Recombination is proposed to be critical for coronavirus (CoV) diversity and emergence of SARS-CoV-2 and other zoonotic CoVs. While RNA recombination is required during normal CoV replication, the mechanisms and determinants of CoV recombination are not known. CoVs encode an RNA proofreading exoribonuclease (nsp14-ExoN) that is distinct from the CoV polymerase and is responsible for high-fidelity RNA synthesis, resistance to nucleoside analogues, immune evasion, and virulence. Here, we demonstrate that CoVs, including SARS-CoV-2, MERS-CoV, and the model CoV murine hepatitis virus (MHV), generate extensive and diverse recombination products during replication in culture. We show that the MHV nsp14-ExoN is required for native recombination, and that inactivation of ExoN results in decreased recombination frequency and altered recombination products. These results add yet another critical function to nsp14-ExoN, highlight the uniqueness of the evolved coronavirus replicase, and further emphasize nsp14-ExoN as a central, completely conserved, and vulnerable target for inhibitors and attenuation of SARS-CoV-2 and future emerging zoonotic CoVs. Recombination is an essential part of normal coronavirus replication, required for the generation of the sub-genomic mRNAs as well as defective viral genome (DVGs) and is also implicated in novel strain emergence. However, the molecular mechanisms and determinants of RNA recombination in CoVs are unknown. Here, we compare recombination in 3 divergent beta-coronaviruses; murine hepatitis virus (MHV), MERS-CoV, and SARS-CoV-2. We show that they have striking similarities in the populations of RNA produced and in the sequences surrounding recombination junctions. Further, we demonstrate that the coronavirus proofreading exoribonuclease (nsp14-ExoN) is required to maintain the rates and loci of recombination generated during infection. These data suggest that recombination and the coronavirus exoribonuclease are conserved and important determinants of replication that may be targeted for inhibition and attenuation to control the ongoing pandemic of SARS-CoV-2 and prevent future outbreaks of novel coronaviruses. The ongoing severe global pandemic of SARS-CoV-2, the etiological agent of coronavirus disease 2019 (COVID-19) underlines the importance of defining the determinants of coronavirus (CoV) evolution and emergence into human populations . FurtherCoronaviruses are a family of positive-sense, single-stranded RNA viruses with genomes ranging in size between 26 and 32 kb . During in vivo virulence, resistance to nucleoside analogues, and immune antagonism [In other RNA virus families including picornaviruses and alphaviruses, regulation of recombination has been mapped to replication fidelity determinants in the viral RNA-dependent RNA polymerase (RdRp) –32. In ctagonism ,42,43.in vitro with broadly similar patterns of recombination, and generate diverse yet similar populations of recombined molecules. We further demonstrate that genetic inactivation of MHV nsp14-ExoN results in a significant decrease in recombination frequency, altered recombination junction patterns across the genome, and altered junction site selection. These defects and alterations result in a marked change in MHV-ExoN(-) recombined RNA populations, including defective viral genomes (DVGs). These results support future studies aimed at illuminating the role of SARS-CoV-2 nsp14-ExoN activity in RNA recombination, the regulation of sgmRNA expression, and its contribution to novel CoV zoonotic emergence. Combined with the multiple critical integrated functions of nsp14-ExoN, the role in recombination further defines nsp14-ExoN as a conserved, vulnerable, and highly specific target for inhibition by antiviral treatments and viral attenuation.In this study, we sought to define the frequency and patterns of recombination of divergent β-CoVs SARS-CoV-2, MERS-CoV, and MHV, and to test the role of nsp14-ExoN in recombination. We used both short-read Illumina RNA-sequencing (RNA-seq) and long-read direct RNA Nanopore sequencing for all three viruses to show that they perform extensive recombination during replication We first sought to quantify recombination frequency and identify recombination patterns in zoonotic CoVs by sequencing both MERS-CoV and SARS-CoV-2 RNA. In three independent experiments for each virus, Vero cell cultures were infected with either MERS-CoV or SARS-CoV-2 until the monolayer displayed >70% virus-induced cytopathic effect (CPE). Total RNA from infected cells was isolated and poly(A)-selected to capture all viral RNA containing poly-A tails, including genomic, subgenomic, and defective viral genome (DVG) RNA molecules. Equal amounts of total cell RNA from each of the three independent experiments for each virus was sequenced by short-read Illumina RNA-sequencing (RNA-seq), and by long-read direct RNA Nanopore sequencing. The depth and low error rate of RNA-seq facilitated the detection and quantification of both high- and low-abundance unique junctions. Long-read direct RNA sequencing on the Oxford Nanopore Technologies MinION platform was used to sequence complete RNA molecules, to define the organization of junctions in the context of intact RNA molecules. By comparing short- and long-read RNA sequencing, we accomplished high-confidence detection and quantification of recombination junctions as well as description of the genetic architectures of molecules formed by the junctions.ViReMa (Virus Recombination Mapper) [ViReMa detected recombination events that generated deletions greater than 5 base-pairs and that were flanked by a 25 base-pair alignment both upstream and downstream of the junction site. ViReMa-detected junctions may be formed from either inter-molecular or intra-molecular recombination during replication. ViReMa aligned both recombined and non-recombined reads in the library and reported the total number of nucleotides aligned to the genome and all detected recombination junctions.For RNA-seq, reads were aligned to the respective viral genomes  using a  Mapper) . ViReMa ViReMa demonstrated nearly identical read coverages for MERS-CoV (1118) and SARS-CoV-2 (1122) (freq) was calculated for MERS-CoV and SARS-CoV-2 . Thus, Jfreq was not biased by the number of virus-mapping reads. Jfreq was multiplied by 104 to scale for library size and was reported as the number of junctions per 104 mapped nucleotides. MERS-CoV had a mean Jfreq of 37.80 junctions detected per 104 mapped nucleotides. SARS-CoV-2 had a mean Jfreq of 475.7 junctions per 104 mapped nucleotides (freq between the two viruses that were infected at similar multiplicity of infections (MOIs), were collected when the cells displayed similar levels of CPE, and had similar viral abundance in sequenced RNA. We considered the possibility that the observed >10-fold difference between Jfreq of each virus could be due to the replication capacity of the parental virus. We compared the number of unique junctions generated by each virus to remove any potential viral replication bias. SARS-CoV-2 generated an average of 56,082 unique junctions per experiment, while MERS-CoV generated an average of 19,367 unique junctions per experiment  . Further2 (1122) . To quanRS-CoV-2 . Jfreq rleotides . This waperiment . Thus, bTo define the patterns of the detected recombination junctions, we mapped forward (5’ ➔ 3’) recombination junctions according to their genomic position Figs . Both MEWe next sought to define and quantify the populations of recombined RNA molecules produced in both MERS-CoV and SARS-CoV-2. SARS-CoV-2 sgmRNAs were identified by the location of recombination junctions within previously defined 65 base-pair regions containing the transcription regulatory sequence (TRSs) of each sgmRNA . SimilarFor each virus, the frequencies of DVGs, canonical sgmRNAs, and alternative sgmRNAs were normalized to total virus RNA. For both MERS-CoV and SARS-CoV-2, canonical and alternative junctions were detected for all sgmRNAs Figs . MERS-CoWe next calculated the mean recombination frequency at each genomic position by comparing the number of nucleotides in detected junctions (both start and stop sites) at that position, and normalized to nucleotide depth at that position. Further, we determined genomic positions with a mean recombination frequency greater than 50% . In MERSFor both SARS-CoV-2 and MERS-CoV, the nucleotide composition of the start and stop sequences resulting in junctions forming DVGs in MERS-CoV and SARS-CoV-2 was determined and compared to the expected nucleotide percentage based on the parental viral genomes . SequencWe next tested whether MERS-CoV and SARS-CoV-2 junction sites favored regions of sequence microhomology at recombination junctions, defined as 2–20 nt regions of identical overlap . The disWe performed direct RNA Nanopore sequencing on the same RNA used for short-read RNA-seq. We analyzed three independent experiments for each virus and sequenced 178,658 MERS-CoV RNA molecules and 1,725,862 SARS-CoV-2 RNA molecules that had 85.6% and 82.2% identity to the parental genome, respectively . To remoTo define the architectures of detected molecules, we filtered for junctions with at least 3 supporting Nanopore reads. For both viruses, junctions were categorized as either a DVG or sgmRNA junction using the same criteria as with the RNA-seq data. In MERS-CoV, we defined 5 distinct species, including 3 sgmRNAs  and 2 DVGs . In SARSViReMa (AY910861.1). In both infected cell monolayers and viral supernatants, MHV-WT and MHV-ExoN(-) had similar mean coverages ranging between 1100 and 1700 reads (We previously have reported that the nsp14 exoribonuclease (nsp14-ExoN) activity is required for high-fidelity replication and proofreading for the β-CoVs murine hepatitis virus (MHV) and SARS-CoV –36. We s00 reads .freq was calculated as described for freq relative to MHV-WT in both infected cells and viral supernatant (Previous studies have shown that MHV-ExoN(-) has decreased genome replication compared to WT . We accoernatant . To addrernatant  Figs. ThRecombination junctions were plotted according to their start (5’) and stop (3’) sites in infected cells and viral supernatant Figs . MHV-WT We next calculated and compared mean recombination frequency at each genomic position in MHV-WT and MHV-ExoN(-) . Both MHCompared with WT, MHV-ExoN(-) had significantly decreased frequencies of DVGs and both canonical and alternative sgmRNAs . MHV-ExoDESeq2 [We next identified junctions with altered abundances in MHV-ExoN(-) compared to MHV-WT using DESeq2 . MHV-ExoTo test whether MHV-ExoN(-) has altered sequence composition at its recombination junctions, we filtered DVG junctions and quantified nucleotide composition of adenosine (A), cytosine (C), guanine (G), and uracil (U) in the start and stop sequences flanking junction sites. Both MHV-WT and MHV-ExoN(-) demonstrated similar patterns of depletion and enrichment of nucleotides in infected cell monolayers and viral supernatant Figs  and S6A.minimap2, MHV-WT datasets contained 102,367 viral molecules and MHV-ExoN(-) contained 19,445  viral