Thousands of RNA species display nonuniform distribution within cells. However, quantification of the spatial patterns adopted by individual RNAs remains difficult, in part by a lack of quantitative tools for subcellular transcriptome analysis. In this study, we describe an RNA proximity labeling method that facilitates the quantification of subcellular RNA populations with high spatial specificity. This method, termed Halo-seq, pairs a light-activatable, radical generating small molecule with highly efficient Click chemistry to efficiently label and purify spatially defined RNA samples. We compared Halo-seq with previously reported similar methods and found that Halo-seq displayed a higher efficiency of RNA labeling, indicating that it is well suited to the investigation of small, precisely localized RNA populations. We then used Halo-seq to quantify nuclear, nucleolar and cytoplasmic transcriptomes, characterize their dynamic nature following perturbation, and identify RNA sequence features associated with their composition. Specifically, we found that RNAs containing AU-rich elements are relatively enriched in the nucleus. This enrichment becomes stronger upon treatment with the nuclear export inhibitor leptomycin B, both expanding the role of HuR in RNA export and generating a comprehensive set of transcripts whose export from the nucleus depends on HuR. Forty-eight hours after transfection, the cells were incubated with 5 μg/ml puromycin in order to select for integrants. Approximately 2 weeks after transfection, a stable population of integrants had been selected.The plasmid containing the Halo fusion also contained a reverse tetracycline-controlled transactivator (rtTA), allowing the expression of Halo fusions to be controlled in a doxycycline-dependent manner. To induce expression of the Halo fusion, selected cell lines were incubated with 1 μg/ml doxycycline for 48 h prior to performing experiments.The H2B fusion was expressed as a C-terminal Halo fusion  while the p65 and fibrillarin fusions were expressed as N-terminal Halo fusions .Halo-DBF was synthesized as previously described . We are d-lysine-coated coverslips and incubated with 1 μg/ml doxycycline for 48 h. The media was then removed and the cells were washed one time with PBS. Cells were then fixed by incubating them in 2% formaldehyde for 30 min at room temperature. The cells were then washed with PBS again, and fluorescent Halo ligand (Janiela Fluor 646 (Promega), 25 nM in PBS) was added. The cells were incubated in the Halo ligand solution for 30 min at room temperature then washed three times with PBS for 5 min each. DAPI was then added to a concentration of 100 ng/ml for 10 min, and the cells were washed again with PBS. The coverslips were then mounted and imaged, usually at 60× magnification, using a Deltavision Elite widefield fluorescence microscope (GE).To verify that the transgenic Halo fusion proteins were targeted to the correct subcellular location, the location of the fusions were visualized using a fluorescent Halo ligand. Cells were seeded on poly-Cells were grown in 10 cm or 15 cm dishes, and the expression of Halo fusion proteins was induced with 1 μg/ml doxycycline for 48 h. Cells were then washed with PBS and incubated with 1 μM DBF halo ligand (in HBSS) at 37°C for 15 min. For negative control samples, the DBF halo ligand was omitted.Cells were then washed with complete media and incubated at 37°C twice for 10 min each. Propargylamine  was then added, and the cells were incubated for 5 min at 37°C. Following the incubation, cells were then irradiated with green light for 5 min from a 100 W LED flood light (USTELLAR UT88831-RGB-US). Dishes were sandwiched between two flood lights in a dark enclosed space so that they were illuminated from above and below. The light above the dish was suspended ∼10 cm from the dish. Total RNA was then isolated from the cells using Trizol (Ambion) following the manufacturer's instructions with the addition of a homogenization step in which the cells in Trizol solution are repeatedly forced through a 20 gauge needle 10 times.The RNA was then DNase treated using DNase I (Thermo Scientific) for 30 min at 37°C. RNA was then again recovered using Trizol, and the final pellet was resuspended in water to a concentration higher than ∼300 ng/ul. This ensures the ability to have the correct concentration in Click reactions downstream. Typical total RNA yields were 25–100 μg if starting with a 10 cm dish and 300–700 μg if starting with a 15 cm dish.Typically, ∼50–100 μg of total RNA was used in the Click reaction. The Click reaction contained 10 mM Tris pH 7.5, 2 mM biotin azide (Click Chemistry Tools), 10 mM sodium ascorbate made fresh (Sigma), 2 mM THPTA (Click Chemistry Tools), and 100 μM copper sulfate. The reaction was incubated for 30 min in the dark at 25°C. The reaction was then cleaned up using a Quick RNA Mini kit (Zymo Research) for small scale biotinylation tests (<10 μg RNA), or through standard ethanol precipitation with sodium acetate for larger amounts of RNA (>10 μg). RNA was then eluted and resuspended to 1 μg/μl in 50 mM NaCl.Typically, between 50 and 100 μg of RNA at 1 μg/μl from the Click reaction was then carried forward for streptavidin pulldowns. 25–50 μl of streptavidin-coated magnetic beads (Pierce PI88816) were used depending on the known amount of labeled RNA present in the reaction. Specifically, we used 1 μl beads per 1 μg total RNA for Halo-p65 and Halo-fibrillarin, and 1 μl beads: per 2 μg RNA for H2B. The beads were washed 3 times in B&W buffer , 2 times in solution A , and 1 time in solution B (100 mM NaCl). The beads were resuspended in an appropriate amount of NaCl to allow for 50 mM final concentration after the addition of the desired amount of RNA to equal 1 μg/μl. The RNA from the Click reaction was then added to the beads, and the beads were rotated for 2 h at 4°C. The beads were then washed 3 times for 5 min each in B&W buffer with rotation at room temperature.RNA was then recovered from the beads using Trizol. First, the beads were resuspended in 50 μl PBS. 150 μl of Trizol was then added, and this mixture was incubated for 5–10 min at 37°C. The eluted RNA (without the magnetic beads) was then recovered from this mixture using a DirectZol kit (Zymo Research) following the manufacturer's instructions and eluted in 10 μl of water. Depending on the location of the Halo fusion protein, typically between 0.5% and 5% of the input RNA was recovered by streptavidin pulldown.2 on a Stratalinker UV crosslinker (Stratagene).RNA biotinylation, both before and after the streptavidin pulldown, was assessed using an RNA dotblot. A 5 cm × 5 cm piece of Hybond-N + membrane (GE) was wet with 2× SSC for 1 min. This was then allowed to dry for 15 min. RNA samples, typically around 5 μg, were then spotted on the membrane and allowed to dry for 30 min. The dried blots were then crosslinked twice to the membrane using 120 000 μJ/cmTo stain for total RNA, the blot was then incubated in 1% methylene blue for 10 min and destained using deionized water. The membrane was then blocked using 5% BSA for 30 min and washed 3 times in PBST (PBS + 0.01% Tween). Biotinylated RNA was then detected using streptavidin-HRP (Abcam ab7403) at a dilution of 1 to 20 000 in 3% BSA by addition to the membrane with rocking overnight at 4°C. The membrane was then washed 3 times for 10 min each in PBST at room temperature. Streptavidin–HRP was then detected using standard HRP chemiluminescent reagents (Advansta) and visualized using chemiluminescent imaging on a Sapphire molecular imager (Azure Biosystems).rRNA-depleted RNAseq libraries were prepared using an RNA HyperPrep Kit (KAPA/Roche). 100 ng of RNA were put into the beginning of the library prep protocol, and 14 PCR cycles were used to amplify the library at the end.Libraries were sequenced using paired end sequencing (2 × 150 bp) on a NovaSeq high-throughput sequencer (Illumina) at the University of Colorado Genomics Core Resource. Typically, between 20 and 40 million read pairs were sequenced for each sample.www.gencodegenes.org, GENCODE 28). Gene abundances were then calculated from these transcript abundances using tximport  versions of transcripts, a custom fasta file was supplied to Salmon which contained two versions of every transcript, one with all introns remaining and one with all introns removed. The custom fasta file was generated using this script: AU-rich element locations were downloaded from the AU-rich element database . AU-richin situ, cell growth and Halo fusion induction were performed as described above with the exception that cells were grown on poly-d-lysine-coated coverslips. The in-cell alkynylation of DBF-proximal molecules was also performed as described above. Then, instead of lysing cells with Trizol to recover RNA, cells were washed with PBS 3 times and fixed by incubating them in fixation buffer  for 30 min at room temperature. Cells were then washed twice for 5 min each in PBS.To visualize the subcellular location of DBF-proximal alkynylated molecules The Click reaction was then performed by incubating cells with 100 μl Click buffer ) for 1 h at 37°C in the dark. As two separate controls, samples in which the DBF had been left out during the alkynylation reaction and the Cy5 had been left out of the Click buffer were used. Following the Click reaction, the coverslips were washed three times for 5 min each in wash buffer . Coverslips were then incubated in DAPI buffer (100 ng/ml DAPI in wash buffer) for 30 min at 37°C and then washed twice with wash buffer for 5 min each. The coverslips were then mounted and imaged using a Deltavision Elite widefield fluorescence microscope (GE).Cells were treated with 40 ng/ml leptomycin B (LMB) for 15 h prior to the beginning of the Halo-seq protocol. To verify the activity of leptomycin B and our conditions, the localization of the Halo-p65 fusion was monitored. The cytoplasmic localization of p65 is sensitive to LMB  Figure . AccordiHalo-seq using a histone H2B Halo fusion was performed in the presence and absence of LMB, generating four conditions: input samples with and without LMB and pulldown samples with and without LMB. To identify genes whose pulldown to input ratio changed between the LMB treated and untreated samples, the software package Xtail was used . Xtail iCells stably expressing HA-miniSOG2 were treated alongside cells stably expressing Halo-HA. The expression of miniSOG2 and Halo-HA was induced with 1 μg/ml doxycycline for 48 h. Cells were then washed with PBS. Halo-expressing cells were then incubated with Halo-DBF ligand at 37°C for 5 min, and both cell lines were then incubated with propargylamine  at 37°C for 5 min. Following the incubation, cells were irradiated with blue light (for miniSOG2) or green light  for a total of 10 min with a 100 W LED flood light (USTELLAR). Dishes were sandwiched between two flood lights so that they were illuminated from above and below. The light above the dish was suspended ∼10 cm from the dish. Total RNA was then isolated from the cells using Trizol (Ambion) following the manufacturer's protocol after homogenization with a 20 gauge needle 10 times. RNA was biotinylated following the protocols described above.A cell line was created expressing a Halo-APEX2 fusion protein to ensure equal expression of HaloTag domain and APEX2 protein. RNA labeling with APEX2 was done alongside Halo-DBF-mediated labeling (described above). Expression of the Halo-APEX2 fusion protein was induced with 1 μg/ml of doxycycline for 48 h. To label RNA with the APEX2 protein, cells were washed with PBS and then incubated with HBSS containing 0.5 mM biotin phenol (APExBIO A8011) for 30 min at 37°C. Cells were then washed in PBS and either treated with hydrogen peroxide at 1 mM in HBSS or plain HBSS (control) for 7 min at 37°C . The reaction was quenched in quenching buffer containing 5 mM Trolox, 10 mM sodium ascorbate and 10 mM sodium azide in PBS. To fully quench the reaction, cells were washed three times in quenching buffer, with 3 min between each wash. Total RNA was isolated from the cells using Trizol (Ambion) after homogenization with a 20 gauge needle. After DNAse treatment, RNA was then directly blotted onto an RNA dotblot.APEX-seq RNAseq data was downloaded from the Gene Expression Omnibus (GSE116008) and processed to calculate transcript and gene abundances as above. Since the libraries were produced using poly-A enrichment of RNA, only protein-coding and lncRNA genes were used for comparisons to Halo-seq data.www.encodeproject.org). Transcript and gene abundances from rRNA-depleted libraries were calculated using Salmon, tximport, and DESeq2 as outlined above. While Halo-seq enrichments were calculated as a gene's abundance in the streptavidin pulldown divided by its abundances in the input to the pulldown, CeFra-seq enrichments were calculated as a gene's abundance in the biochemically defined fraction (e.g. cytosol or nucleus) divided by its abundance in the total RNA samples.CeFra-seq RNAseq data was downloaded from the ENCODE portal . DBF generates singlet oxygen radicals when irradiated with green light ,21. The in vitro Cu(I)-catalyzed azide-alkyne cycloaddition (CuACC) ‘Click’ chemistry in which alkyne-containing molecules and azide-containing molecules are linked  are generally depleted from the cytoplasm ,35. In aWe then turned to the quantification of dynamically processed RNA species. Most protein-coding human RNAs undergo splicing in the nucleus before export to the cytoplasm, and for many RNAs this splicing happens cotranscriptionally . We therNext, we asked whether Halo-seq could quantify intrinsically unstable and short-lived RNAs. During transcription initiation, antisense RNAs are produced from the region upstream of the promoter . These uGiven the assurance in the quality of our spatially resolved nuclear and cytoplasmic transcriptomes, we searched for RNA features that discriminated between nucleus- and cytoplasm-enriched transcripts. Interestingly, we found that genes that contained AU-rich elements (AREs) in their 3′ UTRs were enriched in the H2B pulldown and depleted from the p65 pulldown Figure . AREs arAlthough the results from our analysis of nuclear and cytoplasmic transcriptomes were highly encouraging, these two cell compartments are separated by a well-defined membrane and therefore may not provide the best scenario with which to test the spatial specificity of Halo-seq. We reasoned instead that a more rigorous test of the technique would be to differentiate the RNA contents of non-membrane-bound organelles. We therefore chose to interrogate another subcellular compartment, the nucleolus.2 fold change ≥ 0.5), indicating that the nucleolus contains an RNA population distinct from bulk cellular RNA  and suggests that the RNA populations labeled by Halo-seq in the two compartments were largely distinct.We began by fusing a HaloTag to a specific marker of nucleoli, Fibrillarin . This fuA Figure . When co7SL RNA was enriched in the Fibrillarin pulldown. This RNA is part of the signal recognition particle (SRP) ribonucleoprotein complex. SRP is involved in protein targeting to the endoplasmic reticulum in the cytoplasm, but it is assembled in the nucleolus  Halo ligand; however, this ligand is not commercially available. We therefore tested the ability of this ligand to induce RNA labeling in comparison to the more commonly used and commercially available fluorescein Halo ligand. RNA biotinylation was assayed using dotblots following reactions with these two ligands in HaloTag-expressing cells Figure . RNA labCap-seq, a similar method that uses the blue-light activated singlet oxygen generator miniSOG2 to label RNAs was recently reported . In thisdifference in labeling between the conditions is likely attributable to Halo-mediated labeling.We then performed RNA labeling in these cells under two conditions: either (i) without DBF and using blue light activation or (ii) with DBF and using green light activation. Under the first condition, we would expect any labeling to be due to the activity of miniSOG2. Because miniSOG2 is activated only by light, it is possible that miniSOG2-mediated labeling could also happen in the second condition. However, because Halo-mediated labeling requires DBF, the We observed very little RNA labeling using blue light activation in the absence of DBF. In contrast, we observed robust RNA labeling upon the addition of DBF and green light Figure . We therOne explanation for the striking difference between the two is the singlet oxygen yield, which is related to the stability of both the chromophore and its excited state. The singlet oxygen yield of DBF is 0.42 while that of miniSOG is 0.03 . It is aAnother RNA proximity labeling technique that utilizes radical generation is APEX-seq ,19. SimiTo further compare Halo-seq and APEX-seq, we compared RNA enrichments (pulldown / input) in cytoplasmic, nuclear, and nucleolar samples using hierarchical clustering . UnfortuOther methods to study RNA localization rely on biochemical fractionation rather than proximity labeling . One sucWe then compared gene enrichments from specific gene classes. Whereas both methods showed enrichments for snRNAs in their nuclear fraction, CeFra-seq showed enrichments for snoRNAs in the nuclear fraction while Halo-seq did not Figure . This isGiven that we had established that Halo-seq allows the efficient and accurate profiling of subcellular transcriptomes, we moved to the quantification of RNA localization following perturbation. A subset of transcripts in mammalian cells depend upon the action of CRM1 for export from the nucleus, and the small molecule leptomycin B (LMB) inhibits the action of CRM1 . While tTo more fully investigate the dependence on CRM1 for RNA export, we identified nuclearly enriched RNAs using Halo-seq with a H2B-Halo fusion in the presence and absence of LMB. Following LMB treatment, we expected an increased nuclear abundance for a subset of RNAs whose nuclear export depends on CRM1.We first verified that the nuclear localization of the H2B-Halo fusion protein did not change following LMB treatment Figure . Our cytLMB treated and untreated samples displayed approximately the same amount of labeled nuclear RNA as assayed by streptavidin-HRP RNA dotblot . RNAseq We then asked about the relative nuclear enrichment of different RNA classes Figure . As a clhttps://github.com/TaliaferroLab/FeatureReachR). Using FeatureReachR, we found that the 3′ UTRs of transcripts whose localization was LMB-sensitive were enriched for several AU-rich kmers  using the software package Xtail . To idens Figure .Given the enrichment observed in LMB-sensitive transcripts for AU-rich kmers and motifs, we then directly searched these transcripts for AREs using a previously compiled database of their locations . We founAREs can be bound by several different RBPs, including HuR  . HuR hasSince Halo-seq enrichments are calculated as a ratio of abundances in streptavidin-pulldown and input RNA, the increased enrichment of HuR-bound RNAs following LMB treatment could be due to either an increase in the abundance of HuR targets in the H2B pulldown or a decrease in their abundance in input samples. To distinguish between these possibilities, we compared LMB-induced abundance changes in the input and H2B pulldown samples separately. We found that the abundance of HuR target RNAs in total RNA (input) samples did not change upon LMB treatment. In contrast, the abundance of HuR target RNAs in the nuclear (pulldown) samples significantly increased upon LMB treatment Figure . These rIn this study, we have developed an RNA proximity method called Halo-seq that has the capability to isolate and quantify subcellular transcriptomes. We used Halo-seq to analyze nuclear and cytoplasmic transcriptomes and found that ARE-containing transcripts are relatively enriched in the nucleus and depleted from the cytoplasm. The capacity of AREs to negatively regulate RNA stability has been extensively established ,62, but We then assayed dynamic RNA localization in response to treatment with the nuclear export inhibitor LMB. We found a dose-dependent relationship between the number of 3′ UTR HuR binding sites an RNA contained and the degree to which its abundance in the nucleus was increased following LMB treatment. HuR is known to both increase the stability of the RNAs that it binds  and be iIn one scenario, LMB-induced retention of HuR in the nucleus increases the vulnerability of its target RNAs to cytoplasmic degradation as its ability to protect its targets from instability has been lost. In an alternative scenario, RNAs that depend on HuR for nuclear export are now less efficiently exported, leading to their accumulation in the nucleus.Nuclear RNA makes up a relative minority of total cellular RNA while cytoplasmic RNA makes up the majority. If the primary effect of LMB treatment on HuR-bound transcripts was a loss of stability, particularly in the cytoplasm, then we would expect to find that their levels in total RNA would be decreased. However, if the loss of HuR-mediated export was heavily involved, then we would expect to see HuR-bound RNAs become more abundant in the nucleus. Since, though, nuclear RNA makes up a small fraction of total RNA, their overall abundance may be unchanged. Importantly, we found that the overall levels of HuR-bound transcripts were unchanged following LMB treatment while their levels in the nucleus significantly increased Figure . These rWe believe these results are important in the context of the existing literature describing HuR activity. Although HuR has been previously found to regulate the nuclear export of a limited number of RNAs , the vasUsing known markers of nuclear and nucleolar RNA, we found that Halo-seq can efficiently distinguish the RNA contents of these two compartments. Given their close spatial proximity, these results favorably report on the ability of Halo-seq to quantify RNA localization with high spatial precision.Interestingly, we found a consistent enrichment of RNAs derived from the mitochondrial chromosome in Halo-seq pulldown samples compared to input samples. This was most prevalent in the p65 and Fibrillarin Halo-seq experiments . We beliThe ability of Halo-seq to recapitulate known RNA localization patterns as well as its ability to connect these patterns to activities of specific RBPs gave us confidence that the method performed well. However, a number of approaches for quantifying subcellular transcriptomes had been previously reported ,48. We tCeFra-seq uses biochemical fractionation to define subcellular fractions . AlthougTwo RNA proximity labeling techniques, APEX-seq and CAP-seq, were also recently reported ,18,19. WWe compared RNA labeling efficiencies of all three proximity labeling methods Figure , C. We fAlthough we believe Halo-seq has considerable promise, a number of potential hurdles remain. Halo-seq requires the use of Halo-tagged fusion proteins. The subcellular localization of fusion proteins is often hard to predict and must be empirically verified. The use of transgenes for fusion protein expression is experimentally simple, but the overexpression that occurs with these constructs can often lead to spurious, unintended localization patterns. Tagging endogenous genes with Halo domains may alleviate this problem.Additionally, as with most methods, a certain level of background exists with Halo-seq. This manifests as transcripts that are precipitated by the streptavidin pulldown even though they were not alkynylated in the cell. When querying large subcellular regions when a considerable amount of RNA is expected to be labeled, this level of background is likely overcome by true signal. However, when querying small, specific locations, the background signal may pose serious issues. One potential approach to minimize this effect may be to compare two pulldown samples where one originates from a sample with a Halo fusion localized to the location of interest and another originates from a sample with a broadly localized Halo fusion .One interesting application of this technique may be to the projections of neuronal cells. RNA localization in neurons has been extensively studied on a transcriptome-wide scale ,14,64,65In sum, we view Halo-seq as a flexible, quantitative tool that is well-suited to advance the study of subcellular RNA localization.All high-throughput RNA sequencing data as well as transcript quantifications have been deposited at the Gene Expression Omnibus under accession number GSE172281.gkab1185_Supplemental_FilesClick here for additional data file.


