Background: High-throughput next generation sequencing (NGS) technologies enable the detection of biomarkers used for tumor classification, disease monitoring and cancer therapy. Whole-transcriptome analysis using RNA-seq is important, not only as a means of understanding the mechanisms responsible for complex diseases but also to efficiently identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutations, differential gene expression and fusion-transcripts or chimeric RNA (chRNA).Methods: We usedCrac, a tool that uses genomic locations and local coverage to classify biological events and directly infer splice and chimeric junctions within a single read. Crac’s algorithm extracts transcriptional chimeric events irrespective of annotation with a high sensitivity, andCracTools was used to aggregate, annotate and filter the chRNA reads. The selected chRNA candidates were validated by real time PCR and sequencing.  In order to check the tumor specific expression of chRNA, we analyzed a publicly available dataset using a new tag search approach.Results:  We present data related to acute myeloid leukemia (AML) RNA-seq analysis. We highlight novel biological cases of chRNA, in addition to previously well characterized leukemia chRNA. We have identified and validated 17 chRNAs among 3 AML patients: 10 from an AML patient with a translocation between chromosomes 15 and 17 , 4  from patient with normal karyotype (AML-NK) 3 from a patient with chromosomal 16 inversion (AML-inv16). The new fusion transcripts can be classified into four groups according to the exon organization.Conclusions:  All groups suggest complex but distinct synthesis mechanisms involving either collinear exons of different genes, non-collinear exons, or exons of different chromosomes. Finally, we check tumor-specific expression in a larger RNA-seq AML cohort and identify new AML biomarkers that could improve diagnosis and prognosis of AML. Whole-transcriptome analysis with RNA-seq is increasingly acquiring a key role, not only to learn about mechanisms responsible for complex disease, but also to identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutation, differential gene expression, fusion-transcripts and chimeric RNA (chRNA)4. These cancer biomarkers are generated at DNA level from gene fusions by mechanisms such as translocations, inversions, or more complex chromosomal rearrangements. In some well-documented cases, gene fusions, in addition to contributing to neoplastic transformation, produce fusion RNA and proteins used as therapeutic targets . However recent RNA-seq analyses have revealed the existence of an enlarged “chimeric transcriptome”10 generated by new RNA processing events such as cis- and trans-splicing, whose mechanisms and functional roles are poorly understood. It is crucial to determine whether these events represent artefacts of RNA-sequencing, transcriptional noise with little impact on cell functions or tissue-specific transcripts, or whether these events are important in tumor development. Several significant examples of chRNA without corresponding fusions at DNA level have been described in the context of neoplasia12.For chimeric RNA, a group of fusion transcripts is increasingly used by geneticists in oncology diagnosis13. We recently proposed Crac14, a novel way of analyzing reads that combines genomic locations and local coverage to classify the biological events, to directly infer splice and chimeric junctions within a single read. TheCrac software is based on an innovative algorithm that allows extraction of transcriptional events, irrespective of annotation. The main advantage of using Crac to detect chimeras is its high sensitivity and specificity, allowing detection of rare events with confidence. We developed the complementaryCracTools module to aggregate, annotate, and filter the chRNA reads. The procedure classifies reads into 4 classes, depending on the exon organization. The first class corresponds to fusion transcripts arising from two different chromosomes, the second class includes parts of two genes belonging to the same chromosome strand. The third and fourth classes involve non-collinear transcription on the same chromosome. This categorization suggests that each class represents distinct, complex synthesis mechanisms.Both from a methodological and biological perspective, profiling chimeric RNA is a challenging issue. chRNA’s distinctive features must be identified to provide relevant biological information, including synthesis mechanisms and pertinence as a biomarker. Though RNA-Seq may enable new biomarker discovery, there is a lack of consensus on which analysis tool and algorithmic strategy should be used, especially in the detection of chRNA2. Despite the fact that leukemia chRNAs are well characterized, our study highlights new biological cases of chRNA. We identify and validate 17 chRNAs initially detected in 3 AML patients that belong to the 4 classes. We then explore their specific expression in the publicly available LEUCEGENE cohort15, and propose new criteria for distinguishing chRNAs based on their recurrence, tumor, subgroup, or patient-specific expression. We also classify the chRNAs according to differences in expression of the genes linked to the chimera, in healthy donors vs AML patients. Finally, we identify new biomarkers that could improve diagnosis and prognosis of AML.In order to assess Crac’s potential to identify new biomarkers, we present data related to acute myeloid leukemia (AML) RNA-seq analysis. AML provides a practical cellular model to detect chRNA biomarkers in order to improve classification and patient follow-up in precision medicine6/ml, with or without 0.1µM ATRA, for 3 days. PBMC samples from healthy donors were pooled and used as control sample. The patients and healthy donors provided written informed consent to participate in the study, in accordance with the Declaration of Helsinki. The U937 leukemia cell line , NB4 promyelocytic cell line and NB4-LR2 cell line (provided by CC) were cultured in RPMI 1640  containing 10% decomplemented FBS . For differentiation conditions, NB4 and U937 cells were cultured as previously described17. The chemical agents used for differentiation were 1µM all-trans retinoic acid , or 0.1µM vitamin D (VD) associated with 500pg/ml transforming growth factor beta TGFβ  for the NB4 cell line18. For the U937 cell line, 0.1µM TTNPB associated with 1µM Targretin (LGD1069) and 0.1µM 1 alpha, 25 dihydroxyvitamin D3 (VD) were used. TTNPB, LGD1069 and VD were kindly provided by Dr Klaus , JHiernaux , and L Binderup , respectively. We also used human neuroblastoma cancer SH-SY5Y cells, human breast cancer MCF7 cells and human prostate cancer MDA-PCa cells. Cell pellets were kindly provided by S. Marchal , and by D.Noel  for the latter.Three sets of samples from patients with AML were used in this study. Each patient is designed by a specific ID including the OM or OS code corresponding respectively to bone marrow or blood sample . The fir® ND-1000 spectrophotometer . RNA quality and quantity were assessed using the 2100-Bioanalyzer . Reverse transcription was performed with random primers and MultiScribe Reverse Transcriptase , using 1 µg of total RNA. To check for possible chRNA formation induced by transcriptional artefacts, part of the samples were double reverse transcribed. In this case, the second reverse transcription reaction was performed with ImProm-II™ Reverse Transcriptase .RNA was extracted with the RNeasy Qiagen kit , additional DNase treatment was performed in order to remove residual DNA . Total RNA was quantified using a NanoDrop15, have been used in this study  and AML-inv16) taken from patients OM100011, OM110223 and OS110089, respectively (AML test group) from the Biological Resource Center, CHU-Nîmes, France, were selected for RNA-seq experiments. 4 µg of total RNA taken from bone marrow  or blood PBMCs (OS110089) were sent to GATC biotech and analyzed on an Illumina HiSeq 2000 to generate 100 base pairs of stranded RNA-Seq paired-end reads. The RNA-Seq was performed using polyA-selection with the truSeq RNA Lib-Prep Kit  adjusted with GATC specific procedure for strand specificity. The following publicly available datasets of the LEUCEGENE project dedicated to Acute myeloid and lymphoid leukemia studiesGSE48846 (17 CD34 hematopoietic stem cell),GSE49642 part1 (43 AML-NK),GSE62190 part3 ,5 inv3,7 t, 5 t,6 t and complex karyotypes)ENCODE publicly available datasets (Supplementary Table S7).14. Crac is a software for analyzing reads when a reference genome is available, that is completely independent of annotations. It ignores the sequence quality of reads and classifies reads by detecting diverse biological events  and sequencing errors from a RNA-seq read collection. In this analysis, we used two Crac versions in succession (V1.6 and V2) to extract and classify chRNAs, with the GRCh37/hg19 genome as reference genome. Crac extracts the chimeric reads supporting the chimeric junction (spanning junction) made of a non-collinear arrangement of genomic regions14. CracTools was then used to aggregate, annotate and filter the chRNA reads and extract the chimeric paired reads (spanning PE)  and CracTools (V1.2)ing PE) . Reads w– Class 1, the exons are located on different chromosomes;–Class 2, the exons are collinear but most likely belong to different genes, to be verified through the annotation;–Class 3, the exons are on the same chromosome and same strand, but not in the order in which they are found on DNA;–Class 4, the exons are on the same chromosome but on different strands.For each analyzed chimera, the pipeline provides related information, including a unique read identifier annotated by the