Acropora loripes in the Bolinao-Anda Reef Complex, Pangasinan, northwestern Philippines. Larvae were cultured ex situ following spawning of collected A. loripes colonies in June 2014. Competent larvae were transported to degraded reef areas and approximately 300,000 larvae were introduced in each of three 6 × 4 m plots directly on the reef. Fine mesh enclosures retained the larvae inside each treatment plot for five days. Three adjacent 6 × 4 m plots that served as controls were also covered with mesh enclosures, but no larvae were introduced. Each plot contained ten 10 × 10 cm conditioned settlement tiles cut from dead tabulate Acropora that were used to quantify initial larval settlement. After allowing larval settlement for five days, mean settlement on tiles from the larval enhancement plots that were monitored under stereomicroscopes was significantly higher (27.8 ± 6.7 spat per tile) than in control plots, in which not a single recruit was recorded. Post-settlement survivorship and growth of spat and coral recruits on tiles and reef substrata inside the experimental plots were monitored periodically for 35 months. After 35 months, the mean size of each of the remaining 47 A. loripes coral colonies surviving on the reef substrata was 438.1 ± 5.4 cm3, with a mean diameter of 7.9 ± 0.6 cm. The average production cost for each of the surviving A. loripes colonies at 35 months was USD 35.20. These colonies are expected to spawn and contribute to the natural larval pool when they become reproductively mature, thereby enhancing natural coral recovery in the area. This study demonstrates that mass coral larval enhancement can be successfully used for restoring populations of coral species with different life-history traits, and the techniques can rapidly increase larval recruitment rates on degraded reef areas, hence catalysing the regeneration of declining coral populations.The escalating rate at which coral communities are declining globally requires urgent intervention and new approaches to reef management to reduce and halt further coral loss. For reef systems with limited natural larval supply, the introduction of large numbers of competent coral larvae directly to natural reef substrata provides a potentially useful approach to replenish adult coral populations. While few experiments have tested this approach, only one experiment has demonstrated its long-term success to date. Given the differences in life-history traits among corals, and different sensitivities of larvae to abiotic and biotic factors, coupled with the dynamic nature of post-settlement survivorship and recruitment processes, trials of the larval enhancement technique with larvae of different coral species are needed to test the broader applicability and viability of this approach. Accordingly, in this paper we examine the applicability of the larval enhancement technique to restore a population of  The increasing rate at which coral communities are declining globally requires urgent intervention and new approaches to reef management to reduce and halt the loss, and increase coral cover and diversity on degraded reefs , 2. TherIn the initial stages of coral restoration research, direct coral transplantation gained popularity as one approach to rapidly increase coral cover by re-attaching coral fragments or whole colonies to degraded reefs , 8, 9. AAcropora valida larvae that were settled on coral rubble. After a few months of hatchery rearing, the juvenile corals on substrata were directly attached to reef sites using adhesive. The same approach was employed by Baria et al. /m, where Gr is standardized growth rate, EVf and EVi are final and initial mean ecological volumes, respectively, and m is the number of months elapsed [In situ growth monitoring commenced at this time, with the length ( (l+w)/4 . Growth  elapsed .A. loripes colonies that recruited onto coral tiles and natural reef substratum was monitored at age 23 and 35 months by carefully breaking small branches to check for the presence of gametes [The onset of sexual reproduction in  gametes .To estimate the cost of producing sexual coral recruits from this study, the costs were categorised and then summed for all materials and infrastructure, boat hire and fuel, diving and labour for gravid coral collection (using different wage rates for different skill levels as prescribed by the Department of Science and Technology Grant-in-Aid personnel), spawning and larval rearing, site preparation and capital costs for the larval mesh enclosures. To estimate the cost per coral colony produced at different ages, the total cost was divided by the total numbers of juvenile corals alive at 8 months and at three years of age in the three larval enhancement plots. Costs were in Philippine Pesos and were converted to US Dollars.Data are reported as mean values ± standard error of the means. The three larval enhancement sites and the three control sites were used as statistical replicates (N = 3), with data from the ten tiles in each site averaged to quantify mean initial settlement rates, and subsequent growth and number of surviving recruits at age 35 months.The variability in the benthic cover composition  between larval enhancement and control plots prior the larval enhancement experiment was analysed using Analysis of similarities (ANOSIM). Significant differences in the initial settlement patterns on tiles between larval-enhanced and control plots after five days of settlement was tested using One-way ANOVA. A post-hoc Tukey’s HSD test was conducted to determine any significant differences in settlement patterns among tile surface types .Survivorship of coral recruits on different surfaces of tiles was analysed using survival analysis, a non-parametric pairwise comparison test based on the Kaplan–Meier function . The samR: -0.11, p = 0.8). The benthic communities within the plots corresponded to the category “very poor coral cover” (sensu Wilkinson) [2 (out of the 24 m2) of available substrata  per plot for the larvae to settle.Larval-enhanced and control plots had comparable benthic cover compositions before the larval enhancement experiment   comprisiA. loripes spat settled on the thirty 10 × 10 cm biologically conditioned natural Acropora skeleton tiles that were attached to reef surfaces in the larval enhancement treatment plots during the 5-day settlement period. Mean total settlement on the sets of ten tiles in each of the three larval enhancement treatment plots was 278 ± 67.3 spat, which was significantly higher than for control sites in which no A. loripes spat settled .Mean larval settlement on top surfaces of the tiles was 98 ± 21.4, with 71.3 ± 28.3 and 108.7 ± 36.2 mean settlement on the bottom and side surfaces, respectively . No signMonitoring of coral spat on tiles showed the expected decline in survivorship after the larval settlement period. An almost 50% decline in survivorship on tiles was recorded after 2 months . Survivo3 mean ecological volume) for in situ growth and survivorship monitoring .After eight months, the previously cryptic juveniles that had settled on the natural reef substrata became visible recruits at 1.2 ± 0.04 cm mean diameter .For corals that settled on the natural reef substrata, 100% survivorship was recorded during the first 8 months after they became visible, and survivorship declined slowly and reached 59% survivorship on the last monitoring period in May 2017, 35 months after settlement . Survivo3 and 1.3 ± 0.9 cm3 respectively  to growth rates of recruits that settled on tiles (16 ± 5.7 cm3 mo-1). Thirty-five months after settlement, mean diameters of the colonies ranged from 2.2 cm to 13.9 cm with an average of 7.9 ± 0.6 cm  that recovered from stressors that these local reefs experienced during recent decades, only four colonies of A. loripes were recorded surviving in surveys on ~200 m2 of nearby reef area. With very few adult colonies to supply gametes or larvae to aid natural recruitment and reef recovery, A. loripes population recovery in Magsaysay reef will be very slow and may not occur without active intervention. In addition, the supply of larvae to surrounding reef areas in the Bolinao-Anda Reef Complex may be compromised, as computer model simulations suggest that Magsaysay reef is a source of coral larvae for the nearby reefs [A. loripes larvae to increase settlement and recruitment rates is an active management intervention designed to assist recovery of this coral population in Magsaysay reef. This intervention may also create a potential “spawning hot spot” or “spawning hub” in the future that may provide increased abundance of coral larvae with high genetic diversity for dispersal and restoration of adjacent degraded reef areas [Unlike other by reefs . Therefoef areas .A. loripes larvae on tiles in larval enhancement plots were relatively high, and comparable with those from the A. tenuis larval enhancement experiment, also conducted at the Magsaysay reef area a year earlier [A. loripes among tile surfaces was different from those recorded for A. tenuis. Initial settlement of A. tenuis larvae was significantly higher on tile sides with very few settlers on upper surfaces, whereas for A. loripes, larval settlement was not significantly different among tile surfaces. This suggests that larvae of these species may have different settlement orientation preferences, as recorded among other coral species [The initial settlement rates of  earlier . However species , 70. The species . This pa species  at Ninga species  in Palau species .A. loripes spat on tiles showed a typical Type III survivorship curve. The proportion of surviving spat decreased significantly within the first month and continued to decline until 10 months when survivorship started to stabilise. That survivorship was similar to the survivorship pattern recorded for A. tenuis spat on tiles during the first 10 months after settlement [Survivorship of settled ttlement , and in ttlement , 73. Thettlement , 74, 75.ttlement . Post-settlement . TherefoA. loripes corals that settled on the natural reef substrata and became visible at 10 months after settlement declined slowly over time but remained above 50% at the end of the 35 months monitoring period. This survivorship pattern contrasts with that recorded for A. tenuis, where there was no observed mortality of the faster growing A. tenuis recruits that settled on reef substrata from nine months to 35 months [Acropora species may reflect intrinsic differences in their life-history