Drosophila. These studies on fewestablished models have led to the general assumption that recoding by RNA editing isextremely rare. Here we employ a novel bioinformatic approach with extensivevalidation to show that the squid Doryteuthis pealeii recodesproteins by RNA editing to an unprecedented extent. We identify 57,108 recoding sitesin the nervous system, affecting the majority of the proteins studied. Recoding istissue-dependent, and enriched in genes with neuronal and cytoskeletal functions,suggesting it plays an important role in brain physiology.RNA editing by adenosine deamination alters genetic information from the genomicblueprint. When it recodes mRNAs, it gives organisms the option to express diverse,functionally distinct, protein isoforms. All eumetazoans, from cnidarians to humans,express RNA editing enzymes. However, transcriptome-wide screens have only uncoveredabout 25 transcripts harboring conserved recoding RNA editing sites in mammals andseveral hundred recoding sites in DOI:http://dx.doi.org/10.7554/eLife.05198.001 For living cells to create a protein, a genetic code found in its DNA must first be‘transcribed’ to create a corresponding molecule of messenger RNA(mRNA). DNA and RNA are both made from smaller molecules called nucleotides that arelinked together into long chains; the information in both DNA and RNA is contained inthe sequence of these molecules. The mRNA nucleotides coding for proteins are‘translated’ in groups of three, and most of these nucleotide tripletsinstruct for a specific amino acid to be added to the newly forming protein.DNA sequences were thought to exactly correspond with the sequence of amino acids inthe resulting protein. However, it is now known that processes called RNA editing canchange the nucleotide sequence of the mRNA molecules after they have been transcribedfrom the DNA. One such editing process, called A-to-I editing, alters the‘A’ nucleotide so that the translation machinery reads it as a‘G’ nucleotide instead. In some—but not all—cases, thisevent will change, or ‘recode’, the amino acid encoded by this stretchof mRNA, which may change how the protein behaves. This ability to create a range ofproteins from a single DNA sequence could help organisms to evolve new traits.Evidence of amino acid recoding has only been found to a very limited extent in thefew species investigated so far. There has been some evidence that suggests thatrecoding might occur more often, and alter more proteins, in squids and octopuses.However, this could not be confirmed as the genomes of these species have not beensequenced, and these sequences were required to investigate RNA recoding usingexisting techniques.Alon et al. have now developed a new approach that allows the recoding sites to beidentified in organisms whose genomes have not been sequenced. Using thistechnique—which compares mRNA sequences with the DNA sequence they have beentranscribed from—to examine the squid nervous system revealed over 57,000recoding sites where an ‘A’ nucleotide had been modified to‘G’ and thereby changed the coded amino acid. Many of the identifiedmRNA molecules had been recoded in more than one place, and many more of these thanexpected changed the amino acid sequence of the protein translated from them. Alon etal. therefore suggest that RNA editing may have been crucial in the evolution of thesquid's nervous system, and suggest that recoding should be considered a normal partof the process used by squids to make proteins.DOI:http://dx.doi.org/10.7554/eLife.05198.002 The central dogma of biology maintains that genetic information passes faithfully fromDNA to RNA to proteins; however, with the help of tools such as alternative splicing,organisms use RNA as a canvas to modify and enrich this flow of information. RNA editingby deamination of adenosine to inosine (A-to-I) is another process used to alter geneticinformation . UnlikeDrosophila, an order of magnitude morerecoding sites have been identified, residing in about 3% of all messages homologous to known proteins (We sequenced cDNA from the giant axon system (giant fiber lobe: GFL), the optic lobes(OL) and matching germline gDNA isolated from the same animal. cDNA was also sequencedfrom the vertical lobe (VL), buccal ganglion (BG) and the Stellate Ganglion (SG) fromanother animal . The GFL and OL RNA-seq reads were used toconstruct a transcriptome model . To focuproteins   tissue of the Stellate ganglion, the optic lobe (OL) tissue and a portionof the sperm sack were manually dissected from a single adult male immersed inchilled, filtered seawater. The Buccal ganglia (BG), Stellate ganglion with the giantfiber lobe removed (SG) and Vertical lobe (VL) tissues were dissected from a secondadult male individual. Tissues were also dissected from non-neuronal regions: thebranchial heart, the Gill, the ventral epithelial layer on the pen, the marginalepithelial layer on the pen, the iridophore layer of the skin, and the chromatophorelayer of the skin. Each of these six tissues originated from a different animal. RNAfrom all tissues was extracted with the RNAqueous kit , and genomic DNA was extracted from the sperm sack using Genomic Tip Columns.Specimens of the squid The genomic DNA sequencing library was prepared using the TruSeq DNA Sample Prep kit,as described by the manufacturer , and sequenced using threelanes of Illumina HiSeq 2000 instrument. The RNA-Seq libraries for all the sampleswere prepared using the TruSeq Stranded mRNA Sample Prep Kit, as described by themanufacturer (Illumina), and were sequenced using Illumina HiSeq 2000 instrument. TheGFL and OL libraries were sequenced together on a single lane, the same for the VLand BG libraries (with one unrelated library) and the SG library was sequenced on asingle lane (with one unrelated library).Illumina sequencing was utilized to generate ∼87 million paired-end, 100 ntreads, using RNA from GFL tissue, the same number of reads using RNA from OL tissue,and ∼440 million paired-end, 100 nt reads, using germline DNA. The Trinitytranscript assembly tool  was usedDNA reads and RNA reads were separately aligned against the RNA components usingBowtie2 with local alignment configuration and default parameters . Only unTo focus on editing sites inside coding regions, and avoid repetitive elements thatare prone to assembly and alignment errors, we retained only those components thatwere found to be significant similar  to the Swiss-Protproteins dataset .In thesA)‘weak’ editing sites procedure: a binomial test was applied to find thesignificant modifications between RNA reads and the Swiss-Prot ORFs. The binomialstatistics uses the number of successes (the number of reads with a mismatch of agiven type in a given position), the number of trials  and the error probability. The probability of having asequencing error (the error probability) was estimated using the sequence qualityscore. We counted only mismatches with >=30 quality score, and thereforethe expected error probability was set to 0.1%. The binomial test was applied toevery position inside the Swiss-Prot ORFs. The p-value for each location wascorrected for multiple testing using a Benjamini-Hochberg false-discovery rate of10%. Furthermore, in order to exclude RNA variability due to genomic polymorphisms,we filtered out all modification sites in which any of the DNA reads aligned to thesite does not agree with the transcriptome. This procedure assumes the RNA consensusin the site is identical to the gDNA reads, and thus is not suited to detect sites inwhich the editing appear in most of the RNA reads and therefore also in theTrinity-generated ORFs (‘strong’ editing sites). (B)‘strong’ editing sites procedure: the locations in which all DNA readsshowed a different base than the ORF. The probability of such sites to be not aresult of editing but rather a single nucleotide polymorphism (SNP) was estimated by(1/2)(#DNA reads)  multiplied by theprior probability of a SNP which was taken to be 0.001. Here too, the p-value foreach location was corrected for multiple testing using a Benjamini-Hochbergfalse-discovery rate of 10%.The above alignment data and the ORFs information were used to find the locations ofall DNA-RNA mismatches inside the coding regions. In the following, bases called withquality score Q < 30 were discarded. Note, however, that Trinity consensussequence does take into account these bases, as well as reads that might have notbeen uniquely aligned to the transcriptome. It is often customary to filter outreads' ends when analyzing RNA-DNA mismatches. A main reason for that is the commonmismatches at reads' ends due to alignment artifacts when a splicing junction occursnear the ends. In our case, as alignment is done to the transcriptome, we did notobserve any increase in AG mismatch rate near reads' ends , and thuOverall, 81,930 weak and 5649 strong A-to-G modification sites were detected in 7776ORFs, and only 12,403 weak and 254 strong non A-to-G modifications sites , revealing a high number of ORFs with A-to-G mismatch clusters . Forexabona fide A-to-I editingsites . One possible source of apparent modifications issequencing errors. We thus compared the quality score between A-to-G and non A-to-Gmodification sites. However, no significant difference in the distribution of thequality scores was observed . Anotherbona fide editing sites. To furthercharacterize the tissue-dependence of modifications levels, Illumina sequencing wasagain utilized to generate ∼54, ∼62 and ∼42 million paired-end,100 nt reads, using RNA from Buccal ganglia (BG), Stellate ganglion with the giantfiber lobe removed (SG), and Vertical lobe (VL), respectively, collected from adifferent animal. The same alignment procedure was applied to quantify themodification level at the previously described sites for each of the additionalsamples. Statistically significant differences in the modification levels between allthe five neuronal tissues were detected using a binomial analysis withBonferroni-corrected p-value of 0.05 as a cutoff. The sites with significantlyvariable A-to-G modification levels were subjected for hierarchical clustering,revealing clear tissue selectivity with higher modification levels in the GFL tissueand low levels in the VL tissue  of the detectedA-to-G modifications differ significantly between the GFL and OL tissues. Most ofthese sites  have higher modification levels in the GFLtissue. In contrast, only 6%  of the non A-to-G modifications aresignificantly different for the same two tissues, with no clear tissue preference:45% and 55% of these sites have higher modification levels in the GFL and the OLtissue, respectively. As the same technical artifacts and genomic polymorphisms areexpected to replicate in both tissues, these data increase our confidence that mostof the A-to-G sites are L tissue . The samL tissue .To characterize the modification levels in non-neuronal tissues, Illumina sequencingwas again utilized to generate ∼23, ∼23, ∼19, ∼26,∼19 and ∼14 million paired-end, 150 nt reads, using RNA from thebranchial heart, the Gill, the ventral epithelial layer on the pen, the marginalepithelial layer on the pen, the iridophore layer of the skin, and the chromatophorelayer of the skin. The same alignment procedure was applied to quantify themodification level at the previously described sites for each of the additionalsamples, revealing considerably lower editing levels in the non-neuronal tissues we recorded all the cases in which the beginningor the end of the DNA read was trimmed during the local alignment against the Trinitysequences. (b) If the flanking region could be aligned against the Trinity sequences,even if aligned separately without the rest of the read, it was discarded. (c) If atleast three DNA reads showed the same flanking sequence starting