Here, we develop and apply a semi-quantitative method for the high-confidence identification of pseudouridylated sites on mammalian mRNAs via direct long-read nanopore sequencing. A comparative analysis of a modification-free transcriptome reveals that the depth of coverage and specific k-mer sequences are critical parameters for accurate basecalling. By adjusting these parameters for high-confidence U-to-C basecalling errors, we identify many known sites of pseudouridylation and uncover previously unreported uridine-modified sites, many of which fall in k-mers that are known targets of pseudouridine synthases. Identified sites are validated using 1000-mer synthetic RNA controls bearing a single pseudouridine in the center position, demonstrating systematic under-calling using our approach. We identify mRNAs with up to 7 unique modification sites. Our workflow allows direct detection of low-, medium-, and high-occupancy pseudouridine modifications on native RNA molecules from nanopore sequencing data and multiple modifications on the same strand. Pseudouridine (psi) is an RNA modification that can affect its physiology, including increased half-life. Here the authors identify sites of psi modification in the human transcriptome using direct RNA sequencing and provide a “ground truth” list of psi sites, sites of high psi occupancy, and transcripts that may be modified at multiple sites. Although modifications are less frequent in mRNAs than other RNAs3, mRNA modifications can potentially impact gene expression4, RNA tertiary structure formation5, or the recruitment of RNA-binding proteins6. Pseudouridine (ψ) is synthesized from uridine by one of more than a dozen pseudouridine synthases identified to date7. It was the first discovered RNA modification8 and represents 0.2–0.6% of total uridines in mammalian mRNAs3. Other uridine modifications on mRNAs include 5-methyl uridine9 and dihydrouridine11, but these occur to a lesser extent on mRNAs. Ψ-modified mRNAs are more resistant to RNase-mediated degradation12, and have the potential to modulate splicing13, immunogenicity14, and translation16 in vivo. Further, ψ modifications of RNAs are responsive to cellular stress, leading to increased RNA half-life18. A critical barrier to understanding the precise biological functions of pseudouridylation is the absence of high-confidence methods to map ψ-sites in mRNAs. Ψ modifications do not affect Watson-Crick base pairing19, thereby making them indistinguishable from uridine in hybridization-based methods. In addition, this modification bears the same molecular weight as the canonical uridine, making digested nucleotides challenging to detect directly by mass spectrometry21, and even more difficult to detect within a specific mRNA sequence that is isolated from cells due to the high input and purity requirements.Enzyme-mediated RNA chemical modifications have been extensively studied on noncoding RNAs22. Treatment with a strong base removes the CMC from all the sites except for the N3 position of ψ. Recently, the use of an RNA bisulfite reaction was demonstrated for ψ-specific labeling24. Chemical labeling of ψ combined with next-generation sequencing23 has yielded over 2000 putative ψ sites within mammalian mRNAs, but different methods have identified different sites with somewhat limited overlap25, pointing to a need for alternative detection methods that do not require CMC. Additionally, since these methods are combined with short read sequencing, combinatorial analysis of multiple modifications on one transcript is impossible. Here we aim to develop a direct and orthogonal method for ψ detection on mRNAs without relying on intermediate chemical reactions, but with the ability to detect previously annotated sites and to uncover previously unreported sites.Ψ is conventionally labeled using N-cyclohexyl-N′-b-(4-methylmorpholinium) ethylcarbodiimide (CMC), a reagent that modifies the N1 and N3 positions of ψ, N1 of guanine, and the N3 of uridine31. In these reports, ion current differences for different k-mer sequences (k = 5 nucleotides) as an RNA strand is moved through the pore suggest the presence of a modified RNA base. Detection of ψ using nanopores was also confirmed for ribosomal RNAs (rRNAs)28, on the Saccharomyces cerevisiae transcriptome29, and for viral RNAs31, as indicated by a U-to-C base-calling error at various sequence sites. Algorithms for ψ quantification have been produced30 using combinatorial sequences that contain many ψ sites within close proximity, and control RNAs that contain many natural RNA modifications also in close proximity . While the control molecules in these studies allow analysis of many k-mers, the accuracy of quantifying ψ occupancy at a given site can be highly dependent on the nucleotide sequence surrounding the modification. Moreover, sequence context is particularly important for quantification of RNA molecules wherein the secondary structure can influence the kinetics of translocation through the nanopore32.Recently, several studies have reported using nanopore-based direct RNA sequencing to directly read RNA modificationsHere, we describe a nanopore-based method to identify known ψ modifications and map uridine modifications in a HeLa transcriptome. We compare the sequence alignment to identical negative controls without RNA modifications and show that the number of reads and specific k-mer sequences are critical parameters for defining ψ sites and for assigning significance values based on these parameters. Our approach recapitulates 198 previously annotated ψ sites, 34 of which are detected by 3 independent methods, thus providing a ground truth list of ψ modifications in HeLa cells. Our approach also reveals 1691 putative sites of uridine-modification that have not been reported previously. We show that these previously unreported sites tend to occur within k-mer sequences that are often recognized by pseudouridine synthases, such as PUS7 and TRUB1.Applying our algorithm for detecting ψ modifications using rRNAs which have been comprehensively annotated by mass spectrometry, we assign 38/43 ψ modifications. We demonstrate, however, that rRNA is not suitable for benchmarking RNA modifications by nanopore sequencing due to the frequent clustering of RNA modifications nearby the ψ-site, which interferes with the accuracy of basecalling, thus leading to false positives. Additionally, we synthesize and analyze five 1000 mer RNA controls containing either uridine or ψ within the sequence context of a known pseudouridylated position in the human transcriptome. This analysis reveals that U-to-C mismatch errors are systematically under-called for the detection of ψ, enabling us to identify 40 high-occupancy ψ sites, which we denote as hypermodified type I.Further, we identify 38 mRNAs with up to 7 uridine modification sites, which are confirmed by single-read analysis. Combined, we report a workflow that enables direct identification and semi-quantification of the ψ modification on native mRNA molecules. The long nanopore reads allow for the detection of multiple modifications on one transcript, which can shed light on cooperative effects of mRNA modifications as a mechanism to modulate gene expression.Guppy 3.2.10. Three replicates of the native RNA library produced an average of ~1.2 million poly(A) RNA strand reads, of which ~800,000 have a read quality of 7, with an average of N50 read length (defined as the shortest read length needed to cover 50% of the sequenced nucleotides) of 850 bases and a median length of ~670 bases . Also, the reads with mapping quality score of 20 or higher which corresponds to the probability of correct alignment of 99% or higher were used for the downstream analysis. The second potential source of error is the presence of single-nucleotide polymorphisms (SNPs), whereby the base is different from the reference genome. We identified likely SNP sites based on an equivalent U-to-C mismatch percentage in both the IVT and the direct RNA sequencing samples 23, PARP4 (chr13:24426505)23, PSMB2 (chr1:35603333)23, MCM5 (chr22:35424407)17, and PABPC4 (chr1:39565149)17, representing a range of different k-mers with a putative ψ in the center nucleotide . We chose a range of k-mers because specific sequences can influence the accuracy of base-calling : 96.06 ± 1.16%, PARP4 (chr13:24426505): 91.71 ± 7.56%, PSMB2 (chr1:35603333): 81.07 ± 1.68%, MCM5 (chr22:35424407): 54.82 ± 4.96%, PABPC4 (chr1:39565149): 55.08 ± 3.97%). We confirmed that the IVT samples maintained the standard base-calling error at each site . For each of these positions, we calculated the U-to-C mismatch percentage, the number of aligned reads, and analyzed the surrounding bases of each site. We tabulated their 5 mers for which the target uridine site falls in the center. As expected, higher error rates were observed among low coverage sites Fig. . In addiIt is important to ensure that the targets are not selected based on errors from other sources such as SNPs, basecalling, or alignment. In the cases that the IVT error at a specific position is higher than the average error of that k-mer, the mismatch error from the direct RNA reads is compared with error at the specific site rather than with the average error of that k-mer in IVT. To account for standard basecalling errors, we compare the direct reads to the IVT replicate, using the highest U-to-C error at that site in the direct reads.p < 0.001; Fig. Human rRNA has been extensively annotated using mass spectroscopy and is commonly used to benchmark the specificity of RNA modification. We generated and analyzed direct rRNA and IVT rRNA libraries from HeLa cells. A total of 43 previously annotated rRNA positions from the 18 S and 5.8 S subunits had sufficient coverage for analysis, while the 28 S in the large subunit was omitted from analysis due to insufficient coverage. Of these sites, 38 (88.4%) were detected as ψ  have 1–4 mismatch errors in the direct RNA samples as compared to the IVT controls.Due to the extensive annotation and quantification of ψ sites on rRNA using mass spectrometry, they are considered the gold standard in the field for RNA modification detection and quantification. However, the distance between known modifications falls within the 12-nucleotide window of the biological nanopore, thus likely disrupting expected signal patterns, as shown in Fig. 18 and RNA bisulfite23 , RPL18A (chr19:17862095), CTSA (chr20:45897784), and SLC25A1 (chr22:19177964), each of which had a low mismatch error in direct RNA sequencing. Additionally, FAM168B (chr2:131049504) had high error in the corresponding IVT control, indicative of k-mer specific noise within those sequence contexts.Additional analysis of the positions that were previously annotated by 2 or more independent biochemical methods revealed only 5 positions that have sufficient coverage for analysis but were not identified as having a ψ by our algorithm Fig. . These p10. To minimize the inclusion of sites of random error, we calculated significance based on the higher error of two replicates of analysis. We also