supernatant RNA by direct RNA Nanopore sequencing. When reads were mapped to the MHV genome using d 19,445 . We valid 19,445 .MHV-ExoN(-) had a global decrease in the number of junctions across the genome  Tables. While CoV recombination has long been proposed as a driver of novel strain emergence and is known to be a constitutive aspect of CoV replication, the diversity of recombination products and sequence and protein determinants had not previously been defined. In this study, we show the diversity of the CoV recombination landscape in the β-coronaviruses SARS-CoV-2, MERS-CoV, and murine hepatitis virus (MHV), and we demonstrate that loss of the nsp14 exoribonuclease activity in MHV results in decreased recombination and altered site selection of recombination junctions. Our results support a model in which nsp14-ExoN activity is required for normal recombination. Thus, nsp14-ExoN is a key component of CoV recombination, adding another essential function to the repertoire of those already reported for nsp14-ExoN, specifically CoV high-fidelity replication, RNA synthesis, resistance to antiviral nucleoside analogues, fitness, immune antagonism, and virulence.We show that MHV, MERS-CoV, and SARS-CoV-2 perform extensive recombination and generate diverse populations of RNA molecules, demonstrated by independent short-read Illumina RNA-seq and long-read, direct RNA Nanopore sequencing. These divergent group 2a (MHV), 2b (SARS-CoV-2), and 2c (MERS-CoV) β-CoVs demonstrated many strong similarities in their patterns of recombination junctions across the genomes and in the types of recombined RNAs produced. Specifically, the similarities across all three viruses in the nucleotide composition of sequences flanking DVG junctions and the common increased junction sequence microhomology support the conclusion that recombination mechanisms have been conserved across different evolutionary trajectories and host species specificity.There also were distinct recombination patterns for each virus that were confirmed across independent experiments and by agreement between RNA-seq and Nanopore datasets for all viruses. These differences most likely represent evolutionary divergence of recombination in distinct viruses or sub-genera represented by MHV, SARS-CoV-2 and MERS-CoV. However, it remains possible that observed differences could be impacted by the diversity of the original sample or replication in different cell types. SARS-CoV-2 stock virus was a low passage (P5) population from a clinical isolate that had been passaged in Vero cells, while MERS-CoV and MHV were low passage stocks generated from isogenic cDNA clones. It will be important for future studies to determine the role of the diversity of the viral population, cell environment, virus-specific RNA synthesis kinetics, and virus adaptation/evolution in viral recombination. The extent of the pandemic and availability of genetically diverse viruses will allow investigators to test whether patterns of SARS-CoV-2 recombination show alterations between early and later pandemic isolates, and if any identified differences correlate with or predict changes in other replication or pathogenesis.High-resolution analysis of DVG junctions produced during replication by MERS-CoV, SARS-CoV-2, and MHV reveals that a significant preference for a UUG motif, suggesting a possible conserved core sequence for DVG synthesis that differs from sgmRNA transcriptional regulatory sequences. These results support a model across multiple divergent β-CoVs in which DVGs result from recombination junction selection by the RTC based on both broadly similar sequence identity and specific sequence microhomology of 2–10 bp . This mopicornaviridae and alphaviridae that lack any exonuclease, low-fidelity mutant viruses have altered polymerase speed and processivity [MHV-ExoN(-) mutants showed decreased recombination junction frequency and altered populations of sgmRNAs and DVGs, demonstrating a previously unknown role for nsp14-ExoN in CoV RNA recombination. There is no precedent in RNA viruses for the regulation of recombination by a virus encoded exoribonuclease. In contrast, in DNA viruses such as poxviruses and herpesviruses, virus-encoded exonuclease activity stimulates recombination by single-strand annealing through both exonuclease degradation of nucleic acids and interactions with other proteins ,40. In tessivity  and thesessivity ,55,56. O4-hydroxycytidine  [The similarities between the patterns of recombination across divergent WT β-CoVs, along with the differences observed between recombination in MHV WT and ExoN(-) viruses, support the hypothesis that ExoN mutants will inform our understanding of the evolution of the unique CoV multi-protein polymerase complex. Specifically, the model of DVG synthesis defined in MHV, MERS-CoV, and SARS-CoV-2 will allow for the direct testing of the roles of DVGs in CoV replication. Further, the role of ExoN in CoV recombination, along with the previously defined roles of ExoN in RNA proofreading during replication, native resistance to nucleoside analogues, immune evasion, and virulence and pathogenesis, highlight nsp14-ExoN as conserved and vulnerable target for both antiviral inhibitors and virus attenuation. ExoN(-) viruses are profoundly more sensitive to a range of antiviral nucleoside analogues, including remdesivir, ribavirin, 5-fluorouracil, and β-d-N31/2801) ,38,57. N31/2801) ,12. The 31/2801) ,58,59. TCercopithecus aethiops Vero CCL-81 cells maintained in Dulbecco’s modified Eagle medium (DMEM) (Gibco) supplemented to final concentrations of 10% fetal calf serum (Gibco), 100 IU/ml penicillin (Mediatech), 100 mg/ml streptomycin (Mediatech), and 0.25 mg/ml amphotericin B (Mediatech) were used for MERS-CoV-2 infection. Vero CCL-81 cells were obtained from ATCC. Vero E6 cells maintained in Dulbecco’s modified Eagle medium (DMEM) (Gibco) supplemented to final concentrations of 10% fetal calf serum (Gibco), 100 IU/ml penicillin (Mediatech), 100 mg/ml streptomycin (Mediatech), and 0.25 mg/ml amphotericin B (Mediatech) were used for SARS-CoV-2 infections. Vero E6 cells were obtained from ATCC.DBT-9  cells were maintained at 37°C as described previously . DBT-9 cAll MHV work was performed using the recombinant WT strain MHV-A59 (GenBank accession number AY910861.1  Experime2 flasks were infected with either MHV-A59 or MHV-ExoN(-) at an MOI of 0.01 PFU/cell. Supernatant was harvested at either 16 hours post infection (MHV-A59) or 24 hours post infection (MHV-ExoN(-)) when the monolayer was >95% fused and remained intact. Infection supernatant was clarified by centrifugation at 1500 x g for 5 minutes at 4°C. Viral supernatant was purified on a 30% sucrose cushion by ultracentrifugation at 25,000 RPM at 4°C for 16 hours. The viral pellet was resuspended in MSE buffer . Viral RNA was extracted using the TRIzol-LS reagent according to manufacturer’s protocols. RNA was quantified using the Qubit RNA HS assay. Supernatant data in this paper is the result of three experiments sequenced independently from the infected cell monolayer samples.Subconfluent 150-cm2 flask of DBT-9 cells was infected with either MHV-WT or MHV-ExoN(-) at an MOI or 0.01 PFU/cell. Monolayer was harvested at either 16 hpi (MHV-WT) or 24 hpi (MHV-ExoN(-)) when the monolayer was >95% fused and >75% intact. RNA was extracted with TRIzol according to manufacturer’s protocols. Infected monolayer data in this paper is the result of three independent experiments sequenced independently.In three independent experiments, a subconfluent 150-cm2 flask of Vero CCL-81 cells was infected with MERS-CoV at an MOI of 0.3 pfu/cell. Total infected cell lysates were collected at 72 hpi with the monolayer >70% fused. RNA was extracted in TRIzol according to manufacturer’s protocols.In three independent experiments, a nearly confluent 25-cm2 flasks of Vero E6 cells were infected at an MOI = 0.45 pfu/cell and cellular monolayers were harvested 60 hpi when the monolayer was >90% fused. RNA was extracted in TRIzol according to manufacturer’s protocols.In three independent experiments, a total of 5 subconfluent 25-cmNext generation sequencing (NGS) libraries were generated using 2 μg of RNA of each sample. RNA was submitted to Genewiz for library preparation and sequencing. Briefly, after quality control, polyadenylated RNA was selected during library preparation. Isolated RNA was heat fragmented, RT-PCR amplified with equivalent number of cycles, size-selected, and libraries were prepared for 2 x 150 nucleotide paired-end sequencing performed (Illumina). Genewiz performed basecalling and read demultiplexing.RNA from ultracentrifuge-purified viral supernatant was prepared for direct RNA Nanopore sequencing on the Oxford Nanopore Technologies MinION platform according to the manufacturer’s protocols. Libraries were sequenced on fresh MinION R9.4 flow-cells for 24 hours, or until the pore occupancy was under 20%. Viral supernatant RNA from three independent experiments was sequenced on three separate flow cells for both MHV-WT and MHV-ExoN(-). MERS-CoV RNA from three independent cultures was sequenced on three separate flow cells. SARS-CoV-2 RNA isolated from three independent infections was sequenced on three separate flow cells.Trimmomatic [parameters java -jar trimmomatic.jar PE sample_R1.fastq.gz sample_R2.fastq.gz output_paired_R1.fastq output_unpaired_R1.fastq output_paired_R2.fastq output_unpaired_R2_unpaired.fastq ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGIWINDOW:4:15 MINLEN:36). Reads shorter than 36 bp were removed and low-quality bases (Q score < 30) were trimmed from read ends. The raw FASTQ files were aligned to the MHV-A59 genome (AY910861.1), the MERS-CoV genome (JX869059.2), and the SARS-CoV-2 genome (MT020881.1) using the Python2 script ViReMa  [python2 ViReMa.py reference_index input.fastq output.sam—OuputDir sample_virema/—OutputTag sample_virema -BED—MicroIndelLength 5. The sequence alignment map (SAM) file was processed using the samtools [BAM) file (using command line parameters samtools depth -a -m 0 sample_virema.sorted.bam > sample_virema.coverage).Raw reads were processed by first removing the Illumina TruSeq adapter using mmomatic  default on 0.15)  using thsamtools  suite tofreqJ) was calculated by comparing the number of nucleotides in detected recombination junctions to the total number of mapped nucleotides in a library. Nucleotides in detected recombination junctions were quantified as a sum of nucleotide depth reported at each junction in the BED file generated by ViReMa. Total nucleotides mapped to the MHV-A59 genome were quantified as a sum of nucleotide depth at each position across the genome in the tab-delineated text file generated by the samtools. Jfreq was reported as junctions per 104 nucleotides sequenced. Mean freqJ values were compared between MHV-WT and MHV-ExoN(-) and statistical significance determined by an unpaired student’s t-test. Junctions were mapped across the genome according to their start (5’) and stop (3’) positions. These junctions were first filtered in the forward (5’ ➔ 3’) direction using