kan mutant strain (hereafter csrA mutant), which expresses an impaired CsrA protein, with eight-fold lower affinity for RNA12. The WT and csrA mutant strains grow similarly in rich media, unlike a csrA deletion mutant, which grows slowly and rapidly accumulates suppressor mutations20. Changes in gene expression caused by this csrA mutation likely underestimate the magnitude of CsrA regulatory effects. The integrated application of these approaches provides unprecedented detail of the role of CsrA, revealing vast effects on the expression of structural and regulatory genes throughout the E. coli regulatory network.Here, we investigate the CsrA regulon of 21. It employs UV light to covalently crosslink proteins directly to RNA without crosslinking protein to protein, thus avoiding large covalent complexes common to formaldehyde crosslinking and facilitating the stringent purification of crosslinked RNA. To identify CsrA binding partners in vivo, we conducted CLIP-seq on five biological replicates of both 3xFLAG-CsrA and untagged control strains . This is consistent with a previous systematic evolution of ligands by exponential enrichment study, which revealed that both the consensus sequence ruACArGGAugu and GGA sequence localization in the loop of a stem-loop were required for high-affinity binding24.Sequence and structural motif analysis identified an AuGGAug motif exposed within a short stem-loop structure as highly enriched in the CsrA CLIP-seq peaks Fig. . This mo25, was not enriched in our analysis. Seven peaks were identified in 3′-UTRs of mRNAs, something that to our knowledge has not been studied previously. Thus, we confirmed the in vivo data for one of these 3′-UTRs by testing CsrA binding in vitro. The 3′-UTR of ybjX was chosen for this analysis because its downstream gene is divergently oriented and there is no evidence in our RNA-seq data for expression of an sRNA from this region .A CsrA binding site located 60 nucleotides (nt) within the  nt Fig. , where CCDS Fig. . Recent 29. We used ribosome profiling and paired RNA-seq to analyze differences in active translation, steady-state RNA abundance, and translational efficiency (TE) between the WT and csrA mutant strains. TE is an indicator of post-transcriptional regulation, which is assessed as an interaction term between changes in translation and RNA abundance, i.e., measuring translation while controlling for changes in RNA abundance.Despite the established role of CsrA in regulating translation initiation, until recently there has not been an approach to analyze this on a global scale. Ribosome profiling has emerged as a way to pinpoint the positions of actively translating ribosomes by sequencing the RNA that they protect from nuclease digestion, referred to as ribosome-protected fragments (RPF) Fig. 29. We uscsrA mutant and WT strains in the 3586 protein-coding genes detected. Translation of 1188 genes, as measured by changes in RPF abundance, was significantly different in WT and csrA mutant strains  data were generally well correlated, indicating that our RNA-seq analysis of RNA stability was reliable  for this result were not addressed34. Altogether, there were 203 RNAs with significantly altered stabilities between the WT and csrA mutant strains out of 2564 genes analyzed . Supplementary Fig. csrA mutant strains, although the origin of this pause distribution is not known. There were no differences in the frequency or strength of pauses between the WT and csrA mutant strains globally or associated with CLIP-seq peaks, indicating that CsrA does not regulate ribosome pausing . Some examples of CLIP-seq peaks and associated ribosome pauses are shown in Supplementary Fig. A recent study indicated that CsrA binding entirely within the CDS prevented Rho-dependent transcription termination by stabilizing an alternative mRNA structurehfq30, pnp31, and dksA17, and/or contain conserved CsrA binding motifs. These observations suggest that spurious UV crosslinking is unlikely to be the source of such peaks. Some of these CLIP-seq peaks may represent authentic binding sites that do not affect gene expression under the growth conditions used in our analysis. Perhaps their regulation requires additional factors that are unavailable under this condition or is masked by