required that two out of three direct replicates have the p ≤ 0.01 to be defined as a site of uridine modification. Using this algorithm, we detected 1691 uridine-modification sites (p < 0.01), including 730 positions with a p value cutoff of 0.001 , and possibly Um (2′-O-methyl uridine) as uridine modifications that can occur in human transcriptomep < 0.001 .We characterized the distribution pattern of sites that are most likely ψ modifications on mature mRNA transcripts and observed that ~60% of them are located on the 3′ untranslated region (UTR) and 35% on the coding sequence (CDS), with very few targets detected in the 5′ UTR Fig. . The limite Fig. . Interesp < 0.001). Prior to extracting the distance of the nearest splice junction for each target, we used the RNA isoform analysis tool, FLAIR35 to bin the reads comprising high confidence modified targets into their respective dominant isoform. Overall, targets in the 3′ UTRs are separated from a splice site by a longer distance relative to targets in CDS  were annotated as ψ by two or more previous methods and the other three targets  were detected de novo using the U-to-C mismatch error and our p value cutoff. For each site, we constructed a pair of RNA transcripts, in which the center position of the k-mer is either a uridine or a pseudouridine. We ran these synthetic controls through the nanopore directly and measured the U-to-C mismatch error for each. If the mismatch error were a perfect proxy for ψ, we expected to see 100% U-to-C mismatch in these synthetic controls. In contrast, we observed 38.17% U-to-C mismatch error for PSMB2, 32.16% for MCM5, 69.64% for PRPSAP1, 69.35% % for MRPS14, and 30.08% for PTTG1P , IDI1(chr10:1044099), GTF3C2(chr2:196789267), RHBDD2 (chr7:75888787), HSPD1 (chr2:197486726) that show close to 100% mismatch error . The k-mer UGUAG, representing the PUS7 motif, is also prominent (5/105 sites around 4.8%). In contrast, the k-mers UGUAG , GUUCN, and all others occurred at a similar frequency as the most abundant not-hypermodified targets .We then assessed the k-mer frequencies for detected positions with a U-to-C mismatch error at >40% Fig.  and thosWe assessed the location of putative ψ on the transcript and found that type I hypermodified sites are biased toward 3′ UTRs, which is the same as sites that are not hypermodified , we identified 104 mRNAs with modifications at 2 unique positions, 27 with 3 positions, 4 with 4 positions, 5 with 5 positions, 1 with 6 positions and 1 mRNA with 7 positions , as expected because this error is highly dependent on the k-mer sequence. To demonstrate future applications of long-read sequencing for assessing modifications on the same transcript, we plotted every read for two mRNAs (CHTOP and PABPC4) and labeled each site using the called base . Nonetheless, many of these targets are likely ψ due to the enrichment of k-mer sequences that are motifs for ψ synthases PUS7 and TRUB1. Further studies are necessary to resolve this issue.11 or Um9 that can occur in mRNA. The estimated frequency and stoichiometry of DHU in eukaryotic mRNAs is low (<0.01%10) relative to ψ (0.2–0.6%3), but this modification may also fall within uridine-rich tracks10. Therefore, we recommend that these modified uridine sites be further explored as putative DHU modifications using appropriate DHU-seq10. While the frequency of Um is comparable to Ψ (0.15%9), we performed a meta-analysis of known Um sites from HeLa cells9 and found no overlap with the sites called from our algorithm and de novo analysis (data not shown), indicating that the U-to-C basecalling in nanopore sequencing likely does not report on Um. A better discrimination among different types of uridine modifications occurring as a U-to-C mismatch from direct RNA nanopore sequencing will require additional synthetic controls and improved machine-learning algorithms. Parallel analysis of a transcriptome of interest with the transcriptome of a cell line lacking or reducing the expression of one of the ψ synthases will help to assign a specific modification de novo for putative Ψ sites29.We note that our approach cannot rule out other types of uridine modifications, such as DHU31 for accurate calling of ψ modifications by direct RNA sequencing. To account for the contributions of long-range interactions, we have validated our method by analysis of five synthetic 1000 mers, each containing a site-specific ψ found within a natural target sequence in the human transcriptome. We find that the U-to-C basecalling error is systematically under-called at the modified site. Based on this finding, we defined hypermodification type I as sites that have >40% U-to-C mismatch error with the rationale that higher modification frequencies are more likely to have biological significance. We also define hypermodification type II as mRNAs bearing multiple U modification sites in a specific transcript. Finally, we show that U modifications can occur up to seven times on a single transcript.Previous studies have demonstrated the importance of long-range interactions31, all Us were modified in those studies and consequently, these are not the ideal controls for detection of single ψ modifications within the natural sequence contexts as demonstrated in the comparison of rRNA modifications to mRNA modifications . While high-throughput controls have previously been generatedons Fig. . In addions Fig. . AlthougThe synthetic controls that we have generated demonstrate that the basecalling error is reliable in the calling of ψ at a given site. By setting a cutoff in U-to-C mismatch for a given site, we conservatively draw a list of high-confidence sites that are pseudouridylated with high occupancy, and thus, have a higher likelihood of leading to a measurable phenotype in the cell and conferring a functional impact on the cellular physiology. Our work serves as a foundation for detection and analysis of ψ and other uridine modifications on mRNAs with sequence specificity and single-molecule resolution. Future work should include an expansion of synthetic controls and training of an updated basecaller to improve our ability to distinguish, validate and quantify U modifications in transcriptomes.HeLa cells were cultured in Dulbecco’s modified Eagle’s medium , supplemented with 10% Fetal Bovine Serum  and 1% Penicillin-Streptomycin . To extract sufficient poly-A RNA, three confluent, 10 cm dishes were used for each experiment.G for 15 min at 4 °C and 0.4 ml of aqueous supernatant was transferred to a fresh LoBind Eppendorf tube and an equal volume of 70% ethanol was added to the solution followed by vortexing. In the following steps, PureLink RNA Mini Kit  and the protocol were performed according to the manufacturer’s recommended protocol. Briefly, the solution was transferred to a pure link silica spin column and flow-through was discarded (every two microtubes were loaded on one column). The columns were washed with 0.7 ml of wash buffer I once and then with 0.5 ml wash buffer II twice. The total RNA was eluted using 50 µl nuclease-free water. The RNA concentration was measured using a NanoDrop 2000/2000c Spectrophotometer.The total RNA extraction protocol was performed using a method that is the combination of total RNA extraction using TRIzol  and PureLink RNA Mini Kit . Cells were washed with 3 ml ice-cold PBS and added with 2 ml of TRIzol in each 10 cm dish and incubated at room temperature for 5 min. Every 1 ml of lysed cells in TRIzol was transferred to a LoBind Eppendorf tube and vortexed for 30 s and added with 200 µl chloroform  in each tube and mixed by shaking for 15 s and incubated at room temperature for 3 min. Then the samples were centrifuged at 12000 × NEBNext Poly(A) mRNA Magnetic Isolation Module (E7490L) was used to select poly(A) mRNA. The protocol was followed according to the manufacturer’s protocol. The only modification was pooling 5 samples and performing the experiment in microtubes instead of PCR tubes. A total of 15 samples (3 microtubes) was used in each experiment to get enough Poly-A RNA product. The products were eluted from the NEBNext polyA magnetic isolation  in Tris buffer. The three samples were pooled and ethanol precipitated to get to the concentration that is required for the sequencing step.Primers used are listed in Supplementary Data For rRNA IVT, total RNA was poly-A tailed using E. coli Poly(A) Polymerase  and purified using RNA Cleanup kit  then poly-A selected using NEBNext polyA magnetic isolation . The poly-A tailed total RNA (50 ng) was then IVT according to the above protocol.We constructed five synthetic 1000 mer RNA oligos, each with a site-specifically placed k-mer. Two versions of each RNA were prepared, one with 100% uridine and the other with 100% ψ at the central position of the k-mer. The uridine-containing RNAs were prepared by T7 transcription from G-block DNAs (synthesized by Integrated DNA Technologies), whereas the ψ-containing RNAs were prepared by ligation of left and right RNA arms (each 500 nts in length) to a 15 mer RNA bearing a ψ in the central position (synthesized by GeneLink). A T7 promoter sequence with an extra three guanines was added to all the DNA products to facilitate in vitro transcription. In addition, a 10 nt region within 30 nt distance of ψ was replaced by a barcode sequence to allow parallel sequencing of the uridine- and ψ-containing samples. Finally, each left arm was transcribed with a 3′ HDV ribozyme that self-cleaved to generate a homogeneous 3′-end. Full-length RNA ligation products were purified using biotinylated affinity primers that were complementary to both the left and right arms.TM III Reverse transcriptase . The product was purified using 1.8× Agencourt RNAClean XP beads, washed with 70% ethanol and eluted in nuclease-free water. Then the RNA: DNA hybrid was ligated to RNA adapter (RMX) and purified with 1× Agencourt RNAClean XP beads and washed twice with wash buffer (WSB) and finally eluted in elution buffer (ELB). The FLO-MIN106D was primed according to the manufacturer’s protocol. The eluate was mixed with an RNA running buffer and loaded to the flow cell. MinKnow (19.12.5) was used to perform sequencing. Three replicates from different passages and different flow cells were used for each biological replicate. For Direct rRNA library preparation, total RNA was poly-A tailed using E. coli Poly(A) Polymerase  and purified using RNA Cleanup kit  as in the above protocol.The RNA library for Direct RNA sequencing (SQK-RNA002) was prepared following the ONT direct RNA sequencing protocol version DRCE_9080_v2_revH_14Aug2019. Briefly, 500 ng poly-A RNA or poly-A tailed IVT RNA was ligated to the ONT RT adaptor (RTA) using T4 DNA Ligase . Then the product was reverse transcribed using SuperScriptMulti-fast5 files were basecalled in real-time by guppy (3.2.10) using the high accuracy model.Then, the reads were aligned to the genome version hg38 using minimap 2 (2.17) with the option “-ax splice -uf -k14”. The sam files were converted to bam using samtools (2.8.13). Bam files were sorted by samtools sort and indexed using samtools index and visualized using IGV (2.8.13). The bam files were sliced using samtools view and the signal intensities were extracted using nanopolish eventalign (0.13.2).39. The sequence motifs are generated by kpLogo website40.Gene ontology (GO) analysis of Molecular Function 2021 was performed using enrichr websiteRsamtools package. Mismatch frequency was then calculated for a list of verified ψ sites. We observed that U-to-C mismatch frequency shows a better separation between the modified (IVT) and  modified (Direct) samples.A summary of the base calls of aligned reads to the reference sequence was obtained using the We know from our control sample that U-to-C mismatch frequency depends on both the molecular sequence and coverage Fig. . TherefoAll experiments were performed in multiple, independent experiments, as indicated in figure legends. All statistics and tests are described fully in the text or figure legend.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


