Lucilia sericata is a promising strategy in wound healing. Axon guidance molecules play vital roles during the development of the nervous system and also regulate the capacity of neuronal restoration in wound healing. Netrin-1, one of the proteins that larvae secrete, plays a useful role in cell migration and nerve tissue regeneration. The UNC-5 receptor combines with a netrin-1 signal and transmits the signal from one side of the membrane to the other side, initiating a change in cell activity. In the current study, we identified the full length of the UNC-5 receptor mRNA in L. sericata using different sets of primers, including exon junction and specific region primers. The coding sequence (CDS) of the UNC-5 receptor was sequenced and identified to include 633 base-pair nucleic acids, and BLAST analysis on its nucleotide sequence revealed 96% identity with the Lucilia cuprina netrin-1 UNC-5 receptor. The protein residue included 210 amino acids (aa) and coded for a protein with 24 kD weight. This gene lacked the signal peptide. Furthermore, the UPA domain is conserved in UNC-5. It lied at the interval of 26–131 aa. We identified the CDS of netrin-1UNC-5 receptor in L. sericata. It could be applied to research activities implementing a new essential component design in wound healing.Larval therapy with  The UNC-5 family of receptors mediate the repellent response to netrin-1. Netrins are a group of extracellular proteins that are present in invertebrates, nematodes, and insects UNC-5 is in axon repelence, while the molecular function of the other receptor ‘Deleted in Colorectal Cancer’ (DCC) is to attract and repel axons by netrin-1netrin-1 mediated through different receptors may regulate diverse signaling pathways and play distinct roles in physiological and pathophysiological conditions Drosophila melanogaster fruitfly, netrin A and netrin B have been identified DCC and UNC-5A-D in human and UNC-5H1-4 in rodents DCC receptors were identified as the first netrin receptors UNC -5 receptors can control apoptosis in the presence or absence of netrin-1 protein. Transplantation of bone marrow mesenchymal stem cells that produce netrin-1 improved the function of the sciatic nerve after injury. This method may be used in the future in the treatment of nerve injury netrin-1 UNC-5 receptor is expressed in capillaries. More studies on this new group of molecules will be interesting. Netrin-1, an enzyme known in the guidance of commissural axons, acts similarly in angiogenesis. It was also shown that one of the netrin-1 receptors is expressed at the apical cells of growing small blood vessel. It would be interesting to survey this new group of proteins. in the future. Netrins and netrin receptors have important roles with respect to angiogenesis in wound healing in vivo and enhances the response to vascular endothelial growth factor Netrin-1, as a macrophage maintenance signal in fat tissue during obesity, boosts chronic inflammation and insulin resistance. Netrin-1, as a significant signal, reconciles the dynamic crosstalk between inflammation and constant erosion of the extracellular matrix in abdominal aortic aneurysms. Netrin-1 generates a steady intracellular calcium flow necessary for the transcriptional regulation and persistent catalytic activation of matrix metalloproteinase-3(MMP3) by vascular smooth muscle cells netrin-1 protein with the UNC-5 and DCC receptors are shown to be implicated in axonal regeneration after injury The use of netrin-1 UNC-5 receptor in larvae of L. sericata. This blowfly is a species in the family Calliphoridae, Class Insecta with a critical role in maggot therapy L. sericata larvae are used in maggot therapy and involved in the repair of the damaged nerve tissue, therefore, the identification of the nucleic acid sequence of the UNC-5 receptor of L.sericata and its recombinant expression in the future can be effective in producing drugs for the treatment of ulcers. In this way, using the recombinant protein, it also increases the netrin-1 secretion and accelerates the recovery of the damaged nerve tissue.This study was conducted to identify and characterize the 2.2.1.L. sericata from a colony that had been reared at the School of Health insectarium, Shiraz University of Medical Sciences (SUMS) under constant conditions. Adults were exposed to a 12 hours L/D cycle at a relative humidity of 40–50% and temperature range of 18–25 °C. The larvae were fed on beef. Accurate species identification was routinely confirmed using valid pictorial taxonomic key based on morphological characterization.Experiments conducted on the second instar larvae of 2.2.L. sericata genome has not been sequenced yet and based on our previous study Ceratitis capitata (XM_020859149), Bactrocera cucurbitae (XM_011194897), Musca domestica (XM_020034772), Stomoxys calcitrans (XM_013257881), and Lucilia cuprina (XM_023442922) were obtained from NCBI and aligned using the MEGA 6 software. Then, degeneration primers were determined based on conserve regions  and the extracted RNAs were treated by DNaseI , both according to the manufacturer's instruction and then stored in −70 °C.2.4.Extracted RNA was used for the first strand cDNA synthesis. Then reverse transcription (RT) reaction was performed according to RevertAid First Strand cDNA Syn. kit (Fermentas Company) by the random hexamers primer.2.5.2, 1 unit Taq DNA polymerase, 0.2 mM dNTPs, 2 µL 10 × reaction buffer, and the final volume was adjusted to 20 µL with double distilled water (dd H2O). The amplification program was set as follows: 5 min at 94 °C; followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 58 °C for 30 s, extension at 72 °C for 30 s; and an additional final extension at 72 °C for 10 min. The amplified amplicons were purified using the DNA gel purification kit .All polymerase chain reactions (PCRs) were performed in a 20 µL total volume for 35 cycles using 2 µL of synthesized cDNA or 150 ng genomic DNA in each cycle as a template. The reaction mixture contained 400 nM of each primer, 1.5 mM MgCl2.6.Expected size bonds after gel purification were sequenced, and their analysis was performed by Chromas , DNA Star , MEGA6  and BLAST by NCBI online. Amplicons with a size close to the predicted range sequenced using GSPs forward and reverse primers.2.7.http://blast.ncbi.nlm.nih.gov/Blast.cgi).All primers were designed using the Gene Runner (version 0.4) and Oligo 0.7 software. Alignments were performed by the MEGA software (version 6.0), and their specificity for PCR was checked by nucleotide BLAST on NCBI  of the netrin-1 UNC-5 receptor. The expected protein was determined by D weight . Besidesnetrin-1 UNC-5 receptor from the salivary gland of L. sericata was determined. The PCR reactions were carried out using a combination of NetF1, NetF2, and NetR633 primers, which resulted in finding exactly two amplicons with the expected sizes of 633 bp and 290 bp  receptors, are usually implicated to achieve equal developmental outcomes The discovery of netrins constitutes a grand leap onto the evolution of developmental control mechanisms among neuronal cells. The leading role of netrin-1 and netrin-1 receptors helps to repair injured nerve tissues. Within insects, divergent sets of axon guidance molecules, such as the midline repellant Slit and its L. sericata such as the study of Telford in 2010 on chymotrypsin and Cerovsky in 2010 on lucifensin have been published UNC-5B and netrin-1 in addition to its effects on leukocyte migration Netrin-1 regulates the inflammatory response of neutrophils and macrophages via suppression of cyclooxygenase 2–mediated prostaglandin E2 production during ischemic acute kidney injury Unc5B receptor, induces activation of peroxisome proliferator-activated receptor-γ and other signaling pathways, which causes suppression of IκB degradation and inactivation of NF-κB transcription factor Netrin-1 contributes to the regulation of leukocyte migration and inflammation in peripheral tissues UNC-5B mRNA expression is increased, and the DCC receptor is not changed significantly Some studies on the identification of enzymes secreted from All these and other studies point to the fact that certain enzymes or other molecules derived from larval blowflies or alternative insects could be conducive to the repair, regeneration, and swift resolution of impaired human body tissues. They are thus a promising field of research activity.One of the drawbacks in the current study was that a shortage of basic raw materials essential to launch a wider scale study was impeding to assist the resolution of all research inquiries in this field of study.netrin-1 UNC5 receptor in L. sericata larvae resembles those of other insect species. The findings presented in this research article could be manipulated in future endevours to enhance wound healing using maggot therapy.It is concluded that the coding sequence of the


Saccharomyces cerevisiae. The profiles reveal the distribution of degradation factors between different RNA classes. They are consistent with the canonical degradation pathway for closed-loop forming mRNAs after deadenylation. Modeling based on mRNA half-lives suggests that most degradation factors bind intact mRNAs, whereas decapping factors are recruited only for mRNA degradation, consistent with decapping being a rate-limiting step. Decapping factors preferentially bind mRNAs with non-optimal codons, consistent with rapid degradation of inefficiently translated mRNAs. Global analysis suggests that the nuclear surveillance machinery, including the complexes Nrd1/Nab3 and TRAMP4, targets aberrant nuclear RNAs and processes snoRNAs.RNA degradation pathways enable RNA processing, the regulation of RNA levels, and the surveillance of aberrant or poorly functional RNAs in cells. Here we provide transcriptome-wide RNA-binding profiles of 30 general RNA degradation factors in the yeast  Cells contain a large group of DNA-like molecules called RNAs. While DNA stores and preserves information, RNA influences how cells use and regulate that information. As such, regulating the quantities of different RNAs is a key part of how cells survive, grow, adapt and respond to changes. For example, messenger RNAs (or mRNAs for short) carry genetic information from DNA which the cell reads to produce proteins. RNAs that are not needed can be degraded and removed from the cell by RNA degradation proteins.Most RNA degradation proteins need to be able to bind to RNA in order to work. A technique called “photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation”, often shortened to PAR-CLIP, can detect these proteins on their targets. The PAR-CLIP technique irreversibly links RNA-binding proteins to RNA and then collects those proteins and their bound RNAs for analysis. As with DNA, the