from the sameposition inside the ORFs, it was considered to be an intron fragment. This procedurerevealed the positions of part of the exon–exon junctions and part of theintron sequences that correspond to the junctions. Overall, this procedure resultedin about 2100 regions, containing ∼5% of all the detected editing sites, inwhich the gDNA and the cDNA could potentially be differentially amplified.For the Sanger sequencing, primers were designed to differentially amplify the gDNAand cDNA of 19 ORFs . To alloFor the deep sequencing validations, three groups of targets were tested :(a) twehttp://www.cgl.ucsf.edu/chimera  for consistency. The GO analysis tool GOrillawas used to find enriched GO annotations in the ranked list . In ordeThe extensive recoding due to RNA editing can affect many molecular pathways. Inorder to appreciate the extent of this phenomenon, squid ORFs were mapped to allhuman KEGG pathways , revealiAn important question is whether editing in the squid ORFs tends to avoid recoding bypreferring synonymous modifications or, alternatively, tends to create more recodingsites than expected by chance. In squid, the expected fraction of nonsynonymouschanges is 0.65, estimated by random changes of adenosines in the ORFs, accountingfor the observed local sequence preference of the editing sites , as follA recoding event which creates an amino acid rarely found in homologous proteins mayindicate that the editing is deleterious. Therefore, each squid ORF was alignedagainst the conserved domains in the Conserved Domain Database (CDD) usingDELTA-BLAST . DELTA-BA) to show that the enormous number of AGmismatches in the squid data is real and not an artefact of the analysis pipeline,and (B) to demonstrate that our pipeline could identify RNA editingsites established by previous studies. The results of the analysis are summarized inWe searched publicly available datasets and found two datasets of matched DNA- andRNA-seq data which are comparable in size and read-lengths to our squid data: (i)Human RNA-seq and DNA-seq data of blood samples  (ii) MacIn order to assess the precision of our method in recalling true editing sites wehave looked at the validated editing sites found in the two studies above, andchecked whether they have been picked up by our algorithm as well. The full data ispresented in http://dx.doi.org/10.5061/dryad.2hv7d).The data reported in this paper was deposited to the Sequence Read Archive (SRA),under accession SRP044717. The constructed squid coding sequences and all the A-to-Gmodification sites detected in the coding regions of the squid are available viaDryad digital repository ( reviewprocess). Similarly, the author response typically shows only responsesto the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of thepublished articles . An edited version of theletter sent to the authors after peer review is shown, indicating the substantiveconcerns or comments; minor concerns are not usually shown. Reviewers have theopportunity to discuss the decision before the letter is sent  and 2 reviewers, one of whom is a member of our Board ofReviewing Editors.Thank you for sending your work entitled “The majority of transcripts in thesquid nervous system are extensively recoded by A-to-I RNA editing” forconsideration at The Reviewing editor and the other reviewer discussed their comments before we reachedthis decision, and the Reviewing editor has assembled the following comments to help youprepare a revised submission.eLife. The manuscript also describes a novel bioinformatic methodto identify editing sites. Overall, the manuscript is well written and both Methods andResults are clearly explained.The paper by Alon et al. is a well-performed, concise study that shows extensive RNAediting in the squid genome. Since the extent of editing is several orders of magnitudelarger than that reported in the human genome, and actually in any other metazoanspecies, the results are obviously of biological relevance, and therefore appropriatefor Despite being unexpected, the results appear to be quite robust: the control in primatesshows that they are not an artifact from novel pipeline employed to predict editingsites in absence of assembled genome sequences. The strong bias to AG mismatches, theclustering pattern in genes, the neighbor preferences, the tissue specificity, the resulting recoding events towards the commonamino-acid, etc., all these strongly support the editing sites found by the authors.1) The de novo transcriptome assembly is a very trivial computational issue. Many falsepositives are expected at least in complex mammalian transcriptomes. Paralogs couldaffect the reconstruction of real isoforms leading to a sort of chimeric transcripts. Inaddition, alternative splicing may complicate transcript reconstruction. Are thereestimations about the impact of alternative splicing and paralogs in squid? Any impactof this on the results should be discussed in the text. Also, the text should clarifythat this is not a completely de novo method since genomic sequences are generated.2) The strategy is biased towards the RNA editing prediction in protein coding regions(CDS). Can RNA editing events be detected also in non-CDS regions by the method? If not,this should be clarified in the text. Related to this, evidence of RNA editing inrepetitive regions in squid could potentially be interesting, probably revealing anopposite trend than mammals.3) Regarding methodology, can the statistical binomial test detect any significantchange in the non-AG positions? If yes, how do you explain this finding?The average RNA and DNA coverage is high but regarding RNA editing candidates, are therefilters to exclude low covered sites? What is the minimal coverage for RNA and DNA?Did you apply filters to RNA and DNA reads? I mean reads with low quality and positionsat read ends.4) Have the authors considered the possibility that their results arise from somaticgenomic editing, rather than RNA editing? While for the human and macaque control, theRNA and DNA samples are from the same tissues, in the case of squid, RNA samples arefrom the tissues from the nervous system, while DNA is from the sperm sack. Tounequivocally conclude that the observations are indeed from RNA editing, I guess thatDNA and RNA need to be from the same biological source. Maybe the investigation of thedistribution of the relative proportion of reads supporting and not supporting the editcould help here.5) Related to the above, the authors used RNA only from tissues from the nervous system.Therefore, it is not possible to assess whether the phenomenon observed ischaracteristic of this system, or it is actually systemic in the entire organism. Ithink that sequencing RNA from some other non-nervous tissue could help to distinguishbetween the two hypotheses.6) Regarding the characterization of RNA editing events, events tend to be tissuespecific. Are there events showing tissue specific levels? That is, cases in which thegene locus in expressed at the same level in all tissues but editing levels aredifferent.7) It is a little bit disappointing that there is limited investigation in the potentialmechanisms behind the extensive editing observed. The authors could have at leastinvestigated ADAR with some additional detail. The RNA (and DNA sequence) helps todelineate the ADAR sequence, and the RNA reads to estimate expression levels. Are theremultiple copies of ADAR in the squid genome? Is ADAR expressed at comparatively higherexpression levels than in organism with low editing levels (they can use the mouse andhuman samples to make this comparison? Has the ADAR sequence in squid diverged fasterthan expected? In specific domains? All these questions are quite simple to answer.http://www.sciencemag.org/content/338/6114/1587.full) to test thishypothesis.8) The authors also provide an adaptive explanation to the high levels of editingobserved in the squid genome, and hypothesize that, in contrast to current assumptions,that extensive editing is common as a way to cope with temperature adaption, except inmammals that, as homeotherms, would not require such a process. This is, by the way,reminiscent of the isochore theory by Bernardi that would separate homeothermvertebrates from “cold-blooded” (poikilotherm) vertebrates . If the authors were correct that would indeed bea quite relevant result. They could easily employ their pipeline in available vertebrateRNAseq data . Accordingly, we conclude that the level of non-AG mismatchesdetected, which we used to estimate the false-discovery rate, already includes the sumof all of the above types of error . Thedramatic overrepresentation of A-to-G mismatches over non-AG ones (including T-to-C) istherefore strong evidence that most of the detected A-to-G modifications are due to bonafide ADAR editing. Our conclusions are further supported by the fact that editedadenosines are surrounded by ADAR’s preferred nucleotide motif, that, as withother organisms, editing sites are clustered, and through direct validation of arandomly selected subset of the sites. That being said, we do expect a false discoveryrate of 10-15% in our list of ∼90,000 predicted sites, and these thousands offalse-positives are probably due to inaccurate assembly, SNPs, somatic mutations andmisalignments.1) The de novo transcriptome assembly is a very trivial computational issue.Many false positives are expected at least in complex mammalian transcriptomes.Paralogs could affect the reconstruction of real isoforms leading to a sort ofchimeric transcripts. In addition, alternative splicing may complicate transcriptreconstruction. Are there estimations about the impact of alternative splicing andparalogs in squid? Any impact of this on the results should be discussed in thetext.We completely agree that de novo assembly of complex transcriptomes is bound to resultin many misidentifications of paralogs and splice variants. This may impact our resultsby leading to some false detection of “editing“ sites as well as bymissing true sites. As mentioned above, the total false discovery rate can be estimatedby non-AG mismatches, and is rather low. In fact, it is much lower than genome-awaremethods in model organisms, which identify only ∼40% A-to-G mismatches in codingsequences  to approximate the totalfalse discovery rate, which is an upper bound for false-discovery rate due tomis-assembly. As discussed in the text, it seems that SNPs are a major contributor tofalse discoveries, so not all of these ∼5000 sites are due to mis-assembly.Moreover, by applying our pipeline to primate datasets of similar size we identify≤1000 sites for each mismatch type. Thus one might roughly estimate the number offalse positives due to mis-assembly at a few thousands at most.As for false-negatives, an incomplete transcriptome may lead to missing true recodingsites. The scope of this may be demonstrated by our primate analysis (Table B), wherethe major reason for not identifying about half of the known recoding sites is theabsence of these sites in the de novo transcriptome, or lack of resolution of differentsplice variants (see Table B and footnotes). Accordingly, the full recoding repertoirein the squid may be roughly twice what we report here.In summary, we estimate the number of false-positives due to mis-assembly to be up to afew thousands. This source of error is included in our false discovery rate, and is farlower than the true signal. As for false-negatives, we can only extrapolate from theprimate data, and roughly estimate their number to be sizable, making the full recodingrepertoire of the squid up to twice the size reported here.We have amended the text to the following: “This suggests false-positive rates of15% and 4% respectively, mainly due to transcriptome assembly problems, SNPs, somaticmutations