Plasmodiophora brassicae, an obligate intracellular pathogen, can hijack the host’s carbohydrates for survival. When the host plant is infected by P. brassicae, a large amount of soluble sugar accumulates in the roots, especially glucose, which probably facilitates the development of this pathogen. Although a complete glycolytic and tricarboxylic acid cycle (TCA) cycle existed in P. brassicae, very little information about the hexose transport system has been reported. In this study, we screened 17 putative sugar transporters based on information about their typical domains. The structure of these transporters showed a lot of variation compared with that of other organisms, especially the number of transmembrane helices (TMHs). Phylogenetic analysis indicated that these sugar transporters were far from the evolutionary relationship of other organisms and were unique in P. brassicae. The hexose transport activity assay indicated that eight transporters transported glucose or fructose and could restore the growth of yeast strain EBY.VW4000, which was deficient in hexose transport. The expression level of these glucose transporters was significantly upregulated at the late inoculation time when resting spores and galls were developing and a large amount of energy was needed. Our study provides new insights into the mechanism of P. brassicae survival in host cells by hijacking and utilizing the carbohydrates of the host. Plasmodiophora brassicae, an intracellular obligate biotroph, is dependent on the nutrients of its host and establishes a long-term feeding relationship by stimulating the early steps of phloem differentiation and phloem-specific expression of sugar transporters within developing galls [P. brassicae genome showed a complete glycolytic and tricarboxylic acid cycle (TCA) cycle, indicating a capacity for the utilization of carbohydrates from the host for energy production. In addition, a unique set of potential sugar transporters has been predicted in the P. brassicae genome [P. brassicae may possess a sugar transporter system.The uptake of sugar across the boundary membrane is a primary event in the nutrition of all eukaryotic cells . The regng galls . Inspecte genome . Hence, Plasmodiophora brassicae is still disputed over its classification status, which is currently generally considered a protist [Polymyxa and Spongospora, Plasmodiophora comprises the order Plasmodiophorida, which belongs to the class Phytomyxea (plasmodiophorids) [Plasmodiophorabrassicae has a complex life cycle, generally comprising three stages: survival in the soil, primary infection of root hairs, and cortical secondary infection. Members of the Brassicaceae family are thought to be potential hosts for P. brassicae, parasitizing the host root cell and covering it with a cystic structure to facilitate nutrition exchange between P. brassicae and its host. protist . Along wphorids) ,8. Currephorids) ,10. PlasPuccinia striiformis f. sp. tritici (Pst) colonizes the mesophyll cells and the apoplast of wheat leaves to obtain carbohydrates for energy and carbon [Ustilago maydis causes changes in carbohydrate metabolism in the infected maize leaves [U. maydis susceptibility [Verticillium dahlia, Fusarium oxysporum, Phytophthora infestan, and Rhizoctonia solani, was affected by changes in the sugar metabolism of the host plant [P. brassicae, several studies have reported that soluble sugars (hexose and sucrose) and starch accumulate in the galls of P. brassicae-infected plants, and the development of galls is associated with altered carbohydrate metabolism and partitioning in host plants [Sugar is the main product of photosynthesis, it regulates plant metabolism and acts as a metabolic substrate and signaling molecule ,12. Sucrd carbon . The fune leaves . Notablytibility . The patst plant ,18,19,20t plants ,22. Sugat plants . Furthert plants . Sugar ut plants .SLC50A1 gene, excluding Caenorhabditis elegans, which contains 7 genes [Saccharomyces cerevisiae. A total of 20 genes encoding hexose transporters belonging to the MFS superfamily have been identified in S. cerevisiae, and 18 genes including Hxt1p-Hxt17p and Gal2p function as the transport of glucose, fructose, and mannose. Snf3p and Rgt2p act as glucose sensors to regulate the expression of hexose transporters [AmMst1, AmMst2) from Amanita muscaria [Tmelhxt1, Tmel2281) from Tuber melanosporum [Laccaria bicolor, which function in carbon assimilation [P. brassicae obligately parasitizes the roots of host plants, and plants transport photosynthetic products to the roots, and these products are hijacked by P. brassicae. Thus, it is unclear whether P. brassicae has the same hexose transport system as other fungi.Sugar transporters, including three common sugar transporter families, namely sucrose transporters (SUTs) , monosac 7 genes ,31,32,33sporters ,35,36. Amuscaria , two hignosporum , and 15 milation . Unlike P. brassicae is limited. To bridge this gap, we used data obtained from the sequenced genome of P. brassicae in the National Center for Biotechnology Information (NCBI) database [hxt-null S. cerevisiae strain EBYVW.4000 [Thus far, our knowledge of sugar transporters in database ,6,40,41 YVW.4000  to verifTo determine if the clubroot formation affects soluble sugar content through the carbohydrate metabolism of the host plants, the soluble sugar content  of the roots was analyzed at four different stages after infection (from the second week to the fifth week). Among these, sucrose is the predominant soluble sugar in the roots, followed by glucose and fructose. From the second to the fourth week after inoculation, the sucrose and glucose content did not change significantly, while the fructose content gradually increased. In addition, the sucrose and fructose content was not affected between the infected plants and healthy plants. However, the glucose content increased greatly in the roots of infected plants, which was significantly higher than that of non-infected plants at 5 weeks post-inoculation .P. brassicae genome through a BLAST search and Pfam domain analysis. The selected proteins containing the sugar transporter signature belong to the Sugar_tr (PF00083), MtN3_slv (PF03083), or PQ-loop (PF04193) domains  contained only four or five TMHs (SUTs and MSTs containing the “Sugar_tr” domain (PF00083) belong to the MFS, which usually contains 12 TMHs . SWEETs,ive TMHs a.P. brassicae sugar transporters were submitted to the online MEME program for analysis of the conserved motifs, and the motif sequences and annotations were further predicted by Pfam. Proteins with “Sugar_tr”, “MtN3_slv”, or “PQ-loop” domains were selected for motif analysis. The four proteins with the Sugar_tr domain contained 4–5 conserved motifs, the length of which ranged from 20 to 38 amino acids, and the Sugar_tr domain was predicted in motifs 1, 2, 3, and 4. Furthermore, three motifs  exhibited high conservation. The protein motif distributions of Pbhxt3 and Pbhxt4 were similar, both of which contained motif 1 in the N-terminal region and motifs 3, 2, and 4 in the C-terminal region . Therefore, only 36 protein sequences were employed to construct the phylogenetic tree (Pelomyxa schiedti (KAH3767290.1) and Leiotrametes lactinea (KAH9893501.1), respectively. PQ-10 clustered with the cleft lip and palate transmembrane protein of Phoenix dactylifera (XP 008786461.2) and the CLPTM1-like membrane protein of Arachis ipaensis (XP 016169702.1). In the second group, PQ-7 clustered with the lysosomal Cystine transporter of Spizellomyces punctatus (XP 016608459.1). Interestingly, PQ-9 and PQ-12 were in the third group, without any homologous proteins from other organisms. All MFS family proteins of P. brassicae, including PbHxt1, PbHxt2, PbHxt3, and PbHxt4, were grouped into the fourth group. PbHxt1 and PbHxt2 were homologous with other MFS transporters of Chloroflexi bacterium (RPJ49763.1) and Anaerolineae bacterium (MBI4926876.1). PbHXt4 clustered with some substrate transporters and hexose transporters of fungi or water bears . Based on the above analysis, only PbHXt4 clustered with a characterized hexose transporter and sugar transporter . Considering the lower identity of homologous sequences, these sugar transporters of P. brassicae were probably unique, and sugar transporter activity requires further validation.To better explore the phylogenetic relationship of the sugar transporters identified in tic tree . These hP. brassicae sugar transporters, hexose uptake was evaluated through the heterologous expression of these putative transporters in the hexose transport-deficient yeast strain EBY.VW4000. As shown in To verify the functionality of the P. brassicae. Six of eight glucose transporters, namely PQ-3, PQ-4, PQ-5, PQ-7, PQ-9, and PQ-13, were increased at 4 weeks post-inoculation but were still significantly higher than that at 3 weeks post-inoculation. Only the expression of PbHxt3 and PQ-12 showed no significant differences in the fourth week. These results indicate that sugar transport activity was positively correlated with the expression of related genes during the formation of galls  and sugar permeases were induced during gall formation of P. brassicae, and cytoplasmic invertase mutant cinv1,2 had resistance to clubroot disease at 26 days post-inoculation (DPI), indicating that P. brassicae formed a new sink [Arabidopsis thaliana sweet11, 12 double mutants exhibited delayed P. brassicae development due to impaired sugar transport activity toward the pathogen [P. brassicae-infected Brassica rapa [P. brassicae, and glucose is preferentially utilized. This view has been shown in previous studies to favor hexose over sucrose uptake by fungal biotrophs [in vitro ,44. The in vitro ,46. Trannew sink . Solublenew sink . Arabidoica rapa . At the iotrophs ,51,52,53P. brassicae to take up carbon nutrients, but this mechanism is unclear. To elucidate these mechanisms in P. brassicae, we took advantage of the genome sequencing data of P. brassicae in the NCBI database to identify sugar transporters. A total of 17 potential sugar transporter genes were identified according