characteristics, or may be related to size-escape thresholds [A. tenuis spat growing faster on average and attaining larger sizes more quickly than the A. loripes spat. Similar higher survivorship outcomes have been recorded when outplanting larger size sexually derived Acropora juvenile corals. In a study by Omori et al. [A. tenuis juveniles that had survived for 1.5 years in mid-water nurseries and reached 4 to 5 cm in diameter, had 89% survivorship after six months. In contrast, when smaller 1 cm diameter 6-month old A. valida juveniles were outplanted, a lower survivorship of 67% was recorded after six months [The survival rates of 5 months . The difresholds , 82, witi et al. , A. tenux months .A. loripes was 14 ± 2.9 per larval enhancement plot, which equates to an average of 1.8 coral colonies per m2 of available reef substrata. This density is slightly lower than the mean density of 2.3 A. tenuis colonies per m2 of available reef substrata recorded by dela Cruz and Harrison (2017), and partly reflects the slower growth and lower survivorship patterns for A. loripes colonies recorded in the present study. Edwards [4 settled larvae. The total number of larvae that settled directly on the reef in this experiment could not be quantified because the microscopic larvae are too small to census and tend to have cryptic settlement on the complex reef surfaces. A total of 47 colonies survived after 35 months from an estimated 939,000 larvae added to the larval enhancement plots, which equates to one three-year-old colony per 20,000 larvae supplied. Based on the numbers of larvae settling on the tiles, it is likely that less than 50% of the larvae successfully settled during the five-day settlement period, hence the production rate probably exceeds the threshold suggested by Edwards [After 35 months, the mean number of remaining A. loripes colonies. The growth rates of A. loripes that survived on the tiles and natural substrata (mean diameter 2.6 cm yr-1) was considerably slower than the growth rates of A. tenuis colonies grown from settled larvae at Magsaysay reef (mean diameter 5.1 cm yr-1) [Acropora corals. Cultured A. tenuis larvae that settled on artificial substrata in an outdoor hatchery, and were subsequently transplanted to reef areas in subtropical Okinawa, Japan reached a mean diameter of 20 cm (4 cm yr-1) after 4 years of transplantation [A. millepora corals cultured and maintained in nurseries for 3 years (4 cm yr-1 mean diameter), or transplanted onto reef areas (4.6 cm yr-1 mean diameter) [This study has also provided the first data on growth rates of sexually produced cm yr-1)  and otheantation . Similariameter) , 85.A. tenuis colonies that reached sexual maturity at 35 months when they had grown to a colony size larger than 12.5 cm mean diameter [A. loripes colonies were gravid at 35 months age. This indicates that the minimum age at first reproduction in A. loripes might be at least four or five years, as predicted by Wallace [A. loripes [A. loripes colonies surviving at the end of this study suggests that size-dependent reproduction may not strictly apply in A. loripes, rather sexual reproduction could be influenced by both age and size [Acropora species highlight the importance of choosing the best coral species for achieving specific coral restoration goals.In contrast to sexually derived diameter , none of Wallace . Further loripes , 86. Theand size , 86. TheA. loripes colony after 8 months ($17.41) and 35 months ($35.20) was slightly higher than the production costs for A. tenuis colonies at similar ages  [Acropora palmata colony outplanted onto reef areas two weeks after settlement onto clay tripods, and a higher cost of $325 USD per colony maintained in a land-based nursery. The production costs for the larval enhancement method are significantly lower in large part because settlers are not maintained in ex situ or in situ grow-out nursery installations over long periods of time. The overall cost-effectiveness of the larval enhancement technique will also increase as restored colonies become sexually reproductive and contribute larvae to the natural larval pool that can re-populate and colonize other available reef areas in future.Some of the key challenges for coral restoration are to lower the production cost and to be adaptable to developing nations where most of the world’s coral communities occur and which are subjected to rapidly increasing anthropogenic threats , 87. The months $.41 and 3ctively)  recordedOne of the advantages of the larval enhancement method using temporary mesh enclosures is the applicability and adaptability of the technique to various corals and reef types with different environmental conditions. In the present study, flat mesh matting was used to contain larvae on reef areas with relatively low topographical relief, and the reef experiences occasional strong wave action. For reef systems with more complex substrata, the enclosures can be modified to adapt to the contours of the reef. Furthermore, the availability of plankton nets with various mesh size openings makes the larval enhancement technique applicable to a wide range of spawning and brooding coral species that produce larvae of different sizes. Future larval enhancement interventions could be done without mesh enclosures by releasing competent larvae at optimal densities during periods of low tidal flows and calm weather and low wave action. This technique should also be tested in different reef regions around the world with different environmental conditions and topography, for comparison with other existing coral restoration techniques.While high post-settlement mortality remains a key challenge for coral restoration using sexual production , the res


Our results show that the conventional RNA extraction is fully comparable to modern automated systems regarding analytical sensitivity and specificity with respect to detection of SARS-CoV-2 as evaluated by RT-qPCR. Moreover, the method is easily scalable and implemented in conventional laboratories as a low cost and suitable alternative to automated systems for the detection of SARS-CoV-2.The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly reached pandemic levels. Sufficient testing for SARS-CoV-2 has remained essential for tracking and containing the virus. SARS-CoV-2 testing capabilities are still limited in many countries. Here, we explore the use of conventional RNA purification as an alternative to automated systems for detection of SARS-CoV-2 by RT-qPCR. 87 clinical swab specimens were extracted by conventional phenol-chloroform RNA purification and compared to commercial platforms for RNA extraction and the fully integrated Cobas The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the coronavirus disease 2019 (COVID-19) has rapidly reached pandemic levels, with COVID-19 related morbidities and mortalities rising in many countries . Adequat™ (Invitrogen) and TRI Reagent® (Merck) or can be produced locally from base chemicals at a low cost [The AGPC is a simple method used in many research institutions and laboratories worldwide. The method itself has a long-known track record  and commlow cost . For COV™ tubes containing 1 ml of liquid Amies medium (Copan) and used in this study to evaluate the analytical sensitivity, specificity and accuracy of our in-house SARS-CoV-2 RT-qPCR assay after RNA purification using the AGPC method or the automated Maxwell® RSC 48 instrument (Promega). The patient specimens were selected based on SARS-CoV-2 status from the routine diagnostic analyses to reflect a broad range of viral titers in the sample material as determined by the SARS-CoV-2 E gene  using the fully integrated Cobas®6800 diagnostic system (Roche).A total of 87 clinical sample specimens were obtained from patient material undergoing routine diagnostic analyses at the Department of Clinical Microbiology, Odense University Hospital. The samples were obtained from oropharyngeal or nasopharyngeal swabs collected in ESwab® manufacturer (Merck) with minor modifications  using the Maxwell® instrument according to the manufacturer’s recommendations, without the initial heat incubation step. SARS-CoV-2 was detected according to the real-time PCR protocol established by Corman et al. [®6800 system  was used in this study (set as gold standard) for the evaluation of Maxwell® and AGPC methods of RNA purification. Testing was performed using the Cobas® SARS-CoV-2 test assay with proprietary primers directed against the SARS-CoV-2 ORF1 and E-gene.Phenol-chloroform extraction was carried out according to the instructions of the Tri Reagentn et al. . The ful™ media as an internal control prior to RNA purification using the AGPC method and the Maxwell® instrument. The amount of added NDV had previously been titrated to yield a Ct value ~27.5 as measured by RT-qPCR and routinely used for evaluating sample quality after RNA isolation at the Department of Clinical Microbiology. To determine a Ct cutoff value for NDV after purification with the Maxwell® instrument and the AGPC method, 24 clinical swab samples with known SARS CoV-2 status based on the Cobas®6800 system were processed using both methods and the data for the in-house NDV and SARS-CoV-2 RT-qPCR assays were evaluated. Based on these data and applying a precautionary principle only samples yielding Ct values below 29.5 for the internal control NDV assessed by RT-qPCR on the Maxwell® and TRI Reagent® platforms were included in the analyses.To distinguish between true negative results and reactions affected by inadequate RNA isolation, presence of PCR inhibitors or instrument failure, Nobilis ND C2 vaccine (NDV) against Newcastle Disease (Nobilis) was added to the ESwab® RSC 48 and AGPC method were evaluated by comparison to results obtained for the SARS-CoV-2 E gene using the Cobas®6800 diagnostic system. Sensitivity is defined as the probability of a test result being positive for a SARS-CoV-2 positive sample. Specificity is defined as the probability of a test result being negative for a SARS-CoV-2 negative sample. Accuracy is defined as the probability of a sample being correctly diagnosed. 95% Confidence intervals were calculated using the “exact” Clopper-Pearson method. Pearson correlation coefficients (r) were calculated for Ct correlation studies. Paired Student’s t test was applied for analyses of paired samples with one independent variable.All statistical tests were performed with Prism v8.3 (GraphPad). The sensitivity, specificity and accuracy of our in-house SARS-CoV-2 RT-qPCR assay after RNA purification using the MaxwellException from review by the ethical committee system and informed consent was given by the Regional Committees on Health Research Ethics for Southern Denmark in agreement with Danish law on assay development projects.