RNAs can be identified using genetic sequencing. Degradation often starts at RNA ends, where specialized structures protect the RNA from accidental damage.Saccharomyces cerevisiae. The results highlight the specialization of different proteins to different groups of RNAs. One group of proteins, for example, remove the protective ‘cap’ structure at the start of RNAs. Those mRNAs that are not efficiently producing proteins attracted a lot of these cap-removing proteins. The findings also identify proteins involved in RNA degradation in the cell nucleus – the compartment that houses most of the cell’s DNA.Using PAR-CLIP, Sohrabi-Jahromi, Hofmann et al performed a detailed study of 30 RNA degradation proteins in the yeast Together these findings provide an extensive data resource for cell biologists. It offers many links between different RNAs and their degradation proteins. Understanding these key cellular processes helps to reveal more about the mechanisms underlying all of biology. It can also shed light on what happens when these processes fail and the diseases that may result. The abundance of the different eukaryotic RNA species controls cell type and cell fate, and is determined by the balance between RNA synthesis and RNA degradation. Multiple mechanisms exist for RNA degradation . RNAs arRNA degradation can occur from both ends of a transcript, and these processes are often coupled. During canonical mRNA turnover, degradation is thought to be initiated by shortening of the 3´ poly-adenylated (polyA) tail through two major deadenylation complexes, the Pan2/Pan3 complex and the multi-subunit Ccr4/Not complex  . SpecifiThe second step in mRNA degradation is thought to be the removal of the 5´ cap by the decapping complex . The cap protects the mRNA from degradation by the 5´→3´ exonuclease Xrn1, which requires a 5´ monophosphate at the terminal residue . DecappiAn alternative pathway of mRNA degradation after deadenylation is 3′→5′ degradation by the exosome and its auxiliary factors . The exoFor RNA degradation by the exosome, RNA first passes through either the TRAMP or the Ski complex. TRAMP is a nuclear poly-adenylation complex  that is To degrade eukaryotic mRNAs that are defective in translation, cytoplasmic quality control mechanisms exist . Normal The large variety of different RNA degradation factors poses the question how RNA degradation pathways are selected and whether the RNA sequence can influence this selection. Answering this question requires a systematic analysis of the RNA-binding profiles of the involved protein factors. Although several transcriptome profiles of the RNA degradation factors Xrn1, Rrp44, Csl4, Rrp41, Rrp6, Mtr4, Trf4, Air2 and Ski2 have been reported , we lackSaccharomyces cerevisiae (S. cerevisiae). In-depth bioinformatic analysis and comparisons with previously reported PAR-CLIP data provide factor enrichment on different RNA classes and the binding behavior for mRNAs and their associated antisense transcripts. The results also give insights into how the various degradation complexes, and also different subunits in these complexes, may be involved in the degradation of different RNA species. Several conclusions can be drawn with respect to degradation pathway selection, new functions for known factors can be proposed, and several hypotheses emerge that can be tested in the future. Finally, our dataset provides a rich resource for future studies of eukaryotic RNA degradation pathways, mechanisms, and the integration of mRNA metabolism.Here we used photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) to systematically generate transcriptome-wide protein binding profiles for 30 general RNA degradation factors in the yeast S. cerevisiae using our published protocol  . We perfprotocol , with miprotocol , with Spprotocol . These tWe first compared degradation factor binding over different RNA classes. These included messenger RNA (mRNA), where we distinguished the 5´ untranslated region (5´ UTR), the coding sequence (CDS), introns, and the 3´ untranslated region (3´ UTR). We also included several classes of ncRNAs: ribosomal (r), transfer (t), small nucleolar (sno), and small nuclear (sn) RNAs, as well as stable unannotated transcripts (SUTs), cryptic unstable transcripts (CUTs), and Nrd1-unterminated transcripts (NUTs)  Figure .A first analysis revealed that most PAR-CLIP sequencing reads fall into the mRNA transcript class, although many of the factors also show a considerable number of sequencing reads in ncRNAs, in particular rRNAs . To obtaThe catalytic subunit Pop2 and the core subunits Not1 and Caf40 of the deadenylase complex Ccr4/Not have similar binding preferences for the 5´ UTR, the CDS and 3´ UTR of mRNAs, for rRNAs, tRNAs, snoRNAs, and snRNAs . Pan3 shFor all decapping-related factors we observed similar binding preferences among each other . Dcp2, wFor the exosome we also observed binding to different RNA classes . This coThe two TRAMP complexes TRAMP4 and TRAMP5 show clearly distinct cross-linking patterns . MoreoveThe cross-linking preferences of subunits of the Ski complex differ only slightly from each other . The exoWe next focused on degradation factor distribution on mRNAs. We prepared metagene profiles showing the average occupancy of each factor around the mRNA transcription start sites (TSS) and the poly-adenylation (pA) sites, respectively . The PanThe exosome core subunits  and the catalytically active subunits  cross-link to the 5´ end of the transcript , possiblThe Ski complex components Ski7 and Ski8 occupy the entire mRNA with increasing occupancy towards the pA site, whereas Ski2 and Ski3 show more discrete binding towards the polyA tail . The NMDPervasive transcription of the genome leads to many short-lived aberrant RNAs that must be rapidly detected and degraded in the nucleus. We previously reported that the RNA surveillance factors Nrd1 and Nab3 strongly cross-link to aberrant upstream antisense RNA that stems from bidirectional transcription . In ordeIt has been shown that Nrd1 is involved in terminating transcripts upstream of the TSS. We also observe a strong signal for binding upstream of TSS on the sense strand for Air2 and Mtr4 . This suThese results are consistent with the idea that the nuclear RNA surveillance machinery involves, in addition to Nrd1 and Nab3, the TRAMP4 complex and the nuclear exosome. Indeed, it was reported that TRAMP4 can add a short polyA tail on aberrant RNAs , which mTo find out which groups of factors can work together in degrading transcripts, we analyzed their tendency to co-occupy the same transcripts by calculating the Pearson correlation of their occupancy across all transcripts . We alsoCo-occupancy and co-localization plots for all factors can be found in The analysis contains a lot of new information, forcing us to focus here on a few interesting, novel findings . First, A fourth cluster is formed by mRNA deadenylation factors together with polyA tail binding proteins (Pab1 and Pub1), Ski7, Ski8, Trf5, and the export factor Yra1 (cluster 4). This is consistent with coupled mRNA deadenylation and subsequent degradation from its 3´ end by the exosome with the Ski or TRAMP complex as adaptors. The fifth cluster is formed by mRNA decapping factors, which cluster together with Xrn1, suggesting a coupling of mRNA decapping with degradation from the 5´ end by Xrn1 (cluster 5). The NMD-involved factors Upf1, Upf2, Upf3 and Nmd4, and Ski2 and Ski3 are also found in cluster 5. The high correlation between Xrn1 and Ski2 has been reported in a CRAC experiment . The eloTo study the link between cytosolic mRNA translation and degradation, we compared the occupancy of degradation factors on mRNAs to their average codon-optimality score  –8A. We fTo investigate the significance of the correlation between transcript optimality and binding of the 5´ degradation machinery, we compared the contribution of several mRNA features in explaining the occupancy patterns retrieved from PAR-CLIP experiments. Since mRNA expression, half-life, and translation optimality are inter-correlated , a causaAlthough decapping occurs at the 5´ end of mRNAs, decapping factors show a strong occupancy near the 3´ end . To inveDecapping factors may bind to complete mRNAs or to transcripts that are in the process of being degraded. To quantify these two behaviors, we combined our PAR-CLIP occupancy data with RNA half-life estimates . We modeled the occupancy of factors on mRNA as the sum of binding to all transcripts , where 5´ and 3´ ends are in proximity . It is pComparison of our data with previous profiles of the nuclear surveillance factors Nrd1 and Nab3 reveals many factors that show a similar binding to aberrant non-coding nuclear RNAs, in particular antisense RNA upstream of known promoters, suggesting that these factors are part of the nuclear surveillance machinery. These results are consistent with published data  and withThe exosome is involved in 3´→5´ cytoplasmic mRNA degradation, and in the processing and degradation of long-lived transcripts such as tRNAs, rRNAs and sn(o)RNAs. The differences in exosome co-factor binding to different RNA classes  support Saccharomyces cerevisiae BY4741 strains harboring C-terminally tagged genes  were tested for the correctly inserted tag by Western Blotting using the Peroxidase Anti-Peroxidase  antibody and Pierce ECL Western Blotting Substrate  (data not shown).600 = 0.5. Cells were labeled in 1 mM 4tU final concentration for 4 hr. After labeling, cells were harvested, resuspended in ice-cold PBS and UV irradiated with 10–12 J/cm2 at a wavelength of 365 nm on ice and continuous shaking. Lysis was performed in lysis buffer  by bead beating  using silica-zirconium beads . The cleared lysate was used for immunoprecipitation with rabbit IgG-conjugated Protein G magnetic beads  on a rotating wheel for 4 hr or overnight at 4°C. Beads were washed in wash buffer . IP efficiency was controlled with part of the sample by Western Blot as shown in 5rApp-TGGAATTCTCGGGTGCCAAGG-3ddC 3′ (IDT), 1 U/µL RNase OUT, and 15% (w/v) PEG 8000 overnight at 16°C. 5´ adapter was ligated to the RNA using T4 RNA ligase buffer (NEB) with 6 U/µL T4 RNA ligase 1 (NEB), 10 μM 5′ adapter , 1 mM ATP, 1 U/µL RNase OUT, 5% (v/v) DMSO, and 10% (w/v) PEG 8000 for 4 hr at 25°C and 1 hr at 37°C. Beads were boiled in proteinase K buffer  at 95°C for 5 min. Proteinase K digest was performed with 1.5 mg/mL proteinase K (NEB) for 2 hr at 55°C. RNA was recovered by acidic phenol/chloroform extraction followed by ethanol precipitation in presence of 0.5 µL GlycoBlue (Invitrogen) and 100 μM RT primer . SuperScript III RTase was used for reverse transcription for 1 hr at 44°C and 1 hr at 55°C. NEXTflex barcode primer and universal primer were added to cDNA by PCR