and systematic mis-alignments. Note that these false-positive rates areconsiderably lower than those for similar searches for editing within human codingsequences, where a genome reference was employed.”And also: “Moreover, incompleteness of the de novo transcriptome, as well asincorrect assembly of paralogs and splice variants, may cause our pipeline to miss manyadditional sites (Table B).”Also, the text should clarify that this is not a completely de novo method sincegenomic sequences are generated.Our transcriptome assembly does not use the DNA-reads in any way, and thus is indeed'de novo'. Editing detection does use DNA-reads, and accordingly we do notrefer to our pipeline as a de novo method for editing detection.2) The strategy is biased towards the RNA editing prediction in protein codingregions (CDS). Can RNA editing events be detected also in non-CDS regions by themethod? If not, this should be clarified in the text. Related to this, evidence ofRNA editing in repetitive regions in squid could potentially be interesting, probablyrevealing an opposite trend than mammals.The question of editing in squid repeats is indeed interesting; however without afully-assembled genome available, it is difficult to attack at this point. The mainreason is that non-coding regions in the squid genome tend to be rich in repeats thatdramatically affect the quality of the de novo transcriptome analysis. Thus, while thede novo transcriptome does include some non-coding sequences, and our method may, inprinciple, apply , we chose to focusonly at CDS in order to improve our signal-to-noise ratio.We have amended the text to read: “To focus on editing sites inside codingregions, and avoid repetitive elements that are prone to assembly and alignment errors,we retained only those components that were found to be significant similar  to the Swiss-Prot proteins dataset.”3) Regarding methodology, can the statistical binomial test detect anysignificant change in the non-AG positions? If yes, how do you explain thisfinding?The statistical test is designed to filter out sequencing errors. Thus, many mismatchesthat are not editing sites pass these tests, such as (i) SNPs not identified due toinsufficient DNA-seq reads coverage (ii) somatic mutations (iii) mis-alignments (of DNAand/or RNA reads) and (iv) errors in the underlying transcriptome, due to the issuesmentioned in point #1. In addition, even for the sequencing errors, themultiple-correction scheme we use (Benjamini-Hochberg) does not guarantee zerofalse-positives.All of the above lead to the thousands of non-AG mismatches observed. As mentionedabove, the data presented in The average RNA and DNA coverage is high but regarding RNA editing candidates,are there filters to exclude low covered sites? What is the minimal coverage for RNAand DNA?We thank the referees for raising this important issue that was not properly discussedin our manuscript. In order to minimize the number of arbitrary parameters in ourpipeline, we did not apply any additional DNA and RNA-coverage filter, except for theimplicit requirement for enough read coverage to attain significance in the statisticaltests. Such additional filters do improve the quality of the results , but we still feel they are not necessary  as we now explain:Weak sites detection is based on a binomial test applied to the RNA reads mapped to thetranscriptome. The minimal number of RNA reads to significance in this case is tworeads, both of which are mismatched. There are only two such cases among the 81930 weaksites.One may wonder how come a site with 2 reads reading “G” could have an“A” in the consensus (as is the case for weak sites). The answer is thatwhile our alignments and statistical tests take into account only uniquely aligned readsand quality scores Q≥30, Trinity does not apply these criteria. We chose not tofiddle with Trinity transcriptome assembly and use it as is, and thus there are quite afew cases where the consensus nucleotide of Trinity differs from the majority of thereads we considered in our downstream analysis.Another possible filter is the number of DNA reads. As mentioned above we did not applyany filter of this number. In fact, among our 81930 weak sites there are 1108 sites withno DNA reads at all, and 1148, 1581, 2110 and 2753 sites with 1,2,3,4 supporting DNAreads, respectively. At first, one could have thought that these sites should bediscarded, as they might very well be just genomic SNPs. However, looking at the non-AGmismatches, and repeating our analysis with the additional filter ofminimum-N-DNA-reads, we get the following results:*Note that the counts of AG sites are not exactly the same as the numbers quotedabove for the current dataset. This is because each change of the filters influences thedownstream false discovery rate significance calculation.So, while adding the DNA reads filter does marginally improve accuracy, the number of AGsites lost  is an order ofmagnitude larger than the false-positive sites weeded out (quantified by the number ofTC sites). We therefore choose not to imply the additional filter, and keep thethousands of sites with low DNA reads coverage.Notably, eight of the 143 sites tested in our MiSeq validation were taken from thisgroup. All of these eight sites were confirmed to be editing sites (see Table G). Inaddition, most of these sites show evidence of editing in the additional tissues tested,supporting them being bona-fide editing sites ,2(CT) and 5 (TC), respectively. We therefore agree that, despite our wish to minimizeparameters and avoid arbitrary cutoffs, requiring at least one  RNA read to support editing is reasonable. We thus add this additional filter,and remove the five strong sites from the dataset. Sites with even a single read showing“G” are included .The omission of these five sites (three recoding sites) has no visible effect on thefigures, and thus the figures need not be replaced. Numbers have been modifiedthroughout the paper, when needed, but the changes are always insignificant. Note thatthe table of editing sites provides the full information on the number of DNA and RNAreads, enabling the user to gauge the confidence of the specific site of interest.We have added to the Methods section the following sentences: “We did not applyadditional read-number filters, as these seem to only marginally increase accuracy whilereducing the number of detected sites. However, we did exclude five strong sites forwhich there were no uniquely-aligned, high-quality, supporting RNA reads. This bringsthe number of strong A-to-G and non A-to-G modifications to 5,644 and 219, respectively.The full list of weak and strong sites  Have the authors considered the possibility that their results arise fromsomatic genomic editing, rather than RNA editing? While for the human and macaquecontrol, the RNA and DNA samples are from the same tissues, in the case of squid, RNAsamples are from the tissues from the nervous system, while DNA is from the spermsack. To unequivocally conclude that the observations are indeed from RNA editing, Iguess that DNA and RNA need to be from the same biological source. Maybe theinvestigation of the distribution of the relative proportion of reads supporting andnot supporting the edit could help here.As explained above, somatic mutations might indeed be a partial explanation of our falsediscovery rate, but, as they should not be biased towards A-to-G (and certainly shouldnot introduce more A-to-G than T-to-C mismatches as they do not have strand preference),their contribution is already accounted for in our estimated false discovery rate (basedon the non-AG mismatches), and they cannot account for a sizable fraction of our A-to-Gsites.In principle, one might have speculated that an endogenous squid-specific DNA-editingprocess leads to specific A-to-G mismatches at a level much higher than the randommutations seen in other organisms. But: (i) we have no evidence for such a DNA deaminaseenzyme in the squid (or any other organism) while we do know ADAR enzymes are present;(ii) even such a putative DNA deaminase is not expected to select the coding strand overthe other strand, so there is no explanation for the enrichment of A-to-G overT-to-C.In addition, we can exclude somatic mutations as a major contributor to our putativeediting sites list based on the following:The same sites re-occur in different tissues and different animals see .Randomly sampled sites were validated in additional different animals.Sites appear in clusters, as expected for editing sites but not somatic mutations.Nearby sites are not fully correlated (in the same read). Typically, for a pair ofadjacent sites we find reads showing all four combinations , with only partial correlation. This is not expected for DNA-originatedmutations.The sites show a clear sequence motif resembling the known ADAR motif .5) Related to the above, the authors used RNA only from tissues from the nervoussystem. Therefore, it is not possible to assess whether the phenomenon observed ischaracteristic of this system, or it is actually systemic in the entire organism. Ithink that sequencing RNA from some other non-nervous tissue could help todistinguish between the two hypotheses.We thank the referees for this important comment. We have now checked the editing levelsin six additional non-nervous-system tissues at the sites already identified.Interestingly, the editing level in these tissues is an order of magnitude lower than inthe five nervous system tissues previously studied. The data has been added to We have added to the main text the statement: “Consistently, editing levelsobserved in non-nervous-system tissues are considerably lower ”.We have also added to the Methods section information of the six additional tissuestested: “Tissues were also dissected from non-neuronal regions: the branchialheart, the Gill, the ventral epithelial layer on the pen, the marginal epithelial layeron the pen, the iridophore layer of the skin, and the chromatophore layer of the skin.Each of these six tissues originated from a different animal. RNA from all tissues wasextracted with the RNAqueous kit (Life Technologies), and genomic DNA was extracted fromthe sperm sack using Genomic Tip Columns (Qiagen).”And: “To characterize the modifications levels in non-neuronal tissues, Illuminasequencing was again utilized to generate ∼23, ∼23, ∼19,∼26, ∼19 and ∼14 million paired-end, 150 nt reads, using RNA fromthe branchial heart, the Gill, the ventral epithelial layer on the pen, the marginalepithelial layer on the pen, the iridophore layer of the skin, and the chromatophorelayer of the skin. The same alignment procedure was applied to quantify the modificationlevel at the previously described sites for each of the additional samples, revealingconsiderably lower editing levels in the non-neuronal tissues (6) Regarding the characterization of RNA editing events, events tend to betissue specific. Are there events showing tissue specific levels? That is, cases inwhich the gene locus in expressed at the same level in all tissues but editing levelsare different.In summary, we do see tissue specificity even when expression levels are similar. Thedata provided enables the reader to find such sites using his/her own parameters.7) It is a little bit disappointing that there is limited investigation in thepotential mechanisms behind the extensive editing observed. The authors could have atleast investigated ADAR with some additional detail. The RNA (and DNA sequence) helpsto delineate the ADAR sequence, and the RNA reads to estimate expression levels. Arethere multiple copies of ADAR in the squid genome? Is ADAR expressed at comparativelyhigher expression levels than in organism with low editing levels , however thesquid’s “inactive” ADAR appears equally