Alu is a primate-specific repeat element in the human genome and has been increasingly appreciated as a regulatory element in many biological processes. But the appreciation of Alu has been limited in tumorigenesis, especially for brain tumor. To investigate the relevance of Alu to the gliomagenesis, we studied Alu element-associated post-transcriptional processes and the RNA expression of the element by performing RNA-seq for a total of 41 pairs of neurotypical and diverse glioma brain tissues. We find that A-to-I editing and circular RNA levels, as well as Alu RNA expression, are decreased overall in gliomas, compared to normal tissue. Interestingly, grade 2 oligodendrogliomas are least affected in A-to-I editing and circular RNA levels among gliomas, whereas they have a higher proportion of down-regulated Alu subfamilies, compared to the other gliomas. These findings collectively imply a unique pattern of Alu-associated transcriptomes in grade 2 oligodendroglioma, providing an insight to gliomagenesis from the perspective of an evolutionary genetic element. Taeyoung Hwang et al. evaluate the relevance of Alu repeat elements to gliomagenesis using transcriptomic data from 41 pairs of neurotypical and diverse glioma brain tissues. Their results reveal that grade 2 oligodendroglioma differs from other gliomas with a higher proportion of perturbed Alu subfamilies and the minimally-affected overall A-to-I editing and circular RNA levels, providing further insight into gliomagenesis. Among TEs, Alu is the most abundant repeat element, consisting of about 10% of the human genome1. Alu is primate-specific and expands through retrotransposition, an amplifying process of TE through an RNA intermediate. Alu is transcribed mainly by RNA polymerase III3, generating an about 300 nucleotides (nts)-long noncoding RNA (ncRNA). Alu RNA tends to form double-stranded RNA (dsRNA) as it has two monomers facing each other.A large proportion of the human genome consists of repetitive elements. Transposable elements (TEs), a major repeat element, account for at least 45% of the human genome, while coding sequences comprises less than 3%Alu was considered as junk DNA in the past, there has emerged increasing evidence that this element plays important regulatory roles in diverse cellular processes4. Specifically, Alu’s regulatory roles are manifest at DNA, RNA, and post-transcriptional levels. At the DNA level, Alu insertion can generate regulatory elements, including alternative splicing5 and enhancer function6. The frequent location of Alu in genic regions exert its effect at post-transcriptional regulations including A-to-I editing9, an RNA modification changing RNA sequence at a single nucleotide from adenosine to inosine, and formation of circular RNA10, a single-stranded RNA with a covalently closed loop structure. In particular, it has been reported that Alu-mediated A-to-I editing is tightly regulated11 with a potential to contribute to expand a repertoire of RNA12. The dsRNA structure of Alu is known to facilitates A-to-I editing and circular RNA formation13. Alu also harbors a nuclear localization signal of long noncoding RNA (lncRNA)14. As a short ncRNA, Alu RNA affects transcription by modulating RNA polymerase II (Pol II)16. In addition, Alu RNA has been reported to influence translation17.Although Alu also has implications in human diseases including cancer and neuropathological disorders. In cancer biology, Alu RNA has been studied in hepatocellular carcinoma18 and in a metastatic colorectal cancer cell line19, both showing that increased levels of Alu RNA are associated with tumor development. The dysregulation of Alu-mediated post-transcriptional processes has been cited in the context of neuropathological diseases21. Alu-associated A-to-I editing and circular RNA are abundant in neuronal tissues and involved in neuronal differentiation and potentially in developmental disorders23. In addition, Alu has been hypothesized to potentially play a role in neurodegenerative diseases24. However, the comprehensive study of Alu in the context of brain tumor is limited although previous studies showed that A-to-I editing dysregulation is involved in brain tumors26. This is due at least in part to an underappreciation of the possible role of evolutionary and developmental processes on tumor pathogenesis and the difficulty in studying Alu’s noncoding functions in biological systems.Alu-associated A-to-I editing pattern of glioblastoma (GBM) is similar to that of early-stage neurodevelopment22. In the present study, we extended our analysis to multiple types of gliomas to test whether Alu-associated dynamic processes at both post-transcriptional and RNA levels are related to the progression of glioma. Using a spectrum of glioma samples with up-to-date molecular classification28, we investigated the expression levels of Alu RNA and the two Alu-associated post-transcriptional regulations that are abundant in human brain tissue, i.e., A-to-I editing and circular RNA expression. Our analysis first confirmed a previous finding that A-to-I editing sites and circular RNAs are significantly associated with Alu element in brain tissues. By comparing genome-wide patterns between matched tumor and neurotypical brain samples, we observed that A-to-I editing levels and circular RNA expression are perturbed in gliomas and are globally decreased in high-grade gliomas. In particular, grade 2 oligodendroglioma, a glioma of favorable prognosis, present the least global decrease in both A-to-I editing levels and circular RNA expression. Also, a unique pattern in Alu RNA expression was found in grade 2 oligodendroglioma, specifically wide downregulation of Alu subfamilies in these tumors relative to matched normal brain. Finally, we found that Adenosine deaminase RNA specific B1  was downregulated in gliomas other than grade 2 oligodendrogliomas, potentially contributing to the observed decrease in A-to-I editing and circular RNA formation. Our results demonstrate Alu is associated with gliomas through its own expression and associated post-transcriptional regulations, providing a potential insight into the molecular mechanisms of gliomas from the perspective of a primate-specific repetitive element.We have previously shown that the IDH mutant and 1p/19q-codeleted oligodendroglioma grades 2 and 3 (O2 and O3), IDH mutant astrocytoma grades 2, 3 and 4 , and GBM . When we compared our list of A-to-I editing sites with a public database of A-to-I editing sites 34, 88% of our sites were found in the database. The numbers of A-to-I editing sites normalized by sequencing depth vary across patients and tend to be smaller in tumor compared normal tissues  transformation of the normalized number of A-to-I editing sites: t-statistic = 3.29, p value = 0.002076, two-sided paired Wilcoxon signed rank test: p value = 0.001076). Many A-to-I editing sites were not found commonly across the patients. The proportion of A-to-I editing sites that were shared by patients decreases according to the increasing number of patients observing the sites  due to the small number of patients (n = 2). We first found that the differential sites were enriched with the commonly found sites: 49.0% of 1360 differential sites in any pathology are shared by all the patients while 0.5% of nondifferential sites were such sites. Per pathology, we found that about 0.3~7.7% of A-to-I editing sites showed differential editing between tumor and matched normal tissues . As pathology is a clear factor contributing to variation in our dataset , while the others have significant decreases: O3 , A2 , A3 , GBM . We further observed that the increasing grade in astrocytoma leads to less decrease in average editing levels while oligodendroglioma showed an opposite direction on average. But this trend might be due to differing sample sizes per pathology and should be interpreted cautiously. The loss of A-to-I editing in tumor relative to normal tissues were also confirmed by the Alu editing index (AEI) that was previously proposed to quantify A-to-I editing levels in Alu region35 . About 13.5% of annotated genes  have circular RNA signatures in their gene bodies in our brain tissues. As expected, they are significantly associated with Alu . The global level of circular RNA, measured by RNA-seq as the total number of back-splicing junction reads normalized by sequencing depth, was in general smaller in tumors compared to the matched normal tissues  numbers: t-statistic = 6.96, p value = 2.12*10−8, two-sided paired Wilcoxon signed rank test: p value = 2.87*10−8). We also compared the circular RNA expression rates between tumor and matched normal tissues for a gene per pathology, which are defined by the ratio of the number of RNA-seq back-splicing junction reads relative to the total RNA-seq junction read counts (see methods). We found that 973 genes showed differential circular RNA expression rates in our glioma tissues , O3 , A2 , A3 , GBM .Another Alu Fig. : the groAlu elements themselves can be expressed and affect cellular processes. We estimated the expression levels of annotated 47 Alu subfamilies in our samples and compared their expression levels between tumor and the matched normal tissues using the computational pipeline that can handle repeat elements for differential expression analysis (see Methods). In O2, a higher proportion of Alu RNAs (37 out of 47 subfamilies) was perturbed in tumors relative to matched normal tissues while the other gliomas only had one to three Alu RNAs as significantly changed ones .nes Fig. . Alu RNAnts Fig. . In addiNAs Fig. : averageADAR2 mRNA was downregulated significantly in all gliomas except for O2, which is consistent with the identified decreasing patterns of A-to-I editing. In contrast, ADAR (Adenosine Deaminase Rna Specific), also known as ADAR1, did not show any significant mRNA level change across gliomas. ADARB2 (Adenosine Deaminase Rna Specific B2), also known as ADAR3, known to antagonize ADAR1 and ADAR2 by competing with them37 also showed some decrease in high-grade gliomas. Therefore, ADAR2 among ADAR families seems to be a trans factor underlying the decreased patterns of A-to-I editing that we observed.In order to understand the decreasing pattern of A-to-I