amplification with Phusion HF master mix (NEB). After PCR amplification, cDNA was purified and size-selected on a 4% E-Gel EX Agarose Gel (Invitrogen). Quantification on an Agilent 2200 TapeStation instrument  and 50–75 nt single-end sequencing was performed on Illumina sequencers .PAR-CLIP was performed as described . BrieflyS. cerevisiae genome . After mapping PCR duplicates are removed with UMI-tools.Reads from PAR-CLIP experiments with replicates were merged after making sure that all samples showed high Spearman correlation values comparing binding occupancies of replicates on different genes . MappingWe used two alternative approaches for mapping reads using Bowtie : For allhttps://github.com/soedinglab/Degradation_scripts (copy archived at https://github.com/elifesciences-publications/Degradation_scripts).T→C transitions directly at the edge of the reads or with a Phred quality score lower than 20 are not considered as signature of protein binding as they suffer from higher technical noise. To obtain high confidence cross-link sites, we set a stringent cutoff of 0.005 for the p-value of cross-link sites and require a minimum coverage of 2 per site. Moreover, if we see the same transition in at least 75% of reads in the input library control (SRA: SRX532381) , we annoWe analyzed the distribution of reads from high confidence cross-link sites over the genome . We presc, in comparison to all other factors. We use annotations for rRNA, tRNA, snoRNA, snRNA, coding sequences (CDS), from S. cerevisiae genome sacCer3, version 64.2.1. Untranslated regions around coding boundaries (5´ and 3´ UTRs) were annotated based on TIF-seq experiment . We then normalize according to the total occupancy values,Co-localization measures how likely two factors are to bind near each other in the transcriptome. More precisely, we first calculate the occupancy of a factor f is the number of cross-link sites for factor f, and Occff´,i,j is the occupancy of f at position j around the thi cross-link site from factor f´ . To improve signal-to-noise, we compute from the resulting matrix of co-localizations between all RNA-processing factors Cf,f´, the matrix of Pearson correlations between the rows of Cf,f´,   and mapptotal occupancy of degradation factors on each mRNA relative to such transcript features , 4-mers at position i in the set of binding sequences aligned at their cross-link site i=0, 4-mer[j] is the base at the j’th position of the 4-mer, and bP is the probability of observing base b. We used the probabilities: PA = PT = 0.31 and PC = PG = 0.19 based on frequencies in yeast genome and corrected for the T bias at the cross-link site .Thank you for submitting your work entitled "Transcriptome maps of general eukaryotic RNA degradation factors" for consideration by eLife. However, new submission of a revised manuscript would be welcome if all the reviewers' concerns can be addressed.Our decision has been reached after consultation between the reviewers. All reviewers acknowledge the extensive data sets obtained, the quality of the analysis and the potential value for the RNA turnover field. There are, however, concerns about the reproducibility of the data and the significance of the biological insights, given the absence of any independent validation of the results. We are therefore obliged to reject this paper for publication in We hope that the individual reviews appended below will be helpful in deciding how to move forward.Reviewer #1:In this manuscript the authors describe the genome-wide binding sites for 30 different proteins acting in multiple RNA degradation pathways. The data will be a valuable resource for researchers in this area and should be published. However, there are a numerous places were the manuscript needs to be improved by relatively minor rewrites such that the text more accurately reflects the data. Some of the issues are:1) The description of the reliability and quality of the data in the "Transcriptome maps for 30 RNA degradation factors" section of the Results is not completely congruent with the results.1a) "The high reproducibility of these PAR-CLIP experiments is revealed by a comparison of biological replicates that we collected for eleven out of the 30 degradation factors ." The supplementary figure actually shows high reproducibility for 4 of the proteins , medium reproducibility for 3 factors and modest reproducibility for 4 factors.1b) 19 of the factors were not analyzed in duplicate and thus the reproducibility of these datasets is hard to evaluate.1c) Furthermore, the Materials and methods indicate that "Reads from PAR-CLIP experiments with replicates were merged", thus it is not clear what part of the analyses were actually preformed in duplicate.2) At least seven of the factors studied here have been previously studied by the Tollervey lab . Thus, a valuable extension of this study and the validity of the data would be to compare reproducibility between the author's PAR CLIP data and the published CRAC data.3) "we typically obtained tens of thousands of verified factor-RNA cross-link sites (Table 1)." Table 1 actually shows that about half of the factors meet this tens of thousands threshold while the remaining factors vary between 561 and 16663 sites.4) In the description of "RNA end-processing complexes differ in their targets" section the highlighted results that cytoplasmic factors like Caf40 and Ccr4 bind sparsely to nuclear RNAs such as introns, snRNAs and snoRNAs is not surprising. The authors attribute this to a "strong binding preference", but this may simply reflect that these factors are not exposed to these RNAs instead of binding constants.More importantly, the z-scoring in Figure 1B appears to be inappropriate for these kinds of statements. If I understand the description of the z-scoring correctly the unexpected high z-score of Ccr4 for tRNAs in Figure 1B means that tRNAs have a strong preference for Ccr4 compared to the other 29 factors, not that Ccr4 has a strong preference for tRNAs compared to all other RNAs as implied in the Results section. Another example of problematic description of Figure 1B is "Rrp6.…. shows binding to rRNAs, tRNAs, snoRNAs and snRNAs." However Figure 1A does not appear to show appreciable binding to rRNAs.5) "Dcp2, which harbors the hydrolase activity that removes the cap, cross-links frequently to mRNAs, in particular their 3 ´UTR, and to short noncoding transcripts, in especially to unstable SUTs. This suggests that Dcp2 is involved not only in cytosolic mRNA degradation but also in decapping during nuclear RNA surveillance." However, the defining characteristic that differentiates SUTs from CUTs is that SUTs are degraded by cytoplasmic Dcp2-dependent pathways as shown by several different labs . Thus rather than suggesting a new role for Dcp2, these findings confirm a different known role.6) "The analysis recovers established interactions between subunits of known complexes" but this is not supported by any quantitative analysis. Among the factors the authors analyze, some proteins that are thought to form stable complexes indeed cluster  while other known stable complexes do not (e.g. Ski2/3/8 or the Air1 subunit of TRAMP).7) Some of the names of the clusters in Figure 4C are poorly justified. For example, out of the six proteins in the "nuclear ncRNA processing" cluster four are cytoplasmic proteins that act mostly or exclusively on mRNA . They also function in degradation, rather than processing, thus at least the first two but possibly all three words in this name should be changed.8) "For decapping factors  and Xrn1, low codon optimality is the most determining feature for binding .” However Figure 5C appears to show that this is true for three of these factors, but the opposite is true for Edc2, where high codon optimality appears to be determining. The authors should clarify.9) "Moreover, A-rich 4-mers are abundant around the proximity ( ± 8 nt) of Dcp2-cross-link sites ." However Figure 6C and the legend say the window is ± 5 instead of ± 8. This might be a typo.10) "As expected, most PAR-CLIP cross-link sites fall into the mRNA transcript class, although about one third of the factors also show a considerable number of cross-links to non-coding RNAs". I suggest deleting "as expected" or replacing it with "surprisingly". Several of the factors have known roles in ncRNA processing/degradation and mRNA is only a minor fraction of cellular RNA and therefore I would not have expected most sites to fall into the mRNA class.A related issue may be that the analysis in Figure 1 appears to only include "Fractions of high-confidence PAR-CLIP cross-link sites" and not the actual number of or fraction of high-confidence reads. e.g. if there are 9 sites, in mRNA with 100 reads each and one in a ncRNA with 900 reads this would count as 90% of the sites being in mRNA, but only 50% of the reads are in mRNA. Since the complexity of mRNA is probably the highest , the number of distinct sites in mRNA for a given factor is probably a systematic overrepresentation of that factor's importance in mRNA processing.11) In Materials and methods "various non-overlapping transcript classes c" needs to be clarified. Several of the transcript classes in fact do overlap in the genome. Many snoRNAs are encoded within introns. Many CUTs, SUTs and NUTs overlap with mRNAs and each other. The methods need to be clarified.Reviewer #2:The authors present the PAR-CLIP analysis of 30 proteins implicated in RNA turnover or surveillance. In principal, these results should be of considerable value to the field, but this is much reduced by the lack of biological or even technical replicates for most of the experiments. Moreover, there are some concerns about the mapping of the data to the yeast genome. The downstream bioinformatic analysis of the data appears to have been very well performed, with imaginative approaches being applied and interesting conclusions. The 2D clustering analysis  is particularly good.I regret that the technical problems with the manuscript prevent me from recommending acceptance of the current version.Specific points:1) Subsection “Transcriptome maps for 30 RNA degradation factors”: It appears that biological replicates were performed for only eleven out of the 30 degradation factors tested. For 19 of the factors analyzed, the conclusions are seemingly based on single experiments. While it is the case that the experiments reported involve a lot of work, it is quite unusual for the bulk of data in a manuscript to be published without clearer evidence of reproducibility. It would have been better to analyze fewer proteins in more depth. The authors present data indicating the consistency of the results from the proteins for which replicates were performed. However, while the