similar to all threevertebrate ADARs. The squid ADAR2 sequence has been well characterized and is thesubject of two previous publications by the Rosenthal lab. The squid ADAR1 sequence isthe subject of an ongoing investigation by the Rosenthal lab.It is reasonable to hypothesize that high level of editing in squid is due tocomparatively high ADAR expression. However, it is difficult to compare the expressionlevels in terms of reads per kilobase of exon per million mapped reads (RPKM) in theabsence of a full genome, because we can’t say whether unmapped reads areactually mappable to poorly expressed, or poorly resolved, transcripts. That being said,we did make a rough estimate of ADAR expression to answer this query. By ranking eachcomponent from our transcriptome we found that squid ADAR1 ranks at 0.92 and squid ADAR2ranks at 0.48 for the GFL tissue, where 1 is the most expressed component, and 0 theleast expressed component. These numbers are not very different from the situation inthe human brain, where , ADAR1 is ranked at ∼0.9 and ADAR2 ∼0.5 . This is only a rough estimate, and there areseveral delicate problems in this comparison; therefore we prefer not to make the claimthat the expression levels are indeed similar in the text. However, we are working underthe hypothesis that other differences between squid and mammalian ADARs (see below) aremore likely to be of importance here.Much is known about squid ADAR2 and it does indeed have several interesting featuresthat may explain, in part, high level editing. We’d like to stress, however, thatit is only part of the puzzle. Vertebrate ADAR2’s have an invariant domainstructure: they are composed of 2 N terminal double stranded RNA binding motifs (dsRBMs)followed by a conserved catalytic domain. Squid ADAR2 can have an additional dsRBM atits N terminus which is included in about half the transcripts through splicing. Thus, there is a canonical ADAR2 with 2 dsRBMS and anon-canonical one with three. When produced recombinantly in yeast and tested in vitroon a squid K channel mRNA substrate, the non-canonical version edits far more sites.This version also has a much higher affinity for dsRNA .Another interesting feature of the squid ADAR2 is that its own messages are abundantlyedited, leading to multiple isoforms. The specificities of the individual isoforms haveyet to be tested.We have also cloned and expressed Squid ADAR1. As with squid ADAR2, this enzyme hasnotable differences when compared with its vertebrate counterparts. Vertebrate ADAR1sare normally composed of three dsRBMs followed by a catalytic domain. Squid ADAR1 onlyhas 1 dsRBM followed by the catalytic domain. At its N-terminus, however, it contains ahighly basic domain that contains scores of phosphorylation sites. Squid ADAR1 messagesare also highly edited. A graduate student in the Rosenthal lab has produced recombinantSquid ADAR1 and is currently characterizing its function for her doctoral project.Because it contains many differences from vertebrate orthologs, a full-functionalcharacterization is a complex undertaking. Although its structure is clearlyinteresting, we prefer not to include Squid ADAR1 sequence data in this manuscriptbecause only through structure-function studies can we assess whether its uniquefeatures might contribute to high-level editing. We would like to assure the reviewer,however, that these issues are very much on our minds and that we will be publishingdetailed accountings shortly.We have added the following to the main text: “Have squid ADARs evolved novelstructure that account for the high-level editing? A past study showed that a squidADAR2 ortholog can be expressed as two isoforms due to alternative splicing: one, having2 double-stranded RNA (dsRNA) binding motifs, resembles vertebrate ADAR2s. A second,however, contains an “extra” dsRNA binding motif at its N-terminus. Thisnon-canonical isoform edits RNA more efficiently, edits more sites, and binds dsRNA witha far higher affinity. Further Squid ADAR2 messages themselves contain many editingsites, leading to many subtly different isoforms. An ADAR1 isoform is also present inour transcriptome and is the focus of an ongoing study.”8) The authors also provide an adaptive explanation to the high levels ofediting observed in the squid genome, and hypothesize that, in contrast to currentassumptions, that extensive editing is common as a way to cope with temperatureadaption, except in mammals that, as homeotherms, would not require such a process.This is, by the way, reminiscent of the isochore theory by Bernardi that wouldseparate homeotherm vertebrates from “cold-blooded” (poikilotherm)vertebrates . If the authors werecorrect that would indeed be a quite relevant result. They could easily employ theirpipeline in available vertebrate RNAseq data to test this hypothesis.all cold-blooded animals have extensiverecoding. Published data for Drosophila, the leaf-cutting antAcromyrmex echinatior, and C. elegans all show muchless recoding activity than we report for the squid. What we do intend to suggest isthat in those species where extensive editing does happen, it can be utilized fortemperature adaptation.We thank the referees for this comment. The statement at the closing sentence of themain text was indeed a bit too strong, and we would like to rephrase and clarify ourclaim. We do not claim that Thus, testing another cold-blooded species is not expected to help much, we presume mostspecies will not reproduce the exceptional level found in squid. We do intend toexplore, in future studies, the editing profile of species closely related to squid, inorder to better understand how this extensive phenomenon has evolved.Parenthetically, the data in the reference suggested above cannot be used for ourpurposes, as no matching DNA-reads are provided.We have modified the closing sentence to read: “Most model organisms studied sofar are mammals which are homeotherms. Future studies of more diverse species are neededto reveal the extent to which cold-blooded organisms might utilize extensive editing torespond to temperature changes and other environmental variables”.


C9orf72 ALS/FTLD disease models point at a pivotal role for the nuclear transport system in toxicity. To address whether defects in nuclear transport are indeed implicated in the disease, we reviewed two decades of ALS/FTLD literature and combined this with bioinformatic analyses. We find that both RNA-binding proteins and nuclear transport factors are key players in ALS/FTLD pathology. Moreover, our analyses suggest that disturbances in nucleocytoplasmic transport play a crucial initiating role in the disease, by bridging both nuclear loss and cytoplasmic gain of functions. These findings highlight this process as a novel and promising therapeutic target for ALS and FTLD.Neurodegenerative diseases are characterized by the presence of protein inclusions with a different protein content depending on the type of disease. Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are no exceptions to this common theme. In most ALS and FTLD cases, the predominant pathological species are RNA-binding proteins. Interestingly, these proteins are both depleted from their normal nuclear localization and aggregated in the cytoplasm. This key pathological feature has suggested a potential dual mechanism with both nuclear loss of function and cytoplasmic gain of function being at play. Yet, why and how this pathological cascade is initiated in most patients, and especially sporadic cases, is currently unresolved. Recent breakthroughs in The online version of this article (doi:10.1007/s00401-016-1586-5) contains supplementary material, which is available to authorized users. Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two devastating adult-onset neurodegenerative disorders. In ALS, motor neurons in the motor cortex, brainstem and spinal cord degenerate. This leads to motor problems, muscle weakness and paralysis. These motor impairments are progressive, and ALS is usually fatal within 3–5 years after diagnosis . In FTLDTAR DNA-binding protein gene (TARDBP), encoding TDP-43, in approximately 4 % of familial ALS patients [fused in sarcoma (FUS) are found in ~5 % of ALS cases and rare cases of FTLD [chromosome 9 open reading frame 72 gene (C9orf72), the most common genetic cause of ALS-FTLD [For both ALS and FTLD, hereditary forms of the disease exist. In approximately 10 % of ALS patients and in 40 % of FTLD patients, the disease runs in the family. The neurodegeneration in these familial cases is caused by mutations in a heterogeneous set of genes . Proteins were identified as misregulated when they were found aggregated in inclusion bodies or were mislocalized, based on immunohistochemistry or immunofluorescence in brain or spinal cord (muscle in multisystem proteinopathy patients). The results are graphically presented in Fig. ®, QIAGEN, http://www.qiagen.com/ingenuity). Disease terms significantly enriched included ‘motor neuron disease’ and ‘frontotemporal dementia’, which can be expected given the origin of the input data set  , EWS RNAExtensive efforts have been made to resolve the issue whether disease is caused by nuclear loss of function or cytoplasmic gain of function of the RNA-binding proteins. The available evidence so far suggests that both events are not mutually exclusive and could play an equally important role in the disease . IdentifGiven the nuclear depletion of several RNA-binding proteins in patients, impaired nuclear import has previously been suggested as a pathogenic mechanism, and moreover, as a key initiating event in pathogenesis . Indeed,Most mutations in TDP-43 , TAF15 , EWS 2121, hnRNPAs mentioned above, cytoplasmic inclusion bodies have been proposed to arise from stress granules , 98: morcell = 7.64 µM [droplet = 0.5 µM [Given the strong concentration dependence of stress granule dynamics, nucleocytoplasmic transport seems to be important in regulating this process. Indeed, inhibiting nuclear import of hnRNPs spontaneously induces the induction of stress granules . On the  7.64 µM  > [hnRNP= 0.5 µM ). This iNuclear transport factors themselves, such as importins and Ran, are well-established stress granule components , 72 and Other membrane-less cellular compartments, e.g., the nucleolus or P granules, are also controlled by concentration-dependent phase transitions , 11. ComFUS affect binding to its corresponding importin [Nucleocytoplasmic transport bridges nuclear depletion and cytoplasmic aggregation, the two major pathological findings in ALS and FTLD. Besides this, nuclear transport factors are an important class of pathological proteins Fig. a. Furtheimportin , and conimportin . All togWe reviewed the literature on different ALS/FTLD models and their modifiers to find out whether there is support for a role for nucleocytoplasmic transport in the disease. Many research groups have harnessed the power of yeast and fly genetics to perform high-throughput genetic screens. Figure S2a gives an overview of the screens performed in ALS/FTLD models.C9orf72. Repeat expansions in the non-coding part of this gene result in the generation repeat RNAs [C9orf72 ALS-FTLD, a genome-wide modifier screen in flies expressing an expanded GGGGCC repeat has been performed [C9orf72 patients. As the model used in this study can display effects originating from both RNA and DPR toxicity, it remains unclear which of them causes the defects in nuclear transport. However, it has been suggested that in the current fly models with moderate repeat sizes, DPRs are the predominant mediators of neurodegeneration [Fly ALS models of TDP-43, FUS and VAMP-associated protein B (VAPB) toxicity were used to identify genetic modifiers , 45, 142eat RNAs , 129, wheat RNAs , 82, 148erformed . This leerformed  . Howeveand FTLD .The recent developments in the C9orf72 field point at key pathways in nucleocytoplasmic transport. These findings could explain the mislocalization and aggregation of TDP-43 and other RNA-binding proteins in patients carrying a repeat expansion. Yet, why wild-type proteins mislocalize in other types of the disease, and especially in sporadic patients, remains largely enigmatic.post-mortem ALS and FTLD patient material [ALS and FTLD are typically adult-onset disorders, suggesting that aging and its related processes are crucial to induce the disease. Interestingly, the efficiency and selectivity of nucleocytoplasmic transport deteriorates significantly during aging . This camaterial , 144 . Mutations in Gle1 were recently linked to ALS cases [Gle1 knockdown did induce motor neuron defects in embryonic zebrafish, which could be rescued by wild type but not by mutant Gle1. Besides functioning in mRNA export, Gle1 has been also shown to regulate as well stress granule dynamics [Despite the importance of nuclear transport proteins in the pathology of ALS/FTLD, this group of genes has not been strongly implicated in the genetics of the disease so far. A notable exception is LS cases . Gle1 kndynamics , which cC9orf72 toxicity [Nucleocytoplasmic transport involves protein import and export, mediated by importins and exportins, respectively. Numerous different import and export factors exist, but the most important ones include transportin-1, the importin α2/β1 complex and exportin-1. Interestingly, these different import factors were recently identified in different genetic screens for modifiers of toxicity , 48, 144toxicity , 56. In toxicity , 129. Setoxicity , 81, andtoxicity . EnrichmNuclear export mediated by exportin-1 can be specifically inhibited by leptomycin B . Using qOur analyses of nuclear transport cargo sets illustrate that problems in nucleocytoplasmic transport will affect processes implicated in the disease, especially centering on RNA metabolism. Moreover, the fact that these cargo sets show a strong enrichment for proteins already implicated in ALS/FTLD and related disorders, suggests that defects in nuclear transport could initiate important pathogenic cascades.Nucleocytoplasmic transport is an essential process in cellular organization and functioning. So, if defects in nuclear transport are a cause of ALS/FTLD, why is degeneration largely restricted to specific neuronal populations? Explaining this discrepancy between general pathogenic pathways and selective cell death has been puzzling the neurodegeneration field for years, and applies to numerous established disease mechanisms, e.g., excitotoxicity, mitochondrial damage or protein aggregation .A potential reason why nucleocytoplasmic transport defects could especially harm neurons can be found in their extreme cellular organization: neurons are the longest cells in the human body, e.g., motor neurons can reach lengths of up to 1 m in adults. This means that the synapse, the site of action, is located on a phenomenal distance from the nucleus. Synapses are, for their proper functioning, dependent on local translation and, hence, RNA transport along the axon. Numerous ALS/FTLD-associated genes are implicated exactly in this process , 69. Alsp value = 1.21E−03 and 1.84E−03). Defects in splicing are also a common theme in neurological diseases [On the other hand, the central nervous system is a hot spot for alternative splicing events , 139. Brdiseases . MaintaiC9orf72 expansion consolidated the importance of RNA metabolism in the disease. However, key issues remain to be addressed. Especially, the exact underpinnings of RNA-binding protein mislocalization proved elusive. NLS mutations were identified in FUS, but why wild-type proteins mislocalize was unknown. Only very recently, the identification of nuclear transport genes as modifiers of C9orf72 disease models shifted the focus of the field explicitly to nucleocytoplasmic transport.Landmark discoveries in the last decade have vastly expanded our knowledge on ALS and FTLD. The involvement of RNA-binding proteins has moved the field from viewing the diseases as exclusive proteinopathies to a more holistic view of ALS/FTLD as a problem in ribostasis, linking RNA-binding protein aggregation to problems in RNA metabolism. Mutations in numerous genes encoding such proteins strengthened this hypothesis, and finally, the discovery of the In this review, we tried to discuss all data currently available on the role of nucleocytoplasmic transport in ALS and FTLD. Moreover, we tried to address the question whether transport defects could be an initiating event in the disease. We used meta-analyses and bioinformatics to investigate this option. We found that nucleocytoplasmic transport cargoes are indeed associated with ALS/FTLD and related pathogenic processes. This suggests that a disruption of their proper localization could result in a loss of their normal functions, such as RNA splicing and transport, both vital in neuronal health. The function of RNA-binding proteins is strictly regulated, and largely depends on their subcellular localization. High levels of specific RNA-binding proteins in the cytoplasm will initiate spontaneous compartmentalization of these proteins into fluid-like granules, such as stress granules. These dynamic organelles are the prime suspects as seeds of the pathological aggregates. Compellingly, more than 70 % of all RNA-binding proteins misregulated in ALS/FTLD are known constituents of these stress granules. Recent evidence indeed suggests that RNA-binding proteins can undergo in vitro and in vivo concentration-dependent phase separations, and in vitro droplet maturation to pathological aggregates was observed in real time , 94. TheC9orf72 modifier studies provided us with a list of interesting candidates which could be more broadly implicated in ALS and FTLD. Figuring out whether and how nucleocytoplasmic transport could be perturbed in sporadic patients will be one of the key future challenges. Nucleocytoplasmic transport has been therapeutically targeted before, but mostly by inhibition [The recent hibition . More fuhibition , 144. CoFigure S1: Genes encoding RNA-binding proteins are implicated in ALS/FTLD pathology. Genes linked to ALS and FTLD , 110 wer(a) Overview of all genetic modifier studies in fly and yeast models of ALS/FTLD [C9orf72 studies. (b) Mislocalization and aggregation in cellular TDP-43 models as determined by SILAC quantitative mass spectrometry [Figure S2: Modifiers of and pathological changes in ALS/FTLD models. ALS/FTLD , 144. Wetrometry , 112 (TIBelow is the link to the electronic supplementary material.


Drosophila cells to identify transcripts associated with cytoplasmic ribonucleoproteins (RNPs) containing the RNA-binding protein Imp.Post-transcriptional RNA regulons ensure coordinated expression of monocistronic mRNAs encoding functionally related proteins. In this study, we employ a combination of RIP-seq and short- and long-wave individual-nucleotide resolution crosslinking and immunoprecipitation (iCLIP) technologies in We find extensive binding of Imp to 3′ UTRs of transcripts that are involved in F-actin formation. A common denominator of the RNA–protein interface is the presence of multiple motifs with a central UA-rich element flanked by CA-rich elements. Experiments in single cells and intact flies reveal compromised actin cytoskeletal dynamics associated with low Imp levels. The former shows reduced F-actin formation and the latter exhibits abnormal neuronal patterning. This demonstrates a physiological significance of the defined RNA regulon.Drosophila Imp RNPs may function as cytoplasmic mRNA assemblages that encode proteins which participate in actin cytoskeletal remodeling. Thus, they may facilitate coordinated protein expression in sub-cytoplasmic locations such as growth cones.Our data imply that The online version of this article (doi:10.1186/s13059-015-0687-0) contains supplementary material, which is available to authorized users. To establish a neuronal network, protrusions, in the form of filopodia and lamellipodia, project from the developing axons into their surroundings in search of their appropriate postsynaptic targets. These protrusions are formed at the edge of the outermost part of the axon, the growth cone. This highly motile domain, consisting of bundles of filamentous actin, microtubules and their associated proteins, is an exquisite sensor of chemical gradients and is constantly undergoing dynamic structural changes in response to the surrounding guidance cues (reviewed in ). As thetrans-acting RNA-binding proteins (RBPs) [Drosophila blastoderms have estimated that 71 % of 3370 examined mRNAs are localized in diverse patterns [Drosophila homolog Imp (insulin-like growth factor II mRNA-binding protein), containing the four conserved KH domains, but lacking the N-terminal two RNA recognition motifs. In addition, the Drosophila homolog encompasses a C-terminal Gln-rich extension that is not observed among vertebrates  . The diss (RBPs) . In situpatterns , so the patterns , 7. ZBP1patterns , 9. The tes Fig. . Imp is ectively , 13. Howunctions , and a runctions . Imp is unctions .Fig. 1DIn recent years, high-throughput cross-linking and immunoprecipitation (CLIP) procedures have been introduced to obtain information regarding direct binding sites for individual RBPs on a transcriptome-wide scale in vivo –18. In tDrosophila Imp and thus assess the post-transcriptional RNA regulon hypothesis. The data revealed extensive 3′ untranslated region (UTR) attachments in mRNAs encoding components crucial for actin cytoskeletal remodeling, and subsequent Imp knock-down analysis showed decreased F-actin levels in S2 cells. Moreover, an Imp-deficient fly exhibited a phenotype with difficulties reaching the pharate adult stage, mainly due to defects in the peripheral nervous system. Taken together, our study identifies an Imp-mediated RNA assemblage containing transcripts that encode proteins coordinating F-actin formation.In the present study, we employed three different high-throughput procedures to identify the full targetome of Drosophila S2 cell line, immunostaining of endogenous Imp was performed with co-staining with phalloidin  to the Drosophila genome, demonstrating the high quality of the produced libraries.Since RIP-seq identifies both direct and indirect RNA targets without information regarding the position of interaction, we performed individual-nucleotide resolution cross-linking (iCLIP)  as well In order to quantify the strength of Imp binding and eliminate the contribution from RNA expression, the Imp iCLIP and PAR-iCLIP tag clusters were normalized to transcript levels determined by RNA-seq. To provide a global view of Imp RNA-binding specificity we analyzed the average distribution of tag clusters across mRNA regions, including the 5′ UTR, coding sequence (CDS) and 3′ UTR Fig. . The CLIsquid, chickadee, pAbp and visgun transcripts  compared with the S2 cell line transcriptome . An example of these binding motifs can be found in the highly enriched clusters present in Pendulin and pAbp 3′ UTRs  that models the CA-rich repeat regions and the UA-rich cross-link motif to identify Imp binding sites , Rho GTPases (Rac1 and Cdc42), the exchange inhibitor GDI, the 14-3-3epsilon adaptor, and Arf79f (Arf1 homolog), to name a few.The RNA interaction network described in Fig. luciferase dsRNA , in spite of a lack of any major changes in F-actin organization upon treatment with the ROCK inhibitor in wild-type S2 cells.Imp immunoreactivity and phalloidin staining in an S2 cell are depicted in Fig. imp dsRNA- and luciferase dsRNA-treated cells were carried out. Imp knockdown resulted in a reduction of the Imp protein level