When applied to human transcriptomes, ReCappable-seq identifies Pol II TSSs that are in agreement with orthogonal methods such as CAGE. Additionally, ReCappable-seq reveals a rich landscape of TSSs associated with Pol III transcripts that have not previously been amenable to study at genome-wide scale. Novel TSS from non–Pol II transcription can be located in the nuclear and mitochondrial genomes. ReCappable-seq interrogates the regulatory landscape of coding and noncoding RNA concurrently and enables the classification of epigenetic profiles associated with Pol II and non–Pol II TSS.Determination of eukaryotic transcription start sites (TSSs) has been based on methods that require the cap structure at the 5′ end of transcripts derived from Pol II RNA polymerase. Consequently, these methods do not reveal TSSs derived from the other RNA polymerases that also play critical roles in various cell functions. To address this limitation, we developed ReCappable-seq, which comprehensively identifies TSS for both Pol II and non–Pol II transcripts at single-nucleotide resolution. The method relies on specific enzymatic exchange of 5′ m Current widely used methods to characterize transcriptomes such as whole-transcriptome shotgun sequencing (RNA-seq)  fall shoCurrently methods for TSS determination such as cap analysis of gene expression (CAGE) , NanoCAGWe have previously developed Cappable-seq to identify TSS in prokaryotic species . CappablThe comparison of two data sets, one derived from RNA treated with calf intestinal alkaline phosphatase (CIP) and the other not, permits the discrimination between capped 5′ ends and triphosphorylated 5′ ends. Each TSS can be inferred as derived from either Pol II or non–Pol II polymerases, because Pol II transcripts are capped and not depleted with CIP, whereas non–Pol II transcripts are triphosphorylated and depleted with CIP.We applied ReCappable-seq to the transcriptome of the A549 human cancer cell line with the aims to (1) identify all TSSs irrespective of their transcribing polymerase and (2) classify them into Pol II and non–Pol II TSS. We also performed CAGE on the same cell line in order to benchmark ReCappable-seq for the identification of Pol II TSSs.The principle of ReCappable-seq , a noncapped, nontriphosphorylated transcript, was significantly depleted  were processed using the steps outlined in Supplemental Table S1). In addition, for both the CIP-treated and untreated samples, unenriched libraries were constructed for each from an aliquot taken before the streptavidin enrichment step. Reads were mapped to the human genome using STAR with the ENCODE default parameters (Methods)  .P-value < 2.2 × 10−16) between replicates , showing high reproducibility of the technique. ReCappable-seq replicates were combined and down-sampled to 63 million mappable reads for subsequent analyses.In parallel, we used RNA from the same A549 RNA preparation and performed CAGE for comparison. Analysis of the ReCappable-seq technical replicates at single-nucleotide resolution reveals a high correlation  versus nonprimary transcripts, using the fraction of reads mapping to rRNA as a surrogate for nonprimary transcripts. rRNAs are formed by the processing of a single pre-rRNA 45S transcript to form the mature 18S, 5.8S, and 28S rRNAs , highlighting the specificity of ReCappable-seq and its efficiency in removing transcripts with processed or degraded 5′ ends.We evaluated the specificity of ReCappable-seq for primary transcripts  as well as noncoding transcripts known to be transcribed by Pol III  and reasonable correlation between prefragmented and intact starting material (Pearson corr = 0.76) . Furthermore, reads from prefragmented material predominantly map to the start of annotated genes consistent with the positioning of TSSs . Importantly, the percentage of reads mapping to processed rRNA drops from 65% in the unenriched control libraries to ∼1.3% in the ReCappable-seq libraries derived from prefragmented RNA . Thus, ReCappable-seq is not affected by the large excess of uncapped 5′ ends resulting from fragmentation, and genuine primary 5′ ends were predominant in the ReCappable-seq libraries. Together, these results show that ReCappable-seq performs well on intact and fragmented RNA, adding further support to its high specificity for identifying 5′ ends of primary transcripts.The profile of mapped reads shows a high correlation between both prefragmented replicates (Pearson corr = 0.97) . In short, candidate TSSs were defined as single-nucleotide positions in the genome where the number of 5′ ends of the reads mapping to those is above 1 TSS tags per million mapped reads (TRM). From 63 million primary mappable reads, a total of 42,988 candidate TSSs were identified genome-wide.To evaluate the specificity of ReCappable-seq in capturing capped and triphosphate RNAs, an enrichment score was calculated for each candidate TSS by dividing the TPM in the ReCappable-seq libraries with the TPM in the unenriched control library (Ct) A. CandidSupplemental Fig. S1B). Comparison of the non-CIP ReCappable-seq data set with the CIP-treated data set reveals two populations composed of 33,468 CIP-resistant TSSs consistent with Pol II transcripts and 5269 CIP-sensitive TSSs consistent with non–Pol II transcripts –seq for Pol II, and manually curated human gene annotation. For CAGE, we used data sets derived from the same A549 RNA preparation used for ReCappable-seq.We assessed precision and sensitivity of ReCappable-seq following the published protocol previously described . We compWe analyzed the nucleotide composition for Pol II and non–Pol II TSSs and found that 83.7% and 79.5%, respectively, of the +1 nucleotides are A or G and 70.6% and 61.7%, respectively, of the −1 position are C or T C,D, reveWe examined the conservation profiles around Pol II and the different types of Pol III TSSs for 7SL RNAs (7SL), 7SK snRNAs (7SK), U6 snRNAs (U6), tRNAs, and 5S rRNAs (5S) gene families. We found a peak of conservation for Pol II TSSs consistent with the fact that these sites are functional and therefore under selection E. tRNA cUsing data from ENCODE Pol II ChIP-seq from A549 cells and Pol III ChIP-seq from HeLa cells (the only Pol III ChIP-seq data publicly available) , we inteSupplemental Fig. S3A; Supplemental Text 1; Supplemental Fig. S3A). For example, SREBF2 , a ubiquitously expressed TF known to control cholesterol homeostasis . This result is in accordance with previous findings that show sharp nucleosome positioning differences between tRNA genes and other Pol III genes . This configuration may lead to bidirectionally convergent transcribed enhancers as exemplified in Supplemental Figure S4C or divergent TSSs as exemplified in Supplemental Figure S4D and E. We found a total of 5612 and 1718 Pol II and non–Pol II TSSs, respectively, overlapping A549 enhancers.Pol II TSSs were found for 7186 annotated genes. Consistent with the function of Pol II polymerase, the majority (89%) of ReCappable-seq Pol II TSSs are found either upstream or within protein-coding genes A. AdditiSupplemental Fig. S5). The 5′ RACE results for two uniquely mapped annotated Pol III–transcribed genes (RMRP and RPPH1) confirmed that the start positions were the same as the major TSSs detected from the ReCappable-seq data.Non–Pol II TSSs account for 5269 positions. In addition to the correlation of the TSSs to Pol III ChIP-seq F, we valRMRP and RPPH1, 5S RNA, Vault RNAs, 7SL, and YRNA gene families. The other 444 are located at predicted tRNA genes  represent ∼50% of the total reads despite representing only 14% of all TSS positions, consistent with the fact that Pol I and Pol III transcripts are abundant. Of these non–Pol II TSSs, 4032 are detected within or upstream of 758 genes, of which 314 are annotated as Pol III–transcribed genes such as Supplemental Fig. S7A). This result is in agreement with literature that has assigned transcription of these genes to Pol III (RN7SL1 gene (a member of the 7SL gene family) identifies two strong TSSs at +1 and −1 bp relative to the annotated gene start (RN7SL1 would sharply contrast with the body of literature that describes 7SL genes as being transcribed by Pol III (Around one-third of the non–Pol II TSSs (34%) are derived from reads mapping to multiple positions in the genome, consistent with the role of Pol III polymerase in transcribing repeat elements and multiple-copy genes  such as  Pol III . A closene start B. The −1ne start C. The oc Pol III . AlternaSupplemental Fig. S7A). Consistently, RT-qPCR results showed that neither CIP nor yDcpS treatment affects the recovery of 7SK upon streptavidin enrichment . Furthermore, we show that VCE can remove the gamma-methylated phosphate, indicating that the RNA triphosphatase activity of VCE is not blocked. This suggests that such 5′ ends are subject to in vitro capping . Together, these results explain why 7SK TSSs are classified as non–Pol II (CIP resistant and not dependent on yDcpS decapping.)The TSSs identified at the start of 7SK genes appear resistant to CIP treatment and, as such, were classified as Pol II TSSs , consistent with a nonmethylated canonical triphosphate RNA 5′ end originating at these positions. To rule out an uncharacteristic cell line–specific expression of U6 in A549, we investigated U6 TSSs in data from ReCappable-seq performed on human brain RNA.The same result should have been obtained for the U6 small nuclear RNA, which is also known to possess a gamma-methyl triphosphorylated 5′ end . HoweverSupplemental Text 1; Supplemental Fig. S7C). Nonetheless, TSSs at annotated U6 starts are derived from very few reads in both samples, inconsistent with the abundance of U6 . We did not recover a substantial amount of the expected U6 transcripts, because of either secondary structure or some other unknown feature of the RNA that prevents capture by our enrichment and cloning protocol.To further explore the U6 TSS findings, we performed 5′ RACE on total A549 RNA using a complementary internal U6 primer (Methods). After amplification and sequencing of the resulting cDNA, we found evidence for both the annotated and the upstream TSSs . Notably tRNA-Leu-TAG-1-1 is the highest expressed tRNA , accounting for ∼10% of the total non–Pol II reads in A549 cells.Pre-tRNAs have been notoriously difficult to study owing to the rapid processing of primary transcripts relative to the exceptional stability of the mature tRNA, which consequently accumulates in the cell . With thSupplemental Fig. S8).TSS positions relative to the tRNA annotation highlight a large number of TSSs ∼5 bp upstream of mature tRNA 5′ ends D, consisSupplemental Fig. S9A). The entire mitochondrial genome