replicates appear to cluster well in the 2D diagrams, inspection of the metagene analysis reveals appreciable differences between duplicates , which would presumably be much more marked at the level of individual genes.This is particularly an issue when the results are not as expected, e.g. the apparent binding of the deadenylase Ccr4 to a high proportion of tRNAs, which is based only a total of only 4,540 confident Ccr4 cross-linking sites.2) The authors report that "most PAR-CLIP cross-link sites fall into the mRNA transcript class". This is unexpected, particularly for pre-rRNA processing factors, including the exosome and Mtr4, that should recover high level of rRNA hits. Inspection of the methods indicates that the reads were mapped to a unique database. This would have the effect of eliminating most rRNA and tRNA reads, as well as a subset of mRNAs. In the yeast genomic database, there are 2 copies of the rDNA repeat, so unique mapping will eliminate most rRNA hits. For tRNAs the situation is more complex; most tRNAs are present in multigene families and will be eliminated from unique hits, whereas the few single copy tRNA genes will be retained. This might be a particular issue for the reported Ccr4-tRNA interactions. For mRNAs, duplicated genes such as R-proteins will also be excluded.This point should be clarified. If uniquely-mapped reads were indeed used, the full mapping data should be presented.3) Subsection “RNA end-processing complexes differ in their targets”: SUTs can be cytosolic, so this might not be sufficient to show that Dcp2 has nuclear activity.S. cerevisiae proteins”): It would be helpful to discuss more why Csl4, Rrp4 and Rrp40 have distinct profiles. In particular, the Rrp4 profiles looks very different from all other degradation factors. This raises question of the PAR-CLIP experiment really worked for each cofactor. It is notable that some of these have low numbers of XL sites .4) Subsection “Distinct factor distribution along mRNA”, Figure 1B (and end of subsection “PAR-CLIP experiments of 5) Subsection “Interactions between RNA processing machineries”, Figure 4 and Figure 4—figure supplements 1-2: It would be helpful to discuss why Trf4/Air2 is bound to introns  but not Trf5 while Trf5/Air1 is showing higher co-occupancy / colocalization with splicing factorsReviewer #3:S. cerevisiae RNA decay factors. Analyses of the obtained data are often in-line with current knowledge but also uncover several unexpected binding patterns.The paper by Sohrabi-Jahromi et al. presents genome-wide RNA binding datasets for eLife, should be supported by independent experiments.The presented data are certainly of interest to a wide audience. However, there is no independent validation of any of the unexpected binding patterns. This is especially problematic, given that many datasets are based on single biological replicates. Moreover, some observations challenge our current thinking about the function of the investigated factor(s), which, to be suitable for publication in - Functional relevance:The manuscript presents a series of unexpected and somewhat controversial observations. One example concerns a possible role for Pop2 and Not1 in decay of CUTs. Any functional relevance of such binding should be trivial to validate. This would be relevant for other of the most striking findings. Such validation may be possible by re-analysis of published datasets, for example by intersecting occupancy with RNAseq upon depletion/mutation of the same factors.- Replicates and Technical artefacts:PAR-CLIP is a complex technique, where technical artefacts are not uncommon. For example, binding to highly abundant tRNA, snRNA and snoRNA species is often observed, even for factors where such binding is unlikely to occur or be relevant in vivo. Hence, it is quite possible that binding to such species, especially when based on a single biological replicate, does not reflect biology. Such considerations should be mentioned in the manuscript. Ideally, all factors for which single replicates are presented, should be backed up by additional data. This may be simply an additional replicate, or some kind of independent validation.More generally, more information on how the authors scrutinize the quality of their PAR-CLIP experiments should be given (i.e. more explicitly than simply referring to another publication).[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your revised article has been favorably evaluated by James Manley as the Senior Editor, and three reviewers, one of whom is a member of our Board of Reviewing Editors.Thank you for resubmitting your work entitled "Transcriptome maps of general eukaryotic RNA degradation factors" for further consideration at The manuscript has been considerably improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:1) Even though some of the more controversial suggestions of the original draft are now resolved, comparison with available knock-out or mutant / RNA-seq datasets would greatly help to support, or dismiss, some of the mentioned novel findings.2) If available, western blots and/or autoradiographs of the different PAR-CLIP experiments would be nice to include to allow readers to judge the quality of different IP experiments.3) Some of the conclusions presented in the Results sections still appear vague or unclear. This concerns for example interpretation of the RNA exosome binding profiles.a) i.e. In the first paragraph of the subsection “The exosome and surveillance factors”: unclear what different binding patterns "reflect" the distinct functions of the RNA exosome.b) In the second paragraph of the subsection “Distinct factor distribution along mRNA”: How/why would TRAMP4 binding upstream of the TSS affect early termination of the mRNA? Isn't it more plausible that it serves a role in termination of (unannotated) upstream ncRNA? Similarly, 5' bias of exosome and TRAMP subunits could arise from unannotated NUT-like transcripts.4) Figure 3 and Figure 3—figure supplement 1B: What RNAs generate the high signals for Mtr4 and Air2 5' to the TSS? This region is expected to give rise to very few transcripts. The upstream ncRNAs are further upstream and on the opposite strand. There must be a concern that some sequences have been mis-mapped. Were the sequences mapped only to a small number of loci, which would strengthen this concern? The authors should re-check the data supporting these distributions.5) The second paragraph of the subsection “The exosome and surveillance factors” refers to Figure 2—figure supplement 3 and not Figure 3—figure supplement 3.6) Figure 5C: 3' degaradation => 3' degradation [Editors’ note: the author responses to the first round of peer review follow.]Reviewer #1:1) The description of the reliability and quality of the data in the "Transcriptome maps for 30 RNA degradation factors" section of the Results is not completely congruent with the results.1a) "The high reproducibility of these PAR-CLIP experiments is revealed by a comparison of biological replicates that we collected for eleven out of the 30 degradation factors ." The supplementary figure actually shows high reproducibility for 4 of the proteins , medium reproducibility for 3 factors and modest reproducibility for 4 factors.1b) 19 of the factors were not analyzed in duplicate and thus the reproducibility of these datasets is hard to evaluate.1c) Furthermore, the Materials and methods indicate that "Reads from PAR-CLIP experiments with replicates were merged", thus it is not clear what part of the analyses were actually preformed in duplicate.Thank you for this important comment, which we took very seriously. To ensure the highest possible data quality and reproducibility, we generated 49 new datasets, obtaining between two and four highly reproducible biological replicates for each degradation factor . Upon comparing the replicates, we found poor reproducibility for Pop2, Not1, Rrp4 and Caf40 and therefore omitted old replicates from the analysis. Furthermore, we applied a more conservative threshold for including data and eliminated six of the previous replicates that had less than 10.000 cross-link sites . As a result, only highly reproducible data with a high number of crosslink sites were included in the final data set. This has led to a clearly visible improvement of the analysis and revised manuscript.We now write:“The high reproducibility of these PAR-CLIP experiments is revealed by a comparison of two independent biological replicates that we collected for all 30 degradation factors , with Spearman correlations between 0.87 and 1.00 (mean: 0.94).”The extent of improvement of the PAR-CLIP data can be appreciated by comparing the old Figure 2 with new Figure 3.2) At least seven of the factors studied here have been previously studied by the Tollervey lab . Thus, a valuable extension of this study and the validity of the data would be to compare reproducibility between the author's PAR CLIP data and the published CRAC data.We have included the comparison between metagene profiles obtained from CRAC data and PAR-CLIP in Figure 1—figure supplement 1. The agreement between these studies is indeed good.3) "we typically obtained tens of thousands of verified factor-RNA cross-link sites (Table 1)." Table 1 actually shows that about half of the factors meet this tens of thousands threshold while the remaining factors vary between 561 and 16663 sites.Compare comment above. We have generated new biological replicates for most of the PAR-CLIP experiments. Our new datasets contain between 21,311 and 590,328 cross-link sites per factor .4) In the description of "RNA end-processing complexes differ in their targets" section the highlighted results that cytoplasmic factors like Caf40 and Ccr4 bind sparsely to nuclear RNAs such as introns, snRNAs and snoRNAs is not surprising. The authors attribute this to a "strong binding preference", but this may simply reflect that these factors are not exposed to these RNAs instead of binding constants.More importantly, the z-scoring in Figure 1B appears to be inappropriate for these kinds of statements. If I understand the description of the z-scoring correctly the unexpected high z-score of Ccr4 for tRNAs in Figure 1B means that tRNAs have a strong preference for Ccr4 compared to the other 29 factors, not that Ccr4 has a strong preference for tRNAs compared to all other RNAs as implied in the Results section. Another example of problematic description of Figure 1B is "Rrp6.