to 11–21 % of the endogenous level in S2 cells . To corroborate that the semi-lethal phenotype of impG0072 flies was due to a disturbance at the imp locus, excision of the P-element was induced and both wild-type sequence and phenotype were restored, as previously described by Geng and Macdonald [impG0072 progeny compared with both wild-type and an FM7c balanced/wt was observed during all stages of life. Closer examination of the eclosing flies revealed that only 12 % (n = 383) were hemizygous males compared with the expected 25 %. Moreover, we found 30 hemizygous males struggling to emerge from their pupal cases, and among the 45 hemizygous males that did manage to eclose, all drowned shortly after in the medium.Since reduction of Imp levels resulted in compromised F-actin formation in S2 cells, we hypothesized that an Imp-deficient embryo would exhibit developmental defects. Therefore, we examined the mp locus . Quantitacdonald   marker was present on the X-chromosome balancer FM7c, so that expression of β-gal would be linked to the presence of an FM7c balancer with a wild-type imp allele. In order to record the fraction of embryos with an abnormal neuronal pattern, the nervous system of wild-type embryos, and embryos of crosses between either wt/FM7c or impG0072/FM7c females and FM7c/Y males, was visualized by enzyme-linked immunohistochemistry. Twenty-nine percent of the embryos showed an abnormal phenotype, which suggested that aside from the hemizygous males (impG0072/Y), some heterozygotes (impG0072/FM7c) were also affected by the P-element insertion. The binominal P values of impG0072 embryos compared with either wild-type or wt/FM7c embryos were calculated, showing significantly increased rates of embryos with abnormal morphology and nervous systems , was carried out. Staining with 22C10 showed a large variation among the mutants, ranging from a morphologically normal looking embryo with a nervous system that at least superficially appears normal . We conclude that embryos with reduced Imp levels exhibit abnormal neuronal patterning in the developing nervous system, and that even survivors at the pharate adult stage have profound locomotion problems.To examine the neuronal patterning of mutant embryos, whole-mount immunostaining of the Drosophila Imp protein on a transcriptome-wide scale, utilizing a highly specific polyclonal antibody in six biologically independent experiments with three different methods. In general, both iCLIP and PAR-iCLIP data revealed a striking preference for direct Imp attachment to 3′ UTRs at the expense of especially the coding region. The distinct binding profile in Fig. Drosophila Imp using SELEX [Drosophila Imp to the unpaired 3′ UTR [In this study, we have identified the RNA targets of the endogenous ng SELEX , flankedd 3′ UTR ; the CAUd 3′ UTR . Moreoved 3′ UTR , althougd 3′ UTR .Drosophila Imp and its vertebrate homologs contain the characteristic four KH domains, the former oligomerizes on targets of merely 200 nucleotides  protein synthesis of filopodia components in response to directional guidance cues. This interpretation is supported by experiments performed in mouse primary cortical neurons , Drosoph-neurons , and Xenon cells  that alloskar mRNA anchoring during oogenesis [The embryo phenotype of Imp knockdown we report in this study exhibited a broad range of penetrance — almost to the point of being stochastic — and the few survivors reaching the pharate adult stage showed severe locomotion defects. In a more general fashion, the broad range of Imp knockout phenotypes suggests that Imp provides robustness to cytoplasmic post-transcriptional regulation by supplying RNA assemblages coordinating spatiotemporal protein production essential for cellular extensions. In the same vein, during spermatogenesis Imp exhibits abundant expression in the tail cyst cell encasing the extraordinarily long spermatids , and itsogenesis , suggestNeurogenesis and gametogenesis are characterized by a requirement for plasticity in terms of local cellular elongation, yet preserving a differentiated or totipotent state, respectively. Therefore, coordination of gene expression has to some extent become an RNA-centric task, and the physical manifestation of the post-transcriptional RNA regulon is provided by cytoplasmic RNP granules/hydrogels encompassing functionally related mRNAs associated with similar RBPs, as shown for Imp in the present study. These RNA assemblages facilitate fast sub-cytoplasmic recruitment of genetic information to the translational apparatus, thereby ensuring synchronous on-site translation in response to extracellular cues.Drosophila medium (Biowest) containing 10 % heat-inactivated fetal bovine serum, 100 U/ml penicillin and 100 μg/ml streptomycin.S2 cells (Invitrogen) were cultured at 26 °C in Schneider’s S2 cells were fixed on concanavalin A (Sigma-Aldrich) coated glass bottom dishes with 10 % formaldehyde for 10 min and permeabilized with 0.1 % Triton X-100 in phosphate-buffered saline (PBS) for 3 min . After b6 cells/ml were soaked with 40 μg/ml dsRNA corresponding to the imp or luciferase genes. Each day, cells received a boost of 40 μg/ml dsRNA. After the boost on the third day, cells were treated with 10 μM ROCK inhibitor  for 20 h. The medium was removed and cells were fixed and immunostained as previously described. Cells were examined by structured illumination (SIM) and by confocal microscopy (Zeiss LSM 780) to obtain high resolution pictures and quantification of Imp and F-actin, respectively. Employing a 40× objective, Alexa 568 and Alexa 488 pixels were quantified in ten visual fields randomly selected using the automatic controller of the microscope. Pixels were quantified employing the HISTO functionality of the ZEN 2011 software package (Carl Zeiss). Channels were evaluated separately, and the mean intensity in each field was determined after thresholding. Significant differences were analyzed by a Student’s t-test. For more information see “Supplementary Materials and methods” in Additional file In a 6-well plate, 1 × 10S2 cells were lysed in 50 mM Tris–HCl (pH 8.0), 100 mM NaCl, 1 % NP-40, 1.5 mM EDTA, complete EDTA-free protease inhibitor cocktail (Roche), 1 U/μl RiboLock (Thermo Scientific) and immunoprecipitated using polyclonal anti-Imp antibody coated Protein A Dynabeads (Life Technologies). Following repeated washes in lysis buffer, TRI Reagent (Sigma-Aldrich) was added to the beads and the RNA was isolated according to the manufacturer’s specifications. RNA from two biologically independent RIP-seq experiments as well as poly(A)-enriched RNA from cells isolated on the same day as cells used for RIP-seq, were fractionated, and library preparation was performed as described by , with th7Drosophila S2 cells were used for each iCLIP and PAR-iCLIP experiment. Cells for iCLIP were irradiated with 250 mJ at 254 nm UV light, whereas cells were treated with 100 μM 4-thiouridine for 16 h prior to UV cross-linking with 250 mJ at 365 nm in the PAR-iCLIP procedure. Cells were lysed with lysis buffer ), followed by ribonuclease T1 digestion  in the presence of 0.0025 U/μl DNase I (Thermo Scientific) for 10 min at 37 °C. Imp–RNA complexes were immunoprecipitated using rabbit anti-Imp antibody-coated Protein A Dynabeads (Life Technologies) for 2 h at 4 °C. Beads were washed three times with high-salt buffer  and twice with wash buffer . The RNA purification and library generation were performed as described in [Imp iCLIP and Imp PAR-iCLIP were performed in biological replicates essentially as described in , 23. Bri7 cells using the standard TRI Reagent protocol (Sigma Aldrich). Poly(A) RNA was enriched from 100 μg total RNA using Poly(A) Purist MAG kit (Ambion) according to the manufacturer’s specifications. The poly(A) RNA was fragmented into approximately 280 nucleotides by heating at 95 °C for 3.5 min in the presence of 50 mM Tris–HCl (pH 8.0) and 5 mM MgCl2. Treatment of the RNA with T4 polynucleotide kinase (Thermo Scientific), according to the manufacturer’s specifications, resulted in dephosphorylation of the 3′ ends of the RNA. The remaining part of the RNA-seq library preparation was performed as described in [In order to normalize iCLIP and PAR-iCLIP data, RNA-seq was performed on cells harvested on the same day as cells used in the iCLIP procedure. For each replicate, total RNA was isolated from 5 × 10ribed in  with 17 P value cutoff of 0.0001. As background sample, a list was made of all poly(A) RNAs present in the S2 cell line according to the RNA-seq.The transcripts present in a top 200 ranking of normalized RIP-seq datasets were used as input for the term enrichment tool AmiGO  using a ImpG0072 was obtained from the Bloomington Drosophila Stock Center . The strain was generated by a P-element insertion in the imp locus in a region corresponding to an intron upstream of the initiation codons [impG0072/FM7c females and FM7c/Y males, was visualized by enzyme-linked immunohistochemistry. The monoclonal neuronal marker 22C10, which recognizes the MAP1B-like protein Futsch and labels both the central and peripheral nervous systems [n codons . Embryos systems , was useAll sequencing data were produced by The Danish National High-Throughput DNA Sequencing Centre using Illumina HiSeq and 50–100 bp single-end reads. The mapping procedure for iCLIP, PAR-iCLIP, RIP and RNA-seq controls is described in “Supplementary Materials and methods” in Additional file e of CLIP in a position i of a particular cluster k is calculated as:The iCLIP and RIP libraries were prepared from the same biological sample as that used for the RNA-seq data sets. Therefore, we normalized each RIP and iCLIP replicate with its corresponding RNA-seq replicate. The PAR-iCLIP replicates were normalized to a pool of two iCLIP RNA-seq controls. The enrichment ci is the count of CLIP base calls in position i, and N the total amount of confidently mapped CLIP reads. As rj is the count of RNA-seq reads, g, normalized to the total amount of confidently mapped RNA-seq reads M and the length of the transcript Lg.where To make standardized profiles, the longest protein-coding transcript of each gene with at least 30 % RNA-seq coverage in the S2 cell line was used. The regions  of those transcripts were divided into a fixed number of bins as follows: 20 bins for 5′ UTRs, 50 bins for CDS and 50 bins for 3′ UTRs. The calculation of mean enrichment is described in “Supplementary Materials and methods” in Additional file Drosophila Imp binding across the whole transcriptome  after discarding small clusters (less than six reads) and genes with low expression levels (average of less than five reads per nucleotide), and selected the 25,000 most enriched clusters in 3′ UTRs. These sequences and the enrichment ranking were subjected to the motif discovery method cWords  in two sDrosophila Imp binding sites, we specified a HMM in the anHMM framework [t1 label, which is 0.95. All states have emission probabilities of the letter shown in the diagram set to 0.9. The HMM was used to identify independent instances of the CA-rich and UA-rich motifs and post-processed such that alternating occurrences of the two motifs were merged into one composite motif if they were spaced a maximum of 50 nucleotides apart. All other single motif occurrences were filtered out. We annotated the entire genome on both strands with matches using Viterbi decoding. We summarized binding sites in the top 86 transcripts bound by Imp and in all expressed transcripts with at least 30 % RNA-seq coverage in S2 cells. We found matches in 3′ UTRs for 76 of the top 86 transcripts.To assess the presence of putative ramework . A stateAll the data reported in this paper can be found in the Gene Expression Omnibus database with accession number GSE62997.