editing and circular RNA expression in gliomas relative to normal tissues, we checked the RNA expression levels of ADAR (Adenosine Deaminase Acting on Rna) families that are known to generate A-to-I editing Fig. . ADAR2 mAlu RNA is known to be transcribed mainly by RNA polymerase III. Although we found that a RNA polymerase III subunit, POLR3A expressions tended to be downregulated in some gliomas suggesting a potential contribution of the downregulation of RNA polymerase III on the lower expression of Alu RNA in gliomas, statistical significances were only attained in A2 and A4 gliomas  is moderate but statistically significant   among the 91 genes, the perturbed A-to-I editing sites were observed in the flanking introns of any circular RNAs in the same genes. Second, A-to-I editing and circular RNA were compared in terms of the direction of changes in tumor relative to normal tissue. Specifically, we compared the overall A-to-I editing level changes between the two groups of genes defined by whether a gene shows significant circular RNA expression rate changes between tumor and normal. We did not find the notable consistency between the two processes as the genes with decreased circular RNA expression rates in tumor compared to normal had little decrease in A-to-I levels  Fig. . We alsoels Fig. . Finallyels Fig. . About 1els Fig. . For cir39. These efforts provide interesting hypotheses relating primate-specific genes or genetic elements to neuropsychiatric disorders as well as neurodegenerative disorders. However, brain cancer has not been appreciated well in terms of primate-specific elements as cancer is generally understood as diseases caused by a genetic mutation associated with oncogenes or tumor suppressor genes and with environmental carcinogens. But increasing evidence suggests that evolutionary mechanisms affect brain tumors40. In this study, we looked into a primate-specific Alu element to compare various pathologies in gliomas. We used the most recent version of classification criteria of gliomas and matched neurotypical samples for an elaborate comparison between tumor and normal brain tissues. We also performed extensive computational analyses with strand-specific RNA-seq in order to explore Alu’s dynamic effects on tumorigenesis in human brain tissue. We found that Alu-associated molecular processes, including Alu RNA expression, A-to-I editing, and circular RNA formation are perturbed in gliomas, despite the possibility that we might not capture moderate perturbations due to the limited number of patients in each glioma.There have been many attempts to understand neuropathological disorders from the perspective of genome evolutionAlu element is its expression as RNA. Although Alu is usually suppressed by epigenetic mechanisms, Alu RNA is mainly transcribed by RNA polymerase III and dynamically regulated during development and in various diseases41. However, few studies have been conducted so far on Alu RNA in brain tumor, partly due to difficulty in measurements of repeated sequences in the human genome. We used a rigorous computational pipeline that considers repeat features in counting RNA-seq sequencing reads and controls patient-specific effects in a statistical comparison of Alu RNA expression between tumor and normal tissues. We found downregulation of Alu RNA in almost all pathologies of gliomas at varying degrees. The largest downregulation is observed in grade 2 oligodendroglioma while GBM shows the least downregulation. This perturbation of Alu RNA may contribute to different prognosis between O2 and GBM as a previous study showed that higher levels of Alu RNAs induces epithelial-to-mesenchymal transition in cancer progression19. Interestingly, grade 2 oligodendroglioma is unique among the gliomas we studied in that downregulation of Alu RNA is accompanied by downregulation of other TEs , a repressive histone mark was reported to be increased as a stress response, resulting in downregulation of RNA of transposable elements in mouse brain42. As H3K9me3 was reported to be perturbed differentially depending on glioma pathology43, it will be interesting to test the direct effect of H3K9me3 on the expression of transposable elements including Alu element in different types of glioma.An obvious dynamic mode of TEs Fig. . In otheAlu-associated regulatory processes, two post-transcriptional processes, A-to-I editing and backsplicing generating circular RNA are known to be abundant in brain tissue23. Many previous studies have shown that these processes are affected in gliomas. For example, A-to-I editing is significantly altered, usually reduced in glioma25. And there had been reports highlighting that alterations in specific RNA-editing sites can contribute to tumor progression and classification of molecular signatures or grades in GBM47. Moreover, a recent study comparing relative genome-wide RNA-editing levels among genetic subgroups of glioma showed that the RNA-editing signature can be used for the prediction of isocitrate dehydrogenase (IDH) mutation and chromosome 1p/19q-codeletion status in gliomas48. Regarding circular RNA, emerging evidence is accumulating on aberrant circular RNAs expression and its oncological function in gliomas51. Studies have implicated circular RNAs in the proliferation, invasion, and angiogenesis of gliomas through cancer-associated signaling pathways52. However, these studies mostly focused on individual sites or genes limiting their biological implications in terms of Alu-associated processes in brain cancer. Our results of global decreases of both A-to-I editing and circular RNAs suggest that there are perturbations affecting the molecular mechanism of these processes in glioma, beyond individual sites or genes. We also showed that these changes, especially of A-to-I editing levels might be caused by the downregulation of ADAR2. But it should be noted that we only observed mRNA levels of ADAR, requiring further investigation of protein levels of ADAR enzymes as ADAR mRNA expression can be uncorrelated with protein levels or activity. For example, it has been reported that subcellular localization of ADAR2 proteins is involved in the change of A-to-I editing levels in neural development53.Among Alu element’s contribution to the evolution of the human brain. We and others have shown that A-to-I editing levels and circular RNA are abundant in neural genes that are enriched in the pathways of neurotransmission, neurogenesis, and synaptogenesis55. Alu embedded in neural genes may serve as a mechanism to expand diversities in their functions and regulations that underlie remarkable complexities in the human nervous systems56. The evolutionary benefits that Alu offers in human brain development, however, may turn into Alu element-specific adverse effects in pathological conditions in the nervous systems. Along with this idea, the Alu neurodegeneration hypothesis24 proposes Alu as a double-edged sword, whereby beneficial Alu-related processes also have the potential to disrupt mitochondrial homeostasis in neurodegenerative disorders including Alzheimer’s disease. We propose that dysregulation of A-to-I editing and circular RNAs observed in higher grades of gliomas, affecting many biological processes including glutamate signaling, are also a manifestation of the double-edged effect of Alu in glioma. From this evolutionary perspective, recent studies offer a clue as to how the beneficial effects of Alu might be turned into a tumor-promoting factor. For example, Venkataramani et al.57 reported that neuronal activities associated with glutamate synaptic connections contribute to the progression of glioma. Here, Alu may enhance neuronal activity through A-to-I editing, an evolutionary advantage, but it also makes perturbation of A-to-I editing enzymes in glioma a favorable environment for glioma progression. The experimental investigation will be essential to test the causality of Alu elements on gliomagenesis through the changes of A-to-I editing levels or circular RNA levels in future studies.A-to-I editing and circular RNA are posited to mediate Alu involves both A-to-I editing and the generation of circular RNA, it is intriguing to see whether these two Alu-associated post-transcriptional processes are influencing each other. The relation between circular RNA and A-to-I editing is unclear. Although some studies showed that circular RNA expression has a negative correlation with expression of ADAR1 in human cell lines55, a recent study did not find a global correlation between the two processes in mouse tissues58. We observed little correlations between A-to-I editing levels and circular RNA levels in general although some genes can have interdependent changes between A-to-I editing and circular RNA formation through A-to-I editing in the flanking introns of circular RNA. However, our analyses were performed at a gene level, allowing only a limited understanding of interdependence between the two processes. Also, our results showed the different patterns between Alu RNA expression and A-to-I editing level or circular RNA formation in O2: the Alu expression levels are most strongly changed across all the glioma subtypes, whereas both the A-to-I editing and circular RNA are changed most slightly. We speculate that the molecular mechanisms between Alu expression change and the perturbation of A-to-I editing or circular RNA are distinct. The strong downregulation of Alu expression in O2 is possibly caused by epigenetic mechanisms, such as a repressive histone mark of H3K9me3 as mentioned earlier, whereas the least effect on A-to-I editing and circular RNA in O2 might be due to the little change of ADAR2 expression. Further studies are required to understand the molecular mechanisms behind the uniqueness of oligodendroglioma and to uncover the relations between A-to-I editing and circular RNA.As 28. After performing stranded RNA-seq, we removed one patient for further analysis as its tumor sample has lower sequencing quality based on the number of counted reads for gene expression measurement . This study was performed under the approval of the Institutional Review Board of Seoul National University Hospital, Seoul, South Korea .Surgical specimens and clinical information