…. shows binding to rRNAs, tRNAs, snoRNAs and snRNAs." However Figure 1A does not appear to show appreciable binding to rRNAs.Thank you very much for this remark, which prompted us to come up with a better way to analyse the enrichment of factor binding among transcript classes. The new score now normalizes both by factor and by transcript class and is simply interpretable as a log enrichment score:“For each factor, we counted the number of high confidence reads falling in each transcript class. […] The normalization can be interpreted as subtracting from the log enrichment matrix X’ the first singular component of its singular-value decomposition.”Moreover, we have used non-unique mapping for this analysis to include duplicated genomic regions like rRNAs and tRNA isodecoders. The new procedure and its consequences for the analysis are described in our response to point 2 of reviewer 2.5) "Dcp2, which harbors the hydrolase activity that removes the cap, cross-links frequently to mRNAs, in particular their 3 ´UTR, and to short noncoding transcripts, in especially to unstable SUTs. This suggests that Dcp2 is involved not only in cytosolic mRNA degradation but also in decapping during nuclear RNA surveillance." However, the defining characteristic that differentiates SUTs from CUTs is that SUTs are degraded by cytoplasmic Dcp2-dependent pathways as shown by several different labs . Thus rather than suggesting a new role for Dcp2, these findings confirm a different known role.Thanks for pointing this out. We have revised the text accordingly:“Whereas they [all decapping-related factors] show slight enrichment in binding to mRNAs, in particular to CDS and 3´ UTR, they preferentially bind to SUTs. This is consistent with previous findings that SUTs get degraded via Dcp2-dependent pathways in the cytoplasm .”6) "The analysis recovers established interactions between subunits of known complexes" but this is not supported by any quantitative analysis. Among the factors the authors analyze, some proteins that are thought to form stable complexes indeed cluster  while other known stable complexes do not (e.g. Ski2/3/8 or the Air1 subunit of TRAMP).The new replicate data has led to a generally better agreement between known protein complexes and clusters in our co-occupancy map , such as the decapping and deadenylation complexes. We have updated the Results part to reflect this:“The analysis recovers several established interactions between subunits of known complexes and between different complexes, providing a positive control. For example, all factors of the decapping complex show very high co-occupancy and co-localization, as do Air2 and Mtr4, which reside in the TRAMP4 complex.”However, known complexes do not always correspond to clusters, as highlighted in the Discussion section:“Although our data reflect factor cross-linking signal and measure occupancy on transcripts, and do not directly reveal function, the correlations of occupancies between factors and with transcript properties indicate functional aspects and suggest functional associations between factors that may guide future studies.”7) Some of the names of the clusters in Figure 4C are poorly justified. For example, out of the six proteins in the "nuclear ncRNA processing" cluster four are cytoplasmic proteins that act mostly or exclusively on mRNA . They also function in degradation, rather than processing, thus at least the first two but possibly all three words in this name should be changed.With the new datasets, clusters have gotten more in line with prior expectations. We have also tried to rename the clusters to reflect the biological functions more clearly . However, we still see some factors amongst clusters that do not reflect the general function of the cluster. These are important findings that can be followed up by the community in the future. We highlighted and related unexpected findings in the text, e.g.:“Factors involved in nuclear and cytoplasmic exosomal degradation  form a third cluster (cluster 3). […] Rai1 has been shown to detect and remove incomplete 5´ cap structures, to subject aberrant pre-mRNAs to nuclear degradation .”8) "For decapping factors  and Xrn1, low codon optimality is the most determining feature for binding .” However Figure 5C appears to show that this is true for three of these factors, but the opposite is true for Edc2, where high codon optimality appears to be determining. The authors should clarify.Thank you for pointing this out. We generated two new replicates for Edc2 and those now show the same trends as other decapping factors with low codon optimality as the most determining feature for RNA binding . We do not know the reasons why Edc2 was an outlier in the previous data.9) "Moreover, A-rich 4-mers are abundant around the proximity ( ± 8 nt) of Dcp2-cross-link sites ." However Figure 6C and the legend say the window is ± 5 instead of ± 8. This might be a typo.The correct range is ± 8. This typo was fixed in the manuscript.10) "As expected, most PAR-CLIP cross-link sites fall into the mRNA transcript class, although about one third of the factors also show a considerable number of cross-links to non-coding RNAs". I suggest deleting "as expected" or replacing it with "surprisingly". Several of the factors have known roles in ncRNA processing/degradation and mRNA is only a minor fraction of cellular RNA and therefore I would not have expected most sites to fall into the mRNA class.A related issue may be that the analysis in Figure 1 appears to only include "Fractions of high-confidence PAR-CLIP cross-link sites" and not the actual number of or fraction of high-confidence reads. e.g. if there are 9 sites, in mRNA with 100 reads each and one in a ncRNA with 900 reads this would count as 90% of the sites being in mRNA, but only 50% of the reads are in mRNA. Since the complexity of mRNA is probably the highest , the number of distinct sites in mRNA for a given factor is probably a systematic overrepresentation of that factor's importance in mRNA processing.Thank you for this important comment. We included read distribution over annotated regions in Figure 2A and noted the change in the text:“A first analysis revealed that most PAR-CLIP sequencing reads fall into the mRNA transcript class, although many of the factors also show a considerable number of sequencing reads in non-coding RNAs, in particular rRNA .”11) In Materials and methods "various non-overlapping transcript classes c" needs to be clarified. Several of the transcript classes in fact do overlap in the genome. Many snoRNAs are encoded within introns. Many CUTs, SUTs and NUTs overlap with mRNAs and each other. The methods need to be clarified.This is indeed correct. We clarified how we retrieve our non-overlapping set in the Materials and methods section:“We removed overlapping annotations with the following priority list: rRNA, tRNA, snRNA, snoRNA, intron, CDs, UTR, SUT, CUT, NUT.”Reviewer #2:Specific points:1) Subsection “Transcriptome maps for 30 RNA degradation factors”: It appears that biological replicates were performed for only eleven out of the 30 degradation factors tested. For 19 of the factors analyzed, the conclusions are seemingly based on single experiments. While it is the case that the experiments reported involve a lot of work, it is quite unusual for the bulk of data in a manuscript to be published without clearer evidence of reproducibility. It would have been better to analyze fewer proteins in more depth. The authors present data indicating the consistency of the results from the proteins for which replicates were performed. However, while the replicates appear to cluster well in the 2D diagrams, inspection of the metagene analysis reveals appreciable differences between duplicates , which would presumably be much more marked at the level of individual genes.This is particularly an issue when the results are not as expected, e.g. the apparent binding of the deadenylase Ccr4 to a high proportion of tRNAs, which is based only a total of only 4,540 confident Ccr4 cross-linking sites.Thank you very much for this important comment. We took this very seriously, collected many new datasets, and applied a more conservative threshold to eliminate weak old datasets. We are certain that only high-quality, highly reproducible data are included in the manuscript. Here is the reply to the same issue, raised by reviewer 1, point 1:To ensure the highest possible data quality and reproducibility, we generated 49 new datasets, obtaining between two and four highly reproducible biological replicates for each degradation factor . Upon comparing the replicates, we found poor reproducibility for Pop2, Not1, Rrp4 and Caf40 and therefore omitted old replicates from the analysis. Furthermore, we applied a more conservative threshold for including data and eliminated six of the previous replicates that had less than 10.000 cross-link sites . As a result, only highly reproducible data with a high number of crosslink sites were included in the final data set. This has led to a clearly visible improvement of the analysis and revised manuscript.We now write:“The high reproducibility of these PAR-CLIP experiments is revealed by a comparison of two independent biological replicates that we collected for all 30 degradation factors , with Spearman correlations between 0.87 and 1.00 (mean: 0.94).”The extent of improvement of the PAR-CLIP data can be appreciated by comparing the old Figure 2 with new Figure 3.2) The authors report that "most PAR-CLIP cross-link sites fall into the mRNA transcript class". This is unexpected, particularly for pre-rRNA processing factors, including the exosome and Mtr4, that should recover high level of rRNA hits. Inspection of the methods indicates that the reads were mapped to a unique database. This would have the effect of eliminating most rRNA and tRNA reads, as well as a subset of mRNAs. In the yeast genomic database, there are 2 copies of the rDNA repeat, so unique mapping will eliminate most rRNA hits. For tRNAs the situation is more complex; most tRNAs are present in multigene families and will be eliminated from unique hits, whereas the few single copy tRNA genes will be retained. This might be a particular issue for the reported Ccr4-tRNA interactions. For mRNAs, duplicated genes such as R-proteins will also be excluded.This point should be clarified. If uniquely-mapped reads were indeed used, the full mapping data should be presented.Again, many thanks for this important observation. Unique mapping indeed contributed to low rRNA reads. However, since non-unique mapping negatively impacts the quality of cross-link sites in mRNA regions, we used two mapping strategies in the manuscript for Figure 2 and the rest of presented data :“We used two alternative approaches for mapping reads using Bowtie : For all analyses except the ‘transcript class enrichment analysis’ in Figure 2, reads are uniquely mapped with up to one mismatch. […] For Figure 2, we therefore allowed Bowtie multi-mapping in two regions with –best, –starra options and discarded reads shorter than 30 