® instrument. The samples used had previously been analyzed using the state-of-the-art fully integrated Cobas®6800 diagnostic system capable of analyzing patient specimens from sample to test result without manual interference, using proprietary primers directed against the SARS-CoV-2 ORF1 and E-gene. Analysis of the sample specimens with RNA extracted by the Maxwell® instrument showed a 98.2% sensitivity, 96.4% specificity and 97.6% accuracy, but also reported a single false positive and a false negative sample compared to the Cobas®6800 system  with known SARS-CoV-2 status (57 positive and 30 negative), and performed a side-by-side comparison with the identical samples extracted on a Maxwell0 system . ImportaSARS-CoV-2 E gene in the SARS-CoV-2 positive sample specimens after AGPC extraction of the RNA, to the Ct values from identical samples reported using the Cobas®6800 system and the Maxwell® instrument  and the Maxwell® instrument . Thus, to assess the purity, yield and efficiency of the RNA extraction using the AGPC method we compared the Ct values reported for the NDV to those attained for the same identical samples extracted using the Maxwell® instrument. Direct comparison of the Ct values revealed no significant differences in Ct values for NDV between the AGPC method and the Maxwell® RSC 48 instrument Click here for additional data file. 14 Jan 2021PONE-D-20-39657Phenol-chloroform-based RNA purification for detection of SARS-CoV-2 by RT-qPCR: comparison with automated systemsPLOS ONEDear Dr. Moeller,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.The reviewers find this study well presented and informative.My personal opinion is that Phenol-chloroform-based RNA purification is, in principle, already in the past. For diagnostic purposes, isolation of total RNA or DNA can be avoided.A few comments from reviewer #2 that must be taken into account when preparing the manuscript.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Feb 28 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocolsIf applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: We look forward to receiving your revised manuscript.Kind regards,Ruslan Kalendar, PhDAcademic EditorPLOS ONEJournal Requirements:When submitting your revision, we need you to address these additional requirements.1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found athttps://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf andhttps://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdfhttps://journals.plos.org/plosone/s/submission-guidelines.#loc-statistical-reporting.2. To meet PLOS ONE submission guidelines, in your Methods section, please provide additional information regarding your statistical analyses. For more information on PLOS ONE's expectations for statistical reporting, please see 3. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********5. Review Comments to the AuthorReviewer #1: This is a focused and straightforward examination of the efficacy of the guanidinium thiocyanate-phenol-chloroform (AGPC) method of RNA extraction for RNA sample preparation used in the detection of SARS-CoV-2 by RT-qPCR. The primary objective of the authors was to determine whether the much less expensive and technologically simpler, although more labor intensive, bench top method of RNA isolation from nasopharyngeal and oropharyngeal swabs is as effective as automated RNA isolations carried out by two different instruments currently in use for COVID-19 testing. The rational for this study is that evidence supporting efficacy of the AGPC extraction method can enable COVID-19 testing to be carried out in locations that lack the resources for expensive, high-throughput RNA isolation devices. The results clearly show that the bench top method can reliably be used in place of automated instruments.The study is technically sound and well controlled, and the data is appropriately analyzed for statistical significance. The experiments and results are thoroughly and clearly described in well-written, grammatically correct English.Reviewer #2: The manuscript entitled "Phenol-chloroform-based RNA purification for detection of SARS-CcV-2 by RT qPCR: comparison with automated systems" is a valuable contribution to the field of SARS-CoV-2 detection systems.However, although conventional RNA purification can be a valid alternative to commercial platforms for RNA extraction, it is also true that in a pandemic setting when every moment matters, the use of commercial kits and automated system for the detection of SARS-COv-2 can speed up the identification process of SARS-CoV-2 positive subjects. Maybe the Authors should discuss this point better in their manuscript.line 135: in the Materials and Methods section, the Authors indicated that both oropharyngeal and nasopharyngeal swabs were collected. Please make the required correction.Reviewer #3: In this paper by Moeller lab, they compare the result of the standard phenol choloform isolation of SARS-CoV-2 RNA to automated RNA extraction systems. They use qRT-PCR to detect the inactivated viral RNA and compare the two methods head to head. Appropriate positive controls are used. They find that the PC method is comparable to the automated systems in terms of true positive (TP) TN FP FN. Importantly a detailed protocol is presented in the supplemental data. While this method is typically successfully performed by those skilled in working with RNA and therefore more human error could be introduced during the isolation, it does offer an alternative to automated systems in developing countries that may not have access to those equipment. While PC extraction of SARS-CoV-2 RNA has been preciously reported (their ref 5) this study for the first times does a head to head comparison of the two methods and shows them to be equivalent. 26 Jan 2021Response to Editor and Reviewer’s commentsAcademic Editor:1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. • We have formatted and rearranged the manuscript to meet all PLOS ONE’s requirements.2. To meet PLOS ONE submission guidelines, in your Methods section, please provide additional information regarding your statistical analyses. • We have added additional information regarding statistical analyses used to the Methods section. These changes can be found on line 119-129.3. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.• We have moved the Ethics statement to the Methods section of the manuscript. These changes can be found on line 131-134.Reviewer #1:This is a focused and straightforward examination of the efficacy of the guanidinium thiocyanate-phenol-chloroform (AGPC) method of RNA extraction for RNA sample preparation used in the detection of SARS-CoV-2 by RT-qPCR. The primary objective of the authors was to determine whether the much less expensive and technologically simpler, although more labor intensive, bench top method of RNA isolation from nasopharyngeal and oropharyngeal swabs is as effective as automated RNA isolations carried out by two different instruments currently in use for COVID-19 testing. The rational for this study is that evidence supporting efficacy of the AGPC extraction method can enable COVID-19 testing to be carried out in locations that lack the resources for expensive, high-throughput RNA isolation devices. The results clearly show that the bench top method can reliably be used in place of automated instruments.The study is technically sound and well controlled, and the data is appropriately analyzed for statistical significance. The experiments and results are thoroughly and clearly described in well-written, grammatically correct English.• We thank the Reviewer for their kind review.Reviewer #2:The manuscript entitled "Phenol-chloroform-based RNA purification for detection of SARS-CcV-2 by RT qPCR: comparison with automated systems" is a valuable contribution to the field of SARS-CoV-2 detection systems.However, although conventional RNA purification can be a valid alternative to commercial platforms for RNA extraction, it is also true that in a pandemic setting when every moment matters, the use of commercial kits and automated system for the detection of SARS-COv-2 can speed up the identification process of SARS-CoV-2 positive subjects. Maybe the Authors should discuss this point better in their manuscript.• We appreciate and agree with the Reviewer’s suggestion. We have included a short discussion of the benefits of automated systems for rapid identification of SARS-CoV-2 positive subjects during a pandemic. These additions can be found on line 205-207 and line 222-255.Line 135: in the Materials and Methods section, the Authors indicated that both oropharyngeal and nasopharyngeal swabs were collected. Please make the required correction.• We have corrected this error. The addition can be found on line 198-199Reviewer #3:In this paper by Moeller lab, they compare the result of the standard phenol choloform isolation of SARS-CoV-2 RNA to automated RNA extraction systems. They use qRT-PCR to detect the inactivated viral RNA and compare the two methods head to head. Appropriate positive controls are used. They find that the PC method is comparable to the automated systems in terms of true positive (TP) TN FP FN. Importantly a detailed protocol is presented in the supplemental data. While this method is typically successfully performed by those skilled in working with RNA and therefore more human error could be introduced during the isolation, it does offer an alternative to automated systems in developing countries that may not have access to those equipment. While PC extraction of SARS-CoV-2 RNA has been preciously reported (their ref 5) this study for the first times does a head to head comparison of the two methods and shows them to be equivalent.• We thank the Reviewer for their kind comments.AttachmentResponse to reviewers.pdfSubmitted filename: Click here for additional data file. 9 Feb 2021Phenol-chloroform-based RNA purification for detection of SARS-CoV-2 by RT-qPCR: comparison with automated systemsPONE-D-20-39657R1Dear Dr. Moeller,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Ruslan Kalendar, PhDAcademic EditorPLOS ONE 15 Feb 2021PONE-D-20-39657R1 Phenol-chloroform-based RNA purification for detection of SARS-CoV-2 by RT-qPCR: comparison with automated systems Dear Dr. Moeller:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofProf. Ruslan Kalendar Academic EditorPLOS ONE