the dpylr package (RStudio). The frequency of each junction was calculated by comparing the depth of the unique junction to the total number of nucleotides in all detected junctions in a library. Junctions were plotted according to the genomic position and colored according to log10 of the frequency using ggplot2 in RStudio.Recombination junction frequency  for three independent sequencing experiments by a 2-way ANOVA statistical analysis with multiple comparisons corrected through statistical hypothesis testing using the Sidak test.freq) of each sgmRNA was calculated by dividing the number of nucleotides in a specific sgmRNA population by the total amount of viral RNA . This ratio is multiplied by 10,000 to scale for the number of nucleotides sequenced and is therefore expressed as the number of junctions per 104 mapped nucleotides. The filtered sgmRNA junctions were compiled and DVG junctions were filtered in RStudio by performing an exclusionary anti_join using dplyr on forward junctions identified in each sample. DVG Jfreq was calculated by dividing the number of nucleotides in DVG junctions by the total amount of viral RNA in a sample . The ratio is multiplied by 10,000 to scale for number of nucleotides sequenced and the frequency is expressed as the number of junctions per 104 mapped nucleotides. The percentage of canonical and alternative sgmRNA and DVG junctions was calculated by comparing the depth of all filtered sgmRNA or DVG junctions to the sum of all detected forward junctions. Mean percent canonical and alternative sgmRNAs and DVG was compared between three independent sequencing experiments in viral supernatant RNA. Statistical significance was determined by a 2-way ANOVA test with multiple comparisons and corrected by statistical hypothesis testing using the Sidak test.Forward recombination junctions were classified as either canonical sgmRNA junctions, alternative sgmRNA junctions or DVG junctions based on the position of their junction sites and filtered in Microsoft Excel. Briefly, junction start sites were filtered to those positioned within 30 nucleotides of the TRS-L for each virus. The stop sites were then filtered for those positioned within 30 nucleotides of each respective sgmRNA TRS. This window is supported by other reports defining the flexibility of the CoV transcriptome ,65. Canohttps://github.com/DenisonLabVU) and the R package DESeq2 [bioinfokit [DESeq2 package in RStudio and junctions with a significant alteration of abundance in MHV-ExoN(-) compared to MHV-WT were visualized as either red or green in the graph generated by bioinfokit.To compare the abundance of junctions in MHV-A59 and MHV-ExoN(-), the ViReMa output list of junctions was analyzed by in-house scripts  [BED files listing deletions detected in each sequenced RNA molecule. Both BAM and BED files were filtered for full length molecules using samtools and Microsoft Excel, respectively. Full-length CoV molecules were defined as encoding coverage at in the 5’ UTR and 3’ UTR of the respective viruses. Nanopore junctions output in BED files were compared to junctions in ViReMa RNA-seq BED files to confirm its presence in both datasets. To account for noisiness in Nanopore datasets, a Nanopore junction was considered confirmed if at least 1 RNA-seq junction start and stop sites fell within 20 bp of the Nanopore start and stop sites, respectively. Filtering of Nanopore and RNA-seq datasets was performed in Microsoft Excel. BED files generated by the flair align module were parsed based on the number of junctions were identified. Nanopore reads containing only 1 junction were identified using Microsoft Excel and unique junctions were quantified in RStudio using base-R functions. Sequencing coverage maps were generated from samtools depth analysis of filtered BAM files. All junctions present in sequenced libraries were mapped in Sashimi plots generated by the Integrated Genome Viewer (IGV) [Live basecalling was performed by NanoPlot . Pass re of RNA)  to generer (IGV) . JunctioS1 Figtrans (inter-molecular) recombination and cis (intra-molecular) recombination and produce 3 different types of molecules: subgenomic mRNAs (sgmRNAs) that are translated into structural and accessory proteins, defective viral genomes (DVGs) whose role in viral replication, innate immune antagonism, and viral evolution have not yet been defined, and infectious (complete) genome molecules. sgmRNAs are produced by recombination between transcription regulatory sequences (TRSs) across the genome. DVGs are produced by recombination between sites across the genome outside TRSs that result in sequence deletions. Complete genomes are generated by recombination at the same location between 2 co-infecting molecules. The CoV replication transcription complex (RTC) is shown in gray. (C) Internally deleted recombined RNAs (DVGs) are formed by a recombination junction . In this report, a start site refers to the position where the 5’ segment ends  and a stop site refers to the position where the 3’ segment begins  in the viral genome (blue line). Nucleotides sequences in the genome at both the start and stop sites are numbered according to their position relative to the break formed by the recombination junction (red line). (D) Results in this report support the model in which microhomology (yellow box) between the CoV DVG start and stop sites facilitates formation of the complete RNA molecule through translocation of the CoV RTC (gray).(A) Genome organization of MERS-CoV (gray), SARS-CoV-2 (violet), and MHV (white). Nonstructural (nsps 1–16) and structural  and accessory open reading frames (ORFs) are labelled. The common 5’ leader transcription leader sequence (TRS-L) is denoted with an unfilled red star. Body TRSs are labelled with filled red stars. (B) CoVs perform both (TIF)Click here for additional data file.S2 Figfreq) per 104 mapped nucleotides of MERS-CoV canonical  and alternative  sgmRNA species normalized to total viral RNA. N = 3, error bars represent SEM. (H) Junction frequency (Jfreq) per 104 mapped nucleotides of SARS-CoV-2 canonical  and alternative  sgmRNA species normalized to total viral RNA. N = 3, error bars represent SEM.RNA-seq libraries of (A) MERS-CoV and (B) SARS-CoV-2 were aligned to the viral genomes with ViReMa. Nucleotide depth was calculated at each position and represented as mean nucleotide depth (N = 3). (C) The number of unique junctions detected was compared between MERS-CoV and SARS-CoV-2. N = 3, error bars represent standard error of the mean. Unpaired student’s t-test, *** p < 0.001. Individual recombination junction scatter plots of (D) MERS-CoV and (E) SARS-CoV-2. Recombination junctions were detected by ViReMa and forward (5’ ➔ 3’) junctions were identified by bioinformatic filtering. Junctions are plotted according to their 5’ (start) and 3’ (stop) positions and colored according to their frequency in the population of total junctions. Highly abundant junctions are magenta and opaque and low-frequency junctions are red and transparent. (F) Relative proportions of junctions forming DVGs, canonical sgmRNAs, and alternative sgmRNAs as a percentage of the total population of all recombined RNA in MERS-CoV (black) and SARS-CoV-2 (violet). N = 3, error bars represent SEM. 2-way ANOVA, *** p < 0.001, **** p < 0.0001. (G) Junction frequency (J(TIF)Click here for additional data file.S3 FigRNA-seq libraries of (A) MHV-WT and (B) MHV-ExoN(-) infected cell monolayer RNA were aligned to the viral genomes with ViReMa. Nucleotide depth was calculated at each position and represented as mean nucleotide depth (N = 3). (C) The number of unique junctions detected was compared between MHV-WT and MHV-ExoN(-). N = 3, error bars represent standard error of the mean. Unpaired student’s t-test, ** p < 0.01. Individual recombination junction scatter plots of (D) MHV-WT and (E) MHV-ExoN(-). Recombination junctions were detected by ViReMa and forward (5’ ➔ 3’) junctions were identified by bioinformatic filtering. Junctions are plotted according to their 5’ (start) and 3’ (stop) positions and colored according to their frequency in the population of total junctions. Highly abundant junctions are magenta and opaque and low-frequency junctions are red and transparent. (F) Relative proportions of junctions forming DVGs, canonical sgmRNAs, and alternative sgmRNAs in MHV-WT (blue) and MHV-ExoN(-) (orange) infected monolayer RNA. N = 3, error bars represent SEM. 2-way ANOVA, ** p < 0.01, *** p < 0.001, **** p < 0.0001. (G) Ratios of canonical sgmRNA species in MHV-WT (blue) and MHV-ExoN(-) (orange) infected monolayer RNA. Each sgmRNA species is reported as a percentage of the total sgmRNA population. N = 3, error bars represent SEM. 2-way ANOVA, **** p < 0.0001. (H) Ratios of alternative sgmRNA species in MHV-WT (blue) and MHV-ExoN(-) (orange) infected monolayer RNA. Each sgmRNA population is quantified as a percentage of the total number of minor sgmRNA species detected. N = 3, error bars represent SEM. 2-way ANOVA, * p < 0.05, **** p < 0.0001.(TIF)Click here for additional data file.S4 FigRNA-seq libraries of (A) MHV-WT and (B) MHV-ExoN(-) viral supernatant RNA were aligned to the viral genomes with ViReMa. (C) The number of unique junctions detected was compared between MHV-WT and MHV-ExoN(-). N = 3, error bars represent standard error of the mean. Unpaired student’s t-test, ** p < 0.05. Nucleotide depth was calculated at each position and represented as mean nucleotide depth (N = 3). Individual recombination junction scatter plots of (D) MHV-WT and (E) MHV-ExoN(-). Recombination junctions were detected by ViReMa and forward (5’ ➔ 3’) junctions were identified by bioinformatic filtering. Junctions are plotted according to their 5’ (start) and 3’ (stop) positions and colored according to their frequency in the population of total junctions. Highly abundant junctions are magenta and opaque and low-frequency junctions are red and transparent. (F) Relative proportions of junctions forming DVGs, canonical sgmRNAs, and alternative sgmRNAs in MHV-WT (blue) and MHV-ExoN(-) (orange) viral supernatant RNA. N = 3, error bars represent SEM. 2-way ANOVA, *** p < 0.001, **** p < 0.0001. (G) Ratios of canonical sgmRNA species in MHV-WT (blue) and MHV-ExoN(-) (orange) viral supernatant RNA. Each sgmRNA species is reported as a percentage of the total sgmRNA population. N = 3, error bars represent SEM. 2-way ANOVA, ** p < 0.01, **** p < 0.0001. (H) Ratios of canonical sgmRNA species in MHV-WT (blue) and MHV-ExoN(-) (orange) viral supernatant RNA. Each sgmRNA population is quantified as a percentage of the total number of minor sgmRNA species detected. N = 3, error bars represent SEM. 2-way ANOVA, *** p < 0.001.