The past decade has seen a tremendous increase in interest and progress in the field of sperm epigenetics. Studies have shown that chromatin regulation during male germline development is multiple and complex, and that the spermatozoon possesses a unique epigenome. Its DNA methylation profile, DNA-associated proteins, nucleo-protamine distribution pattern and non-coding RNA set up a unique epigenetic landscape which is delivered, along with its haploid genome, to the oocyte upon fertilization, and therefore can contribute to embryogenesis and to the offspring health. An emerging body of compelling data demonstrates that environmental exposures and paternal lifestyle can change the sperm epigenome and, consequently, may affect both the embryonic developmental program and the health of future generations. This short review will attempt to provide an overview of what is currently known about sperm epigenome and the existence of transgenerational epigenetic inheritance of paternally acquired traits that may contribute to the offspring phenotype. The principal function of spermatozoa is to deliver the haploid paternal genome to an oocyte during fertilization. Spermatozoa are highly specialized cells generated in the testis through a differentiation process called spermatogenesis. They are equipped with specific structures to achieve fertilization, such as a flagellum which confers mobility and a very compact nucleus which ensures protection of the paternal genome. Their chromatin organization differs significantly from that of somatic cells: while, in the latter, the chromatin is packed into nucleosomes containing 146 bp of DNA wrapped around an octamer of basic proteins named histones  DNA methylation and associated modifications, (ii) the histone/chromatin code which consists mainly in histone variants and their post-translational modifications, and (iii) Coding and non-coding RNA.Epigenetics (from “epi” in Greek which means above/upon/near) is the study of heritable changes in gene expression that are not caused by modifications in the primary DNA sequence  or RNA.de novo methylation, while DNMT1 is involved in methylation maintenance  a phenomenon known as paternal or maternal imprinting depending on whether it takes place in male or female germ cells  and IAP  and, a loss in spermatocytes Overall, depending on the cellular type and development period, the genome is more or less methylated with two major reprogramming time points during mammalian development: in primordial germ cells (PGC) and pre-implantation embryos.During the formation and migration of PGC at E12.5 to E13.5 days in mouse, DNA methylation is erased up to a point that solely 10% of total CpG remain methylated. This is required because PGC are derived from embryo cells that have already acquired a somatic fate  or on spermatogenesis implicated genes such as for example Dazl (deleted in azoospermia like) were shown to be associated with oligozoospermia, i.e. low sperm concentration  present altered DNA methylation on imprinted genes . This structure is ~6–10 times more condensed than a classical somatic nucleosome  and methylation, which depending on the residue can impact on gene expression either by activation or repression. For example, histone H3 trimethylated at lysine 27 (termed H3K27me3) deposited by the histone-lysine N-methyltransferase enzyme EZH2, leads to chromatin compaction and thus to inhibition of transcription. This mark is notably found in the constitutive heterochromatin. In contrast, H3K4me3  is associated with actively transcribed genes  is maintained in spermatozoa, and those persisting histones bear PTMs and include non-canonical histones . It is worth noting that a majority of histone variants are encountered at one point during spermatogenesis; they are involved in establishing a male germ cell-specific gene expression program and/or in histone-to-protamine chromatin remodeling process  chromatin structure, in which megabase-size genomic regions form topologically associated domains , microRNAs clusters, genes subjected to genomic imprinting and binding sites of the chromatin insulator protein CCCTC-binding factor . They can be grouped according to their size in long or small non-coding RNA. In this review, we will focus on small non-coding RNA (sncRNA) as they are the most described in male germ cells. In spermatozoa, sncRNA could be the basis of epigenetic information transmitted to the embryo as they enter the oocyte upon fertilization. At the end of spermatogenesis, most of the cytoplasm and RNA content of spermatozoa are ejected. Only a limited number of RNA molecules remain, and they have been the focus of several studies. In addition to mRNA fragments, several small non-coding RNA (sncRNA) were shown to be present in sperm , it was shown that 80% of the protamines were removed within 3 h post-fertilization  and associated PTMs, the question about the contribution of those persisting histones to the embryo was asked . Of course, in view of the ongoing debate regarding the genomic location of sperm histones, their contribution and functional role in the embryo cannot be fully understood. Most data regarding the location of sperm histones after fecundation are based on immunodetection. It was reported that some sperm persisting histones variants are removed after fertilization. For example, H2AL1/2 rapidly disappear after fertilization in the mouse  that allow the formation of the male pronucleus and the replication of the zygote DNA following fertilization  an active DNA demethylation process dependent on Tet enzymes, including Tet3, and the successive formation of 5 hmC, 5 caC, and 5 fC  are ultimately transformed into non-methylated cytosine by the DNA repair pathway TDG-BER (standing for thymidine DNA glycosylase-base excision repair) that is independent of replication  and imprinted genes  by preventing the binding of the Tet3 protein and maintaining the presence of the histone methylation mark H3K9me2  initially identified in the PGC. It is assumed that Stella protects from DNA demethylation, at particular paternal imprinted loci  and suspected them to participate to paternal pronucleus formation and to control the events of early embryo development. Regarding sperm-borne non-coding small RNA, studies have shown their impact in preimplantation embryo development. A striking example is that of miR-34c, a male germ line-specific miRNA that is transmitted to the embryo. Mir-34c was demonstrated to have a key role in the first division of the mouse embryo, as injection of a miR-34c inhibitor into zygotes was shown to induce an arrest in embryo cell division  gene expression and on its anti-proliferative function.Since the discovery of sperm mRNA and non-coding RNA transmission to the embryo after fertilization, studies were conducted to determine the impact(s) and role(s) of these RNAs in embryonic development  are exposed to multiple environmental insults including nutritional stress, psychological stress, toxins and drugs exposure  or a Folate-deficient diet] could result in the appearance of metabolic disorders in the offspring that were directly linked to changes in sperm epigenetics . Any modification of these persisting sperm histones may also be considered as part of the epigenetic information received by the future embryo. Very few studies have succeeded to establish a relation between sperm histone changes and transgenerational defects. In one particular study, Vassoler et al.  demethylase KDM1A during spermatogenesis. Spermatozoa of these mice showed reduced H3K4 dimethylation within CpG islands of genes implicated in development. In their progeny were observed severely impaired embryonic development and reduced survival rates across three generations  in transgenerational inheritance because they are long known to be present in the germ cell lineage and their involvement in the regulation of DNA methylation and histone modifications is well documented  exhibited an altered sperm miRNA profile associated with an increase in metabolic disorders in their offspring  constitute a specific epigenetic landscape, shown, in case of disruption, to result in male infertility, abnormal embryo development and/or transgenerational inheritance. Future studies will have to elucidate and clarify the role and underlying mechanisms of sperm epigenetics in those processes, in particular its impact on the offspring health. If one takes into account the additional effects due to variations in the oocyte epigenome, predicting the risk of diseases in the offspring associated with germ cell epigenome alterations will certainly be a rather complex issue.AC, JC, JH-B, and AK: drafted the manuscript; JD: critically reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Comprehensive delineation of complex cellular networks requires high-throughput interrogation of genetic interactions. To address this challenge, we describe the development of a multiplex combinatorial strategy to assess pairwise genetic interactions using CRISPR-Cas9 genome editing and next-generation sequencing. We characterize the performance of combinatorial genome editing and analysis using different promoter and gRNA designs and identified regions of the chimeric RNA that are compatible with next-generation sequencing preparation and quantification. This approach is an important step towards elucidating genetic networks relevant to human diseases and the development of more efficient Cas9-based therapeutics. Complex cellular processes that control cell state and decision-making are orchestrated through highly interconnected regulatory networks. Quantitative genetic interaction mapping enables the systematic discovery of how gene-gene interactions give rise to complex cellular processes . Uncovermultiplex strategy for assessing genetic interactions using CRISPR-Cas9 (MoSAIC).The CRISPR-Cas9 system enables efficient genome engineering of mammalian cells through a programmable guide-RNA (gRNA) that targets Cas9 to a desired locus for editing –8. Thus 2 in high-Glucose Dulbecco’s modified Eagle’s medium (DMEM) containing 10% fetal bovine serum and 1% Penicillin/Streptomycin (Life Technologies). HEK 293T cells containing eGFP were a gift from Stephen Goff (Columbia University). 293FT cells were obtained from Life Technologies and were maintained in the same medium formulation and supplemented with 0.1 mM non-essential amino acids, 2 mM L-glutamine and 500 ug/ml Geneticin.HEK 293T cells were obtained from the American Tissue Collection Center (ATCC) and grown at 37°C, 5% COhttp://www.broadinstitute.org/rnai/public/resources/protocols).Lentivirus was produced in 293FT cells and stable Cas9-eGFP cells were transduced as previously described  inducible Cas9 cells were generated as follows. 293T cell clones stably expressing eGFP-Cas9 under dox inducible promoter were generated by transduction of PLX301-eGFP-Cas9/Bsd  using LT1 transfection reagent (Mirus) followed by selection with 10mg/ml Blasticidin (Bsd). 