Mapping RNAPI by Miller chromatin spreads or UV crosslinking revealed 5′ enrichment and strikingly uneven local polymerase occupancy along the rDNA, indicating substantial variation in transcription speed. Two features of the nascent transcript correlated with RNAPI distribution: folding energy and GC content in the transcription bubble. In vitro experiments confirmed that strong RNA structures close to the polymerase promote forward translocation and limit backtracking, whereas high GC in the transcription bubble slows elongation. A mathematical model for RNAPI elongation confirmed the importance of nascent RNA folding in transcription. RNAPI from S. pombe was similarly sensitive to transcript folding, as were S. cerevisiae RNAPII and RNAPIII. For RNAPII, unstructured RNA, which favors slowed elongation, was associated with faster cotranscriptional splicing and proximal splice site use, indicating regulatory significance for transcript folding.Transcription elongation rates influence RNA processing, but sequence-specific regulation is poorly understood. We addressed this   •in vivoStructures in the nascent RNA correlate with rapid elongation by RNAPI •in vitroStable RNA structures limit RNAPI backtracking •GC content in the transcription bubble tunes transcription elongation rate•in vivoNascent transcript folding modulates dynamics of all three RNAPs  Transcription elongation is a stochastic process that can go backward as well as forward. Here Turowski et al. report that folding of the nascent transcript immediately behind the polymerase resists backtracking and promotes forward movement. This results in faster elongation relative to unstructured sequences and is potentially important for cotranscriptional processing. Transcription elongation is composed of many successive cycles of nucleotide addition, in which the translocation step is based on Brownian motion without input of external energy. The major driver of transcription elongation is nucleotide addition because pyrophosphate release is essentially irreversible, allowing this step to act as a ratchet A. Depend–1  growing in YPD medium, containing 2% glucose + 1 M sorbitol at 30°C, as described previously . To anal6)-tobacco etch virus (TEV) protease cleavage site-2xProtA (HTP). Following growth in SD medium with 2% glucose at 30°C, nascent RNA was covalently crosslinked to RNAPI using 254-nm UV irradiation. After 3-step purification, including stringent denaturing wash conditions, cDNA libraries were prepared and sequenced using Illumina technology. The CRAC protocol used exclusively recovers RNAs with 3′ hydroxyl groups  and tomography .hmo1Δ strain still showed a 5′ bias and stable peaks over the 5′ region of the rDNA  or the 5′ ETS alone (p < 5 × 10−3). Unwinding of the template DNA in front of the transcription bubble could potentially be slowed by high GC content. However, the first 10 nt downstream of peaks and troughs showed no clear correlation with GC for the 5′ ETS (p ≫ 0.05) or even an opposing trend for RDN37 (p = 5 × 10−4) (We next assessed whether features in the nascent pre-rRNA could affect RNAPI elongation kinetics. A short RNA:DNA hybrid is present in the transcription bubble in the RNAP elongation complex A. For hu × 10−4) E. The GCThe data indicate that elevated GC content in the RNA:DNA hybrid in the transcription bubble is associated with increased RNAPI occupancy, presumably reflecting slowed or transiently paused RNAPI.spearman [Rsp] = 0.65; because a window of 80 nt is used, the folding energy line commences at +80).The yeast 5′ ETS folds into 10 stable, extended hairpin structures C. To exasp = 0.78; structures are plotted as ΔG, with lower values representing greater stability). Conversely, regions of low RNAPI occupancy were correlated with stable structures in the nascent transcript  but was also observed when the RDN37 gene was analyzed excluding the 5′ ETS (−7).To better understand the relationship between pre-rRNA folding and elongation, the analysis was repeated using a range of window sizes to calculate folding energy. In addition, an “offset” was added because the terminal ∼15 nt of the transcript is located within the polymerase and unable to participate in folding J. The beWe conclude that weak structures in the nascent pre-rRNA behind RNAPI coincide with sites of slowed elongation (high RNAPI density), whereas strong pre-RNA structures correlate with rapid elongation (low RNAPI density).in vivo  relative to the 5′ region of the 18S rRNA  despite having low GC content. This suggests that structure in the 5′ ETS may have been selected to promote elongation.The 5′ ETS has very stable overall folding  mol−1), precluding their experimental determination. To validate the conclusion that the structure in the nascent pre-rRNA limits backtracking, we used an in vitro RNAPI transcription system (The effects of nascent RNA are expected to operate over 1–2 s because of the fast elongation rate of RNAPI (∼40 nt sn system . In thisn system A. Withinn system . BacktraRNAPI was purified via Rpa135-HTP and bound to immunoglobulin G (IgG)-conjugated magnetic beads to allow rapid exchange of transcription buffer. Nascent transcripts are retained on the beads in association with the polymerase. The template DNA included a sequence that generates a stem-loop structure in the RNA, close to the 5′ end of the transcript. The transcript lacked A residues other than a sequence of three adenines (AAA) close to the 3′ end of the template A and 3B.Cleavage by Rpa12 should reposition the 3′ end of the nascent transcript in the active site . Consistin silico a construct with four random nucleotides . In the backtracking assay, samples were first incubated in [−ATP] transcription buffer to induce stalling and then washed and incubated without nucleotides ([none]) for 15 min at 28°C to allow RNAPI backtracking (−1). We predict that this represents the strength of RNA structure needed to efficiently block further backtracking. Moreover, the cleavage product was more abundant for the construct with ΔG –15 than for the constructs with ΔG –12 or –13. These results confirm that stable structures in nascent RNA limit backtracking by RNAPI.To compare sequences with different folding energy, we designed Weaker structures did not generate stable stalls at 28°C but might still affect RNAPI back-translocation. To assess this, the RNAPI backtracking assay was analyzed at short time points and with reduced temperature (18°C) to slow the polymerase E–3G.For the strongest hairpin, CGGC (ΔG −15 at 28°C and ΔG −17 at 18°C), we observed very rapid backtracking even at 18°C E and S3BWe next tested two hairpins that did not generate stable products at 28°C: GGCG (ΔG −7 at 28°C and ΔG −9 at 18°C) and GUGU (ΔG −5 at 28°C and ΔG −6 at 18°C). At 18°C, both transcripts generated a clear but transient gel band corresponding to backtracked RNAPI that was most prominent at 2 min and destabilized during longer incubation H and S3FAltogether, these kinetic assays revealed that strong structures block backtracking, whereas weaker structures slow the kinetics of back translocation proportional to their stability.To better understand the contributions of the different components to overall transcription, we developed a mathematical model for RNAPI transcription. The model is based on simulations of individual RNAPI molecules initiating and transcribing a 7,000-nt RNA. The key parameters of the model include the (stochastic) initiation frequency and the probability of forward or reverse translocation. The latter is influenced by several factors: (1) the effects of DNA torsion on the probability of elongation versus backtracking, (2) the effects of structure in the nascent transcript, and (3) the stability of RNA-DNA duplex in the transcription bubble  and S4.FThe parameters are very briefly described below and discussed in more detail in the −1, limited by the requirement that the preceding RNAPI has cleared the initiation region. A mean stochastic initiation rate of 0.8 s−1 generated RNAPI loading similar to that observed with Miller spreads (∼50 per rDNA unit)  I.in vivo transcription rate across the entire yeast 35S pre-rRNA is ∼40 nt s−1  correlated with increased probability of rapid elongation and decreased probability of backtracking. The correlation between RNAP density and stability of the RNA:DNA duplex in the transcription bubble was incorporated with high stability  correlated with decreased probability for rapid elongation. The effect of each feature was calculated for every nucleotide position. For ease of implementation, these were combined in the model as “RNA elements.”We constructed a set of dynamic models that were run to achieve equilibrium states C–4G.in vivo electron microscopy (EM) and CRAC data. Inclusion of a 5′ low entrainment region generated a distribution that more closely matched the in vivo data because we now see a clear 5′ bias in modeled RNAPI density, with polymerases moving more slowly and more closely positioned over the initial 2 kb . The RNAPI footprint was estimated at 38 bp by cryo-EM, so strong structures within this region will potentially accelerate promoter clearance and increase rDNA loading. We analyzed a set of in silico constructs with the 5′ ETS fixed to ΔG = −20 kcal mol−1 starting at positions 0 nt, 10 nt, etc., up to 80 nt. The 5′ ETS with structures starting at early positions (0–30 nt) indeed increased effective initiation and rDNA loading .The  5′ bias A, similaae RNAPI E. MetaplSCR1 gene encodes the 522-nt-long scR1 ncRNA component of the signal recognition particle. Previous RNAPIII CRAC data showed a very uneven distribution across SCR1 .Some of the variation in RNAPII occupancy reflects nucleosome positioning, with maximal density  seen at the center of nucleosomes , which aTo determine whether nascent RNA structure may have regulatory potential, we used pre-mRNA splicing as a model process. The splicing machinery co-transcriptionally recognizes the 5′ splice site (SS), branchpoint (BP), and the 3′ SS (acceptor site). We predicted that stronger structure in the nascent RNA would reduce the time available for co-transcriptional selection of the 3′ SS, disfavoring rapid cotranscriptional splicing. Analyses using extremely fast metabolic labeling previously ranked yeast pre-mRNAs by splicing speed