autoregulation, the latter of which was shown previously for dksA17. Because the csrA mutant in these studies retains partial RNA-binding activity12, some transcripts might be regulated in the mutant strain via this residual activity. It is also plausible that some sites do not regulate the target’s expression but instead compete with CsrA binding to regulated targets, similar to CsrB/C or fimA mRNA of Salmonella9. In this regard, all CsrA binding sites within the transcriptome compete with one another for limiting CsrA16. Therefore the global composition and expression level of transcripts would affect the regulatory dynamics of the Csr system.Many genes associated with CLIP-seq peaks did not exhibit differential expression in our analyses Fig. . However9, and a few are explored in more detail in the following sections.Functions enriched in genes with CsrA CLIP-seq peaks are shown in Fig. 13. Recent transcriptomics and metabolomics studies have confirmed that CsrA activates glycolysis, while it represses gluconeogenesis and the TCA cycle14. However, the mechanisms used to mediate these changes have remained elusive. Metabolic flux analysis has suggested that phosphofructokinase, encoded by pfkA, is key to effects of CsrA on glycolysis38, but the molecular basis of this finding remained unclear. We identified a CLIP-seq peak in the 5′-UTR of pfkA and this mRNA was destabilized in the csrA mutant , galactitol (gatABC), N,N′-diacetylchitobiose (chbBCA), N-acetylglucosamine (nagE), and 2-O-alpha-mannosyl-d-glycerate (mngA), while it activates genes for sorbitol (srlABE) and N-acetylmuramic acid (murP) uptake , with regulation often occurring in the opposite direction , which allows E. coli to restructure its envelope in response to a variety of stressors42. RseA is an inner membrane-bound anti-sigma factor that antagonizes RpoE and is encoded just downstream of rpoE . We confirmed that CsrA specifically binds to the 5′-UTRs of both rpoE and rseA in vitro with apparent Kd values of 5 nM and 33 nM, respectively , ferrous iron transport (feoABC and efeUOB), and transport of iron chelates   Fig. . mRNAs esed Fig. . CsrA alsed Fig. , the sRNaks Fig. . ConceivftnA, which encodes the major iron storage protein in E. coli50  participate in oxidative stress resistance by sequestering iron52, donating iron to iron–sulfur clusters damaged by oxidation (FtnB)52, and by binding and protecting DNA from oxidizing Fenton chemistry (Dps)51. Although no CLIP-seq peaks were associated with these genes, the concurrent increase in ftnB, dps, and bfr expression in the csrA mutant may be sufficient to cause a decrease in free intracellular iron. This might lead to an accumulation of apo-Fur in the csrA mutant and the observed changes in iron-related gene expression. However, this hypothesis remains to be further explored. Due to the crucial role of iron scavenging during growth and especially during infection46, it is conceivable that CsrA effects on iron homeostasis contribute to fitness in the host environment.To this end, we identified several differentially expressed genes involved in iron storage that may affect free intracellular iron accumulation. Although the effect was modest, CsrA activated the expression of 50 Fig. . On the bfr Fig. , which ince Fig. . Intrigunts Fig. . These fcsrA mutant without associated CLIP-seq peaks , perhaps contributing to these effects. We also examined effects on sRNAs as a source of post-transcriptional regulation. CLIP-seq analysis identified peaks overlapping 8 sRNAs, and CsrA affected the abundance of 11 sRNAs 53. The mRNA targets of GcvB showed complex responses to CsrA, several of which likely involve direct CsrA binding  or gentamycin (10 μg mL−1) when appropriate. Glycerol stocks were used to inoculate 2.5 mL of LB, and cultures were grown overnight at 37 °C with shaking at 250 rpm. Fresh media were inoculated from these overnight cultures to an OD600 of 0.01, and samples were collected at OD600 of 0.2 , 0.5 , 1.0 (transition to stationary phase), and ~4 (stationary phase), depending on the experiment. FLAG-tagged proteins were created using the λ red recombinase method, as described previously62.Bacterial strains used in this study are in listed in Supplementary Data Oligonucleotides synthesized for