HNF1A, TBX3, ACLY, ECI1 and ERDR1 are related to embryonic development and cell division.Posttranscriptional modification of mRNA sequences through RNA editing can increase transcriptome and proteome diversity in eukaryotes. Studies of fetal and adult tissues showed that adenosine‐to‐inosine RNA editing plays a crucial role in early human development, but there is a lack of global understanding of dynamic RNA editing during mammalian early embryonic development. Therefore, here we used RNA sequencing data from human, pig and mouse during early embryonic development to detect edited genes that may regulate stem cell pluripotency. We observed that although most of the RNA editing sites are located in intergenic, intron and UTR, a few editing sites are in coding regions and may result in nonsynonymous amino acid changes. Some editing sites are predicted to change the structure of a protein. We also report that  HNF1A can change its protein secondary and tertiary structures while ECI1 only changed the secondary structure.RNA sequence data from early embryonic development in human, mouse, and pig were used to detect edited genes that may regulate stem cell pluripotency. Non‐synonymous editing has lower frequency than synonymous editing. Humans and pigs share more editing genes during embryonic development. Edited  ADARadenosine deaminases acting on RNAA‐to‐Iadenosine‐to‐inosineCDScoding regionEssEssentialGEOGene Expression OmnibusGICGene Importance CalculatorGOGene OntologyNonessnonessentialRNA‐seqRNA sequencingSNPsingle‐nucleotide polymorphismEmbryonic stem cells have totipotency and can develop into various tissues to form a complete embryo . ConsideRNA editing is the process of altering genetic information at the mRNA level. Specifically, it will present in the transcription of genes. Due to deletion, insertion or substitution of a nucleotide , 3, the Currently, studies have shown that RNA editing is associated with a variety of human cancers and contributes to the production and maintenance of cancer stem cells that control cancer progression and drug resistance . In the HNF1A, ECI1 and HOX families, were found.In this work, we studied the dynamic process of RNA editing based on RNA sequencing (RNA‐seq) datasets about early embryonic development. The cell types of early embryonic development include two‐cell, four‐cell, eight‐cell, morula and blastocyst. Three species were analyzed in our work, including human, mouse and pig. RNA editing occurs throughout the early embryonic development process, and the highest number of editing sites is generally in the eight‐cell stage. Interesting, although the majority of editing sites are in non‐CDSs, a few nonsynonymous edited genes occur. Overall, this study comprehensively analyzed and compared the RNA editing of early embryonic development in human, mouse and pig. Finally, some edited genes related to stem cell pluripotency, such as https://www.ncbi.nlm.nih.gov/geo/)  was used with 1 µg total RNA for the construction of sequencing libraries. RNA libraries were prepared for sequencing using standard Illumina protocols. Smart‐Seq2 method was used to amplify the single‐cell sample. The sample cDNA was fragmented into 300 bp; then we performed terminal repair, addition of A, and ligation of sequencing adapters, and amplified. The PCR amplification product with 350–450 bp was extracted. RNA sequences of human and mouse used to identify editing sites were generated for this study and obtained from Liu W et al. [et al. [et al. [et al. [et al. sequenced the libraries as single‐end reads [https://github.com/FelixKrueger/TrimGalore/releases) to filter out reads from all studies that contained adapters and low quality using ‘‐‐quality 33 ‐‐phred33 ‐‐stringency 3 ‐‐length 20 –paired’. Reads were aligned to the human genome (hg38), the pig genome (susScr11) and the mouse genome (mm10) with HISAT2 (http://github.com/infphilo/hisat2) [http://broadinstitute.github.io/picard/), and clean reads were integrated. Candidate RNA editing sites were detected by REDItools [We obtained RNA‐seq data of whole early embryonic stages from Gene Expression Omnibus  Table . The RNAW et al.  Wu J et  [et al. , Yan L e [et al.  and Fan  [et al. . RNA libnd reads , whereasnd reads  was used/hisat2) , respectEDItools .P‐value using the p.adjust function of r program, and editing sites with a false discovery rate >0.05 were removed. The bcftools [http://www.ncbi.nlm.nih.gov/SNP/), Ensembl (ftp://ftp.ensembl.org/pub/release‐98/variation/) and Sanger Institute (ftp://ftp‐mouse.sanger.ac.uk/REL‐1807‐SNPs_Indels/mgp.v6.merged.norm.snp.indels.sfiltered.vcf.gz). Then all SNPs in the databases were removed with bedtools [http://genome.ucsc.edu). Alu sequences were extracted with the Python program; then the editing sites on the Alu were analyzed.We used REDItools to detect candidate sites. Finally, we collected all mismatches between the earlier reads and the reference genome. Multiple test correction was performed to correct the bcftools  was usedbedtools . For rarbedtools . So we aannovar software [annovar was written by the Perl programming language, and it classifies the RNA editing sites we have obtained to determine which positions of the genes are causing protein mutations. Finally, the results showed that there were not many mutations in the exon.We used the software  to annot2 test and F test for the earlier data was based on r program.Essential (Ess) and nonessential (Noness) genes from human and mouse were obtained from the OGEE  database. The human gene in the database contained datasets from multiple laboratories and was processed by different experiments. Therefore, this may misclassify many human Ess genes as Noness genes and mismatch some Noness human genes into Ess genes. There is only one dataset of mice in the database, resulting in insufficient data. So we used Gene Importance Calculator (GIC)  to calcur package clusterProfiler [http://metascape.org) [The gene set enrichment analysis was performed on Profiler  and Metaape.org) . To idenhttps://www.3dsbiovia.com/products/collaborative‐science/biovia‐discovery‐studio/) was used to visualize structures.We downloaded the gene sequence from National Center for Biotechnology Information and modified the editing site in the sequence. PSIPRED was used to predict the secondary structure of the two sequences . We retaet al. found that the RNA editing activity of Alu in human fetal tissue samples decreased relative to adult tissue samples [We analyzed five stages in the early embryonic development of human, mouse and pig, including two‐cell, four‐cell, eight‐cell, morula and blastocyst Table . We can  samples . The reaAccording to our analysis, RNA editing in early embryo occurs mainly in intergenic regions, introns and UTRs, regardless of species Fig. . At the et al. [ADAR1, ADAR2 and ADAR3 genes at different stages [To explore the relationship of RNA editing level in early embryonic development, we observed the distribution in the three species. RNA editing level is the ratio of the number of RNA‐seq reads, which is different from the reference base by the total number of reads covering the site . We reseet al. . They bet stages . TherefoP = 0.0003, Fig. P < 0.0001, Fig. F test on their editing frequency. Strikingly, there was a significant difference in the editing frequency of Ess and Noness genes  [We combined the earlier genes, including 2065 genes in human, 1925 in mouse and 4015 in pig Fig. . Based oee.org/)   and are involved in developmental differentiation  Fig.  and S6. et al.  and Suzu [et al. , in whicHOX family genes and RIF1; the pig genes included CD46, ACLY, CLN3, among others; and the mouse genes were H2‐DMA, KHDC1B, HJURP, ERDR1 and so on. The functions are mainly related to the regulation, proliferation and development of embryonic stem cells and energy metabolism  are known to be involved with the regulation of proliferation and migration of esophageal adenocarcinoma cells [et al. [ECI1 is related to lipid metabolism. Among these proteins, ECI1 is an auxiliary enzyme in the beta‐oxidation of polyunsaturated fatty acids.Finally, we selected representative genes, such as the human res Fig. . Moreovenge Fig. . HNF1A‐rma cells . And Zha [et al.  study foRNA samples were extracted from early embryonic development tissues to detect RNA editing events. We found that RNA editing does exist in early human embryogenesis. After obtaining the candidate genes, we not only compared the editing events of five embryonic stages but also analyzed the differences of RNA editing among human, mouse and pig. Finally, we selected candidate edited genes that are related to the characteristics of stem cells.et al. [HOX family genes were more novel. In the early embryonic development of pig, we found that these genes are mainly involved in fat metabolism. Moreover, Zhang et al. studied the reprogramming of iPSCs in pig and analyzed the expression of pig polymorphic genes [ECI1 changed the secondary and tertiary structure of the protein by RNA editing. As an important domestic animal, pig is not only the main source of meat for human beings but also an indispensable model animal in biomedical research. In the breeding process of pigs, it is very important to increase the exploration of economic traits of pigs. We performed a detailed comparative analysis of the RNA editing levels of pig embryo development to expect to find genes associated with pig economic traits, such as participation in fat metabolism. In general, we have provided new insights into the breeding of pigs.Our study found that the number of RNA editing sites has little to do with the number of samples and is related to physiological needs during embryonic development. We found that the number of A‐to‐I RNA editing in Alu sequences was significantly higher in human, which is consistent with previous studies , 44, 45.et al.  describeet al. . They poet al.  showed set al. . After ric genes . The resOur study was novel because we performed RNA editing analysis on the early embryonic developmental stages of three species. The purpose of this study was to mine genes that have