D. Consisde novo using previously described features , suggesting a kinetic model for 3′ SS selection based on a “window of opportunity.” To assess the potential role of nascent RNA folding as a decisive factor, we defined all yeast introns features H, focusiWe conclude that stimulation of transcription elongation by nascent RNA structure is a conserved feature of all three eukaryotic polymerases and that regulation of co-transcriptional processes is at least partially determined by local folding of nascent RNA.in vivo will therefore be predictive of processing decisions.Analyses of eukaryotic transcription by multiple techniques reveal uneven polymerase occupancy, reflecting variable elongation rates. This is important because many RNA processing factors act very quickly on the nascent transcript. For example, splicing of pre-mRNA is strikingly speedy in yeast  but moreIn eukaryotes, RNAPI is most amenable to these analyses because it transcribes only a single product from the nucleosome-free rDNA. EM analyses of Miller chromatin spreads revealed uneven distribution of RNAPI across the rDNA, with an excess of polymerases in the 5′ region. In an orthogonal approach, we determined the distribution of RNAPI by CRAC UV crosslinking. This confirmed the 5′ enrichment for RNAPI density but also revealed a strikingly uneven, local polymerase distribution, most notably over the 5′ ETS region of the pre-rRNA E and 1F.Analysis of features that correlate with peaks and troughs of RNAPI density showed a modest correlation with the stability of the RNA-DNA duplex in the transcription bubble but strong correlation with the calculated folding energy of the nascent pre-rRNA transcript close to the polymerase . RNAPs oin vitro, we confirmed that strong structures in the nascent transcript effectively resist backtracking and defined the stability of stems that can block or slow backtracking by RNAPI .Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, David Tollervey , except for the 25 rDNA strain which derives from NOY1071  medium + 1 M sorbitol such that after growth at 30°C for 6 h the culture reached a density of ODescribed . In brieChromatin spreads on multiple grids from several cultures were methodically examined, grid square by grid square, using a JEOL 100CX transmission electron microscope. Areas of dispersed chromatin containing 35S rRNA genes were photographed. In Miller chromatin spreads, active rRNA genes are recognized as a consequence of their high transcription frequency. The multiple Pol I molecules engaged in transcribing the genes lend electron density to the DNA template thus enhancing the visibility of the genes.Micrographs of chromatin were examined and all 35S genes that could be unambiguously traced from 5′ to 3′ ends were scanned on an Epson Perfection V750 Pro flatbed scanner. Polymerase positions were measured on these digital images using ImageJ software. The position of the center of each polymerase was recorded relative to the 5′ end of the gene on which it was observed. These positions, measured in pixels along the DNA strands, were then normalized by setting the position of the first polymerase to a value of 0 and that of the last polymerase to a value of 100. While we cannot exactly determine if a first polymerase is at the promoter or occupies a spot a polymerase width or two downstream, we based our determination of “full length” genes on the distance between first and last polymerase and the relative distances between upstream and downstream 5S genes, neighboring 35S genes, together with the characteristic features of polymerases and transcripts associated with initial and final positions 600 = 0.5. Actively growing cells were cross-linked in culture media using megatron UVC cross-linker (Strains for CRAC experiments were grown in synthetic dextrose (SD) medium with 2% glucose, lacking Trp to ODs-linker  typicalls-linker  for 40 s2, 10 mM CaCl2, 5 mM β-mercaptoethanol, 50U of DNase RQ1 and a protease-inhibitor cocktail (1 tablet / 50 mL) with zirconia beads in a 50 mL conical. The cells were lysed with five one-minute pulses, with cooling on ice in between. The supernatant was spun for 20 minutes at 21,000 g. The cleared lysate was incubated with the IgG Sepharose for two hours at 4°C, with nutating. Subsequently, the beads were washed three times with TMN600  and two times TMN100 . The eluate was transferred to a fresh tube containing 350 μL TMN100, 2.5U of RNace-IT was added and samples were incubated for 5 minutes at 37°C to fragment protein-bound RNA.Samples were processed as previously described . HoweverProtein:RNA complexes were eluted by incubation with HaloTEV for 2h at 18°C with shaking. The supernatant was separated and adjusted for nickel affinity purification with the addition of 400 mg guanidine hydrochloride, 45 μL NaCl (3M) and 7 μL imidazole (1 M) and added to 50 μL of washed nickel beads.2 and 5 mM β-mercaptoethanol), three times with PNK buffer  and transferred to a spin column. Subsequent reactions  were performed in the columns, and afterward washed once with WBI and three times with PNK buffer:1.3′ linker ligation (1x PNK buffer(NEB), 10% PEG8000, 20U T4 RNA Ligase II truncated K227Q, 80U RNasIN, 80 pmol preadenylated 3′ miRCat-33 linker (IDT); 16°C overnight).2.32P-γATP; 37°C for 45 min, with addition of 100 nmol of ATP after 30 min).5′ end phosphorylation and radiolabeling (1x PNK buffer (NEB), 40 U T4 PNK (NEB), 80U RNasIN, 40 μCi 3.5′ linker ligation (1x PNK buffer (NEB), 10% PEG8000, 40 U T4 RNA ligase I (NEB), 80 U RNasIN, linker, 200 pmol 5′ linker, 1 mM ATP; 22°C for 4h).Following 4h incubation, the nickel beads were washed three times with WBI  and acetone precipitated overnight. RNPs were pelleted at 21000 g for 20 minutes, and resuspended in 20 μL 1X NuPAGE sample loading buffer supplemented with 8% β-mercaptoethanol. The sample was denatured by incubation at 65°C for 10 minutes, and run on a 4%–12% Bis-tris NuPAGE gel at 130 V. The protein:RNA complexes were transferred to Hybond-C nitrocellulose membranes with NuPAGE MOPS transfer buffer for 2 h at 100V.Labeled RNA was detected by autoradiography. The appropriate region was excised from the membrane and treated with 0.2 μg/μL Proteinase K  in a 500 μL reaction. The RNA component was isolated with a standard phenol:chloroform extraction followed by ethanol precipitation with 1 μL of GlycoBlue. The RNA was reverse transcribed using Superscript III and the miRCat-33 RT oligo (IDT) for 1 hr at 50°C in a 20 μL reaction. The resulting cDNA was amplified by PCR in 50 μL reactions using La Taq  PCR reactions were combined, precipitated in ethanol, and resolved on a 3% Metaphore agarose gel. A region corresponding to 140 to 200 bp was excised from the gel and extracted using the Min-elute kit. Libraries were measured with Qbit and sequenced using Illumina HiSeq with 50bp single-end reads or Illumina MiniSeq with 75bp single-end reads.2, 20% glycerol). For elution, beads were finally resuspended in 100 μl of buffer B200, supplemented with 3 μl TEV protease  and incubated for 2 h at 16 °C in a thermomixer . The supernatant was collected and aliquots were stored at 4°C for short term or at −80 °C for longer. For buffer exchange assays, TEV elution was skipped and aliquots were stored only for short term at 4°C. 10% of the purified fraction was analyzed via SDS–PAGE to monitor the purification success. Protein concentrations were determined by comparing the intensity of Coomassie-stained RNA polymerase subunits to the defined amount of Coomassie-stained HaloTEV protease used.The protein content of the supernatant was determined using the Bradford assay. Equal protein amounts  were incubated with 50–75 μl of immunoglobulin-G  coupled magnetic beads slurry  for 1–2 h on a rotating wheel. The beads had previously been equilibrated with lysis buffer. The beads were washed four times with 1 ml buffer B1500  and three times with 1 ml buffer B200 2SO4, 20 mM HEPES/KOH pH 7.6, 8 mM MgSO4, 10 μM ZnCl2, 10% glycerol, 10 mM DTT) supplemented with 1 mM NTPs. The samples were incubated at 28°C for 5 min. For backtracking assays, reaction tubes were placed on a magnetic rack, and supernatant was removed. Beads were washed with 200 μL buffer B200, re-suspended in 12 μL 1x TB without NTPs and incubated at 28°C for 10 min. All reactions were stopped by addition of 2x RNA loading dye . Samples were heat denatured at 95°C for 5 min and resolved on 8 M urea 20% polyacrylamide gels. Fluorescently labeled transcripts were visualized using a Fugi FLA-5100 phosphoImager and images were processed using Multi Gauge software (Fuji).The ied from . For 1 rTwo major aspects of the CRAC data were investigated: Contamination with mature rRNA or processed pre-rRNA and bias in sequence recovery.In total RNA, mature rRNAs  are much more abundant than the spacer regions  present in the nascent transcript. However, the recovery of reads mapping to the rRNA sequences was not clearly elevated relative to the spacers and there was no accumulation at the mature rRNA boundaries E. This sDuring pre-rRNA transcription, the nascent transcript is cleaved at four sites; A0, A1, A2 and B0. Cleavages at A0-A2 are coupled and predominately cleaved in the nascent transcript, but processing occurs when RNAPI has traveled ∼1.2Kb downstream of site A2 . Sequenc1: To reduce the possibility of non-specific cross-linking to Rpa190, we analyzed a range of shorter UVC cross-linking times. These showed minimal changes D.2: To exclude steric preferences in RNA cross-linking, we HTP-tagged the second largest subunit of RNAPI, Rpa135 E. This s3: We performed a PAR-CRAC experiment, in which RNA was metabolically labeled with 4-thiouracil (4SU) and cross-linked using UVA G. 4SU cr4: Wild-type yeast strains generally have ∼150-200 ribosomal repeats, of which around 50% are reported to be actively transcribed, making it conceivable that the apparent 5′ end bias E arises 5: We considered the possibility that RNA interacting with the outside of the polymerase might contribute to the signals, although the requirement that recovered RNA has a 3′ OH group made this unlikely. To test this, we considered only cDNA sequences shorter than 20 nt, since this region will be almost entirely located within the transcription bubble and RNA exit channel. This analysis also revealed the distinctive peaks for RNAPI distribution J.6: To address the possibility that periodic peaks with the 5′ ETS are generated by ambiguous mapping to repetitive sequences, we prepared a reference genome with single copy of the rDNA. We then performed unambiguous mapping, which does not report any sequences that map to more than one location. Unambiguous mapping returned a spiky profile, closely matching the results of random mapping K, showinhmo1Δ strain  nascent RNA interacting with RNAP is buried inside the channel in its extended, unstructured form, therefore, there should be no influence of structure on the UV crosslinking efficiency. (2) The CRAC protocol involves highly denaturing conditions to reduce the background. Following protein denaturation, the RNA could be susceptible to folding, potentially sequestering RNA ends and hindering adaptor ligation. In such a case we would expect lower recovery of highly structured RNAs, e.g., hairpin loop regions. However, this is in marked contrast to our results F.We also performed experiments to validate the Rpa190 CRAC data and detect potential bias in target recovery. Notably, all of these analyses yielded RNAPI distributions that were consistent with the results of CRAC with Rpa190 F.1: To rin vivo. All subsequent analysis was performed using the median of six biological replicates , corresponding to ∼2,000 ribosomes miny active . Each tr-1. We tested rates of stochastic initiation over this range, limited by the requirement that the preceding RNAPI has cleared the initiation region. A mean stochastic initiation rate of 0.8 s-1 generated RNAPI loading consistent with data from Miller spreads (∼50 per rDNA unit)  I.