were obtained from glioma patients who underwent surgery at Seoul National University Hospital, Seoul, South Korea. Informed consent was obtained from all patients for the usage of samples. A total of 42 patients with a matched pair of tumor and normal samples were enrolled. Normal brain tissues were obtained when the surgical approach to the tumor involves brain areas without evidence of microscopical involvement of the tumor. The final diagnosis was rendered using the most recent update of cIMPACT-NOW guidelines59, using the primary assembly and gene annotation obtained from GENCODE (GRCh38 ‘primary_assembly’ and ‘comprehensive gene annotation (regions: CHR)’ version 27). The potential PCR duplicates were marked by the Picard MarkDuplicates (version: 2.6.0). The numbers of reads assigned to gene bodies were counted in a strand-specific way by using FeatureCounts (version: subread 1.6.2) with the command-line options of ‘-g gene_id -C -s2 -p’60.RNA-seq libraries were generated with total RNA extracted from tissues by using the commercial kit of Illumina TruSeq Stranded Total RNA LT Sample Prep Kit. We performed paired-end sequencing, generating a 101 nucleotides sequencing read for each end. Sequencing reads were aligned to the human reference genome by the STAR software (version: 2.6.0c)61, with TPM (transcripts per million reads) values. Statistical tests for differential expression between tumor and matched normal tissues were done by using DESeq262 with a regression model that has patient information as a covariate.t-SNE was performed using the R package Rtsne (version: 0.15)We developed a computational pipeline to call RNA-editing sites from strand-specific RNA-seq. First, the following reads were filtered to reduce potential technical errors in the identification of RNA-editing sites: (i) reads were suspected as PCR duplication, which was determined by Picard MarkDuplicates, (ii) reads were mapped at multiple loci, (iii) reads had more than or equal to 5 nucleotides that were clipped by aligner, (iv) reads had more than 10 nucleotides composing homopolymers, where a homopolyer was defined as 4 or more contiguous same nucleotides, (v) reads had any insertion or deletion. Second, mismatches relative to the reference genome were identified. Third, for every genomic site that has reads with any mismatch whose sequencing quality is greater than 20 , the sequencing reads were counted separately for the reference sequence or mismatches. Fourth, filter sites if mismatches were found closer to either 5’ or 3’ sequencing ends (<=5 nucleotides) in more than half the number of reads with mismatches. Also, if there were multiple alternative alleles, keep the sites only when there is a major alternative allele such that the major allele has at least five sequencing reads and more than two times of sequencing reads than any other alternative alleles. Finally, if a site has more than or equal to 3 sequencing reads for an alternative allele and does not overlap with SNP (dbSNP 150 common except for variants discovered using a cDNA template), it was called as a potential RNA-editing site whose type is determined by considering the strand information of RNA-seq.63. The A-to-I editing level for a given sample was defined as the ratio of inosine-supporting read counts relative to the adenosine or inosine-supporting read counts. Multiple test correction was done by using false discovery rate (FDR). The sites whose FDR-adjusted p-value is less than 0.05 were called as differentially-edited sites.The sites shared by all the patients for a given pathology were statistically tested for whether they showed different A-to-I editing levels between tumor and matched normal tissues. Specifically, beta-binomial regression was performed for a given site while controlling patient-specific effect by adding patient information as a covariate in the regression. The fitting of the regression model was done with R functions in Redit package64 with STAR (version: 2.6.0c)59 options of ‘--clip3pNbases 1 --chimSegmentMin 10’. We focused on exonic circular RNAs based on the gene annotation from GENCODE (version 27). In order to compare circular RNA expression rates  across the samples, we performed beta-binomal regressions for every annotated genes, where the number of back-splicing junction reads identified by CIRCexplorer2 was modelled to follow binomial distribution with a total number of both back-splicing and linear junction reads as a parameter. Statistical significances of differences between tumor and matched normal samples were calculated while controlling patient-specific effect with a covariate in the regression if a gene body has more than three back-splicing junction reads in any comparing samples.Circular RNAs were identified using CIRCexplorer2 (version: 2.3.8)65 with STAR (version: 2.6.0c)59 options of ‘--clip3pNbases 1 --winAnchorMultimapNmax 100 --outFilterMultimapNmax 100’ and the gene annotation from GENCODE (version 27). Briefly, TEtranscripts is a software that utilizes both uniquely and ambiguously mapped reads to quantify RNA expression levels of transposable elements from RNA-seq. Statistical tests for differential expression between tumor and matched normal tissues were done by using DESeq262 with a regression model that has patient information as a covariate.We measured expression levels of Alu elements using TEtranscripts (version: 2.1.4)66. The terms in the biological process whose FDR-adjusted p-value is less than 0.1 were called as over-represented terms.Repeat annotation was according to the UCSC RMSK track. The intersection analysis and Gene Ontology (GO) analysis in the Fig. N = 6, 9, 4, 5, 15 patients for O2, O3, A2, A3, GBM, respectively; Fig. N = 58,288 genes; Fig. t test, N = 41 patients; Figs. N = 12, 18, 8, 10, 4, 30 tissues for O2, O3, A2, A3, A4, GBM, respectively; Fig. N = 22725 genes; Fig. N = 1578 A-to-I editing sites.Statistical analysis was performed using the statistical computing software R (version 3.6). The number of samples in a statistical analysis is specified in the result of the statistical test or the corresponding figure legend. For main figures, see the followings. Figure This study involving human tissue and cells was approved by the Ethics Committee of the Seoul National University Hospital (IRB No. H-1404-056-572). All tissue and data were anonymized. This study was performed in accordance with the Declaration of Helsinki.Further information on research design is available in the Transparent Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Reporting Summary


Molecular analyses of normal and diseased cells give insight into changes in gene expression and help in understanding the background of pathophysiological processes. Years after cDNA microarrays were established in research, RNA sequencing (RNA-seq) became a key method of quantitatively measuring the transcriptome. In this study, we compared the detection of genes by each of the transcriptome analysis methods: cDNA array, quantitative RT-PCR, and RNA-seq. As expected, we found differences in the gene expression profiles of the aforementioned techniques. Here, we present selected genes that exemplarily demonstrate the observed differences and calculations to reveal that a strong RNA secondary structure, as well as sample preparation, can affect RNA-seq. In summary, this study addresses an important issue with a strong impact on gene expression analysis in general. Therefore, we suggest that these findings need to be considered when dealing with data from transcriptome analyses. To examine the expression of individual genes, quantitative RT-PCR (qRT-PCR) is a widely used method. However, in the past, it became more and more important to analyze the complete transcriptome within cells in an unselected manner for an in-depth understanding of, e.g., development and disease . StartinDNA microarray analyses, which are based on the hybridization of fluorescently labeled probes, require a reference genome/transcriptome for designing the microarray probes. Therefore, cDNA microarrays are dependent on the choice of probes and their binding specificity. Here, background signals and cross-hybridization can reduce the specificity and sensitivity for some genes, which can lead to pitfalls and misallocations during an analysis ,4,5,6,7.Due to these limitations of microarrays and the advantages of RNA-seq, such as cost reduction through massively parallel sequencing, a shift from cDNA array analysis to RNA-seq experiments has been observed in recent years ,2,8,9. NGenerally, transcriptome analyses, especially of large sample sets, are usually performed with the newest and most advanced methods. Hardly ever is the same sample set analyzed with both methods in parallel. So far, only a few comparisons that indicate possible discrepancies between the different methods for transcriptome analyses exist ,14.SOX gene family and YAP/TAZ effectors of the Hippo pathway) in various melanoma cell lines with different transcriptome analysis methods—cDNA array, RNA-seq, and classical quantitative RT-PCR—and revealed striking differences in gene detection. When comparing the expression analysis results using these techniques, we observed that some genes were detectable with qRT-PCR and cDNA arrays, but not with RNA-seq, and vice versa. In this study, we focused on analyzing the loss of information within RNA-seq and, therefore, compared our transcriptome data in different melanoma cell lines with the qRT-PCR and cDNA microarray data, as well as with datasets from different research groups. Our results demonstrate the urgent need for an in-depth understanding regarding RNA-seq data and the related methodological challenges, and we show that there are variances in outcomes with different processing methods.In this study, we compared the detection of two groups of genes  and RNA-seq, we first analyzed the differences in gene expression obtained by these two methods exemplarily based on melanoma cell lines. We compared the transcriptome analysis results  and perfanalysis A. Surprianalysis . To demoMel Juso . Here, tglyceraldehyde-3-phosphate dehydrogenase (GAPDH), gamma-actin1 (ACTG1)), which were similarly expressed in each method, cell line, and dataset (GAPDH: ACTG1: SOX (=SRY-related HMG-box) family members, especially SOX21. We were able to detect SOX21 with the cDNA array; however, we observed no read counts with RNA-seq   B and proqRT-PCR) C and, thSOX21 in RNA-seq data was due to the library preparation, the mapping algorithm  in the raw RNA-seq fastq files, allowing up to three mismatches. The SOX21 pattern search yielded no hits with up to one mismatch. Random hits started to accumulate with two or more mismatches. A GAPDH pattern served as a positive control and yielded numerous hits with no mismatches. Thus, we can exclude that the detected loss of SOX21 read counts was a mapping artifact , or thev2.5.2a, . We usedartifact E.SOX family, we chose additional SOX genes  and compared the cDNA array data  and TAZ  within the datasets. Therefore, we analyzed the fluorescence signals detected with the microarray analysis for YAP1 and TAZ , just to name a few . GA. GA® 480www.kfb-regensburg.de (accessed on 8 December 2020)). Samples were generated according to the manufacturer’s instructions for the Affymetrix GeneChip WT Plus reagent kit . The fluorescence signals were measured with an Affymetrix GeneChip Scanner 3000 7G and normalized with the RMA algorithm. In this study, two datasets were used. The first dataset contained two different cell lines (Mel Im and Mel Juso) that were analyzed in two biological replicates  in a single replicate each.After the cellular RNA was isolated, sample processing was performed at an Affymetrix Service Provider and Core Facility, “KFB—Center of Excellence for Fluorescence Bioanalytics”  analyzed with a cDNA microarray. The second dataset . In both datasets, the cell lines were analyzed in two biological replicates.RNA-seq samples and libraries were prepared as described previously . Library dataset  containehttp://rna.tbi.univie.ac.at/ (accessed on 2 March 2021)). This tool calculates the minimum free energy of an RNA sequence based on the Zuker algorithm [http://www.endmemo.com/bio/gc.php (accessed on 11 March 2021)) was used.The free energy of the RNA secondary structure was analyzed with the RNAfold server of the ViennaRNA Web service ) [The results are shown as the mean ± SEM (standard error of the mean) calculated with the GraphPad Prism software . A correlation analysis was performed using R v.4.0.3 with the help of the ggplot2 and ggpubr v.0.4.0 packages by A. Kassambara (r 2020)) ,39,40.The data were deposited in the NCBI Gene Expression Omnibus under GEO:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112509 (accessed on 11 January 2021).Kunz et al. ; GSE1125https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4845 (accessed on 11 January 2021).Hoek et al. ; GSE4845This study indicates that genes with strong secondary-structured mRNA are difficult to determine in RNA sequencing after chemical shearing. Chemical shearing seems to fail in breaking up strong secondary RNA structures. However, we cannot state whether the method of chemical shearing also has advantages with regards to the detection of other genes. Therefore, we strongly suggest that, as for cDNA array analyses at the time that that method was established, all pros and cons must be defined in detail and made public; if possible, they also need to be incorporated into bioinformatical analyses. Although RNA-seq offers immense advantages over microarray analysis, there are challenges that need to be addressed. Therefore, we recommend validating the results of transcriptome analyses by using at least one additional method.


RNA synthesis in respiratory syncytial virus (RSV), a negative-sense (−) nonsegmented RNA virus, consists of viral gene transcription and genome replication. Gene transcription includes the positive-sense (+) viral mRNA synthesis, 5′-RNA capping and methylation, and 3′ end polyadenylation. Genome replication includes (+) RNA antigenome and (−) RNA genome synthesis. RSV executes the viral RNA synthesis using an RNA synthesis ribonucleoprotein (RNP) complex, comprising four proteins, the nucleoprotein (N), the large protein (L), the phosphoprotein (P), and the M2-1 protein. We provide an overview of the RSV RNA synthesis and the structural insights into the RSV gene transcription and genome replication process. We propose a model of how the essential four proteins coordinate their activities in different RNA synthesis processes. Orthopneumovirus, family Pneumoviridae, order Mononegavirales [The human respiratory syncytial virus (HRSV or RSV) is an enveloped virus with a negative-sense (−) genome in the genus avirales . Since iavirales . RSV is avirales . The RSVavirales . Unfortuavirales ,6,7,8.Mononegavirales is also called nonsegmented negative-sense (NNS) RNA viruses, a class of pathogenic and sometimes deadly viruses that include measles (MeV), rabies (RABV), Nipah (NiV), Hendra (HeV), Ebola (EBOV), Marburg (MARV), and RSV. The family Pneumoviridae has two genera Orthopneumovirus and Metapneumovirus. The genus Orthopneumovirus consists of bovine RSV (BRSV), murine pneumonia virus (MPV), and human RSV. HRSV has two antigenic subgroups, A and B, with genome-wide sequence divergence. The genus Metapneumovirus consists of human metapneumovirus (HMPV) and avian metapneumovirus (AMPV) [The order of s (AMPV) .The RSV virions are either spherical particles of 100–350 nm in diameter or long filaments up to 10 μm and 60–200 nm in diameter. The RSV virion comprises an RNA synthesis ribonucleoproteins (RNP) complex packaged in a lipid envelope derived from the host cell membrane. The RNA synthesis RNP consists of four proteins essential for the RSV RNA synthesis: the nucleoprotein (N), the large polymerase protein (L), the phosphoprotein (P), and the processivity factor M2-1. The RSV envelope contains three membrane proteins: the glycoprotein (G), the fusion protein (F), and the small hydrophobic protein (SH). The matrix protein (M) lays between the RNP and the envelope, acting as the cushion.RSV encodes ten sequential viral genes in the order of 3′ NS1–NS2–N–P–M–SH–G–F–M2–L, and each gene is flanked by conserved gene start (GS) and gene end (GE) sequences. The genes responsible for the RSV RNA synthesis are highlighted in blue . Each geRSV initiates viral infection by delivering into the host cell a virus-specific RNA synthesis RNP required for both replications of the full-length genome and transcriptions of individual viral genes . RNA synThere is only one promoter (green arrow) for transcription at the 3′ end of the Le region (position 1–11) . The polMuch of our current understanding of RNA synthesis comes from studies of prokaryotic or eukaryotic DNA- or RNA-dependent RNA polymerases ,16,17,18Further, studies using the RSV minigenome replicon in which the RNP complexes are reconstituted in cells by supplying the trans-acting protein and RNA components have shown that the replication requires N, L, and P, whereas transcription requires N, L, P, and M2-1 ,6. RemarMononegavirales RNA synthesis RNPs has provided rich insights into RSV RNA synthesis and viral replication. For the comparison purpose, we use the well-studied vesicular stomatitis virus (VSV) and human parainfluenza virus (HPIV), the representative members of the Rhabdoviridae and Paramyxoviridae familes, respectively, and HMPV as a control in the Pneumoviridae family. Comparing the RNP structures of RSV and other representative viruses sheds light on the shared and different transcription and replication strategies. As highlighted above, N, L, P, and M2-1 are the minimal components for the in vitro reconstitution of the RSV RNA synthesis machinery. This review focuses on RSV and summarizes structural insights of those four proteins that constituted the RSV RNA synthesis RNP. Readers who are interested in more targeted reviews may read recommendations in each subsection.In the past two decades, the advance of structural and biochemical studies on the Mononegavirales and distinct to the capping in the host cells.The RSV L is a single polypeptide of 2165 residues and is the catalytic core of the RSV polymerase. The RSV L exists as a monomeric but multifunctional enzyme, bearing three distinct enzymatic domains, namely, the RNA-dependent RNA polymerization (RdRp), the cap addition (Cap), and the cap methylation (MT) domains. Besides three functional domains, the RSV L also contains two structural domains, the connector domain (CD) and the C-terminal domain (CTD). The RSV RdRp activities share similarities with the activities of other viral RNA-dependent RNA polymerases (RdRPs), but the Cap and MT activities are unique to the Mononegavirales L are color-coded in blue, green, yellow, pink, and cyan, respectively, in Mononegavirales RdRPs may consult [The RdRp, Cap, CD, MT, and CTD domain organizations of  consult ,28,29, a consult ,11.NTD (magenta), POD (red), and PCTD (orange), respectively , the oligomerization domain (OD), and the C-terminal domain (CTD), which are indicated as Pectively A.NTD that interacts with M2-1 and POD (red) and PCTD that binds to L are shown with the terminal residue numbers indicated. The flexible or unmodeled regions are linked by the dotted lines, and only one copy of the M2-1-binding domain is shown , N:RNA complex, and M2-1 proteins ,6. Thus is shown B. The M2Mononegavirales P proteins. Previously, the co-crystal structures of the N°-binding motif, the N:RNA-binding motif, and the oligomerization domain of P interacting with respective binding partners have been determined [NTD that interact with the CTD, RdRp, and CD domains of L [The VSV P is the most extensively studied among termined ,32,33  sits on the RdRp domain of HPIV L (CTD (orange) of one chain of P also interacts with L. Interestingly, there are several notable differences among the P proteins presented here from different families: (1) the RSV P and HPIV P are tetramers, but the VSV P is a dimer; (2) the length of the oligomerization domain of HPIV P is more than twice the length of the RSV POD or VSV POD; (3) the sizes of the RSV P and VSV P are similar (241 aa vs. 265 aa), but HPIV P is much longer (392 aa).The HPIV P model was extracted from the structure of the HPIV polymerase  Figure D. POD .The RSV N is a protein of 391 residues, consisting of two core domains: the N-terminal domain  and the C-terminal