undergone RNA editing during early embryonic development, and to explore the distribution of RNA editing in CDSs and non‐CDSs, with particular attention to the editing sites associated with embryonic stem genes, to provide insights into the maintenance of stem cell characteristics. Our next step is to show a conservative relationship among the three species. Also, the relationship between RNA editing differences and RNA editing enzymes in early embryonic development remains to be further revealed.The authors declare no conflict of interest.MZL, JLH and ZHL conceived and supervised the study; TTL, QL and HL designed experiments; XX, AWD and XGW performed experiments; TTL, QL and HL analysed data; TTL, NZ and ZYC drafted the manuscript; XGW, JYH, AWD and TL made manuscript revisions. All authors read and approved the final manuscript.Fig. S1. Enrichment analysis of the edited genes occurring on chromosome.Fig. S2. The ratio of RNA editing before and after exonerate v2.4.0 was used.Fig. S3. Dynamic changes of nonsynonymous editing genes.Fig. S4. Gene enrichment analysis of the intersection of nonsynonymous editing genes in three species during the development of the early embryo.Fig. S5. The editing frequency of the intersection genes of human, mouse and pig in different cells.Fig. S6. Tertiary structure of protein before and after RNA editing.Click here for additional data file.Table S1. Statistics of the samples.Click here for additional data file.Table S2. Numbers and percentages of RNA editing sites in the human identified by REDItools using RNA reads as inputs.Click here for additional data file.Table S3. Statistics for nonsynonymous and synonymous editing sites in different stage.Click here for additional data file.Table S4. Essential and nonessential genes in human and mouse.Click here for additional data file.Table S5. GO analyzed 169 genes that intersect in human, mouse and pig.Click here for additional data file.Table S6. GO analysis for genes that produced nonsynonymous editing.Click here for additional data file.


Even though persistent neural activity has been proposed as a mechanism for maintaining eligibility trace, direct empirical evidence for active maintenance of eligibility trace has been lacking. We recorded neuronal activity in the medial prefrontal cortex (mPFC) in rats performing a dynamic foraging task in which a choice must be remembered until its outcome on the timescale of seconds for correct credit assignment. We found that mPFC neurons maintain significant choice signals during the time period between action selection and choice outcome. We also found that neural signals for choice, outcome, and action value converge in the mPFC when choice outcome was revealed. Our results indicate that the mPFC maintains choice signals necessary for temporal credit assignment in the form of persistent neural activity in our task. They also suggest that the mPFC might update action value by combining actively maintained eligibility trace with action value and outcome signals. Empirical studies have found evidence for molecular synaptic tagging in the striatum and cortex in rodents6 as well as in the insect brain7. Second, spike timing-dependent synaptic plasticity beyond millisecond timescale may associate two events that are separated in time at longer timescales8. In the hippocampus, a variant of spike timing-dependent synaptic plasticity rule  allows potentiation of synapses that are activated by presynaptic activity up to 2 s before9. Third, the information about an action may be maintained for a short period of time in the form of short-term synaptic weight changes11.Making a causal link between an action and its outcome can be trivial when sensory information about the chosen action is available at the time its outcome is revealed. It is often the case, however, that the outcome of an action is revealed after a substantial delay without sensory information about the chosen action. In everyday lives, feedbacks of a large number of actions are available not immediately, but with a delay on the timescale of seconds. Hence, we are frequently faced with the problem of attributing credit of an outcome to an action we committed seconds before. There are several candidate neural processes that can be used to solve this temporal credit assignment problem. First, synapses activated during action generation may be tagged biochemically, and their weights are modified by feedback signals12. This will be referred to as ‘active’ eligibility trace to contrast to the other candidate processes mentioned above (synaptic tagging and synaptic plasticity) which will be referred to as ‘silent’ eligibility traces. Active eligibility trace is a plausible neural process given that many brain structures maintain working memory in the form of persistent neuronal ensemble activity13. It has also been shown that many different brain areas in rats and monkeys maintain choice signals as persistent activity over multiple trials14. However, to the best of our knowledge, choice-related persistent activity has not been