pipeline, class, number of spanning junctions and spanning reads:1. ID: A unique read ID for each chimera, composed of ‘sample name: chimera ID’2. Fusion gene names (left-right)3. Chr(left): Chromosome number of the 5' part of the chimera4. Pos1: Genomic position of the 5' part of the chimera5. Strand1: Genomic strand of the 5' part of the chimera (+1 or -1).6. Chr(right): Chr number of the 3' part of the chimera7. Pos2: Genomic position of the 3' part of the chimera8. Strand2: Genomic strand of the 3' part of the chimera139. ChimValue: the chim-value takes into account methodological parameters and ambiguities, including the read mapping (P_loc) and the read coverage (P_support).10. Spanning junction normalized: Spanning junction reads coverage . A spanning junction read is the read that contains the chimeric junction.11. Spanning PE normalized: Coverage of paired-end reads  that contains the chimeric junction in the non-sequenced part12. Class: Chimeric class from 1 to 4.The filtering process with CracTools considered the following thresholds:a.Only candidate fusions (chRNA) with at least one read covering the fusion breakpoint,b.Spanning reads must be associated with pair-end reads, c.An annotation of the fusion junction matching almost a known expressed sequence (gene A or Gene B),d.A ChimValue of up to 60, allowing the removal of false positives corresponding to pseudogenes and splicing events detected by GSNAP.Candidate fusion transcripts involving adjacent genes within a 3Kb distance region were discarded. To estimate the number of supporting reads for a chimeric candidate, CracTools extracted the count number of spanning junctions and spanning PE reads. All candidate fusion transcripts were validated using qPCR and Sanger sequencing except the class 3 overlap which requires systematic reconstruction of the fusion transcript for the design of primers. BLAT, UCSC) to the human genome GRCh37/hg19 in order to identify complex biological events .Complementary annotations were identified using the ENSEMBL genome browser to determine exons and spliced variants involved in the transcript. Protein sequences and functional annotations were also verified to identify affected protein domains and to evaluate potential protein damage in selected chRNAs.The potential chimeras are listed in®480 sybr green I Master, Roche Diagnostics, GERMANY) and 0.66 µM forward and reverse primers. Mix and cDNA were loaded onto the 384-well PCR plate using an epMotion 5070 automated pipetting system . Primer sequences were designed using thePrimer3Plus web interface, with some constraints as described in the PCR strategy . Amplifications were performed according to the following conditions: 95°C for 5 min, then 55 cycles as follows: 95°C for 10s, followed by T°C depending on Tm for 10s, and 72°C for 10s. Ultimately, a melting curve analysis ranging from 60°C to 95°C was performed to control primer specificity. For each sample, the graph of the negative first derivative of the melting curve gave a specific peak corresponding to the amplified transcript. Samples with TM value peaks different from those found in the negative control were considered potential positive targets and retained for sequencing. PCR products were purified with the Minelute PCR purification kit  and sequenced on the ABI 3730XL .Reverse transcription was performed as described above. 1 µl of each cDNA sample (2ng/µl) was added to a 5 µl of reaction mix containing 3 µl of Master Mix  for 30 minutes at 37°C, followed by dehydration in 3 baths of increasing ethanol concentration: 70%, 85% and 100%, each for 1 minute. Finally, slides were air dried at room temperature. To confirm the putative t translocation related to the PAN3-NONE fusion gene, a fusion probe was designed using bacterial artificial chromosome (BAC) technology, framing the following regions of interest: RP11-239J16, RP11-339H12 in chromosome 2p21 (labeled in Cy3 Orange) and RP11-179F17, RP11-95G6 in chromosome 13q12 (labeled in Alexa 488 Green) . 1 μl of each of the BACs was mixed and diluted in 9 μl of hybridization BlueFISH Buffer and then applied to the slide. Chromosomes fixed on the slide and probe of interest were denatured in a single step using Thermobrite . Codenaturation was done at 75°C for 5 minutes, followed by hybridization at 37°C in a humid atmosphere overnight. To remove the probe that would not properly hybridize, two successive washes in stringent conditions were performed: the first one in 0.4xSSC and 0.1% Igepal at 73°C for 2 minutes, the second one in 2xSSC and 0.3% Igepal for 1 minute at room temperature. After complete drying, 10 µl of counterstaining reagent  was added. Slides were observed on an epifluorescence microscope (Olympus BX60). For both probes, positivity was defined as the presence of two fusion signals (co-location) in addition to a red signal and a green signal.https://github.com/jaudoux/countTags) that searched for sequences and their reverse complement with an exact match in the FASTQ files. For each FASTQ file and each tag, the total number of tags  is counted. Final value is given in a delimited table, with a tag count normalized per 5 billion of k-mers.The tag search approach consisted of extracting representative sequences (Tags) 30 nucleotides in length, centered on the chimeric junctions. The latter were then searched in LEUCEGENE and ENCODE publicly available RNA-seq datasets. A FASTA file, listing these tags of interest, designed for each chRNA, was submitted to a specific pipeline ,ii.Short fusion distance (max 300kb),iii.Short exon-end distance (max 20bp).Tandem repeat chimeras were further selected based on three criteria:i.Chimera annotated as "Class 3"ii.Overlaping chimeric fragments (the two parts of the chimeric read correspond to overlapping sequences on the genome)iii.Both chimeric fragments are located on the same exon of the same gene.19. The normalization of counts was performed with DESeq2 (version 1.14.1) (design ~ 1), so values used in the clustering were normalized counts transformed with the variance stabilization method provided in DESeq2 package. Heatmaps were produced with heatmap.2 function from the gplots package (R version number 3.3.2), using default parameters (i.e. complete-linkage clustering and Euclidean distance).For each chimera type (read-through and tandem-repeat), only genes involved in at least 2 different chimeric events (either from the same or different samples) were finally selected as candidates for the clustering of gene expression. LEUCEGENE data were downloaded from SRA using the fastq-dump utility (version 2.5.4) and converted to FASTQ. Using Kallisto 0.42.4 software and Ensembl 84 annotations, we determined transcript expression. Transcript counts computed by Kallisto were merged at gene-levelWe performed RNA-seq on samples from 3 AML patients. One presented with a normal karyotype (NK), while the other two presented with an abnormal karyotype (AK), one with an Inv16 and the other with a t translocation , 4/18 from AML-NK, and 3/3 from AML-inv16 . The val–Class 1: 5–Class 2: 3–Class 3: 5–Class 4: 4.Among the Class 1 chRNAs more frequently associated with genomic translocation, we identified 4 chRNAs associated with PML and RARA genes in patient OM110223 suffering from AML-t. The remaining chRNA of this class was associated with the PAN3 gene and a non-annotated region (NONE), and was found in patient OS110089 suffering from AML-inv16. We highlighted two types of Class 2 chRNAs that depend on the genomic distance between the two parts of the read. A short distance was consistent with read-through, whereas a long distance would be associated with other mechanisms. We found two kinds of Class 3 chRNA - in the first, the chRNA processes the 3’ exon before the 5’ start of the same gene. In the second, the chimera involve distant exons from different genes. Finally we also validated several Class 4 chRNAs, among which the CBFB-MYH11 chimera associated with Inv16.A new Class 1 chRNA was identified in patient OS110089 presenting the inv16 associated fusion transcript CBFB-MYH11. The new fusion junction read  in normal and tumor tissues using the approach described previously combining digital Gene expression (DGE) and RNA-seq datasamples . The RNAsamples  confirmesamples . We conf22. PCR and FISH analyses performed during diagnosis showed that patient OM110223’s leukemia cells present a short bcr3 transcript which connects exon 3 of the PML gene and exon 9 of the RARA gene .Four Class 1 junctions involving PML and RARA genes were identified from the analysis of the AML-t OM110223 sample . Acute p23. Besides these fusion transcripts already described24, we observed the presence of two new fusion transcripts. The first corresponds to a fusion RNA shorter than bcr3, joining PML exon3 and RARA exon 12 /pos 77557943-77558014 segment is fused with exon2-CTDP1-001 or with exon13-CTDP1-001 .The Class 4 chRNA corresponds to reads whose 5’ and 3’ parts match on the chromosome’s opposite strands . This kiTo extend our analysis, we tested the expression of the validated chRNA in a large cohort of AML samples with different karyotypes  using qPIn order to validate our strategy on a large cohort, we analyzed a publicly available dataset of 125 AML and 17 normal CD34+ HSC RNA-seq using a tag search approach . For this purpose, we selected qPCR validated chRNA  and candWe identified four new types of tumor-specific chRNA; TRIM28-TRIM28, DHRS7B-TMEM11, PLXNB-BLRD1 and SLC16A3-METRNL, expressed in all AML groups . DHRS7B-25 that could influence tumorigenesis, we analyzed the impact of gene expression on AML with unsupervised clustering. As described for the read-through chRNA subcategory, we compared RNA-seq data from normal CD34+ HSC and AML-AK subtypes .In order to verify whether genes involved in chRNA have an aberrant expression profile26, are already used in diagnosis, but many other chimeric fusion products generated by transcriptional mechanisms such as read-throughs, cis or trans-splicing9, also have the potential to be used in diagnosis if correctly categorized. In this study, we successfully developed new tools to classify these fusion transcripts methodologically and biologically into chRNA categories and subcategories, and associated them with biological mechanisms.The use of RNA-seq to provide a detailed view of the transcriptome and to detect new RNA transcripts, opens up new opportunities for improving diagnosis and treatment of human diseases. The characterization of new chRNA presents as a great opportunity, as it could reveal new transcriptomic biomarkers for cancer and therefore could be useful in personalized medicine. ChRNAs, also known as “fusion RNA” or “canonical chimeras”27. Bioinformatics processes also generate artefacts associated with the algorithm’s approach for mapping raw reads to the complex reference genome. Many attempts have been made to improve the bioinformatics analysis of chRNAs by proposing multistep filtering pipelines including gene annotation, lists of known fusion genes or machine learning approaches to improve prediction13. However, pipeline choice remains a difficult task for biologists and bioinformaticians. We have developed a benchmarking system that enables the calibration and selection of pipelines optimised for the detection of fusion RNAs. Our work also entailed developing, within Crac, a machine learning model used to optimise the selection of fusion RNAs . Crac offers precise prediction of all chRNA categories, and the CracTools pipeline helps the biologist increase biological rate validation, producing a ChimValue that takes into account methodology and annotation.One of the major challenges in chRNA detection is to distinguish true candidates from false positives during RNA-seq analysis. False positives can result from technical artefacts that occur during sample preparation, mainly produced by reverse transcription or downstream PCR errors29, performed on large cohorts with thousands of experiments, have focused on “canonical fusion genes”. Most of the molecular cancer markers are based on fusion genes because of their ease of detection. It is more difficult to identify features that distinguish chRNAs into new categories, and to elucidate their biological mechanisms and functions. Integrating DNA-seq and RNA-seq data to improve classification is a possible solution30, but it is time and cost consuming. In this work, we confirm that RNA-seq is a good solution for canonical and new chRNA extraction, and propose a classification system based on subcategories and specific expression profiles.As mentioned above, what is at stake is the possibility of exploring the “whole chimeric transcriptome” to classify chRNA, and hence identify cancer biomarkers. High throughput studies, like the Cancer Genome Project31. MRD and patient follow-up in APL is usually performed by PML-RARA transcript QPCR, and relapse is associated with an increase of bcr1, bcr2 or bcr3 fusion transcripts32. Then, it would be useful to have a picture of the complete isoforms, to best address treatment in this context. Though many patients with AML-inv16 or AML-t can benefit from effective treatment, some may develop resistance, leading to adverse outcomes. The appearance or increase of fusion transcripts during treatment could be an indicator of such resistance.The present study also reveals new chRNA candidates among the well characterized subcategories. We identified a Class 1 PAN3-NONE chRNA transcript associated with a new translocation in a tumor subclone of a characterized Inv(16) AML, that could be used in patient follow-up. We also identified novel PML-RARA isoforms, shorter than the isoforms currently used in diagnosis, which could again be used in patient follow-up. A recent publication revealed that several isoforms can coexist in leukemia cells from the same patient. The authors showed that the ATRA cell response is isoform-dependent, as the short isoform lacks sensitivity to ATRA33. The real advantages of RNA-seq in highlighting tandem repeat sequences are its open nature and its capacity to detect outside “hotspots”. Furthermore, we also detected fusion transcripts resulting from “read-through transcription”, described in chRNA studies on cancer.Besides the translocation and inversion mechanisms, our pipeline highlights other events that correlate with chromosomal rearrangement and cancer diagnosis and prognosis. We find Class 3 overlap fusion transcripts like FLT3, corresponding to tandem duplication that could be of use in the prognosis and MRD of AML34. It is unlikely that they are the products of genomic abnormalities, since they are expressed in healthy samples. However, the pathogenetic impact of these chimeric fusions remains unclear. Recent findings have demonstrated the role of read-through chRNA in renal carcinoma and breast cancer35, and our data demonstrates that genes involved in these events are differently expressed in AML. More studies are needed to elucidate the physiopathological impact of these chRNAs.Most newly identified chRNAs used canonical splice sites and were detected in normal hematopoietic tissues. This observation confirms previously published works concerning the recurrence of chimeric fusion RNAs in healthy cells2. However, to achieve this, efforts must be made to facilitate the interpretation of complex high throughput data. For chRNA, this is feasible only if the fusion transcripts are well classified and characterized. New technologies are available to simplify disease follow-up at reduced costs. Here, we propose a robust, open method based on a single process to identify different classes of chRNA. This approach provides a chRNA transcriptome map of biomarkers for disease characterization and monitoring including known canonical gene fusions and new chRNA. In combination with a tag-based approach and gene expression profile, this map can give a global picture of the complex physiological processes and could correlate with current leukemia classification.The potential of NGS technologies, particularly of RNA-seq, in increasing the capabilities of personalized medicine is clearAbbreviations: chRNA, chimeric RNA; AML, acute myeloid leukemia; NK, normal karyotype; UK, unknown karyotype; AK, abnormal karyotype; APL, Acute Promyelocytic Leukemia; PBMCs, peripheral blood mononuclear cells; Inv16, chromosome 16 inversion; t, translocation of chromosomes 15 and 17; qPCR, quantitative polymerase chain reaction; NONE, non-annotated region; LincRNA, Long intergenic noncoding RNAs; Bcr, break chromosomal region; FISH, Fluorescencein situ hybridization; ATRA, all-trans retinoic acid; VD, vitamin D; TGFβ, transforming growth factor beta; MRD, minimal residual disease.The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2017 Rufflé F et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).http://crac.gforge.inria.fr/.The Crac and CracTools software is hosted onE-MTAB-5767 in the ArrayExpress database at EBI.Raw data (FASTQ files) for OM100011, OM110223 and OS110089 patients are available under accession numberhttps://www.ENCODEproject.org/.The publicly available ENCODE datasets (a detailed list is provided in Supplementary Table S7) used in the study are available onhttps://www.ncbi.nlm.nih.gov/geo/The following LEUCEGENE datasets: GSE62190 (82AML-AK), GSE48846 (17 CD34), GSE49642 (43AML-NK) . I am satisfied with the authors responses and feel the report is now suitable for indexing.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In this report, Ruffle et al. present a new approach for identifying novel chimeric transcripts using CracTools. They classify the transcripts into four categories: 1)  different chromosomes , 2) co-linear but from different genes , 3) exons from same chromosome but are not in the expected order based on the reference , or 4) exons on same chromosome but different strands. The authors then go on to validate a subset of putative new chimeric transcripts using RT-PCR and FISH. These types of studies have been previously performed. One new aspect of this study is the stranded library preparation which allows for the identification of the class 4 chimeras. In addition, they tried to minimize the bias in their analysis pipeline by not relying on a reference genome, which enabled the discovery of new transcripts. Overall, their approach provides a validated new strategy for identifying novel transcripts in RNA-seq data.Major ConcernsThe authors do not discuss circular RNA, which are likely to make up a large portion of their class 3 chimeras as found in many recent studies. The low numbers of class 3 chimeras also raises concerns about the sensitivity of the approach, as most recent studies have found thousands of circular RNA isoforms per sample. The total RNA underwent RT with random primers, which would retain circular RNA. It is not clear if there was a polyA-selection step after that point or if the total RNA was ribosomal depleted. If it was the former, the circular transcripts would not be present.If the RNA was not polyA-selected, the authors should specifically discuss the PML-RARA circular transcripts recently discovered in acute promyelocytic leukemia. I am not aware of any independent validation of that work.Minor ConcernsThe authors should read the manuscript closely for typos. For example in the AML samples and cells lines section there are commas where there should be periods, in the FISH methods section there are degree signs instead of percent, and two paragraphs before the discussion there is MDR instead of MRD.The authors should include the kits used for ribosomal RNA-depletion/polyA-selection, as well as stranded library preparation.What mechanisms do the authors have in mind for a structural variant and/or alternative splicing event that would result in class 4 chimeras, as they would not occur as a result of transcriptional read through of the same strand?The resolution is too low for Figure 5A.