use in this study are listed in Supplementary Data 63 and Holmqvist et al.10 with modifications. Five biological replicates of 3xFLAG-CsrA and WT untagged control strains were grown to mid-exponential phase in 22 mL LB. Cells in the growth medium were then exposed to 400 mJ of UV at 254 nm, washed with PBS, and snap frozen in liquid nitrogen. Samples were thawed, resuspended in 2 mL of RIPA buffer  with an EDTA-free protease inhibitor cocktail (Roche), and lysed using three rounds of sonication at 0.5 s on/0.5 s off for 10 s on ice. Lysates were cleared by centrifugation then treated with Turbo DNase (Ambion) and RNAse I (Ambion) at 37 °C for 5 min. Samples were treated with anti-FLAG M2 paramagnetic particles (Sigma M8823) to immunprecipitate CsrA for 4 h at 4 °C. The paramagnetic particles were washed three times with high-salt wash buffer (RIPA with 1 M NaCl), once with RIPA, and once with T4 polynucleotide kinase buffer (NEB). Samples were then radiolabeled with [γ32P]ATP using T4 polynucleotide kinase (NEB). Protein–RNA complexes were eluted from the beads with Laemmli buffer without β-mercaptoethanol, separated by 12% Bis Tris SDS–PAGE, and transferred to Protran 0.2 μm nitrocellulose membranes. RNA was eluted from the membrane by treatment with proteinase K (Roche). RNA was then purified with phenol-chloroform extraction and ethanol precipitation. The RNA was then depleted of ribosomal RNA (rRNA) with the Ribo-Zero rRNA Removal Kit for Gram Negative Bacteria (Epicentre), according to the manufacturer’s instructions. Sequencing libraries were generated with the NEBNext Small RNA Library Prep Kit (NEB), according to the manufacturer’s instructions with one modification. Adaptors and reverse transcription primers were diluted 1 in 5 in water before use. Libraries were sequenced using HiSeq 2500 50SE (Illumina) by the HudsonAlpha Genomic Services Laboratory.UV crosslinking and immunoprecipitation method was adapted from Huppertz et al.64 to remove adaptor sequences. Reads longer than 12 bases were then mapped to E. coli rRNA sequences with bowtie65 and unmapped reads were retained. The rRNA depleted reads were then mapped to the E. coli genome. Integrated genome viewer was used to visualize the alignments66. Duplicate reads were removed using Picard MarkDuplicates. The 3xFLAG-CsrA reads were merged into a single file and used as input into PIPE-CLIP67 to identify CLIP-seq peaks. These peaks were used to count reads per peak with HTseq count68 for each individual sample file. DESeq269 was used to identify peaks significantly enriched  in the 3xFLAG-CsrA samples over the untagged controls. The average length of the CLIP-reads was 17 bases and the significantly enriched CLIP-seq peaks was 136 bases.Sequencing reads were demultiplexed and trimmed with cutadapt70. CMfinder was used with default settings to identify the top enriched structural motif71. To compare the frequency of the AuGGAug in the CLIP-seq peaks to the E. coli genome, FIMO 72 software was used to identify this motif in both sets of sequences. A two-sided χ2-test was used to determine statistical significance of the enrichment of this sequence in CLIP-seq peaks.Zagros was used to identify enriched sequence motifs in the CLIP-seq peaks using both secondary structure and sequence information73 and Ingolia et al.21 with modifications. Briefly, five biological replicates of WT and csrA mutant cultures were growth to mid-exponential phase in 500 mL of LB. Cultures were treated with 100 μg mL−1 chloramphenicol for 5 min at 37 °C with shaking at 250 rpm then quick chilled with an equal volume of ice containing 100 μg mL−1 of chloramphenicol. Cells were washed with ice-cold wash buffer  and snap frozen in liquid nitrogen. Thawed cell pellets were resuspended in lysis buffer  and lysed by bead beating using 0.1 mm zirconium beads. Samples were vortexed for a total of 8 min in 1.5 increments. Lysate was centrifuged at 4 °C for 30 min at 13,000 rpm to remove cell debris in a tabletop microcentrifuge. Total RNA was extracted from lysates using the miRNeasy Kit (Qiagen), treated with Turbo DNase (Ambion), and purified with the RNeasy Kit (Qiagen). Lysates were diluted to contain an estimated 1.5 mg of RNA and were treated with 1000 U of micrococcal nuclease for 1 h at 25 °C