demonstrated in an experimental setting that requires temporal credit assignment. In almost all studies so far, sensory information about the chosen action was available at the time its outcome is revealed. In this regard, we have previously examined striatal neural activity in a task in which a choice must be remembered until its outcome is revealed for correct credit assignment; however, we failed to find persistent neuronal ensemble activity linking a choice and its outcome in the dorsal striatum15. In another study in which monkeys performed a task requiring temporal credit assignment across target-cue presentation and choice outcome, neural activity related to a target cue subsided following cue offset and then arose again at the time of outcome onset16. Hence, even though active eligibility trace is a plausible mechanism for temporal credit assignment, it is yet to be demonstrated empirically.As another candidate mechanism, which is the subject of the present study, memory of the committed action may be maintained by persistent activity of a population of neurons20 we reasoned that the mPFC may maintain short-term memory of a chosen action based on active discharges. Our results show that the mPFC neuronal population carries significant choice signals in the form of persistent activity during the time period between the animal’s choice and its outcome.In the present study, we examined whether the medial PFC (mPFC) maintains eligibility trace in the form of persistent neuronal ensemble activity. Given its importance in working memory15  task in an elevated Y-shaped maze as in our previous studyThe central stem of the maze was narrow (5 cm) so that the rat’s return trajectories from the left and right targets to the reward zone converged on the central stem Fig. b. As a cThe rats were over-trained in the task  before unit recording began. Figure We recorded total 446 single units from the anterior cingulate cortex (ACC), prelimbic cortex (PLC) and infralimbic cortex (ILC) in the right hemisphere in three rats (excluding those units recorded near the borders between these structures) performing the dynamic TAB task over 12–21 days ] or outcome [R(t)] in the current trial. As shown, neuronal populations in all three subregions carried significant choice signals  throughout the memory stage and also during the reward stage. Overall, the return time  = 3.407; p < 0.01). Similar results were obtained when we performed the same analysis excluding those sessions with significantly  different return times between high and low reward-probability-target choices  or reward stage . Analyzing the same time period, of 160 PLC neurons, 27 and 71 were significantly responsive to choice and outcome, respectively, and 19 were significantly responsive to both choice and outcome; of 60 ILC neurons, 11 and 32 were significantly responsive to choice and outcome, respectively, and 10 were significantly responsive to both choice and outcome. Both PLC and ILC neurons were significantly more likely to encode both choice and outcome than expected by chance . These results indicate that choice and outcome signals are conjunctively combined in the mPFC during the reward stage.To test whether choice and outcome signals are conjunctively encoded in the mPFC, we examined whether mPFC neurons are more likely to encode both choice and outcome signals than expected by chance. Of 78 ACC neurons, 13 and 48 were significantly responsive to choice and outcome, respectively, during the first 1-s time period since reward stage onset , and, of these, 11 were significantly responsive to both choice and outcome. There was a trend for ACC neurons to encode both choice and outcome more than expected by chance . As shown in Fig. 25. Chosen value and outcome signals would be sufficient to compute reward prediction error and update value of the chosen target in a given trial. However, in our previous study in the striatum that used the same task as the present one, we failed to observe elevation of chosen value signals at the time of choice outcome15. Instead, action value signals were elevated in the dorsolateral  striatum. Given that choice and outcome signals were also significant at the time of choice outcome, these results suggest that the dorsolateral striatum my update value of the chosen target by combining signals for action value, choice, and outcome. To examine neural signals for action value and chosen value in the mPFC, we used a regression model that included left action value, right action value, and chosen value along with the animal's current choice and its outcome  in the ACC and PLC  and PLC , but not in the ILC  , but not in the ACC (p = 0.11) or ILC (p = 0.49). In addition, in the ACC, chosen value signals were significantly weaker than left action value signals . These results indicate that chosen value signals are not elevated in the mPFC at the time choice outcome is revealed in our task. They also indicate that all the signals necessary for updating action value, namely choice, outcome, and action value signals converge