The discovery of experience-dependent brain reactivation during both slow-wave (SW) and rapid eye-movement (REM) sleep led to the notion that the consolidation of recently acquired memory traces requires neural replay during sleep. To date, however, several observations continue to undermine this hypothesis. To address some of these objections, we investigated the effects of a transient novel experience on the long-term evolution of ongoing neuronal activity in the rat forebrain. We observed that spatiotemporal patterns of neuronal ensemble activity originally produced by the tactile exploration of novel objects recurred for up to 48 h in the cerebral cortex, hippocampus, putamen, and thalamus. This novelty-induced recurrence was characterized by low but significant correlations values. Nearly identical results were found for neuronal activity sampled when animals were moving between objects without touching them. In contrast, negligible recurrence was observed for neuronal patterns obtained when animals explored a familiar environment. While the reverberation of past patterns of neuronal activity was strongest during SW sleep, waking was correlated with a decrease of neuronal reverberation. REM sleep showed more variable results across animals. In contrast with data from hippocampal place cells, we found no evidence of time compression or expansion of neuronal reverberation in any of the sampled forebrain areas. Our results indicate that persistent experience-dependent neuronal reverberation is a general property of multiple forebrain structures. It does not consist of an exact replay of previous activity, but instead it defines a mild and consistent bias towards salient neural ensemble firing patterns. These results are compatible with a slow and progressive process of memory consolidation, reflecting novelty-related neuronal ensemble relationships that seem to be context- rather than stimulus-specific. Based on our current and previous results, we propose that the two major phases of sleep play distinct and complementary roles in memory consolidation: pretranscriptional recall during SW sleep and transcriptional storage during REM sleep. Rats exposed to novel objects during periods of wakefulness generate neural activity that is correlated with patterns observed in subsequent sleep episodes Sleep is important for the consolidation of newly acquired memories . The disstrictu sensu neuronal reactivation during sleep in mammals has only been investigated in the hippocampocortical loop  representing larger-scale neural rhythms were simultaneously recorded from four different brain regions: HP, primary somatosensory “barrel field” CX, ventral posteromedial thalamic nucleus (TH), and putamen (PU)  and S7. In our studies, neural signals were continuously recorded across the natural sleep–wake cycle for 48–96 h, with a single 1-h exposure to four complex objects placed in the four corners of the recording box C. All obn= five templates per animal). Control templates were selected from epochs of alert WK 24 h or 48 h before novel stimulation , during which familiar tactile stimulation was produced by the contact of whiskers with the smooth walls of the recording box, to which animals had been habituated. Templates were matched against the entire record of neuronal activity using the neuronal ensemble correlation method . This indicates that the neuronal firing patterns concomitant with novel stimulation persisted significantly more during the ensuing time than patterns sampled 24 h or 48 h before novel stimulation, when animals were in the same behavioral state , but without novel objects to explore. The effect was independently observed, to a variable degree, in all the five animals studied . First, we tested whether the neuronal ensemble correlation method could detect any trace of neuronal reverberation lasting at least more than 1 h after exposure to novel stimulation. For this, we examined correlation profiles obtained for all recorded neurons (three to four brain areas pooled together) in each animal. As shown in p values generated by Bonferroni comparisons between post- and prenovelty correlation distributions . This indicates that the neuronal changes associated with exposure to novel stimuli were highly distributed through the neuronal populations sampled. Furthermore, judging by the maximum neuronal ensemble correlations observed . Indeed, experience-dependent changes were not statistically different across different forebrain areas . The temporal evolution of p values (Bonferroni comparison) associated with single-area correlation profiles shows that significant reverberation was present in 16 of 18 recording sites for several hours after exposure to novel stimulation , with SW ratios being significantly higher than those of both WK  and REM . Indeed, significant state-specific differences in post-/prenovelty correlation ratios were individually detected in four of five animals .Single-area results indicate that neuronal ensemble correlations often peak during discrete epochs that last a few hours. We also noticed marked oscillations of the correlation trace in several recorded sites . These observations suggest that some underlying biological process, with slow evolution but with sharp phase transitions, governs the long-term reverberation of neuronal firing patterns. To test whether transitions in the wake–sleep cycle could amount for these effects, we investigated how experience-dependent changes in neuronal correlations varied across the three major rat behavioral states: WK, SW sleep, and REM sleep. A comparison across states of post-/prenovelty correlation ratios calculated from averages of entire recordings indicated a significant state-specific effect . Next, we tested the possibility reported in hippocampal place cells  that expp values can be observed for speed factors 10 times and 20 times faster than normal WK rates, while speed factors near the physiological range (2×–0.5×) show stronger and similar effects. This was the case even in the HP, in contrast with previous findings in hippocampal place cells recorded in highly trained animals performing a spatial navigation task  are substantially less detectable over time.Our results fend off three major objections to the notion that neuronal reverberation during sleep may underlie memory consolidation. First, significant experience-depen-dent changes in neuronal ensemble correlations can be tracked as late as 48 h after the reference novel experience, being therefore compatible with memory impairment effects of sleep deprivation applied hours or days after training . Second,An important result of the present study is the inverse correlation between neuronal correlations and concurrent firing rates. Although all neuronal activity templates were taken from epochs of high arousal WK when firing rates were generally high, their neuronal reverberation during subsequent WK was not very prominent , agreeing with values previously reported for pairwise  or many-A third important point regards the observation that neural activity sampled when animals were aroused, but not touching the objects, yielded neuronal reverberation that was nearly identical to that obtained when animals made sensory contact with the objects. This indicates that the kind of experience-dependent neuronal reverberation detected by the neuronal ensemble correlation method  does notIt has been suggested that the neuronal reverberation of newly acquired synaptic changes during SW sleep may lead to the recall and storage of new memories by way of “calcium-mediated intracellular cascades” capable of opening the “molecular gates to plasticity” . This hyThe fact that neuronal reverberation is sustained for long epochs during SW sleep suggests that unconsolidated synaptic changes may not only be recalled, but also amplified over time during SW sleep. Indeed, a progressive increase of neuronal correlations across single SW sleep episodes was often observed  so as to reach steady-state behavioral activity and baseline wake–sleep cycles. Behaviors were continuously recorded by way of two infrared-sensitive CCD video-cameras; infrared illumination was used to monitor behavior when visible lights were off. Behavioral states were coded as WK, SW sleep, or REM sleep based on a spectral analysis of LFPs and visual inspection of videotaped behaviors, according to previously described criteria  Figure .TM  code running in a computer cluster comprising 32 CPU . Neuronal ensemble correlations were calculated following the method in CxN matrices corresponding to simultaneously recorded spike trains of C neurons binned into N intervals of a certain length. Our results were obtained using 9 s-long templates binned with 250 ms-long bins with as many neurons as were available for the brain areas in question. Hence, each template pattern yielded a Cx36 matrix. Target data comprising 24 h or 48 h were sparsely sampled (every 30 s) and binned into matrices of the same dimensions. For a C×N template data matrix x and a target data matrix y sampled at time t, the ensemble correlation index Ct is obtained by:Data were processed and analyzed by custom-made MATLABwherec, y¯, and σy). and MATLABTM software were used for descriptive statistics and hypothesis testing.Templates were selected by careful scrutiny of behavior recorded on videotapes and of the corresponding spectral characteristics of hippocampal LFPs, so as to assure sampling during alert WK . ParticuFigure S1Four different objects were used to produce CSS.(9.7 MB PPT).Click here for additional data file.Figure S2All animals were highly habituated to the recording box, so that exposure to novel complex objects caused a general increase in the animals' arousal. Four of five animals showed an increase in time spent in WK with respect to SW and REM sleep during CSS (A), as compared to adjacent pre- and postnovelty periods of equal length (60 min). The only exception was rat 1, which showed nevertheless a marked exploratory drive, spending nearly 20% of the exposure period in direct whisker-contact with the objects (B). Individual object preferences were moderately varied, as indicated in (C).(746 KB PPT).Click here for additional data file.Figure S3Teflon-coated tungsten wires  were assembled in multielectrode arrays shaped to fit different neuroanatomical targets.(282 KB PPT).Click here for additional data file.Figure S4Frontal brain sections stained for cresyl-violet were used to determine the sites of electrode placement. Electrode tracks, tissue scars, and reference electrolytic lesions performed a few days before sacrifice were used to delimit the implant sites, indicated in red in the figure below. Numbers on the right represent standard AP coordinates  in milli(6.3 MB PPT).Click here for additional data file.Figure S5To record neuronal activity, differentiated neural signal was preamplified  and digitized at 40 KHz. Up to four neuronal action potentials per recording channel were sorted online  and validated by offline analysis  according to the following cumulative criteria: voltage thresholds greater than two standard deviations of amplitude distributions; signal-to-noise ratio greater than 2.5 (as verified on the oscilloscope screen); less than 1% of interspike intervals smaller than 1.2 ms; and stereotypy of waveform shapes, as determined by a waveform template algorithm and principal component analysis. In order to continuously record individual neurons for up to 96 h, we used an adaptive algorithm  that adjusts waveform templates based on the recent accumulated mean shapes (1% of midline every 20 min). This allows for the same neuron to be tracked across consecutive days, as verified by the superimposition of waveforms acquired thoughout the experiment .(3 MB PPT).Click here for additional data file.Figure S6Up to 159 neurons were recorded from three to four different brain areas.(1.4 MB PPT).Click here for additional data file.Figure S7LFPs were recorded in parallel with spikes from the same electrodes. Neural signals were split, preamplified , and filtered (0.5–400 Hz) by way of a Plexon LFP board. Signals were then fed to the MAP acquisition principal component through a NIDAQ card and digitized at 500 Hz. Behaviors were constantly recorded in videotape by two diametrically opposed infrared-sensitive CCD cameras . A millisecond-precision timer  was used to synchronize the acquisition of spikes, LFPs, and videotape records.Behavioral states were identified by the combined inspection of videotapes and the spectral content (1–20 Hz) of LFPs. Behaviors were classified according to the following criteria: (1) alert WK: active exploration with whisking, plus strong hippocampal θ rhythm; (2) quiet WK: stillness or grooming, with eyes open and low-power hippocampal θ rhythm; (3) SW sleep: stillness with eyes closed, plus large-amplitude hippocampal δ rhythm; (4) REM sleep: overall stillness with intermittent whisking, eyes closed, strong hippocampal θ rhythm.The inspection of videotape records readily separates alert and quiet WK from sleep states, but the separation of SW and REM sleep relies strongly on LFP analysis. Hippocampal LFP is particularly useful to disambiguate SW and REM sleep: SW sleep has a strong δ band (2–4 Hz), while REM sleep shows increased θ band (5–8 Hz). The distinction between alert and quiet WK was used only for template selection . For all other purposes, alert and quiet WK data were combined into a single WK category. The graphs depict the θ/δ hippocampal spectral ratios (mean ± SEM) of the three major behavioral states for rat 5 (entire recording).(2.1 MB PPT).Click here for additional data file.Figure S8The figure shows the effect of using different bin sizes to calculate neuronal ensemble correlations. We observed quantitative differences , but qualitatively the correlations profiles are equivalent, i.e., have very similar shapes.(404 KB PPT).Click here for additional data file.