domain , plus the N-terminal motif  and C-terminal motif  near the terminus of the core domains A.The crystal structure of the RSV N:RNA pseudo-ring revealed that N has two core lobes (NTD and CTD) with the RNA bound in the central groove, and each N coats 7-nt of RNA . Three cMononegavirales N:RNA were the VSV N:RNA by Green et al. and RABV viral N:RNA by Albertini et al. in 2006 [Mononegavirales N:RNA structures were determined, including those of RSV, HMPV, HPIV, MeV, and EBOV [CTD that binds to the VSV N:RNA [CTD (orange) binds to the CTD and the C-arm of two adjacent N proteins  binds to a similar location where the RNA is located in the N:RNA complex  monomers and delivers them to nascent RSV genomes or antigenomes . The PNT VSV N°P . While t complex E. The stractions ,51,52,53emplates ,55,56. R consult ,58,59.M2-1 acts as an antiterminator to ensure the full-length transcription of all ten RSV mRNAs, and it is impossible to rescue infectious RSV from cDNAs without M2-1 . M2-1 exapo RSV M2-1 in two different space groups, which revealed the symmetrical tetramer configuration [apo HMPV M2-1 showed an asymmetric tetramer, with three of the protomers in a closed conformation and one protomer in an open conformation [The 194-residue RSV M2-1 consists of three distinct domains: the zinc-binding domain (ZBD: 1–31), an oligomerization domain (OD: 32–68), and a core domain (CD: 69–194). The domain organization of M2-1 is shown in guration  , showing the location of P(90–110) fragment on the surface of M2-1  Figure D. In thede novo RNA synthesis (C). These domains then adopt an open conformation upon promoter recognition and remain open during elongation (D–G), resulting in higher mobility of the CD, MT, and CTD domains of L. Depending on N°, RNA synthesis goes to either transcription  or replication (G). Even within this proposed framework, some ambiguity remains. For example, (1) P may bind to L all the time. (2) The domains of L undergo significant conformational rearrangements in response to not only P but also other cofactors, such as M2-1 and N. (3) There is also the possibility that the active RNA synthesis needs the coordination of multiple  L proteins at the same time.On the basis of current data, we proposed a model of the sequential RSV RNA synthesis . In geneUndoubtedly, the visualization of the RSV RNA synthesis RNP has provided enriched insights into how RSV integrates viral transcription and replication as the essential part of the viral life cycle. The elucidation of the interaction surfaces and catalytic domains of the RNA synthesis RNP distinct from the host cell counterparts will facilitate the rational design of novel therapeutics ,68,69,70


The 7SK small nuclear RNA (snRNA) found in most metazoans is a key regulator of P-TEFb which in turn regulates RNA polymerase II elongation. Although its primary sequence varies in protostomes, its secondary structure and function are conserved across evolutionary distant taxa.D. melanogaster. We examined the structure of the corresponding gene and determined the expression profiles of the encoded RNA, called snRNA:7SK:94F, during development. It is probably produced from the transcription of a lncRNA which is processed into a mature snRNA. We also addressed its biological function and we show that, like dm7SK, this alternative 7SK interacts in vivo with the different partners of the P-TEFb complex, i.e. HEXIM, LARP7 and Cyclin T. This novel RNA is widely expressed across tissues.Here, we describe a novel ncRNA sharing many features characteristic of 7SK RNAs, in D. melanogaster.We propose that two distinct 7SK genes might contribute to the formation of the 7SK snRNP complex in The online version contains supplementary material available at 10.1186/s12860-021-00381-7. Drosophila) which methylates its 5′ end [Transcriptional elongation by RNA polymerase II (RNA Pol II) is regulated by the positive transcription elongation factor b (P-TEFb) composed of Cyclin T1 and cyclin-dependent kinase 9 (CDK9) , but alss 5′ end , and wits 5′ end –17. Thess 5′ end –20.Caenorhabditis species [Drosophila [The genes encoding 7SK are found in all vertebrates and the sequence of the snRNA is highly conserved in mammals, whereas basal vertebrates only exhibit poorly conserved sequences (e.g. ~ 68% similarities in lamprey) . Neverth species , althoug species . A struc species , 26. A r species . Overallosophila . This inDrosophila genome with a bioinformatic analysis based on the structure of the Drosophila RNA Pol III-specific promoters [, embryogenesis and organogenesis of Drosophila. We also demonstrate that this snRNA physically interacts with dmHEXIM, and co-immunoprecipitates with dmP-TEFb. We propose that this novel snRNA named snRNA:7SK:94F is a Drosophila-specific alternative 7SK snRNA. This is the first description of two distinct 7SK gene products which could participate in the formation of the P-TEFb snRNP complex.In this work, we identified a novel non-coding snRNA in romoters . We arguDrosophila genome. Instead, we derived a profile Hidden Markov Model based on a structural alignment of chordates 7SK sequences . Then we analysed the upstream region of this gene by comparison to the well-known organization of the promoters of Drosophila snRNA genes [D. melanogaster, the promoters of RNA Pol II (U1 to U5) and RNA Pol III (U6) snRNA genes contain two elements: an upstream Proximal Sequence Element (PSE), and a PSEB (for RNA Pol II snRNA genes) [Drosophila  were spurious hits arising from bioinformatic noise and were not studied further. First, we identified the Transcriptional Start Site (TSS) of this gene by 5′ RACE PCR , which is an adenine located at 3R:23432869 (Flybase release FB2018_06)   on 2–4 h embryos  revealedThis locus probably encodes a lncRNA that we named lncRNA:94F according to the current nomenclature. The 5′ end of this transcription unit corresponds precisely to the 7SK-like RNA sequence identified in the bioinformatic screen. It is likely that the lncRNA:94F RNA is a precursor transcript later processed into the short mature 7SK-like snRNA.Drosophilidae species but not in other insects, particularly in the 5′ and 3′ ends  followed by PCR with primers located in the predicted transcription unit (PU11 and PU12) Fig. b. No staWe first profiled snRNA:7SK:94F expression using RT-sqPCR, from the embryonic stages until adulthood  , homolog control , 43, as Drosophila [7SK snRNA homologues have been found in bilaterians and follow precisely the phylogenetic distribution of HEXIM . This isosophila . There aosophila . All theosophila . The steosophila . We wereAn intriguing fact is that the sequence of snRNA:7SK:94F presents several internal tracks of thymidines which act as RNA Pol III transcriptional termination signals. It is therefore highly unlikely that it is an RNA Pol III gene. An interesting hypothesis would be that the RNA Pol III promoter structure evolved over time into an RNA Pol II promoter which overcomes premature termination. Accordingly, the promoter specificity is not dictated by the TATA box but rather by the sequence of the PSE . Such a Drosophila? Do they functionally cooperate or is their functions antagonistic or independent of each other (despite interacting with the same regulatory complex)? The fact that more of one 7SK sequence is present in a genome is not new. For example, mammalian genomes contain a single functional 7SK gene but they are populated with numerous 7SK-like retro-pseudogenes [C. elegans genome, but the expression of only one could be detected by Northern blot, probably due to the very low expression level of the other. The authors suggest that they are paralogues subjected to concerted evolution because their sequence is nearly identical [Drosophila is quite different from C. elegans: the sequences of the dm7SK and the proposed snRNA:7SK:94F are poorly conserved and display distinct expression patterns during development. This suggests that the two genes are not fully redundant and may have specific biological functions. Also, one can note that the phylogenetic distribution of snRNA:7SK:94F is restricted to the Drosophila genus  between these two 7SK genes found in udogenes . This raudogenes . Moreoveudogenes , 52. In dentical . LikewisProteins can often accommodate various molecular functions. For example, Actin proteins are components of the cytoplasmic acto-myosin network, but they are also nuclear components of the chromatin remodeling complex . Beta-cawww.hmmer.org) to look for signatures of 7SK sequences. To this end, a structural alignment built from the 7SK sequence of humans, mice, chicken, xenopus, fugu, tetraodon, lamprey, myxin and amphioxus was used to set up a nucleotidic multiple alignment, which was used to derive a profile HMM model with hmmbuild. The model was used to scan the Drosophila genome (FB2018_06 release) [Given that ncRNA 7SK sequences are poorly conserved, we used the HMMER package (release)  with hmmhttp://hgdownload.soe.ucsc.edu/admin/jksrc.zip. Visualization of reads density and the genomic environment is carried out with the JBROWSE genome browser [https://www.ncbi.nlm.nih.gov/bioproject/PRJNA644503) from wild type pupae are from [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131160) are from Ueberschar et al. [Sequence reads were mapped on Dmel v6 genome available from UCSC website by using the bowtie short reads aligner  with str browser . RNA-Seqare from  and PRO-r et al. .Ten micrograms of RNA, extracted from adult flies (Qiagen RNeasy kit), were reverse- transcribed with the SuperScript II reverse transcriptase . The 5′ end of the snRNA:7SK:94F was identified by the Rapid Amplification of cDNA End (RACE) system (Invitrogen) as previously described , using s6 to 58-fold). Then the relative dilutions of the cDNAs were used to estimate relative snRNA:7SK:94F and dm7SK PCR quantities.Semi-quantitative RT-PCR (RT-sqPCR) were carried out with RT II and hexamer oligonucleotides from Invitrogen. Series of PCRs were performed from purified cDNAs to amplify PCR fragments of snRNA:7SK:94F, dm7SK, and RP49 RNAs with corresponding primers  and larval tissues were carried out with standard procedures  using apCo-immunoprecipitations were performed using rabbit antibodies against isoform A of