is known to be transcribed from both strands as long polycistronic transcripts  has historically been proposed to explain the higher abundance of the rRNAs. However, the two-promoter model remains controversial as more recent experiments . Most of the transcripts starting at the major HSP1 TSSs show signs of stuttering with nontemplated adenosines at the start of the transcripts . Although such stuttering has been reported previously during initiation of transcription by phage polymerases in vitro  is located 6 bp upstream of the proline tRNAs and shows similar conformation to the HSP1 with stuttering of nontemplated adenosines at the start of the novel transcripts. Similar nucleotide composition starting at the TSS can be found for HSP1, LSP, and the predicted novel TSS (position MT:16029), with AAAGA as the common motif.We did not find TSSs at the HSP2 position, reinforcing the absence of an HSP2 promoter as indicated by previous studies . InsteadIn this work, we describe a novel technology, ReCappable-seq, which identifies eukaryotic TSSs for all primary transcripts genome-wide at single-nucleotide resolution. Our method relies on decapping and recapping RNA with two sequential enzymatic treatments using the enzymes yDcpS and VCE, as well as subsequent enrichment with streptavidin. Starting with 2–5 µg of total RNA and using standard ligation-based Illumina library kits, without the need of custom adaptor/primers, one can obtain TSS information with very low ribosomal reads content. Compared with standard CAGE protocols , our method requires less time, is technically less difficult to perform, uses fewer steps, does not require customized reagents, and should be more accessible to researchers.RN7SK) levels can lead to drastic changes in mRNA expression. The ability of ReCappable-seq to quantitatively measure the TSS usage of all transcripts irrespective of the transcribing polymerase will permit the inclusion of Pol I and III TSSs into transcriptome studies that have been, so far, either ignored or studied separately from Pol II transcripts.ReCappable-seq identifies TSSs independently of the transcribing polymerases because it captures both capped and triphosphorylated primary transcripts. In this respect, ReCappable-seq represents a significant departure from existing technologies that either target prokaryotic TSSs  or eukarSimilarly, ReCappable-seq can be applied to complex communities composed of both prokaryotic and eukaryotic organisms with the expected outcome of detecting all TSSs regardless of the organism or transcribing polymerases while simultaneously depleting mature rRNA.Supplemental Figs. S1E, S10A). Notably, Pol II and non–Pol II TSSs were identified genome-wide at base resolution for Jurkat and K562 cells .In this study, we focused on the human A549 transcriptome and showed that ReCappable-seq identifies TSSs from transcripts derived from Pol I, Pol II, Pol III, and POLRMT RNA polymerases. Additionally, we have performed ReCappable-seq on various other cell lines and tissues, which showed broad applicability and reproducibility of ReCappable-seq  were cultured in F12 media supplemented with L-glutamine. Total RNA was purified from cell passage number 3 using the RNeasy RNA purification kit (Qiagen 75142). A260:280 ratio > 2 and RIN > 9.Human brain total RNA was obtained from Takara (636530). The RNA was isolated by a modified guanidinium thiocyanate method and has RIN > 9.Total RNA was  dephosphorylated using the Quick CIP , and the resulting RNA was purified with the “Clean & Concentrate” kit (Zymo Research R1013) using the standard protocol.Decapping of 5 µg total RNA was performed with 200 units of yDcpS (NEB M0463S) in 10 mM Bis-Tris-HCl (pH 6.5), 1 mM EDTA in 50 µL total volume for 1 h at 37°C. The decapped RNA was purified with the “Clean & Concentrate” kit as above.Capping with 3′ desthiobiotin GTP (DTB-GTP) was performed in 50 µL total volume with 5 µL VCE (NEB M02080) and 0.5 mM DTB-GTP (NEB N0761), in the absence of SAM for 40 min at 37°C. The RNA was purified with the “Clean & Concentrate 5, Zymo Research R1013” kit and eluted in 40 µL.The RNA was then fragmented in 0.25× T4 polynucleotide kinase (PNK) buffer (NEB M0201) (1 µL) for 2.5 min at 94°C and immediately put on ice. The solution was further supplemented with 3 µL PNK buffer to 1× and 1 µL of PNK  and incubated for 20 min at 37°C. The RNA was purified by AMPure beads in 50% ethanol . The material was eluted in water, and a portion (20%–25%) was kept as an unenriched control sample. The remaining RNA was mixed with an equal volume of hydrophilic streptavidin magnetic beads (NEB S1421), which had been prewashed two times and resuspended in 2 M NaCl, 10 mM Tris-HCl (pH 7.5), 1 mM EDTA (SA Binding buffer). The suspension was incubated by rotation for 1 h at room temperature. The unbound material was removed by magnetic separation and discarded. The beads containing the bound RNA were washed three times by resuspension in 300 µL of SA binding buffer and three times in 0.25 M NaCl, 10 mM Tris-HCl (pH 7.5), 1 mM EDTA (SA wash buffer). The bound RNA was eluted by incubation of the beads in 0.5 M NaCl, 10 mM Tris-HCl (pH 7.5), 1 mM EDTA containing 1 mM D-biotin (SA elution buffer) by incubating for 30 min at 37°C with occasional resuspension. The eluted material was purified with AMPure beads as above by thoroughly washing the beads and sides of the tube four times with 80% ethanol to eliminate any traces of biotin before proceeding to a second round of streptavidin binding as above.The eluted material from the second streptavidin purification was purified with AMPure beads as above and the RNA eluted in 32 µL water. This material, in parallel with the unenriched control sample from above, was treated with 4 µL RppH (NEB M0356) in 1× ThermoPol buffer (NEB B90004) in a total volume of 40 µL for 1 h at 37°C. Afterwards RppH was deactivated by adding 0.5 µL 0.5 M EDTA and incubating for 3 min at 94°C. These samples were purified with AMPure beads as above and the RNA eluted in 15 µL of water.This material was used for generating sequencing libraries using the small RNA library prep set for Illumina (Multiplex compatible) kit (NEB E7330). Typically, 11–13 cycles of PCR were used for the unenriched control RNA and 14–16 cycles for the streptavidin-enriched fraction. Indexed libraries were sequenced using Illumina NextSeq 500 (75-bp paired-end sequencing).FLUC; 5 pg per 5 µg RNA). The mixture was divided into three aliquots, which were subjected to the ReCappable-seq procedure as described above with only one streptavidin enrichment step. One aliquot was first dephosphorylated (CIP sample), a second aliquot was not (no CIP sample), and the third was processed without the decapping step (no DcpS sample). After streptavidin enrichment, all three samples were handled identically.Fifteen micrograms of total RNA from A549 cells was used for the control RT-qPCR experiments after addition of spike-in controls: ERCC mix 1 (0.1 µL per 5 µg RNA) and an in vitro transcription-generated transcript of firefly luciferase . qPCR reactions were performed in duplicate. The percentage of recovery for each target transcript, for each RNA sample, was calculated from the Cq values of enriched compared with those of unenriched control, which was set to 100% using the following formula: % enrichment = 100 × 2exp − (average Cq input − average Cq SA), where input is the sample before enrichment and SA is the streptavidin enriched sample.RNA eluted from streptavidin enrichment and the corresponding unenriched control was purified with AMPure beads, and a portion corresponding to 1 µg input RNA was converted to cDNA using the LunaScript RT supermix cDNA synthesis kit (NEB E3010). One percent of the cDNA was used for each qPCR reaction performed with the Luna universal qPCR mastermix (NEB M3003) and the PCR primers listed –) = 32.10%; for the No DcpS sample, 100 × 2exp − –) = 0.14%.For example, The RNA-seq libraries were prepared as follows: the rRNA was depleted from 0.5 µg of A549 total RNA using the NEBNext rRNA depletion kit (NEB E6310). The resulting rRNA-depleted RNA was used for library construction with the NEB Ultra II directional RNA-seq kit (NEB E7760). The library was sequenced on Illumina MiSeq (75-bp paired-end sequencing). The corresponding sequencing results are referred to as A549 RNA-seq and A549 rRNA-del RNA-seq.Two replicate samples of total RNA (10 µg per sample) were sent to DNAFORM , who performed library preparation and sequencing using the nAnT-iCAGE protocol previously described . First-sRPPH1, RMRP, and RN7SL1 . Total RNA from A549 cells (3 µg in 30 µL) was treated with RppH (NEB M0356) for 1 h at 30°C and purified by spin column; 0.5 µg of the treated RNA was used for ligation to an RNA oligonucleotide with T4 RNA ligase 1 (NEB M0204) for 1 h at 25°C and then converted to first-strand cDNA with random priming and the ProtoScript II reverse transcriptase mix (NEB E6560). Amplification reactions from this cDNA were performed with the 5′ PCR primer and a reverse primer corresponding to the target sequence using LongAmp Taq polymerase (NEB M0287). The PCR products were sequenced, and the junction between the ligated oligo revealed the start sites.We determined the nucleotide sequence of transcript 5′ ends obtained from the amplified RACE products for Supplemental Table S2.Sequences of the RNA ligation oligonucleotide and the 5′ forward and reverse primers used are shown in the https://www.ncbi.nlm.nih.gov/geo/] accession number GSE33213) for polymerase binding analysis in We used A549 RNA polymerase II ChIP-seq data sets  with the following identifiers: ENCFF103COS ENCFF897HCS ENCFF519XGR ENCFF290AKW ENCFF331NAZ ENCFF447XHR ENCFF603JLN ENCFF538HPA ENCFF473YHH ENCFF000MYX ENCFF387NIL ENCFF721WCM ENCFF359GMW ENCFF623DEN ENCFF931GUX ENCFF000NBM ENCFF897YZE ENCFF102ZYB ENCFF648PQD ENCFF747CVL ENCFF801BLX ENCFF640ILD ENCFF322NBG ENCFF599JTK ENCFF897SFR ENCFF950NVT ENCFF172GSY ENCFF658EZL ENCFF000NDT ENCFF908FKC ENCFF137VHW ENCFF668LIG ENCFF870WJP ENCFF000NEY ENCFF454PBC ENCFF565TWZ ENCFF776IJI ENCFF785XZN ENCFF374NIY ENCFF585RGO ENCFF796BZA ENCFF512ZYH ENCFF997KRD ENCFF967MRB ENCFF829OSK ENCFF063VTN ENCFF000NIP ENCFF967EEA ENCFF539BSO ENCFF604QPX.For TF and histone ChIP-seq used in Supplemental Figure S4, C through E, we used the following ENCODE data: ENCFF640LCC ENCFF872MQN ENCFF312BJX ENCFF874AAY ENCFF189DIW ENCFF843JEO. bigWig files are used as it is. BAM files were compared to the appropriate control BAM files using bamCompare from deepTools (version 3.3.0) (For H3K27ac1 and H3K4me1 ChIP-seq used in n 3.3.0)  with then 3.3.0) , and theSupplemental Figure S2 .We used the K562 CAGE data set previously generated  for rRNAHuman genome version GRCh38/hg38 was used for all the analyses.ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_24/gencode.v24.primary_assembly.annotation.gtf.gzThe comprehensive gene annotation on the primary assembly GRCh38.p5 (GENCODE v24) was downloaded from GENCODE: Supplemental Figure S6.To further classify gene types, we added predicted tRNA genes derived from GENCODE v24 tRNAscan into the comprehensive gene annotation for TSS assignment in The UCSC annotation used for precision and sensitivity assessment was created using the UCSC Table Browser and the following options: assembly, Dec2013 GRCh38/hg38; group, Genes and Gene Predictions; track, All GENCODE v24.https://github.com/FelixKrueger/TrimGalore).Because CAGE reads were 50 nt, we shortened the A549 ReCappable-seq and CIP ReCappable-seq reads to retain the first 50 nt from the 5′ end. Then we processed the ReCappable-seq reads and the paired-end RNA-seq reads by trimming the Illumina adapter using Trim Galore!  using STAR  version To calculate the percentage of reads from processed rRNA genes, we mapped the ReCappable-seq and CAGE reads from each replicate to a human rRNA reference set  using BWA-MEM (with default parameters) and counted using SAMtools. We did not include the 5S rRNA genes because these genes are transcribed by Pol III polymerase, and therefore, 5S is triphosphorylated.% rRNA = (number of reads mapped to rRNA genes / number of reads mapped to human genome) × 100.https://github.com/elitaone/cappable-seq) and normalized as tags per million primary mappable reads (TPM). Here, 5′ end tag refers to the 5′ most nucleotide of a read mapping to the reference genome, and it corresponds to the TSS position.The number of 5′ end tags for each position on the genome was calculated using a custom script CountTssGTF.py . Because the two replicates were highly correlated, in order to get more information and to compare between methods for all the ReCappable-seq and CAGE treatments, we merged the aligned reads from two replicates and randomly sampled 63 million primary mappable reads for TSS analysis. In this study, TSS is defined as a position with TPM ≥ 1 .To assess reproducibility, we compared the TPM for each position between the two technical replicates starting from the same RNA starting material , we classified the TSSs into Pol II (TPM Ratio < 4) and non–Pol II (TPM Ratio ≥ 4) B.To compare the defined TSSs from both ReCappable-seq and CAGE to GENCODE-annotated genes, including tRNA prediction, for each TSS, we identified the closest exon using BEDTools  closest https://github.com/elitaone/transcript_body_coverage) to analyze the coverage of reads (RNA-seq) or 5′ end tags (ReCappable-seq) across the transcript body. Briefly, we calculated the number of reads or tags covering each position of transcripts >300 bp. For RNA-seq we discarded low expression transcripts (FPKM < 10). For coverage analysis of ReCappable-seq and the corresponding unenriched control, we discarded transcripts whose overlapping 5′end tags contained less than n reads, where n = number of mappable reads divided by 1 million.We used a custom script  were defined as the high-confidence ReCappable-seq Pol II TSSs overlapping each reference, including UCSC TSS annotation, CAGE positions, and DNase-seq peaks as mentioned above. False positives (FPs) were those not overlapping each reference.x-axis  were defined as the UCSC TSS annotations that (1) were within any protein coding transcripts with FPKM ≥ cutoff as shown in sensitivity plot ng RSeQC  based onWe determined the overlap using BEDTools intersect using the high-confidence ReCappable-seq Pol II TSS as -a and each reference as -b with the addition of the following parameters: (1) -s option for UCSC TSS annotation comparison; (2) -s option for comparison with CAGE positions within a window of ±1 nt.Precision A = TP/ under accession number GSE132660. All custom scripts used for data analysis are uploaded in GitHub (https://github.com/elitaone) and provided in Supplemental Code. The Sanger sequences are available as Supplemental Material. High-confidence TSSs defined by ReCappable-seq have been uploaded as a custom track in the UCSC Genome Browser: http://genome.ucsc.edu/s/rezo/ReCappable-seq. High-confidence TSSs are defined by ReCappable-seq, bedGraph format; negative data values represent CIP-sensitive TSS (quadrant II); positive data values represent CIP-resistant TSSs (All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO;


The purpose of the study was to evaluate predicted maturity offset (time before age at PHV) and age at PHV  in a longitudinal sample of 58 under-13 club level soccer players in central Portugal for whom ages at PHV were estimated with the SITAR model. Two maturity offset prediction equations were applied: the original equation which requires CA sitting height, estimated leg length, height and weight, and a modified equation which requires CA and height. Predicted maturity offset increased, on average, with CA at prediction throughout the age range considered, while variation in predicted maturity offset and ages at PHV within CA groups was considerably reduced compared to variation in observed ages at offset and at PHV. Predicted maturity offset and ages at PHV were consistently later than observed maturity offset and age at PHV among early maturing players, and earlier than observed in late maturing players. Both predicted offset and ages at PHV with the two equations were, on average, later than observed among players maturing on time. Intra-individual variation in predicted ages at PHV with each equation was considerable. The results for soccer players were consistent with similar studies in the general population and two recent longitudinal studies of soccer players. The results question the utility of predicted maturity offset and age at PHV as valid indicators of maturity timing and status. Predicted maturity offset, defined as time before peak height velocity (PHV) ,2, and eCurrent interest in the application of the maturity offset prediction equations in samples of youth athletes is considerable –6. PrediIn the context of the preceding, the purpose of the present study is to evaluate predicted maturity offset and derived ages at PHV with the original  and modiCoimbra Soccer Longitudinal Project  cautioned that "Fourth, intra-individual variation in predicted ages at PHV with each equation was considerable in the present sample of soccer players and in each of the longitudinal studies. Ranges of predicted ages at PHV were reduced with the Moore compared to the Mirwald equation.The dependency of predicted maturity offset upon CA and body size at prediction merits attention. Means for predicted maturity offset are plotted relative to means for CA and height in the present study and in samples of male soccer players extracted from the literature ,46,51–69Studies reporting maturity offset by relative age categories within an age group and studies classifying players across variable age ranges as pre-, at/circa- or post-PHV based on predicted offset were not included in the graphs. In the former, players born early in the year were, on average, older and taller than those born later in the year, while in the latter, CA and height systematically increased from pre-, to at/circa- to post-PHV groups (see above). Given the trends, studies applying predicted maturity offset as a maturity indicator beg the following question: Is predicted maturity offset an indicator of time before age at PHV or is it an indicator of CA and size at prediction? By inference, the validity of predicted maturity offset as an indicator of the time before or after PHV can be questioned.The preceding has implications for studies using predicted maturity offset as an indicator of maturity status among youth athletes in soccer and other sports. Predicted maturity offset is used most often to classify youth as pre-, at-/circa- and post- PHV, although mean CAs, heights and weights show, on average, a clear gradient across the respective maturity groups. Many studies simply compare the three groups with analysis of variance without controlling for the variation in CA and body size among groups ,36,40,70Although studies of youth athletes do not ordinarily indicate the ethnic composition of samples, the issue of ethnic variation is relevant as the maturity offset prediction equations were developed and validated on samples of European ancestry. The original Mirwald equation  requiresper se or variations of the method to identify when players enter and exit the interval of the adolescent growth spurt should employ these methods with caution. If predicted offset is used to inform training design and prescription, it is essential that variation in chronological age at prediction and error associated with the prediction equations be considered. Perhaps additional or alternative methods might be used as a complement, for example, percentage of predicted adult stature attained at the time of observation. As noted above, the utility of estimated velocities of growth in height based on short term height increments has limitations.Results of the current study also have practical implications for those working with youth athletes. The interval of PHV is central to the Long Term Athlete Development (LTAD) model for youth athletes ,79, whicThe inability of the maturity offset prediction methods to effectively differentiate between early and late maturing youth implies that they should not be used to group players by maturity status as in bio-banding , or to aResults of the study of applying maturity offset prediction equations to the sample of Portuguese soccer players were consistent with similar studies of soccer players and of the general population. Predicted maturity offset increased, on average, with CA at prediction throughout the age range considered. Variation in predicted maturity offset and ages at PHV within CA groups was consistently reduced compared to variation in observed ages at offset and at PHV. Predictions also varied with maturity status defined by observed age at PHV; predicted maturity offset and ages at PHV were consistently later than observed maturity offset and age at PHV among early maturing boys, and earlier than observed in late maturing boys. And, intra-individual variation in predicted ages at PHV with each equation was considerable and ranges of predicted ages at PHV were reduced more with the Moore compared to the Mirwald equations.S1 TableMixed effects linear models analysis of differences between predicted ages at PHV with the Mirwald  and Moor(DOCX)Click here for additional data file.