Programmed cell death is a universal mechanism in animal development and tissue maintenance, which facilitates the elimination of surplus or poorly functioning cells. Many conserved regulators of programmed cell death have been identified in model organisms including the fruit fly Drosophila melanogaster. In the latter, the four members of the RHG gene family function as critical inducers of programmed cell death. Despite this important role, RHG genes had thus far only been found in a surprisingly small number of insect groups, i.e., other flies and butterflies. This study reports the much deeper conservation of RHG genes in winged insects, ranging from cockroaches to beetles. In addition to opening new opportunities to study programmed cell death in a wide range of insects, the bioinformatic search strategy developed for this work will be of general use for studying gene families with challenging sequence evolution dynamics.sickle (skl), the Drosophila paralogs reaper (rpr), head involution defective (hid), and grim (RHG) control a critical switch in the induction of programmed cell death. RHG homologs have been identified in other dipteran and lepidopteran species but not beyond. Revisiting this issue with a “taxon hopping” BLAST search strategy in current genome and transcriptome resources, I detected high confidence RHG homologs in Coleoptera, Hymenoptera, Hemiptera, and Dictyoptera. Analyses of gene structure and protein sequence conservation revealed aconserved splicing pattern and highly conserved amino acid residues at both the N- and C-terminal ends that identify hid as the most ancestrally organized RHG gene family member in Drosophila. hid-like RHG homologs were also detected in mosquitoes, redefining their michelob_x (mx) genes as an expansion of derived RHG homologs. Only singleton homologs were detected in the large majority of other insect clades. Lepidopteran RHG homologs, however, stand out by producing an evolutionarily-derived splice isoform, identified in previous work, in addition to the newly detected hid-like isoform. Exceptional sequence diversification of select RHG homologs at the family- and genus-level explain their previous elusiveness in important insect genome model species like the red flour beetle Tribolium castaneum and the pea aphid Acyrthosiphon pisum. Combined, these findings expand the minimal age of the RHG gene family by about 100 million years and open new avenues for molecular cell death studies in insects.Together with  Caenorhabditis elegans [Drosophila and mice, uncovered the functional conservation of caspases as executive forces in the programmed cell death pathway [C. elegans, a similar, but less complex regulatory protein machinery appears to be in place [Drosophila, caspases are constitutively expressed but blocked by default through physical interventions by members of the inhibitor of apoptosis (IAP) gene family [reaper (rpr), head involution defective (hid), and grim, besides sickle (skl) [Programmed cell death results from the unleashed activity of caspases, a deeply conserved gene family of cysteinyl aspartate proteases. First characterized for their executive role in programmed cell death in the nematode  elegans , subsequ pathway . However pathway . In C. ein place . In Drose family . Pendinge family , which ile (skl) ,8.Rpr was the first characterized Drosophila RHG gene family member [hid [grim [skl [Drosophila species genome drafts revealed the conservation of all four clustered genes in the Drosophilidae [Anopheles gambiae, however, were unsuccessful [Drosophila RHG homologs corroborated the high conservation of the N-terminal IBM (IAP-binding motif) sequence [Drosophila paralogs was only tentatively supported by sequence similarity [y member , followeber [hid , grim [9rim [skl ,11,12. Sphilidae . Similarccessful . At the sequence ,15: A-[Ksequence . Comparasequence  and the sequence ,18. Thismilarity .Lucilia cuprina and L. sericata [Anastrepha suspensa (Tephritidae) [Megaselia scalaris [Bombyx mori and the fall armyworm Spodoptera frugiperda [Today, caspase and IAP genes have been identified in a wide range of insect species , but thesericata ,22, the ritidae) , and, moscalaris . In the ugiperda ,25,26. Tugiperda  the shorugiperda ,19. Thisugiperda ,29. HereIAP-binding motif 1 (IBM1) (NP_001159813.1) as a query in BLASTp searches against the NCBI nr database (accessed on 1–31 December 2020) with Diptera and Lepidoptera excluded from the taxonomic search range [Bemisia tabaci , Nilaparvata lugens , and Laodelphax striatellus . All of these sequences started with the RHG homology-defining IAP-binding motif (IBM) [Initial searches for RHG homologs outside Diptera and Lepidoptera were conducted using the silkworm RHG homolog ch range . This efTribolium castaneum, which represents the first coleopteran genome draft that has since been improved by a number of revisions [Agrilus planipennis (XP_018330969.1), detected the protein product of T. castaneum locus LOC103313285 (XP_008194456.1) as a candidate RHG homolog with an e-value of 0.05. ReBLAST of the T. castaneum LOC103313285 protein sequence against the conceptual A. planipennis proteome returned the putative A. planipennis RHG homolog as the best matching hit. Using the putative T. castaneum RHG homolog as a query against coleopteran transcript and protein sequence databases expanded the compilation of coleopteran RHG sequences to 25 (Asbolus verrucosus and Zophobas atratus) and five additional homologs from families in the Tenebrionoidea  and Hymenoptera (bees + wasps). This approach produced significant hits in over 50 hymenopteran species, 21 of which were compiled for detailed analysis , and fivevisions ,31. I thes to 25 . This inionoidea .Blattella germanica (PSN40724) and the termite species Cryptotermes secundus and Zootermopsis nevadensis  residue  residue followed by tryptophan (W) 5–7 residues away from the C-terminus in all clades except Diptera . The latresidues .hid as the most ancestrally organized RHG paralog in Drosophila given the complete lack of C-terminal consensus residues in rpr, grim, and skl  detected candidate RHG homologs in A. gambiae. One of these, named michelob_x (mx), was studied in detail and found to induce apoptosis in cell culture as well as transgenic Drosophila. Moreover, A. gambiae Mx was shown to bind Drosophila Diap1 in vitro and in an IBM-dependent manner [The first RHG homologs outside the genus squitoes . Complet elusive ,34. Devet manner .hid-like C-terminal consensus amino acid residues in mosquito mx homologs  and Toxorhynchitinae (Toxorhynchites spec.)  as the C-terminal residue  . Combine residue .Drosophila grim, rpr, and skl are localized on single exons, while the ORF of hid spreads out over four exons [hid ortholog of the scuttle fly Megaselia scalaris [B. mori [T. castaneum [Nasonia vitripennis [Oncopeltus fasciatus [The open reading frames (ORFs) of ur exons , an orgascalaris . To prob[B. mori , the redastaneum , the jewripennis , and theasciatus .Plutella xylostella, was characterized by the acquisition of an exceptional fourth ORF encoding exon  was identified among the curated protein sequence predictions [P. xylostella, i.e., the representative of the Yponomeutoidea, implied at least 140 million years of evolutionary conservation in the Lepidoptera [In many Lepidoptera, initial BLASTp searches recovered two types of RHG orthologs per species. In these cases, the two apparent homologs were sequence identical in the N-terminal region but diverged C-terminally. This preliminary evidence of differential splice isoforms was confirmed by the gene structure analyses. In the genome draft of the monarch butterfly, dictions  and a seA. pisum, an important pest species and genome evolution model [S. flava RHG homolog uncovered single copy hits in nine additional aphid species including A. pisum (A/V)GP(S/L/P)(H/Q)” with six perfect copies of “SAGPSH” (A. pisum RHG gene model in the NCBI gene database (not shown). Similarity blotting of the A. pisum RHG coding sequence confirmed corresponding repetitiveness as the nucleotide level, which is typical for slippage extended simple sequence repeats  as the N-terminal residue in place of the deeply conserved ancestral arginine (R) residue in the Hemiptera and other insect orders A. Combinhid is now clearly established as the most ancestrally organized member of the four Drosophila RHG paralogs via outgroup comparison. Further significant, the protein product of hid, in contrast to rpr, skl, and grim, is localized to mitochondria due to its hydrophobic C-terminus (392–409), which has therefore been defined as the mitochondria-targeting sequence (MTS) domain [Drosophila cell death is still not clearly defined, the conservation of N-terminal residues, i.e., a hid-like MTS domain, in ancestral RHG homologs across winged insects constitutes compelling evidence that mitochondrial localization might be a critical aspect of insect RHG protein function. The possibility that the hid MTS domain promotes IAP degradation by virtue of mitochondrial colocalization, therefore, continues to be an attractive model [hid’s cell death-inducing capacity [S. frugiperda [hid vs. grim, rpr, and skl in Drosophila.The expanded panel of insect RHG homologs clarifies a number of previously elusive aspects of this critical cell death gene family. Most importantly, perhaps, and consistent with previous speculations , hid is ) domain . Thus, wve model ,46. Thiscapacity . Interesugiperda . At firsrpr, skl, and grim are not the only examples of RHG gene family expansions resulting in paralogs with simpler gene organization, i.e., a lower number of coding exons, and substantially shorter protein sequences. This is also true for the mx paralogs in mosquitoes and the derived S-isoforms of the lepidopteran RHG genes. The discovery of the latter further suggests that the dipteran RHG gene family expanded via the selective duplication of the first ORF sequence containing exon, which encodes the short, but cell death induction sufficient IBM. This duplication conduciveness likely explains the spawning of RHG paralogs and isoforms in mosquitoes and Lepidoptera, respectively [The updated insect RHG homolog database further reveals that ectively .mx homologs in mosquitoes had been noted earlier [hid-like homologs of mosquitoes. While these efforts may reveal connections to the exceptional pathogen load of mosquito vector pest species, it is also possible that they represent functionally neutral outcomes of gene duplication in line with the “duplication–degeneration–complementation” trajectory [hid, rpr, grim, and skl, given their largely non-overlapping expression patterns based on modENCODE data [The existence of multiple  earlier . Tissue-ajectory ,49. ThisODE data .Ibm1 of B. mori [B. mori RHG homolog locus. Paralleling the situation in mosquitoes, it is the ancestrally organized L-RHG isoform that now awaits functional study [The first RHG homologs identified outside dipterans via a bioinformatic search in a new genome sequence was  B. mori , which ial study ,26. FutuA. pisum, arguably the genomically best documented representative of its clade [T. castaneum. It is notable that the RHG homologs of both A. pisum and T. castaneum were only detected after more closely related homolog sequences were at hand as queries, a strategy that may be referred to as “taxon hopping”. Future applications of this approach will benefit from computational automatization and refinements that optimize sensitivity and specificity.It has been over 10 years since the last RHG homolog was detected in a new insect order. This hiatus is, of course, in part explained by the well recognized challenges of finding RHG homologs, i.e., their short sequence lengths, relatively unconstrained evolution, and low number of constrained residues. However, the updated RHG compilation also reveals a role of historical contingencies. The previously identified homologs in mosquitoes and lepidopterans both represent derived homologs or isoforms that lack the conserved C-terminus. This may, in part, explain subsequent failures to identify RHG homologsin Hemipterans . The latts clade  and the Varied BLAST searches in genome and transcriptome databases of older insect clades, i.e., Paleoptera and apterygote Hexapoda, as well as crustaceans and invertebrates in general, did not uncover further RHG homologs at this point. Given the success of the “taxon hopping” strategy in identifying new homologs, it seems reasonable to assume that the RHG gene family is restricted to neopteran insects. Thus, besides identifying new powerful insect model systems for the study of RHG function, the expanded compilation of RHG homologs suggests a new hypothetical time point of RHG family origination at the base of the Neoptera and predicts the existence of different IAP inhibiting regulators in other clades.Using the BLAST search interface of the National Center for Biotechnology Information (NCBI), homolog searches were conducted with BLASTp, tBLASTn, or Position-Specific Iterated BLAST (PSI-BLAST) in the non-redundant (nr) protein sequence, Transcriptome Shotgun Assembly (TSA), and Whole Genome Shotgun contig (WGS) sequence databases ,52,53. MMultiple protein sequence alignments were generated using Clustal Omega, webPRANK, and T-Coffee all at default settings ,55,56.Gene structures were analyzed in current assemblies available in the NCBI Genome Data Viewer .


Poor self-rated health (SRH) is a known predictor of frailty and mortality in the general population; however, its role among older adults with cancer is unknown. We evaluated the role of SRH as a potential screening tool to identify frailty and geriatric assessment (GA)-identified impairments.Cancer & Aging Resilience Evaluation (CARE) registry underwent a GA at the time of initial consultation. We measured SRH using a single-item from the Patient-Reported Outcomes Measurement Information System global health scale and dichotomized responses as poor  and good . We evaluated the diagnostic performance of SRH in measuring frailty, and GA impairment (≥2 deficits among a set of seven GA domains). We examined the impact of SRH with survival using a Cox model adjusting for confounders, exploring the mediating role of frailty.Adults ≥60 years diagnosed with cancer in the UAB n = 274) reported poor SRH. Poor SRH demonstrated high sensitivity and specificity for identifying frailty  and GA impairment . In a Cox regression model, poor SRH was associated with inferior survival  after adjusting for confounders; frailty mediated 69% of this observed relationship.Six hundred and three older adults with cancer were included, with a median age of 69 years. Overall, 45% (Self-rated health may be used as a screening tool to identify older adults with cancer with frailty and GA impairments. Poor SRH is associated with inferior survival, which is mediated by frailty. This article evaluates the role of self-rated health as a potential screening tool to identify frailty and geriatric assessment-identified impairments. In this prospective cohort study involving 603 older adults ≥60 years with cancer, poor self-rated health at the time of initial consultation with a medical oncologist demonstrated a high sensitivity and specificity for frailty  and GA impairment , which was independently confirmed in an external replication cohort. Poor SRH was associated with inferior survival after adjusting for age, sex, race, cancer type/stage, and planned therapy. A single item question on self-rated health may be used to screen older adults with cancer who are likely to have frailty and geriatric assessment identified impairments.1 Although, older adults with cancer are at high risk of treatment-related toxicity and inferior survival, chronologic age by itself does not adequately explain inter-individual variability in outcomes.2 A geriatric assessment (GA) can identify vulnerable or “frail” older adults at greatest risk for treatment-related adverse events3–5, health care utilization,4 and mortality6. Currently, the American Society of Clinical Oncology7 and the National Comprehensive Cancer Network (NCCN)8 recommend using GA to personalize and guide clinical care in older adults. Yet, widespread use of GA in oncology practice remains limited due to perceived complexity as well as time/resource constraints.9 While there are few screening tools to identify patients at greatest risk of GA impairments,11 a primarily self-administered single-item screening tool represents an unmet need.More than 60% of new cancer diagnoses occur among adults aged 65 years or older.12 It is typically measured using a single item question, commonly worded as “In general would you say your health is” with the response items “excellent”, “very good”, “good”, “fair” or “poor”. While, SRH captures an individual’s perception of their overall health, it has been shown to correlate well with performance-based measures of health.13 Among community-dwelling older adults, SRH predicts functional limitation, cognitive impairment, health care utilization, and survival.14–17 Yet, there is limited data regarding the utility of this tool to predict adverse outcomes among older patients with cancer.Self-rated health (SRH) is a widely used measure of health, where patients are asked to report their health status based on their own definition of health.Here, we evaluated SRH as a screening tool to identify older adults with cancer at risk of frailty and GA-identified impairments and its association with mortality.19 We chose 60 years of age as criteria for enrollment in this registry given recognition of the uncertainty of the “right” age cutoff and to allow for meaningful age-related sub-analyses such as the current study.20 For the current report, we included patients completing GA at the time of initial consultation to the UAB medical oncology clinic between 9/2017 and 10/2019. The Institutional Review Board of UAB (IRB-300000092) approved this study.We used participants from the University of Alabama at Birmingham (UAB) Cancer and Aging Resilience Evaluation (CARE) study—a prospective registry enrolling older adults (≥60 years) undergoing cancer treatment at UAB Hospitals and Clinics.21 The question is worded as: “In general, would you say your health is?” with the following possible responses scaled on a 5-point Likert scale, “excellent”, “very good”, “good”, “fair”, or “poor”. We then dichotomized these responses to poor  and good , consistent with other studies.23We measured SRH using a single-item question from the Patient-Reported Outcomes Measurement Information System (PROMIS) global health scale, incorporated as a part of the GA.19 Briefly, CARE GA is a modified version of the Cancer and Aging Research Group GA24 and includes the following domains26: functional status,28 comorbidity,30 cognition, mood disorders,31–33 nutrition,34 social support,35 and HRQoL33, consistent with recommendations from the International Society of Geriatric Oncology.26All participants completed patient-reported GA as previously published .18,19 Br36 and following the standard procedures outlined by Searle et al37 We used 44 GA variables from the CARE survey (Supplementary), and categorized patients as robust (0-0.2), pre-frail (0.2-0.35), and frail (>0.35), as previously described.37 An index constructed with ≥30 items has been previously shown to predict adverse outcomes and survival among older adults.38We constructed a frailty index using the principle of deficit accumulation approach originally described by Rockwood et al,39: impairment in any activities of daily living (ADL); impairments in ≥2 instrumental activities of daily living (IADL); limitation in walking one block; significant weight loss (3% in 3 months or 6% in 6 months); presence of ≥4 comorbidities; presence of depression (T-score ≥60) and cognitive impairment (T-score ≤40) (Supplementary). Additionally, we conducted a sensitivity analysis varying our definition of GA impairment as ≥1 GA impairment as well as ≥3 GA impairments.We classified patients as having significant GA impairment if they had ≥2 impairments among the following seven domains40 which uses death information from Social Security Administration records, obituaries and state death records, and supplemented by review of medical records. We abstracted information on demographic and clinical characteristics including age at the time of GA evaluation, sex, race/ethnicity, cancer type, cancer stage, and planned systemic therapy from medical records.Information on vital status was updated to January 12, 2019, linking the study cohort to Accurint database,n = 208) and four exercise intervention trials  at the University of North Carolina at Chapel Hill.41 We measured and similarly categorized SRH based on a single-item question from the PROMIS global health scale. Frailty was defined using the 36-item Carolina Frailty Index as previously reported.6 Meanwhile, GA impairment was defined as the presence of ≥2 impairments among the following seven domains: impairments in ≥2 IADL; abnormal timed get up and go test (≥20 s42); limitation in walking one block; significant weight loss (5% within 6 months); presence of ≥4 comorbidities using OARS comorbidity assessment; presence of depression ; and cognitive impairment (Blessed Orientation Memory Concentration test score ≥1144). Of note, data on ADL were not available and abnormal TUG ≥20 s was used as a surrogate for ADL, as shown before.45We obtained data on 372 older adults ≥60 years with cancer enrolled in the Carolina Senior Registry  and GA impairment in terms of sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic curve (ROC AUC). We computed binomial exact 95% confidence intervals for sensitivity, specificity, positive predictive value, and negative predictive value. Meanwhile, the AUC and its asymptotic normal 95% confidence interval was calculated using the non-parametric method proposed by DeLong et al48 To account for variation in time between the date of diagnosis and time of GA evaluation, we used a landmark analysis using the date of GA evaluation as the start of follow-up. We used Kaplan–Meier methods and log-rank test to compare the survival distributions between those reporting good vs poor SRH. We used Cox proportional hazards regression to evaluate the independent impact of SRH on overall survival adjusting for potential confounders including age, sex, race, cancer type/stage, and planned systemic therapy. We tested proportionality assumption of the Cox model using Schoenfeld residuals and testing for interaction using log person-time; covariates violating this assumption were used as stratification variables in the final model.The median follow-up of the study cohort was estimated using reverse Kaplan–Meier method.49 We estimated mediation with “exposure” defined as a dichotomized measure of self-reported health (poor/fair vs. good/excellent) and the mediator focused on frailty index as a continuous variable. We specified an accelerated failure time model with Weibull distribution and used the Med4way package in STATA50 to estimate the causal contrasts that arise in the above decomposition.Finally, we hypothesized that frailty might act as a mediator between SRH and survival. Conceptually, a mediation analysis attempts to break down the total effect of an exposure (SRH) on outcome  into two parts: (1) direct effect: effect of an exposure on an outcome in the absence of mediators and (2) indirect effect: effect of an exposure on outcome explained by a set of mediator (Frailty). To allow for interaction between SRH and frailty, we used the four-way decomposition as proposed by Vanderweele to conduct this mediation analysis. This method breaks down the overall effect  of an exposure  on outcome , in the presence of a mediator (frailty) into four components; (1) the effect of exposure in the absence of the mediator, (2) effect due to interaction only, (3) effect due mediation and interaction, and (4) due to mediation only.All hypothesis testing was two-sided, and the level of the significance was set at .05. We conducted statistical analysis using SAS statistical software version 9.4  and STATA v13.0 .Of the 708 older adults diagnosed with cancer in the CARE registry, 603 (85%) underwent GA and reported on SRH at the time of initial consultation with a medical oncologist . The medn = 203) or fair  (n = 274). When compared with patients with good SRH, those reporting poor SRH were more likely to be female , have less than high school education , diagnosis of pancreatic cancer , and stage IV disease   . Upon diP = .01) .P < .001). Similarly, patients with poor SRH were more likely to have GA impairment (≥2 GA domain impairments) When compared with those with good SRH  as well as domain-specific impairments in ADL, IADL, depression anxiety, multi-morbidity, polypharmacy, malnutrition, and one or more falls  versus poor (poor or fair) was also the optimal cut point offering the best balance between sensitivity and specificity . Using tP < .001; P < .001) after adjusting for age, sex, race, cancer type, and planned chemotherapy as well as accounting for cancer stage as stratification variable  (P < .001) after adjusting for age, sex, race, cancer type, cancer stage, SRH, and planned chemotherapy. Interestingly, the prognostic impact of SRH remained no longer significant  after adding frailty into the multivariable analysis, suggesting a possible mediating role  . Furthering role .We conducted a four-way decomposition analysis to identify the proportion of the observed relationship between SRH and mortality  that is mediated by frailty. The details of the mediation analysis are presented in supplementary files , where tOur replication cohort comprised of 372 older adults ≥60 years with cancer, with a median age of 73 years (range 60-91 years), 47% females, 86% non-Hispanic white and with multiple myeloma (18%), breast cancer (17%), and prostate cancer (13%) being most common diagnoses. In this cohort, the prevalence of frailty was 17%, whereas 26% had significant GA impairment (≥2 GA domain impairments). Self-rated health demonstrated moderate sensitivity (61%) and high specificity (82%) for capturing frailty, as well as GA impairment  .In this study of 975 older adults with cancer, we found that a single-item SRH question has a moderate-to-high sensitivity and a high specificity for capturing frailty and GA impairment, suggesting its value as a simple, one-item screening tool for identifying vulnerable older adults with cancer who require further evaluation with a GA. Furthermore, we demonstrated that poor SRH is independently associated with inferior survival, and this effect is largely mediated by a higher prevalence of frailty in this population.51 To our knowledge, this is the first study to demonstrate the utility of single-item SRH question as a potential screening tool for identifying older cancer patients with frailty and GA impairments. Among older adults ≥60 years with chronic kidney disease, single item SRH demonstrated a 70.0% sensitivity and 92.0% NPV in identifying frail individuals using Fried phenotype as well as Dalhousie Clinical Frailty Score52. Similar findings were reported in 243 community dwelling older adults aged ≥75 years, where SRH had a sensitivity of 62.5%, specificity of 93.6%, and a PPV/NPV of 67.5%/92.2%53. Notably, both studies report a high NPV of this tool (≥90%), arguing that patients reporting excellent, very good or good SRH are very unlikely to have frailty and a comprehensive GA can be likely deferred.Despite overwhelming evidence regarding the role of GA in capturing frailty and GA impairments among older adults with cancer, widespread implementation remains limited due to perceived complexity, interruption in clinic workflow, and time/resource constraints.54 IADL impairments,55 cognitive impairment,57 malnutrition,57 and multimorbidity57. Additionally, SRH independently predicted overall survival in our cohort; a finding that has been reported among community dwelling older adults,14 older adults presenting to the ED,54 or hospital,58 and those with chronic kidney disease,59 coronary artery disease,59 and cognitive impairment.56 Only few studies, however, have evaluated the association of SRH with mortality among patients with cancer60–63; among 735 adults with advanced malignancy, SRH predicted survival independent of the individual’s performance status.61 In another population-based observational study, SRH status predicted non-prostate cancer mortality among men with localized prostate cancer.62 Furthermore, we show that this association is mediated by frailty, demonstrating the value of SRH in capturing frailty among older adults with cancer.We found that SRH was associated with several domain-specific impairments among older adults with cancer. This is consistent with prior observations that older adults reporting poor SRH have a higher prevalence of ADL impairments,10 Similarly, the Flemish version of the Triage Risk Screening Tool (fTRST), a five-item screening tool, had a sensitivity of 67% and specificity of 74% in identifying older adults ≥70 years with cancer who had ≥2 geriatric impairments.39 Finally, among older adults ≥70 years with prostate cancer, Mohile et al showed that the 13-item Vulnerable Elders Survey (VES-13) had a sensitivity of 73% and specificity of 86% in identifying older adults with ≥2 GA impairments.11 It is important to note that both G-8 and VES-13 include a self-rated health as a component. However, in contrast to the above screening tools, SRH is a simple, practical, and an easily scalable single-item screening tool. Moreover, we show for the first time that SRH can identify frail older adults with cancer as well.Recognizing the challenge to identify older adults with cancer for whom a comprehensive GA would be most beneficial, several screening tools have been previously explored. Bellera et al evaluated the G-8 tool, incorporating age and seven items from the Mini Nutritional Assessment and reported a sensitivity of 85% and 92% as well as specificity of 65% and 48% respectively for identifying patients with ≥1 and ≥2 GA impairments, respectively.2 We had limited clinical information regarding type and intensity of chemotherapy, tumor grade, lymph node involvement, and tumor markers that may adversely affect survival in our cohort. Our follow-up time was relatively short; additional follow-up with more mature survival data is needed. We were not able to directly compare SRH with established GA screening tools such as G-8,10 fTRST,64 and VES-1365. Finally, due to the self-reported nature of this instrument, SRH may not be useful as a screening tool for older adults with moderate to severe cognitive impairment.Our study has limitations. Over 60% of our cohort had gastrointestinal malignancies recruited from a single institution in the southeastern USA. As such, our findings may not be readily applicable to other malignancies or populations. Of note, our replication cohort from a different site with different cancer types had similar results. We cannot rule out the possibility of selection bias, where fit patients with good health may be more likely to complete the CARE survey as opposed to those in poor health. However, over 92% of all newly diagnosed gastrointestinal malignancies presenting to the GI oncology clinic are currently included in the CARE registry.In conclusion, a single-item SRH question is a potential screening tool for capturing frailty and GA impairments among older adults with cancer. Future studies should validate our findings in geographically diverse cohorts across different cancer types and compare it against established screening tools.oyab020_suppl_Supplementary_MaterialClick here for additional data file.