dmHEXIM , and sheURA3 gene and express snRNA:7SK:94F fragments fused to two MS2 sites. The pACTII-derived plasmids carry the LEU2 gene and drive the production of the dmHEXIM isoform A (dmHEXIM A) [BamHI and XhoI sites of pACTII, after amplification from the pET21-HEXIMA plasmid [D. melanogaster CantonS strain (Bloomington Drosophila Stock Center) and cloned into the SmaI sites of pIIIA/MS2.1 using specific primers   fused to plasmid  using spAdditional file 1: Fig. S1 Sequence of the snRNA:7SK:94F region. Positions of PSE and TATA box are underlined. The transcription start site is indicated over the + 1 nucleotide. The RNA sequence is in bold case and the untranscribed region is in lower case. The position and orientation of the different structural motifs i.e. M1, M3 and M8 and stems A and B are indicated by arrows onto the sequence. R: reverse orientation; F: forward orientation. Fig. S2 Secondary structure of hs7SK snRNA, snRNA:7SK:94F and dm7SK snRNA. The structures have been defined by mfold software [DG, is indicated next to the structure. Fig. S3 Quantification of the relative expression levels of dm7SK RNA and snRNA:7SK:94F at different developmental stages. The histogramm displays the ratio of dm7SK RNA expression levels versus snRNA:7SK:94F. Expression levels normalized over RP49. Average of two to three different representative experiments. Fig. S4 Expression patterns of snRNA:7SK:94F during embryogenesis and organogenesis. a Expression patterns of snRNA:7SK:94F were monitored by in situ hybridization. Embryos are oriented anterior to the left, dorsal uppermost. They are ordered by developmental stages. b In situ hybridization during organogenesis in eye-antenna, leg and wing imaginal discs, and brain from third-instar larvae. Fig. S5 Uncropped image of the gel used to analyse the localisation of the transcript unit of snRNA:7SK:94F gene, as shown Fig. Fig. S6 Uncropped image of the gels used to analyse the expression of snRNA:7SK:94F during the life cycle of D. melanogaster, as shown Fig. Fig. S7 Uncropped image of the gels used to analyse by co-immunoprecipitation the interactions between HEXIM and cyclin T, as shown Fig. Fig. S8 Uncropped image of the gels used to analyse by co-immunoprecipitation the interactions between HEXIM, cyclin T, and dm7SK or snRNA:7SK:94F as shown Fig. Fig. S9 Uncropped image of the photographies used to analyse direct interaction between snRNA:7SK:94F and dmHEXIM by three-hybrid system, as shown Fig. Table S1 Name and sequence of primers used in this work.software  to allowsoftware , the lessoftware  or the hsoftware  are indi


There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems.We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop  on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available ( In 2013, the NIH Common Fund launched the Extracellular RNA Communication Consortium to stimulate research into the fundamental biology of exRNA and its clinical applications in disease diagnosis and treatment. One product of the first stage, ERCC1 (2013–2018), is the exRNA Atlas, a database of small RNA-sequencing and RT-qPCR data. To date, the Atlas holds over 7,700 samples from 14 biofluids and 16 disease conditions . The curRob Kitchen opened the workshop by outlining open problems in exRNA data analysis. One key challenge is that exRNA data quality varies widely and systematically between different experimental methods. Isolating and purifying exRNA and extracellular vesicles (EVs) from experimental samples is itself difficult, and RNA isolation kits used for these tasks are known to be a major source of variability in the resulting exRNA data. Compensating for this variation is a central challenge, as each kit and RNA sequencing method has different sequence biases that must accounted for when doing larger analyses . Dr. KitAnother central question of interest in exRNA and EV biology is determining tissue and cell type of origin of different clusters of exRNAs in a biofluid. Dr. Kitchen argues that it might be possible in peripheral biofluids like urine and saliva, but more challenging in circulating blood serum and plasma, where the exRNA complement may be too diverse to parse. For vesicular exRNAs, the problem should be made simpler by improvements in experimental techniques to isolate EV sub-fractions, such as selecting for EVs with cell-type-specific surface proteins. As fractionation techniques improve, however, it will be necessary to compensate for variable enrichment efficiency.Juan Pablo Tosar focused specifically on the quality of non-coding RNA annotations. He outlined how the mechanisms of biogenesis of miRNA and piRNA serve as the basis of existing annotations like miRbase. The problem is that such databases often lack strict curation, resulting in many sequences that are not miRNAs or piRNAs under any reasonable definition. Tosar described two examples from miRbase annotations, showing that miR-1202 is, in fact the small nucleolar RNA, SNORD126, and miR-1246, a microRNA enriched in EVs, is likely a contaminant from fetal bovine serum (FBS) in the cell culture media and likely fragment of the small nuclear RNA RNU2-1 . This isTosar emphasized that piRNAs have a complex biogenesis that imposes a strong bias to start with U or to have A in the 10th position, and they are expressed from genomic clusters with a high density of piRNA sequences . PiRNA ahttps://app.nih-cfde.org/). Justin Chang gave a preview of the exRNA Explorer tool, a data exploration and visualization tool that will soon be integrated into the public exRNA Atlas. Joel Rozowsky later outlined the exceRpt pipeline used to process short exRNA sequencing data in the exRNA Atlas  using both small RNA-sequencing and mRNA-capture sequencing. Introducing short and long synthetic spike-in RNAs before and after RNA extraction, enabled comparison of the absolute miRNA and mRNA content between samples, revealing large differences between fluids. Mestdagh also summarized efforts to identify the relative contribution of different tissues to exRNA transcriptomes and how this varied across biofluids. Deconstruction of exRNA profiles into contributing tissues can be achieved through computational deconvolution, a topic explored in-depth later in the workshop. He showed that the accuracy of deconvolution depends on several factors, including 1) proper transformation and normalization of the exRNA-seq read count, 2) choice of deconvolution algorithm and 3) quality and completeness of the reference data. Mestdagh also presented evidence suggesting that circular RNAs (circRNAs) are present in biofluids, potentially at a higher proportional abundance relative to linear transcripts than they are in cells and tissues.Pieter Mestdagh presented the Human Biofluid RNA Atlas , which cKlaas Max discussed healthy reference profiles of extracellular miRNA in serum and plasma . Based oHelicobacter pylori bacteria and Epstein-Barr virus associated with GC for determining disease state when analyzing saliva samples of Asian origin. This highlights the need for ensuring ethnic diversity in human genome and microbiome sequences in the age of personalized medicine. More recently, the researchers have found that performing deconvolution and variance partition analyses to isolate extraneous sources of variation improves their ability to identify exRNA biomarkers.Karolina Elżbieta Kaczor-Urbanowicz discussed bioinformatic analysis of salivary RNA sequencing data  in the cbona fide mRNA fragments. In a longitudinal study of hematopoietic stem cell transplant recipients, mRNA fragments segregated into several distinct temporal co-expression signatures associated with the transcripts’ likely tissue of origin .Ryan Spengler emphasized that standard small RNA-seq library preparation methods require RNAs to have 5′ phosphate and 3′ hydroxyl groups, but a substantial fraction of exRNAs lack these end chemistries . By incuLeonora Balaj discussed efforts to identify extracellular mRNA signatures associated with glioma. They examined long RNA-seq reads from EVs isolated from glioma patients compared to healthy individuals matched by age and sex. They also demonstrated a two-hybrid capture method which uses exome capture panels to enrich for exRNA reads from protein-coding mRNAs. Including a ribosomal RNA depletion step, they were able to substantially enrich for mRNA sequences and largely eliminate the non-mRNA reads that dominate non-captured libraries. They also performed long mRNA-seq on exosomal RNAs isolated from patients before and after undergoing dacomitinib treatment for recurrent glioblastoma and found exosomal mRNA signatures that distinguished responders from non-responders to the treatment. Importantly, in follow-up validation cohorts, these signatures showed promise in predicting which individuals would respond to treatment.de novo, not relying on mapping reads to annotated RNAs. In fact, they found that most expressed smRCs emanate from unannotated genomic regions in a cell-type- and biofluid-specific manner and have EV-specific properties which can be exploited for biomarker discovery. Three such smRCs were found to be strong biomarkers for early-stage liver cancer , significantly outperforming the clinical surveillance standard in an independent Phase 2 clinical study. These findings raise the possibility of a blood-only, operator independent, minimally invasive liquid biopsy test for HCC.Bojan Losic presented a method for the discovery of small RNA clusters  expressed in circulating liver cancer EVs . The metin vitro motifs and RNA interactions in K562 and HepG2 cell lines for over 350 RNA binding proteins . Some exRNAs are resistant to RNase digestion, e.g., Gly/Glu tRNA fragments that can form stable homo- and hetero-dimers . Other eproteins .Emily LaPlante described initial work scanning the exRNA Atlas for ENCORE RNA binding sites and establishing the infrastructure to allow Atlas users to study their own regions of interest. Bogdan Mateescu outlined current knowledge about exRNA-associated RBPs (exRBPs)  as well The next step in the PRISM group’s strategy is experimental validation of candidate exRBPs in a model system. After using CRISPR to create knockout strains in the 293T cell line for each candidate exRBP