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We thank the referee for his careful reading of our manuscript and for his remarks which led us to clarify the protocol used in this work.Major Concerns The authors do not discuss circular RNA, which are likely to make up a large portion of their class 3 chimeras as found in many recent studies. The low numbers of class 3 chimeras also raises concerns about the sensitivity of the approach, as most recent studies have found thousands of circular RNA isoforms per sample. The total RNA underwent RT with random primers, which would retain circular RNA. It is not clear if there was a polyA-selection step after that point or if the total RNA was ribosomal depleted. If it was the former, the circular transcripts would not be present.  Response: The remark is relevant and we agree with the referee that it was not clearly stated that we performed polyA selection for the RNAseq experiment. This point is only described in online data availability (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5767/) and doesn’t appear explicitly in mat&methods section. We added the following comment on page 4: “The RNAseq was performed using polyA-selection with the TruSeq RNA Lib-Prep Kit (Illumina) adjusted with GATC specific procedure for strand specificity”. We also agree with the referee that class3 fusion transcripts could arise from circular RNA (circRNA) when performing ribosomal depleted RNA-seq. However, as our polyA+ RNA-seq study doesn’t enable the identification of this RNA subtype, we do not discuss about circRNA in the manuscript. Concerning class3 chimeric RNA, our pipeline detects a great number of candidates before applying specific filters. This category, as well as the class2, is the most represented in all datasets we analyzed. As described in mat&methods section, we used stringent criteria to minimize bias and greatly reduced the number of candidates. For example, from a typical AML stranded and paired- end RNA-seq experiment , starting from the raw data (CRAC and CracTools process) we extracted 1406 chRNAs including 35% of class3chRNA reduced by 16 fold with the filtering process (26 class3chRNA).If the RNA was not polyA-selected, the authors should specifically discuss the PML-RARA circular transcripts recently discovered in acute promyelocytic leukemia. I am not aware of any independent validation of that work.    Response: As we choose polyA RNA-seq, we can only characterize new linear fusion transcripts. From ribo-depleted RNAseq data analysis, the characterization of circRNA arising from fusion gene requires the identification of linear junction (lin-J) and circular junction (circ-J) described as spliced and back spliced junction respectively by Guarnerio et al : 289-302). The linear and circular fusion transcripts share the same linear junction.https://bio2m.montp.inserm.fr/papers/AMLchimera2017). Moreover, in our polyA+ dataset it surely corresponds to a linear transcript. It is most probably an alternative splicing product transcribed from the PML-RARA bcr3 translocation since it coexists with the well-known (3-9) PML-RARA bcr3 transcript. However we could not exclude in the biological sample the presence of circRNA emerging from the PML-RARA fusion gene. However polyA RNAseq protocol doesn’t enable to reveal them. The new (3-12) PML-RARA junction we discover corresponds necessarily to a linear junction because of its fusion junction sequence (see figure atBioProjectID: PRJNA315254) with our pipeline to detect fusion junctions (lin-J and Circ-J) specific tocircRNA arising from the PML-RARA translocated genes. We found 3 PML-RARA and 1 RARA-PML linear fusion junctions but we did not detect the circRNA ones. We performed a tag search approach specific to the PML-RARA circ-junction (F-circ1) described in Fig S1B supplemental information of Guarnerio et al manuscript. We did not find it in their fastQ files. To conclude we were unable to detect PML-RARA cirRNA in Guarnerio J dataset but we confirmed the data recently described by You, X. and Conrad, T. OF  with the  specific cirRNA Acfs pipeline . The circRNA search for low abundance will certainly require more deepness in RNAseq. To answer to the referee remark, we performed complementary experiments and analyzed the Guarnerio dataset  adjusted with GATC specific procedure for strand specificity”.  What mechanisms do the authors have in mind for a structural variant and/or alternative splicing event that would result in class 4 chimeras, as they would not occur as a result of transcriptional read through of the same strand ?Response: Two mechanisms would result in class 4 chimeras. The first one could involve chromosomic duplication and inversion as described in Newman et al:208-20. doi:10.1016/j.ajhg. 2014.12.017. Epub 2015 Jan 29). The second one could involve splicing events as described by Gingeras et al :206-11doi: 10.1038/nature08452.)  The resolution is too low for Figure 5A.Response: We changed the resolution of the figure 5A, increasing the font size.   Ruffle et al., performed RNA-Seq on three AML patients, and identified a number of new chimeric RNAs. They grouped them into reasonable categories, and discussed their generating mechanisms. For some, they performed tag search in larger sets of RNA-Seq data of AML and CD34+ HSCs. In general, the analysis is solid. Multiple levels of evidence were provided for some fusion RNAs (for instance FISH for a Class I fusion). The conclusion is justified. No major issues were noted.It would be nice to perform the same RNA-Seq pipeline analysis on bigger datasets, such as the data from LEUCEGENE.It is well known that different software tools behave differently and false positive/negative is a big issue. Would be nice to use another independent software for crosschecking.The METRNL001 Class 3 chimera may be a product of backsplicing if the sequencing protocol is not restricted to polyA RNAs. Minor suggestions:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Response to referee Report 1. It would be nice to perform the same RNAseq pipeline analysis on bigger datasets, such as the data from leucegene.2. It is well known that different software tools behave differently and false positive /negative is a big issue. Would be nice to use another independent software for crosschecking.3. The METRNL001 class3 chimera may be a product of backsplicing if the sequencing protocol is not restricted to polyA RNAsWe want to thank the referee for taking time to read the manuscript and provide constructive feedback.6 reads/per sample. However, our first aim in the present manuscript was to test our pipeline and to propose a new way to analyze, select and classify chRNA. We thus choose to analyze a small dataset and used the Leucegene cohort only to explore specific expression of the new chRNAs . This Expression with a tag search approach was based on their recurrence, tumor, subgroup, or patient-specific expression. The complete leucegene chRNA research represents a big task and constitutes a full-fledged work, we are now performing our RNAseq pipeline on this dataset. In this new task, we will check at a large scale the pipeline performance, in term of memory computing and time consuming, and also validate its capacity to detect all the well-known AML fusion genes. We then should be able to discover new chimeric RNA in AML.1. The first remark concerning LEUCEGENE analysis is relevant as this cohort is a unique dataset of 450 RNA-seq from AML patients and normal hematopoietic cells. This cohort includes diverse types of AML and clinical data with a deepness of 100-300x10. CRAC was developed in the Lab and compared with other software before we choose it for this study  and we recently published in September 2017 a benchmark method dealing with this topic . CRAC tools has been developed in the Lab in response to biologists requests to better characterize, filter and classify predicted fusion transcripts. Moreover, we recently compared the pipeline CRAC and CRACtools with Fusioncatcher in our AML dataset, as it provides interesting and complementary annotation  but the process is time consuming and the filtering step is more stringent, many of the new fusion transcripts we validated are ruled out. 2. We totally agree with the referee and are well aware of potential bias generated with different pipelines. Our group is particularly concerned about software comparison for chimeric RNA detectionWhatever the bioinformatics pipeline used, bias will subsist. In order to improve chRNA discovery, we undertook to couple the bioinformatics approach with biological information and propose a tag search approach targeting the chimeric junctions in normal and tumoral large data set. We demonstrate that this complementary approach works well and will help the biologist to determine the biological relevance of chimeric events.3. Since we use polyA+ RNA to perform RNAseq, the METRNL001 class3 chimera, which involves a single exon (end of exon 2 METRNL transcribed before the start of the same exon) more probably corresponds to a linear polyA+ transcript involving a short repeat sequence of 5’ part of exon 2 as described in Fig4 rather to a circularization of a single exon formed by backsplicing.