Reverse genetics is a technology that allows the production of a virus from its complementary DNA (cDNA). It is a powerful tool for analyzing viral genes, the development of novel vaccines, and gene delivery vectors. The standard reverse genetics protocols are laborious, time-consuming, and inefficient for negative-strand RNA viruses. A new reverse genetics platform was established, which increases the recovery efficiency of the measles virus (MV) in human 293-3-46 cells. The novel features compared with the standard system involving 293-3-46 cells comprise (a) dual promoters containing the RNA polymerase II promoter (CMV) and the bacteriophage T7 promoter placed in uni-direction on the same plasmid to enhance RNA transcription; (b) three G nucleotides added just after the T7 promoter to increase the T7 RNA polymerase activity; and (c) two ribozymes, the hairpin hammerhead ribozyme (HHRz), and the hepatitis delta virus ribozyme (HDVrz), were used to cleavage the exact termini of the antigenome RNA. Full-length antigenome cDNA of MV of the wild type IC323 strain or the vaccine AIK-C strain was inserted into the plasmid backbone. Both virus strains were easily rescued from their respective cloned cDNA. The rescue efficiency increased up to 80% compared with the use of the standard T7 rescue system. We assume that this system might be helpful in the rescue of other human mononegavirales. The measles virus (MV) is an enveloped, non-segmented, negative-sense, single-stranded RNA virus. It carries a single copy of the genome, which is 15,894 bp in length. The genome of MV encodes six major structural proteins, i.e., nucleocapsid protein (N), phosphoprotein (P), matrix protein (M), fusion protein (F), hemagglutinin (H), and the large protein (L). Two additional nonstructural proteins, V and C, are produced from the P gene by RNA editing. The viral genome is encapsidated by N, P, and L proteins forming the ribonucleoprotein complex (RNP), surrounded by M protein. H and F proteins mediate virus attachment and fusion, respectively .Reverse genetics is a molecular biology method for creating a new virus from its cloned cDNA. This technique facilitates the understanding of viral biology and encourages the further development of novel vaccines and vectors for gene delivery. The first recovery of a non-segmented negative-strand RNA virus entirely from cloned cDNA was achieved in 1994 for the rabies virus . Since tThe original reverse genetics system for rescuing the MV is based on the co-transfection of two plasmids, the full-length cDNA plasmid p(+) MV323-eGFP and the helper plasmid for the expression of the large (L) protein pEMC-La, into the helper cell 293-3-46, which stably expresses the MV proteins N, P, and the T7 RNA polymerase , wild-tyIn the present study, we constructed a plasmid platform that uses two promoters, the RNA polymerase II promoter (CMV) and the bacteriophage T7 promoter, in a uni-direction. In this construct, three G nucleotides were added just after the T7 promoter to enhance the T7 RNA polymerase activity, and two optimized ribozymes HHrz and HDVrz sequences were used to cut the exact termini of the antigenome RNA of MV. Full-length antigenome cDNA of MV, either the wild type IC323 strain or the vaccine AIK strain, was inserted into this plasmid backbone. Both virus strains were rescued efficiently from their cloned cDNA. This reverse genetics platform can increase the rescue efficiency up to 80% compared to the standard T7 rescue system.Vero cells expressing human SLAM (Vero/hSLAM)  and the The plasmid p(+)MV323-eGFP ,21 was uE. coli NEB-5-alpha. The resulting plasmid pCi-T7-HHrz-HDVrz was then subcloned into a low-copy plasmid piRFP [The plasmid pCi-neo-CAV1  was usedid piRFP  by digesThe DNA fragment of the vector backbone plasmid pT-HHrz-HDVrz was produced by a PCR using primer pair: MV_Trailer_s and MV_Leader_as . This PC5 helper cells 293-3-46 in each well of a 6-well plate were transfected with 5 µg full-length-genome plasmids and two helper plasmids, 10 ng of pEMC-La plasmid expressing the measles large (L) protein [Recombinant MV IC323-eGFP and MV AIK-eGFP were recovered from the full-length cDNA original plasmids p(+) MV323-eGFP and p(+) MVAIK-eGFP or the newly cloned plasmids pT(+)MV323-eGFP and pT(+)MVAIK-eGFP using the reverse genetics method as reported previously with a modification ,9,25. Br protein  and 250  protein  using ca5/well) in 6-well plates were infected with MV at a MOI of 0.01 for 2 h at 37 °C. The supernatant was removed and replaced with an equal volume of fresh medium. Virus titers were determined on Vero/hSLAM cells by 50% tissue culture infective dose (TCID50) titration [A multiple-step growth kinetics study was performed to assess the difference in replication of the viruses rescued from cloned cDNA plasmids (origin and new). Vero/hSLAM cells (2 × 10itration .Immunohistochemistry assays were used for the detection of MV N antigens in transfected cells. Vero/hSLAM cell monolayers grown on glass coverslips were infected with MV at a MOI of 0.01. Two days post-infection (dpi), the cells were fixed in 4% paraformaldehyde for 10 min, washed with PBS, and blocked for 1 h at RT with 10% normal goat serum  in PBS—0.2% triton X-100. After blocking, the coverslips were incubated with primary antibodies F227 mouse-anti-MV-N , 1:5 dilution in PBS 0.3% triton X-100, overnight at 4 °C. The glass coverslips were washed three times with PBS 0.2% triton X-100 and incubated with fluorescence-labeled secondary antibodies  1:500 dilution in PBS 0.2% triton X-100 for 1 h. After washing, the glass coverslips were mounted using Entellan fluorescence mounting medium  and viewed under a fluorescent microscope.The vector backbone pT-HHrz-HDVrz was generated by molecular cloning methods through several steps as schematically described in This vector backbone pT-HHrz-HDVrz was used as a vector backbone for inserting the full-length cDNA antigenome of MV between the HHrz and HDVrz sequences in the next step.As shown in To test whether the newly constructed plasmids can improve virus rescue efficiency, recombinant MVs were rescued from four plasmids: two were original plasmids, and the other two were constructed in this paper. The helper cells 293-3-46 were transfected with each of the full-length plasmids listed in The rescue event of the MV from the original plasmid was rare if any. Only one rescue event was found in one well after five independent rescue experiments  the RNA polymerase II (CMV) promoter and the bacteriophage T7 RNA polymerase promoter were placed on the same plasmid to increase viral RNA transcription. (2) An intron was inserted downstream of the CMV promoter to stabilize the RNA and transport it to the cytoplasm. (3) Three G nucleotides were added directly downstream of the T7 promoter to enhance T7 RNA polymerase. (4) Two ribozymes, HHrz and HDVrz, flanking the full-length cDNA termini, were used for cleavage, leading to exact antigenome RNA termini.The transcription of the full-length antigenomic MV RNA and three helper proteins N, P, and L in the standard reverse genetics system was carried out under the control of T7 RNA polymerase. The source of the T7 RNA polymerase was the helper cell line 293-3-46, which constitutively expressed T7 RNA polymerase, MV N, and P proteins ). There To improve transcription of the viral RNA, two promoters, the cellular RNA polymerase I and RNA polymerase II promoter, were used to transcribe the viral RNA. These two promoters could raise the chance of transcriptions of the virus gene by cellular RNA polymerase I and II in a single cell ,28,29.In our novel plasmid platform, two promoters, the cytomegalovirus (CMV) and the T7 promoter, were used. We first constructed a rescue system with only the CMV promoter  and compTo increase the efficiency of T7 RNA polymerase transcription, three guanidine residues were added just after the T7 promoter sequence. These three guanidine residues formed an optimal initiation sequence for T7 RNA polymerase ,34,35. HThe critical factor for efficient recovery of the negative-strand RNA virus is the exact termini of the resulting transcribed full-length RNA. More recently, Ghanem et al. designed an optimized HDVrz ribozyme with an enhanced cleavage activity compared with the standard HDVrz ribozyme . They adIn conclusion, an improved reverse genetics plasmid platform was engineered with several advantages over standard reverse genetics methods. Using this plasmid platform, recombinant MVs were rescued from their cloned cDNA with high recovery efficiency . The res


Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus in humans, has expanded globally over the past year. COVID-19 remains an important subject of intensive research owing to its huge impact on economic and public health globally. Based on historical archives, the first coronavirus-related disease recorded was possibly animal-related, a case of feline infectious peritonitis described as early as 1912. Despite over a century of documented coronaviruses in animals, the global animal industry still suffers from outbreaks. Knowledge and experience handling animal coronaviruses provide a valuable tool to complement our understanding of the ongoing COVID-19 pandemic. In this review, we present an overview of coronaviruses, clinical signs, COVID-19 in animals, genome organization and recombination, immunopathogenesis, transmission, viral shedding, diagnosis, treatment, and prevention. By drawing parallels between COVID-19 in animals and humans, we provide perspectives on the pathophysiological mechanisms by which coronaviruses cause diseases in both animals and humans, providing a critical basis for the development of effective vaccines and therapeutics against these deadly viruses. Coronavirus, family Coronaviridae, order Nidovirales. The genus Coronavirus can be subdivided into four clusters based on genetic and serologic properties  [Alphacoronavirus includes transmissible gastroenteritis virus (TGEV) in swine, porcine respiratory coronavirus (PRCV), porcine epidemic diarrhea virus (PEDV), swine acute diarrhea syndrome-coronavirus (SADS-CoV), canine coronavirus (CCoV), feline coronavirus, ferret and mink coronaviruses, human coronavirus (HCoV) 229E, HCoV NL63, and bat coronaviruses (Bat CoVs). Betacoronavirus includes murine hepatitis virus (MHV), bovine coronavirus, equine coronavirus, canine respiratory coronavirus (CRCoV), HCoV OC43, HCoV HKU1, Human enteric CoV-4408 (HECoV-4408), porcine hemagglutinating encephalomyelitis virus, rat coronavirus, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome-related coronavirus (MERS-CoV), SARS-CoV-2, and Bat CoVs. Gammacoronavirus includes avian coronaviruses, such as avian infectious bronchitis virus (IBV), turkey coronavirus (TCoV), goose coronavirus, duck coronavirus, and Asian leopard cat coronavirus. Interestingly, beluga whale coronavirus belongs to Gammacoronavirus. Finally, Deltacoronavirus includes wigeon coronavirus, bulbul coronavirus, moorhen coronavirus, munia coronavirus, thrush coronavirus, and porcine deltacoronavirus (PDCoV) [Coronaviruses are enveloped viruses with a large, capped, and polyadenylated RNA genome of approximately 24,500–31,800 nucleotides [Tracing the history of coronaviruses, the first coronavirus-related disease recorded may have been a case of feline infectious peritonitis (FIP), discovered as early as 1912 . DiseaseAnimal coronavirus diseases involve multiple body systems, such as gastrointestinal, respiratory, and central nervous systems, with clinical symptoms varying from encephalomyelitis, hepatitis, and nephritis to peritonitis . FIP wasTGEV, PEDV, and PDCoV cause diarrheal diseases in piglets and sows. The main clinical signs are mild to severe watery diarrhea, vomiting, and anorexia. Mortality can be extremely high (up to 100%) in neonatal piglets, as disease severity and the age of infected pigs are negatively correlated ,13. MostAlphacoronavirus) and CRCoV (Betacoronavirus), respectively [Among rodents, MHV can induce hepatitis as well as neurological diseases and enteritis depending on the strain . BCoV inectively . The cliectively . TCoV caectively . The taxCOVID-19 is a zoonotic disease believed to have originated in animals . HoweverCaninae, Felinae, and Mustelidae [In early April 2020, nondomestic felids, such as tigers and lions, infected with SARS-CoV-2 were reported . Notably−2 to 10−5 nucleotide substitutions per site per year [Positive-sense single-stranded RNA viruses have approximately 10per year . Similarper year . The genper year . Anotherper year .Non-structural genes encoding polyproteins 1ab are subdivided into approximately 16 NSPs involved in proteolytic processing, genome replication, and subgenomic mRNA synthesis . SimilarBetacoronavirus suggest a high potential for recombination within group members [Betacoronavirus HECoV4408 isolated from a child with diarrhea is thought to have originated from the bovine species [A unique feature of coronaviruses is the high frequency of genome recombination events during the evolution of the lineage . Recombi members . Cross-s species . Additio species . TherefoHCoV and avirulent FCoV, observed prior to the SARS-CoV outbreak, are non-pathogenic and induce mild inflammation. In contrast, SARS-CoV, SARS-CoV-2, and virulent FCoV are highly pathogenic coronaviruses in humans and cats, respectively. They cause diseases with similar immunopathogenic features; both are characterized by an intense inflammatory/cytokine storm that compromises normal physiological function and contributes to progressive, debilitating manifestations, such as fever and systemic disease ,64,65. G+ T cells [Similar to FIP caused by virulent FCoV, severe COVID-19 is associated with the substantial suppression of natural killer (NK) cells and regulatory T (Treg) cells, as reflected by lower cell counts and reduced NK cell functionality ,73,74. A T cells .Antibodies that bind to S proteins might contribute to immunopathogenesis, especially if they are weakly neutralizing or present at sub-neutralizing levels. Antigen–antibody complexes that are deposited in the blood vessel walls and induce complement activation can lead to vasculitis and edema, contributing to the development of the effusive form of FIP . The opsThe evasive mechanisms associated with ADE have yet to be thoroughly investigated. However, recent studies have suggested that interactions between antibody-opsonized virions and other surface receptors, such as leukocyte Ig-like receptor-B1 suppress antiviral responses . In addiEnterotropic coronaviruses, such as TGEV, PEDV, PDCoV, SADS-CoV, CCoV, BCoV, TCoV, and enterotropic MHV, can be transmitted by direct fecal-oral or indirect routes. Several coronaviruses are spread by aerosol droplets and the ingestion of contaminated food or water. Therefore, research on the viral shedding period is important. In a natural PEDV outbreak, high levels of PEDV shed by feces can be seen for a few days post-infection, and the viral titer gradually decreases after one week. However, intermittent viral shedding can be detected up to 2 months post-infection . In a 7-Serological and molecular tests are often utilized for the detection of specific antibodies and antigens in veterinary and human infectious diseases. However, several issues are of concern with respect to the clinical diagnosis of coronaviruses. (i) Is the presence of the virus and antibodies an indicator of disease status? The answer is sometimes not straightforward. For instance, when PEDV infects naïve pigs, especially young pigs, the disease manifestation can be very severe. However, during the recovery phase after PEDV infection, the lack of long-term persistent infection would indicate that the risk of virus transmission is reduced . In cont(ii) Is differentiating infected from vaccinated animals (DIVA) possible? The principle of DIVA is to test the possibility of serological surveillance for the presence of wild-type infection. The DIVA strategy is available for some important transboundary animal diseases, such as foot-and-mouth disease, classical swine fever, and avian influenza . However(iii) Can the specificity and sensitivity of diagnostic tools, especially novel diagnostic methods and strategies, be improved? At present, viral nucleic acid detection by molecular tests remains the gold standard for COVID-19 . HoweverGalanthus nivalis agglutinin) [Supportive care is usually required in animal coronavirus cases. For FIP, treatment is focused on reducing the inflammatory and hyperimmune response, with several anti-FCoV agents and immunosuppressants also considered. Drugs that target FCoVs include carbohydrate-binding agents (lutinin) ,97,98, Hlutinin) , interfelutinin) ,104,105,lutinin) ,107, antlutinin) , anti-fulutinin) , immunoslutinin) ,111, 3C-lutinin) ,113, andlutinin) . Howeverlutinin) ,115. As lutinin) ,78, and lutinin) ,117.The immunopathogenesis of FIP and COVID-19 are similar, and disease treatments aimed at the inhibition of coronavirus replication alone will not be sufficient. Indeed, the molecular pathways modulated by viruses and disease severity are temporally distinct . PossiblHigh-level biosecurity measures and vaccines are still the best strategies to prevent coronavirus diseases in animals and humans. Unfortunately, these diseases continue to spread worldwide due to the convenience of global trade ,123. ForA recent review has shown that the neutralization levels of each type of vaccine (seven current vaccines) are highly predictive of immune protection . As SARSAnimal coronavirus diseases are emerging or reemerging worldwide. Veterinarians and animal coronavirus researchers are gaining experience and a better understanding of prevention and control strategies. Therapeutic strategies aimed at reducing viral loads and attenuating inflammatory/cytokine storms are the most promising against both FIP as well as COVID-19. Although COVID-19 was initially transmitted from animals to humans, we still cannot discount the potential for transfer from infected animals to humans and vice versa. Therefore, high-level biosecurity , effective vaccines, and herd immunity are still the best strategies to prevent and control the spread of COVID-19. Finally, due to the similar features and characteristics of animal and human coronavirus, detailed insights from animal coronaviruses, especially porcine and feline coronaviruses, might provide a basis for understanding COVID-19.