gene, exRNA profiles from wild-type and knockout conditioned media are compared. A case study including knockout lines for 10 genes in the RNA silencing pathway led to changes in expression in classes of exRNA beyond miRNA: tRNA, snRNA, snoRNA, and YRNA. Whereas the bioinformatic pipeline identifies individual sites of exRNA interaction with RBPs, the model system provides a global perspective on the impact of each RBP on expression of all exRNAs and perhaps gives insight into exRBP function. The ERCC2 PRISM group plans to identify over 100 candidate exRBPs and create an atlas of exRNA profiles from the model system exRBP knockout strains.Two major open problems in the field are identifying tissue of origin of the exRNAs in a biofluid and associating them with their molecular carrier, whether that be an RNA binding protein, a lipid like HDL or LDL, or a variety of classes of extracellular vesicle. Computational deconvolution, a method for partitioning a heterogeneous dataset into contributions from different independent constituents, can complement experimental approaches to addressing such challenges. There are two broad classes of deconvolution algorithms—reference-based and reference-free. Reference-based algorithms require a known signature matrix of gene expression profiles from all the cell types or molecular carriers to be separated. Reference-free methods estimate simultaneously both the signature matrix and the relative ratios of each cell type or carrier in the mixture.http://DREAMchallenges.org/).In a session on deconvolution, Brian White described a Tumor Deconvolution DREAM Challenge  to assesin vitro and in silico admixtures spanning 14 different immune, stromal, and cancer cell types and asked teams to predict the relative ratios of each cell type in the admixtures. To help assess its potential relevance for deconvolving exRNA data, Dr. White described CIBERSORTx, one of the baseline reference methods used in the Challenge. CIBERSORTx is based on the earlier Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm .In the Tumor Deconvolution DREAM Challenge, the organizers provided teams RNA expression profiles from lgorithm . Both CIlgorithm  is a twoIn Silico Mixing and Deep Neural Network) used to win the Tumor Deconvolution DREAM Challenge. Dr. Yu explained that the choice of a neural network method was guided by the non-linearity of the input data. He showed that expression levels of different genes vary across cell types in a non-linear way. Neural networks perform better than linear regression in that case. The problem with using DNN is that it requires a very large number of training datasets, on the order of ten thousand. The team’s solution was to augment the existing training datasets by shuffling them together in silico with expression data from target cells, either bulk RNAseq from purified cell samples or single-cell RNAseq. Finally, although DNN models are generally considered to be difficult to interpret black boxes, it is possible to apply methods such as the SHapley Additive explanation (SHAP) model from game theory (Rongshan Yu described the DAISM-DNN algorithm (Data Augmentation through e theory  to outpue theory .Finally, Aleks Milosavljevic described the XDec algorithm for analyzing small RNA-seq datasets in the exRNA Atlas. XDec is a two-stage reference-free deconvolution algorithm that separates exRNAs in a biofluid sample into sets associated with different molecular carriers—extracellular vesicles, lipoproteins HDL and LDL, and three categories of RNA binding proteins .A major goal of the workshop was to lay the foundation for creating an exRNA-themed data analysis challenge. The DREAM challenge framework is ideal for presenting problems in data analysis to the wider scientific community. The last session of the workshop was chaired by Gustavo Stolovitzky, co-founder of the DREAM challenges. He gave a talk asking the question: “Can we use a crowdsourcing challenge to benchmark exRNA transcriptomics analyses?” Building on that foundation, Roger Alexander discussed specific classes of challenges that the community might put forth. The ensuing discussion and a post-workshop survey narrowed the field to two classes of challenge: first, a biomarker discovery challenge, and second, a deconvolution challenge to address two problems: identifying cell type of origin and molecular carrier of groups of exRNAs within a biofluid.Given a set of case-control experiments for a specific disease, what are the most informative sets of exRNA biomarkers for diagnosing and tracking treatment progress for that disease? One of the most difficult aspects of the challenge is acquiring the number of case-control experiments necessary to have sufficient statistical power for biomarker discovery. Improved batch correction algorithms would ease the problem by making it easier to stitch together datasets from multiple related studies. Given large testing and training datasets, teams would compete to develop algorithms that identify exRNA biomarkers that best discriminate cases of disease from controls. The main challenge would reward algorithms that can solve the batch-to-batch variation problem to combine public datasets into a larger testing dataset. A sub-challenge would select for algorithms that perform well even as the original training dataset is repeatedly sub-sampled to be smaller and less powerful. The authors welcome community participation in this effort.Is it possible to determine the cell type of origin or molecular carrier of different clusters of exRNAs in a biofluid? These questions can begin to be addressed in a data analysis challenge using synthetic datasets. Beyond improved algorithms, a beneficial outcome of such a challenge would be the creation of better gold standards of cell-, tissue-, and molecular carrier-associated exRNA profiles for use with reference-based deconvolution methods.Moving beyond a synthetic deconvolution challenge requires the generation of an experimental gold standard, which is more challenging for exRNA than for tumor deconvolution. For tumor deconvolution, 14 cell lines were chosen to mimic a tumor microenvironment. For exRNA, the community must come together to agree on a similar proxy environment, perhaps by collecting culture media from cell lines representing several different tissues, with or without a vesicle purification step before RNA isolation and sequencing. Finding that proxy environment was beyond the scope of the workshop but will be necessary to make possible a non-synthetic exRNA deconvolution challenge.The workshop introduced experimental and data scientists to the field of exRNA data analysis. The standard for small RNA-seq data is uniform processing by the exceRpt pipeline and storage in the exRNA Atlas. Long extracellular RNA is less thoroughly studied, and methods for its analysis are still under development. It is important to be mindful that standard RNA-seq methods are blind to many RNA base modifications, and mis-annotated RNAs can lead to misinterpretation. Biomarker and other studies requiring comparison across datasets would benefit greatly from improved algorithms to compensate for batch-to-batch variation and other systematic errors.With the discovery of new classes of exRNA  and deve


Our study aimed at investigating the way not having the choice to be reassigned was associated to a poorer experience of reassignment among health care workers (HCWs) during the first wave of the coronavirus disease 2019 (COVID-19), and indirectly to a lower workplace well-being and reduced intent to stay at the hospital. We also investigated the moderating role of the perceived hospital management responsiveness on these associations. A cross sectional survey was sent to all professionals from 11 hospitals and clinics in the French-speaking part of Switzerland, in July 2020. Out of 2811 professionals who completed the survey, 436 were HCWs reassigned to COVID-19 units during the first wave of the pandemic and constituted our analysis sample. Results indicated that hospital management responsiveness moderated the association between lack of choice and reassignment experience, indicating that the more HCWs perceived responsiveness, the less the lack of choice affected their experience of reassignment and thus their intent to stay and workplace well-being. Lack of choice during reassignments can reduce intent to stay and workplace well-being, in particular if hospital management is not perceived to be responsive during the crisis. Attempts by hospital management to find solutions, such as flexibility in working hours or extraordinary leaves, can alleviate the perceived constraints of reassignment and be considered signs of responsiveness from hospital management. Coronavirus disease 2019 (COVID-19) became a major public health concern in March 2020 ,4,5. MorActually, reassignments during pandemics are quite unavoidable. In exceptional circumstances, such as the COVID-19 pandemic, duty to care justifies staff redeployment or mobilisation. This duty to care, present in the conduct codes of most health care professions, is based on the fact that health care professionals (i) possess specific irreplaceable skills, (ii) have freely accepted to practice health care professions knowing the associated risks and obligations, and (iii) have a social contract with the community ,11. Up tSeveral factors, such as control over work or having the choice to work in frontline care units, were shown to play a key role in HCWs’ workplace well-being and intent to stay during pandemics . For exaThe objective of this study was thus to describe the associations between the experience of reassignment in frontline units and both workplace well-being and intent to stay among HCWs. More precisely, we proposed that the lack of choice in being reassigned was associated to a negative experience of the reassignment and indirectly to lower well-being and decreased intent to stay (mediation). We also proposed that these associations were conditional on how reassigned HCWs perceived hospital management responsiveness: the more hospital management was perceived as responsive towards the crisis, the weaker was the association between lack of choice and reassignment experience  were obtained