Emiliania huxleyi, Chrysochromulina rotalis and Prymnesium polylepis) to address the effects of temperature and nutrient limitation on elemental content and stoichiometry (C:N:P), total RNA, cell size, and growth rate. We found that the main gradient in elemental content and RNA largely was related to nutrient regime and the resulting differences in growth rate and degree of P-limitation, and observed reduced cell volume-specific content of P and RNA  and higher N:P and C:P in the slow growing −P cultures compared to the fast growing +P cultures. P-limited cells also tended to be larger than nutrient replete cells. Contrary to other recent studies, we found lower N:P and C:P ratios at high temperature. Overall, elemental content and RNA increased with temperature, especially in the nutrient replete cultures. Notably, however, temperature had a weaker–and in some cases a negative–effect on elemental content and RNA under P-limitation. This interaction indicates that the effect of temperature on cellular composition may differ between nutrient replete and nutrient limited conditions, where cellular uptake and storage of excess nutrients may overshadow changes in resource allocation among the non-storage fractions of biomass (e.g. P-rich ribosomes and N-rich proteins). Cell size decreased at high temperature, which is in accordance with general observations.Temperature and nutrients are key factors affecting the growth, cell size, and physiology of marine phytoplankton. In the ocean, temperature and nutrient availability often co-vary because temperature drives vertical stratification, which further controls nutrient upwelling. This makes it difficult to disentangle the effects of temperature and nutrients on phytoplankton purely from observational studies. In this study, we carried out a factorial experiment crossing two temperatures (13°and 19°C) with two growth regimes  for three species of common marine haptophytes ( Marine phytoplankton constitute nearly 50% of the global primary productivity  and are Direct effects of temperature on phytoplankton are related to kinetic responses. Temperature increases enzyme and ribosome activity and thereby processes like nutrient uptake and translation. Ultimately, this leads to higher rates of overall processes like protein synthesis, light saturated photosynthesis, and cell division when temperature is elevated towards the optimum and other factors are non-limiting . TemperaSeveral observational studies have revealed a positive correlation between the N:P ratio of marine seston and global temperature . Yet thein situ studies, at least for intraspecific changes in cell size. Experimental studies on phytoplankton indicate contributions both from ambient nutrient levels and temperature per se to cell size , total RNA content, and cell size, and hypothesized that (1) high temperature will decrease elemental and RNA content, (2) high temperature will increase N:P and C:P ratios, (3) high temperature will reduce cell size, (4) growth rate and elemental content will increase under nutrient replete growth, and (5) the effect of temperature on especially P and RNA content will differ between P-limited and nutrient replete growth. This is expected, because under P-limitation P and RNA more directly reflect the non-storage requirements, and P-content is not confounded by excess storage (luxury consumption), which may be significant under nutrient replete conditions .Emiliania huxleyi (Lohmann) Hay & Mohler, 1967; Chrysochromulina rotalis Eikrem & Throndsen, 1999; and Prymnesium polylepis (Manton & Parke) Edvardsen, Eikrem & Probert 2011—all haptophytes in the class Coccolithophyceae (=Prymnesiophyceae). The specific strains were initially isolated from the outer Oslo fjord (Norway), and have been kept in culture at the University of Oslo for several years. We cultivated the algae in IMR 1/2 medium, which is the original IMR medium with nutrient levels reduced to 50%  and two temperatures (13 °C and 19 °C) in triplicates for three species. This yielded 36 experimental units. Prior to the experiment, the algae were cultivated in IMR 1/2 medium for approximately two weeks at 16 °C before being acclimated to their respective temperatures for seven days.E. huxleyi and C. rotalis, and 40% for P. polylepis. Hence, the dilution factors (DFs) were 1∕(1 − 0.5) = 2 and 1∕(1 − 0.4) = 1.667, respectively.The P-limited cultures (hereafter referred to as “−P”) were grown in IMR 1/2 medium reduced to 1 µM phosphate, resulting in a dissolved N:P ratio of 124:1 (mol:mol). We grew the −P cultures as batch cultures in semi-continuous mode. Every second day, a constant fraction of the culture volume was removed and replaced by fresh medium. The fraction removed was 50% for −1 for E. huxleyi and C. rotalis and 100,000 cells mL−1 for P. polylepis. This ensured that the cells were P-saturated and always growing exponentially.With the semi-continuous mode, we ensured that cell densities eventually reached a quasi steady state which was limited by P due to the high N:P supply ratio (124:1). Here the state of the algal cells is the same in each dilution cycle, but will change somewhat over the inter-dilution growth period due to the periodic dilution with fresh medium. The high P treatment (hereafter referred to as “+P”) received standard IMR 1/2 with 12.5 μM phosphate resulting in a dissolved N:P ratio of 10:1. To make sure that we had no nutrient limitation in the +P cultures, these were run as batch cultures in turbidostat mode, and diluted down to the same, low cell density every 2–3 days; 50,000 cells mL−2s−1 from fluorescent white tubes, and the light: dark-cycle was set to 14 h:10 h. The cultures were maintained for >20 weeks, with regular samples for determination of cell numbers and cell size. Elemental content , RNA and alkaline phosphatase activity (APA) was measured as indices of nutrient status of the cultures.All experiments were run in 40 mL nunclon filtercap flasks . The cultures received an irradiance of 170 μmol photons m660) using a plate reader equipped with a spectrophotometer . A660 was converted to cell numbers based on species- and treatment-specific calibration curves constructed from parallel measurements of A660 and cells numbers in the CASY .Cell size  was measured by a CASY electronic cell counter . Mean and median ESD were analyzed twice a week after the cultures had stabilized. During the first weeks, cell densities were estimated from CASY cell counts, but when the cultures had stabilized, we started monitoring cell densities by measuring absorbance at 660 nm (ANt+Δt∕Nt)∕Δt, where Nt was the cell density after dilution, Nt+Δt the cell density at the end of the growth interval (before the next dilution), and Δt the time interval between measurements (in days). For the −P cultures, growth rate was calculated as log(Nt+Δt∕(Nt∕DF))∕Δt, where Nt was the cell density before dilution, DF the dilution factor (see above), and Nt+Δt the cell density at the end of the growth interval.For the +P cultures, specific growth rate was calculated as log(Since the cultures (and growth rates) remained stable over time, we here report mean growth rates for each species and treatment. Cultures were kept at the same light:dark cycle (14:10), and samples were taken at the same time in the morning to ensure that cells were harvested from the same cell cycle phase.E. huxleyi could be allocated to the calcified structures. Hence in order estimate the amount of particulate organic carbon (POC) and particulate inorganic carbon (PIC), additional samples of E. huxleyi were treated with 2 M hydrochloric acid (HCl) to remove PIC . Some fraction of particulate C in move PIC . We did −1).RNA was measured three times during steady state using a modified version of the RiboGreen fluorescence protocol . From each culture, 1–4 mL was filtered onto nitrocellulose membranes , which were snap-frozen (in liquid N) in nuclease-free tubes. Prior to analysis, the tubes were added 120 μL of the extraction buffer , and while still frozen, homogenized by ice-cold sonification  for two minutes. The samples were again put on ice, and diluted 1:5 with TE buffer . Duplicates with 75 µL from each sample were produced in individual slots of a 96-well plate . The duplicates were inserted pairwise into columns after the first column, which was reserved for an RNA standard. Into each of the first duplicate columns a total of 20 μL RNase-free water  was added. The other duplicates were then added 20 μL of 0.1% RNase A . Immediately after the RNase mixture was added, the well plate was heat incubated at 37 °C on a shaking table to ensure homogenous dispatch of the RNase, and digestion of RNA. After the incubation, 75 μL of 100 times diluted RiboGreen dye  was added to each well with an automatic 8-channel pipette. Finally, the sample plate was inserted into a plate reader (described earlier) and fluorescence of the samples measured using an excitation wavelength of 480 nm (20 nm bandwith) and an emission wavelength of 525 nm (20 nm bandwith). Since RiboGreen yields fluorescence from both RNA and DNA, we subtracted the fluorescence in the RNase-treated samples from the non-RNase treated samples to obtain the signal from RNA alone. Concentrations of RNA were obtained by calibrating the fluorescence of the unknown samples against the fluorescence of the known RNA standards. We converted the weight of total RNA to moles of ribonucleotide monophosphates (which each contains one P-atom) using the average molecular weight of the four different ribonucleotide monophosphates  may serve as an independent biomarker for P-limitation, and was analyzed along with RNA for all treatments and species. APA was analyzed by the CDP-star chemoluminescence method according to the protocol of η is the total number of cells in the sample and φ the median ESD (in µm) of the cells in the samples for C, N, P, RNA and APA. The factor 10−15 converts from µm3 to L. The resulting bulk cell volume-specific concentrations have units of moles (L cell volume)−1. We will also present results of cell quota, i.e., the number of moles of C, N, P, and RNA per cell.Because we observed significant differences in cell size between the experimental treatments, we chose to normalize the molar amounts of C, N, P and RNA, and APA, by the total cell volume in each sample. The total cell volume (in liters) in a sample was calculated as adonis in the vegan package). Moreover, we tested whether any of the underlying gradients in elemental content and RNA were related to growth rate and the degree of P-limitation by including growth rate and APA as passive variables in the ordination , which was expected since we used the same dilution rate at high and low temperature. High temperature increased growth rate in the +P cultures of E. huxleyi and C. rotalis (p < 0.05). In contrast, the +P cultures of P. polylepis grew slower at 19 °C compared to 13 °C (p < 0.05). APA was strongly elevated in the −P cultures for all species and temperatures, except for C. rotalis at 13 °C, where APA was similar for both P-regimes (p > 0.05), but there was a significant interaction between temperature and species (p = 0.035) manifested by elevated APA at 19 °C in P-limited E. huxleyi.Within the three species, 84–94% of the variation in specific growth rate was explained by P-regime , with the +P cultures growing significantly faster than the −P cultures (p < 0.05). Species differences accounted for 28%. Both specific growth rate and APA correlated strongly