to the annotations. Four proteins contained sugar_tr (PF00083) domains belonging to the MFS. MFS transporters usually contain 12 TMHs [Trichoderma reesei [Vitis vinifera [MtN3/saliva domains were identified. Thus, these proteins may encode SWEET transporters in P. brassicae. Our analyses revealed that PQ-loop transporters of P. brassicae contain a diverse number of TMHs  of the protist supergroup Rhizaria [P. brassicae to identify highly homologous sugar transporters from other species. However, most of the proteins of P. brassicae failed to cluster with known sugar transporters from other species. In addition, the alignment results of the protein sequences showed very low similarity. This suggests that these proteins are unique to the P. brassicae genome.Many microbial sugar transporters have been identified and functionally validated, such as in revisiae , Colletoorioides , ectomycnosporum , Bradyrhaponicum , arbuscuaponicum , and Pseri A1501 . NeverthRhizaria . We perfP. brassicae sugar transporters with glucose and fructose transport activities and to determine whether they have sucrose transport activity. We next plan to further validate the function of these P. brassicae sugar transporters using a Förster resonance energy transfer (FRET) sensor-based screen [P. brassicae glucose transporters. Previous studies have found that a soluble disaccharide, trehalose, which is synthesized from glucose as a substrate by the trehalose-6-phosphate synthase gene (TPS), is abundant in the resting spores of Brassica plants, and the accumulation pattern of trehalose correlated with the PbTPS1 gene from P. brassicae [P. brassicae may be used to synthesize trehalose, which needs to be verified in the future.Hexose-deficient yeast EBY.VW4000 has been used in many studies to verify whether the sugar transporter has hexose transport activity ,65. In td screen . The relrassicae . The gluBrassica rapa during clubroot development, and 17 potential sugar transporters were identified. Eight upregulated candidate sugar transporters transported glucose with the development of P. brassicae. Based on the above results, we propose that P. brassicae may possess a transporter system to transport host plant sugars from the apoplast into the cytoplasm through its own sugar transporters.In conclusion, a large amount of glucose was accumulated in the roots of BJN3-2’. The plants were maintained in the culture room under a 16 h light/8 h dark photoperiod at 25 °C. A single-spore isolate (Pb4) of P. brassicae was applied. The resting spores were collected from homogenized clubbed roots and diluted to a density of 1 × 107 spores/mL with sterile distilled water until inoculation. The 2-week-old seedlings of Chinese cabbage were inoculated with P. brassicae by injection, and 1 mL of a resting spore suspension was injected into the soil near the hypocotyl for each plant. The roots of cabbage were sampled at 1, 2, 3, 4, and 5 weeks after inoculation and used for the determination of soluble sugar content and gene expression analysis. Non-inoculated plants were used as the control. Each treatment was carried out with three biological replicates, and each replicate contained six plants. This experiment was carried out in the greenhouse of Shenyang Agricultural University in 2021.The plant material used as a host was the Chinese cabbage susceptible variety ‘P. brassicae infection. The soluble sugar was extracted using a method reported previously [Soluble sugars were extracted from the roots of Chinese cabbage sampled at 2, 3, 4, and 5 weeks after eviously . The col−ΔΔCt [The total RNA was extracted from the root using TRIZOL reagent , following the manufacturer’s guidance. Reverse transcription was performed using a Thermo Scientific ReverAid Kit. Quantitative real-time RT-PCR was performed using the SYBR Green Premix system . PCR reactions were carried out in triplicate with three independent RNA samples, and the primers are listed in −ΔΔCt  method wsugar-tr”, “MtN3_slv”, or “PQ-loop” from the whole genome sequencing data of P. brassicae in the National Center for Biotechnology Information database (NCBI)  [Putative sugar transporters were screened according to the keywords “ry 2021) ,40,41,68http://pfam.xfam.org/, accessed on 14 April 2022). Redundant sequences and fragment sequences were removed. Proteins with “sugar-tr” domains were named Pbhxt1–Pbhxt4, and those with PQ-loop or MtN3_slv domains were named PQ-1 to PQ-13. The protein molecular weight (kDa) and isoelectric points (pI) were calculated according to the online ExPASy2 database .Proteins including Sugar_tr (PF00083), MtN3_slv(PF03083), and PQ-loop (PF04193) domains were selected and used for further domain verification by the Pfam database  was utilized to forecast the protein structure of sugar transporters in P. brassicae. The online MEME tool  was used to identify conserved protein motifs of putative sugar transporters [The online tool TMHMM Server 2.0 sporters . MEME waE value < 1 × 10−20; query coverage, 60%) were selected to construct the phylogenetic tree [ClastalW , the unrooted phylogenetic tree was constructed by MEGA6 with a bootstrap of 1000 replicates using the neighbor-joining (NJ) method, and Poisson correction was chosen as the distance parameter.Each protein sequence was subjected to protein BLAST in NCBI’s nr (Non-Redundant Protein Sequence Database). Proteins from other organisms having a BLASTP hit , following the manufacturer’s instructions. AtSWEET1 was used as a positive control for glucose uptake, and AtSWEET16 was used as a positive control for fructose uptake. Empty vector pDR195 was used as a negative control. The constructed expression vector was transferred to the hexose transport-deficient yeast strain EBY.VW4000, which cannot grow on hexose medium due to concurrent knockout of 20 endogenous transporter genes, but can grow on maltose medium. Transformants of the EBY.VW4000 strain were grown on liquid synthetic deficient (SD)/-uracil media supplemented with 2% maltose. For complementation growth assays, the concentration of EBY.VW4000 yeast cell suspension was adjusted to OD600 = 0.5. Serial dilutions  were plated dropwise on solid SD/-uracil medium consisting of 2% maltose and 2% glucose/fructose. These plates were grown at 30 °C for 3 days and used for imaging.The yeast expression vector pDR195 was applied to the analysis of the hexose transport function. The CDSs of these sugar transporter genes with t-tests at * p < 0.01 or ** p < 0.05 with the SPSS statistical software . The data were graphically analyzed using GraphPad Prism V8.0.2 .The data on sugar content are presented as the means ± SEs (standard errors). Other statistical evaluations and significance tests were performed via Student’s


Inflammatory bowel disease (IBD) is a chronic inflammatory disorder characterized by damage to the intestinal mucosa, which is caused by a combination of factors. These include genetic and epigenetic alterations, environmental influence, microorganism interactions, and immune conditions. Some populations with IBD show a cancer-prone phenotype. Recent studies have provided insight into the involvement of RNA modifications in the specific pathogenesis of IBD through regulation of RNA biology in epithelial and immune cells. Studies of several RNA modification-targeting reagents have shown preferable outcomes in patients with colitis. Here, we note a new awareness of RNA modification in the targeting of IBD and related diseases, which will contribute to early diagnosis, disease monitoring, and possible control by innovative therapeutic approaches. NOD2), interleukin-23 receptor (IL23R), caspase recruitment domain family member 9 (CARD9), E3 ubiquitin-protein ligase, ring finger protein 186 [RNF186], and adenylate cyclase 7 (ADCY7)  characterized by damage to the intestinal mucosa, which is believed to be caused by a combination of factors, including abnormal immunity of the intestinal mucosa . In acco ADCY7) . Second, (ADCY7) . Third,  . Second5C) in a study of a nucleic acid constituent in crystalline picrate from hydrolyzed tuberculinic acid [Escherichia coli, yeast, rabbit liver, wheat germ, and viruses, which indicate that a methylated thymine base and three methylated adenine bases are generated in RNA [In 1925, around 40 years before the discovery of the DNA double helix structure by Watson and Crick , who recnic acid . This finic acid  and of bnic acid ,14,15. Id in RNA . In 1958d in RNA .6A methylation of CD44 mRNA, a cancer stem-like cell marker, to enhance the formation of CD44v3, a splicing variant. This led to the development of biologically malignant cell phenotypes, such as vascular invasion and distant metastasis in hepatocellular carcinoma, suggesting an association between RNA modification and NNMT [As a biochemical base of RNA methylation, S-adenosyl methionine (SAM) was identified as an active methyl donor in 1952  and was and NNMT . Furtherand NNMT . Furtherand NNMT . It was 5C, m6A marks are the most prevalent RNA modifications followed by N1-methyladenosine (m1A), m5C, and 7-methylguanosine (m7G) [1A modification is analogous to m6A, which indicates that their molecular mass is exactly the same and that they cannot be discriminated by mass spectrometry, while m7G provides the critical structure of the cap site of mRNA [6A on microRNAs (miRNAs), which are small non-coding RNAs regulating gene expression, is useful for the detection of early-stage and relapsing cancers [In contrast to DNA modifications, which predominantly occur on mne (m7G) . The m1A of mRNA . These e of mRNA . Further cancers . These s6A, which is the most prevalent epitranscriptomic marks on human transcriptome, is catalyzed by N6-adenosine-methyltransferase complex catalytic subunit (METTL3) and methyltransferase-like 14 (METTL14) by forming a stable heterodimer core complex [6A marks [6A reader YTHDF1 is controlled by the oncogene c-MYC [m complex . During  complex . The cor6A marks . In cont6A marks  and α-ke6A marks . The res6A marks , and at ne c-MYC . Taken t6A modification of RNA and provided two X-ray crystal structures of Mycoplasma capricolum TrmM with and without S-adenosyl-l-homocysteine [Given that next generation sequencers cannot detect chemical modifications without base substitutions of RNAs, alternative technologies, such as the mass spectrometry (MS) and tunneling-current-based sequencing, are considered as important methods for the analysis of modified nucleosides, especially for miRNAs and tRNAs that play crucial roles in mRNA translation and decoding . Recentlcysteine . Althougcysteine , severalcysteine . Moreovecysteine . Taken tAmong DNA repeats in telomeres within chromosomes, the guanine-rich strand (5′-TTAGGG-3′) is lined with four guanines. This strand forms a planar structure called, G-quartet, in the presence of monovalent ions, such as K+, in addition to the double helix . The G-qRecent studies have indicated the activity of DNA G4 structures as small molecules against breast cancer by targeting the promoter of the human oncogene c-MYC and telomeres . These sRecent studies have