-1) and transcript release rate  predicts a spacing of 120 bp. Measurements of the relative positions of RNAPI in Miller spreads by tomography, indicated minimal center to center separation of 15 nm  and rDNA repeats (75-100), giving a range of 50-60. This figure is in good agreement with quantification of RNAPI complexes from Miller chromatin spreads ∼50; . The numperiment . The aveof 15 nm , which iin vivo and in vitro many times and some examples were summarized in in vitro measurements are systematically lower than in vivo.The velocities of RNA polymerases have been determined -1 is expected to be the overall average velocity of transcribing RNAPI. However, pause-free elongation is very unlikely in vivo. Therefore, we used 50 nt sec-1 as the intrinsic, average RNAPI elongation rate , yeast doubling time (100 min), pre-rRNA length (∼7000 nt) and number of transcribing RNAPI molecules (5000 - 6000).in vitro determined the distribution of nucleotide incorporation (elongation) rates at a nucleotide level in E. coli using single-molecule measurements . The second function reflects active elongation and centered at 12.8 nt sec-1 DNA torsion(b)Promotion of RNAP elongation by nascent structure forming behind the polymerase(c)Decrease of RNAP elongation by a strong RNA:DNA hybrid within the transcription bubble.This probability can be modified by following factors:RNAP elongation along a DNA helix requires two types of movement: forward and rotary. In principal, either the DNA or polymerase can rotate with a frequency of ∼240 rpm. However, the combined mass of all polymerases plus nascent pre-ribosomes is very much greater than that of the rDNA. The rDNA is nucleosome-free and loaded with multiple RNAPI complexes (∼50 at 0.5 MDa each), each associated with up to 7 Kb of pre-rRNA transcript (up to 2.3 MDa) and a multi-megadalton pre-ribosome  containing many assembly factors . The difhttps://www.yeastgenome.org) based on multiple analyses: Top1; 4130 ± 2517 molecules per cell. Sum of the largest subunits of all three RNA polymerases; 33674 ± 12715. Moreover, topoisomerases can unwind a minimum of one complete turn of the DNA, whereas a stalling force is generated by substantially less overwinding for polymerases with spacing typical for the rDNA (120 bp)  can relax positive or negative supercoils and are necessary to maintain transcription of the rDNA . However(120 bp) .E. coli RNAP  We assumed that highly packed and viscous environment of the nucleolus causes RNAPI to transcribe as a convoy. However, in vivo the ability of RNAPI to rotate around the rDNA will be greater than zero. Therefore, an increased limit of sigma in vivo are ≥ 2 fold higher than in vitro  and c i-1. This gives values of DNA stiffness constant The 00 nt as A. Then wity VInt E. Based The RNAP convoy model is justified by the energetic cost of spinning the DNA, friction, and the ratio between topoisomerases and all three eukaryotic RNAPs. Theoretically, only two flanking topoisomerases might be sufficient act as swivels to release torsion generated from DNA rotation by an entire convoy of RNAP. Notably, experimental data demonstrated that depletion of both topoisomerases causes severe perturbation in rDNA transcription when RNAPI is around 2 Kb into the transcription unit . This waWe interpret this observation as showing that RNAPI molecules are initially able to spin around the DNA, allowing changes in their relative positions without generating torsion, but become locked by torsion at around +2 Kb. To incorporate this mechanism into the model, we progressively engaged torsion within RNAP convoys over the initial 2 Kb of the rDNA. We used a linear engagement scheme, where at position 0 RNAP moves according to discrete stochastic elongation, at position 1000 DNA torque was applied in 50% and becomes fully engaged at position 2000 and later. This Low Entrainment Region was implemented as three elements: (1) Decreased DNA stiffness constant Finally, we introduced our findings on the effects of sequence in the RNA:DNA hybrid within the transcription bubble and the structure of the extruded RNA into the model.Nascent RNA interacts with template strand of DNA within transcription bubble. Stronger hybrids usually have a higher G+C content, particularly with G in the DNA sequence, and this correlates with slower RNAP translocation. We calculated the ΔG of RNA:DNA hybrids over an 8 nt rolling window (ibed see .-1). This excluded an artificial situation when long, but very weak structures would apparently have a sufficiently low ΔG to promote translocation.The folding energy of nascent RNA was calculated using a 65 nt rolling window, offset by 15 nt  and noted that there were no significant differences. All remaining simulations were performed with this time step.The principle constraint on the time step is that the distance moved in a single time step should be 0, 1 or −1 (since only single nucleotide jumps are permitted). As an estimate, we note from We determined the total time necessary to run the model by monitoring expected values, such as the number of particles and the mean separation, requiring that these had reached equilibria. The main purpose of this was to remove bias caused by initiation of the RNAP molecules along the transcription unit. We found no significant differences when the total time was between 1500 s and 3000 s.Increasing the number of independent simulations decreases the statistical noise in the final result. This can also be achieved by increasing the total time of each simulation, but due to parallelization, it is more efficient to increase the number of simulations. We performed convergence studies for a range of parameters and determined that there is no significant difference between results with 256, 512, and 1024 independent simulations. For the parameter studies below, due to the large number of parameter combinations, we used 256 simulations, whereas for the single chosen parameter set we used 1,024.The model was optimized toward two major criteria: (1) The number of RNAPI molecules present on the transcription unit I. (2) Thc = {400,500,600}, 5 = 243 sets of parameters, with each varying approximately 10%–20% from the chosen value. Given the constraints on the parameters discussed above, we tested all parameter combinations with transcription initiation (addProb) = {0.7,0.8,0.9}11, DNA stiffness constant in silico density profiles are normalized so that they have unit area; they are probability distributions. To maintain this, the ‘experimental’ densities are renormalized after the In order to mimic the experimental measurement process, we applied a smooth cutoff function to the data, essentially reducing the measurement of RNAPI in areas of low density/high velocity. The cutoff function is given byTo calculate the relative contributions of different forces to the modeled elongation, absolute values were used. RNA structures always act positively, RNA:DNA hybrids act negatively, whereas DNA Torsion can act both, positively or negatively. All three modifiers were summed for each nucleotide position and their relative contributions were calculated as a percent of that sum.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) considering specificity of CRAC data. Raw reads were collapsed to remove PCR duplicates using FASTX-collapser v0.0.14 (http://hannonlab.cshl.edu/fastx_toolkit/) then inline barcodes were removed using pyBarcodeFilter.py script from pyCRAC package v3.0  with –r random and saved in novo or sam file format. Second quality control step was performed using pyReadCounters script (pyCRAC package) which calculates overlaps between aligned cDNAs and yeast genomic features. The 3′ end or the 5′end of reads were selected using in-house awk script and 1 nt resolution BigWig files were generated using bamCoverage v3.1.3 script from deepTools package  were summed up to 1 and fraction of reads was used further, adding 10−7 pseudo count. There are two copies of the RDN37 gene in the reference genome; RDN37-1 and RDN37-2. Subsequent analyses used the RDN37-1 gene. For simplicity, this is referred to as RDN37 in the text.Downstream analyses were performed using python 2.7 Jupiter notebooks, python libraries  and in-house scripts submitted as an update of gwide toolkit v0.5.27 (The data at 1 nt resolution were quite noisy and we therefore smoothed them with centered Blackman function (window 10). CRAC profiles were presented similar to boxplots of six biological replicates L: medianThe data were randomized to compare obtained profile with random distribution of similar data, especially within part of 25S rRNA. To generate random data raw reads were shuffled using random functions  and post-processed . Spearman correlations for independent generation of randomized datasets confirmed that distribution is non-random M.Peak/trough calling was performed using argrelextrema function from signal processing library scipy.signal (v1.3.0) using order value most appropriate to applications: 50 for comparison