then quenched with EGTA. This treated lysate was ultracentrifuged over a sucrose cushion (wash buffer plus 1 M sucrose) for 4 h at 72,000 rpm  at 4 °C in an MLA-130 rotor. RPF were then extracted from the pelleted ribosomes with the miRNeasy Kit (Qiagen). Sequencing libraries were prepared for RPF and RNA samples as described in Becker et al.73 except for using the PCR primers listed in Supplementary Data This method was adapted from Becker et al.64 to remove adaptor sequence from the 3′ end and the first base at the 5′ end that often appeared to be a result of untemplated base addition by reverse transcriptase. Sequences longer than 12 bases were mapped to E. coli rRNA sequences with bowtie65 and unmapped reads were retained. The rRNA depleted reads were then mapped to the E. coli genome and read counts per gene calculated with HTSeq count68. Integrated genome viewer was used to visualize the alignments66.Sequencing reads were trimmed with cutadapt74. A linear model consisting of three contrasts was fitted to the data to identify genes with changes in translation 2(0.5) were considered be significant. To identify changes in RNA abundances for both protein-coding and non-protein-coding genes, all genes with an average of 15 counts per RNA sample were also analyzed separately using limma74. The linear model (RNAcsrA − RNAWT) was fit to the data to identify genes with differences in their RNA abundance. False discovery rate (FDR) of 5% was used to adjust for multiple testing. Differences were considered significant when the adjusted p-value was <0.05 and fold changes were larger than ±log2(0.5).For the integrated analysis of differential translation, RNA abundance, and TE, non-protein-coding genes were removed. Genes with expression lower than an average of 15 counts per sample in both RPF and RNA samples were also removed. The remaining 3586 protein-coding genes were analyzed using limma75, with a few modifications. The first and last 30 nt of CDS were not included to avoid considering initiating or terminating ribosomes as pauses. Positions with >10-fold occupancy over the average occupancy of each CDS were considered to represent pauses. Only pauses shared across at least four biological replicates were considered significant. The frequency of pause sites within trimmed CDS and CLIP-seq peaks ±30 flanking bases was calculated. A two-sided χ2-test was used to assess the statistical significance of the difference in the frequency of pausing. The strength of pausing was also compared between WT and csrA mutant strains using the fold occupancy over the average occupancy of the trimmed CDS for all pauses and pauses associated with CLIP-seq peaks.Analysis of ribosome pausing was carried out as previously described8. WT and csrA mutant strains were grown to mid-exponential phase in LB at which time rifampicin was added to a final concentration of 200 μg mL−1 to arrest transcription initiation. Samples were collected 0, 2.5, 5, 10, and 15 min after rifampicin addition and mixed with 2 vol of RNA Protect Bacteria (Qiagen) to immediately stabilize the RNA. The RNA was purified according to the manufacturer’s instructions with the miRNeasy Kit (Qiagen), treated with turbo DNAase I (Ambion), and purified with the RNeasy Kit (Qiagen). RNA concentrations were normalized and ERCC RNA spike-ins (Ambion) were added to represent 5% of the total RNA. rRNA was then depleted using the Ribo-Zero rRNA Removal Kit for Gram negative bacteria (Epicentre). Sequencing libraries were then constructed and sequenced using HiSeq 2000 PE100 (Illumina) by BGI America.RNA decay curves were conducted as previously describedE. coli genome and the ERCC spike-in RNA sequences with bowtie65, and read counts per gene determined using HTseq count68. Each time point (Tn) was scaled relative to the time zero (T0) within its time course to normalize the raw read counts. Normalization factors, k, were calculated using each exogenous RNA in the ERCC spike-in mix with n and ERCC0 are the total reads for this RNA in library Tn and T0, respectively; and Totaln and Total0 are the total library sizes in library Tn and T0, respectively. k values were calculated for the 25 most abundant RNAs in the ERCC spike-in, and the geometric average of these was used as the library