Arabidopsis thaliana mutants with defects in various RNA metabolism pathways. We observed a global production of siRNAs caused by inefficient pre-mRNA cleavage and polyadenylation leading to read-through transcription into downstream antisense genes. In addition, in the lsm1a lsm1b double mutant, we identified NIA1, SMXL5, and several miRNA-targeted mRNAs as producing siRNAs, a group of transcripts suggested being especially sensitive to deficiencies in RNA metabolism. However, in most cases, RNA metabolism perturbations do not lead to the widespread production of siRNA derived from mRNA molecules. This observation is contrary to multiple studies based on reporter transgenes and suggests that only a very high accumulation of defective mRNA species caused by specific mutations or substantial RNA processing defects trigger RNAi pathways.Defects in RNA maturation and RNA decay factors may generate substrates for the RNA interference machinery. This phenomenon was observed in plants where mutations in some RNA-related factors lead to the production of RNA-quality control small interfering RNAs and several mutants show enhanced silencing of reporter transgenes. To assess the potential of RNAi activation on endogenous transcripts, we sequenced small RNAs from a set of  Arabidopsis thaliana, where different small RNA pathways can be distinguished based on the nature of small RNA (sRNA) precursors, processing enzymes, and effector complexes. Major plant small RNA classes include microRNA (miRNA), small interfering RNA (siRNA), trans-acting siRNA (ta-siRNA), natural antisense transcripts siRNA (nat-siRNA), and heterochromatic siRNA (het-siRNA) (reviewed in1). We recently described two mutants involved in mRNA metabolism with elevated production of siRNAs from protein-coding genes3. Here, we tried to determine if such a phenomenon could also be observed in a broader set of RNA-related mutants.Small RNA interference (RNAi) pathways are conserved mechanisms implicated in the defence against invading nucleic acids and in the regulation of gene expression. These processes are extensively studied in a model plant species 4. In the case these transcripts are antisense to mRNAs, they may trigger double-stranded RNA (dsRNA) formation and, consequently, siRNA synthesis5. Transcripts arising from defective maturation or impaired degradation are considered aberrant. Their features include, but are not limited to, lack of cap structure, as is the case for read-through transcripts in the xrn3 mutant, lack or shortened poly(A) tail, defective pre-mRNA 3′ end formation and incomplete splicing6. It has been postulated that mRNA surveillance and RNAi pathways compete in removing such faulty RNA molecules6. Several studies have demonstrated the existence of a new class of sRNAs called rqc-siRNA 7 or ct-siRNA (coding transcript-derived siRNAs)8, which are generated mainly due to defects in RNA degradation machinery from mRNAs that do not normally produce siRNAs. In line with this, our recent work demonstrated a strong accumulation of siRNAs in the mutant of the DXO1 gene involved in mRNA cap surveillance3.Arabidopsis genome is relatively small and densely packed with genes, which makes regulation of Pol II transcription especially important. We and others showed that XRN3 5′–3′ exoribonuclease is a crucial factor for Pol II transcription termination and defects in this process give rise to read-through transcripts11 or 5′–3′ and 3′-5′ RNA degradation18 initiate production of siRNAs from mRNAs mediated by RNA-dependent RNA polymerase (RDR) and Dicer-like proteins, mainly but not exclusively by RDR6 and DCL2/4 (reviewed in19). It has been demonstrated that severe phenotypes of decapping dcp2 and vcs mutants and the RNA degradation double mutant ski2 xrn4 are partially suppressed by elimination of siRNA production from several hundreds of mRNAs18, which underscores the significance of this new class of siRNAs. These RNA degradation factors and many others have also been identified as suppressors of transgene silencing mediated by siRNA26. However, it seems that transgene high expression renders them more sensitive to any disturbances in RNA metabolism than most endogenous mRNAs. Except for mRNA decapping, biogenesis of rqc-siRNA from endogenous mRNAs is strongly increased only when both 5′ and 3′ cytoplasmic RNA degradation pathways are defective, while a disturbance in either of these mechanisms results in an enhanced accumulation of small RNAs from a limited number of loci or reporter transgenes15. This is also true in the Ler accession of A. thaliana, which has an active form of SOV (SUPPRESSOR OF VCS) gene, encoding a homolog of DIS3L2 3′–5′ cytoplasmic exonuclease27. Notably, although a point mutation in the RRP4 gene of the exosome core complex subunit increases transgene silencing28, high-throughput sequencing of small RNAs from the RRP4 RNAi line showed a minor impact on the production of siRNAs from mRNAs29. Also, aberrant mRNA biogenesis may lead to the production of new siRNAs and the increase of transgene silencing. Enhancement of transgene silencing was observed in the case of defects in mRNA splicing31, cleavage and polyadenylation33, transcription regulation16 and termination5. A whole plethora of RNA maturation and degradation mutants showing stronger transgene silencing led to the hypothesis that defects in mRNA metabolism give rise to accumulation of aberrant mRNA species and production of unwanted siRNAs, with a potentially toxic effect on cellular functions6.Mutations in factors directly engaged in mRNA decappingcstf64-2 transcription termination mutant showed a more robust siRNA accumulation from about a thousand mRNAs, mainly due to read-through transcription from downstream convergent genes. However, sequencing of small RNAs from mutants in other factors involved in pre-mRNA 3′ end formation , mRNA poly(A) tail control , NMD , RNA 5′–3′ decay (lsm1a lsm1b) and splicing (ncb-4) revealed that the majority showed only weak or no signs of increased siRNA production from mRNAs. These results suggest that different RNA degradation pathways may substitute for each other or that the rqc-siRNA generating mechanisms are limited only to a specific group of aberrant transcripts.Here, we set out to validate this assumption by sequencing small RNA libraries from a set of mutants, which potentially are candidates for rqc-siRNA production. Our analysis of xrn3-8 mutant lead to small RNA production sourcing from convergent protein-coding gene pairs2. The crucial step of Pol II transcription termination is pre-mRNA 3′ end processing conducted by the multisubunit cleavage and polyadenylation complex (CPA). One of its components is the CstF64 protein responsible for the recognition of GU-rich elements in nascent RNAs34. We performed small RNA profiling of the cst64-2 mutant, which exhibits delayed Pol II release due to dysfunction of the cleavage and polyadenylation machinery. Mutant plants are sterile and show characteristic morphology35, allowing for the selection of 21 day-old cstf64-2 homozygous plants grown on soil along with Col-0 wild-type. Since widespread defects in Pol II transcription termination due to the cstf64-2 mutation have not been directly demonstrated in Arabidopsis, we checked the level of read-through transcripts downstream of several genes that we previously reported to have deficient termination in the xrn3-8 mutant2. All tested regions showed significant up-regulation of transcripts that was often unrelated to changes in the expression of upstream mRNAs . Contrary to our expectations, we do not observe an enrichment of small RNAs exclusively at 3′ ends of affected genes  tail length control. Importantly, two Arabidopsis putative deadenylating enzymes PARN/AHG2 and CCR4a act as suppressors of transgene silencing dependent on RDR6 and SGS3 (SUPPRESSOR OF GENE SILENCING3) and co-localise with siRNA-bodies24. However, it should be emphasised that data on the cellular localisation of AHG2 is not consistent with its function in mitochondria42. In turn, Poly(A)-binding (PAB) proteins regulate mRNA deadenylation and translation, contributing to mRNA cytoplasmic turnover and stability43. Out of eight Arabidopsis PAB proteins, PAB2, PAB4 and PAB8 are expressed in vegetative tissues. While triple mutants are not viable, the combination of pab2 and pab4 knock-out mutations shows the strongest phenotypic defects44.We chose the ahg2-1, ccr4a, rsr1-2, fy-2 and pab2 pab4 mutant seedlings grown on MS solid medium, sequenced (Table crr4a and fy-2) to 273 (ahg2-1) genes with significantly elevated levels of small RNAs . Interestingly, upf1 and upf3 mutants have been shown to enhance transgene silencing through the production of transgene-derived siRNAs. Moreover, UPF1 protein co-localises with cytoplasmic siRNA-bodies, which represent sites of siRNA production24. COILIN is a conserved structural protein required for the assembly of peri-nucleolar organelles called Cajal bodies, which in different organisms are implicated in the formation and function of small nuclear (snRNA), small nucleolar (snoRNA) and small Cajal body-specific RNAs (scaRNA). Consequently, Cajal bodies have a role in mRNA splicing and rRNA maturation (reviewed in47). Although in plants composition and role of Cajal bodies are much less studied, they contain COILIN and several components of snRNP complexes including SmD3, SmB, U2A' and U2B''50. Interestingly, splicing defects have been shown to enhance siRNA production for transgene reporter transcripts51 and SmD1, which is involved in splicing and has been reported to play a role in transgene silencing31. Finally, Arabidopsis LSM1 protein is a subunit of the cytoplasmic heptameric LSM1-7 complex engaged in mRNA decapping and degradation53. In yeast and Metazoa, mRNA deadenylation and oligouridylation attract the LSM1-7 complex, which recruits decapping proteins that trigger XRN1-mediated RNA degradation from the 5′ end and at the same time protects RNA 3′ end against degradation by the exosome (reviewed in54). Although information concerning the role of LSM proteins in mechanisms engaging small RNAs is lacking, XRN4, a plant homologue of yeast Xrn1, acts as a suppressor of silencing by small RNAs, and its absence leads to the accumulation of decapped mRNAs that are targeted for siRNA production25.A very limited number of endogenous mRNAs producing siRNAs in mutants that have been shown to act as suppressors of transgene silencing was unexpected. Therefore, we set out to extend our analysis again by including mutants in factors affecting other aspects of mRNA