Recent results showed that certain DEAD box protein RNA helicases, DDX3 and DDX1, play an important role in the HIV infection cycle by facilitating the export of long, singly spliced or unspliced HIV RNAs from the nucleus via the CRM1-Rev pathway. Close examination of an extensive microarray expression profiling dataset obtained from cells latently infected with HIV induced to undergo lytic viral replication indicated that additional DEAD box proteins, beyond DDX3 and DDX1, exhibit differential expression during lytic HIV replication, and in latently infected cells prior to induction into active replication. This finding provides additional evidence that the involvement of DEAD box proteins and other RNA-binding proteins may play roles in active HIV replication and in the control of viral latency. Agents targeting these functions may offer new approaches to antiretroviral therapy and the therapeutic manipulation of HIV latency. The DEAD box proteins, a family of RNA helicases containing the conserved amino acid motif Asp-Glu-Ala-Asp (D-E-A-D in the single letter amino acid code), play an essential role in many aspects of cellular RNA metabolism , inclIn a directed analysis of a large data set, which describes global changes in cellular gene patterns before and after a latently infected cell line was induced into active viral replication , we founYedavalli et al.  identifiOur work on large scale expression profiling in HIV actively and latently infected cells has been guided by the hypothesis that there is one set of cellular conditions which is ideal for normal cellular growth and homeostasis, that there is another set of conditions which may be better suited to supporting viral replication, and that HIV has evolved ways of altering the host cell so as to better support viral replication. An interesting corollary of the hypothesis is that targeting the products of the differentially expressed genes may inhibit viral replication by making the host cell environment less hospitable to viral replication . Sev, the data were subjected to statistical analyses using univariate parametric and multivariate permutation analyses, based on the one sample random variance t-statistic, where significance was based on P < 0.001 and the proportion of false discoveries was limited to 0.10 with a 90% confidence level. 1740 genes showed differential gene expression at a minimum of one timepoint during lytic replication. Hierarchical clustering analyses were performed using mAdb clustering tools, as well as Treeview . Since the data was obtained from latently infected cell lines, there may be some concern that infected primary cells may behave in a somewhat different fashion. However, the advantages of using latently infected cell lines are significant: Activation into active replication is reasonably synchronous and includes essentially all the cells, so that the signal from the cells supporting active replication is not diluted by the signal from uninfected cells or cells with virus at different stages of viral replication. Also, the signal comes only from infected cell and not from cells responding to effects from the exposure to very large numbers of defective viral particles that are in high multiplicity of infection inocula.In our study, we compared RNA samples obtained from HIV latently infected cell lines prior to induction of active replication by the integrated HIV-1 provirus and following such induction with phorbol myristyl acetate (PMA). We profiled ACH-2 cells treated with PMA side by side with similarly treated HIV-1 naïve parental A3.01 cells to assess differential cellular gene expression patterns associated with HIV lytic replication. The dataset was generated from samples obtained from three independent biological replicate experiments and at least two microarray hybridizations were done for each time point from each biological replicate (for a minimum of 6 microarrays per time point). A detailed description of the cell culture, induction, RNA isolation, and microarray labeling and hybridization methods are contained in reference . Followi[DDX10, a DEAD box protein with expression in many tissues having tumorigenic activity when fused to the nucleoporin NUP98 [DDX21, a DEAD-box protein originally identified as a nucleolar protein thought to be involved in ribosomal RNA metabolism[DDX23, a DEAD box protein first identified in U5 SnRNPs with significant homology to the yeast Prp28p splicing factor[DDX52, a human DEAD box protein identified through its homologies to a yeast gene [Figure . We founin NUP98 ,23, DDX2etabolism, DDX23, ng factor, and DDXast gene .EIF4G1 [NCBP1 (CBP80) [SMARCA2 and SMARCA5 [Other genes encoding proteins with RNA splicing/binding and RNA transport activity were also up regulated during this period, including the methylated mRNA cap binding proteins EIF4G1  and NCBP (CBP80) , which f SMARCA5 ,30. Anot SMARCA5 , indicatOur findings that several DEAD box protein genes are upregulated during HIV replication lend support to the published finding that two cellular DEAD-box proteins, including one (DDX3) that was identified in a broad-based screen for differentially expressed genes , may be In our recent publication , we showCellular genes may be differentially expressed during viral replication for many different reasons. Differential expression of cellular genes may conceivably occur because of viral actions on the host cell designed to optimize the cell for viral replication, because of cellular responses to infection aimed at inhibiting viral infection, or may be fundamentally unrelated to key aspects of viral replication. However, the findings that several DEAD box protein genes are differentially expressed during HIV replication, together with the recently published observations that two DEAD box genes, DDX3 and DDX1, exhibit differential expression during HIV replication and have important functions in HIV replication lend additional credence to the hypothesis that a careful, large scale study of differentially expressed cellular genes can provide insights into host cell factors involved in viral replication and pathogenesis. Future studies may reveal additional human co-factors for HIV replication. These cellular co-factors many represent important new therapeutic targets.The authors declare that there are no competing interests.VK designed and performed the experimental work and the data analysis. SZ directed and coordinated the study and participated in the data analysis. VK and SZ wrote the manuscript.


Severe acute respiratory syndrome (SARS) emerged in later February 2003, as a new epidemic form of life-threatening infection caused by a novel coronavirus. However, the immune-pathogenesis of SARS is poorly understood. To understand the host response to this pathogen, we investigated the gene expression profiles of peripheral blood mononuclear cells (PBMCs) derived from SARS patients, and compared with healthy controls.The number of differentially expressed genes was found to be 186 under stringent filtering criteria of microarray data analysis. Several genes were highly up-regulated in patients with SARS, such as, the genes coding for Lactoferrin, S100A9 and Lipocalin 2. The real-time PCR method verified the results of the gene array analysis and showed that those genes that were up-regulated as determined by microarray analysis were also found to be comparatively up-regulated by real-time PCR analysis.This differential gene expression profiling of PBMCs from patients with SARS strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection, as we observed a complete lack of cytokine genes usually triggered during a viral infection. Our study shows for the first time how the immune system responds to the SARS infection, and opens new possibilities for designing new diagnostics and treatments for this new life-threatening disease. Severe acute respiratory syndrome (SARS) emerged in 2003, as a new epidemic form of life-threatening infection . As of SDuring a viral infection, most cell types in the body respond by secreting high levels of type 1 interferons (IFN-α and IFN-β) . IFN-α/βTo study the differential immune-gene expression patterns induced by SARS coronavirus, PBMCs from patients with SARS and normal subjects were examined using microarray technology. We shooed to use PBMCs as these cells are more easily obtained from patients compared to other infected tissues, and the SARS-CoV has recently been shown to infect PBMCs ,10. The P < 0.01) was performed and significant genes were selected. Cluster analysis was performed to find distinct groups of genes that were significantly changed. The criteria used for cluster analysis is different from filter criteria used for differentially expressed genes. 248 genes were selected for Hierarchical clustering  but share a common three-dimensional structure ,22. Tissvia remodeling of the actin component of the membrane [There is also up-regulation of expression of genes, such us, Bactericidal Permeability Increasing Protein (BPI) and Carcino Embryonic Antigen related Cell adhesion Molecule 8 (CEACAM 8 or CD66b). BPI is released from activated neutrophils and is an endogenous antibiotic, which rapidly kills Gram negative bacteria by high affinity binding to the LPS component of the cell wall . CEACAM membrane . Up-regumembrane .There is moderate up-regulation of the kappa light chain of the Nuclear Factor NFκB 1A) and the B-cell lymphoma 3-encoded protein (BCL3) , or genes involved in complement mediated cytolysis, suggesting that the immune response against the SARS-CoV may be different from other viral infections, or that the virus may be using a unusual strategy to evade the host immune system and cause the pathogenesis and mortality.This differential gene expression profiling of PBMCs from patients with SARS, strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection. There is no significant level of up-regulation of MHC-I genes or major cytokines, including IFNs, or complement mediated cytolysis, suggesting that the immune response against the SARS-CoV may be different from other viral infections or that the virus may be using a different strategy to evade the host immune system and cause the pathogenesis and mortality. The severity of the disease does not seem to change the genetic profile of the PBMCs: 5 of our patients were in intensive care, whereas the other 5 patients did not require intensive care, at the time the blood was taken. However, no substantial gene-expression differences were observed that could correlate to disease severity. Moreover, we confirmed  that cytokines usually associated with viral infections (such as interferons and other cytokines) were not detected in the SARS samples, but were triggered