nt."This approach resulted in many more mapped reads in rRNA regions for Figure 2:“A first analysis revealed that most PAR-CLIP sequencing reads fall into the mRNA transcript class, although many of the factors also show a considerable number of sequencing reads in non-coding RNAs, in particular rRNA .”3) Subsection “RNA end-processing complexes differ in their targets”: SUTs can be cytosolic, so this might not be sufficient to show that Dcp2 has nuclear activity.We have changed the text to better reflect this point:“For all decapping-related factors we observed similar binding preferences. […] This is consistent with previous findings that SUTs get degraded via Dcp2-dependent pathways in the cytoplasm .4) Subsection “Distinct factor distribution along mRNA”, Figure 1B (and end of subsection “PAR-CLIP experiments of S. cerevisiae proteins”): It would be helpful to discuss more why Csl4, Rrp4 and Rrp40 have distinct profiles. In particular, the Rrp4 profiles looks very different from all other degradation factors. This raises question of the PAR-CLIP experiment really worked for each cofactor. It is notable that some of these have low numbers of XL sites .We have repeated the experiments for members of the exosome to obtain 2-4 biological replicates with high reproducibility. In our new data-set we have at least 175,000 cross-link sites for each of the core exosome subunits and the metagene profiles show a consistent trend between them .5) Subsection “Interactions between RNA processing machineries”, Figure 4 and Figure 4—figure supplements 1-2Figure 4: It would be helpful to discuss why Trf4/Air2 is bound to introns  but not Trf5 while Trf5/Air1 is showing higher co-occupancy / colocalization with splicing factorsWith our new definition of enrichment , we see a stronger enrichment for Mtr4, Air1, and Air2 for intronic regions. This is reflected in the colocalization analysis , where both TRAMP4 and 5 show high correlation with splicing factors. In contract, co-occupancy reflects the tendency to bind to the same transcripts regardless of the binding position. Because TRAMP4 and 5 bind to most mRNAs, not just intron containing ones, its low co-occupancy with splicing factors is expected.Reviewer #3:[…] The presented data are certainly of interest to a wide audience. However, there is no independent validation of any of the unexpected binding patterns. This is especially problematic, given that many datasets are based on single biological replicates. Moreover, some observations challenge our current thinking about the function of the investigated factor(s), which, to be suitable for publication in eLife, should be supported by independent experiments.Thank you very much for this important comment. Here is the reply to the same issue, raised by reviewer 1, point 1:To ensure the highest possible data quality and reproducibility, we generated 49 new datasets, obtaining between two and four highly reproducible biological replicates for each degradation factor . Upon comparing the replicates, we found poor reproducibility for Pop2, Not1, Rrp4 and Caf40 and therefore omitted old replicates from the analysis. Furthermore, we applied a more conservative threshold for including data and eliminated six of the previous replicates that had less than 10.000 cross-link sites . As a result, only highly reproducible data with a high number of crosslink sites were included in the final data set. This has led to a clearly visible improvement of the analysis and revised manuscript.We now write:“The high reproducibility of these PAR-CLIP experiments is revealed by a comparison of two independent biological replicates that we collected for all 30 degradation factors , with Spearman correlations between 0.87 and 1.00 (mean: 0.94).”The extent of improvement of the PAR-CLIP data can be appreciated by comparing the old Figure 2 with new Figure 3.- Functional relevance:The manuscript presents a series of unexpected and somewhat controversial observations. One example concerns a possible role for Pop2 and Not1 in decay of CUTs. Any functional relevance of such binding should be trivial to validate. This would be relevant for other of the most striking findings. Such validation may be possible by re-analysis of published datasets, for example by intersecting occupancy with RNAseq upon depletion/mutation of the same factors.Thank you for the comment. We have generated replicates and sometimes triplicates to show reproducibility of our data and significance of our observations. We observed problems with reliability of some factors in the older dataset, particularly Not1 and Pop2. We repeated the experiments for these factors at least twice. According to our new dataset, Pop2 and Not1 behave similarly to other members of the deadenylation machinery .- Replicates and Technical artefacts:PAR-CLIP is a complex technique, where technical artefacts are not uncommon. For example, binding to highly abundant tRNA, snRNA and snoRNA species is often observed, even for factors where such binding is unlikely to occur or be relevant in vivo. Hence, it is quite possible that binding to such species, especially when based on a single biological replicate, does not reflect biology. Such considerations should be mentioned in the manuscript. Ideally, all factors for which single replicates are presented, should be backed up by additional data. This may be simply an additional replicate, or some kind of independent validation.More generally, more information on how the authors scrutinize the quality of their PAR-CLIP experiments should be given (i.e. more explicitly than simply referring to another publication).Thank you for the comment. We have generated replicates and sometimes triplicates to show reproducibility of our data and significance of our observations. We observed problems with reliability of some factors in the older dataset, particularly Not1 and Pop2. We repeated the experiments for these factors at least twice. According to our new dataset, Pop2 and Not1 behave similarly to other members of the deadenylation machinery .[Editors' note: the author responses to the re-review follow.]The manuscript has been considerably improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:1) Even though some of the more controversial suggestions of the original draft are now resolved, comparison with available knock-out or mutant / RNA-seq datasets would greatly help to support, or dismiss, some of the mentioned novel findings.While there are some RNA-seq experiments available to investigate functions for some of these factors, they were performed with different strains and under different conditions. However, we previously published a dataset of microarray expression measurements for the same strain background. In this work, synthesis and degradation rates upon knock-out of several degradation factors have been determined .We correlated the PAR-CLIP-measured occupancy across mRNA transcripts with the change in degradation rate upon knock-out from this dataset. The heat map shows the Pearson correlations.As expected, these values are anti-correlated, meaning that transcripts that are bound more by degradation factors experience a decrease in degradation rate upon knock-out. But unfortunately, we see little structure in the correlation matrix that would connect a PAR-CLIP profile to its respective knock-out experiment. Interestingly, we observed strongest anti-correlation of degradation rates with other degradation factor knock-outs for the deadenylation factor Pop2 with deadenylation and exosome components and the 5’→3’ exonuclease Xrn1 with decapping and NMD factors. This indicates a function for Pop2 in RNA degradation from the 3’ end, and an effect on RNA degradation from the 5’ end for Xrn1. We have previously shown a Xrn1 dependent buffering of transcript levels .We believe this rather unstructured pattern is largely due to secondary effects based on the extensive buffering that balances changes in degradation rates by adapting synthesis rates when one of the factors is knocked-out . Moreover, the viability of these knock-out strains suggests that such bound transcripts could get processed by alternative degradation pathways. Finally, the lack of data on non-coding transcripts makes it difficult to validate further hypotheses. We have therefore not included this analysis in the manuscript.2) If available, western blots and/or autoradiographs of the different PAR-CLIP experiments would be nice to include to allow readers to judge the quality of different IP experiments.We included one representative Western Blot of the immunoprecipitation for each factor to show the quality of the different experiments as Figure 1—figure supplement 2.3) Some of the conclusions presented in the Results sections still appear vague or unclear. This concerns for example interpretation of the RNA exosome binding profiles.a) i.e. In the first paragraph of the subsection “The exosome and surveillance factors”: unclear what different binding patterns "reflect" the distinct functions of the RNA exosome.Thank you for pointing this out. We have changed the text to make the conclusions clearer: “This complex distribution of cross-links for different exosome subunits to different RNA classes reflects the distinct functions of the exosome in nuclear RNA surveillance, processing of stable non-coding RNAs, and cytoplasmic mRNA degradation .”b) In the second paragraph of the subsection “Distinct factor distribution along mRNA”: How/why would TRAMP4 binding upstream of the TSS affect early termination of the mRNA? Isn't it more plausible that it serves a role in termination of (unannotated) upstream ncRNA? Similarly, 5' bias of exosome and TRAMP subunits could arise from unannotated NUT-like transcripts.Many thanks for pointing this out. Prompted by point 4 – as explained below – we have shown better now that the binding of TRAMP4 to regions upstream of TSS originates from NUT-like transcripts . Since this explains the signal more readily, we have substituted the analysis with the one concerning attenuated genes. We have adapted the text in the second paragraph of the subsection “Surveillance of aberrant nuclear ncRNA”.We also observed that upon removal of the cross-link sites that fall into previously annotated NUTs, and CUTs, the 5’ bias of the exosome remains intact . This suggests that such transcripts are apparently not the source of the binding bias on the 5’ end for exosome factors.4) Figure 3 and Figure 3—figure supplement 1B: What RNAs generate the high signals for Mtr4 and Air2 5' to the TSS? This region is expected to give rise to very few transcripts. The upstream ncRNAs are further upstream and on the opposite strand. There must be a concern that some sequences have been mis-mapped. Were the sequences mapped only to a small number of loci, which would strengthen this concern? The authors should re-check the data supporting these distributions.Thank you for this word of caution, which prompted us to investigate in more detail the origins of this signal. The reads are mapped uniquely to the whole genome with a minimum length of 20 bases and just one mismatch allowed. This strict mapping criteria makes it very unlikely for reads to get mis-mapped. However, we showed before that upon Nrd1 knock-out aberrant transcription upstream of TSS becomes visible . To show that the responsible Nrd1-unterminated transcripts (NUTs) are indeed the RNAs for which we see TRAMP4 binding signal, we performed two further analyses that are explained in the text:“It has been shown that Nrd1 is involved in terminating transcripts upstream of the TSS. […] Comparison of the observed antisense profiles  with those obtained after excluding cross-link sites in previously annotated NUT and CUT regions  confirms that most of the signal originates from transcripts that are targeted by the Nrd1/Nab3 machinery.”5) The second paragraph of the subsection “The exosome and surveillance factors” refers to Figure 2—figure supplement 3 and not Figure 3—figure supplement 3.Thank you. We corrected the text accordingly.6) Figure 5C: 3' degaradation => 3' degradationThank you. We corrected the figure accordingly.


