The ability to design a protein to bind specifically to a target RNA enables numerous applications, with the modular architecture of the PUF domain lending itself to new RNA-binding specificities. For each repeat of the Pumilio-1 PUF domain, we generate a library that contains the 8,000 possible combinations of amino acid substitutions at residues critical for RNA contact. We carry out yeast three-hybrid selections with each library against the RNA recognition sequence for Pumilio-1, with any possible base present at the position recognized by the randomized repeat. We use sequencing to score the binding of each variant, identifying many variants with highly repeat-specific interactions. From these data, we generate an RNA binding code specific to each repeat and base. We use this code to design PUF domains against 16 RNAs, and find that some of these domains recognize RNAs with two, three or four changes from the wild type sequence. The PUF RNAbinding domain has an eight-repeat structure with the ability to bind an eight-base RNA sequence. Here the authors generate a comprehensive library of variants of a PUF domain to find an RNA binding code and design PUF domains that recognize an arbitrary RNA sequence. More than 30 structures of RRMs in complex with RNA are available, making it the most intensively studied class of RNA-binding domain5. However, it is still difficult to predict recognition specificity solely from the amino acid sequence of an RRM domain. The RRM domain can form distinct sub-states at the RNA-binding surface to allow for high or low-affinity binding, and inter-domain interactions and the linker region between multiple RRMs within a protein also contribute to RNA recognition2.RNA targeting in living cells presents a unique opportunity to monitor and manipulate the diverse biological functions performed by RNA. One approach toward such targeting is via the engineering of protein-based RNA-binding domains to recognize an arbitrary RNA sequence. This engineering requires that the RNA-binding domain can be programmed for specific recognition with limited off-target effects. However, decoding the RNA-binding specificity of most types of RNA-binding domains is challenging, as these domains can associate with RNA via complex networks of interactions. For example, the most abundant RNA-binding domain in vertebrates, the RRM domain10. PPR proteins can be insoluble in heterologous systems, which has hampered biochemical characterization of specificity and hence design9. PPR redesign has identified proteins that bind to a new RNA target12, but complications exist. For example, a comprehensive analysis of the sequence specificity of the native protein PPR10 showed that four of the 17 nucleotides in a target RNA are specified in a manner that cannot be explained by the current PPR code13.Another major class of RNA-binding domains—the pentatricopeptide repeat (PPR)—uses a modular structure arranged in tandem repeats, with each repeat binding primarily to a single RNA base. PPR proteins have two amino acids that provide specificity and make this domain highly useful for targeting RNA by design, although binding sites for certain naturally occurring variants are unclear16 and has been applied in cells ranging from bacteria to mammalian cells21. Reliance on RNA–RNA interactions provides a strong underpinning for rational design. Challenges include the requirement to deliver multiple components, the large size of the protein, and off-target effects20.The Cas13 family and CRISPR RNA technology provide a powerful approach to manipulate RNAs in vivo. The strategy achieves specificity through guiding RNA interactions with a targeted RNA sequence. The approach has been applied to detect, modify, cleave, image, edit, and manipulate RNA processing25. They contain a conserved RNA-binding domain, known as the Pumilio homology domain or PUF domain, that is generally composed of eight 36-amino-acid repeats  combination28, on the concave surface of the protein. A target RNA sequence of eight bases is bound as an extended strand to the concave surface. X-ray structural analysis of the complex indicates that the recognition is highly modular, with each repeat binding to a single RNA base27. Residues at positions 12 and 16 in each repeat directly interact with a Watson-Crick edge of a base, whereas the residue at position 13 is involved in a stacking interaction with the base27.PUF proteins are involved in regulating eukaryotic processes that include embryogenesis and developmentats Fig. 26,27. Ea31. For example, at the 12 and 16 positions, respectively, cysteine and glutamine bind adenine; asparagine and glutamine bind uracil; and serine and glutamate bind guanine. No TRM combinations in natural PUF proteins have been found to specifically bind cytosine.The identities of the residues in a TRM combination  play a key role in RNA-binding specificity30 mutated repeats 1, 3, and 7 of the human Pumilio-1 domain and changed specificities to guanine and uracil. Ozawa et al.32 mutated multiple repeats of the Pumilio-1 domain to target sequences of a mitochondrial mRNA. Efforts to identify TRM combinations that recognize cytosine imply that amino acid sequence context can influence TRM specificity. Residues of Pumilio-1 that are capable of specifying cytosine have been identified, and are applicable in different positions in the RNA34. A study using C. elegans FBF-2 determined the specificities of 25 natural and engineered PUF variants in