with the ordination . The passive variables are plotted as dashed arrows in A PCA plot of the cell volume-specific concentrations of C, N, P, and RNA from all species revealed an underlying gradient from high to low concentrations along PC axis 1, which explained 78.7% of the variation in the dataset . CultureE. huxleyii = 4.67 µm [sd: 0.22 µm]; C. rotalis: 4.0 µm [sd: 0.1 µm] P. polylepis: 8.49 µm [sd: 0.55 µm]).The corresponding analyses for cell quota of C, N, P, and RNA  revealedE. huxleyi; 1.7 at 13 °C and 6.3 at 19°for C. rotalis; and 1.3 at 13 °C and 1.8 at 19 °C for P. polylepis. For C and N, the effect of P-regime was less consistent, but generally, the concentrations increased or were unaffected by P-regime , C:N was not significantly affected by P-regime nor temperature (data not shown). For comparison, the correlations between C and P, and N and P were 0.92 and 0.85, respectively.For elemental and RNA content , Table 2P-regime , Table 2quotas of C, N, P, and RNA were generally also higher in cultures growing at +P. However, for cell quotas the effect of P-regime was weaker and in more cases non-significant compared to the cell-volume specific concentrations C, N, P and RNA  under both P-regimes. N:P ratios were above Redfield (N:P = 16) for P-limited cultures, but below or close to Redfield when growing under nutrient replete conditions , P-regime (p = 0.025), and species  in the three species ranged from 4 to 66% and was significantly related to temperature  in the P-limited cultures compared to the exponentially growing nutrient replete cultures, which is consistent with classical Droop kinetics . Also, tE. huxleyi and C. rotalis, but not in P. polylepis. A corresponding, positive effect of temperature on N and P content under nutrient replete conditions, but a negative effect under P-limitation (RNA decreased with temperature in both P-regimes), was also observed a similarly designed experiment with the freshwater chlorophyte Chlamydomonas reinhardtii  the fraction of total cellular P bound to RNA was higher at low temperature in both P-regimes. Under P-limitation, the changing fraction was due to a slightly more negative temperature response in RNA than in P. At +P, however, total cellular P increased more than RNA when moving from 13 °C to 19 °C. This points to an increased fraction of storage P at high temperature, perhaps due to more efficient uptake kinetics, which again lead to lower RNA:P.The tendency for opposite effects of temperature on elemental content and RNA in nutrient replete and P-limited cultures is quite striking, and could be related to differences in uptake and storage strategies of P in the two growth regimes. Under P-limitation, cells cannot store extra P, and must allocate the available P to essential, non-storage macromolecules that are required to sustain growth, e.g rRNA . Hence, nhardtii . Under nnhardtii , which mμ) at both 13 °C and 19 °C. Since the maximum growth rate (μmax) varied with temperature, the cultures also experienced differences in the RGR (μ: μmax). Given that the maximum growth rate increased with temperature, the P-limited cultures at 19 °C experienced a lower RGR and were presumably more strongly P-limited than those growing at 13 °C. This would further be expected to result in lower P and RNA content (following Droop kinetics), which we observed to some extent in E. huxleyi and C. rotalis. In P. polylepis, we observed that cellular P and RNA clearly increased with temperature also under P-limitation. Interestingly, the maximum growth rate of P. polylepis was lower at 19 °C than at 13 °C, meaning that its RGR was higher at 19 °C.The responses of cellular P and RNA to temperature in the P-limited cultures may also be related to differences in relative growth rate (RGR). The P-limited cultures of a given species were grown using the same semi-continuous dilution rate, which led to the same quasi steady state growth rate ; “replicate”, denotes replicate 1–3; “mmol.RNA.L.cyto”, the concentration of RNA in mmol of RNA nucelotides per L of cell volume; “molN.L.cyto”, the concentration of nitrogen (N) per L of cell volume, “molC.L.cyto”, the concentration of carbon (C) per L of cell volume, “molP.L.cyto”, the concentration of phosphorus (P) per L of cell volume, “APA.L.cyto”, alkaline phosphatase activity normalized to L of cell volume (relative units); “mu”, specific growth rate ; “meanESD”, mean equivalent spherical diameter .Text file containing the raw data from the experiment. Columns contain the follwing data and information: “Species”, denotes which species the data are from; “temp”, the temperature of the experimental unit (13 or 19 degrees C); “P”, the P-regime of the experimental unit  given temperature (13 °C and 19 °C) and species.Alkaline phosphatase activity (APA) plotted as a function of P-regime  and alkaline phosphatase activity (APA) were included as passive variables in the PCA plot  and plotted as dotted arrows. The arrows points in the direction to which the linear change in the variable is the fastest. Both were significantly correlated to the ordination .PCA plot based on log-transformed and standardized cell quota of C, N, P, and RNA in the 36 experimental units. Due to the factorial design consisting of three species, two temperatures, and two P-regimes, we obtained 12 unique experimental combinations which are represented in the diagram as follows: The letters E, P, and C represents the species Click here for additional data file.10.7717/peerj.3743/supp-4Figure S3P), pluses P-replete cultures (+P). Solid lines are drawn if the difference between temperatures was significantly different from zero within a given P-regime . Dotted lines indicate non-significant trends. If two lines are drawn for an element or a ratio, there was a significant difference between P-regimes for at least one of the two temperatures. If no line is drawn (as for N in P. polylepis), no P-regime- nor temperature-effect was present.Responses of cell quota of C, N, P, RNA, C:P and N:P to temperature and P-regime. The data are plotted on log-scale. Dots represent P-limited cultures , pluses P-replete cultures (+P).Mean cell size, calculated as the average equivalent spherical diameter (ESD) from weekly samplings over the >20-week experimental period, in the different treatments and species. Dots represent P-limited cultures  is calculated from the sum of squares (Sum sq) of each term divided by the total sum of squares.Click here for additional data file.10.7717/peerj.3743/supp-8Table S3Results from linear models of cell quota of C, N, P and RNA. Legend as in Click here for additional data file.10.7717/peerj.3743/supp-9Table S4p-values for the explanatory terms  and their interaction) in a linear model with mean cell size as the response variable. ANOVAs were done separately for each specie to remove the large variance in cell size between species.ANOVA tables showing the sum of squares (Sum sq), fractions of variance explained (% var), and Click here for additional data file.


Parents around the world engage in the practice of parenting by lying, which entails lying to manipulate children’s emotional states and behavior. The current study is the first to examine whether exposure to parenting by lying in childhood is associated with later dishonesty and psychosocial maladjustment in adulthood. Female undergraduate adults retrospectively reported their experiences of parenting by lying during childhood, the current frequency at which they lie to their parents, and their current psychosocial functioning. We found that adults who recalled relatively high levels of parenting by lying during childhood both lie to their parents more often and experience greater psychosocial adjustments problems in adulthood than adults who recalled relatively low levels of parenting by lying during childhood. This study is the first to suggest that parenting by lying during childhood may be associated with negative moral and social outcomes later in life. Parents strongly believe in the importance of instilling the value of honesty in their children from a very young age . ParadoxAlthough childhood lying has been studied for many decades see . In thisBuilding on this initial work, researchers have examined the lies parents tell their children in the United States and China , 2013. TThere is intriguing evidence suggesting there may at least be negative short-term effects when children are exposed to adults who lie: Prior research has indicated that children’s lying is associated with a host of negative outcomes, such as an increased risk of developing externalizing and antisocial personality problems . For exaThe goal of the current study was to take a first step in investigating the moral and social associations of exposure to parenting by lying during childhood. To do so, female undergraduate adults completed three self-report questionnaires measuring their recollection of experiencing parenting by lying in their childhood, the current frequency at which they lie to their parents, and their current psychosocial functioning . Based on the existing, albeit limited, evidence, we hypothesized that: (1) greater recall of experiencing parenting by lying in childhood will be related to more frequent lying to parents in adulthood; (2) more frequent lying to parents in adulthood will be associated with greater psychosocial maladjustment; and (3) recalling greater exposure to parenting by lying in childhood will predict greater psychosocial maladjustment in adulthood.Mage = 20.73 years, SD = 4.83 years). We limited our sample to females due to difficulty in recruiting male participants. Participants were recruited from a large North American university through online advertisements and flyers posted throughout campus. The ethnic composition of our sample reflected the diversity of the city from which the participants were recruited. According to self-report data, 63% of the participants were Asian, 16% were White, 3% were Black, 2% were bi-racial/multiethnic, 2% were Hispanic, 11% indicated another ethnicity, and two participants (3%) chose not to indicate their ethnicity. Participants were compensated $5.00 for their time.Fifty female undergraduate students participated in the current study  in a quiet room on campus. The nature, procedure, and methods used in this study were approved by the university’s research ethics board and all regulatory standards were upheld during data collection.The parenting by lying questionnaire, adapted from “you need to finish all your food or you will get pimples all over your face”; (2) lies related to leaving/staying, “if you do not come with me now, I will leave you here by yourself”; (3) lies related to misbehavior, “if you do not behave, I will call the police”; and (4) lies related to money, “we do not have enough money to buy that toy.” Participants were asked to read each lie (four items/lies per lie-category) and indicate whether they remember their parent telling that lie to them as a child by indicating “yes,” “no,” or “I don’t know.”The parenting by lying questionnaire is based on four categories of lies commonly told to children by parents: (1) lies related to eating, Following the coding scheme used in “…lie to your parents about what you do with your friends” , “…sometimes do not tell the truth so you do not have to hurt someone else’s feelings” , and “…exaggerate to your parents about the things you experience” (lying by exaggeration). Participants rated each statement on a 5-point Likert scale