The first 50 residues of Ntail extricate the disordered chain from the nucleocapsid, thereby loosening the otherwise rigid structure, and the C-terminus contains a linear motif that binds P. Recent results show how the 5′ end of the viral RNA binds to N within the nucleocapsid and also show that the bases at the 3′ end of the RNA are rather accessible to the viral polymerase. P is a tetramer and most of the protein is disordered; comprising 507 residues of which around 380 are disordered. The first 37 residues of P bind N, chaperoning against non-specific interaction with cellular RNA, while a second interaction site, around residue 200 also binds N. In addition, there is another interaction between C-terminal domain of P (XD) and Ntail. These results allow us to propose a new model of how the polymerase binds to the nucleocapsid and suggests a mechanism for initiation of transcription.Measles virus is a negative strand virus and the genomic and antigenomic RNA binds to the nucleoprotein (N), assembling into a helical nucleocapsid. The polymerase complex comprises two proteins, the Large protein (L), that both polymerizes RNA and caps the mRNA, and the phosphoprotein (P) that co-localizes with L on the nucleocapsid. This review presents recent results about N and P, in particular concerning their intrinsically disordered domains. N is a protein of 525 residues with a 120 amino acid disordered C-terminal domain, N Paramyxoviridae family. The viral RNA, 15,894 nucleotides long, is bound by thousands of copies of the viral nucleoprotein, forming the helical nucleocapsid (NC). The viral polymerase complex, comprising the viral L protein  and phosphoprotein, uses the NC as the template for transcription and replication. The only known structure of L of the viruses of Mononegavirales order, to which also paramyxoviruses belong, is the one from vesicular stomatitis virus (VSV)  and nucleoprotein (N) of measles virus. The classical paradigm that a protein must have a defined and stable structure and that the functional mechanisms of the protein in the cell can be derived from this structure, is not applicable to P and N. This is because most of P is disordered, as is the C-terminal tail of N Tompa, . As showloop (residues 377–457).P and N have multi-domain structures with alternating ordered and disordered regions . P startcore and Ntail, an intrinsically disordered domain, containing a linear motif with α-helical propensity  bound to the surface of the neighbor i-1 and the Carm folding onto neighbor i+1. N can bind RNA, either viral RNA (vRNA) during infection, or cellular RNA during expression in cellular expression systems, making recombinant nucleocapsid-like structures. In order to avoid non-specific binding to cellular RNA, N is chaperoned by the N-terminal residues of P forming an N0P complex  made from measles, Marburg, rabies and VSV viruses, for example, have similar structure as the viral nucleocapsids , with no RNA bound (arm and Carm become broader (the peaks disappear) as they bind to the neighboring N particles in the NC. In addition, using RNA labeled with a fluorescent probe, the kinetics of the assembly can be followed by measuring the changes in fluorescence anisotropy. Free RNA tumbles rapidly, however once it is bound to N and then assembles into NCs, the anisotropy increases. The resulting assembly can then be visualized with negative staining EM and NC growth can be followed during several hours  and the others turn outward . If the in vitro, we can identify both the binding of the 5′ end of the RNA to the first N protomer of the NCs, and the binding of the 3′ end on the final protomer of the NCs . This region δα4 binds residues 96–127 on N0  up to residue 299, to form a zinc-binding domain of the V protein  in the transcription and replication processes of measles virus. Additionally it should be mentioned that N and P are both known to be phosphorylated, which can be important for the viral cycle regulation, in particular phosphorylation is involved in the regulation of viral gene expression. However the mechanism of such regulation was not studied yet (Lamb et al., The measles virus polymerase stutters on the template and adds extra G bases for making several mRNAs and therefore different proteins (Thomas et al., tail and only one Ptail. P could be bound to N with the XD-Ntail complex and with α1 in the free α1-site on N (tail and Ntail could keep L away from the RNA. However, if δα4 region also binds to its binding site on N, then L could come closer to the vRNA (Cox and Plemper  have sugite on N  and L coite on N . For thethe vRNA . To concRR and MB wrote the manuscript. SM and SG contributed to reviewing relevant literature, interpretation of studies, drafting the manuscript, and preparation of figures. All authors approved the article for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Prochlorococcus, and high microbial community alpha diversity. Seawater from the less protected system of Los Canarreos, Cuba had elevated microbial community beta‐diversity whereas waters from the most impacted nearshore reefs in the Florida Keys contained high organic carbon and nitrogen concentrations and potential microbial functions characteristic of microbialized reefs. Each reef system had distinct microbial signatures and within this context, we propose that the protection and offshore nature of Jardines de la Reina may preserve the oligotrophic paradigm and the metabolic dependence of the community on primary production by picocyanobacteria.There are a few baseline reef‐systems available for understanding the microbiology of healthy coral reefs and their surrounding seawater. Here, we examined the seawater microbial ecology of 25 Northern Caribbean reefs varying in human impact and protection in Cuba and the Florida Keys, USA, by measuring nutrient concentrations, microbial abundances, and respiration rates as well as sequencing bacterial and archaeal amplicons and community functional genes. Overall, seawater microbial composition and biogeochemistry were influenced by reef location and hydrogeography. Seawater from the highly protected ‘crown jewel’ offshore reefs in Jardines de la Reina, Cuba had low concentrations of nutrients and organic carbon, abundant  The collective impacts of climate change, overfishing, and coastal development have caused shifts in functioning and energy transfer in coral reef ecosystems and these changes have been documented at the level of macro‐organisms  is a protected reef‐system in Cuba that may provide useful insights into the microbial ecology of relatively healthy Caribbean coral reefs. The reefs of JR were historically protected from human activities due to their remote nature and are now further protected because maritime traffic, fishing, and recreational diving and tourism are limited within the boundary of a Marine National Park (est. in 1996) that encapsulates most of the archipelago. Additionally, Cuba does not currently have large‐scale industrialized agriculture or extensive development along most of its coastline , Cuba lies ~230 km to the west of JR. CAN encompasses three important keys that have less stringent protection compared to JR: Cayo Largo is an Ecological Reserve and the Rosario and Cantiles Keys are Faunal Refuges. Due to increased accessibility, reefs within CAN are more impacted by humans through subsistence and illegal fishing, tourism, and the diving industry compared to the remote and protected reefs within JR. Fishing has resulted in overexploitation of important finfish and invertebrates in most of Cuba's waters with the exception of the central area within JR Baisre, . The proet al., et al., et al., et al., et al., et al., The reef‐system of the Florida Keys (FK) is located in close proximity to JR and CAN, but has experienced more anthropogenic impacts relative to the Cuban reef‐systems. Reefs within FK (spanning ~570 km) are situated close to developed land within FK and South Florida and development activities have influenced the water quality in these waters  gradients across each reef‐system. At each reef, divers surveyed the reef composition and then sampled surface (<1 m) and reef‐depth seawater (within 1 m of the reef).Water sampling and reef surveys were conducted at reef locations across the three reef‐systems: Jardines de la Reina, Cuba , Los Canarreos, Cuba  and Florida Keys, USA  Fig. . The samF = 4.89, p = 0.025] in JR and FK compared to CAN and was similar between JR and FK  = 5.82, p = 0.014) in CAN compared to JR and similar to the algal cover in FK , F,14 = 4.8F,14 = 4.8F,14 = 4.8F,14 = 4.8F,14 = 4.8F,14 = 4.8F = 78.19, p < 0.05), total organic nitrogen (TON)  = 53.44, p < 0.05), and dissolved inorganic nitrogen   = 4.21, p < 0.05) were significantly higher within the JR gulf as well as nearshore FK compared to the offshore JR reefs  = 7.38, p < 0.05) and nitrate  = 3.39, p < 0.05) were significantly higher in CAN and FK compared to JR  = 14.11, p < 0.05) were significantly higher  in FK nearshore seawater compared to seawater from other reefs  in JR, 5.2 m (1–14 m) in CAN, and 3.8 m (1–6 m) in FK  within JR offshore reef‐depth and surface seawater compared to JR gulf reef‐depth seawater, CAN surface and reef‐depth seawater, and nearshore FK seawater, but not significantly different from abundances in FK offshore reef‐depth and surface seawater  in offshore JR (surface and