between experiments, 35 for comparison with folding energy and 20 for analysis of GC-richness. To generate peak/trough metaplot for each peak or trough two sided window around the feature was superimposed with all other peaks. Mean for all windows were calculated and data for each dataset were presented as peak/trough metaplot F.For RNAPII analysis, due to different length of transcripts, reads were additionally normalized to fraction of reads in the window followed by calculation of mean. For each position an average of folding energy was calculated for a 40 nt window around each feature. Wilcoxon signed rank test was applied.RDN37 profiles were subtracted from each other and regions where the difference was ≥ 0.0005 were treated as specific for UVA (PAR-CRAC) or UVC (CRAC). For all specific positions an average frequency of nucleotides in a short (1 to 15 nt) window was calculated and two tailed student test was applied. p value < 0.005 was found for windows 1 to 3 nt.To investigate differences between CRAC and PAR-CRAC their normalized Content of guanine (G) or cytosine (C) bases in a given window is called G+C-richness. A given peaks or troughs G+C-richness was calculated depending on application using window: 10 nt upstream, 10 nt downstream or 10 nt upstream plus 10 nt downstream. P values were calculated using two-sided t test.w nt, where w was the length of RNA considered to form structure . The folding energy at 30°C was calculated using hybrid-ss-min from UNAfold package v3.8  find all branch points (BP), (2) for each BP find the 5′ SS, upstream to the BP and non-overlapping with it, (3) find the 3′ SS at least 4 nt away from the last nucleotide of the BP. This approach was able to predict correctly positions of 236 of 256 annotated yeast introns. For some genes an additional, consensus 3′ SS was predicted but skipped in the spliced product. Only genes where the predicted but skipped 3′SS and the utilized 3′ SS were separated by at least 40 nt were selected for this analysis. Folding energy is presented, normalized to the 3′ SS, to highlight changes in folding as the polymerase moves downstream of this position. P values were calculated using Wilcoxon rank-sum test for ± 10 nt around the position 60 nt downstream of each potential 3′ SS.For analysis of the 3′ SS selection in yeast the features of known introns were extracted as described  using thAll plots and statistical analyses of this work were performed using python 2.7 Jupiter notebooks and python library scipy v1.2.0. Wilcoxon rank-sum test was used unless stated otherwise. t test was used for G+C richness analysis B and S2End and 3rd quartile, line marks median and whiskers range between 5th and 95th percentile.Boxplots present 2


Ribosomal RNAs are complex structures that presumably evolved by tRNA accretions. Statistical properties of tRNA secondary structures correlate with genetic code integration orders of their cognate amino acids. Ribosomal RNA secondary structures resemble those of tRNAs with recent cognates. Hence, rRNAs presumably evolved from ancestral tRNAs. Here, analyses compare secondary structure subcomponents of small ribosomal RNA subunits with secondary structures of theoretical minimal RNA rings, presumed proto-tRNAs. Two independent methods determined different accretion orders of rRNA structural subelements: (a) classical comparative homology and phylogenetic reconstruction, and (b) a structural hypothesis assuming an inverted onion ring growth where the three-dimensional ribosome’s core is most ancient and peripheral elements most recent. Comparisons between (a) and (b) accretions orders with RNA ring secondary structure scales show that recent rRNA subelements are: 1. more like RNA rings with recent cognates, indicating ongoing coevolution between tRNA and rRNA secondary structures; 2. less similar to theoretical minimal RNA rings with ancient cognates. Our method fits (a) and (b) in all examined organisms, more with (a) than (b). Results stress the need to integrate independent methods. Theoretical minimal RNA rings are potential evolutionary references for any sequence-based evolutionary analyses, independent of the focal data from that study. The ribosomal dimeric RNA core surrounding the peptide synthesis site14 also resembles tRNA dimers linked by complementary anticodons, according to the self-referential hypothesis on the origin of translation19. Evidence for this process exists also in modern vertebrate mitochondrial ribosomes: regular mitochondrial tRNAs constitutively fulfill 5S rRNA functions21. In the latter case, extreme mitogenome reduction perhaps reversed evolution to a tRNA-insertion stage, enabling further mitogenome reduction. These evidences suggest that rRNAs derived from tRNAs.Ribosomes presumably evolved through serial accretions of tRNAs and tRNA-like RNAs41. A similar hypothesis exists for 5S rRNAs42.Several hypotheses suggest different historical scenarios for tRNA evolution, all assuming accretions of smaller sequences50. Other analyses show striking similarities in nucleotide triplet biases of tRNAs and protein coding genes52. Theoretical RNA rings, sequences artificially designed according to coding constraints54 seem homologous to tRNAs57.Some evidence suggests that tRNAs originate from stem-loop hairpins initiating replication58 and cladistic comparisons to infer accretion history from comparisons among numerous sequences59. This classical comparative biology method uses parsimony as its main conceptual tool60 and was also used to recover evolution of molecular functions62 and protein accretion64. Various empirical tests show that this method recovers actual histories better than chance75.Two main approaches have been developed and used to recover accretion histories of ribosomal RNAs. Both consider secondary structure subcomponents of rRNAs as units undergoing this process. One approach is based on homology, character polarity78. The method corresponds to that of spatial comparisons in disciplines such as plant community ecology. Structures encompass large amounts of information: in ribosomes, contact biases between amino acids and nucleotide triplets recover the very ancient evolution of genetic code codon-amino acid assignments79. Though reasonable, the structural method lacks to our knowledge further empirical tests in contexts of reconstructing biomolecular histories, but one of its merits is that for each taxon for which accurate structural data are available, it produces (slightly) different histories, enabling to search for consensuses.A second approach is structure-based, and assumes that the ribosome grew from its spatial core towards its periphery, with the most ancient structural subcomponents located at the physical center of the ribosome, and the more recent ones at its periphery80; genetic code evolution81; embryology83; and ecological communities84. Spatial variation in vegetation can reconstruct the ontogeny of forests (forest succession85), but plant colonization at forest periphery and clearings differ from de novo colonization of areas where no forest is adjacent and no humus exists: primary and secondary successions differ86. In addition, the structural model unrealistically assumes equal ribosomal growth in all directions from the core to the periphery89. Its name, the onion peeling model, is formally incorrect , reflecting emphasis on structure rather than historical process90.The theoretical premises of the structural approach are in observations that ontogenies of different structures recover their phylogenies: chemical prebiotic evolutionOverall, one can assume that both approaches complete each other, one recovering history using phylogenetic methods, and the other using principles from ecology and embryology for historical reconstruction. Accretion ranks of the 16S rRNA secondary structure subcomponents according to cladistic- and structure-based methods differ . Ancient groups tend to be more diverse because more time is available for “evolutionary radiation” (this term from species evolution might not be adequate in context of RNA species). The same rationale was applied to functional tRNA species, ranking as most ancient those with the highest diversity of isoacceptor tRNAs93.A method clustering RNA secondary structures found two main RNA secondary structure groups, one characterized by small, presumably ancient tRNA-like secondary structures, and a presumed more derived group, characterized by larger, rRNA-like secondary structures, including virusesThe decision to consider the other RNA cluster as rRNA-like was because this cluster included all subdomains of small and large rRNAs. Note that this clustering is phenotypic, based on secondary structure similarities, not phylogenetic. The assumption that tRNA-like structures are primitive, and that rRNA-like structures are more derived is in line with the tRNA-accretion hypothesis for rRNA formation. Results show that tRNA-like RNAs have few unpaired nucleotides within stems (bulges); for rRNA-like secondary structures, the proportion of bulges among all unpaired nucleotides is greater. Bulges are targets for regulation and enzymatic degradation, properties of advanced metabolism. In prebiotic conditions, these might be disadvantageous, increasing degradation risks.91 were estimated, projecting each tRNA secondary structure on a presumed tRNA-rRNA axis of RNA secondary structure evolution. Then correlations were calculated between the genetic code inclusion rank of the tRNA cognate amino acids94 and this tRNA-rRNA similarity score, expecting that tRNAs with relatively recent cognates have more rRNA-like secondary structures, and those with ancient cognates, are more typically tRNA-like. Results were overall positive (weakest in Eukaryota), confirming tRNA-rRNA polarity: two independent scales of evolutionary ranks, one for amino acids, and one for RNA secondary structures, converge56. Here again, polarity is not deduced from phylogenetic reconstructions, but from presumed orders of integration of the tRNA’s cognate amino acid.This assumption about the evolutionary direction of secondary structures was tested explicitly on tRNAs from diverse organisms . First, similarities of all tRNAs from specific organisms with tRNA-like vs rRNA-like groups95, in the latter, from structure: the more peripheral a structural element in the ribosome, the more recent, including information on stacking interactions among subdomains96. These results strengthen the hypothesis that tRNAs are