in the ACC and PLC during the early reward stage.Choice and outcome signals must be combined with value signals in order for the animal to update expected outcomes associated with two target choices. In our previous studies using conventional dynamic foraging tasks, signals for chosen value  were elevated as the animals approached the target locations, so that they were combined with outcome signals at the time choice outcome was revealed in numerous brain areasms; Eqs. ) which wWe examined whether the mPFC neuronal population carries memory signals for a chosen action in the form of persistent activity in a task that requires credit assignment across a temporal gap of ~ 1 s. We found that all subregions of the mPFC  maintain memory signals for a chosen action in the form of persistent ensemble activity during the time period between the animal's choice of action and its outcome. We also found that, unlike in conventional dynamic foraging tasks, chosen value signals are not elevated at the time of outcome onset. Instead, action value signals converged with choice and outcome signals in the ACC and PLC. These results show that all subregions of the mPFC actively maintain eligibility trace signals, and that they are combined with action value and outcome signals in the ACC and PLC when the animal’s choice outcome is revealed.2, may also contribute to solving the temporal credit assignment problem in the mPFC. Also unknown is whether and how different brain structures rely on different neural processes to solve the temporal credit assignment problem. We failed to obtain evidence for active eligibility trace in the dorsal striatum in the same task as used in the present study15. This might be an indication that the mPFC plays a more important role than the striatum in temporal credit assignment in the current task . Alternatively, the mPFC and striatum may rely on different neural processes for temporal credit assignment. In this regard, previous studies have shown evidence for synaptic tagging in the striatum5. Note that experimental settings vary widely across studies on the neural basis of temporal credit assignment. A given brain structure may rely on different neural processes to solve temporal credit assignment depending on task requirement such as the length of a temporal gap between an action and its outcome27. Clearly, further studies are needed to clarify whether and how different brain structures utilize different neural processes to solve temporal credit assignment problem in diverse behavioral settings.Our results provide evidence for active eligibility trace in the mPFC in the timescale of seconds. However, it remains to be determined whether such active eligibility trace actually contributes to assigning credit of the experienced outcome to the chosen action. Even in the case it does, it is unknown whether the mPFC relies solely on active eligibility trace to solve the temporal credit assignment problem. Other neural processes than active eligibility trace, such as synaptic tagging28, conveys working memory signals in the form of dynamically-changing, rather than stably-maintained, neuronal ensemble activity20. Readout of such dynamically-changing neuronal ensemble activity would be trivial if the duration of delay is fixed; a specific ensemble activity pattern at the end of delay can be matched to a specific sensory cue presented before delay. However, working memory signals are conveyed by dynamically-changing mPFC neuronal ensemble activity even when delay duration is not fixed, such as when delay duration is randomized across trials29. We also found the maintenance of previous choice signals in the form of dynamically-changing neuronal ensemble activity during the time period that varied across trials in the dorsomedial striatum22. Together, these results raise the possibility that the brain might be equipped with a mechanism for reading out signals reliably from dynamically-changing neuronal ensemble activity. Further studies are needed to elucidate whether this is the case and, if so, how such readout may be implemented.Assuming that the mPFC relies on active eligibility trace to solve the temporal credit assignment problem in the present task, it is unclear how the mPFC ascribes credit of a choice outcome to neuronal ensemble activity representing a specific choice. Even though the mPFC neuronal population maintained choice signals throughout the memory stage, only a small fraction of individual mPFC neurons showed persistent choice-related activity throughout the memory stage. In other words, choice signals were maintained in the form of dynamically changing neuronal ensemble activity during the memory stage so that neuronal ensemble activity at the time of outcome onset differed from that at the time of action selection. It is currently unclear how a choice outcome might alter mPFC neural network so that it affects neural representation of the chosen action in a way to guide future choices. In fact, this is a problem not limited to temporal credit assignment. The rodent