metabolism. UPF1 and UPF3 proteins are key factors of Nonsense-mediated decay (NMD), which is an important translation-dependent mRNA surveillance mechanism that eliminates aberrant transcripts containing premature translation stop codons and also contributes to the regulation of gene expression  mutant 14 day-old MS-grown seedlings were prepared, sequenced . Most of the tested mutants showed a significant fraction of genes with small RNAs produced only from one strand (21–74%), except for the cstf64-2 mutant  or containing an upstream ORF 46 as they were identified among the genes producing more sRNAs in upf3 or upf1 mutants but only from their coding strand . Consequently, these small RNA hotspots may have elevated DNA methylation. We used Col-0 DNA methylation data56 to check the methylated cytosine fraction in the affected genes and noticed that it is often much higher than the average for all protein-coding genes. This effect was not observed for dcp2, vcs7 and ski2 xrn4 mutants8, which represent the golden standard of mutants producing rqc-siRNAs . We cannot exclude that genes with high DNA methylation are the true source of rqc-siRNAs, but they would constitute atypical cases as they can be silenced even in wild-type plants. The presence of methylation and siRNA production hotspots in different parts of the gene can also be explained by spreading of small RNA synthesis57. Therefore, we decided to filter out genes with high level of DNA methylation using an arbitrarily chosen criterion of methylation level higher than 20% of all cytosines in a given gene, which is six times more than the average for protein-coding genes. Using both the strandedness and hotspot criteria, most libraries showed 53–88% dropouts, except for the cstf64-2 mutant with only 10% of the excluded genes . Assuming that restricting the analysis to this class of siRNAs will enhance our results, we repeated the differential analysis for 21–22 nt long small RNAs  mRNAs and some of the observed mutant phenotypes18. In contrast, in the lsm1a lsm1b mutant, the NIA2 gene produced only moderately more siRNAs , while a moderate increase in siRNA would not affect mRNA levels (SMXL5). Interestingly, when used as a transgenic reporter, NIA2 induces silencing of its endo- and exogenous copies60, whereas SMXL5 is one of the six protein-coding genes which produce more siRNAs when DCL4 is mutated59. These observations suggest that a low number of genes accumulating rqc-siRNAs in the lsm1a lsm1b mutant may represent those especially sensitive to induction of rqc-siRNA biogenesis. Recent work has shown the potential physiological role of 22 nt siRNAs produced from NIA1/2 mRNAs as they are induced not only by xrn4 ski2 mutations but also by nitrogen starvation and ABA treatment18.We observed a very high accumulation of siRNAs generated from the ant Fig. A. Similalsm1a lsm1b mutant, we also observed a modest siRNAs increase for AGO1 and several other miRNA targets that produce siRNAs also in wild-type plants 24, pab2 pab4 (SALK_026293 SALK_113383)44 , cstf64-2 35, fy-264 , lsm1a lsm1b 53, ncb-449 , rsr1-2 41, upf1-5 and upf3-1 65 were used in this study. We had obtained permission to grow genetically modified plants and we handled them in accordance with the institutional, and national guidelines and legislation. Seeds were sown on MS plates and stratified for 2 days at 4 °C. Plants were grown at constant 21 °C under long-day conditions and harvested at the age of 14 days. The cstf64-2 mutant was grown in soil at constant 21 °C under long-day conditions, and homozygous 21-day-old plants were harvested based on their phenotype. Wild-type Col-0 plants for this experimental set were grown together at the same time and condition but their small RNA sequencing analysis results are part of the earlier GEO submission (GSE99600). RNA was isolated using the Tri Reagent method and analysed with Bioanalyzer. All library preparation steps using NEB Next Small RNA Library Prep Set for Illumina, including PAGE selection of small RNAs and sequencing with Illumina HiSeq4000 in 50 bp single-end mode, were conducted by BGI Sequencing Services. Obtained fastq files were quality checked using FastQC  with all the replicates showing high-quality RNA-seq data. Adaptor sequences  were removed using cutadapt , and reads were quality trimmed with sickle se  with the following command-line parameters: -t illumina -q 20 -l 15. Reads were mapped to the TAIR10 A. thaliana genome from Ensembl66 (release v29) using bowtie67 (v1.0.0) with the following command-line parameters: -phred33 -v 0 -k 10 -m 10. Mapped reads were sorted using samtools sort (v1.1)68, counted with HTseq-count69 (v0.6.0) with or without respect to the strand and using exon features from Araport11 gene annotation release 20160436. In that annotation, exon stands for any gene fragment (both protein-coding and non-protein-coding) included in a mature RNA molecule. Differential expression and PCA was performed using DESeq237 (v1.8.2) R (v3.2.2) package with parameter alpha = 0.05. Genes with FDR < 0.05 and absolute log2FC > 1 were considered significantly changed. Analysis of siRNAs of different lengths was performed on reads grouped according to their length using reformat.sh with parameters minlength and maxlength from bbmap v35.x (https://sourceforge.net/projects/bbmap/). For DNA methylation analysis, we extracted the number of methylated cytosines for each gene using an R script and published data for Col-0 ecotype56; GSM1085222). Alignment coverage graphs were calculated with genomeCoverageBed from bedtools70 (v2.17.0) for all the alignment files with normalisation to the number of reads and were converted to bigwig format with bedGraphToBigWig (v4) from the UCSC Genome Browser application binaries collection (http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/) for visualisation in Integrated Genome Browser71. GO enrichment analysis was performed using g:Profiler (https://biit.cs.ut.ee/gprofiler/gost) with FDR < 0.001 and enrichment > 1.5. RT-qPCR was carried out on 2 µg of total RNA following DNase I digestion  with Random Primers and SuperScript III Reverse Transcriptase (Thermo Fisher Scientific). Quantitative PCR was performed using SYBR Green I Master Mix (Roche) using the Roche qPCR platform (LightCycler 480). Results were normalised to UBC9 or ACT2 mRNA. Primers sequences used for qPCR are listed in Supplementary Table Wild-type Col-0 and mutant lines: Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


Acute gastroenteritis (AGE) has a significant disease burden on society. Noroviruses, rotaviruses, and astroviruses are important viral causes of AGE but are relatively understudied enteric pathogens. Recent developments in novel biomimetic human models of enteric disease are opening new possibilities for studying human-specific host–microbe interactions. Human intestinal enteroids (HIE), which are epithelium-only intestinal organoids derived from stem cells isolated from human intestinal biopsy tissues, have been successfully used to culture representative norovirus, rotavirus, and astrovirus strains. Previous studies investigated host–virus interactions at the intestinal epithelial interface by individually profiling the epithelial transcriptional response to a member of each virus family by RNA sequencing (RNA-seq). Despite differences in the tissue origin, enteric virus used, and hours post infection at which RNA was collected in each data set, the uniform analysis of publicly available datasets identified a conserved epithelial response to virus infection focused around “type I interferon production” and interferon-stimulated genes. Additionally, transcriptional changes specific to only one or two of the enteric viruses were also identified. This study can guide future explorations into common and unique aspects of the host response to virus infections in the human intestinal epithelium and demonstrates the promise of comparative RNA-seq analysis, even if performed under different experimental conditions, to discover universal and virus-specific genes and pathways responsible for antiviral host defense. Caliciviridae family), rotaviruses (double-stranded RNA viruses belonging to the Reoviridae family), adenoviruses (double-stranded DNA viruses belonging to the Adenoviridae family), and astroviruses (single-stranded RNA viruses belonging to the family Astroviridae) [Acute gastroenteritis (AGE) is a major source of illness globally. It is defined as the inflammation of the mucus membranes of the intestinal tract accompanied by rapid onset diarrhea, nausea, vomiting, and abdominal pain. Five major virus families have been identified as etiological agents of viral gastroenteritis: noroviruses, sapoviruses (both single-stranded positive-sense RNA viruses belonging to the viridae) . Only 20viridae) . Rotavirviridae) . Human aviridae) . A full Human intestinal enteroids (HIEs) are patient-derived endoderm-only 3D structures composed of heterogeneous cell populations recapitulating intestinal tissues in vivo, providing a more faithful experimental model than immortalized and transformed cells. A large degree of epithelial cellular diversity is observed in HIEs: Columnar intestinal epithelial, stem/progenitor, enteroendocrine, and secretory cells can be observed depending on the culture conditions . SpheroiHIEs provide a uniquely complex system to study host–virus interactions at the intestinal epithelial interface and have been used to profile the epithelial transcriptional response to viral causes of AGE by RNA sequencing (RNA-seq) in several studies. Non-classic human astrovirus (VA1 strain) upon infection of duodenal HIEs triggered type I and type III interferon (IFN) signaling . Ileal HHere, our goal was to re-analyze three publicly available datasets from HIEs infected with three representative viruses that cause AGE to gain further insights into shared and unique transcriptional changes in the intestinal epithelium upon virus infection from a qualitative assessment standpoint.6 viral RNA copies) or mock-treated. Samples were collected at 48 hpi before sequencing [Each public RNA-seq dataset was generated in a different institution with variations in the experimental protocol used for viral infection, which we briefly summarize here. To generate the RNA-seq dataset from duodenal HIEs infected with astrovirus, HIEs were seeded in 48-well plates as 2D monolayers in complete L-WRN medium (GMCF+) and infected with astrovirus strain VA1 at an MOI of 1 based on genome copies per cell for 1 h at 37 °C or mock. Total RNA was extracted for sequencing at 24 h