(TIF)Click here for additional data file.S5 Fig2(Fold Change) of abundance .Mean recombination frequency at each genomic position is shown for MHV-WT (blue) and MHV-ExoN(-) (orange). (A) 5’ UTR, (B) the non-replicase nonstructural proteins (nsp1–6), (C) the replicase proteins (nsp7–16), (D) the structural and accessory proteins, (E) 3’ UTR. Key RNA elements including the TRS-leader (TRS-L) and body TRSs (TRS1–7) are labelled. Positions with statistically significant differences in MHV-ExoN(-) recombination frequency were identified by a 2-way ANOVA with multiple comparisons. Recombination junction abundance was compared in MHV-ExoN(-) to MHV-WT by DESeq2 in infected cell monolayer RNA (A) and viral supernatant RNA (B). Volcano plots of junctions colored by statistical significance  and by the log(TIF)Click here for additional data file.S6 Fig(A) Nucleotide composition was calculated and reported as the percent adenosine (A), cytosine (C), guanine (G), and uracil (U) at each position in a 30-base pair region flanking DVG junction start and stop sites in MHV-WT (blue) and MHV-ExoN(-) (orange) viral supernatant RNA. Each point represents a mean (N = 3) and error bars represent SEM. 2-way ANOVA with multiple comparisons corrected for false discovery rate (FDR) by the Benjamini-Hochberg method. * q < 0.05, ** q < 0.01, *** q < 0.001, **** q < 0.0001. (B) Distribution of microhomology overlaps in MHV-WT (blue) and MHV-ExoN(-) (orange) compared to an expected probability distribution (gray). The frequency of each overlap length is displayed as a mean (N = 3) and error bars represent SEM.(TIF)Click here for additional data file.S1 TableNumber of reads in RNA-seq libraries and mapped to viral genome reported for MHV, MERS-CoV, and SARS-CoV-2. The percent mapping to the viral genome is reported as a mean of 3 libraries, ± standard error of the mean (SEM).(PDF)Click here for additional data file.S2 TableFor direct RNA Nanopore sequencing of MHV, MERS-CoV, and SARS-CoV-2, the percent identity of aligned reads, the mean read length, mean read quality, the read length N50 (fiftieth percentile), number of total sequenced reads, number of mapped reads, and number of unique detected junctions are reported. The percentage of junctions detected in Nanopore reads also detected in RNA-seq datasets is also reported.(PDF)Click here for additional data file.S3 TableDirect RNA Nanopore reads spanning the entire SARS-CoV-2 genome are listed. The mapping start site (Read Start), mapping end site (Read End), and unique read identifier (Read Name) are all listed. Each read represents a single detection (Count), and contains most of the SARS-CoV-2 genome (Read Length).(PDF)Click here for additional data file.S4 TableDirect RNA Nanopore reads aligning to viral genome by minimap2. Individual reads are listed by read name. Genomic positions of read alignment are listed . Read segments aligning to the genome are noted (“# Segments”) and start positions and aligned segment lengths listed .(XLSB)Click here for additional data file.S5 TablePositions with significantly altered recombination frequency in MHV-ExoN(-) infected monolayer RNA compared to MHV-WT and in MHV-ExoN(-) viral supernatant RNA compared to MHV-WT as determined by a 2-way ANOVA with multiple comparisons are listed. Genomic regions are noted. (PDF)Click here for additional data file.S6 TableJunctions with altered abundance in MHV-ExoN(-) infected monolayer RNA compared to MHV-WT and in MHV-ExoN(-) viral supernatant RNA compared to MHV-WT are listed. P-values calculated by DESeq2. (XLSB)Click here for additional data file.


N = 792) attending a higher secondary grade in Chittagong district. Data were collected using a structured and self-administered questionnaire. Descriptive statistics and multiple linear regression analyses were used to summarize the SRH-related KAP and identify the associated factors, respectively. The level of knowledge about puberty, family planning, maternal health, and HIV/AIDS was not satisfactory among the older adolescent girls. Different myths are common in the rural area with regards to menstruation, which impose several restrictions on adolescent girls and adult women. Standardized coefficients of beta (β) and p value < 0.05 in linear regression analyses demonstrated that being a student of the science group  and reading about or watching SRH issues on media  were significantly associated with older adolescent girls’ high level of knowledge in this regard. Furthermore, being a student of the science group , urban residence , regular SRH communication (at least once a month) with a mother/sister/friend , and reading or watching any SRH content on media  appeared as predictors of adolescent girls’ positive attitude towards SRH issues. Moreover, being a student of the science group , urban residence , regular SRH discussions with a mother/sister/friend , pre-knowledge on periods before menarche , and reading or watching any SRH content on media  are the most important factors influencing a regular hygienic practice of SRH. This study suggests strengthening SRH-related comprehensive education programs incorporated into the curriculum, the effective use of mass media, and supplying behavioral change communication materials.Improving the sexual and reproductive health (SRH) of adolescent girls is one of the primary aims of the Sustainable Development Goals (SDGs). Adequate and accurate knowledge, a favorable attitude, safe behavior, and regular practice contribute to adolescent girls’ SRH, maternal health, and child health. Considering this, this study aims to explore the level of knowledge, attitudes, and practices (KAP) of SRH among college-going older adolescent girls in Chittagong district, Bangladesh. An institution-based cross-sectional study was conducted in four colleges among the older adolescent girl age group of 16–17 years old ( Adolescence, a near-universal period of the socialization cycle, is characterized as a phase of human growth and development that occurs after childhood and before adulthood and includes individuals between the ages of 10 and 19 . BangladIn Bangladesh, the sexual and reproductive health (SRH) status of the adolescent population, including those who are unmarried and married, remains an area of significant concern. Many adolescents, especially adolescent girls in Bangladesh, are not given adequate opportunities to enhance their overall health during their process of growing up . In makiSRH is still a cultural taboo in Bangladesh, especially for adolescents, and SRH information and services present a critical gap for unmarried adolescents, particularly girls, which leaves them vulnerable to health risks and discriminatory care . ParentsHowever, Bangladesh has no nationally representative data that measure the level of knowledge, attitudes, and practices (KAP) on SRH among adolescent populations . The BDHAn institution-based cross-sectional study was conducted from 17 June 2019 to 23 July 2019. Our study population was older adolescent girls aged 16–18 years, who had reached puberty at least two years preceding the survey and were studying at an intermediate level in government/autonomous or private colleges located in Chittagong district.N = 792) were incorporated into our study. The mean age of the respondents was 16.59 years (SD = 0.49).For selecting the colleges, a probability sampling method was used for urban and rural areas, respectively. For urban participants, two colleges were randomly selected for the study from the lists of two types of colleges of Chittagong City. Similarly, for rural respondents, two colleges were taken from Satkania Upazila (sub-district) following the lottery method. Adolescent girls who attended the selected colleges in the higher secondary classes participated in our study. Finally, 792 respondents  value was acceptable among 10 attitude-related items (α = 0.78), but low among 10 knowledge-related items (α = 0.55) and 10 practice-related items (α = 0.54), which should be kept in mind when interpreting the results. The response options regarding KAP involved a five-point Likert scale . For the percentage distribution of respondents’ responses regarding KAP, five scales of each section were recoded into three categories because of the low frequency at the endpoint of the scale. In our study, regular SRH communication indicates discussions of adolescent girls with their mothers or sisters at least once in a month that covered safe practices and complexities during menstruation, physical and mental changes during puberty, safe sexual health and behavior, maternal health, pregnancy, and the delivery process, as well as sexual harassment and sexually transmitted diseases (STDs).F = 17.418, p < 0.001), SRH attitude , and SRH practice  showed that our multiple linear regression model performed well and would be a good predictor of the main outcome variables. Variables with p < 0.05 were considered statistically significant.Because 32 respondents (3.88%) were excluded from the analysis due to incompleteness, the final number of participants employed for data analysis was 792. Descriptive statistics with the frequency and the mean with a 95% confidence interval were used to describe the SRH-KAP of older adolescent girls. Multiple linear regression analyses were used to examine respondents’ SRH knowledge, attitude, and practice. The confounding and multicollinearity were checked. Analysis of Variance (ANOVA) values for the overall SRH knowledge .Moreover, more than half (52%) of older adolescent girls had read or watched SRH content on media, while 30% had talked with a doctor or health worker about SRH matters. Percentage distributions with a mean score of older adolescent girls’ SRH knowledge-related items are reported in Percentage distributions with a mean score of older adolescent girls’ SRH practice-related items are reported in p < 0.001) and reading about or watching SRH issues on media  are significantly associated with older adolescent girls’ high level of knowledge in this regard. Furthermore, being a student of the science group , urban residence , regular SRH communication with a mother/sister/friend , having knowledge on periods before experiencing them , and reading or watching any SRH content on media  appeared as predictors of older adolescent girls’ positive attitude towards SRH issues. Moreover, it is obvious that being a student of the science group , urban residence , mothers’ regular TV watching , respondents’ regular SRH discussions with a mother/sister/friend , pre-knowledge on periods before menarche , students whose primary source of reproductive health was their mother , reading or watching any SRH content on media , and visiting and talking with a doctor or health worker about SRH issues  are the most important factors influencing a regular hygienic practice of SRH of older adolescent girls.The results of the linear regression analysis depicting factors associated with older adolescent girls’ SRH knowledge, attitude, and practice are reported in Our study demonstrates that that majority of older adolescent girls did not have prior knowledge of menstruation while experiencing menarche. This finding is consistent with others ,19. For The present study shows that most of the older adolescent girls considered their mothers to be their key informants of SRH issues. Mothers also appeared as the primary source of SRH information. This is because daughters have a trustworthy relationship with their mothers due to gender