indicated that alterations in chemical modifications  in transfer RNA (tRNA)-derived fragments, such as tRNA-derived RNA fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs), are emerging as new research hotspots . These a5C, 2-thiouridine, and m1A) suppress PKR activation in cells, whereas unmodified or lightly modified (fewer than three nucleosides per one tRNA copy) tRNA can activate PKR [As a member of the eukaryotic translation initiation factor 2A (eIF2α) family of protein kinases that inhibit translational initiation in response to stress stimuli, protein kinase RNA-dependent (PKR) functions as a key mediator of the interferon-induced antiviral response . After Pvate PKR ,57. ThesPrevious reports demonstrated that aberrant processing of human tRNAs can induce a mucosal immune response. For example, sideroblastic anemia with B-cell immunodeficiency, fever, and developmental delay (SIFD) is an inherited recessive disease with a mutation in tRNA nucleotidyltransferase 1 (TRNT1) . It has miR-1280, a fragment of tRNALeu and pre-miRNA, suppresses stem cell-like cells and metastasis by repressing the jagged canonical notch ligand 2 (JAG2)-notch signaling pathway [miR-1280-related tumors were associated with mucosal immunity via innate lymphoid cells, including natural killer (NK) cells and innate lymphoid cells , which are innate immune system cells derived from common lymphoid progenitor cells. These cells secrete signaling molecules in response to pathogenic tissue damage and control both innate and adaptive immune responses [Recent studies have indicated that tRFs regulate cell proliferation as tRFs are fragments of tRNAs and share some similarities with other nucleotides, such as miRNAs. The biological functions of tRFs include translational regulation and gene silencing, which are critical processes in the occurrence and development of inflammation and cancers . Recentl pathway , suggestesponses . It was esponses . It is p6A mRNA methylation in susceptibility to IBD. Interestingly, given that single nucleotide polymorphisms (SNPs) located near or within m6A motifs have been proposed as possible contributors to disease, a recent study identified five candidate genes corresponding to two of the major IBD subtypes: ubiquitin-conjugating enzyme E2L3 (UBE2L3) and solute carrier family 22 member 4 (SLC22A4) for Crohn’s disease and transcription factor 19 (TCF19), chromosome 6 open reading frame 47 (C6orf47), and small nuclear RNA-activating complex polypeptide 4 (SNAPC4) for ulcerative colitis [6A modifications is altered in IBD, while m6A regulators displayed extensive differential expression, including that of some shared common differentially expressed genes [6A marks.Recent studies have revealed the implication of m colitis . UBE2L3  colitis . SLC22A4 colitis , while S colitis . Anothered genes . However6A, m1A, m5C, and m7G are the major forms of RNA methylation modifications, a recent study presented evidence of epitranscriptomic marks on long non-coding RNAs  in colorectal cancers and described a methylation-related lncRNA signature for predicting “hot tumors” with a better prognosis and “cold tumors” with a poorer prognosis [AC156455.1 and ZEB1-AS1, were increased in the high-risk group of cervical cancer. A signature of seven m6A-related lncRNAs can independently predict disease prognosis and is closely associated with immune cell infiltration, suggesting new therapeutic targets for disease control [Given that mrognosis . Another control . AlthougReported as variations with respect to the reference sequence of miRNAs in the miRbase database , isomiRsAlthough extensive efforts have been made to investigate epigenetic modifications in IBD to clarify how epigenetic mechanisms regulate IBD , little 6A) is an important modulator of RNA stability and decay as well as protein translation on ribosomes. Recent studies have indicated that the m6A modification is essential for maintaining homeostatic self-renewal of colonic stem cells by the regulatory function of the methyltransferase 14 (Mettl14) gene in mice [6A enzymes in multiple physiological and pathological processes. It is suggested that the m6A modification may be a potential therapeutic target in IBD, considering that oncogenic signals, such as PI(3)K-AKT-mTOR pathway and RAS-RAF-MEK-ERK MAPK pathway, can be control by a m6A modification-dependent translation, and the m6A mRNA modification maintains epithelial cell homeostasis via NF-κB-mediated antiapoptotic pathway in bowel [6A-related genes and regulatory factors are expected to be potential predictive markers and therapeutic targets against inflammation and cancer [mRNA modification of adenosine  activation and T-cell homeostatic proliferation and differentiation [Citrobacter rodentium-mediated colitis-induced intestinal disease, suggesting the potential importance of STAT5 in other idiopathic forms of colitis, such as IBD [The involvement of RNA modifications in T-cell homeostasis was first reported in a study of the IL-7/STAT5/SOCS pathways . This stntiation . It has ntiation . In addintiation . The opth as IBD . These s6A demethylases contribute to the efficacy of immunotherapy and identify ALKBH5 as a potential therapeutic target to enhance immunotherapy outcomes in melanoma and colorectal cancer [It was reported that RNA modifications regulate immune responses . One stul cancer . It is sl cancer  (Table 2The chemical modifications without base substitutions of RNAs cannot be detected by standard next generation sequencers, and thus, a special sequencing technique suitable for this purpose is required. The results of previous studies strongly suggest that RNA modifications play specific roles in the pathophysiology of IBD. To elucidate this mechanism and develop breakthrough diagnostics and innovative therapies, it is necessary to clarify the phenomenon of RNA modifications at the single-cell level. By doing so, it is possible to enhance the effects of standard genomic medicine, molecular-targeted therapy, and cancer immunotherapy that are currently being administered and to substantially contribute to the development of precision medicine .


The global coronavirus disease 2019 (COVID-19) pandemic has led to the rapid development of vaccines against this disease. Despite the success of the international vaccination program, adverse events following vaccination, and the mechanisms behind them, remain poorly understood. Here we present four cases of death following receipt of a second dose of COVID-19 vaccine, with no obvious cause identified at autopsy. Using RNA sequencing, we identified genes that were differentially expressed between our post-vaccination cases and a control group that died of blood loss and strangulation. Three hundred and ninety genes were found to be upregulated and 115 genes were downregulated in post-vaccination cases compared with controls. Importantly, genes involved in neutrophil degranulation and cytokine signaling were upregulated. Our results suggest that immune dysregulation occurred following vaccination. Careful observation and care may be necessary if an abnormally high fever exceeding 40°C occurs after vaccination, even with antipyretic drugs. While there is growing interest in the use of vaccination for reducing disease severity, there are some reports of vaccination-related side effects, such as immune thrombocytopenia, myocarditis, and death (Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, with the number of infected cases exceeding approximately 550 million as of July 2022, defining it as a significant global health concern [WHO coronavirus (COVID-19) dashboard. Geneva: World Health Organization, nd death . Meanwhind death , 5. We eThe profiles of four cases who died at home after receiving the COVID-19 vaccine are summarized in RNA extraction from blood samples was performed using ISOGEN-LS  following the manufacturer’s protocols. Transcriptome profiling using next-generation sequencing was conducted by Azenta Japan Corp . In brief, total RNA was quantified and qualified by Qubit RNA assay  and TapeStation RNA ScreenTape . Total RNA (400 ng) was treated using an NEBNext rRNA depletion kit (Human/Mouse/Rat)  to eliminate rRNA and prepare the cDNA library. Synthesis of cDNA followed by transcriptome library preparation was conducted using the MGIEasy RNA directional library prep kit v2.0 , in which dUTP was incorporated in second-strand cDNA synthesis instead of dTTP, to block PCR amplification against the second-strand templates and enable strand-specific transcriptome profiling. A 12-cycle PCR amplification was performed to increase library yield. The resulting transcriptome sequencing libraries were quantified by Qubit DNA Assay (Thermo Fisher Scientific) and their fragment size distribution was confirmed by TapeStation D1000 ScreenTape (Agilent). The adapter sequences used in the library preparation were:forward: AAGTCGGAGGCCAAGCGGTCTTAGGAAGACAA;reverse: AAGTCGGATCGTAGCCATGTCGTTCTGTGAGCCAAGGAGTTG.The resulting double-stranded library fragments were pooled/multiplexed in equimolar amounts and further processed into single-stranded circular DNA (sscDNA), which was the final form of the MGI library. The sscDNA libraries were quantified with the Qubit ssDNA assay kit (Thermo Fisher Scientific) and used to generate DNA nanoballs (DNBs) by rolling circle replication reaction. DNBs were then loaded into a flow cell for sequencing on a DNBSEQ-G400 platform (MGI tech) with 150-bp PE configuration, in accordance with the manufacturer’s instructions. RNA sequencing data reported are available in the DDBJ Sequenced Read Archive under the accession numbers DRA013514.Quality control for the RNA sequencing raw reads were performed with FASTQC v0.11.9, and trimming was done with Trimmomatic v0.39. Read counts were estimated by using Kallisto v0.46.0 with default settings . Kallisthttp://metascape.org/gp/index.html#/main/step1) pathway analysis  paired-end (PE) reads per sample. Of the 43,751 genes with nonzero total read counts, there were 505 significantly differentially expressed genes between individuals with unknown cause of death (unknown group) and the control group. Volcano plots showed a clear difference in mRNA expression between the unknown and control groups  for editing a draft of this manuscript. This work was carried out at the Analysis Center of Life Science, Natural Science Center for Basic Research and Development, Hiroshima University.We thank Edanz (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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The analysis of biomarkers in biological fluids, also known as liquid biopsies, is seen with great potential to diagnose complex diseases such as cancer with a high sensitivity and minimal invasiveness. Although it can target any biomolecule, most liquid biopsy studies have focused on circulating nucleic acids. Historically, studies have aimed at the detection of specific mutations on cell-free DNA (cfDNA), but recently, the study of cell-free RNA (cfRNA) has gained traction. Since 2020, a handful of cfDNA tests have been