post-infection (hpi) . Ileal Hquencing . For thequencing .The sequencing reads obtained from RNA-seq experiments in each one of the studies re-analyzed were obtained from the Gene Expression Omnibus (GEO) or the European Bioinformatics Institute (EMBL-EBI) ArrayExpress collection. FASTQ files from a total of 12 HIEs samples (6 mock-treaded and 6 virus-infected) grown from two patients (TI006 and TI365) were obtained for the norovirus-infected ileal HIEs study from the GEO database (accession number GSE117911) . The metTranscript abundances from pseudoalignments (provided in transcripts-per-million) were generated for each RNA-seq dataset using Kallisto  and usedEstimated gene count matrices were then generated for each RNA-seq dataset from transcript abundances using the package tximport . Subsequhttps://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) [2 FC > 0.58) with a false discovery rate (FDR) cutoff of 1%  was used to determine whether a gene was differentially expressed. Volcano plots were generated to display the list of DEG for each RNA-seq dataset using the R package EnhancedVolcano [http://www.interferome.org/interferome/home.jspx last accessed on 30 March 2021).Filtered estimated gene count matrices generated for each RNA-seq dataset were normalized (median of ratios) with the R package DESeq2 version 1.32.0 (q2.html)  and subjq2.html)  to contrdVolcano . Additiohttps://rpkgs.datanovia.com/factoextra/index.html and https://github.com/jkrijthe/Rtsne accessed on 30 March 2021, respectively.Estimated gene count matrices were also used for the exploratory analysis of the dimensionality of the data . Dimensip-value cutoff of <0.001 using the Benjamini–Hochberg (BH) step-up procedure. To determine whether any BP categories were enriched, the gene ratio  and background ratio  are calculated and then compared. Visualization of the DEG assigned to each BP category was done using the R package ComplexHeatmap version 2.6.2 [https://github.com/jokergoo/ComplexHeatmap.Over-representation analysis was used to determine whether known biological processes (BP) were enriched in a list of differentially expressed genes (DEGs) for each one of the RNA-seq datasets analyzed. The package clusterProfiler  was usedon 2.6.2  availablTFEA using the R package TFEA.ChIP  was usedhttps://gitlab.com/rjcieza/HIEs-GI-viruses-comparative-transcriptomic.git, accessed on 30 March 2021. The datasets that have been used in this study are publicly available from the GEO and EMBL-EBI as indicated above.The source code used for analysis of the datasets and to generate the figures in this study are available at: The RNA-seq datasets from each of the studies that we analyzed ,11,12 we2 values > 10). No genes coding for IFNs were found in the list of shared genes from all RNA-seq datasets. However, IFN-λ (IFNL1-3) was uniquely identified in rotavirus-infected jejunal HIEs, while IFN-ε (IFNE) was partially shared across two of the RNA-seq datasets  present in all the datasets A. Among datasets . Biologi2 FC > 0.58) with a false discovery rate (FDR) cutoff of 1%  using an Empirical Bayes (EB) approach with the R package ashr [To determine the differentially expressed genes (DEGs) between infected versus mock controls in each RNA-seq dataset, we re-analyzed each dataset individually for differential expression with the package DeSeq2 and compared the list of DEGs we obtained in this analysis with those previously published ,11,12. Fage ashr . RelativIn the astrovirus-infected duodenal HIE dataset, 42 DEGs were identified out of a total of 11,824 non-weakly expressed genes, the smallest number of DEGs of the three RNA-seq datasets analyzed and all were up-regulated genes A. When r2 FC cutoff.A large degree of similarity was found in the list of DEGs identified between our analysis and those previously published ,11,12. AIFIT1, IFIT2 and IFIT3) as well as the signal transducer and activator of transcription proteins 1 and 2 (STAT1 and STAT2). Shared DEGs were also evaluated in the Interferome database, which revealed that 16 out of the 33 DEGs shared across RNA-seq datasets were associated with type I, II, III IFN responses (OASL) and Interferon Induced Protein 44 (IFI44), as well as the Solute Carrier Family 15 Member 3 (SLC15A3), which potentiates MAVS- and STING-mediated IFN production [IFIT1 showed greater up-regulation in the rotavirus dataset versus either the norovirus or astrovirus datasets. In addition, 30 genes were differentially expressed in only two out of the three datasets (CXCL11) and Bone Marrow Stromal Cell Antigen 2 , which was up-regulated in response to norovirus and astrovirus but not rotavirus infection. A different pattern was observed for example for the transcription factors IFN Regulated Factor 7 and 9 , which were differentially expressed in the astrovirus and rotavirus but not norovirus datasets, and the rotavirus and norovirus but not astrovirus datasets, respectively.To identify shared DEGs, we compared the list of DEGs for each individual RNA-seq dataset. There were 33 DEGs common to all three datasets, suggesting that they are part of a shared response against viruses by the intestinal epithelium A. Twentyesponses . Hence, oduction , showed datasets C. These p value < 0.001) with the Benjamini–Hochberg (BH) step-up procedure  over-representation analysis comparing biological processes (BP). The dot plots highlight the top ten over-represented biological process (BP) categories identified in the list of DEGs for each dataset A–C. 18, rocedure . Ten overocedure D. The torocedure .Overall, a shared set of DEGs informing a shared set of biological processes (BP) were identified in the three RNA-seq datasets. This conserved, predominant response of the intestinal epithelium to viral infection is mediated by antiviral defense and IFN signaling pathways.IFI27 and REV3L) not overlapping with the other datasets were identified. Interferon Alpha Inducible Protein 27 (IFI27) expression sensitizes cells to apoptotic stimuli [IFI35 and IFIT5 as well as the innate immune receptor toll-like receptor (TLR) 3 and several tripartite motif containing (TRIM) genes . Among the 18 DEGs found exclusively in the rotavirus-infected jejunal HIE dataset were IFN-λ .In addition to shared or partially shared DEGs, we also identified 2, 47, and 18 DEGs uniquely identified in astrovirus-infected, norovirus-infected, and rotavirus-infected HIEs, respectively . In the  stimuli , which m stimuli . HoweverIFIT1, HERC5, DDX60, DHX58, and STAT1) (CCL5 and IFN-λ (IFNL1-3) were important for 3 out of the 4 BP over-represented uniquely in rotavirus-infected HIEs  . A likelted HIEs . These gted HIEs A. Norovited HIEs .While the top shared over-represented BP categories were the same in the three RNA-seq datasets , we noteIn summary, shared and unique DEGs inform similar biological processes that are triggered in HIEs as part of an intestinal epithelial response to viral infection.p value < 0.05) in the list of DEGs when compared to non-differentially expressed background genes. Among the top enriched TFBS were ones for STAT1, STAT2, IRF1 and RELA  that were enriched in the list of shared and partially shared DEGs across the three RNA-seq datasets. Our rationale was that these DEGs are likely regulated by common transcription factors (TFs) or TFs from the same family to drive conserved responses of HIEs against viral infection. A total of 63 DEGs  sequenced at different institutions, (b) obtained from HIEs grown from different sections of the small intestine , and (c) collected at different times post-virus infection . These differences were noticeable when the data was subjected to dimensionality reduction, where t-Distributed Stochastic Neighbor Embedding (t-SNE) showed that the primary factor driving the differences between the three RNA-seq datasets was likely the anatomical origin of the biopsy used to generate HIEs . DespiteIFNE) was detectable in the datasets generated from astrovirus-infected duodenal HIEs and norovirus-infected ileal HIEs, but IFNE was not differentially expressed. This is in contrast to our previously published research, where we found that colonic and ileal HIEs showed detectable IFNβ gene expression in response to astrovirus infection by quantitative PCR [IFNL1, IFNL2, and IFNL3) being differentially expressed, consistent to what the authors originally reported [Several over-represented biological processes were part of type I IFN production, signaling, and response  and thustive PCR . This hitive PCR ,42. As sreported . Changesreported  was ableType I and III IFN production and signaling typically occurs in response to stimulation of pattern recognition receptors (PRRs), including toll-like receptors (TLRs). We found TLR3 to be the only TLR differentially expressed and only in ileal HIEs in response to norovirus (≈fold-change of 2). While TLR1, 2 and 3 were detectable across all datasets, they were only differentially expressed in response to norovirus infection. TLR3 is primarily present in the endosomal compartment and senses double-stranded (ds) RNA ,43. DsRNIn addition to the shared enrichment of DEGs in the type I IFN and response to virus pathways, a variation in the DEGs between the three viruses was also observed. The factors resulting in this variance are likely multi-fold and represent a balance of experimental differences, including in sampling timepoints, and inherent differences in the biology of these three viruses, including their ability to antagonize innate immune responses. For example, the host response to viral infection is dependent on the expression kinetics of type I and III IFNs, among others. The expression of IFNβ and IFNλ mRNA peaked at 16 and 24 hpi, respectively, in intestinal enteroids infected with reovirus , consistOverall, while a comparative analysis of dissimilar RNA-seq datasets from the literature has limitations, our processing pipeline and filters  limited these caveats and allowed us to draw several conclusions. Specifically, we identified key shared players across all datasets, which are part of the response to virus , demonstrating that HIEs employ a generic antiviral response. However, this is augmented by more tailored responses depending on the virus . We hope this comparative study provides a useful resource to the scientific community that stimulates further explorations into common and unique aspects of the host response to virus infections in the human intestinal epithelium. These studies should be complemented by comparative studies between different viral pathogens that cause gastroenteritis in the same host genetic background.