homogeneity . SimilarAs was observed, about half of the adolescent girls neither read about nor watched any SRH-related content on media. The lack of perceived importance of the adolescent girls of the rural area may result in a low media exposure of SRH issues. Moreover, less than one-third of the students had ever consulted with health care providers regarding the SRH problem. Inadequate health care services across the country and the tendency to conceal SRH-related problems because of the perception of them being taboo may contribute to this low percentage of access to health care.This study found that about one-third of the older adolescent girls had inaccurate knowledge regarding puberty health. The absence of open and frequent discussions on this important topic within the family, classroom, and social network; a lack of SRH health campaigns; and inadequate SRH-related content on mass media due to the perceived taboo of the issues have led to the restriction of a steady flow of SRH information and ignorance about adolescence health among the college-going girls. Furthermore, a portion of the study participants were also unaware of family planning and maternal health issues. According to social perception, regardless of their age, women are considered to have matured after their marriage. Therefore, SRH discussions are forbidden for unmarried girls, and significant numbers of female students are uninformed at their most crucial transitional phase of life .Discussions on sexuality and sexually transmitted diseases are prohibited in social spaces in the country. Ideas about HIV/AIDS have been given in an elementary form in textbooks prescribed by educational institutions as these matters are considered taboo. In general, ideas that have been given are that HIV/AIDS can occur if anyone uses a syringe used by an HIV-infected person or uses untested blood, or when a child is born by an infected mother. The most important messages, such as unsafe sexual intercourse, are often left out of discussions. Additionally, empirical evidence revealed that content delivery in school education continues to remain inefficient, and teachers often skip the chapters, or ask students to study them at home . TeacherDifferent myths are common in the rural area with regards to menstruation, creating religious dogmas and cultural orthodoxy that impose restrictions on adolescent girls and adult women. These restrictions prohibit adolescents from going outside, even to school; entering the kitchen; touching any male; brushing their hair; and seeing themselves in the mirror. Some respondents, most of them from rural areas, believed that they should follow these restrictions. Various types of restrictions related to menstruation also exist in other countries, such as India, Nepal, and Saudi Arabia ,24,25,26Our study findings also reported that about half of the adolescent girls feel shy and fearful of puberty issues; consequently, they are reluctant to reveal SRH-related problems. In the rural area, mothers still feel uncomfortable while discussing SRH-related matters with their daughters, due to traditional values and conservative attitudes. Therefore, it is not a particular matter for female students to feel discomfort about reproductive health, whereas a very dear one like a mother feels uncomfortable discussing periods. Our study depicts that the majority of adolescent girls used clean cloth during their menstrual cycle, which is very unhealthy because it can cause fungal infections and urine infections. In Bangladesh, the use of sanitary napkins is a very recent trend. As a result of publishing advertisements on media, currently, the level of awareness is gradually increasing. Purchasing capacity also influences the use of a sanitary napkin. Though it is more convenient for a female student who lives in a city, the use of napkins depends on the financial well-being and availability of a student living in a village. However, irrespective of the area of residence, the average use rate of the sanitary pad is higher than the findings of some studies conducted in India ,24 and lThis study also examined the factors better predicting the KAP of SRH of the study participants. We found that being a student of the science group, urban residence, regular SRH discussions, prior knowledge on SRH, mother as the source of SRH information, ever reading or watching SRH content on mass media, and ever talking with a health professional regarding SRH problems are the significant factors associated with a better status of SRH knowledge, attitude, and practice. Some of these factors are also supported by other studies ,29.This survey among older adolescent girls aged 16–18 years has shown, overall, a higher than average level of sexual and reproductive health knowledge and practice and better status of attitude in this regard. However, knowledge gain, a positive attitude, and regular practices are augmented by factors such as studying in a science group, urban residence, regular SRH communication, and reading or watching SRH-related content on media. Although the college-going study participants had already reached the late adolescence stage, their knowledge regarding menstruation and HIV/AIDS was poor, and their attitude towards sexual education was inappropriate. Furthermore, different myths and misconceptions that should be refuted were common among one-fifth of the adolescent girls. About two-thirds of the respondents used cloth instead of sanitary pads during their menstruation cycle. Moreover, the girls usually felt comfortable expressing themselves as sick, whereas most of them thought of periods as a disease.This study questions the existing school curriculum and teaching method which are employed to teach SRH education among adolescents and constructs new ground for conducting policy research to develop an apposite strategy for improving the SRH status through cultural appropriateness. This study also suggests conducting studies among adolescent boys to investigate their SRH status.Given the context, it is crucial to meet the sexual and reproductive health needs and rights of adolescent girls, as a significant number of this cohort are married before the age of 18. These needs can be met by ensuring the provision of quality and age apposite sexuality education commencing with very young adolescents, and the delivery of quality age and gender appropriate SRH information in line with global standards, adapted to suit the present context of Bangladesh. Reproductive and menstrual health and hygiene should be more detailed and comprehensive in the school curriculum. Adolescent girls should receive counseling services at government health facilities and schools. The use of radio, television, and social media could also be an effective strategy for supplying teenagers with information about puberty health, especially those living in remote and rural areas. Sanitary pads should be made available by the government to all adolescent females at a subsidized rate to make them affordable.The study participants’ information regarding their knowledge, attitude, and practice of sexual and reproductive health might have been influenced by social desirability, which may have affected the validity of the result. Moreover, Cronbach’s alpha on the knowledge and practice measure is lower than we would have liked, and this measure requires further refinement. Moreover, no qualitative data collection method was used, so this study could not obtain a better explanation and more in-depth insight into such a taboo topic.


Saccharomyces cerevisiae RNase P  co-purify with yeast telomerase, another RNA-protein complex. The three telomerase-associated proteins, Pop1, 6 and 7, bind to TLC1, the RNA subunit of telomerase. In a recent study , we used temperature sensitive alleles of the essential POP genes to determine their role in telomerase biogenesis. At permissive temperature, pop mutant cells grow normally, and the abundance of most proteins, including protein subunits of telomerase, is similar to wild type (WT). However, telomeres are short, and the amount of the mature telomerase holoenzyme is low. Unlike the RNA subunit of RNase MRP, TLC1 is more abundant in pop cells and properly folded, except at the Cs2a/TeSS domain where the Pop proteins bind. These defects correlate with defective movement of TLC1 from the cytoplasm, where it associates with telomerase proteins, back to the nucleus where it lengthens telomeres. Thus, Pop proteins are needed for the stable association of telomerase proteins with TLC1, and their reduction sequesters mature telomerase in the cytoplasm, away from its nuclear substrates.RNase P, an RNA-protein complex, is essential for processing tRNAs. Three of the ten protein subunits of  Escherichia coli RNase P consists of a 350 nt RNA and a single protein. The RNA subunit is sufficient for catalysis in vitro. Eukaryotic RNase P's have a similarly sized RNA subunit (369 nt), but an expanded number of proteins (10), and their catalytic activity is protein-dependent (sizes and protein number are for S. cerevisiae) . A few non-tRNA RNAs are also processed by both bacterial and eukaryotic RNase P.RNase P is a ubiquitous RNA-protein complex that is essential for the processing of the 5' ends of tRNAs. Fig. 1b). Other protein subunits are recruited to the two RNAs via interactions with Pop1. Thus, Pop1/6/7 are the organizing core for both RNase P and MRP. All of the RNase P/MRP subunits in S. cerevisiae are encoded by essential genes, and most of the yeast proteins have human homologues .RNase MRP, which processes ribosomal RNA and one mRNA, is a eukaryotic-limited RNA-protein complex. Although the RNA components of RNase P and MRP are different, all but a few of the protein subunits are present in both complexes. The two RNA subunits are mostly unrelated in sequence and structure, but each contains a P3 region of almost identical sequence and secondary structure. In budding yeast, a heterodimer of two subunits, Pop6 and Pop7, binds P3, and this complex recruits the larger Pop1 protein . Telomerase RNAs vary in size and sequence, but in all organisms, they are highly structured. In addition, all telomerase RNAs contain a short stretch whose sequence is complementary to the G-rich strand of telomeric DNA. This region base pairs to and provides a template for extension of the G-rich strand. The catalytic subunit of all telomerases, called Est2 in S. cerevisiae, are reverse transcriptases. Additionally, S. cerevisiae telomerase contains Est1 and Est3 that are also essential for telomerase action in vivo. Est1 promotes binding of the telomerase holoenzyme to telomeres, but its essential role is activating telomerase, perhaps by recruiting Est3 into the holoenzyme. Est1 is the only telomerase subunit whose abundance is cell cycle regulated, peaking in late S/G2 phase, the time of telomerase action.Like RNase P/MRP, telomerase is an RNA-multi-protein complex that maintains the ends of chromosomes (telomeres) in most eukaryotes  and inactive (G1 phase) telomerase. Their association is DNase-resistant. Thus, the