Next, less common RNA modifications including m5C, Nm and ac4C were investigated and also found to be present on viral RNA. It now appears that viral RNA is littered with a multitude of RNA modifications. In biological systems that are under constant evolutionary pressure to out compete both the host as well as newly arising viral mutants, it poses an interesting question about what evolutionary benefit these modifications provide as it seems evident, at least to this author, that these modifications have been selected for. In this review, I discuss how RNA modifications are identified on viral RNA and the roles that have now been uncovered for these modifications in regard to viral replication. Finally, I propose some interesting avenues of research that may shed further light on the exact role that these modifications play in viral replication.The field of mRNA modifications has been steadily growing in recent years as technologies have improved and the importance of these residues became clear. However, a subfield has also arisen, specifically focused on how these modifications affect viral RNA, with the possibility that viruses can also be used as a model to best determine the role that these modifications play on cellular mRNAs. First, virologists focused on the most abundant internal mRNA modification, m The post-transcriptional regulation of mRNA function by the covalent modification of individual nucleotides, referred to as epitranscriptomic gene regulation, has attracted increasing interest in recent years. Through the development of better mapping techniques to identify the sites of RNA modification, the ability to quantify these modified residues through mass spectrometry, and the identification of modification writer and reader proteins , researc6A, comprises an additional methyl group at the N6 position of adenosine and constitutes ~0.4% of all adenosine residues on human cellular mRNA [5C) at ~0.05% is also quite prevalent on cellular mRNAs [m) are also widespread, where the methyl group is added to the ribose base as opposed to the nucleoside as is the case for the previous three modifications. 2′O-methylation occurs on all four nucleosides on cellular mRNAs. Common 5′cap modification 7-methylguanosine (m7G) has also been recently reported to be found internally on cellular mRNAs, though not a great deal is known about the abundance at this early stage [6,6A), and N1-methyladenosine (m1A) have also been proposed to be present on cellular mRNAs, though if they are indeed present, they are at extremely low levels [4C) rounds out the list of common mRNA modifications and this arises from the addition of an acetyl group to cytosine. Interestingly, this is the first and, so far, only acetylation event reported on eukaryotic mRNAs [RNA modifications include the addition of generally small biochemical groups to adenosine, cytosine, uracil and guanosine, with a methyl group being the most common addition. The most common mRNA modification, mlar mRNA . The secar mRNAs . 2′O-metw levels . Finally6A, on RNA from a variety of RNA and DNA viruses, as detailed in 6A modifications are highly prevalent on viral RNA, seemingly present on RNA from every virus that has been investigated. Many of the studies compiled in The abundance of these modifications on viral RNA is less well understood. However, in recent years, there have been a growing number of publications investigating the presence of RNA modifications, mostly m6A, m5C, 2′O-methylation and ac4C have now been mapped on HIV-1 RNAs [6A having additionally been mapped on RNA from IAV, MLV, SV40 and a number of flaviviruses [The presence of these modifications has been found to increase the replication of HIV-1, SV40, HBV and IAV ,11,12,13V-1 RNAs ,11,14,15iviruses ,17.While the function of these modifications on mRNA is slowly being elucidated, with roles in splicing ,19,20, t6A identification, before methods were adapted to identify m5C, m1A and ac4C [With the commercial availability of modified nucleoside-specific antibodies, novel mapping techniques started to be developed . This waand ac4C ,23,48. I6A, m5C and ac4C modification identification [6A and m5C using these methods [6A modifications have also been mapped in this way.These antibody-based methods, or slightly altered versions of them, have since been used to great effect to map a large number of modifications on RNAs from a whole host of viruses. HIV-1 has been the most extensively studied by this method, with antibody mapping having been used for mfication ,10,11,14 methods , SV40 [1 methods , Zika vi methods , hepatit methods  and hepa methods  m6A modiHowever, antibody-based mapping of modifications is inherently noisy, with input or IgG controls being required for a number of these methods to remove background signal . In addi6A-specific RNA-binding proteins, known as m6A ‘readers’ first described by Dominissini et al. [6A residue, to a similar resolution to some antibody-based approaches such as PA-m6A-seq  and 5-formycytidine (f5C). Unfortunately, the efficiency of bisulfite conversion is affected by RNA secondary structure. This may be a problem for the use of this technique to map modifications on viral RNAs, which are notoriously rich in secondary structures and may produce too many artifacts [RNA bisulfite sequencing is another well-used biochemical method, which has been used to map mo uracil . This isrtifacts . This wiN-cyclohexyl-N′-(2-morpholinoethyl) carbodiimide metho-p-toluenesulfonate (CMC) to selectively modify ψ residues. This large CMC modification on each ψ results in a total block to reverse transcription and these prematurely stopped cDNA fragments can be deep sequenced and identified bioinformatically , while also mapping sites of modification on a range of other flaviviruses. These authors found that m6A modification had a direct effect on viral RNA retention in virus replication factories, effectively slowing down the infection and potentially leading to a prolonged chronic infection as is characteristic of HCV infection in the liver. However, when m6A was depleted, these viral transcripts are more readily bound by viral Core protein and are successfully trafficked to sites of virion packaging within the cell [6A on HIV-1 and found that the presence of two m6A sites in the Rev-response element (RRE) increased the affinity of Rev for the RRE that in turn increased the nuclear export of RRE containing HIV-1 RNA. While Eckwahl et al. [5C modification of MLV RNA, which the authors found, through an association with ALYREF, also increased nuclear export of viral RNA and thus increased viral replication. The differing roles for RNA modifications in the trafficking of HCV and retrovirus RNA further adds a fascinating layer to the complexity of modification mediated post-transcriptional regulation of viral RNA.The correct trafficking of viral RNA to sites of replication, translation and packaging is critical to the successful completion of the viral replication cycle. RNA modifications have been identified in a number of cases as having an important role in this process of RNA trafficking. Gokhale et al.  investigthe cell . Two furthe cell  exploredl et al.  focused 6A, have been shown to dysregulate the stability of cellular mRNAs generally through interaction with the YTH domain-containing family of proteins [6A by methyltransferase knockdown or simply depletion of YTHDF proteins by siRNAs, that m6A contributed to the destabilisation of viral RNA mostly likely through interactions with YTHDF proteins [6A induced RNA destabilization does not appear to be the case. The presence of m6A residues on the RNA of HIV-1 [RNA modifications, most notably mproteins . This haproteins ,41. Howeof HIV-1  and IAV of HIV-1  have beeof HIV-1 . This in6A, a recent study by Tsai et al. investigating the role of ac4C modifications in HIV-1 RNA found that these modifications to increase the stability of HIV-1 RNA [4C that found a similar phenotype on cellular mRNAs [4C are known so the authors were unable to speculate as to whether this increase in RNA stability is due to RNA structural changes or RNA-protein interactions.Aside from mIV-1 RNA . This fiar mRNAs . The aut5C is generally present at specific locations across the HIV-1 mRNA genome [5C modification was perturbed due to writer knockout or mutagenesis to prevent modification, alternative splicing at one specific site, namely the D1/A2 splice junction, was altered. Interestingly, this reduction in splice acceptor usage was found for both early (~1.8 kb) and late (~4 kb) HIV-1 classes of transcripts. For adenoviral RNA, Price et al. investigated the role of m6A modifications on viral splicing [6A modifications globally, by siRNA mediated knockdown of METTL3 expression, significantly reduced the expression of specifically late adenoviral transcripts. They went on to determine that this phenotype was caused by a reduction in splicing efficiency. These studies imply that the presence of RNA modifications, which are already known to affect splicing on cellular mRNA [RNA modifications have been implicated in the alternation of splicing events for both HIV-1 and adenovirus RNA ,18. RegaA genome . Howeverlar mRNA , are bei6A or ψ diminished the innate immune signalling by TLRs and RIG-I, respectively. However, it should be noted that both these studies used RNAs with high levels of modified nucleosides much greater than would be physiologically relevant for viral RNAs.This idea of RNA modifications preventing innate immune sensors from recognising foreign RNAs is not a new concept ,66. Kari6A modified and this is found to prevent recognition by RIG-I, in validation of the phenomenon described previously by Durbin et al. These studies perform experiments to reduce the modification of virally encoded adenosines using methods such as mutation of the viral genome or treatment with 3-deazaadenosine (DAA), an inhibitor of S-Adenosylhomocysteine (SAH) hydrolase and find that this in turn increases the cellular type 1 interferon response to infection.However, in what feels like a seamless follow on to this thought-provoking work, one exciting study into the role of RNA modifications in the viral replication cycle was recently published by Ringeard et al.  looking 6A and into other common modifications already found on cellular mRNAs. Lead candidates would include m5C, ψ and Nm residues, of which very little is currently known about in regard to viral RNA [As more research is performed and published surrounding viral epitranscriptomics there are several ways in which scientists in the field can improve on our current knowledge base. First, we need to expand our interests beyond miral RNA ,36. WhenAnother methodology for the study of viral epitranscriptomics that may be better utilised in the future is the generation of viral stocks where the genomic RNA is entirely unmodified. This would help answer important questions about the role of modifications during the initial stages of infection, prior to transcription. This concept has rarely been utilised so far , but thi6A, the writer complex for viral RNA is almost always METTL3/METTL14/WTAP, but for the likes of m5C, it is not as straightforward as the writer may be one out of a family of NSUN proteins or even DNMT2. Through the identification of the correct writer protein, the sites of modification can be better validated, by knocking out or knocking down expression of the writer and then remapping the modification on viral RNA. If the correct writer is no longer present, it would be expected that modification would be ablated. The identification of the primary writer protein will also allow researchers to better investigate the relationship between these proteins and the viral polymerases. A number of RNA modifications have been found to be added co-transcriptionally to cellular mRNAs. Therefore, it is a valid hypothesis that the polymerases of viruses that exploit RNA modifications would associate with writer machinery. This is an area of study that is significantly lacking but may reveal surprising discoveries in the coming years that could impact on our knowledge of both viral and cellular RNA modification utilisation. If these potential interactions are proven to be essential to viral replication, this would also be an interesting avenue of research into the development of antivirals.As the research progresses, we also need to do better at identifying the exact writer protein for the viral RNA being investigated . RegardiThe field of viral epitranscriptomics is just at the cusp of a research explosion over the next few years as the line that separates molecular virologists and RNA biologists begins to blur. Exciting questions, as discussed above, will be answered in the coming years and we will hopefully establish the key roles for individual RNA modifications in the replication cycles of a wide variety of viruses. Technological advances will only aid in our understanding of these modifications, with enhanced mapping techniques and better quantification of virus modified sites hopefully not far off.