ancestral and rRNAs derived.Note that the phylogenetic and the structural methods also make polarity assumptions. In the former, these are deduced from cladistic parsimony principlesin silico according to few basic constraints: the shortest possible sequence coding for a start and a stop codon, and once for each of the 20 biogenic amino acids.The tRNA-rRNA evolutionary axis score is based on a sample of known RNA secondary structures. Hence, it suffers from sampling biases, and from some level of circularity: biological data are used to infer on biological phenomena, a caveat it shares with the phylogenetic method. A possible solution to this is to use as reference theoretical minimal RNA rings, a set of short sequences designed 98, with a predicted anticodon and its corresponding cognate amino acid for each RNA ring55.These constraints define at most 25 circular RNA sequences of 22 nucleotides, which code according to partially overlapping codons, along three consecutive translation rounds, for a start codon, 20 different amino acids, and a stop codon. The stop codon is physically next to the start codon, closing the RNA ring. These RNA rings, mainly defined by coding sequences, resemble ancestral tRNAs102 and primary and secondary structure properties57. These properties coevolve with the genetic code integration order of the cognate amino acid matching the anticodon defined by homology of the RNA rings with ancestral tRNAs102. Considering that the design of RNA rings is purely rational and mainly based on the structure of the genetic code, this means that the genetic code’s structure intrinsically embeds information on the evolution of these various properties. However, we do not yet understand what determines these complex evolutionary trajectories.The theoretical minimal RNA rings realistically mimic primitive RNAs and their evolution, along several coding properties56, with the evolutionary ranks of integration of the cognate amino acids matching their predicted anticodons57. This parallels the result described in the previous section for regular tRNAs and the genetic code integration order of their cognate amino acid56. Here too, the polarity results from this order, not from phylogenetic reconstruction.Notably, the tRNA-rRNA scores obtained for secondary structures of these RNA rings, correlate, as observed for real tRNAs102. Analyses use similarities between RNA ring secondary structures and those of structural subelements of 16S rRNAs. The method assumes that high similarities with RNA ring secondary structures indicate ancient structural subelements, and low similarities recent 16S rRNA structural subelements. These similarities are then compared with accretion ranks produced by each of the phylogenetic and the structural hypotheses, expecting: 1. negative correlations if the different methods are producing congruent accretion ranks; 2. these correlations should be most negative for RNA rings with ancient cognate amino acids, and gradually be more positive for RNA rings with recent cognate amino acids.Hence, RNA rings are designed as proto-mRNAs but have also properties that are expected for proto-tRNAs. As plausible proto-tRNAs, they are used here as references for ancestral RNAs, in line with results of evolutionary analyses of their different properties92. Optimal secondary structures of spliced RNA rings were predicted by Mfold103.The quantification of similarities between secondary structures is identical to previous analysesThermus thermophilus 16S rRNA . Similarities are estimated by r, the more positive r, the more similar the secondary structures.Similarities between two secondary structure pairs are estimated by Pearson correlation coefficients r between these four variables as obtained for each secondary structure Fig. , in thisThermus thermophilus and Sulfolobus solfataricus104 .There are 25 RNA rings, each 22 nucleotide long. These are considered according to the splicing matching homology with ancestral tRNAs, as shown previously (Table 103) exists for RNA ring sequences spliced at each potential splicing position. The 25 RNA rings form 25 × 22 = 550 secondary structures.Each RNA ring can be spliced at 22 positions, and a different optimal secondary structure (predicted by MfoldFour secondary structure variables are extracted from each of these 550 secondary structures. Table For each of the about 45) structural subelements of small rRNA subunits of the 6 examined organisms, the four secondary structure variables are extracted, as was done for the 550 RNA ring structures at step 3. These variables are presented for the 6 × 45 = 270 secondary structure subelements presented in Tables 5 structuThermus thermophilus and two RNA ring 25 secondary structures, one obtained by splicing that ring at position 7, and one at position 19.The secondary structures of RNA rings are compared to the secondary structures of rRNA structural subelements by analyses as presented in Fig. For each of the 550 RNA ring secondary structures, there are as many rS as there are rRNA secondary structure subelements, about 45.According to our hypothesis, the (about) 45 rSs comparing a given RNA ring secondary structure to all rRNA structural subelements are potential estimates of the accretion order of the rRNA secondary structures.These rSs are compared to the accretion order of the rRNA secondary structure subelements, as these were determined by other methods and published by other authors . This comparison is done by calculating the Pearson correlation coefficient between the rS and the accretion orders, producing rH, one for the cladistic method, rHphyl, and one for the structural method, rHstru. Note that rS are z-transformed before calculating rH using the formula z = −ln((1 + r)/(1 − r)). The z transformation linearizes the scale of r, which is not linear.Hence, each of the 550 RNA ring secondary structures produces one rHphyl and one rHstru per organism. For each organism, there are 550 rHphyls and 550 rHstrus. The minimal and maximal rHphyls and rHstrus for each organism are in Table For any given RNA ring secondary structure, there are 6 rHphyls and 6 rHstrus, because analyses were done for 6 organisms. There are in total 6 × 550 = 3300 rHphyls and 3300 rHstrus. Further analyses describe general patterns within these data, according to RNA rings, and according to splicing positions.For each RNA ring, there are 22 secondary structures which produce 22 rHphyls and 22 rHstrus per organism, hence 6 × 22 = 132 rHphyls and 132 rHstrus across all 6 organisms. An alternative way to explain this is: for each of the 25 RNA rings, there are 3300/22 = 132 rHphyls and 132 rHstrus across all 6 organisms.Percentages of negative rHphyls and rHstrus for each RNA ring  are used in the y axis of Fig. There are 25 RNA rings. Hence, for a given splicing position, there are 25 rHphyls and 25 rHstrus. Pooling these data across 6 organisms, for any given splicing position, there are 6 × 25 = 150 rHphyls and 150 rHstrus across all 6 organisms. Percentages of negative rHphyls and rHstrus for each splicing position, calculated from these 150 rHphyls and 150 rHstrus, consist the y axis in Fig. Analyses in Table 106, . This result fits the prediction that the most positive correlations between accretion ranks and secondary structure similarities would correspond to RNA rings with recent cognates. In other words, these secondary structures would not be references for initial RNAs starting the accretion process, but for the latest RNAs in the accretion process.A second noteworthy point is that the most positive correlations are in 7 among 12 cases with RNA ring 2, which has a predicted anticodon for a stop codon, coding sometimes for selenocysteine. This is presumably one of the latest amino acids integrated in the genetic code (21Figure 55. This observation is also in line with the working hypothesis that RNA rings are proto-tRNAs, and that accretion of proto-tRNAs, tRNAs and tRNA-like RNAs formed rRNAs. Note that the assumption that RNA rings are proto-tRNAs is under debate107. Nevertheless, and apparently confirming this status of proto-tRNAs, pseudo-phylogenetic analyses of RNA ring sequences reveal two clusters of RNA rings, one coinciding with RNA rings whose presumed cognate amino acid is the cognate of tRNAs for which the tRNA acceptor stem includes a primitive code108.Figure 106. These analyses compared structural subelements of the large rRNA subunit with the same RNA ring secondary structures as those used here. As described here for the small rRNA, for the large rRNA subunit, comparisons with RNA ring secondary structures show that: a. are slightly more congruent with the phylogenetic than the structural method; b. results are strongest for comparisons with RNA rings with predicted ancient cognate amino acids; c. weakest for comparisons with RNA rings with predicted recent cognate amino acids.Particularly noteworthy is that results of analyses presented here for the small rRNA subunit are in line with results obtained for the large rRNA subunit92, expanding upon evidences for common origins for tRNAs and rRNAs9. Analyses presented here for the small rRNA subunit show greater congruence between accretion orders derived from the secondary structure method used here and the phylogenetic method than between the former and the structural method. Similar analyses done for the large rRNA subunit produce qualitatively similar results, independently confirming our method and evolutionary conclusions. Overall, both phylogenetic and structural methods produce accretion orders that are congruent with the secondary structure method applied through the tRNA-rRNA axis of RNA secondary structure evolution. It is probable that the structural methods are more prone to errors due to evolutionary convergences than the phylogenetic method, though convergences remain the main difficulty in reconstructing evolution.Results are strong corroboration of the working hypothesis that tRNA accretions formed rRNAs. They show that RNA rings are likely proto-tRNAs, and that these are good reference points for primitive RNAs in general, and tRNAs in particular. Results confirm that RNA ring cognates are good estimates for RNA ring evolutionary ranks, and that similarities between secondary structures bear information on evolutionary direction of RNA secondary structures, from tRNA to rRNA-like, also among rRNA structural subelements. This has been suggested by several previous lines of analyses presented in the Introduction