in another viral infection (influenza patients). We also showed that genes triggered in the SARS patients were not triggered in the influenza patients.Our study shows, for the first time, how the immune system responds to the SARS infection and opens new possibilities for designing new diagnostics and treatments for this new life-threatening disease.260 nm.Our study included ten adult patients (S1-S10) who were diagnosed with SARS according to the World Health Organization (WHO) SARS criteria and admitted to the Tan Tock Seng Hospital, Singapore, for treatment and four additional normal human subjects (C1-C4). Our protocol was approved by the Tan Tock Seng Hospital Research Ethics committee. Peripheral blood was collected by vein-puncture of the brachial vein; mononuclear cells (PBMC) were prepared using histopaque  according to manufacturer's recommendations. Sample preparation and processing procedures were carried out as described in the Affymetrix Gene Chip Expression Analysis Manual . Briefly, total RNA was extracted from PBMCs, using Trizole method  and further purified using RNeasy columns according to manufacturer's instructions . Integrity of total RNA was confirmed by formamide gel electrophoresis and quantification was carried out by measuring the Ain vitro transcription (IVT) using T7 RNA polymerase at 37°C for 5-6 hr as per manufacturers' instructions . Labeled cRNA obtained after IVT was purified using RNeasy columns (Qiagen). Purified cRNA was fragmented using fragmentation buffer  at 94°C for 35 min.5 μg of total RNA was used to synthesize double stranded cDNA using T7-(dT24) oligonucleotide primer and Superscript reverse transcriptase (Invitrogen). The resultant cDNA was purified by phenol:chloroform extraction and ethanol precipitation in presence of 7.5 M ammonium acetate. 1 μg of purified cDNA was subsequently used to synthesize biotin labeled cRNA by Fragmented cRNA (10 to11μg/ probe array) was used to hybridize to human focus array (HG-Focus Array) at 45°C for 16 hr with constant rotation of 60 rpm in a Gene chip hybridization oven 640 (Affymetrix). The chips were washed and stained using Gene chip fluidics Station 400 (Affymetrix). Staining was performed using streptavidine phycoerythrin conjugate , followed by the addition of biotinylated antibody to streptavidine , and finally with streptavidine phycoerythrin conjugate. Probe arrays were scanned using Agilent Gene Array Scanner Series US 74900593 .P value for each gene. Any gene for which this P value was < 0.01 was considered to be differentially expressed. We selected 186 genes for further analysis that met the following criteria:An absolute expression analysis was performed using Microarray Suite Software 5.0 (Affymetrix) and relative mRNA expression levels were expressed as plus or minus fold changes compared to normal controls. Each chip was scaled to an overall intensity of 500 to correct for minor differences in overall chip hybridization intensity, and to allow comparison between chips. The data from ~8700 genes was imported in to MicroDB 3.0 and Data Mining Tool 3.0 (Affymetrix) for further analysis. Pearson Correlation coefficient (r) was used to ensure the reproducibility of the data using signal from normal samples. T-test was performed to identify genes that were differentially expressed in SARS patients over normal samples. The statistical significance of the differential expression of any gene was assessed by computing (i) Changes in expression of at least 2 fold higher or lower comparing the normal(ii) Signal >500P < 0.01 and(iii) Detection (iv) Genes which met the above criteria in at least 30% of samples.Finally, data representing 10 SARS patients fold changes over control samples were averaged..Genes were annotated according to biological process using the Gene ontology: tool for the unification of biology from the Gene Ontology Consortium . The comUnweighted average linkage Hierarchical clustering was applied for samples using the 'Genesis' software . Genes w® instrument (Roche) as follows: reverse transcription at 61°C for 20 min, initial denaturation at 95°C for 2 min; amplification for 45–65 cycles of denaturation , annealing  and extension . A single online fluorescence reading for each sample was taken at the end of extension step. Quantitative results were expressed by identification of the second derivative maximum points, which marked the cycles where the second derivatives of the fluorescence signal curves are at maximum. These points were expressed as fractional cycle numbers. Then, these cycle numbers were plotted against the logarithm of the concentrations of serially 2-fold diluted standard samples to obtain a standard curve. The concentrations of unknown samples were calculated by extrapolation from this standard curve. Positive sample specificity was confirmed by determining the melting curve .Real-time PCR was performed for ten genes, namely: Lactoferrin (Primers: 5' tcg tcc tgc tgt tcc tcg ggg 3' and 5' tcc agc ggt cct gcg aag gcc 3'); Lipocalin (Primers: 5' aag ccc ctg ctc ctg gcc atc agc 3' and 5' cga cct gat gct gta tgc cac gtg 3'); S100P (Primers: 5' cat gat cat aga cgt ctt ttc 3' and 5' aca cga tga act cac tga agt 3'); TLR2 (Primers: 5' gta tct gca agg gca gct cag gat 3' and 5' ttc ctc aag gaa ggt aag tcc agc 3'); FCGR3A (Primers: 5' ctc cgg ata tct ttg gtg act 3' and 5' tgc aga gca gtg ttc ttc cag 3'); IFNA (Primers: 5'atg gcc ttg acc ttt gct tt 3' and 5'tgg aag att tcc tca tag c 3'); IFNB (Primers: 5'atg acc aac aag tgt ctc ctc caa a 3' and 5' ttc ttc cag gac tgt ctt ca 3'); IL-12 p40 (Primers: 5'atg tgt cac cag cag ttg gtc atc 3' and 5'ctg aat gtc aaa tca gta ct 3'); TNFA (Primers: 5'gag tga caa gcc tgt agc cca tgt tgt agc 3' and 5'gca atg atc cc a aag tag acc tgc cca gac 3'); GAPDH (Primers: 5'acc aca gtc cat gcc atc ac 3' and 5'tcc acc acc ctg ttg ctg ta 3'). For amplicon detection, the Light Cycler RNA Master SYBR Green Kit (Roche) was used as described by the manufacturer. PCRs were performed in a LightCyclerRR carried out the sample preparation, hybridization and drafted the manuscript. MJ carried out statistical analysis, data filtration, and data deposition and helped with manuscript preparation. LYH, HHC and DT provided the blood samples and the clinical comments for the study. BPL advised on study design and development. AJM devised and coordinated the study, revised and finalized the manuscript. All authors read and approved the final manuscript.


Blocks of duplicated genomic DNA sequence longer than 1000 base pairs are known as low copy repeats (LCRs). Identified by their sequence similarity, LCRs are abundant in the human genome, and are interesting because they may represent recent adaptive events, or potential future adaptive opportunities within the human lineage. Sequence analysis tools are needed, however, to decide whether these interpretations are likely, whether a particular set of LCRs represents nearly neutral drift creating junk DNA, or whether the appearance of LCRs reflects assembly error. Here we investigate an LCR family containing the sulfotransferase (SULT) 1A genes involved in drug metabolism, cancer, hormone regulation, and neurotransmitter biology as a first step for defining the problems that those tools must manage.Sequence analysis here identified a fourth sulfotransferase gene, which may be transcriptionally active, located on human chromosome 16. Four regions of genomic sequence containing the four human SULT1A paralogs defined a new LCR family. The stem hominoid SULT1A progenitor locus was identified by comparative genomics involving complete human and rodent genomes, and a draft chimpanzee genome. SULT1A expansion in hominoid genomes was followed by positive selection acting on specific protein sites. This episode of adaptive evolution appears to be responsible for the dopamine sulfonation function of some SULT enzymes. Each of the conclusions that this bioinformatic analysis generated using data that has uncertain reliability (such as that from the chimpanzee genome sequencing project) has been confirmed experimentally or by a "finished" chromosome 16 assembly, both of which were published after the submission of this manuscript.SULT1A genes expanded from one to four copies in hominoids during intra-chromosomal LCR duplications, including (apparently) one after the divergence of chimpanzees and humans. Thus, LCRs may provide a means for amplifying genes (and other genetic elements) that are adaptively useful. Being located on and among LCRs, however, could make the human SULT1A genes susceptible to further duplications or deletions resulting in 'genomic diseases' for some individuals. Pharmacogenomic studies of SULT1Asingle nucleotide polymorphisms, therefore, should also consider examining SULT1A copy number variability when searching for genotype-phenotype associations. The latest duplication is, however, only a substantiated hypothesis; an alternative explanation, disfavored by the majority of evidence, is that the duplication is an artifact of incorrect genome assembly. Experimental and computational results estimate that 5–10% of the human genome has recently duplicated . These eAt the same time, the appearance of LCR duplicates may be an artifact arising from one of a number of problems in the assembly of a genome of interest. Especially when classical repetitive sequences are involved, it is conceivable that mistaken assembly of sequencing contigs might create in a draft sequence of a genome a repeat where none exists. In the post-genomic world, rules have not yet become accepted in the community to decide when the burden of proof favors one interpretation (a true repeat) over another (an artifact of assembly). Again, these rules will emerge over time through the study of individual cases.Through the assembly of many case studies, more general features of duplication and evolutionary processes that retain duplicates should emerge. Although each LCR family originates from one progenitor locus, no universal features explain why the particular current progenitor loci have been duplicated instead of other genomic regions. From an evolutionary perspective, duplicated material is central to creating new function, and to speciation. One intriguing hypothesis is that genes whose duplication and recruitment have been useful to meet current Darwinian challenges find themselves in regions of the chromosome that favor the generation of LCRs.Browsing a naturally organized database of biological sequences, we identified human cytosolic sulfotransferase (SULT) 1A as a recently expanded gene family with biomedically related functions. SULT1A enzymes conjugate sulfuryl groups to hydroxyl or amino groups on exogenous substrates (sulfonation), which