ranging from 0 = never to 4 = very often. The 12 items were summed together to create a total participant lie score, referred to henceforth as “lying to parents,” with higher scores indicating more frequent lying to parents. A total lying score was used to better understand the global nature of participant lying to parents. Reliability for this measure is acceptable with high internal consistency (α = 0.90).The lying to parents questionnaire, adapted from The ASR is a 126-item questionnaire measuring adults’ overall adaptive functioning  and behavioral, emotional, and social problems based on the DSM-V scales  . For theThe goal of the current study was to determine if remembering experiences of parenting by lying in childhood is associated with lying to parents and psychosocial maladjustment in adulthood. To evaluate this possibility, we conducted a series of linear regressions to explore relationships between: (1) exposure to parenting by lying in childhood and the frequency of lying to parents in adulthood; (2) the frequency of lying to parents in adulthood and psychosocial maladjustment ; and (3) exposure to parenting by lying in childhood and psychosocial maladjustment in adulthood. Finally, we conducted mediation analyses, with the frequency of lying to parents in adulthood as a mediator, to try to explain the significant associations uncovered between exposure to parenting by lying in childhood and psychosocial maladjustment in adulthood.R2 = 0.08, ΔF = 4.33, p = 0.043. Thus, as exposure to parenting by lying in childhood increased, the frequency of lying to parents during adulthood also increased, bparent lying = 0.29, SE = 0.89, t(49) = 2.08, p = 0.043, 95% CI , rpart = 0.29.We conducted a simple linear regression to determine whether parenting by lying is associated with the frequency of lying to parents. Parenting by lying was entered as the predictor variable and lying to parents served as the dependent variable. Parenting by lying significantly predicted lying to parents, explaining 8% of the total variance, ΔTo determine whether the frequency of lying to parents in adulthood is associated with psychosocial maladjustment, we conducted three separate simple linear regressions. For each regression, the frequency of lying to parents in adulthood was entered as the predictor variable and one of the three psychosocial maladjustment variables  was entered as the dependent variable.Table 1; step 1). That is, as the frequency of lying to parents increased, the severity of psychosocial adjustment problems also increased. Specifically, lying to parents explained 26% of the variance in internalizing problems, 31% of the variance in externalizing problems, and 36% of the variance in antisocial personality problems.Results for all three regressions demonstrated a significant positive linear relationship with the frequency of lying to parents in adulthood , after controlling for the frequency of lying to parents in adulthood, exposure to parenting by lying in childhood did not uniquely account for a significant portion of the variance in internalizing problems or externalizing problems. However, exposure to parenting by lying in childhood significantly and uniquely accounted for 7% of the variance in antisocial personality problems in adulthood. These results indicate that as parenting by lying scores increase, antisocial personality problems in adulthood also increase, even after controlling for the frequency of lying to parents.As shown in X) and self-reported internalizing, externalizing, and antisocial personality problems (dependent variables: Y) .Using the path a × b). We then examined whether the independent variable also influences the dependant variable directly   indirect-only mediation: the indirect path is significant but the direct path is not; (3) complementary mediation: both the indirect and direct paths are significant and occur in the same direction (both positive or both negative coefficients); and (4) competitive mediation: both the indirect and direct paths are significant but occur in opposite directions (one positive and one negative coefficient) (We explored the presence of a mediation by first determining whether an indirect relation exists between the independent and dependent variable of interest through the mediator (indirect effect: ediator) . Based oficient) .Unlike in the Table 1). Parenting by lying and internalizing problems were not significantly related .As discussed above, we found significant relationships between exposure to parenting by lying and lying to parents in adulthood (path a), and lying to parents in adulthood and internalizing problems , explaining 53% of the total effect. After controlling for lying to parents, we found no significant direct effect of parenting by lying on internalizing problems  . Together, these results indicate that an indirect-only mediation occurs between parenting by lying in childhood and internalizing problems in adulthood, with lying to parents acting as a mediator , and parenting by lying and externalizing problems  .As mentioned above, we found significant relations between exposure to parenting by lying and lying to parents in adulthood (path a), lying to parents in adulthood and externalizing problems , explaining 44% of the total effect. After controlling for lying to parents, we found no significant direct effect of parenting by lying on externalizing problems  . Thus, similar to internalizing problems, these results indicate that an indirect-only mediation occurs between parenting by lying in childhood and externalizing problems in adulthood, with lying to parents acting as a mediator , we uncovered a significant relationship between parenting by lying and antisocial personality problems .In addition to the significant relations between exposure to parenting by lying and lying to parents in adulthood (path a) and lying to parents in adulthood and antisocial personality problems , explaining 41% of the total effect. We also found a significant direct effect between parenting by lying and antisocial personality problems  . Both the direct and indirect pathways contained positive coefficients, thus indicating that a complementary mediation exists between parenting by lying in childhood and antisocial personality problems in adulthood, with lying to parents acting as a mediator  and external actions of anger and hostility , thereby demonstrating an indirect relationship between parenting by lying and these specific maladjustments.Additionally, we found that both direct and indirect pathways occur between parenting by lying and antisocial personality problems. As discussed above, the direct pathway may reflect a socialization environment where there is a lack of opportunity for learning conflict resolution skills, thus inadvertently promoting the development of antisocial personality problems . However, we found that the associations between parenting by lying and participants’ antisocial personality problems also exist through an indirect pathway between participants’ lying to their parents. Similar to the interpretation above, this suggests that parenting by lying may affect the development of antisocial personality problems through participants’ tendency to lie to their parents in adulthood. It may be that children who are exposed to more parenting by lying not only implicitly learn that it is okay to lie to family members, but also generalize this understanding to other behaviors and situations, such as aggression, disobeying rules, and cheating. This may also help explain why lying to parents in adulthood mediates the relationship between parenting by lying in childhood and externalizing problems in adulthood.The present study has several limitations that should be addressed in future research. One major limitation is the relatively small sample size that primarily consisted of Asian female adults. Future research will be needed to examine the present research questions in a larger, more representative gender-balanced sample. Including male adults will be important given that males are more likely to experience externalizing problems, whereas females are more likely to experience internalizing problems . AdditioAn additional significant limitation is that we relied on the retrospective reports of young adults to assess parenting by lying. Doing so means that there are likely memory errors that add noise to our data. Although we attempted to mitigate such errors by instructing our adult participants to only answer the items in which they were confident about, the potential for memory errors in this study could not be eliminated. An additional problem with using retrospective reports is that individuals cannot accurately report on parenting by lying without recognizing the lies their parents told at the time or after the fact. It is likely that parents who lie frequently have children who recognize statements as lies more frequently, but this is an empirical question. Having researchers observe parent–child interactions has the potential to resolve some of these issues, such as examining lies that children might not be aware of. However, this method would also likely to lead to difficulties assessing which statements are lies given that parents sometimes make false statements for reasons that are not lies  and that researchers may have difficulty assessing whether some statements are true .Future research should also involve child participants to directly assess how parenting by lying affects psychosocial adjustment during childhood. It would be best if such a study incorporated a longitudinal design to investigate parenting by lying throughout development. Future research is also needed to explore other possible associations between parenting by lying and psychosocial development, including associations with cognitive variables, such as how children evaluate evidence . AdditioThe present study is the first to investigate associations between exposure to parenting by lying in childhood and psychosocial development into adulthood. We discovered that remembering greater exposure to parenting by lying in childhood is associated with increased dishonesty toward parents in adulthood and with the development of internalizing, externalizing, and antisocial personality problems. These findings point to the need for further research on understanding the mechanisms that can account for these effects.The following study was approved by the University of Toronto Research and Innovation department. As per ethical protocol, all research participants were guided through an informed consent process in which they were made aware of the purpose of the study, potential risks and benefits to participating, and the right to withdrawal consent. Participants provided written consent to participate. Additionally, all participants were compensated for their time. No exclusions were made and vulnerable populations were not used.The execution of this paper was truly a combined effort on behalf of all authors. KL developed the original study idea and concept. RS expanded the study concept, selected the appropriate measures, and designed the study. SK collected and entered the majority of the data used for this study. KL, RS, and SZ conducted the subsequent data analyses and interpretations. RS drafted the initial manuscript before SZ, GH, and KL contributed to the writing of the manuscript. All authors approved the final version of the paper for submission.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.