from the hospitals’ responsible body. Agreements were signed with the hospital responsible body to ensure data security and confidentiality, and communication was made with the professionals by their managements at least 2 days before the beginning of the study. All professionals were then invited to participate in an anonymous electronic survey in July 2020. A reminder was sent 2 weeks after the launch of the survey. Results presented in this article focus on HCWs having indicated that they were reassigned to COVID-19 units during this period. Data from professionals  who indicated they were not reassigned were not included. Since information about the number of HCWs reassigned was not available from hospital management, the total number of HCWs actually reassigned cannot be provided.The current study is part of a larger project involving all types of professionals working at hospitals during the first wave of COVID-19. In the latter project, professional e-mail addresses of all employees working in 11 hospitals and clinics of the French-speaking part of Switzerland  to match with the context of the pandemic. For the latter, the wording of items was based on a literature search of previous SARS, H5N1, or H1N1 epidemic studies and on interviews conducted with staff physicians, a quality and safety expert, and mediators involved in support services to the teams during the first wave of the pandemic. The reliability of the measured constructs was assessed with principal component analyses and Cronbach’s alphas.The 2 outcomes of this study were workplace well-being and intent to stay.Workplace well-being was assessed with the 8 items of the Psychological Well-Being Scale  adapted Intent to stay was measured with a single item that assessed the perceived motivation of professionals to stay after the first wave  on a 5-point Likert scale.The following independent variables were measured:Lack of choice regarding reassignment was measured with a single-item asking if participants had had the choice to accept or decline the reassignment on a 5-point Likert scale ranging from 1 (possibility to choose) to 5 (no choice).Reassignment experience was measured with 4 items using a 5-point Likert scale . Internal consistency was quite good  and a mean score was computed on the 4 items that ranged from 1 (negative experience of reassignment) to 5 (positive experience of reassignment).The perceived responsiveness of hospital management was measured with the 5 following items using 5-point Likert scales: “I feel that the crisis was managed effectively within the institution”; “I feel that the management has been responsive to the situation”; “The resources put in place by the management to support the professionals  met my needs”; “I feel that the management was present and mobilised during the whole period”; “Overall, I felt supported by the hierarchy during the coronavirus first wave”. Internal consistency on the 5 items was good  and a mean score was computed that ranged from 1 (poor perceived responsiveness) to 5 (strong perceived responsiveness).Age, gender, length of professional experience, professional groups (dummy variables), the threat of being infected or of infecting others , and being involved in intensive care (yes/no) were considered confounding variables.We treated all variables as continuous variables (except where specified) after checking for distribution linearity and normality. Since the missing value rate was low (<0.5%), we did not estimate missing values and opted for a listwise deletion procedure.We first conducted descriptive analyses to characterize the data, after which we computed Pearson correlations to assess potential covariations that were due to confounding variables. Finally, as our hypothesis involved both a mediation and a moderation effect, suggesting that the mediation effect depended on the level of our moderator, we used the PROCESS program proposed by Hayes . This prDescriptive analyses and the PROCESS macro for moderated mediation analyses were performed with SPSS Statistics 26 ; the software program GPower  was used to check that the sample size was adequate for estimation analyses .According to the local ethics committee, the study did not need ethics approval (Req-2020-00695).n = 436), all respondents, and eligible HCWs are reported in Of 8645 contacted professionals, 2811 completed the electronic survey (32.5% response rate), and 818  reported having been reassigned during the first COVID-19 wave. Among the latter, 382 were reassigned to non-COVID-19 units because they had personal vulnerabilities or lived with vulnerable relatives, and 436 were specifically reassigned to COVID-19 units. As the study concerned workplace well-being and intent to stay experienced by HCWs reassigned to frontline units specifically, our analyses concerned the latter 436 respondents . Characteristics of the study sample , 28.1% of the 436 respondents expressed a high level of well-being , 50.8% reported a medium level , and 21.1% expressed a low level . Concerning lack of choice , 32.3% of the 436 respondents answered they had choice , 19.5% answered they had a limited choice , and 48.2% answered they had no choice . Concerning reassignment experience , 25.6% of the 436 respondents reported a good experience with reassignment , 45.3% reported a neutral experience , and 29.1% reported a negative experience . Concerning the perceived responsiveness of hospital management in front of the pandemic crisis , 36.0% of the 436 respondents felt that management had been responsive during the first wave of COVID-19 , 40.1% felt it had been moderately responsive , and 23.9% thought it had not been responsive .Bivariate correlations  showed wWe expected trust in hospital management of the COVID-19 crisis to moderate the indirect path between perceived lack of choice and both intent to stay and workplace well-being through the reassignment experience. The two moderated mediation analyses showed a significant overall index of moderated mediation for both outcomes , suggesting a moderated mediation effect on both outcomes.The results reported in Finally, as hypothesized, we observed that the indirect effects of lack of choice on intent to stay and workplace well-being (through reassignment experience) were conditional on the perceived responsiveness of hospital management. In fact, the more that HCWs perceived their hospital management to be responsive, the less that lack of choice affected their experience of reassignment  and thusIn the current COVID-19 pandemic context, our results suggest that both having the choice to be reassigned and perceived responsiveness of hospital management can increase intent to stay and workplace well-being. Despite the fact that many studies have already reported the negative consequences of the pandemic situation on the physical and mental health of frontline HCWs, including reassigned HCWs ,33,34,35The association found in our study between having the choice to be reassigned and well-being or intent to stay could be explained by the fact that having the choice can provide HCWs with a sense of control over events. Actually, control over work has been described as a strong determinant of nurses’ retention  and as aThis is coherent with research suggesting that reassignment should be organized in the form of a contract between the hospital and the HCWs rather than in the form of a coercive one-way decision . The autBeyond the ethical issue raised by the duty of care in times of a pandemic, our study suggests that lack of choice, or, conversely, imposed reassignments, may be associated with reduced professional well-being and a willingness to leave the profession. This could be because HCWs’ perceptions of duty to care have evolved in recent outbreaks ,44, showAn interesting result in our study concerns the mediating role of reassignment experience between lack of choice and both workplace well-being and intent to stay. In fact, perception of control over work, autonomy, or lack of choice are usually found to be directly associated with well-being, as proposed in the job-demand-control model  or with The main strengths of this study are that it involved investigating an understudied topic and that it included diverse reassigned HCWs  in 11 Swiss hospitals. However, the following two limitations need to be considered. First, the response rate may be considered low. Such response rates are, however, close to those from similar studies, especially in a pandemic context, which may have had an impact on participation in a survey. In addition, the characteristics of our respondent sample and those of the eligible professionals were almost identical. A second limitation is the one-time cross-sectional measurement of well-being at work. Indeed, as the current pandemic period is characterized by recurrent waves and uncertainties, several measurements would allow fluctuations in well-being to be captured. Moreover, a longitudinal design could help confirm causality. The current acute and highly worrisome pandemic context nonetheless prevents oversolicitation of professionals and makes the performance of a longitudinal study unrealistic.Our study highlights that, for HCWs reassigned during the first wave of COVID-19 to unfamiliar work environments under demanding conditions, having the choice to accept or decline the reassignment may have an indirect impact on their well-being and intent to stay. However, this can be compensated by the fact that hospital management is perceived as responsive during crises.This result has concrete implications for hospital administration, who should consider reassignments not as an obligation imposed to HCWs but as a contract of reciprocity between hospital management and HCWs. In addition, lack of choice is mainly relevant when HCWs perceive barriers to working during pandemics . Understanding these barriers and proposing solutions for overcoming them, such as flexible working hours or agreements about extraordinary leave, for example, can be concrete signs of responsiveness that hospital management can provide to HCWs. Considering these solutions, which need to be anticipated in preparedness planning, is crucial to ensure engagement of workforces during demanding periods.