approved for therapy selection by the FDA, however, no cfRNA tests are approved to date. One of the main drawbacks in the field of RNA-based liquid biopsies is the low reproducibility of the results, often caused by technical and biological variability, a lack of standardized protocols and insufficient cohorts. In this review, we will identify the main challenges and biases introduced during the different stages of biomarker discovery in liquid biopsies with cfRNA and propose solutions to minimize them. In the past few years, there has been an increased interest in finding minimally invasive methods for disease-specific biomarker detection . FollowiEven though other biofluids are of special interest for certain diseases . These are molecules of DNA shed by the tumor and present in the circulation . CtDNA bChanges in RNA expression in cells are a dynamic process that can reflect tissue damage or disease . MoreoveThe field of cell-free RNA biomarkers has mostly focused on the study of microRNAs (miRNAs) as biomarkers of disease in the circulation due to their higher stability in blood . HoweverAlthough more technically challenging, there is one main advantage associated with the study of long RNAs: the number of known long RNAs is much higher than that of known miRNAs , cell fragments and nucleic acids, among other types of biomolecules . In blooAlthough intrinsically different, there is limited information about which biofluid provides more biologically relevant information. Promising results have derived from both the study of serum and plasma. Even though not studied in depth, some groups reached opposite conclusions while attempting to quantify the differences associated with the analysis of serum vs plasma. For instance, Dufourd et al. suggested that circulating miRNA profiles of healthy subjects may not be affected by the type of biofluid studied  while otWhether using plasma or serum, a common limitation of the study of these biofluids for biomarker discovery is the release of RNAs derived from red blood cells (RBC) and/or platelets during sample processing . RegardiIn order to assess the compositional bias induced by platelet-derived EVs on the plasma transcriptome, Kim et al.  examinedWhile hemolysis controls are more common than platelet assessment, there is still a lack of consensus about the optimal steps necessary to quantify the presence of RBC RNA. In an attempt to characterize the effect of hemolysis during the study of circulating RNA biomarkers, Kirschner et al.  suggesteDespite the absence of studies performed on cfRNA, a study found that for cellular RNA freeze-thaw cycles have a detrimental effect on the quality of the RNA obtained downstream, resulting in significantly shorter fragments . AdditioIn summary, the obtention of plasma or serum from whole blood can result in contamination from different cellular sources, such as platelets or erythrocytes. Avoiding and assessing cellular contamination, with correct processing and quality controls, is a critical step in order to prevent and monitor the introduction of unwanted biases that could lead to wrong conclusions. In addition, avoiding freeze-thaw cycles and long-term storage improves the quality of the RNA extracted, leading to more robust and reproducible results.The cfRNA obtained from a blood sample represents an approximation to a snapshot of the transcriptome of the individual. However, the use of different methodologies for RNA isolation leads to biases that can mask any relevant biological information.The most common strategy for RNA isolation from biofluids is based on RNA extraction kits designed and optimized for plasma and/or serum. Commercial column-based kits are more commonly used than traditional guanidium-thiocyanate or phenol-chloroform methods. Classic methods tend to favor the isolation of selective RNA populations and often lead to reduced quantities of RNA . HoweverOne major concern regarding RNA isolation from plasma is DNA contamination. The majority of cfRNA isolation kits recover a fraction of the cfDNA present in the biofluid . During In order to limit the effect of other technical biases associated with RNA isolation, some groups have proposed the addition of exogenous RNAs as controls to compare the efficiency of the RNA extraction , 47. SpiThe quantity and quality of input RNA has a strong impact on downstream processes . HoweverDifferent strategies for RNA isolation are linked to different recovery rates and to the enrichment of selective RNA types. However, several strategies are available to minimize these biases: consistency in the extraction kit used, comparison of the RNA extraction efficiency with spike-ins, assessment of samples with low RNA quality and removal of DNA contamination.During the last decade, RNA sequencing (RNA-seq) has become the gold standard methodology for biomarker discovery. It allows the detection of known and novel transcripts and their quantification in a sample, with higher sensitivity and accuracy than other methods, such as microarrays . While yApproximately 80% of the cellular RNA is ribosomal RNA (rRNA) , and thiDuring library preparation, another step leading to the introduction of biases is adaptor ligation and PCR amplification. Due to secondary structures and enzyme affinity, some sequences are more prone to ligate to adaptor sequences and amplify than others , 58. In Following the same rationale used to evaluate biases during RNA isolation, spike-ins are also useful in measuring technical variability introduced during library preparation. Similar to the spike-ins used to assess RNA isolation, they are added at a known concentration to correct for the amplification bias during library preparation  and can Finally, another possible step of bias introduction is the sequencing of the libraries. Often, batch effects are observed when samples used in the same study are sequenced in different rounds . CommercIn summary, the main problems associated with library preparation and sequencing are PCR amplification and batch effects caused by technical variability. However, the use of UMI sequences and spike-ins to minimize this bias has become very prevalent in RNA-seq studies during the last years and has shown to increase the reproducibility of the results .One of the most challenging steps in biomarker discovery using RNA-seq is the computational analysis of the generated data. This analysis allows to correctly interpret the molecular processes that occur in the patient. Bioinformatics analysis goes from data quality control to transcript quantification and various downstream analyses. However, since the results of the analysis are closely linked to the input quality of the data, quality assessment is a critical step in this workflow .One of the most prevalent issues with the processing of raw RNA-seq data is normalization. It consists in correcting in silico for technical biases that could mask biological information . RNA-seqOnce the raw data is processed, the identification of potential biomarkers can follow two different routes: comparative analysis of cfRNA profiles and machine learning (ML) methods. The former strategy is the simplest: it consists in finding genes whose expression is associated with a phenotype. This approach attempts to determine the presence of a disease or its prognosis only from the expression of a few genes. An example of this methodology is the recent study of Rasmussen et al., where they found a signature of 7 RNAs associated with preeclampsia, a condition marked by maternal hypertension that is a significant cause of maternal morbidity. This signature has a positive predictive value of 32.3%, higher than the current clinical state-of-the-art models , presumaIn relatively small datasets, ML algorithms tend to generate models that fit artificially the initial cohort of samples, causing poor replication of the results in new cohorts of patients. This is especially prevalent in omics data, where the number of variables is very high . This prAdditionally, other research groups have proposed new approaches to enhance the reproducibility of the findings. For instance, Larson et al.  have focOn the one hand, the comparative analysis of cfRNA profiles is easier to implement in the clinical practice than more complex ML signatures due to its lower cost and improved practicality. On the other hand, ML signatures tend to have higher accuracy. Both methods are useful in obtaining signatures of high value for diagnosis and prognosis.The main issues limiting the applicability of liquid biopsies as screening and diagnostic tools in the clinic are technical and biological reproducibility biases. This is often linked to a lack of gold standard methods for sample processing and data analysis . Due to Besides the technical bias introduced during sample handling, external factors such as age or gender of the individual, have a strong effect on the cfRNA profile . To contA biological limitation of the study of cfRNA is that the interpersonal variability is very high , with soAccording to a recent market research study, the liquid biopsy industry is expected to exceed 5.8 billion dollars by 2026 , althougOne of the main challenges in the field of liquid biopsies is caused by a limitation on the number of donors in the training cohorts. Most of the studies comparing cases with controls use small retrospective cohorts to detect a disease after it is clinically reported , 89–91, Another important aspect for the incorporation of liquid biopsies into the clinical practice is the ability of the physicians to be able to interpret the results of the biomarker model. This can be an arduous task for the medical staff that is not proficient in bioinformatics and statistical analysis. For this purpose, several research centers and companies have created cloud computing pipelines that take directly the sequencing data and generate comprehensive reports , 94.The field of liquid biopsies, and more specifically cell-free long RNA liquid biopsies is promising, but still young. With a relatively reduced number of studies published, there are candidate biomarkers undergoing clinical trials, but none have been approved by the regulatory agencies at the moment. In the last few years, there has been an increasing interest in liquid biopsy-based biomarkers using RNAs. However, it has been only in the last 5 years that the focus has started to switch from miRNAs to long RNAs, leading to the discovery of new disease-associated RNAs. Although there is still much work left to do to translate long cfRNA into clinical practice, a number of recent promising results suggest that long cfRNA-based liquid biopsies could be one of the next big revolutions in the field of screening and diagnosis.


Affiliations. Instead of “Department of Cardiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China”, it should be “Department of Cardiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China”.Due to a production error, there was an error in the The publisher apologizes for this mistake. The original version of this article has been updated.