typically facilitates elimination of the xenobiotic by the excretory system . Sulfonap-nitrophenol and α-naphthol .The July 2003 human reference genome (based on NCBI build 34) was queried with the ent tool , and seaent tool . Two disent tool  as the SSULT1A3 and ~500 kbp of sequence surrounding SULT1A4 were downloaded from NCBI and compared using PIPMAKER  (SULT1A3) and chimpanzee whole genome shotgun sequences [Genbank:AACZ01082721] (SULT1A1), [Genbank:AADA01101065] (SULT1A2), and [Genbank:AACZ01241716] (SULT1A3) using PIPMAKER exon analysis. These new SULT1A genes have been deposited with the GenBank Third Party Annotation database under accession numbers [Genbank:BK004887-BK004892]. DNA sequences were aligned with CLUSTAL W [The MASTERCATALOG was used for performing initial inspections of the SULT gene family and for delivering a non-redundant collection of SULT1A genes. Additional SULT1A ORFs were extracted from gorilla working draft contigs [Genbank:AC145177]  of two-fold redundant codons  where the amino acid had been conserved in pairs of aligned sequences, and the number of these codons where the third position was identically conserved (c) were counted by the DARWIN bioinformatics platform , where k is the rate constant of nucleotide substitution, t is the time separating the two sequences, and Eq. is the equilibrium state of the TREx value [k) was estimated as 3.0 × 10 -9 changes/site/year for placental mammals . These estimates were determined by sampling all pairs of mouse:rat and mouse:human orthologs in the public databases and following accepted placental mammal phylogenies and divergence times [k.Starting with aligned DNA sequences, the number  value . TREx vaEx value . The equce times ,83. TherHuman-chimpanzee genome alignments were inspected at the UCSC Genome Browser. Human-rodent genome alignments were examined with the VISTA Genome Browser ,51,84. VSULT1A3 were downloaded from NCBI UniGene [SULT1A3 or SULT1A4.All expressed sequences ascribed to  UniGene  and aligkbp (kilobase pairs); LCR (low copy repeat); Mbp (million base pairs); Ma (million years ago); ORF (open reading frame); SULT (sulfotransferase); TREx (transition redundant exchange); VISTA .M.E.B carried out the study and drafted the manuscript. S.A.B participated in designing the study and preparing the manuscript.Multiple sequence alignment of SULT1A genes.Multiple sequence alignment of SULT1A genes used in all phylogenetic analyses. Characters conserved in all sequences are indicated with asterisks.Click here for filen and c values for SULT1A genes. Pairwise n and c values between SULT1A genes. The names of the sequences are the row-headers and the column-headers. Lower triangular matrix contains n values, and upper triangular matrix contains c values.Pairwise Click here for file


Arabidopsis thaliana (mustard weed) and Oryza sativa (rice) for the effectors of these signalling molecules.Cyclic nucleotides are ubiquitous intracellular messengers. Until recently, the roles of cyclic nucleotides in plant cells have proven difficult to uncover. With an understanding of the protein domains which can bind cyclic nucleotides (CNB and GAF domains) we scanned the completed genomes of the higher plants Our analysis found that several ion channels and a class of thioesterases constitute the possible cyclic nucleotide binding proteins in plants. Contrary to some reports, we found no biochemical or bioinformatic evidence for a plant cyclic nucleotide regulated protein kinase, suggesting that cyclic nucleotide functions in plants have evolved differently than in mammals.This paper provides a molecular framework for the discussion of cyclic nucleotide function in plants, and resolves a longstanding debate about the presence of a cyclic nucleotide dependent kinase in plants. The discovery of cyclic 3'5'-adenosine monophosphate (cAMP) by Earl Sutherland in the late 1950s was one of the most significant paradigm shifts in biochemistry .The ubiquitous presence of cyclic nucleotides may be due to several characteristics which make it an ideal second messenger. Cyclic nucleotides are derived in a energetically favourable reaction from common metabolites (ATP and GTP), and can be broken down into non-toxic products (inorganic phosphate and AMP/GMP). The synthesis and degradation of cyclic nucleotides are controlled by enzymes termed adenylate (or guanylate) cyclases and cyclic nucleotide phosphodiesterases, respectively ,4.In plants, cyclic nucleotides have endured a checkered research history fraught with complications and setbacks. Despite this, recent work has shown unequivocally that cyclic nucleotides are present in plant cells ,6, and tCyclic nucleotides are able to bind to two distinct protein domains, CNB domains and GAF domains. CNB domains were first identified in the regulatory subunit of mammalian cAMP-dependent protein kinase (RI and RII). Since several CNB domain containing plant proteins have been shown to be directly modulated by cyclic nucleotides, this indicates that the CNB domain in plants is functionally similar to CNB domains in other organisms -15.Anabaena cyclic nucleotide stimulated adenylate cyclase and several other proteins ). This yielded no new inclusions to our list of proteins, but did confirm each of our previous entries. For examination of the Oryza sativa spp. Japonica genome we performed BLAST searches using the aforementioned baits, as well as each of the Arabidopsis proteins. This search was performed using the Blast utility of the TIGR rice database . A subsequent BLAST search using this gene found another isoform of this gene in Arabidopsis (atCNTE2) and one isoform in Rice (osCNTE1) which we also included in our analysis.We generated a model of the atCNTE1 cyclic nucleotide binding domain (residues 28–117) based on an alignment of atCNTE1 with the CNB domains of RIIβ [pdb: 1CX4] , RIα pd, CAP pd, HCN2 p and Epac32P-γ-ATP (Amersham-Pharmacia), 30 μM Kemptide substrate, 50 mM HEPES pH 7.4, 1 mM dithiothreitol and 10 μM cyclic nucleotide as specified. Reactions were allowed to occur for 10 minutes at 30°C and assays were terminated by spotting onto squares of P81 paper followed by extensive washing with 75 mM phosphoric acid [Unless otherwise indicated all chemicals were purchased from Sigma-Aldrich. Assays were performed on extracts of Arabidopsis cells grown in suspension culture  or isolaric acid . Assays ric acid .Bovine heart PKA catalytic subunit and RII were purified to homogeneity ,79. PuriCAP catabolite activator proteincAMP 3'5'-cyclic adenosine monophosphateCGK2 cGMP dependent protein kinase 2cGMP 3'5'-cyclic guanosine monophosphatecNMP 3'5'-cyclic nucleotide (cAMP or cGMP)CNB cyclic nucleotide bindingCNGC cyclic nucleotide gated channelCNTE cyclic nucleotide dependent thioesteraseEpac exchange protein directly activated by cAMPGMP-phosphodiesterases, adenylyl cyclases and FhlAGAF domain found in cGORK guard cell outward rectifying potassium channelPKA protein kinase ASKOR stellar potassium outward rectifying channelSMART simple modular architecture research toolDB performed the bioinformatic analysis, modeling and biochemical studies and drafted the manuscript. MEF participated in the modeling. GBG conceived of the study and participated in its design and co-ordination. All authors have read and approved the final manuscript.Co-ordinate file of model generated for Figure Click here for fileArabidopsis thaliana and Oryza sativa. Phylogenetic analysis of CNGC and shaker-type channels from Un-rooted neighbor-joining trees were constructed for (A) shaker-type and (B) CNGC channels using full-length sequences. For the shaker-type channels, numbers at nodes indicate number of trees out of 100 in which the node occurred. These are omitted in (B) for clarity. Scale indicates number of differences per residue. Trees were generated using ClustalX [ClustalX  and visuClustalX .Click here for fileRelevant Sequence Alignments Protein sequence alignments upon which phylogenetic analyses in Figure Click here for fileArabidopsis thaliana and Oryza sativa. Cyclic nucleotide binding proteins in Proteins containing CNB domains including species, name, aliases, accession number, other domains present , sequence length, residues encompassing the CNB domain and E-value for the CNB domain as determined by SMART [.by SMART . All accClick here for fileProtein sequences of all sequences analysed in this manuscript. Sequences are named according to details in the text and are in FASTA format.Click here for file


Previous studies have conducted time course characterization of murine colitis models through transcriptional profiling of differential expression. We characterize the transcriptional landscape of acute and chronic models of dextran sodium sulfate (DSS) and adoptive transfer (AT) colitis to derive temporal gene expression and splicing signatures in blood and colonic tissue in order to capture dynamics of colitis remission and relapse. We identify sub networks of patient-derived causal networks that are enriched in these temporal signatures to distinguish acute and chronic disease components within the broader molecular landscape of IBD. The interaction between the DSS phenotype and chronological time-point naturally defines parsimonious temporal gene expression and splicing signatures associated with acute and chronic phases disease . We show these expression and splicing signatures are largely orthogonal, i.e. affect different genetic bodies, and that using machine learning, signatures are predictive of histopathological measures from both blood and intestinal data in murine colitis models as well as an independent cohort of IBD patients. Through access to longitudinal multi-scale profiling from disease tissue in IBD patient cohorts, we can apply this machine learning pipeline to generation of direct patient temporal multimodal regulatory signatures for prediction of histopathological outcomes. Longitudinal changes in gene expression, splicing patterns and adaptive immune repertoire in murine models of colitis are examined and associated with histologic inflammation in mice and in patients with inflammatory bowel disease. Murine models of colitis have provided some valuable insights into the molecular and histopathological changes associated with disease progression and therapeutic intervention. However, they have also been criticized for failing to fully recapitulate the complexity of CD and UC, as specific models depict different aspects of colitis development and progression. For example, the dextran sulfate sodium (DSS)-induced colitis mouse model induces colitis via barrier disruption and provides key insight into innate immunity-driven inflammation. On the other hand, adoptive T-cell transfer (AT) models represent T-cell-mediated colitis and can serve as a model for CD. Nonetheless, the DSS model has been successfully used to assess the effects of various perturbations, including therapeutic agents4 and genetic perturbations9, on both chronic and acute diseases.Inflammatory bowel disease (IBD) is characterized by a breakdown of intestinal immune homeostasis, mediated by cyclical flares of inflammation and driven by a complex interplay of genetics and environmental factors. Clinically, two major subsets of IBD are defined as Crohn’s disease (CD) and Ulcerative colitis (UC) but their exact molecular etiologies remain elusive10, histopathological11, and immunological14 aspects of the disease, however, a multiscale model of the molecular alterations associated with disease dynamics demands genome-wide and systems approaches. In their earlier work, Breynaert et al.15 developed a multi-DSS-cycle model mimicking the relapsing–remitting course of human colitis while exploring colonic gene expression alterations contrasting acute and chronic inflammation using microarray technology15. They identified distinct expression signatures for acute and chronic inflammation, which included overexpression of keratins associated with wound healing15. More recently, Holgersen et al.16 performed RNA sequencing (RNAseq) on colonic tissue from IBD patients and three murine models of colitis: interleukin-10 (IL10) knockout, AT, and DSS16. They showed that, among the 115 genes previously identified in biomarker studies of IBD, 92 were differentially expressed in human colonic tissue and there was significant overlap with the genes deregulated in the Il10KO and adoptive transfer models16. They concluded that the colonic transcriptional profiles of human IBD and the murine colitis models are homologous16. We and others have also demonstrated the considerable utility of DSS models and their high resolution within human IBD ex vivo models as part of a comprehensive strategy for validating key driver genes17.Previous studies aimed at the characterization of the DSS model have focused on clinicalThe temporal dynamics of disease onset in colitis are of key importance, and are still largely uncharacterized, with clear potential applications in disease staging and potentially in developing treatment course prediction and management. In order to further parse the temporal dynamics of colitis and establish connections to key disease inflection points in IBD, we characterized the temporal variance of colitis-associated transcriptomic alterations in both acute and chronic mouse models. We characterize the disease-specific expression and splicing alterations across time and estimate their relative predictive power for histopathological measurements compared to other more traditional signatures. By projecting the most predictive signatures onto existing human IBD causal networks, we show that it is possible to explicitly identify acute and chronic subnetworks which elucidate potential transition points of acute to chronic colitis and thereby provide a quantitative framework of disease evolution.We evaluated the induction and evolution of DSS-induced colitis by measuring the primary endpoint phenotypes of this model: body weight, colon length, and histopathology scores. In line with disease model endpoints, mice in the disease group showed weight losses of 20% and 10% after DSS cycles one and three, respectively Fig. . No decrWe performed RNAseq on intestine and whole blood samples collected on days 5, 12, 17, and 36. At each timepoint, we quantified gene expression and differential exon-usage patterns (cassette exon excision and retention), and carried out V(D)J alignment and CDR3 de novo assembly to quantify adaptive immune response magnitude and clonality  defined as logFC > 0 with FDR < 0.05 show an increase in transcriptomic alterations from day 5 (2040 DE genes) to day 17 (5740 DE genes), with a decrease in the levels of disease gene expression at day 36 (2771 DE genes) Fig. , suggest18, with the genes that show strong evidence for differential splicing having low gene-level expression fold changes and vice versa , was found to be much weaker than gene-level expression changes in terms of number of genes involved at the same significance threshold. The highest number of significant DS genes peaked at day 12 with 125 genes having evidence of differential splicing at FDR < 0.05 Fig. . Both si−4 for differential splicing at least one timepoint and (b) whose differential exon-usage pattern can be explained by differential expression of annotated transcripts (in Refseq and/or Ensembl). Two genes met those criteria: Il1rl1 (ST2), a receptor in both membrane-bound and soluble forms belonging to the Toll-like receptor superfamily whose ligand is Il-33, and Lama3, a secreted protein that belongs to the laminin family and acts as the alpha subunit of laminin-5 heterotrimer. The early DSS-specific Lama3 isoform has an FDR of 2.39e-18  and performed a pathway enrichment analysis  on the resulting isoform-specific co-correlated gene-lists. As the majority of the genes that are co-correlated with the long, TIR-containing isoform are also co-correlated with the short one (95.7%), none of the significantly enriched pathways are specific to the long-transcript (TIR-containing) gene list , especially in the ileum, cecum, right colon, and rectum of CD patients, and in the cecum and rectum of UC patients (Supplementary Data R > 0.7) with either Lama3 transcript in RNAseq data show a shift towards immunity-related pathways, including Il-5 and Jak-STAT signaling, for the genes correlated with the short transcript  RNAseq reads mapping to VDJ loci, normalized by total library size. The non-targeted and sparse nature of these data prevents a deep characterization of the lymphocyte receptor repertoire which is directly comparable to that of targeted amplicon DNA-based sequencingpval ~0.05). Clonal diversity was reduced in later rest cycles. VDJ clones were detected in >99% of the DSS models at a median of 500 clones per sample, while on average 44% of samples in the adoptive T cell models had detections of only about 8 clones per sample , tissues (blood and colonic), and colitis model type (DSS and AT)). For each signature, we constructed a random forest model using the same DSS whole-colon training set to predict histological scoring that incorporated predictive terms related to the specific fixed and trajectory expression, splicing, and adaptive VDJ signatures evaluated on the validation data  ~8), and that this predictive power depends mostly on the gene expression features, not on adaptive inflammation or exonic usage. Similarly, dynamic trajectory signature B (immune influx) has moderate predictive power in DSS blood  ~3) again with expression features dominating the model. For the adoptive transfer model, we see that the DSS day 36 average control vs disease fixed signature is predictive for histological scoring using distal mucosal expression, and notably highlights the predictive power of differential exon usage . Finally, averaging all DSS day-specific signatures into one, we observe a negative correlation between histology predicted in the adoptive transfer model using proximal mucosal expression, supporting that these models manifest disease in different colonic regions and time course.The murine model molecular signatures were then tested for their ability to predict the Nancy (UC) and GHAS (CD) histological scores, as well as other related scores, in human IBD patient biopsies Fig.  across d9. The overlap of the timepoint-specific DSS signatures with the human GRN yields four subnetworks that show considerable overlap, with 52% of the genes appearing in two or more subnetworks  built from gene expression and genotyping data from intestinal biopsies of anti TNFα CD patients undergoing a Phase II trial for Ustekinumabrks Fig. . In agrerks Fig. . The tworks Fig. .Fig. 6DSSome of the altered pathways involved in colitis have timepoint-specific enrichment. While day 5 shows enrichment for innate immune (phagosome), anti-viral response  and mucosal immunoglobulin-mediated response pathways , day 36 is specifically enriched for adaptive/MHC-mediated immune pathways  and genes that have been implicated in IBD pathogenesis Fig. . These rAs with the pathways, days 5 and 36 show the strongest enrichment in genes of three IBD relevant gene-sets: an intestinal inflammatory signature of CD patients (CD signature), the list of candidate genes identified in IBD GWAS studies (IBD genes) and the genes involved in very early onset IBD (veoIBD genes) Fig. . While tWe have used integrative analysis of RNAseq expression profiling of the blood and intestine to determine the degrees to which two experimental murine models of colitis relate to human IBD. We leveraged the power of RNAseq to characterize the differential splicing induced by disease, which highlighted isoform-specific contributions of the Lama3 and Il1rl1 loci. We then evaluated the predictive power of differential expression, differential splicing, VDJ repertoire and temporal clusters spanning acute and chronic phases within the same tissue and across tissue, disease model and species, for histological measurements. The temporal signatures had the most predictive power and integrating these regulatory readouts over time provided greater predictive power over single timepoints. We have also shown that human causal IBD networks can be intersected with the longitudinal murine data to parse acute versus chronic stages and putative transition phases. We were also able to highlight the conservation of intermediate molecular phenotypes as the murine trajectory signatures overlapped with the transcriptional causal networks derived from the intestine from IBD patient populations. We have also shown that the human causal IBD networks can be intersected with the longitudinal murine data to parse acute versus chronic stages and putative transition phases.The predictive power of these colitis model signatures extends to histological scores in a cohort of IBD patients. We used GHAS max as an outcome measure because the area of worst disease/inflammation determines the score. However, there could be limitations with averaging and regressing out regional effects as intestinal transcription is known to vary in a region dependent manner, even with the exclusion of ileum. While the sample representation was outnumbered in rectum, leading to a dominance in transcriptional signal, regressing out region may affect biology of disease signal as it is thought that there could be regional differences in disease activity. Another limitation is that averaging intestinal tissues across regions may be dilutive to the expression