PH20 is a member of the human hyaluronidase family that degrades hyaluronan in the extracellular matrix and controls tumor progression. Inhibition of DNA methyltransferases (DNMTs) leads to elevated hyaluronan levels; however, whether DNMT inhibitors control PH20 remains unclear. Here, we report that the DNMT1 inhibitor, decitabine, suppresses PH20 expression by activating the long non-coding RNA PHACTR2-AS1 (PAS1). PAS1 forms a tripartite complex with the RNA-binding protein vigilin and histone methyltransferase SUV39H1. The interaction between PAS1 and vigilin maintains the stability of PAS1. Meanwhile, PAS1 recruits SUV39H1 to trigger the H3K9 methylation of PH20, resulting in its silencing. Functionally, PAS1 inhibits breast cancer growth and metastasis, at least partially, by suppressing PH20. Combination therapy of decitabine and PAS1-30nt-RNA, which directly binds to SUV39H1, effectively blocked breast cancer growth and metastasis in mice. Taken together, DNMT1, PAS1, and PH20 comprise a regulatory axis to control breast cancer growth and metastasis. These findings reveal that the DNMT1-PAS1-PH20 axis is a potential therapeutic target for breast cancer. LncRNAs are transcripts with more than 200 nucleotides, lacking protein-coding potential.4 Although thousands of lncRNAs have been identified, only a small number of them are well characterized in cancer progression.6 The lncRNA PHACTR2-AS1  was originally found to promote cell proliferation and invasion in hepatocellular carcinoma.8 However, PHACTR2-AS1(PAS1) plays the opposite role in breast cancer. PAS1 expression is suppressed in breast cancer and lower PAS1 level predicts poor outcomes for breast cancer patients.9 PAS1 directly binds to ribosome DNA and inhibits ribosome synthesis, ultimately blocking breast cancer development.9 Nevertheless, the precise mechanism by which PAS1 attenuates breast cancer metastasis is unclear.RNA-binding proteins are important regulators of mRNAs and non-coding RNAs in cancer. Vigilin, also called high-density lipoprotein binding protein Hdlbp, is a highly conserved RNA-binding protein, containing 15 tandem KH domains. It could bind over 700 RNAs to regulate mRNA translation, transport, and stability.11 Hyaluronidases are responsible for degrading hyaluronan; thus, they play important roles in cancer progression.12 The family of hyaluronidases includes Hyal-1, Hyal-2, Hyal-3, Hyal-4, PH20, and a pseudogene in the human genome.13 Hyal-1, Hyal-2, and PH20 have hyaluronidase activity for hyaluronan hydrolysis.14 The hyaluronidase family members have different expression profiles in different cancers. Hyal-1 is significantly overexpressed in bladder cancer and has been used as a diagnostic marker for bladder cancer.15 The activities of Hyal-1, 2, 3, and PH20 are increased in colorectal cancer, and higher levels of Hyal-1 and Hyal-2 expression correlate with more aggressive stages.16 In breast cancer, Hyal-1 acts as a tumor promoter, and its overexpression promotes cancer cell growth and motility.17 Moreover, Hyal-2 is also implicated in the invasiveness of breast cancer cells.18 Hyal-1 and Hyal-3 have elevated expression in lung cancer cells.19 PH20  is specifically expressed on the surfaces of sperm and participates in the process of sperm-egg fusion.20 Aberrant expression of PH20 is involved in the progression of prostate, laryngeal, and breast cancers.23 However, the mechanism through which PH20 expression is regulated in cancer remains unknown.Hyaluronan is critical for forming the extracellular matrix and mediating cell to cell interactions.24 DNMT inhibitors can induce the re-expression of silenced tumor suppressors by decreasing DNA methylation levels.25 There are two FDA-approved DNMT inhibitors, 5-aza-2′-deoxycytidine (decitabine) and 5-aza-cytidine (azacitidine), which act as nucleoside analogs for incorporation into newly synthesized DNA strands.26 These two agents have been successfully used to treat myelodysplastic syndrome (MDS) and myeloid hematologic malignancies. However, the efficacy of DNMT inhibitors in the treatment of solid tumors remains unclear.27 In this study, we reported that a DNMT1 inhibitor controls PH20 expression by activating PAS1. The combination therapy of DNMT inhibitor decitabine and PAS1-30nt-RNA efficiently blocked breast tumor growth and metastasis.DNA methyltransferases (DNMTs) are key enzymes that maintain DNA methylation.To scan the transcriptional regulation of PAS1, the small interfering RNAs (siRNAs) targeting 183 transcription factors (TFs) were transfected into MCF7 cells, and the fold changes of PAS1 were determined by RT-PCR. We found that 32 TF siRNAs resulted in the activation of PAS1 (fold change ≥2), whereas 18 TF siRNAs led to the suppression of PAS1 (fold change ≤0.5) Fig. . GO analTo explore the relationship between DNMT1 and PAS1 expression, we firstly examined the PAS1 RNA level by RNA in situ hybridization and DNMT1 protein levels by immunohistochemical staining of human breast cancer tissue arrays. As expected, the expression of DNMT1 was negatively correlated with that of PAS1 Fig. . The levTo verify the regulation of DNMT1 on PAS1, we examined the levels of PAS1 upon knockdown or overexpression of DNMT1. DNMT1 knockdown resulted in an upregulation in the expression of PAS1 Fig. , whereasTo determine the protein interactome of PAS1, RNA pull-down combined with mass spectrometry was performed. Four candidates are found including vigilin, matrin-3, SFPQ, and hnRPQ Fig. . We thenP-value was calculated using the Rank method  against lncRNAs. We apply another strategy to restore RNA level with synthetic RNA molecules. We synthesize a functional fragment (PAS1-30nt-RNA) of a lncRNA (PAS1) and add chemically modifications for in vivo RNA delivery. PAS1-30nt-RNA naturally exists in epithelial cells and thus has rather low toxicity. The combination of decitabine with PAS1-30nt-RNA showed a significant anti-tumor activity superior to that of decitabine alone. The marked efficacy of the combination not only depends on the synergistic effects of the DNMT inhibitor and PAS1-30nt-RNA, but also on the enhanced expression of PAS1 induced by the DNMT inhibitor. Thus, an investigation of the combination of DNMT inhibitors with small-molecule RNA might be expected in the future.Given the action of DNA methylation in cancer and the reversibility of epigenetic modifications, DNMT inhibitors have a strong potential for use in cancer therapy. Combination therapy with a DNMT inhibitor and a histone deacetylase inhibitor has shown encouraging results.2 in a humidified incubator.Human breast cancer cells were obtained from the Cell Resource Center, Peking Union Medical College . The species origin of the cell lines was confirmed with PCR. The identity of the cell lines was authenticated with short tandem repeat profiling. The cell lines were checked free of mycoplasma contamination by PCR. Cells were grown in DMEM medium (Gibco) supplemented with 10–15% fetal bovine serum at 37 °C under 5% COTotal RNA was extracted using TRIzol reagent (Invitrogen). RNA nuclear or cytoplasmic fraction was performed using the Cytoplasmic & Nuclear RNA Purification Kit  according to the manufacturer’s instructions. A HiScript II Q RT SuperMix Kit  was used to synthesize the cDNA. A ChamQ SYBR qPCR Master Mix  was used for real-time PCR with a Light Cycler 96 detection system (Roche). Primer sequences:PAS1-F-GTCAGGCTCCTGGATCTTCTAG; R-AAGGGCTAAGGAACTCGATTG.2) was heated to 95 °C for 2 min, on ice for 3 min, and then at RT for 30 min. Then RNA was incubated with pre-cleared cell lysates (containing 1 mg proteins) in 500 μl binding buffer  and then incubated at RT for 1 h. Next, 25 μl of washed streptavidin Dynabeads (Invitrogen) was added to each binding reaction and incubated at RT for 1 h. The streptavidin Dynabeads were washed with binding buffer for 5 times and boiled in SDS buffer. The retrieved proteins were analyzed by Western blot.Plasmid of pcDNA3.1-PAS1 was linearized as DNA templates. Biotin-labeled RNA was produced with the MEGAscript T7 Kit (Ambion) with biotin-16-UTP (Ambion). The biotin-labeled RNA was further purified using the MEGA clear Kit (Ambion). 10 μg biotin-labeled RNA in RNA structure buffer  was used to carry out the RNA immunoprecipitation assay. According to the manufacturer’s instructions 5 μg of anti-FLAG, anti-Vigilin, anti-mouse IgG, or anti-rabbit IgG antibody was used. The co-precipitated RNAs were extracted, and RT-PCR was performed to analyze the samples.® Enzymatic Chromatin IP Kit . The cells were cross-linked for 10 min at 37 °C with 1% formaldehyde added directly into the culture medium. Cross-linking was quenched by the addition of glycine to a final concentration of 0.125 M, followed by incubation at RT for 5 min. Cells were then lysed, and the cytoplasmic fraction was discarded. Nuclear fractions were sonicated to obtain 300–500 bp DNA fragments. After centrifugation, the supernatant was transferred to a new tube. The pre-cleared supernatant was immunoprecipitated with anti-H3K9me2 , anti-H3K9me3 , and anti-DNMT1 (Abcam) antibodies overnight at 4 °C, with rotation, followed by incubation with protein G-conjugated magnetic beads for 2 h at 4 °C. After elution and reversal of cross-linking, the precipitated DNA was purified and resuspended in Tris-EDTA for qPCR. The primer sequences used for qPCR are listed below:A chromatin immunoprecipitation (ChIP) assay was performed using the SimpleChIPPAS1 CHIP1 F-CAGTAAGTAAGGCAGTGAGC; R-CTGCGTCAGATGAAGAAGA;PAS1 CHIP2 F-TGGCATCTGCTTGGC; R-GAGGTGTTTGCGTCAT;PH20 CHIP F-AAGAGGCAGCATTTGA; R- AGGTGGAGCCAGTCAT.TM-modified PAS1 probe  was diluted in hybridization buffer  and placed on a slide for denaturation at 80 °C for 2 min, and hybridization at 54 °C for 30 min. After hybridization, cells were washed with 0.1 SSC at 65 °C for 3 × 10 min. Streptavidin Alexa Fluor 488 conjugate (Invitrogen) was then added and incubated for 4 h at 37 °C. Nuclei were counterstained with DAPI. Images were captured using a confocal laser-scanning microscope (Carl Zeiss).Cells were briefly rinsed with phosphate-buffered saline (PBS) and fixed with 4% formaldehyde for 15 min at room temperature, followed by permeabilization with pepsin (0.1% in 10 mM HCl) for 1 min at 37 °C. After washing, the cells were dehydrated using 70, 80, 90, and 100% ethanol, then air dried. A 5′biotin-labeled LNATM-modified PAS1 probe  at 54 °C overnight. Subsequently, streptavidin was conjugated to Poly Horseradish Peroxidase (Poly-HRP) using the detection kit . Criteria for staining were classified into four groups based on staining level: no staining,0; low staining, 1+; moderate staining, 2+; and strong staining, 3+.Human breast cancer tissue microarrays were purchased from the National Human Genetic Resources Sharing Service Platform2005DKA21300 . Tissue sections were deparaffinized and gradually rehydrated. After treatment withpepsin, tissues were hybridized with 5′biotin-labeled LNACells were harvested and washed once with cold PBS. Cells were then lysed in lysis buffer . For each immunoprecipitation assay, proteins were incubated with anti-HA antibody at 4 °C overnight, followed by incubation with protein A/G agarose beads (Santa Cruz Biotechnology). Beads were washed three times with NP40 buffer, the bound proteins were eluted with 2×SDS loading buffer and then boiled at 100 °C for 5 min. Precipitated proteins were detected using western