three Pop proteins behave like integral subunits of telomerase. In contrast, none of the other RNase P/MRP proteins has significant telomerase association.In a 2015 paper, we used mass spectrometry to identify proteins that co-purify with Fig. 1c). When the P3-like region is deleted, Est1 binding to the mutant TLC1 is low when measured by RNA immuno-precipitation (RNA-IP).A 2016 paper from the Wellinger and Krasilnikov labs confirmed our finding that Pop1, 6 and 7 are telomerase associated. They demonstrated that the three Pop proteins bind to the Cs2a/TeSS domain, a P3-like region, in TLC1 RNA, which is near the Est1 binding site on the RNA . At this temperature, pop1 and pop6 cells grow at near WT rates, and most proteins, including Est1 and Est2 are present at near WT amounts. However, levels of the mutant Pop proteins are significantly reduced. Many experiments were also done at 30°C, a semi-permissive temperature, where pop cells grow slowly but indefinitely, and protein levels are reduced, including Est1 (and to a lesser extent Est2). We reasoned that telomere phenotypes in pop cells at 24°C  are likely direct effects of Pop proteins on telomerase. All the mutant phenotypes we detect are suppressed by introducing a WT copy of the mutant POP gene.Meanwhile, we were carrying out experiments to determine if Pop proteins affect telomerase assembly and/or activity pop cells are significantly shorter than WT telomeres, even at 24°C and shorter still at 30°C . This short telomere phenotype is not due to reduced Est1 or Est2. As the abundance of NME1, the RNA subunit of RNase MRP, is reduced in pop cells (~50 and 25% of WT at 24° and 30°C), we predicted that TLC1 would also be present in low amounts. However, TLC1 is actually more abundant in pop cells at both 24°C and 30°C, ~2 and 6x higher than in WT cells. The increased abundance of TLC1 in Pop-limited cells was surprising.Telomeres in pop cells might be defective in TLC1 processing, and this defect might somehow lead to higher levels of TLC1. However, post-transcriptional changes at both the 5' and 3' ends of TLC1 occur normally in pop cells. Thus, Pop proteins do not affect processing of TLC1.Given the well documented role of RNase P/MRP in RNA processing, we hypothesized that pop cells, even at 24°C (~14% of WT). This low binding is not due to reduced Est1 or 2. Binding of mutant Pop proteins to TLC1 is also low at 24°C, but in this case, lower binding can be explained by reduced Pop proteins. Likewise, the telomere association of the telomerase holoenzyme is reduced in both mutants at 24°C. The defects in telomerase assembly and its lower binding to telomeres likely explain the short telomere phenotype of pop cells.Next, using RNA-IP, we tested if defect(s) in TLC1 prevented its binding to its associated proteins. By RNA-IP, Est1 binding  to WT TLC1 is highly compromised in in vitro data suggest that the complex secondary structure of NME1 is Pop-protein dependent. If Pop protein deficiency results in improper folding of TLC1, this defect could explain its impaired binding to telomerase proteins. To test this idea, we used DMS-MaPseq, a method developed by Sylvia Rouskin's lab (MIT), for high through put analysis of RNA secondary structures. We determined the structure of both NME1 and TLC1 RNAs in pop and WT cells at both 24 and 30°C in DMS treated and untreated cells. As anticipated from earlier work, the secondary structure of NME1 was globally perturbed in pop cells even at 24°C. Strikingly, seven nucleotides in the P3-like region, where Pop6 and Pop7 bind, is the only portion of TLC1 that is altered in 24°C grown pop cells.Previous pop mutant cells. However, we were surprised that the structures of Est1, Est2, Yku, and the Sm7 ring binding sites are indistinguishable in pop and WT cells, as by RNA-IP, Est1 (and to a lesser extent) Est2 binding to TLC1 is low at 24°C. One possibility is that our methods are not sensitive enough to detect structural differences at these sites. However, the structure of NME1 RNA in the same cells is globally perturbed at 24°C. Also, the Est1 binding site on TLC1 is significantly altered at 30°C in pop cells, consistent with less Est1 in pop cells at this temperature. The data are also sensitive enough to detect greater DMS-sensitivity in the P3-like domain in pop6 compared to pop1 cells, consistent with the Pop6-dependence of Pop1 binding to this region. The significance of the differences we detect are robust due to the large number of reads  and biological triplicates of DMS treated and untreated controls for each strain. Finally, the statistical methods used by our collaborator Sharon Aviran (UC Davis) can detect and assess the importance of structural changes, even if they are present in only a subset of molecules.The altered DMS accessibility of the P3-like domain was expected as levels of Pop proteins are reduced at 24°C in in vivo. The WT structure of the Est1, Est2, Yku and Sm7 binding sites in pop cells seen by this method suggest that these binding sites are occupied to a similar extent in pop and WT cells. In contrast, RNA-IP is measured in cell extracts and does not use cross-linkers to preserve RNA-protein interactions. Thus, WT-like occupancy of these binding sites in pop cells, as measured by DMS-MaPseq, and their low occupancy as measured by RNA-IP suggest that Est1 (and to a lesser extent Est2) bind much less stably to TLC1 in pop compared to WT cells. Preliminary RNA-IP data suggest that Yku binding to TLC1 is also impaired. We suggest that Pop proteins are required for higher order folding of TLC1, and this folding stabilizes the binding of Est1, Est2 (to a lesser extent), and perhaps Yku to TLC1. Thus, unlike the described roles for Pop proteins in tRNA, ribosomal RNA, and mRNA processing, in telomerase, they play a structural role that maintains the integrity of the holoenzyme.The DMS-MaPseq method monitors the secondary structure of RNA pop cells can be explained by the downstream consequences of unstable binding of proteins to TLC1. However, this instability does not provide an obvious explanation for increased TLC1 abundance. To address this question, we collaborated with Harold Kim (Georgia Institute of Technology). His lab used a novel FISH approach to examine the subcellular localization of TLC1 in pop and WT cells. In WT cells, TLC1 is transcribed in the nucleus, transits to the nucleolus where it acquires a tri-methylated 5' cap and moves to the cytoplasm where it binds protein subunits to form the holoenzyme. Specific nuclear import factors escort the holoenzyme back to the nucleus where it binds and lengthens telomeres . By FISH, TLC1 is detected in the nucleoplasm, the nucleolus and the cytoplasm in both WT and pop cells. However, compared to WT cells, TLC1 is significantly over-represented in the cytoplasm at 24°C, an effect exacerbated at 30°C. The nucleases that degrade TLC1 are nuclear-localized. Thus, the over-representation of TLC1 in the cytoplasm provides a satisfying explanation for its elevated copy number. We propose that this mis-localization is due to inefficient recognition of the unstable protein-associated TLC1 by nuclear import factors that facilitate nuclear re-entry of the holoenzyme. Figure 2 presents our view of the impact of Pop proteins on TLC1 biogenesis.Almost all of the telomere phenotypes in S. cerevisiae? By mass spectrometry, we find that Pop1 co-purifies with fission yeast telomerase so Pop proteins may affect telomerase RNA in other organisms. In addition, there may be other as yet undiscovered RNA-protein complexes that are stabilized by Pop proteins.Is the unanticipated role of Pop proteins in TLC1 biogenesis unique to


Structural disorder represents a key feature in the mechanism of action of RNA-binding proteins (RBPs). Recent insights revealed that intrinsically disordered regions (IDRs) linking globular domains modulate their capability to interact with various sequences of RNA, but also regulate aggregation processes, stress-granules formation, and binding to other proteins. The FET protein family, which includes FUS (Fused in Sarcoma), EWG (Ewing Sarcoma) and TAF15 (TATA binding association factor 15) proteins, is a group of RBPs containing three different long IDRs characterized by the presence of RGG motifs. In this study, we present the characterization of a fragment of FUS comprising two RGG regions flanking the RNA Recognition Motif (RRM) alone and in the presence of a stem-loop RNA. From a combination of EPR and NMR spectroscopies, we established that the two RGG regions transiently interact with the RRM itself. These interactions may play a role in the recognition of stem-loop RNA, without a disorder-to-order transition but retaining high dynamics. The capability of RBPs to bind RNA has been generally ascribed to the globular domains that possess a defined secondary structure, such as the RNA recognition motif (RRM), the K homology domain (KH), the zinc fingers (ZnF) and the DEAD box helicase motifs5. Indeed, recent reports revealed that intrinsically disordered regions (IDRs) are largely distributed in various RBPs sequences and contribute to RNA binding, cooperate with globular domains, and should play relevant roles in RNA interaction9.Some studies revealed that 3–11% of the human proteome is involved in RNA binding, but only a small fraction of proteins could be classified as typical globular RBPs12.The second most common class of intrinsically disordered regions in RBPs is represented by RGG-boxes (or GAR) that contain repeats of arginine and glycine residues. The absence of a folded conformation of the RGG motifs is due to the abundance of hydrophilic and charged amino acids and to the lack of bulky hydrophobic groups that promote the formation of stable three-dimensional structures. RGG repeats are usually associated with other repetitive sequences, including polyalanine, polyaspartate, polyglutamate, polyglutamine, and polyglycine repeats, as well as other diglycine-containing motifs such as FGG, PGG, SGG and YGG. The positively charged guanidinium group of arginine can form different types of interactions with different elements of nucleic acids, including cation-π interactions, π–π stacking, hydrogen bonding, charge-charge/dipole and van der Waals interactions. On the other hand, glycine is the smallest amino acid with a single hydrogen atom as side chain and therefore it represents the key-factor for the conformational flexibility of RGG domains. Moreover, post-translational modifications (PTM), such as arginine methylation, may represent another important factor for modulating the functions of the RGG boxes. PTMs could alter protein–protein and protein-RNA binding capability and could regulate the interaction of the protein with a group of RNA sequences and structures14. FUS is a multi-domain protein formed by a QGSY-rich region at the N-terminus, a globular RNA-recognition motif (RRM) with the classical βαββαβ fold, a zinc finger domain (ZnF), three different RGG boxes , a G-rich region and a C-terminal nuclear localization signal (NLS)  protein is a nuclear protein