In this study, by combining nuclear magnetic resonance (NMR), circular dichroism (CD), liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS), and gel electrophoresis, we report an unusual topological structure of the RNA G-quadruplex motif formed by human telomere RNA r(UAGGGU) containing 8-bromoguanosine. Results showed that the RNA sequence formed an antiparallel tetramolecular G-quadruplex, in which each pair of diagonal strands run in opposite directions. Furthermore, guanosines were observed both in syn- and anti-conformations. In addition, two of these G-quadruplex subunits were found to be stacking on top of each other, forming a dimeric RNA G-quadruplex. Our findings provide a new insight into the behavior of RNA G-quadruplex structures. G-quadruplexes are also believed to be important for telomere maintenance, involved in cell aging or death mechanisms3. Their structure has recently become an attractive therapeutic target for drug design8. Both X-ray crystallography and NMR have revealed their high resolution structures10. The topologies of G-quadruplexes can be classified into several types: antiparallel, parallel, and hybrid G-quadruplex, among others20. Four guanines in a plane bind to each other through Hoogsteen hydrogen bonds, forming the G-tetrad. Usually, the syn/anti-glycosidic conformation of guanines is considered to be an important factor in the G-quadruplex structure folding.G-quadruplexes are higher-order DNA and RNA structures. Structure and function of G-quadruplexes have become an area of great interest. Recently, they have been associated with some human genetic neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD)21. According to the crystallographic analysis, the unprecedented complex loops, and not the G-stem itself, were described as “non-parallel”. A recent study tried to govern the formation of a forced RNA antiparallel orientation in G-stem, suggesting that the RNA G-quadruplex was difficult to control in an antiparallel orientation in G-stem than the DNA G-quadruplex22. Until now, the studies on RNA G-quadruplexes showed that the RNA sequences can only adopt parallel G-quadruplex. For example, we and other groups demonstrated that human telomere RNA, a newly found telomeric repeat-containing RNA, can fold in a variety of ways to form parallel G-quadruplexes31.Contrary to their DNA counterparts, RNA G-quadruplexes are less diverse in terms of stem strand orientations. Recently, a crystal structure study of a RNA aptamer revealed that the RNA folds into a new RNA G-quadruplex motif with two groups of loops in different orientations33. Recently, in contrast to previous reports stating that rG residues favor anti-conformation in parallel RNA G-quadruplexes, both rG (syn) and rG (anti) have been observed in RNA crystal structures21. Additionally, many works have reported the existence of C2′-endo sugars in RNA G-quadruplexes34. These observations motivated us to investigate whether an antiparallel RNA G-quadruplex could form in a special sequence context.There has not been any consistent explanation of why RNA sequences cannot form antiparallel G-quadruplexes, although some studies attribute it to the anti-glycosidic conformation of guanosine (rG) and the C3′-endo conformation of sugars36. Galeone et al. showed that the substitution of 2′-deoxyguanosine (dG) with 8-methyl-2′-deoxyguanosine (8mdG) affects the folding topology of DNA G-quadruplex structures38. We previously substituted the dG with 8-bromo-2′-deoxyguanosine (8BrdG) in the sequence of human telomere DNA and examined the resultant structures and thermal stabilities, revealing a new mixed-parallel/antiparallel DNA G-quadruplex structure39. Similarly, using the 8mdG, we also determined the arrangement of the anti/syn conformations of the G-quadruplex structure at the 5′ end of the Rb gene40. The incorporation of a methyl or bromine group at the C8 position of dG causes a steric hindrance between the 8-substituent and the ribose ring that favours a syn conformation of 8mdG or 8BrdG. This allows the examination of the structure and thermal stability of G-quadruplexes via appropriate substitutions of dG with 8mdG or 8BrdG.The chemical and biochemical properties of G-quadruplexes could be better understood by suitably modifying nucleobases in G-quadruplexesBrrG) and examined the resultant structures by using NMR, CD, LC-ESI-MS, and gel electrophoresis. For the first time, we successfully observed a new antiparallel RNA G-quadruplex formed by the modified RNA sequence, and further found that two of these G-quadruplexes as subunits, could stack on top of each other and form a dimeric RNA G-quadruplex. Multimethod approaches, CD, NMR, LC-ESI-MS, and gel electrophoresis, have been used to provide several complementary lines of evidence for the existence of antiparallel RNA G-quadruplexes. The availability of the antiparallel RNA G-quadruplex structure now allows us to reconsider the nature of RNA G-quadruplexes, and suggest that RNA may be more polymorphic than initially assumed. These results provide valuable information to allow further understanding of the structural features of RNA G-quadruplexes.Here, we substituted the rG in the sequence of human telomere 6 nt RNA with 8-bromoguanosine  with 8–3 Table –S8, and  nm Fig. . Surpris nm Fig. . The CD  nm Fig. 26. The tnts Fig. . The Tm nts Fig. . The ORNn Figure . These dm Figure .Table 1R1H NMR spectrum of ORN-1 obtained in the presence of K+, three well-defined signals were observed between 10.0 and 11.5 ppm , homonuclear NOESY, and heteronuclear 9− and [(4 M)2 + 4 K+  + Na+ − 15 H+]10– 2−3Br– + 3 K+ − 15 H+]9−, [(4 M)2 − 6Br− + K+  + Na+ − 17 H+]9−, [(4 M)2 − 3Br− + 3 K+ − 16 H+]10−, and [(4 M)2 − 6Br− + K+  + Na+ − 18 H+]10− peaks , the ORN-2 showed a reduced mobility compared with the reference DNA oligonucleotide dT24 Fig. , indicat27. The crystal structure study of RNA aptamer (PDB code: 3IVK) revealed the complex loops of G-quadruplex structure, which were described as “non-parallel”21. To find the typical antiparallel RNA G-quadruplex with different orientations in G-stem, Mergny et al. applied the hybrid duplex–quadruplex structure and tried to induce the formation of a RNA antiparallel orientation in G-stem22. Furthermore, Defrancq et al. applied a template strategy for constraining RNA to adopt an antiparallel topology, they were unable to form antiparallel RNA46. The DNA G-quadruplex was successfully transformed into an antiparallel conformation, unlike the RNA G-quadruplex that was found difficult to form an antiparallel orientation in G-stem. The results suggested that the G-stem structure itself, and not the loops, maybe the key element when it comes to forcing RNA G-quadruplexes into a parallel conformation. Accordingly, the syn/anti-glycosidic conformation of rGs is an important factor in the G-stem orientation of RNA G-quadruplexes47. Consequently, stabilizing the syn-glycosidic conformation of rGs in G-stem can drive the formation of antiparallel G-quadruplexes.Although there is no report that confirmed any RNA sequences can form an antiparallel G-quadruplex conformation, recently, accumulated evidence showed that RNA G-quadruplexes present some antiparallel features , suggesting that the RNA maybe more polymorphic than initially assumed. For example, Gabelica and coauthors have reported that the relative intensity of CD band at 295 nm was observed for some RNA sequences, suggestive of a mixed arrangement of the guanine steps in RNA G-quadruplexesBrrG to stabilize the syn conformation, we found that the modified ORN-2 RNA sequence was able to form an antiparallel RNA G-quadruplex, which had never been reported before, and further observed a dimeric RNA G-quadruplex, adopted by stacking two of these G-quadruplexes. Interestingly, the replacement of the central rG in the G-stem results in the formation of an antiparallel G-quadruplex (ORN-2), while the replacement of the first rG in the sequence does not affect the parallel (ORN-1) and the third rG substitution does not induce any folding (ORN-3), suggesting that the rG at different positions of G-stem exerted differential effects on G-quadruplex folding. A recent report also suggested the similar position-dependent effect48. The G at central position has more effect on G-quadruplex structure than the G at the other positions. Antiparallel RNA G-quadruplex formation provides new insights into the RNA structure and leads to a better understanding of the essential biological role of RNA molecules.In this study, by using the 8-substituted purine 8The syntheses of the 8-bromoguanosine-containing RNAs were carried out by phosphoramidite chemistry. All RNAs were synthesized on the 1 μmol scale with an automatic DNA/RNA synthesizer . After automated synthesis, the oligonucleotides were detached from the support and deprotected according to the manufacturer’s protocol. All oligonucleotides were purified by Reverse phase-HPLC (JASCO). All synthetic procedures and compound characterizations are described in the Supporting Information. RNA sequences are Table CD spectra were measured using a Jasco model J-810 CD spectrophotometer. Samples were prepared by heating the oligonucleotides at 90 °C for 5 min and gradually cooling them to room temperature. The melting curves were obtained by monitoring a 265 and 295 nm CD band. Solutions for CD spectra were prepared as 0.3 mL samples at 0.01 and 0.1 mM concentrations in the presence of 10 and 100 mM KCl or in the absence of KCl, 10 mM potassium phosphate buffer (pH 6.8).2O/10% D2O, 10 mM potassium phosphate, pH 6.8, 100 mM KCl. Assignments of the proton resonances were initially made by using the methods previously described for assignments of G-quadruplex RNA structures. These assignments were confirmed by comparison of NOESY spectra to those of the related telomere RNA50. HDX Experiments: RNA samples (5.2 mM in 0.15 mL H2O) were lyophilized overnight prior to the HDX experiment. 0.15 mL D2O was added to resolve the sample just before measurement. Three samples were prepared, and the 1H NMR spectra were recorded, respectively. For each sample, the spectrum was recorded after the specific D2O exchange time, measurement time is 10 min.NMR experiments were performed a BRUKER (AV-400M) magnetic resonance spectrometer. Spectra were recorded at 25 °C. RNA samples (0.6–5.2 mM) were dissolved in 0.15 mL of 90% H2O with 50 mM triethylammonium acetate); B solution (H2O: MeOH = 1: 1 with 50 mM triethylammonium acetate); flow rate was 0.2 ml/min. A spray voltage 3.5 kV was used to generate the negative ion mode with a heated capillary temperature of 250 °C. An ion accumulation time was 100 ms.LC-ESI-MS experiments were performed on Q Exactive Quadrupole-Orbitrap LC-MS System (Thermo Scientific) equipped with a XBridge Oligonucleotide Separation Technology C18 column . Data was acquired using Xcalibur software. Sample was prepared at 0.05 mM concentration in 100 mM KCl. LC condition: A solution  staining. RNA samples concentration ranged from 90 to 140 pmol. DNA oligonucleotides dT12 and dT24 were used as molecular markers.Supplementary information