mPFC, which plays a crucial role in diverse working memory tasks30. These results suggest that these brain areas might update action value by combining chosen value and outcome signals. However, we found that action value signals are weak in the mPFC at the time of outcome onset in the present task. In our previous study that used the same task as the present one, we showed that chosen value signals are weak and, instead, action value signals are elevated during the reward stage in the dorsolateral striatum15. These results suggest that dynamics of neural signals related to evaluating choice outcome might differ drastically depending on the requirement for temporal credit assignment in the mPFC and dorsal striatum. These brain structures may rely on action value rather than chosen value signals in updating value of the chosen action when sensory information on the chosen action is not available at the time of outcome. It remains to be determined whether this is a common rule that can be applied to other brain structures as well.A large body of studies have shown that chosen value signals arise at the time of outcome onset in multiple regions of the brain including the mPFC in behavioral tasks in which sensory information is available at the time of outcome onsetThree young male Long-Evans rats  were individually housed in a colony room and initially allowed free access to food and water. They were then gradually deprived of water with free access to food with extensive handling for 7 days. Their body weights were maintained at > 80% ad libitum throughout the experiments. Experiments were performed in the dark phase of a 12-h light/dark cycle. All experiments were performed in accordance with protocols approved by the directives of the Animal Care and Use Committee of the Korea Advanced Institute of Science and Technology .The rats performed a dynamic TAB task in a Y-shaped maze that was made of black acrylic Fig. a. The cet-test, p > 0.05) and maintained that way for at least three time points (100 ms) was marked as the onset of the memory stage  was updated as follows:α is the learning rate and R(t) is the reward  in the t-th trial. The total action value [Qa(t + 1)], that includes the action value [qa(t + 1)] and the win-stay-lose-switch term, were calculated as follows:aWS and aLS are the win-stay and lose-switch terms, respectively. The action value for the unchosen action did not change. The probability for selecting the left goal β is the inverse temperature which defines the degree of exploration during the action selection and 31.We used the same hybrid model we used in our previous study32. Unit recording locations were determined based on the locations of marking lesions and tetrode advancement history. Those units that were determined to be near the border between the secondary motor cortex and ACC, between the ACC and PLC, or between the PLC and ILC were excluded from the analysis  and the rest were classified as putative interneurons . The identity of a unit signal was determined based on the clustering pattern of spike waveform parameters, averaged spike waveforms, baseline discharge frequencies, autocorrelograms and interspike interval histogramst. Neural activity related to the animal’s choice and its outcome was examined using the following regression model:t. A(t) is a set of autoregressive terms consisting of spike discharge rates during the same analysis time window in the previous three trials as the following:Neural activity related to action value and chosen value was examined with the following model:A(t) in the regression model to prevent such slowly drifting neural activity from inflating action value signals.Action values are correlated across trials because they are updated incrementally according to the animal’s choice outcomes. If neural spikes are also correlated across trials, they may appear as encoding action value even though the source of spike serial correlation is unrelated to action value. We included 15 in which dorsal striatal neural activity was examined in the same behavioral task as used in the present study. Student’s t-tests were used to determine the significance of a regression coefficient and the difference between left- and right-choice trajectories. Permutation tests were used to determine significance of neural signals for a given variable. We circularly shifted trials of spike count data by a random number  and assessed how many neurons were significantly responsive to a variable of interest. This was repeated 100 times, and the p value for a given variable was determined by the frequency in which the number of neurons significantly responsive to a given variable was exceeded by that obtained after trial shuffling. χ2-tests were used to test whether choice and outcome signals are encoded conjunctively. All tests were two-tailed, and a p value < 0.05 was used as the criterion for a significant statistical difference. Results are expressed as mean ± SD.Sample sizes were determined based on the sample sizes in our previous studySupplementary Figure S1.