implicated in the transcription, splicing and transport of mRNA. The scientific interest for this protein is due to its direct implication in neurodegenerative disorders, specifically in Amyotrophic Lateral Sclerosis (ALS)1-2 and the RRM could be of importance in determining the function of the protein, particularly in the contest of RNA binding. Through a combined approach based on NMR and EPR spectroscopies, we provided insights concerning the conformation and dynamics of these domains, but also some hints on the possible roles exerted by RGGs regions in the RRM-RNA binding.In the present study we focused on the core RRM structured domain of FUS and on its two flanking IDRs both containing RGG motifs, to further characterize these parts of the protein and assess whether the interplay between the RGG18; however, biophysical investigations on a FUS fragment comprising the RRM and the two extended flanking RGG disordered regions  underline that these regions of FUS are positively charged under physiological conditions (pH 7.0–7.4), thus being capable of interacting with negative charges of nucleotides or others proteins. The main type of interactions between RBPs and RNA are π-π contacts, exerted by nucleobases and aromatic amino acids. Trp, Tyr and Phe residues in FUS165–422 are present in the RRM domain only, consistent with this region being the principal site for RNA interaction22.The resulting 258-residues long construct, with a molecular weight of 25 kDa, is composed of about 20% charged residues, 10% polar residues, and 37% glycine residues, with many repeats beside the canonical GG and RGG motives . This unique content of glycine in FUS165–422. The far-UV CD spectrum of the protein in MES buffer solution at pH 6.5  and 75% ± 3.1% of disordered conformations. These data are in agreement with the previous work by Song and co-workers24 and also with the estimated content of secondary structural elements based on the disorder predictor GlobPlot2.325 and the available structural models (see Ref.26 and PDB 2LA6), which account for 11% β-sheet and 7% α-helix and 72% disordered polypeptide.CD spectroscopy was used to confirm the secondary structure content present in FUS26. We thus took advantage of the available sequence specific assignment (BMRB ID 17508) to assign the RRM signals in our experimental conditions by using a sub-set of NMR experiments  was determinednts see “”. The 1H24. We acquired and analyzed 15N relaxation properties of the RRM domain to investigate the conformational dynamics of the folded domain in our longer construct. The 15N longitudinal relaxation rate (15N R1) and the 15N transverse relaxation rate (15N R2) confirm the general features of the domain, without major variation of the ns-ps dynamics of the folded domain and with little changes consistent with an overall increase in correlation time induced by the presence of the additional residues of the two dynamical flanking regions , while a decreased mobility determines broader lines and higher τC values. This methodology has been extensively applied to characterize intrinsically disordered proteins and to follow possible disorder-to-order transitions30.EPR spectroscopy combined with site-directed spin labeling (SDSL) is a powerful method to investigate protein dynamics. From the analysis of the lineshape of the EPR spectrum of a spin label  selectively grafted on an appropriate residue  and the determination of its mobility, protein conformation can be characterized1 and the RGG2 regions, as well as the RNA-binding site, five different mutants of FUS165–422 to insert a cysteine residue in the protein sequence were designed  have a large degree of fluctuations and lack a defined conformation. The CW-EPR spectrum of D258R1  and a minor component with a higher τC value   together with a conformation with reduced dynamics (component 3 = 35 ± 4% with τC = 1.40 ± 0.09 ns). Similarly, the EPR analysis of the S360R1 mutant  higher than that determined for the same component in A349R1 (τC = 1.40 ± 0.09 ns). These differences in correlational time values between the two labelling positions could be caused by the spin label position. In fact, the less restricted environment for the label grafted on the α2 helix should confer a larger tumbling rate  to MTSL compared to the S360R1 mutant.CW-EPR spectra were also recorded for the A349R1 and S360R1 mutants bearing the spin label on the α2 helix and on the flexible loop connecting α2 and β4 of the RRM domain, respectively  NMR experiments to determine possible long-range contacts between different protein regions1 and the RRM domain. On the RGG2 side, the paramagnetic effects produced by the label in position S412R1 27, we performed all the spectroscopic measurements with a RNA sequence containing these specific nucleotide quartets. Previous studies measured the affinity of the full length FUS and various protein segments for different nucleotide sequences, revealing that the RGGs motifs strongly influence the capability of the protein to bind RNAs18.In order to test the capability of FUS165–422, at 1:1 molar ratio, no major changes in the structure of the protein occur. The far-UV CD spectrum showed a minor shift of the maximum absorbance band (from 207 to 210 nm) and a weak variation in the spectral lineshape . On each of these mutants the 4-pulse Q-band DEER traces were recorded in the absence and in the presence of stem-loop RNA to understand the spatial arrangement of the RGG boxes compared to the RRM region and to identify their role in the RNA interaction process.To get insights on the structural variations of FUSBackground corrected DEER time traces, recorded for both doubly labelled mutants with and without RNA, are reported in Fig. 1 and RRM induced by RNA binding. Analogues Q-band DEER experiments performed on the A349R1/S412R1 system with stem-loop RNA  during RNA-coordination, as previously determined for others RRG motifs of FUS and similar proteins38. However, no drastic variations have been observed for RGG1 in complex with RNA in this study thus we can infer that also this IDR of FUS does not undergo a defined disorder-to-order transition in presence of nucleotides. Summarizing, the experiments performed in this work were not able to detect any direct interaction of the RGG regions with stem-loop RNA.Although the RGG39. Flanking models are a category of complexes that lack a defined conformation where IDRs increase the propensity of the folded regions toward binding of competent form and regulate the entropy of the process40. We speculate that this type of conformational model could be adopted by RGG1 upon coordination of stem-loop RNA, as also previously demonstrated for another splicing factor complex (U2AF65/SF1) sharing various similarities with the FUS protein41.Recently, several studies evidenced that a large class of proteins that contain IDR connecting globular domains form fuzzy complexes with partners (i.e. protein or nucleic acids) with different topologies42. Moreover, Allain and co-workers speculated about the role exerted by the RGG regions of FUS in RNA destabilization and remodelling, without assuming a folded conformation27. Thus, the possibility that RGG1-2 of FUS protein could bind transiently the hnRNPA2/B1 stem-loop RNA cannot be ruled out.Various RBPs have evolved a great variety of pathways to remodel RNA. These proteins, which generally contains a large number of cationic residues, transiently interact with nucleotides enabling the RNA backbone to assume wide conformations27. We thus investigated the structural effects of stem-loop RNA coordination on the RRM region of FUS. In solution, this domain has a more compact conformation compared to RGG1 and RGG2 but it experiences a more pronounced mobility than generally observed for a normal globular protein43. Based on CW-EPR spectra recorded in the presence of RNA, the RRM domain revealed concomitant increase and a decrease of local flexibility, underlining that remodelling of the conformation takes place upon nucleotide binding. This capability to modulate its structure, in combination with the RNA destabilization exerted by RGG motifs, supports the recent speculation that RRM nucleotide recognition is more based on RNA shape than on nucleobase sequence. However, further studies are needed to confirm this finding.The presence of the adjacent RGG motifs significantly increases the affinity of the RRM domain for different nucleotide sequences, and structured RNA in particular2 helix and the flexible loop connecting α2 and β4 of the RRM domain during stem-loop RNA coordination. RGG motif should represent a key-factor for determining the physiological and also pathological pathways of different human proteins related to fatal neurodegenerative diseases, such as ALS and FTLD. Further biochemical and biophysical efforts on the characterization of the role exerted by these domains in protein activities should be performed in order to contribute to the development of possible therapies for the treatment of the related pathologies.Summarizing, our results provide additional structural information on the RGG domains of FUS and their implication in RNA binding activity. Through an integrated approach of EPR and NMR spectroscopies, the degree of disordered of the RGG boxes of FUS have been characterized even if future experiments aimed to individuate their role in RNA-binding are necessary. The experimental data revealed also a dynamic effect of these flanking domains exerted on the folded region and a variation in terms of terms of tumbling rates for the α165–422 fragment (GENEWIZ) was cloned in the pENTR vector for the Gateway cloning technology and subsequently in the pDEST-HisMBP vector , utilizing the pENTR/TEV/D-TOPO cloning kit (Invitrogen). E.coli BL21 (DE3) cells were transformed with the plasmid pDEST-HisMBP-FUS165–422, grown in LB medium (or in 15N M9 medium for NMR experiments) to mid-log phase at 37 °C, induced with 0.50 mM IPTG, and then they were kept at 20 °C for at least 16 h. For protein purification we adapted the protocol established by Ozdilek et al.18. In brief, the cells were harvested and re-suspended in 50 mL of binding buffer  supplemented with protease inhibitor tablets (Bayer) and successively lysed by sonication. The lysate was passed through a 5 mL HisTrap FF affinity column  and washed with the elution buffer . The His-MBP tag was cleaved incubating the protein solution overnight with TEV protease and successively separate from the fusion protein with a second step of purification with 5 mL HisTrap FF affinity column using the same elution buffer reported above.The gene encoding for FUS165–422 from possible contaminants and to transfer the protein in the final buffer (20 mM MES at pH 6.5 with 150 mM NaCl). The corresponding MALDI-TOF spectrum  was performed to separate FUS165–422 sequence using the kit Quick Change II Site-Directed Mutagenesis Kit (Agilent). The primers of each variant  were inserted into FUS165–422 vector carrying already the A349C mutations and then introducing D258C and S412C mutations respectively.The plasmids encoding the