of true disease signal, if there is a preponderance of noninflamed affected regions included. However, this analysis can also capture sub clinical molecular changes on a pathological escalation. Furthermore, capturing transcriptional predictive power in CD will differ from UC in disease region since UC is continuous progression distal to proximal whereas and CD is sporadic in its regional intestinal manifestation.Nevertheless, this time series predictive pipeline has significant potential in being applied to large human datasets where longitudinal molecular profiling data is available from intestinal biopsy and/or blood. It may be used to generate IBD patient sourced temporal trajectories for the prediction of disease severity, mucosal healing and therapeutic response and could be of particular value if non-invasive tissue, such as the blood, can be used to predict local disease status in the intestine. This study highlights the utility of profiling aspects of experimental animal disease models and will facilitate more precise use of the models to test candidate therapies. Furthermore, this machine learning pipeline can be adjusted to generate expression or other data regulatory scale based histological annotation in convolutional neural networks to better classify the molecular signatures of histological stages and pathological tissue changes and identify non-linear predictive features. Further refinements of this model may enable the classification of patient subsets based on temporal signatures of disease if more routine longitudinal patient molecular profiling can facilitate a personalized medicine approach where a patient remission and flare can be better predicted in addition to defining sequential and/or combinatorial therapies tailored to specific intervention time points. As more longitudinal molecular data becomes available at various data scales, it can be paired with clinical data in order to annotate and track variables which may be driving the temporal clusters. Based on the conserved homology, various perturbations can be tested in experimental models and then profiled and re-integrated with human data models leveraging longitudinal sample collection and profiling in order to better predict efficacy of in silico interventions for human clinical trials.On Day 0, female C57Bl/6 mice were weighed and divided into 2 groups of 90 mice per group such that the average weight of each group is similar. Mice were enrolled onto study at body weight of 16–18 grams. Enrolling mice at a range of 16–18 grams ensures consistent disease across the groups while minimizing losses due to death. Mice in Group 1 received 2.5% DSS in drinking water at times shown below while mice in group 2 received plain drinking water at all times. Food was provided ad libitum. See Table Mice were weighed daily for the duration of the study.At various times during the study  15 mice from each group were chosen randomly and sacrificed after an overnight fast. Colon was excised and feces were harvested aseptically and placed into cryotubes and frozen. After feces collection, all contents of the colon were removed and colons flushed with cold PBS. Feces collection only occurred on days 5, 17, and 34. For all mice of each group, the 1 cm distal adjacent to the rectum was sectioned and placed in 10% buffered formalin for subsequent histological analysis. The colons from 15 mice per group at each time point were collected for RNA analysis. 1 cm distal adjacent to rectum was used for histology and the remaining tissue was used for RNA. The remaining colon tissues were frozen in RNAlater at −80°C for RNA extraction. Blood was collected by cardiac puncture from mice from every group at termination. Approximately 250 μl of whole blood was collected into PAXgene tubes. Whole blood was assessed for RNA. Colons from all groups were measured for length and weight. RNA was isolated with Qiagen RNeasy kits according to manufacturer’s instructions.In this model, CD45RBhigh CD4+ T cells from C57Bl/6 J mice were transferred to RAG1−/− mice which lack T and B cells.5 cells/ml (100 μl/mouse injection). Control RAG1−/− mice served as a control.On study day 0, spleens for CD45RBhigh CD4+ T-cell isolation were obtained from female C57Bl/6 J mice using the RAG1−/− mice as recipient mice. After cells had been obtained and sorted, each female RAG1−/− mouse received an IP injection of 5 × 10Mice were weighed 2–3 times a week for the duration of the study.At various times during the study , 10 mice from each group were chosen randomly and sacrificed after an overnight fast. Large bowel was excised and contents flushed with cold PBS. The 1 cm part of distal colon was sectioned and placed in 10% buffered formalin for subsequent histological analysis. The remaining part of colon from the 10 mice, per group, per time point, were collected for RNA analysis. Colons were sectioned such that proximal ends and distal ends of mucosa were placed into separate tubes for RNA profiling. We froze the remaining colon tissues in RNAlater at −80°C for RNA extraction. Blood was collected by cardiac puncture from mice from every group at termination. Approximately 250 μl of whole blood was collected into PAXgene tubes. Whole blood was assessed for RNA. Colons from all groups were measured for length and weight.Tissues were fixed in 10% buffered formalin, embedded in paraffin and cut into sections. Sections were stained with hematoxylin and eosin. Stained sections were examined for evidence of colitis using as criteria the presence of mononuclear inflammatory cells, erosions, glandular loss, elongation or distortion of crypts and hyperplasia.high and CD45RBlow cell isolation (Using IBD Cell Separation protocol). After cells were obtained and sorted, control animals (Group 1) received ~4 × 105 CD45RBlow cells (100 µl/mouse injections) and disease animals (Group 2) received an IP injection of ~4 × 105 CD45RBhigh cells (100 µl/mouse injections). On study day −1, mice were weighed and randomized into treatment groups (described below) based on body weight. 10 animals were randomly selected from each group and necropsied on study day 14, 21, 28, 35, 42, 49, 56, and 63. Intestinal tissue was flushed with cold PBS, mucosa removed and placed into separate tubes and flash frozen.160 C57 RAG(−/−) mice, 80 C57Bl/6 RAG(−/−) and 80 C57Bl/6 from Taconic. Mice were 6–7 wks old C57Bl/6 were 12 weeks, and all mice were female. Mice were acclimated for at least 7 days after arrival. Mice were housed at 5 animals/cage. On Study day 0, C57Bl/6 mice were terminated and spleens obtained for CD45RBi.n = 15 mice = 45 samplesDSS colon: 3 timepoints , ii.n = 15 mice (or pooling samples) = 45 samplesDSS blood: 3 timepoints, iii.hi mucosa: 3 timepoints, n = 10 mice = 30 samples*Rbiv.hi muscularis: 3 timepoints, n = 10 mice = 30 samples*Rbv.hi blood: 3 timepoints, n = 10 mice = 30 samplesRbTotal: 180 samples Quality control further limited sequenced samples for analysis.21 and gene- and exon-level read summarizations were performed with the featureCounts program in subRead22. Ensembl annotations were used for both the alignment and the read summarization. For the exon-level read summarization, a collapsed version of the annotations file where gene designs are described based on non-overlapping “counting- bins” was used to avoid over- or under-counting of reads. The raw data files, the gene- and exon-level read count matrices, a sample descriptor file and an experimental procedure descriptor file have been uploaded to the Gene Expression Omnibus (GSE*).Raw RNAseq reads were aligned against the mm10 reference genome using STAR (version 2.4.0g1)23.The differential expression analysis was performed with the DESeq2 package in R24 was applied to the gene- and exon-level read count matrices and the resulting log2-transformed counts per million (log2cpm) matrices and the observation-level weights were fed to the lmFit function to fit the mixed linear model described in equation (1). The p values associated with the DSS vs. control effect on each gene and exon were computed using empirical Bayes moderated t statistics. The differential exon-usage estimations were done using the diffSplice function (limma) on the exon-level fit object and only those genes with an FDR < 10% as given by either moderated F-statistics or simes-adjusted t statistics were considered as differentially spliced.A voom transformationX and a reduced model without such term Y.For the identification of the genes that show different temporal trajectories of expression in the DSS and control groups, DESeq2 was used (11) to perform a likelihood-ratio test between a full model containing a disease-time interaction term fitted on gene-level count data t test to obtain gene-wise significance of disease-time interaction using diffSplice (limma).The identification of genes that show differential splicing trajectory across time in control and disease was done by fitting a model containing a disease-time interaction term on exon-level data and performing a simes-moderated Each box in Fig. HistoScore ~ PC1_{gene} + PC2_{gene} + PC3_{gene}+ PC1_{exon} + PC2_{exon} + PC3_{exon}y axis we allow this training function to predict HistoScore. For each signature and each validation set we have a model with a predicted HistoScore, and in this model we can perform variable selection to pick which predictors are the most important to that model. Using k-fold cross validation, we avoid superflous predictors and generally mitigate overfitting. Also note that for each model, we keep the same number of predictors even though the number of genes in each signature can vary tremendously. In each box, we only report the top three predictors for that model.+ median_expression + median_splice + VDJ_{read_{cpm}} + VDJ_{clonality} in the Janssen WC colonic dataset. This gives a function relating HistoScore to gene, exon usage, V(D)J burden and clonality, trained only on the 40 mice of the DSS Janssen cohort and only on the colonic expression signatures so derived. Here, PC1,2,3 are the first three principal components of the DSS WC signature for either genes or exon usage, median is the median expression (relative isoform expression) of the genes in that signature, and VDJ-read-cpm, VDJ-clonality are the adaptive inflammation scores for each mouse in that dataset, respectively. In order to validate the predictive power of the training function above, we use it to predict the HistoScore using each validation dataset. For each validation set of the 26 and the transcript variants described in RefSeq27  for those genes.The differential splicing of Il1rl1 and Lama3 was interpreted in terms of differential transcript expression by comparing the differential exon-usage pattern identified by diffSplice, the alignment pattern observed in the exploration of BAM files using the Integrative Genome Viewer (IGV)9. The overlap between the murine differential expression signatures and the human GRN was done using Cytoscape 3.2.128.To explore the relevance of the transcriptomic alterations identified in the DSS model for human disease, we used a colonic gene-regulatory network (GRN) built from mi- croarray gene expression and genotype data from a population of Crohn’s Disease (CD) patientsExperimental validation of the differential transcript usage for Il1rl1 and Lama3 was performed through a qPCR experiment using transcript- specific primers spanning exon junctions  on the QIAsymphony. RNA from whole blood collected in PAXgene tubes was isolated using QIAsymphony Blood PAXgene RNA kit (cat.