normalization factor. Raw read counts were divided by the appropriate normalization factor to produce normalized read counts. Curves were fit to the normalized data with linear regression on a semi-log plot with Prism 6 (Graphpad). Genes with starting read counts <50 and R2 values <0.7 were excluded from further analysis. Half-lives were calculated for the remaining 2564 genes with the equation csrA mutant strains were analyzed with Student’s two tailed t tests and adjusted for multiple comparisons using a FDR of 5%. Genes with FDR < 0.05 and fold changes >log2 (0.5) were considered to have significant changes in their RNA stability.Sequencing reads were demultiplexed, mapped to the csrA were analyzed with DAVID to identify clusters of functional terms76. The default parameters and databases were used and multiple testing adjustments were done with a FDR cut off of 10%.Genes identified in the CLIP-seq analysis and genes whose expression was regulated by 617 and RNA synthesized in vitro with MEGAshortscript Kit (Ambion) or the Ampliscribe T7 flash Transcription Kit (EpiCentre). The template DNA for in vitro transcription of sRNAs was generated by PCR from MG1655 genomic DNA, using the primers listed in Supplementary Data 32P] ATP and T4 polynucleotide kinase. Binding reactions contained 0.1–0.5 nM RNA, 10 mM MgCl2, 100 mM KCl, 32.5 ng total yeast RNA, 20 mM DTT, 7.5% glycerol, 4 U SUPERasin (Ambion), and various concentrations of CsrA (0–400 nM), and incubated at 37 °C for 30 min. Reactions mixtures were separated on native polyacrylamide gels using 1× TBE as the electrophoresis buffer and labeled RNA was analyzed using a PMI phosphorimager (Bio-Rad) and Quantity One software (Bio-Rad), as described previously17. Apparent equilibrium binding constants (Kd) presented in the figures are averages of two independent experiments.Binding of CsrA to RNAs was determined by EMSA with recombinant CsrA-His8. The primers used are listed in Supplementary Data qRT-PCR was used to analyze RNA abundance with an iCycler thermocycler (Bio-Rad Laboratories) and iScript One-Step RT-PCR Kit with SYBR Green (Bio-Rad), according to the manufacturer’s instructions, as described previously8. Cultures were harvested by centrifugation, resuspended in Laemmli sample buffer, and lysed by boiling for 5 min at 95 °C followed by sonication. Samples were separated using Bis Tris SDS–PAGE with a Criterion cell (Bio-Rad) and electroblotted onto 0.2 μm polyvinylidene difluoride membranes with a Criterion blotter (Bio-Rad). Membranes were blocked with SuperBlock T20 (Thermo Fisher) and probed with Anti-FLAG M2 monoclonal antibody  or anti-RpoB monoclonal antibody . Signal was detected with horseradish peroxidase-linked anti-mouse secondary antibody  and SuperSignal West Femto Chemiluminescent Substrate . Blots were imaged using the ChemiDoc XRS+ system (Bio-Rad), and signals were quantified using Quantity One image analysis software (Bio-Rad). The western blots and fold change values shown are representative from two independent experiments.Western blotting was conducted as previously describedrpoE′–′lacZ fusion strain was constructed using the conditional-replication, integration, and modular (CRIM) system, as previously described26. The fusion contains from −292 to +26 relative to the rpoE translation initiation codon. β-galactosidase experiments were performed as described previously26.The csrA mutant strains were grown in LB at 37 °C with shaking at 250 rpm to mid-exponential phase, washed with PBS, serially diluted, and plated on LB alone or LB supplemented with EDTA and/or SDS. Plates were incubated at 37 °C for 18 h then colonies were counted.WT and p < 0.05, **p < 0.005, ***p < 0.0005.Statistical analysis was carried out in the R computing environment and in Prism 6 (Graphpad). Relevant statistical information is included in the methods for each experiment. Error bars show standard deviation from the mean. Asterisk representing statistical significance denote the following *The CLIP-seq, ribosome profiling, and RNA-seq raw sequencing data have been deposited in the GEO repository with accession code GSE102386. All other relevant data are available in this article and its Supplementary Information files.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11