reef‐depth) as well as JR gulf surface seawater compared to JR gulf reef‐depth seawater   = 15.98, p = 3.38E‐5; Tukey multiple comparisons of means, p < 0.05)  = 4.032, p = 0.030) in FK (16%) compared to JR (5%)   Fig.  but chanχ2 = 21.41, df = 4, p = 2.6E‐4) higher alpha diversity compared to CAN , as indicated by the number of minimum entropy decomposed (MED) nodes of bacterial and archaeal SSU rRNA gene amplicons [median of 333.5 (range: 185–359) MED nodes] Fig. . Offshory‐axis (NMDS2) and separated from JR and FK microbial communities  test on the Bray–Curtis dissimilarity index of reef seawater bacterial and archaeal SSU rRNA gene amplicons grouped into MED nodes indicated that region , subregion, reef location, and sampling depth influenced the composition of reef seawater communities, with reef location contributing the most to variation in community dissimilarity Table . Additioies Fig. A. Surpriies Fig. B.R2 = 0.11, p = 0.040) and nitrite  and silicate  concentrations were significantly correlated with the ordination of microbial communities in the NMDS.Within the NMDS, microbial communities sampled within JR ordinated together by location; microbial compositions from reefs JR 1 and 2 were more similar to each other than to the other communities sampled from sites located within the JR gulf Figs . CompareProchlorococcus sequences were 99.2%–99.6% identical to MIT9313, a low‐light ecotype of Prochlorococcus, and this was the only ecotype identified within the amplicon‐based survey. Bacteroidetes was most represented in FK reef seawater, with an average relative abundance of 16.8 ± 8.3%, and less abundant in JR (12.7 ± 5.7%) and CAN (8.6 ± 6.4%)  Fig. . Verruco4%) Fig. . Euryarc4%) Fig. .p‐adjusted <0.05, Benjamini–Hochberg correction for multiple testing)   Fig. . The 34 ng) Fig. . Cyanoba JR Fig. .Metagenomic sequencing and analysis of the whole microbial community  in reef‐depth seawater from JR and FK resulted in 163 significantly different functional genes Fig. . These g2 consumed L−1 day−1  had positive community respiration rates that ranged from 0.3 to 16.7 μmol of Oy−1 Fig. . The hig 24 Fig. . These v 23 Fig. .R2 = 0.33, p = 0.010, Fig. R2 = −0.02, p = 0.49, Fig. Prochlorococcus and Synechococcus cell abundances) and coral cover across the reef‐systems  and the hydrodynamic regime created by the complex array of cays and channels. The variability in FK may be mostly impacted by increased concentrations of organic and inorganic macronutrients, higher productivity, and/or terrestrial sources of sediments from developed land. The nearshore reefs in FK exhibited a few signs of microbialization, but this process was not as evident on the FK offshore reefs surveyed in this study. The microbial regimes observed across the reef‐systems appear to be determined by the intersection of local anthropogenic impacts as well as oceanographic processes.et al., et al., A majority of the macronutrient concentrations were low or barely detectable in JR and were similar to concentrations measured in other oligotrophic systems including the Sargasso Sea, the North Subtropical Pacific Gyre, the Red Sea, and other reefs in the Caribbean and Pacific, suggesting rapid turnover of these nutrient pools by microorganisms  have fewer data points compared to the other categories due to sampling limitations and that care needs to be taken when interpreting the statistical differences between subregions. That being said, measurements from these locations were similar within each subregion and are likely representative of the environmental conditions. However, future studies would benefit from collecting samples at a higher spatial or temporal resolution in order to unravel the process of microbialization on reefs. Additionally, there are other examples of reefs that are subjected to high loads of organic and inorganic nutrients as well as pollution, like Vardero reef in Colombia , enhanced community respiration, higher concentrations of inorganic and organic macronutrients, and shifts from coral to algal dominance on the reefs along the gradient of human impact. However, we did not observe significant changes in most of these parameters. Overall, hydrogeography and subregion were the largest influences contributing to reef similarity. Offshore reefs in both JR and FK were oligotrophic, had high abundances of picocyanobacteria, high microbial alpha diversity, and more constrained microbial beta diversity, although the magnitudes of the contrasts were different within each reef‐system. The only reefs that supported some of the predictions of the DDAM model were the nearshore reefs in FK. These two nearshore reefs in FK had significantly higher concentrations of organic macronutrients, very low abundances of et al., et al., et al., et al., et al., et al., et al., et al., Coral and algal coverage varied locally  and CAN/FK (April/May 2015) during the Caribbean dry season. Due to sampling limitations within JR, we were only able to survey and collect samples from two reef sites in the JR offshore subregion and four reefs within the JR gulf subregion. Scuba divers conducted reef surveys at all JR and five CAN reef locations to assess the percent cover of different reef organisms and substrates (Supporting Information Methods). Coverage of a wider diversity of biotypes was assessed on FK reefs using the same methods but with a different research team (Supporting Information Methods).At each reef location, hydrographic profiles of the water column were obtained and seawater from surface (<1 m depth) and reef‐depth (~ 1 m above reef) was collected for a variety of different analyses  were analysed using a continuous segmented flow system  concentrations for each sample were calculated by subtracting the NO2− concentration from the NO2− + NO3− concentration. TON concentrations were calculated by subtracting the sum of the inorganic nitrogen species concentrations (NO2− + NO3− and NH4+) from the TN concentrations for each sample.Total non‐purgeable organic carbon (TOC) and total nitrogen  concentrations were measured using a Shimadzu TOC‐Vet al., et al., et al. et al. (2015). The converged initial pigment ratio matrix was used because the phytoplankton assemblages in these samples were not determined with a microscope.Pigment analysis was conducted using high‐performance liquid chromatography , picoeukaryotes, and unpigmented cells (proxy for heterotrophic bacteria and archaea)  and a laser excitation wavelength of 488 nm. Unstained and stained  subsamples were analysed to estimate the abundance of picocyanobacteria . Briefly, V4 hypervariable regions of the 16S rRNA gene were amplified using the Fluidigm® microfluidics quantitative PCR platform and prepared for 2 × 250 bp paired‐end Illumina MiSeq sequencing (Supporting Information Methods). The Fluidgim V4 primer set 515F‐Y: 5′‐GTGYCAGCMGCCGCGGTAA (Parada n = 4) and FK (n = 5) using a modified cetyl‐trimethylammonium bromide—phenol:chloroform:isoamyl alcohol extraction (Supporting Information Methods). No samples from CAN were chosen because the DNA extracted from CAN yielded 16S rDNA sequences that were highly variable between sites. Genomic libraries were prepared using the Hyper Library construction kit  and sequenced at the W. M. Keck Center using 2 × 150 bp paired‐end Illumina HiSeq 4000 sequencing. Fastq files were demultiplexed, and library adaptors were trimmed from the 3′ ends of the reads (Supporting Information Methods). BBTools . Raw files can also be found in the NCBI SRA under bioProject PRJNA517146.Total genomic DNA was extracted from nine reef‐depth seawater samples from JR (in situ temperatures as possible in the remotefield work locations. The incubation temperatures were 26.6 ± 0.5 °C (JR), 25.0 ± 0.2 °C (CAN), and 26.5 ± 0.5 °C (FK). Initial oxygen measurements were taken every hour (h) for the first 4 h and then approximately every 4 h after that. Ten oxygen measurement readings were taken for each incubation bottle at each timepoint. Prior to calculating respiration rates, oxygen data were quality controlled to remove any individual readings greater than one standard deviation from the mean value at a given timepoint. Linear fitting to the time course oxygen data was done in matlab  using the ‘polyfit’ function.Seawater samples (4–6 per site) were collected from reef‐depth using a submersible groundwater pump and kept in the dark. Respiration rate incubations (~24 h) were conducted with ~5× replication with seawater collected from 19 reefs in acid‐cleaned 60 ml glass Biological Oxygen Demand bottles with glass stoppers. Incubations using seawater from sites 2, 12, and 24 were conducted twice. Acid‐washed bottles were equipped with oxygen optode ‘dot’ sensors (PreSens) affixed to the glass using food‐grade silicone adhesive. In the laboratory, the perimeter of the glass stopper was filled with water using a squeeze bottle to prevent gas exchange between the water in the bottle and the atmosphere. The concentration of oxygen in the bottles was measured over time using a handheld Fibox 4 (PreSens). Incubations were conducted in a static water bath in a darkened cooler located inside a darkened room at as close to p value <0.05). For data that were not normally distributed, Kruskal–Wallis rank‐sum tests, followed by either Dunn's or Conover–Iman tests using Bonferroni corrections were used to assess significant differences . We recognize that JR offshore and JR gulf subregions have fewer data points compared to the other categories, but measurements from these locations were similar within each subregion and are likely representative of the environmental conditions. We performed linear regressions using ggplot2, geom_smooth, and the method = ‘lm’  and tested for significance across different qualitative  and quantitative (e.g. TOC concentrations) parameters. To compare differences in reef cover, macronutrient concentrations, and cell abundances, data were inspected for normality using Shapiro–Wilk normality tests. Normally distributed data were tested using ANOVA followed by post hoc Tukey multiple comparisons of means tests using a 95% family‐wise confidence level . Reef‐depth seawater collected from site FK 23 was omitted from the analysis because reef‐depth TOC was not collected from this site. Nested PERMANOVA (Adonis) tests, also available within ‘vegan’, were conducted using the Bray–Curtis dissimilarity index (999 permutations) (p < 0.05). To conduct this test, the factors of reef‐depth, reef location, and subregion were nested within the region (reef‐system). The package ‘phyloseq’ (McMurdie and Holmes, et al., Amplicon‐based microbial community statistical analyses were completed using R studio (R Core Development Team, Appendix S1: Supporting InformationClick here for additional data file.