Brassica napus) is an allopolyploid hybrid (AACC genome) of turnip rape  and vegetable cabbage . Rapeseed oil is one of the main vegetable oils used worldwide for food and other technical purposes. Therefore, breeding companies worldwide are interested in developing rapeseed varieties with high yields and increased adaptation to harsh climatic conditions such as heat and prolonged drought. One approach to studying the mechanism of the epigenetically regulated stress response is to analyze the transcriptional changes it causes. In addition, comparing the expression of certain genes between stress- and non-stress-tolerant varieties will help guide breeding in the desired direction. Quantitative reverse transcription PCR (RT-qPCR) has been intensively used for gene expression analysis for several decades. However, the transfer of this method from model plants to crop species has several limitations due to the high accumulation of secondary metabolites, the higher water content in some tissues and therefore problems with their grinding and other factors. For allopolyploid rapeseed, the presence of two genomes, often with different levels of expression of homeologous genes, must also be considered. In this study, we describe the optimization of transcriptional RT-qPCR analysis of low-expression epigenetic genes in rapeseed, using Kinetochore Null2 (KNL2), a regulator of kinetochore complex assembly, as an example. We demonstrated that a combination of various factors, such as tissue homogenization and RNA extraction with TRIzol, synthesis of cDNA with gene-specific primers, and RT-qPCR in white plates, significantly increased the sensitivity of RT-qPCR for the detection of BnKNL2A and BnKNL2C gene expression.Rapeseed ( Brassica napus) is a 30–150 cm tall herbaceous plant species of the cruciferous family (Brassicaceae). It is an allopolyploid hybrid (AACC genome) from a sexual cross between turnip rape  and vegetable cabbage . The number of chromosomes is 38, including 20 from B. rapa and 18 from B. oleraceae. According to Rapeseed  plants were cultivated in a greenhouse with a 16-h light phase from 6 a.m. to 10 p.m. at a temperature of 20/18°C day/night and humidity level between 55 and 70%.BRoot tissue material, rosette leaves, stem, stem leaves, buds, flowers, pollen, siliques, young embryos, and the upper part of the seedlings were collected in liquid nitrogen and distributed in 2 mL microcentrifuge tubes with two 1/8” stainless steel beads in equal amounts of approximately 150 mg. To compare tissue disruption efficiency, homogenization was performed using two different methods: (1) by grinding the frozen tissue in an MM400 CryoMill  for 2 × 30 s at a frequency of 30 Hz; (2) by adding 100 μL TRIzol to the thawed samples before homogenization under the aforementioned conditions.® Plant Mini Kit , according to the manufacturer’s instructions. In the second method, isolation was performed using TRIzol reagent . After homogenization of the samples in 100 μL TRIzol, an additional amount of 900 μL TRIzol was added for RNA extraction. In the regular protocol, samples were mixed with 1,000 μL TRIzol after homogenization. After thorough mixing and incubation of the samples at room temperature for 5 min, further extraction was performed according to the manufacturer’s instructions. The RNA pellet was dissolved in 40 μL RNase-free MilliQ-H2O. If the pellet did not completely redissolve or if co-precipitates were formed, the sample was frozen in liquid nitrogen for 2 min and then thawed directly at 28°C and 400 rpm in a shaker for 5 min. After centrifugation at 14,800 rpm and 4°C for 5 min, the supernatant was transferred to a new tube, and the pellet was discarded. For RNA isolation and subsequent RT-qPCR analysis, the materials listed in Total RNA was isolated from approximately 150 mg of tissue powder using two different methods. In the first method, isolation was performed using the RNeasyIn case of low RNA yield, samples from the same tissues were pooled, precipitated, and resolubilized in smaller volumes. For precipitation, 0.1 times the sample volume, but not more than 20 μL, of 3 M sodium acetate and 1 μL of 2% glycogen were added to the sample. Samples were thoroughly mixed and centrifuged at 14,800 rpm and 4°C for 15 min, followed by addition of 2.5 times the sample volume of 96% ethanol. After centrifugation, the supernatant was discarded, and the pellet was washed once from all sides with 700 μL of 70% ethanol. The samples were then centrifuged at 14,800 rpm and 4°C for 5 min, and the supernatant was removed. Further, the pellet was pushed from the bottom to the inner wall of the tube with a pipette tip to ensure efficient evaporation of the residual solvent below the pellet. The pellet was dried in a shaker at 400 rpm at room temperature for 15 min, and the RNA was resolubilized with 40 μL of RNase-free MilliQ-H2O.® RNA Set for NucleoZOL  according to the manufacturer’s instructions.A portion of the TRIzol-extracts was additionally purified using NucleoSpin–1. RNA concentration and purity were confirmed using the NanoDrop ND-1000 spectrophotometer, and RNA integrity was validated by 1% denaturing RNA gel electrophoresis in 1 × MOPS at 100 V for 30 min. Reverse transcription was performed using the first-strand cDNA synthesis kit  according to the manufacturer’s instructions. For comparison, 100 pmol oligo (dT)18 or 20 pmol gene-specific antisense oligonucleotides were used for synthesis, together with 4 μg of RNA samples after DNase treatment. In order to assess the effectiveness of the DNase treatment, an RNA-qPCR was performed  according to the manufacturer’s instructions. Concentration of the digested total RNA was adjusted to approximately 1 μg⋅μLerformed .TB Green Premix Ex Taq I  using a QuantStudio 6 Flex system  according to the manufacturer’s instructions. For comparison, an additional measurement with a fully skirted, transparent 384-well plate  was conducted. cDNA equivalent to 200 ng of total DNase-digested RNA and 5 pmol of each corresponding sense and antisense primer  and e primer  were use–ct without a calibration and normalization  raw data were used for comparison between the samples. Ct-values were transformed to 2lization . Validatlization . Meltinglization , which i0 to 10–4 was prepared using this cDNA mix. The dilution series was measured using the RT-qPCR method in white plates, as described above. Using the defined relative cDNA quantities and raw Ct data, a linear function was generated for each gene ((–1/slope) – 1] ⋅100 , and p-values were identified in frame of t-test analysis and descriptive statistics using GraphPad Prism version 8.0.1 for Windows1 .In all experiments, three to eight independent RNA samples were extracted for each variant. The replicates were then pooled into a single biological sample. Each sample was represented by at least three technical replicates, which were analyzed during the same RT-qPCR run. The cycle threshold (Ct) raw data transformed to 2 samples , 5. 2–ctBnACT7 and BnUBC21, were selected as controls because they exhibit high specificity in RT-qPCR and similar levels of transcription in different tissues  on the centromere. Comparative in silico analysis of KNL2 gene transcription in Arabidopsis using Arabidopsis eFP Browser . Below, we present several technical approaches that have been applied to increase the sensitivity of RT-qPCR for the analysis of the expression of genes with low levels of transcription.To optimize the sensitivity of the RT-qPCR method, two classical housekeeping genes,  tissues . As a lo Browser  showed t–1, with an A260/A280 ratio of 1.825 and A260/A230 ratio of 0.45, whereas homogenization of plant tissue with 100 μL of TRIzol resulted in an RNA yield of 1.67 μg⋅μL–1, A260/A280 ratio of 1.95, and A260/A230 ratio of 0.775. Thus, homogenization of samples in the presence of TRIzol resulted in a strong increased RNA concentration because addition of TRIzol strongly increased the quality of homogenization . Additionally, many tissues are coarser than those of Arabidopsis, such as stems, old leaves, and roots. In most cases, such frozen samples can hardly be homogenized  of total RNA compared to the primary TRIzol RNA extract used for analysis. The A260/A280 ratio increased from 1.30 to 1.39 (6.47%), and the A260/A230 ratio increased from 1.22 to 1.45 (15.86%) due to precipitation. Comparison of the amplification data showed significantly higher 2–ct values for the precipitated compared to the untreated samples  of total TRIzol RNA extract; further elution steps were not analyzed. The A260/A280 ratio of the first elution was increased from 1.34 to 2.14 (62.6%), and the A260/A230 ratio increased from 1.21 to 2.23 (54.3%). Interestingly, amplification data for BnUBC21 shows significantly lower 2–ct values, when samples were purified after TRIzol extraction, while for BnACT7, no significant differences were detected . The yield of the first elution of purified RNA was 54.4% 18 cDNA )18 cDNA . Howevery strong . NeverthBnKNL2 genes from the same sample was significantly higher in all cases when using white plates than that with transparent plates , could be achieved only after additional concentration of RNA by precipitation.18 18  with tho18 . For botnt plate . In summRT-qPCR-based transcriptional analysis is a powerful tool to provide important information about gene activity in different organs/tissues at a specific stage of plant development. It is well known that the transcription levels of different genes in an organism vary significantly . StudiesBnACT7 and BnUBC21) from samples homogenized with TRIzol compared to samples without prior TRIzol addition. Thus, the addition of TRIzol before homogenization of the plant material is recommended as it increases the RNA stability and sensitivity of RT-qPCR.Because TRIzol contains phenol and chaotropic salts , the chaFor a long time, we successfully used the TRIzol method for RNA isolation from various tissues and organs of different plants. Compared to other commercially available methods of plant RNA isolation, TRIzol is inexpensive in terms of cost per sample. In addition, several TRIzol-similar reagents are available from different companies: TRIzol (Invitrogen), QIAzol (Qiagen), TRI Reagent and RNAzol (Sigma-Aldrich), TRIpure Reagent (Aidlab), and TriSure (Bioline). However, the use of TRIzol to isolate RNA from some tissue types, such as stems, seedlings and siliques, may result in its degradation.The reason can be specific for each tissue type. For instance: young seedlings usually contain large amounts of RNases and nucleases required for nucleotide remobilization; stems are cellulose-rich and contain dead xylem tissue; problems of RNA isolation from siliques most likely are related to the high content of polysaccharides and phenols .® plant mini kit, although this method is more expensive than TRIzol-based methods. The differences in sample purity in favor of column isolation, as we have observed, did not negatively influence the effectiveness of RT-qPCR because a slight increase in RT-qPCR sensitivity for BnACT7 was not confirmed for the BnUBC21 gene. A similar effect for GAPDH and lamin A has already been demonstrated in RNA extracts from mouse livers 18 cDNA )18 cDNA . A simil18 and the second, according to our recommendations, with white plates and gene-specific cDNA. In principle, both BnKNL2A and BnKNL2C showed a significant increase in the RT-qPCR signal when a combination of specific cDNA and white plates was used.To demonstrate the influence of the two factors, the cDNA synthesis strategy and the choice of RT-qPCR plates, tissue-specific analysis of the two low-transcription genes BnKNL2C gene. In particular, the transcription level of BnKNL2C is so weak that it is nearly impossible to determine its expression level in different tissues using the standard RT-qPCR protocol. By using our recommended changes to the RT-qPCR protocol, it was possible to increase detection sensitivity and, in general, accurately determine the real expression level of BnKNL2C in most tissues.The detection of the transcriptional levels of both genes in different tissues improved significantly when our recommendations were used, but the strongest effect was found for the In summary, the following strategy has proven to be optimal:1.Sample homogenization is recommended with the addition of 100 μL TRIzol per 150–200 mg tissue, as it provides a homogeneous sample, stabilizes RNA, and increases RNA yield.2.To achieve high RNA yield, RNA isolation using TRIzol is recommended without additional purification of the RNA sample on spin columns. However, in some tissue types, such as stems, seedlings and siliques, TRIzol RNA may degrade; in this case, column-based isolation can be performed. RNA precipitation is recommended to increase the RNA purity and concentration.3.18 is recommended.To improve the amplification of weakly expressed genes, specific cDNA synthesis instead of oligo (dT)4.The use of white instead of transparent plates significantly increased the signal intensity, which improved the detection of weakly expressed genes.The most successful option in such cases is to combine several approaches, for example, the addition of TRIzol by sample homogenization, precipitation of RNA when its concentration is low, synthesis of cDNA with a specific primer, and use of white plates for RT-qPCR.The original contributions presented in the study are included in the article/MM, DD, and IL conceived the study, designed the experiments, and wrote the manuscript. MM and HK performed the experiments. All authors have read and approved the final manuscript.