protein bearing the double cysteine variations (D258C/A3494C and A349C/S412C) were obtained as described previously using the pENTR-FUS165–422 single and double cysteine variants, was incubated with a tenfold molar excess of DTT at room temperature in order to reduce the cysteine thiol group. After 30 min, the reaction mixture was eluted into a PD-10 desalting column to remove the reducing agent using MES buffer (20 mM MES at pH 6.5 with 150 mM NaCl) as mobile phase. Successively S- methylmethane-sulfonothiolate (MTSL) was added to the solution with 1:10 protein:label molar ratio; the reaction was kept at room temperature under gentle stirring and in absence of light for at least 10 h. The unreacted MTSL was removed using a PD-10 desalting column using MES buffer. The fractions containing single  and double (D248R1/A349R1 and A349R1/S412R1) MTSL-labelled positions of FUS165–422 were individually checked recording the relative X-band CW-EPR spectrum, to assure the complete removal of the free tag. Successively all the fractions containing labelled samples were pooled together and the solution was concentrated using AMICON ultra centrifugal filters (MWCO = 10 kDa). The final protein concentration was calculated observing the absorbance at 280 nm  with a UV/Vis Varian Cary 50 spectrophotometer. The labelling yield of each sample  was synthetized by Eurofins Genomics. The lyophilized product has been dissolved in RNAase free buffer (20 mM MES at pH 6.5 and 150 mM NaCl) and the concentration of stock solution was checked measuring the absorbance at 260 nm with UV/vis spectroscopy. For CD, EPR and NMR analysis of FUS165–422 in solution was recorded with a final protein concentration either of 15 μM or 50 μM in 20 mM of MES buffer (pH 6.5) and 150 mM of NaCl from 200 to 250 nm. At wavelength below 200 nm the absorbance due to the presence of NaCl, necessary for protein stability, saturate the photomultiplier and the data were discarded. Experiments in the presence of RNA were recorded placing in solution the protein with 1:1 FUS: hnRNPA2/B1 RNA molar ratio. Each spectrum was accumulated at least 10 times in order to improve signal-to-noise ratio. Secondary structure elements of the protein in different conditions were derived from CD spectra using Dichroweb analysis tool23. The prediction of the percentage of folded and disordered region based on the primary sequence of FUS165–422 were realized through the PDB structural model of the RRM (PDB ID 2LA6) and GlobPlot 2.3 program25.Far-UV Circular Dichroism (CD) experiments were performed at room temperature with a Jasco CD-J-815 spectropolarimeter using a quartz cuvette with a path length of 1 mm length. CD spectrum of FUS165–422 variants  in solution and with RNA was recorded with an ELEXYS E580 spectrometer equipped with a Super High Q resonator (SHQE) operating at X-band (9.8 GHz) at room temperature. Each CW-spectrum for spin labeled FUS165–422 variants were obtained with a final protein concentration of approximately 60 μM in 20 mM of MES buffer (pH 6.5) and 150 mM of NaCl. For the experiments in the presence of hnRNPA2/B1 RNA, a 1:1 protein:RNA ratio was used.Each CW-EPR spectrum of MTSL-labeled FUSThe following spectroscopic parameters were applied to record each spectrum: microwave power = 10 mW; magnetic field amplitude = 1G; field sweep = 150 G; receiver gain = 60 dB; modulation frequency = 100 kHz.44 in order to obtain spectral component percentages and the corresponding correlational times (τC). The error on the components’ percentages and the correlational times values for each spectrum were obtained performing three independent simulations of the same spectra. The correlation times and the component percentages reported in the text for each labelling position represent the average values arising from the spectral simulations.All EPR spectra were successively simulated with the program SimLabel (a GUI of Easyspin software)165–422 doubly spin labeled mutants (D258R1/A349R1 and A349R1/S412R1) in buffer solution (20 mM MES at pH 6.5 with 150 mM of NaCl) or in the presence of 1:1 protein:RNA were performed on Elexys E580 spectrometer coupled with the standard resonator EN5107D2 operating at Q-band (34 GHz) and a 3 W microwaves amplifier  setting π/2 = 20 ns and π = 40 ns . The inter-pulse t1 was set to 200 ns and the t2 was adjusted based on the phase memory constant time recorded for each labelled mutant. The pump ELDOR pulse (πPUMP = 36 ns) was positioned at the center of the MTSL absorption spectrum and the frequency difference between the pump and the observer pulses was 65 MHz. The DEER traces were accumulated at least 14 h in order to minimize the signal to noise ratio.DEER experiments were performed using the four-pulse dead-time free DEER sequence  was selected into the corner, according to L-curve criteria.1H-15N HSQC NMR spectra of a 100 μM 15N-FUS alone or in complex with hnRNPA2/B1 stem-loop RNA (1:1 FUS:RNA) were acquired at 298 K with a 22.3 T Bruker Avance III NMR spectrometer operating at 950.06 MHz for 1H and equipped with a cryogenically cooled triple resonance probehead (TCI).The 2D 1H-15N HSQC NMR spectrum of unlabelled and MTSL-labelled 15N-FUS165–422 variants  in solution were acquired at 298 K with a 22.3 T Bruker Avance III NMR spectrometer operating at 950.06 MHz for 1H and equipped with a cryogenically cooled triple resonance probehead (TCI).Each 2D 1, R2 and 1H-15N NOEs) were acquired using 15N labeled FUS at about 200 µM. The spectra were recorded at 298 K on a 16.4 T Bruker Avance NEO spectrometer operating at 700.06 MHz for 1H and equipped with a cryogenically cooled triple resonance probehead (TCI). The 15N R1 and R2 experiments were acquired with 8 scans per increment (2048 × 320 points). To determine the 15N R1 the following delays were used: 20 ms, 60 ms, 120 ms, 180 ms, 240 ms, 400 ms, 600 ms, 800 ms, 1 s, 1.2 s. To determine the 15N R2 the following delays were used: 32 ms, 64 ms, 128 ms, 160 ms, 190 ms, 220 ms, 260 ms, 320 ms, 360 ms, 440 ms, 500 ms. The relaxation delay for 15N R1 and R2 experiments was 3.0 s. The 1H-15N NOEs experiments were acquired with 64 scans (2048 × 320 points) and a relaxation delay of 6.0 s, used for 1H saturation when necessary.All the heteronuclear relaxation experiments  were determined by fitting the cross-peak intensity measured as a function of variable delay, to single-exponential decay using the Bruker Dynamics Center 2.4, available as stand-alone ancillary software of TopSpin by Bruker. The error bars are generated by the software using the peak intensities under the assumption that their uncertainties are unknown but equal for all Y values and the non-linear fit determines errors for the fitted parameters.1H-15N NOEs values were obtained as a ratio between peak intensity in spectra recorded with and without 1H saturation. The error can be estimated about 10% measuring the noise level of the spectrum without saturation.The 46 available online at http://nmr.chem.rug.nl and the BMRB 17508 data.The secondary structure propensity  by using UCSF ChimeraSupplementary Information


Coronavirus infection (COVID-19) is spreading rapidly in the world beyond the health care capacity. The World Health Organization (WHO) has announced an emergency state that needs quick and effective actions. In the lack of specific medicine and vaccine, integration of mental health and nutritional care in the platform of a powerful managerial technique named the “Logical Framework Approach” (LFA) could be helpful for successful control of this pandemic. The strengths of the LFA for coronavirus management program are stakeholders’ involvement, integrative teamwork in research and medical procedures, as well as Inter-sectoral cooperation. The related organizations like WHO and ministries of health of every country could easily adopt this approach and act more efficiently to manage this pandemic. Coronavirus disease 2019 (COVID-19) pandemic has raised serious concerns about health care capacity worldwide. The spread of COVID-19 was so fast that nearly all countries are engaged by its infection  causes and effects related to coronavirus infection. Furthermore, priorities are determined. This technique is similar to the problem tree analysis . 2.   Stakeholder analysis: Is the process of identifying any individual, group, organization or system that could help in coronavirus control project or could be suffering from the coronavirus control project. The brain storming technique can be used to identify the stakeholders. The project manager also can map the interest & power matrix of stakeholders separately outside the LFA matrix . 3.   Analysis of objectives: The main objective should be determined according to the core problem. Then the main objective should be broken down to the smaller and more manageable objectives in order to achieve the goal. The hierarchy of objective could be plotted like an objective tree . 4.   Analysis of strategies: Identifying and comparing different solutions for coronavirus issue in order to control the pandemic . These s5.   Analysis of the other factors: The process of identifying any systems, ethnics, countries, societies, religious, cultures and the other parameters as social, political, economical, legal, environmental and technological, which have the effects on the pandemic; and analyzing the symptoms to find the causes, and decreasing the problems and then control it . The above analytical approach is used to set up a management framework named Logical Framework Matrix. This Logframe summarizes the essentials of project, hierarchy of objectives and activities and assumptions (vertical logic; columns 1 and 4 in The strengths of the logical framework approach for coronavirus management program are stakeholders’ involvement, integrative team work in research and medical procedures, as well as Inter-sectoral cooperation in all parts of society. Moreover, in the logframe responsibilities, cost and budget are determined carefully for each task, which leads to an arranged process to achieve the goals. In the absence of specific medicine and vaccine for coronavirus infection, adjunction of the regular medical care for the patients with complementary medicine, mental health care and nutritional care should be addressed , 23. We are at the beginnings steps of the new coronavirus pandemic. Hence, lack of literature related to coronavirus is obvious. However, using the experience of previous outbreaks such as SARS, MERS and Ebola infections could be helpful.Currently weakness in coronavirus control programs is obvious. In the absence and also in presence of specific medicine for COVID-19 infection, mental health and nutritional interventions could be cooperative. LFA is a powerful management instrument that could be used by policy-makers for integration of the mental health and nutritional care to the prevention and treatment protocols of coronavirus infection. The key points of LFA for coronavirus control programs are integrative and teamwork approach, involvement of all stakeholders, government and people, and powerful portfolio leadership.