blot. Antibodies against anti-DNMT1 (Abcam), anti-GAPDH (ZSGB-BIO), anti-SUV39H1 (Active Motif), anti-vigilin (Abcam), and anti-PH20 (Abcam) were used.4 cells was added to the upper chamber of a modified Boyden Chamber , and 0.6 ml media containing 20% FBS was added to the lower chamber. After 12–24 h, non-migratory cells were removed by wiping the top of the membrane with cotton swabs, and cells that had passed through the membrane were fixed, stained with crystal violet, and counted by microscopy.A cell suspension containing 5 × 10MDA-MB-231-Luc-D3H2LN cells were inoculated orthotopically onto the abdominal mammary fat pad or injected into tail vein of 6-week-old female NOD-SCID mice. Control RNA or the PAS1-30-nt RNA were chemically synthesized with modifications of 2′-O-methylation and 5′-Cholesterol from Ribobio Co.  for in vivo RNA delivery. Control RNA or the PAS1-30-nt RNA (Ribobio) was injected into the tail vein of NOD/SCID mice twice a week for total 4 times. Decitabine (Sigma) was injected intraperitoneally at 2 mg/kg twice a week for total 4 times. The mice were then injected with D-luciferin abdominally , and anesthetized; finally, images were taken using a Xenogen IVIS Lumina system . Images were analyzed using the Living Image software ver. 3.0. . Photon flux was normalized to background.38.The Ethics Committee of Peking University Health Science Center approved the mouse experiments (Permit Number: LA2014122) for this study. The mice were handled in accordance with the ethical standards of the Helsinki Declaration of 1975 and the revised version in 1983. We also referred to the procedures by Workman et al.p < 0.05.All statistical analyses were performed using GraphPad Prism software. Two-tailed unpaired t-tests were used to analyze the two groups. One-way analysis of variance was used to analyze multiple groups. All results are presented as mean ± SEM. Statistical significance was set at Supplementary figures and legendsTable S1. Mass SpectrometryTable S2. PAS1 co-expression


Hordeum vulgare) to its interacting fungal pathogens. We found barley EVs that were 156 nm in size, containing predominantly 21 and 19 nucleotide (nts) siRNAs, starting with a 5′-terminal Adenine. Although a direct comparison of the RNA cargo between HIGS and SIGS EV isolates is improper given their underlying mechanistic differences, we identified sequence-identical siRNAs in both systems. Overall, the number of siRNAs isolated from the EVs of dsRNA-sprayed barley plants with sequence complementarity to the sprayed dsRNA precursor was low. However, whether these few siRNAs are sufficient to induce the SIGS of pathogenic target genes requires further research. Taken together, our results raise the possibility that EVs may not be mandatory for the spray-delivered siRNA uptake and induction of SIGS.The demonstration that spray-induced gene silencing (SIGS) can confer strong disease resistance, bypassing the laborious and time-consuming transgenic expression of double-stranded (ds)RNA to induce the gene silencing of pathogenic targets, was ground-breaking. However, future field applications will require fundamental mechanistic knowledge of dsRNA uptake, processing, and transfer. There is increasing evidence that extracellular vesicles (EVs) mediate the transfer of transgene-derived small interfering (si)RNAs in host-induced gene silencing (HIGS) applications. In this study, we establish a protocol for barley EV isolation and assess the possibilities for EVs regarding the translocation of sprayed dsRNA from barley ( RNAi-based plant protection strategies represent powerful tools to address the goals of the European “farm to fork” strategy in order to reduce the usage of pesticides by approximately 50% by 2030. As an alternative to conventional pesticides, RNAi-based plant protection holds enormous potential to prevent further drastic losses of biodiversity. Over the last two decades, more than 170 studies have demonstrated the feasibility of controlling agronomically and horticulturally relevant plant diseases by utilizing the transgenic expression (host-induced gene silencing (HIGS) [Arabidopsis thaliana leaf extracts and apoplastic fluids contain transgene-derived small interfering RNAs (siRNAs) [5 preprint]. Furthermore, RNA sequencing (RNA-seq) analysis reveals that EVs from plants expressing CYP3RNA, a 791 nucleotide (nts) long dsRNA originally designed to target the three CYP51 genes of the fungal pathogen Fusarium graminearum (Fg), contain CYP3RNA-derived siRNAs [Fg can take up long, unprocessed dsRNA that is processed by its own RNAi [Fg–barley system. In the present study, we isolated, for the first time, EVs from dsRNA-sprayed barley leaves and analyzed their RNA cargo to verify whether barley EVs contain dsRNA spray-derived siRNAs. In addition, we compared our RNA-seq results with existing datasets of EVs isolated from CYP3RNA-expressing Arabidopsis plants to seek sequence similarities between siRNAs in EVs isolated using HIGS and SIGS strategies, respectively.Recent data suggest that, analogous to the role of mammalian exosomes in cell-to-cell communication, fungi rely on a bidirectional sRNA transport system mediated by EVs . Support(siRNAs)  preprint(siRNAs)  preprintown RNAi ,7. This own RNAi , indicatown RNAi . ConsequHordeum vulgare) EVs contain dsRNA spray-derived siRNAs, we established a protocol for EV isolation from barley leaves by adjusting the EV isolation protocol we previously adopted for Arabidopsis preparation [Arabidopsis leaves required only one minute per round to achieve full leaf infiltration. To harvest the apoplastic fluid, the centrifugation speed was adapted from 700× g to 1000× g for 20 min at 4 °C. Purified barley EVs exhibited a highly diverse size distribution with a mean size of 156 +/− 12.2 nm, which is slightly larger than the mean size of EVs isolated from Arabidopsis  [Arabidopsis as the plant model organism further impedes efforts to identify possible barley EV markers. Based on the amino acid similarity, we located 10 homologs for PEN1 and seven homologs for TET8 in barley   are the n barley c,d. HoweArabidopsis plants was less informative and reliable, because of the divergent dsRNA origins. While a CYP3RNA transgene leads to the constitutive expression and formation of endogenous dsRNA that can be easily incorporated into intracellular vesicles, exogenously applied dsRNA needs to overcome several cellular barriers before being loaded into EVs. Moreover, the amount of dsRNA continuously decreases after foliar spraying due to degradational and dilutional effects. Consequently, the siRNA quantities that reach the lower unsprayed leaf section for loading into EVs might be reduced compared to prerequisites in HIGS. Given these considerations, it was not surprising that the amount of CYP3RNA spray-derived siRNAs was low. In other words, we found less siRNA in barley EVs than in Arabidopsis EVs, which led to a low read coverage (number of reads that overlapped at a certain position of the sequence) compared to Arabidopsis EVs . Moreover, we assume that the fungal uptake of SIGS RNA triggers may depend on the lifestyle of the interacting fungal pathogen. Given this assumption, further research is required to unravel the routes of dsRNAs and siRNAs necessary to determine the strengths and limitations of the SIGS strategy in a pathosystem-specific manner.One hundred and sixty second leaves of barley cv. Golden Promise were harvested from plants grown for 3 weeks under long day conditions . The leaves were transferred to square Petri dishes with 1% agar and divided into two groups. The upper part of the first group was sprayed with CYP3RNA diluted in TE buffer and the second group was sprayed with TE buffer as the mock control, as previously described [For TEM, copper formvar-coated 300-mesh electron microscopy grids were glow discharged prior to sample application for 40s. Subsequently, 5 µL of the sample, resuspended in PBS, was applied to the grids. Samples were dabbed using Whatman filter paper and grids were washed three times in 50 µL of 2% uranyl acetate and once with distilled water. Needless staining or fixing solutions, buffers and water were removed using Whatman paper between each step. Finally, the grids were air-dried. Preparations were inspected at 120 kV under zero-loss conditions (ZEISS EM912a/b) and images were recorded at slight underfocus using a cooled 2 k × 2 k slow-scan ccd camera (SharpEye/TRS) and the iTEM software package (Olympus-SIS). Two replicates per sample were invested and at least ten meshes per grid were checked to avoid grid to grid or mesh to mesh variations.For size and concentration predictions, purified barley EVs were diluted (1:50) with PBS. Subsequently, 500 µL of the vesicle suspension was loaded into Nanosight NS300 . Five measurements were performed at 25 °C and size, concentration prediction and statistical analysis were performed by the NTA 3.2 Dev Build 3.2.16 software.Arabidopsis thaliana PEN1 (AT3G11820) and TET8 (AT2G23810) paralogs of Hordeum vulgare subsp. vulgare (Hv) were predicted based on their amino acid sequences. These were obtained from The Arabidopsis Information Resource (tair) (Available online: https://www.arabidopsis.org/ (accessed on 10 February 2021)). Paralogs were forecasted by the NCBI’s protein BLAST service (Available online: https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 10 February 2021)) and the phylogenetic tree was built using ETE 3 [The ng ETE 3 .® Small RNA Library Prep Kit  according to the manufacturer’s instructions. Indexed sRNA libraries were pooled and sequenced on the Illumina MiSeq Platform (1 × 36 SE) and the sequences were sorted into individual datasets based on the unique indices of each sRNA library. The RNAseq libraries can be accessed on the European Nucleotide Archive (https://www.ebi.ac.uk/ena/browser/home (accessed on 11 February 2021); Accession ID: PRJEB45864). The quality of the datasets was examined with FastQC (version 0.11.9) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 11 February 2021)) before and after trimming. The adapters were trimmed using cutadapt (version 2.8) [Vesicle RNA was isolated using the Single Cell RNA Purification Kit  according to the manufacturer’s instructions for cells growing in suspension. RNA concentrations were determined using the NanoDrop spectrophotometer  and RNA was stored at −80 °C before samples were sent for RNA sequencing. Indexed sRNA libraries were constructed from RNA isolated from vesicles with the TruSeqion 2.8) . To filtion 2.8)  was usedion 2.8) . All reaion 2.8)  to idention 2.8)  and thenion 2.8) . These fion 2.8) . Read coHordeum vulgare (IBSC v2—release 47) were downloaded from EnsemblPlants [https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ (accessed on 12 February 2021)), which used cutadapt (version 2.8) [https://github.com/agordon/fastx_toolkit (accessed on 12 February 2021)). The bacterial contaminations were filtered out as demonstrated in the previous section. The remaining reads were aligned to the reference genome using STAR (version 2.7.3a) [Arabidopsis. The following RNA types were examined: “lncRNA”, “pre_miRNA”, “mRNA”, “ncRNA_gene”, “rRNA”, “snoRNA”, “snRNA” and “tRNA”. All alignments that could not be assigned to a feature were considered as “not assigned”.To determine the RNA species, the reference genome and annotation of blPlants . The adaion 2.8) . In this 2.7.3a) . feature