293T cells were infected with lentiviral particles at MOI of 0.3 followed by clonal selection. We selected a clone with highest differential Cas9 expression following 48 hour induction using immunostaining of FLAG-tagged Cas9, followed by flow cytometry.The eGFP-Cas-9 clone was infected with lentivirus containing gRNA constructs targeting eGFP and STAT1 or eGFP-only. Twenty-four hours post-infection, the media was changed and supplemented with 10 ug/ml blasticidin (Life Technologies) and cells were selected for three days, prior to doxycycline induction of Cas9. Cells were harvested on days 14, 21, and 28 post-induction. Gene knockout efficiencies were measured by either flow cytometry or SURVEYOR assay. Flow cytometry was performed using a LSRII or LSR Fortessa to quantify fraction of eGFP positive cells.MV.1, MV.3, MV.5, MV.6, MV.7 originated from lentivector v_w0, originally called plxsgRNA (Addgene 50662). A point mutation was made in the PGK promoter to eliminate the BsmB1 restriction site for all down-stream cloning (v_w0).MV.2 originated from pLenticrispr (49535). Vs.d1 was amplified with primers containing eGFP gRNA 1 / STAT1 gRNA2 and cloned into the pLenticrispr vector to generate an all-in-one vector containing two gRNAs.To clone MV.1 backbone, pLenticrispr was used as a template with vs_p39(f) and vs_p40 (r) to amplify an insert containing the reverse direction chimeric RNA, filler region with BsmB1 restriction sites and a forward direction chimeric RNA sequence. The chimeric- filler-chimeric was cloned into v_w0. To clone in gRNAs, vs.d5 (dsDNA) containing reverse direction H1 promoter, LoxP site and forward direction U6 promoter, was amplified with primers containing eGFP gRNA 1 and STAT1 gRNA 2 as well as BsmB1 restriction sites. The PCR product containing both gRNAs and both promoters was cloned into the MV.1 backbone to generate MV.1.1 and MV.1.2.To clone MV.3 backbone, H1 promoter expressing short tracr RNA was cloned into v_w0 from px261 (Addgene 42337). To clone in gRNAs, vs.d11 (containing U6 promoter) was amplified with primers vs_p79 and vs_p80/ vs_p81 / vs_p82 and PCR products were cloned into MV.3 backbone.To clone MV.5 backbone, the U6 promoter, filler region and chimeric RNA was cloned into v_w0 from lenticrispr_v1 (Addgene #49535) using vs_p59/vs_p40 primers. To clone in gRNAs,v_w2 containing chimeric RNA-LoxP-site and H1 promoter were amplified with primers containing eGFP gRNA 1 and STAT1 gRNA 2 as well as BsmB1 restriction sites.To clone MV.6 backbone, U6 promoter, the filler region with BsmB1 restriction sites and the chimeric RNA v2, was cloned into v_w0 using vs_d10. To clone in gRNAs,v_w2 containing chimeric RNA-loxP-site and H1 promoter were amplified with primers containing eGFP gRNA 1 and STAT1 gRNA 2 as well as BsmB1 restriction sites.In MV.7, used the backbone established in MV.6. To clone in gRNAs, oligo pairs (vs_p75 / vs_p76 and vs_p77/vs_p78) containing -BsmB1 Overhang-gRNA1-chimeric RNA-gRNA 2 were synthesized, annealed and ligated into backbone.Unless noted, all DNA constructs and primers were obtained from IDT (Geneblocks) and used for PCR and assembly steps as described above.vs_d1: chimeric RNA—LoxP site—U6 promoter:GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTATAACTTCGTATAGCATACATTATACGAAGTTATGAGGGCCTATTTCCCATGATTCCTTCATATTTGCATATACGATACAAGGCTGTTAGAGAGATAATTAGAATTAATTTGACTGTAAACACAAAGATATTAGTACAAAATACGTGACGTAGAAAGTAATAATTTCTTGGGTAGTTTGCAGTTTTAAAATTATGTTTTAAAATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACCvs_d5: reverse H1 promoter—LoxP site—U6 promoter forward:TAGATCTGTGGTCTCATACAGAACTTATAAGATTCCCAAATCCAAAGACATTTCACGTTTATGGTGATTTCCCAGAACACATAGCGACATGCAAATATTGCAGGGCGCCACTCCCCTGTCCCTCACAGCCATCTTCCTGCCAGGGCGCACGCGCGCTGGGTGTTCCCGCCTAGTGACACTGGGCCCGCGATTCCTTGGAGCGGGTTGATGACGTCAGCGTTCGAATTATAACTTCGTATAGCATACATTATACGAAGTTATGAGGGCCTATTTCCCATGATTCCTTCATATTTGCATATACGATACAAGGCTGTTAGAGAGATAATTAGAATTAATTTGACTGTAAACACAAAGATATTAGTACAAAATACGTGACGTAGAAAGTAATAATTTCTTGGGTAGTTTGCAGTTTTAAAATTATGTTTTAAAATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACCv_w2: chimeric RNA—LoxP site—forward H1 promoter:GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTATAACTTCGTATAGCATACATTATACGAAGTTATAATTCGAACGCTGACGTCATCAACCCGCTCCAAGGAATCGCGGGCCCAGTGTCACTAGGCGGGAACACCCAGCGCGCGTGCGCCCTGGCAGGAAGATGGCTGTGAGGGACAGGGGAGTGGCGCCCTGCAATATTTGCATGTCGCTATGTGTTCTGGGAAATCACCATAAACGTGAAATGTCTTTGGATTTGGGAATCTTATAAGTTCTGTATGAGACCACAGATCTAvs_d10: Xho1 site—forward U6 promoter—BsmB1 filler region (36nt)—Chimeric RNA version 2—Nhe1:ATTCGAACTCGAGGAGGGCCTATTTCCCATGATTCCTTCATATTTGCATATACGATACAAGGCTGTTAGAGAGATAATTAGAATTAATTTGACTGTAAACACAAAGATATTAGTACAAAATACGTGACGTAGAAAGTAATAATTTCTTGGGTAGTTTGCAGTTTTAAAATTATGTTTTAAAATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACCGGAGACGGTTTTCTTGCTCTTTTTTGTACGTCTCTGTTTTAGAGCCGGAAACGGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTGCTAGCGCTAACvs_d11: forward U6 promoter:GAGGGCCTATTTCCCATGATTCCTTCATATTTGCATATACGATACAAGGCTGTTAGAGAGATAATTAGAATTAATTTGACTGTAAACACAAAGATATTAGTACAAAATACGTGACGTAGAAAGTAATAATTTCTTGGGTAGTTTGCAGTTTTAAAATTATGTTTTAAAATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACCvs_p26 :AATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCGvs_p64 :TATTTTAACTTGCCGTTTCCGGCvs_p59 (forward):ACGGACTCGAGGAGGGCCTATTTCCCATGATTCvs_p40 (reverse):GATCACGGAGCTAGCCTGCCATTTGTCTCAAGATCTAGAATTCvs_p75:CACCGGAGCTGGACGGCGACGTAAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTGAAGTTCGAGGGCGACACCCvs_p76:AAACGGGTGTCGCCCTCGAACTTCAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAACTTTACGTCGCCGTCCAGCTCCvs_p77:CACCGGAGCTGGACGGCGACGTAAAGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTCCCCGGGGAAGTTCGAGGGCGACACCCvs_p78:AAACGGGTGTCGCCCTCGAACTTCCCCGGGGAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAACTTTACGTCGCCGTCCAGCTCCvs_p79 (forward):AGGGATCCTGAGGGCCTATTTCCCATGAvs_p80 (reverse):GCGCTAGCTAAAAACAGCATAGCTCTAAAACGGGTGTCGCCCTCGAACTTCACAGCATAGCTCTAAAACTTTACGTCGCCGTCCAGCTCGGTGTTTCGTCCTTTCCACAAvs_p81 (reverse):TTAGCGCTAGCTAAAAGTTTTGGGACCATTCAAAACAGCATAGCTCTAAAACGGGTGTCGCCCTCGAACTTCGTTTTGGGACCATTCAAAACAGCATAGCTCTAAAACTTTACGTCGCCGTCCAGCTCGGTGTTTCGTCCTTTCCACAAGvs_p82 (reverse):AGCGCTAGCTAAAAGTTTTGGGACCATTCAAAACAGCATAGCTCTAAAACGGGTGTCGCCCTCGAACTTCGTTTTGGGACCATTCAAAACAGCATAGCTCTAAAACTTTACGTCGCCGTCCAGCTCGTTTTGGGACCATTCAAAACAGCATAGCTCTAAAACGGTGTTTCGTCCTTTCCACAeGFP gRNA 1:GAGCTGGACGGCGACGTAAAeGFP gRNA 2:GAAGTTCGAGGGCGACACCCSTAT1 gRNA 1:GATCATCCAGCTGTGACAGGSTAT1 gRNA 2: CCTGTCACAGCTGGATGATCeGFP sequence:GAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTACGTCCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGCGCCGAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAGGGCATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTACAACTACAACAGCCACAACGTCTATATCATGGCCGACAAGCAGAAGAACGGCATCAAGGTGAACTTCAAGATCCGCCACAACATCGAGGACGGCAGCGTGCAGCTCGCCGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCACCCAGTCCGCCCTGAGCAAAGACCCCAACGAGAAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCACTCTCGGCATGGACGAGCTGTACAAGTAAATGGTGAGCAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCWe used the crystal structure of the sgRNA-targetDNA-cas9 complex to model the UA31CG-AU32GC-sgRNA . The corIn order to comprehensively map genetic interactions in a gene network, all possible single and double knockouts (KO) need to be simultaneously interrogated. MoSAIC achieves this in a single step through PCR of a common DNA template with gRNA primer pools . The firTo optimize the system for simultaneous targeting of Cas9 to multiple loci, we designed and tested two MoSAIC-compatible strategies:1) dual promoter, dual gRNA transcripts, and 2) single promoter, single RNA transcript . We explored several designs that use RNA Pol III promoters U6 and H1 in different positions and orientations , having We began by benchmarking a previously described approach utilizing two unidirectional U6 promoters to express dual gRNAs  designs. We founWe then explored whether the promoter choice and orientation impacted KO efficiency in a position-dependent fashion  sequences is sufficient for multiplexed Cas9-mediated KO in the absence of the SpRNase III RNA cleavage enzyme [Previously, studies demonstrated that targeting Cas9 to multiple loci could be achieved by co-expressing the RNA cleavage enzyme ge sites . Anothere enzyme . In ordee enzyme  Fig 4A)Csy4 alonWe further explored MV7 designs that incorporated transcripts containing two tandem gRNA-chimeric RNA sequences and thus did not require a tracrRNA . This deS. pyogenes CRISPR-Cas9 crystal structure (PDB-4OO8) [MoSAIC is designed such that gRNA pairs serve as barcodes that can then be PCR amplified and identified using next-generation sequencing. We achieved this by altering the second chimeric RNA sequence such that placement of a reverse sequencing primer results in PCR amplification of both gRNAs with an amplicon size that is NGS compatible . Primer DB-4OO8)  to prediHere we report on the development of a generalizable strategy for multiplex targeting of Cas9 for mammalian genome engineering. We describe the implementation of this approach to combinatorially target two genes simultaneously and monitor the mutational efficiency of several gRNA designs. We find that gRNA pairs expressed from dual U6-H1 promoters lead to optimal Cas9-mediated genome editing, which can be combined with single transcript multiple gRNA designs (MV7) to increase editing efficiency. In addition, we find that a unidirectional single-transcript gRNA system resulted in low KO efficiency. While the precise reason for this observation has not been fully elucidated, we speculate that MV3.3 and 3.4 designs that lead to contiguous unprocessed gRNA products may cause a lower Cas9 targeting efficiency by virtue of Cas9 binding to either of the two gRNA positions, but not simultaneously. In addition, we observed that unidirectional dual gRNA designs resulted in increased KO efficiency for the second gRNA position. While further experiments are needed, we speculate that higher KO efficiency of the second gRNA may be the result of transcription read-through of first position gRNA, leading to increased levels of second gRNA transcripts and Cas9 targeting.To enhance multiplexing of our system, we identified specific chimeric RNA variants at positions 11–12 and 17–18 corresponding to the tetraloop repeat-anti-repeat duplex that are amenable to alteration. We showed that these chimeric RNA variants still maintained Cas9 targeting while also allowing compatible barcode amplification in a pooled format to enable multiplex assessment of cell populations using NGS. In this manner, the gRNA pairs serve as unique molecular barcodes linking their abundance in the cell population with their screened phenotype. This strategy is also compatible with the use of different Cas9 variants, including CRISPRi  and CRISMoSAIC overcomes several key technical hurdles associated with high-throughput generation and measurement of dual loci perturbations in mammalian cells. Additionally, to facilitate subsequent iterative introduction of gRNA constructs and enable higher-order combinatorial genetic perturbations, the integrated lentiviral vector design includes loxP “landing-pad” sequences. The MoSAIC system expands the toolbox for genetic modification of mammalian genome and extends our knowledge of Cas9 targeting design parameters. Recently, Wong et al described the CombiGEM approach to generate combinatorial gRNA libraries for Cas9-based genomic interrogations by barcoded sequencing . The Com