High grade serous ovarian cancer (“HGSOC”) is the seventh most common cancer worldwide among women. The disease has no unique symptoms and often women are diagnosed in advance stages. The World Health Organization estimates that 225,500 women are diagnosed and 140,200 will die of this disease per year. In the United States, the 5-year survival rate is 43%, and 85% of all HGSOC patients experience recurrence despite aggressive treatment. More efficacious drug combination treatments are needed. Recently it was found that platinum drugs are inactivated in dimethyl sulfoxide (DMSO). Thus drug discovery platforms that use it as a solvent are not able to study platinum drugs in combination with other drugs easily. Advances in Echo technology at the National Center for Advancing Translational Sciences (NCATS) enabled acoustic transfer of platinum drugs in aqueous solutions. This allows us to find novel small molecule/platinum drug combinations that can be tested in clinical trials.We ran single-agent dose-response experiments to correctly estimate the dose-ranges needed for the matrix screens utilizing the OVCAR8 human carcinoma cell line. Then, we conducted our pilot matrix screen with compounds that are commonly combined with cisplatin for HGSOC treatment. Lastly, we spotted cisplatin against the Mechanism Interrogation PlatE drug library to identify novel combinations for clinical use against HGSOC. Compounds that show synergistic cell killing will give insight into novel drug combinations to treat HGSOC.Several synergistic and antagonistic effects were found. These results were refined with smaller-scale screening and tested in other HGSOC cell lines.The novel combinations discovered will allow chemotherapy patients to receive lower dosages of platinum drugs while maintaining or improving efficacy of cytotoxicity of cancer cells. This will help alleviate the side-effects of platinum drugs experienced by HGSOC patients.


PAX3-FOXO1 in two ARMS patients, and EWSR1-FLI1, EWSR1-ERG, or EWSR1-NFATC2 in four URCS patients. For the 11 remaining patients, 11 newly identified fusion transcripts were confirmed by RT-qPCR, including COPS3-TOM1L2, NCOA1-DTNB, WWTR1-LINC01986, PLAA-MOB3B, AP1B1-CHEK2, and BRD4-LEUTX fusion transcripts in ARMS patients. Additionally, recurrently detected secondary fusion transcripts in patients diagnosed with EWSR1-NFATC2-positive sarcoma were confirmed . In conclusion, this study shows that mRNA capture sequencing enhances the detection rate of pathognomonic fusions and enables the identification of novel and secondary fusion transcripts in sarcomas.We assess the performance of mRNA capture sequencing to identify fusion transcripts in FFPE tissue of different sarcoma types, followed by RT-qPCR confirmation. To validate our workflow, six positive control tumors with a specific chromosomal rearrangement were analyzed using the TruSight RNA Pan-Cancer Panel. Fusion transcript calling by FusionCatcher confirmed these aberrations and enabled the identification of both fusion gene partners and breakpoints. Next, whole-transcriptome TruSeq RNA Exome sequencing was applied to 17 fusion gene-negative alveolar rhabdomyosarcoma (ARMS) or undifferentiated round cell sarcoma (URCS) tumors, for whom fluorescence in situ hybridization (FISH) did not identify the classical pathognomonic rearrangements. For six patients, a pathognomonic fusion transcript was readily detected, i.e.,  Fusion genes are chimeric genes arising from a chromosomal translocation that fuses parts of two genes. Such genes have been shown to drive tumorigenesis in hematological disorders and solid tumors, such as bone and soft tissue sarcomas, and are prototypical examples of disease-defining genomic aberrations. As such, fusion gene detection has become an integral part of routine clinical practice for these diseases ,2. Over Different types of massively parallel sequencing technologies have been applied for fusion gene identification, using both DNA- and RNA-based approaches . Whole-gIn parallel to the advent of various RNA sequencing-based methodologies for fusion transcript identification, a plethora of specific bioinformatics software packages for fusion transcript calling was developed ,16,17. MIn this study, we aim to establish an optimized workflow for fusion transcript identification using RNA-based hybridization capture sequencing in FFPE tissue, the routinely used and most common resource of archived biomaterial in pathology departments. For this purpose, two Illumina mRNA capture sequencing methods, i.e., TruSight RNA Pan-Cancer Panel and TruSeq RNA Exome sequencing, are applied to FFPE tissue samples of sarcoma patients to identify both pathognomonic and novel fusions. Additionally, we provide an RT-qPCR-based workflow to validate the identified fusion transcripts.To validate our workflow for the identification of fusion transcripts using mRNA capture sequencing, we used TruSight RNA Pan-Cancer Panel profiling to analyze a first cohort (cohort I) of formalin-fixed paraffin-embedded (FFPE) biomaterials of six cancer patients for whom the diagnostic workup demonstrated the presence of a specific chromosomal rearrangement in the tumor . For allPAX3-FOXO1 fusion, whereas FOXO1 break-apart FISH demonstrated 0% positive tumor cells, and patients were diagnosed based on clinicopathological and morphological findings only. For the URCS patients (P26–P29), we detected an EWSR1-ERG, EWSR1-NFATC2, or EWSR1-FLI1 fusion, but the EWSR1, FUS, CIC, and BCOR break-apart FISH results were negative. This means that the percentage of positive cells is below the cutoff of 15% (10–12% for EWSR1) or that 0% positive tumor cells were detected . Based on clinicopathological, morphological, and immunohistochemical findings only, P26 and P29 were diagnosed as Ewing sarcoma, and P27 and P28 as undifferentiated round cell sarcoma  of FFPE tumor RNA of 17 patients diagnosed with alveolar rhabdomyosarcoma (ARMS) or undifferentiated round cell sarcoma (URCS), that were designated fusion gene-negative by FISH analysis . This meAlu-Sq, Alu-Sx, and Alu-J) and a dilution series of normal FFPE tissue RNA of patient P15, i.e., the cohort I patient with the highest normal FFPE tissue RNA yield, enclosing six 10-fold dilution points ranging from 2.5 ng cDNA to 0.000025 ng cDNA per reaction. Based on the relatively high Cq values of the first dilution point (18.43 to 20.13), 4 ng cDNA was put forward as input for RT-qPCR. As such, the pathognomonic fusion transcripts detected in cohort II could be validated using RT-qPCR, with Cq values ranging from 27.01 to 34.69  additional fusion transcripts were identified in each of the patients of cohort II . For patnscripts .ATXN3-THAP11, AC245595.1-IGK@, and ELMO1-AOAH) were detected in ARMS, and four  in URCS  with fusion events at exon-exon borders were selected. For six fusion transcripts  could not be detected in the other EWSR1-rearranged patients of cohort II and are thus specifically expressed in sarcomas with an EWSR1-NFATC2 fusion.Apart from the identification of novel fusion transcripts in enigmatic sarcomas, the obtained fusion transcript lists were also screened for recurrently detected fusions besides the pathognomonic fusions in ARMS and URCS patients see . At the  in URCS , more spnscripts  RT-qPCR nscripts , in vitrWe assessed the performance of two mRNA enrichment sequencing methods, i.e., TruSight RNA Pan-Cancer Panel and TruSeq RNA Exome sequencing, for fusion transcript identification in FFPE tumor tissue. Although both panels have previously been reported to detect pathognomonic fusions ,18, theyEWSR1-FLI1 fusion. This finding is in line with the reported simultaneous detection of multiple fusion transcripts in the same tumor caused by EWSR1 splice variants in Ewing sarcoma .,63.+, 3


As fundamental components of RNA silencing, small RNA (sRNA) molecules ranging from 20 to 32 nucleotides in length have been found as potent regulators of gene expression and genome stability in many biological processes of eukaryotes. Three major small RNAs are active in animals, including the microRNA (miRNA), short interfering RNA (siRNA), and PIWI‐interacting RNA (piRNA). Cnidarians, the sister group to bilaterians, are at a critical phylogenetic node to better model eukaryotic small RNA pathway evolution. To date, most of our understanding of sRNA regulation and its potential contribution to evolution has been limited to a few triploblastic bilaterian and plant models. The diploblastic nonbilaterians, including the cnidarians, are understudied in this regard. Therefore, this review will present the current‐known small RNA information in cnidarians to enhance our understanding of the development of the small RNA pathways in early branch animals. The pin6Hydra magnipapillata head regeneration, although its mechanisms have yet to be elucidated (Krishna et al., Nematostella and Hydra, in addition to transposon expression, many piRNAs also map to protein‐coding genes such as the hydra non‐transposon targets involved in cell cycle regulation. However, no piRNA‐targeted transcripts are shared across Nematostella and Hydra, indicating that targeting protein‐coding genes has a high turnover rate and is unique to each species (Juliano et al., Although the most well‐studied and conserved function of the PIWI‐piRNA pathway is transposable element repression, other functions have also been revealed. The PIWI‐piRNA pathway is necessary for 7An increasing number of small RNAs will undoubtedly be identified in many distinct and diverse species because of recent advances in deep sequencing technology and bioinformatics tools. These rapidly growing studies allow us to investigate and re‐evaluate the evolution stories of small RNAs. This review summarizes the current findings of the small RNA pathways in cnidarians, the sister group of bilaterians, and how it advances our knowledge of the evolution of the small RNA pathways in ancestral eukaryotes. The cnidarian miRNA pathway is comparable with the bilaterian and plant miRNA pathways, implying that the eukaryotic miRNA pathway evolved from a common ancestor. Somatic piRNA expression in cnidarians also supports the view that somatic PIWI function is an ancestral feature in metazoans. Despite advances in our knowledge of small RNA evolution and regulatory research in cnidarians, most studies are still descriptive. Further studies are expected to resolve several long‐standing conundrums, such as the functional interaction of small RNAs and their targets, the implications for the gene regulatory networks, the influence of specific cell types and their impact on the organism, and the functional importance of the small RNA pathways in a larger hierarchy of regulatory mechanisms. We expect new technologies that allow in‐depth analysis in animals to provide the means of answering these questions and yield a better knowledge of the role of small RNA in eukaryotes.The authors declare no conflict of interest.