# 762635). One microgram of total RNA was used for the preparation of the sequencing libraries using the RNA Tru Seq Kit (Illumina (Cat # RS-122-2001-48). Ribosomal RNA from biopsy tissue was depleted from total RNA using the Ribozero kit (Illumina Cat # MRZG12324), and globin RNA along with ribosomal RNA was depleted from total blood RNA using Globin zero gold rRNA removal kit (Illumina cat.# GZG1224) to enrich poly-adenylated coding RNA as well as non-coding RNA. The rRNA and globin + rRNA depleted RNA from biopsy and blood total RNA, respectively was used for preparation of the sequencing library using RNA Tru Seq Kit supplied by Illumina (Cat # 1004814). The ribozero and globin zero RNASeq libraries were sequenced on the Illumina HiSeq 2500 platform using 100 bp paired end protocol following manufacturer’s procedure.Biopsy RNAseq data were generated as previously described. Briefly biopsy and Blood RNA was extracted and processed in randomly allocated batchers as previously described21. Default parameters for STAR were used, as were those for the quantification of aligned reads to GRCh37.75 gene features via featureCounts22. Multimapping reads were flagged and discarded. Raw count data was pre-filtered to keep genes with CPM > 0.5 for at least 3% of the samples. After filtering, count data were normalized via the weighted trimmed mean of M-values30.Genomic alignment to GRCh37 of single-end RNAseq reads was performed using 2-pass STARlimma framework. Expression matrices were also adjusted for age, gender, and genetic PCs for further analysis.Gene expression matrices were generated using the voom transformation and adjusted for technical variables  using the Icahn School of Medicine at Mount Sinai and Boulder Biopath received Institutional Animal Care and Use Committee (IACUC) approval for conducting these murine disease model experimental studies and have complied with all relevant ethical regulatory requirements. All human data were from previously published cohorts and therefore, IRB and informed consent was not required for this study.Authors submit individual source files to ensure readability. This file type is published in raw format and is not edited or composed.Statistical analysis was designed as described in the Methods section. All analysis was reproduced in at least two independent experiments.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Code 1Reporting Summary


Sustaining higher frequency of mast cells in the allergic lesion site has been recognized. Factors causing high numbers of mast cells in the local tissues are not fully understood yet. RAS signaling plays a role in sustaining certain cell activities. This study is aimed at elucidating the role of RAS activation in the apoptosis resistance induction in mast cells and at employing semaphorin 3A to regulate RAS activities in sensitized mast cells and alleviating the allergic response in the intestine. A food allergy (FA) mouse model was developed. Mast cells were isolated from FA mouse intestinal tissues by flow cytometry. Mast cell apoptosis was assessed by staining with annexin V and propidium iodide. We found that aberrantly higher p21-activated kinase-1 (Pak1) expression in FA mast cells was associated with mast cell aggregation in the intestine. Sensitization increased Pak1 expression and apoptosis resistance in intestinal mast cells. RAS and Pak1 mutually potentiated each other in sensitized mast cells. Semaphorin 3A (sema3A) suppressed the Pak1 expression and RAS activation in mast cells. sema3A restored the apoptosis sensitivity in sensitized mast cells. Administration of sema3A potentiated allergen-specific immunotherapy in experimental FA. In conclusion, mast cells of FA mice showed higher Pak1 expression and high RAS activation status that contributed to apoptosis resistance in mast cells. Administration of sema3A restored the sensitivity to apoptosis inducers and promoted the therapeutic effects of specific immunotherapy on experimental FA. Mast cells are one of the major effector cells in allergic response . SpecifiOur previous studies showed apoptosis resistance in intestinal mast cells of mice with experimental food allergy (FA) . Those mThe p21-activated kinases (Paks) are a family of serine/threonine kinases activated by Cdc42 (cell division control protein 42) and Rac1 (Ras-related C3 botulinum toxin substrate). They were discovered as binding proteins of small GTPase in rodent brain tissue . Pak1 isThe semaphorin family has soluble and membrane-bound proteins that are associated with angiogenesis, neuronal development, cancer progression, and organogenesis . SemaphoThe animal experimental procedures were approved by the Animal Ethical Committee at Zhengzhou University.μm first, then 40 μm) and collected by centrifugation . With CD117 and FceRI as cell markers, mast cells were purified via flow cytometry (FCM). The purity of isolated mast cells was 95%-97% as checked by FCM. The viability of isolated mast cells was 97%-99% as assessed by the Trypan blue exclusion assay.Small intestinal segments were excised and cut into small pieces about 2 × 2 × 2 mm in size. The tissues were incubated with collagenase IV (0.5 mg/ml) for 30 min at 37°C with mild agitation. Single cells were filtered through a cell strainer  was excised and irrigated with 2 ml saline; the irrigation fluids were collected and centrifuged at 5000 × t test or the Mann–Whitney test; ANOVA followed by Dunnett's test was performed among more than two groups. The Pearson correlation coefficient test was performed to determine the correlation between the two groups. p < 0.05 was set as a significant criterion.The data are presented as mean ± SEM or median (IQR). The difference between two groups was determined by Student's Reagent sources, experimental procedures of mice, generation of bone marrow-derived mast cells, flow cytometry, real-time quantitative RT-PCR, Western blotting, enzyme-linked immunosorbent assay, immunoprecipitation, knockdown of plexin A2 or Sos1 expression in BMMCs by RNA interference, overexpression of Pak1 in BMMCs.Our previous reports showed apoptosis resistance in FA mast cells (mast cells of FA mice) ; here, wThe data of As Pak1 can be activated by Ras-related small G-proteins , the higPublished data indicate that sema3A can downregulate RAS activation by facilitating RAS.GTP to transform to RAS.GDP status and blocking the Ras/mitogen-activated protein kinase (MAPK) signaling pathway . We thenWe next tested a possible role of sema3A in restoring apoptosis sensitivity in sensitized mast cells. BMMCs were sensitized via exposure to anti-OVA IgE in the culture Figure . SensitiFinally, we developed an FA mouse model with ovalbumin (OVA) as the specific antigen Figure . FA miceOur previous studies showed that FA mast cells had the apoptosis resistance feature ; the prein vitro experiments. The RAS activation levels were positively correlated with the mast cell counts in the intestine, as well as with the apoptosis resistance in FA mast cells. Previous reports also mentioned that RAS was associated with the pathogenesis of allergic diseases; for example, Zhang et al. suggested that RAS activities were involved in several aspects of the pathophysiology of asthma, such as airway hyperresponsiveness, airway smooth muscle contraction, and airway remodeling [ε, an effector of Ras and Rap small GTPases, can induce dermatitis [The mast cell aggregating in the lesion tissues is a pathological feature of allergic diseases, some chronic inflammations, and tumors. The underlying mechanism is not fully understood yet . The premodeling . Overexprmatitis . The preOur previous studies  showed tOur previous studies showed that Bcl2L12 is bound to c-Myc, the transcription factor of FasL, to prevent the FasL expression. As FasL plays a critical role in the initiation of apoptosis , the preSon of Sevenless (Sos) is a key factor in RAS activation . Our datThe data show that inhibition of RAS restores the sensitivity to apoptosis inducers in sensitized mast cells. The apoptosis inducer of this study is the exposure to specific antigens, an event similar to the “activation-induced cell death” (AICD). The phenomenon of AICD was firstly found in T cells  that wasIt is well known that mast cells are one of the major effector cells in allergic diseases . This stThe data showed that administration of sema3A not only reduced the mast cell number in the intestine but also attenuated the Th2 cytokines in the intestine and downregulated serum sIgE. This may result from the fact that the aberrant RAS activation is also involved in the development and maintenance of Th2 polarization in the intestine of FA mice. Others also noted this phenomenon; for example, Wu et al. found that fungal allergen could activate the RAS/RAF/ERK signal pathway to induce allergic inflammation in the airway tissues . Mast ceWe also observed that, after the treatment with both sema3A and AIT, the eosinophil number was reduced in the intestinal tissues; eosinophilic mediators were reduced in GLF. Previous reports also found that administration of sema3A inhibited eosinophils from entering the experimental allergic conjunctiva in mice . This maεRI on the surface of mast cells by specific antigen promotes mast cell proliferation and survival [Our data show that sensitization confers mast cells the apoptosis resistance. Previous studies also suggest that aggregation of Fcsurvival , 33. OneIn summary, the present data show that sensitized mast cells are at a higher activation status and express higher levels of Pak1. The RAS activation and Pak1 expression mutually potentiate each other, sustaining the development and maintenance of apoptosis resistance in sensitized mast cells. Administration of sema3A can markedly promote the effects of AIT on alleviating experimental FA, suggesting that sema3A has the translation potential in the treatment of FA and other allergic disorders.


Acute kidney injury (AKI) is common and associated with adverse outcomes as well as important healthcare costs. However, evidence examining the epidemiology of acute kidney disease (AKD)—recently defined as AKI persisting between 7 and 90 days—remains limited. The aims of this study were to establish the rates of early AKI recovery, progression to AKD and non-recovery; examine risk factors associated with non-recovery and investigate the association between recovery timing and adverse outcomes, in a population-based cohort.All adult residents of Tayside & Fife, Scotland, UK, with at least one episode of community or hospital-managed AKI using KDIGO creatinine-based definition during the period 1 January 2010 to 31 December 2018 were identified. Logistic regression was used to examine factors associated with non-recovery, and Cox modelling was used to establish associations between AKI recovery timing and risks of mortality and development of de novo CKD.Over 9 years, 56,906 patients with at least one AKI episode were identified with 18,773 (33%) of these progressing to AKD. Of those progressing to AKD, 5059 (27%) had still not recovered at day 90 post AKI diagnosis. Risk factors for AKD included: increasing AKI severity, pre-existing cancer or chronic heart failure and recent use of loop diuretics. Compared with early AKI recovery, progression to AKD was associated with increased hazard of 1-year mortality and de novo CKD .These findings highlight the importance of early AKI recognition and management to avoid progression to AKD and long-term adverse outcomes.The online version contains supplementary material available at 10.1186/s12916-022-02428-8. Globally, 13 million people worldwide are thought to be affected by acute kidney injury (AKI) every year , making The aim of this study is to (i) establish the rates of early recovery, progression to AKD and non-recovery following AKI using population based routinely collected healthcare data, (ii) understand which factors are associated with progression to AKD and non-recovery and (iii) explore the relationship between recovery timing and survival as well as development of de novo CKD.This was a population-based cohort formed of all adults (aged 18 or above) in Tayside & Fife, Scotland, UK, who had at least two serum creatinine measurements on different days and presented an AKI episode between 1 January 2010 and 31 December 2018. Cohort entry (index date) was defined as the first day of the first AKI diagnosis during the study period.Data were provided by the Health Informatics Centre (HIC)  at the ULinkage to SMR01 data provided information on all hospital admission and discharge dates as well as reasons for admission. Deprivation category was derived from the Scottish Index of Multiple Deprivation . InformaThe primary outcomes were AKI recovery/non-recovery, death and progression to chronic KRT (Kidney Replacement Therapy). These were assessed at day 7 and day 90 post AKI diagnosis.Secondary outcomes included progression to de novo chronic kidney disease and recovery timing—in terms of “days to recovery from the first day of AKI diagnosis”. Secondary outcomes were only assessed in a subset of the cohort: amongst patients without pre-existing chronic kidney disease and amongst patients with hospital-managed AKI respectively. The association between recovery timing and 1 year-mortality as well as 1-year de novo CKD were also explored.Detailed descriptions of all the concepts defined below are also available in Table AKI definition was based on the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine-based criteria , using tAKD was defined as a loss of kidney function for a duration between 7 and 90 days after exposure to an AKI initiating event, as per the ADQI Workgroup definition . By defaCKD was defined according to the KDIGO definition  where eGth day following the index date.Therefore, the presence of 2 eGFR records below 60 mL/min/1.73 m2 separated by more than 90 days was used to define CKD. Pre-existing CKD was determined using all SCr measurements strictly prior to the index date (first day of AKI diagnosis) whilst de novo CKD was determined using all SCr measurements sampled strictly after the 90Progression to CKD was only investigated in patients who had no pre-existing CKD identified prior to the index date.1) (for AKI identified by creatinine ratio) or < 1.2 times higher than RV1 and < 26.5 μmol/L higher than reference value 2 (RV2) (for AKI identified by creatinine increment) [Creatinine-based recovery was defined as having a creatinine measurement within 90 days post AKI diagnosis that was either < 1.2 times higher than reference value 1  or kidney transplantation. The date of chronic dialysis initiation or kidney transplantation is recorded in the Scottish Renal Registry for all patients starting chronic KRT in Scotland with 100% coverage.Characteristics of the study population were summarised by medians and interquartile ranges for continuous measurements  and as percentages for categorical factors. Scottish Index of Multiple Deprivation (SIMD) quintiles were summarised as a categorical factor. Age was converted to a categorical variable with approximately similar numbers within each category  and youngest patients (< 65 years old) taken as the reference level. Multivariable logistic regression models were implemented to identify risk factors associated with progression to AKD, taking patients with early recovery as the reference level. We then considered patients who entered the AKD phase and determined risk factors associated with non-recovery at day 90 post-AKI diagnosis, using another multivariable logistic model. For both models, we excluded patients who died or initiated chronic KRT, between day 1 and day 7, and between day 8 and day 90 respectively. In a sensitivity analysis, the models were rerun keeping patients who died or initiated chronic KRT during the period considered in the non-recovery group, and risk factors for non-recovery were re-identified. The same candidate risk factors were included in both models: demographic characteristics ; baseline comorbidities  and medications  received in the 90 days prior to the index date. Those variables were checked for multicollinearity using a correlation matrix and the variance inflation factor. Frequency of creatinine measurements can provide additional important information that other variables cannot capture and was therefore included in the models as a continuous variable.Associations between recovery timing and 1-year mortality or de novo CKD were evaluated in the recovery cohort (patients with proven recovery within the 90 days following AKI diagnosis) amongst those who had been tested within the first 7 days, using multivariable Cox proportional hazards (PH) models. People in the recovery cohort were followed up from the recovery date (time 0) until either occurrence of one of the study outcomes (death or de novo CKD) or censored at the last date of data availability 29-05-2019). Development of de novo CKD was assessed using a cause-specific Cox proportional hazard model with all-cause mortality as a competing endpoint. All AKI categories were included when exploring the association between delayed versus early recovery and adverse outcomes. However, the association between days to recovery and adverse outcomes was only investigated in patients with hospital-managed AKI, since the testing frequency (number of SCr measurements divided by number of days from AKI to recovery) was too low in those with community-managed AKI to allow for a precise determination of recovery timing, hence the exclusion of this AKI subgroup from this specific analysis. Days to recovery was included as a continuous variable using P-splines  to allow-05-2019.All data were analysed using the R statistical programming language  using the following packages: dplyr, data.table, survival, survminer, networkD3, graphics and sjPlot.The study cohort consisted of 56,906 patients who had at least one AKI episode during the period 1 January 2010 to 31 December 2018  commenced chronic KRT after the AKI episode. This was strongly associated with AKI severity, with 6.9% of patients with AKI stage 3 further initiating chronic KRT.Additional file At day 8, 18,773 (33%) patients from the initial cohort entered the AKD cohort. Of these, 7698 (41%) had a delayed recovery, with a similar proportion in the different AKI categories, whilst 5059 (27%) had still not recovered at day 90. A total of 3695 (19.7%) patients with AKD died between day 8 and day 90, with a higher proportion amongst those who were managed in hospital . Of note, 11.7% of those who had been tested but had not recovered at day 7 were not retested between day 8 and day 90 whilst 36.2% of those who had not been tested within the first 7 days had still not been tested at day 90 . The adjusted odds of progressing to AKD were 2.3 times higher (95% CI 2.2 to 2.5) in those with community-acquired/community-managed AKI than in those with community-acquired/hospital-managed AKI. An older age was negatively associated with progression to AKD, however this trend disappeared in the sensitivity analysis.Risk factors associated with progression to AKD amongst tested individuals are summarised in Table p < 0.001). Risk factors associated with non-recovery at day 90 are summarised in Table The results also showed that a higher number of SCr tests performed over the first 7 days was associated with early AKI recovery .No multicollinearity was detected between the different predictors investigated, with all correlation coefficients below 60%  with 14,486 individuals free of pre-existing CKD. Of those, 2805 (19.4%) subsequently developed de novo CKD with similar proportions across the different AKI categories .Tested people with either early or delayed proven recovery formed a recovery cohort  and de novo CKD  in the subsequent year following the AKI episode . Compared with early AKI recovery, progression to AKD was associated with increased risks of 1-year mortality and de novo CKD . The first 14 days following an AKI episode were identified as a critical window where each additional day was associated with a rapid increase in risk for adverse outcomes.It is concerning that in our cohort, a remarkably high proportion of patients with community-acquired/community-managed AKI (67%) were untested at day 7. Amongst those, 36% remained untested at day 90. Patients untested within the first 7 days appeared to be younger, with a higher baseline eGFR and milder AKI which may explain the lack of repeat testing in this fitter population. Furthermore, it is worth noting that in this area of Scotland, repeat testing within 7 days post AKI diagnosis only became more common after the introduction of the National Health Service England Acute Kidney Injury electronic alert algorithm in 2015 . TherefoThe association between AKI, AKD and CKD is complex and mortality as well as progression to CKD after an AKI episode have been documented in many studies . Our resSimilarly, we found that risk of death and de novo CKD increased progressively with recovery timing. This is in line with previous work conducted in a cohort of adult US veterans, suggesting that recovery timing may act as an independent predictor for future loss of kidney function . BhatrajOur study has several strengths. These include the comprehensive nature of the unselected population-based cohort covering a large geographical population of Scotland , the large number of AKI episodes recorded over a 9-year period and the robust methodology accounting for major confounders, with sensitivity analyses ensuring the consistency of findings. The inclusion of community-managed AKI brings new insights regarding level of care and risks associated with treatment outside hospitals, for which data are currently lacking. Finally, our strict definition of sustained recovery reduces misclassification of relapse as recovery. This work helps fill important knowledge gaps in the current understanding of renal recovery after AKI but also comes with a number of limitations. Firstly, this study was conducted in a specific geographic area of Scotland and may not be generalizable to other AKI cohorts worldwide. However, it remains an unselected population-based cohort whose characteristics are similar to that of previous studies and therefore generalizable to other high-income countries. It should be noted that a large proportion (67%) of patients with community-managed AKI were untested during the first 7 days post AKI diagnosis; hence, all conclusions made on this subgroup were based on the subset of patients who had available follow-up SCr data, with subsequent risks of ascertainment and selection bias. However, our sensitivity analysis showed that patients with community-acquired/community-managed AKI tested within the first 7 days have very similar characteristics to that of patients with hospital-acquired AKI, making comparisons relevant. It should be noted that in the absence of any accepted definition , we chosOur data demonstrates that AKD is common in patients with AKI and associated with deleterious outcomes such as early mortality or de novo CKD, especially when AKI management takes place outside hospitals. Patients with community-managed AKI should be more widely tested within the first 7 days post AKI diagnosis to ensure optimal management. As risks for adverse outcomes increase sharply during the immediate period (2 to 3 weeks) following AKI diagnosis, this work stresses the importance of early AKI recovery to avoid long-term consequences. Patients with cancer, chronic heart failure and those exposed to diuretics may be at particularly high risk of progression to AKD and non-recovery, therefore deserving extra attention. Although more evidence is needed to guide clinical practice, our results suggested that ACE/ARB may have a protective effect in a context of AKI, with improved recovery amongst recently exposed individuals. Increased awareness and strategies for the management of patients with AKD are needed to maximise early recovery and minimise AKI-related harms.Additional file 1: Figure S1: Survival curve stratified by AKI category. Table S1: Recovery status at 7- and 90-days post-AKI diagnosis. Table S2: Characteristics of tested versus untested patients with CA-CM AKI. Table S3: 90-day status of patients untested within the first 7 days post AKI diagnosis. Figure S2: Association between individual predictors and progression to AKD . Table S4 and Figure S3: Association between individual predictors and non-recovery at day 7 . Figure S4: Association between individual predictors and non-recovery at day 90 in patients who entered the AKD phase . Table S5 and Figure S5: Association between individual predictors and non-recovery at day 90 in patients who entered the AKD phase . Figure S6: Correlation matrix for pairs of candidate risk factors. Table S6: Association between progression to AKD and subsequent risk of death or development of de novo CKD.