repeat 7 of this protein and proposed a code for guanine, uracil, and adenine recognition, but did not detect a cytosine-specific code. The differences in these studies prompted the suggestion that the context in which the TRM appears can influence specificity28. Multiple studies of PUF redesign and specificity35 support this conclusion, and suggest that any PUF code may not be generic for all repeat locations34. Furthermore, PUF proteins can have an elongated RNA-binding surface that recognizes nine bases, with the central bases flipping away from this surface36, which may also affect PUF design prediction.PUF domains have been engineered to design new sequence specificities and repurposed to control multiple steps in RNA biology. Different TRM combinations can shift specificity from one nucleotide to another, while others broaden rather than switch specificity. For example, Cheong and Hall Saccharomycescerevisiae. We explored the binding specificity and affinity of TRM combinations that can specify any of the four RNA bases in any of the eight positions. Beginning with the human Pumilio-1 RNA-binding domain and its eight-nucleotide binding site, we tested libraries of ~8000 possible variants of TRM combinations in each repeat against sets of four RNAs altered at the cognate RNA position. By combining the yeast three-hybrid method with next-generation sequencing, we scored the binding activities of all of these PUF variants for each of the four possible RNA bases. We identified many variants with highly specific interactions, suggesting that each repeat may need to be optimized for maximal binding to an arbitrary RNA sequence.We sought to further understand the basis of PUF specificity using a broad selection strategy in37 , a competitive inhibitor of His3. In order to facilitate the identification by next-generation sequencing of both a protein variant and its RNA target, we encoded both components on a single plasmid. This “all-in-one” plasmid includes both a protein module encoding the human Pumilio-1 PUF domain fused to the Gal4 activation domain (PUF-AD) and an RNA module encoding the Pumilio-1 RNA recognition sequence, the nanos response element (NRE)38, fused to the MS2 coat protein binding sequence , as UGUAAAUA is the most common Pumilio-1 binding motif based on a comparative analysis of mRNA targets for the human PUF family proteins39, a structure exists for human Pumilio-1 bound to this sequence30, and A was one of three preferred bases at that position when Pumilio-1 binding was tested in the yeast three-hybrid assay ; the synonymous changes were likely to have a negligible effect on protein function.The PUF domain variants were designed to contain the 8000 possible combinations of amino acid substitutions at residues 12, 13 and 16 through NNK libraries at each position  of nonsense variants had an enrichment score >0, providing an estimate of the fraction of the loss-of-function missense variants that were also false positives.Based on enrichments in the post-selection pool, we scored the RNA-binding activities of 169,587 PUF domain variants. This dataset contains 24,751 nonsense variants and 144,836 missense variants from the eight repeats. The interaction score distribution of all variants revealed that, in general, nonsense PUF variants were deleterious for interaction with any RNA sequence, and missense PUF variants were present as a bimodal distribution Fig. . Some noR = 0.952–0.982). We assigned a specificity score for each PUF variant as the difference between its highest and second-highest interaction score. Using a threshold of interaction score >5, and a specificity score >4, we identified many PUF variants with highly specific interactions  was preferred in position 1234.The PUF domain–RNA interaction score for each PUF variant showed a high degree of overlap between two experimental replicates  for each repeat . We found that this code specified guanine in repeat 3 as well (the interaction score for the other three bases was <40% of the score for G). If, instead, tryptophan was present in position 13 (SWD), G-specific recognition could be achieved in repeats 6 and 7. Moreover, if position 13 was glycine (SGD), G-specific recognition could be achieved in repeat 1, with SNE and SWD not found RQats Fig. . Howeverats Fig. . While Nats Fig. . These rFor U-specific binding, the natural TRM combination NYQ was found highly specific in repeat 1, 3, and 7 Fig. . Asparag34 as well, which potentially explains why C-specific combinations identified from repeat 6 in human Pumilio-1 did not confer this recognition to repeat 7 of C. elegans FBF-243.For C-specific binding, a polar, uncharged amino acid  in position 12 and a positively charged amino acid  in position 16  were the preferred combinations Fig. . However27.Many combinations showed non-specific binding. For example, repeat 1 combinations with positively charged amino acids in position 12 and 16, repeat 3 combinations with negatively charged amino acids in position 12, and repeat 6 combinations with an aromatic in position 13 and an arginine in position 16 bound to more than a single base, with some of these combinations previously characterized as capable of non-specific RNA recognitionGiven this new set of TRM combinations specific for each of the eight Pumilio-1 PUF repeats, we sought to determine their utility to bind in combination to RNA sequences possessing multiple changes to the UGUAAAUA wild-type binding site. Toward this end, we generated 16 8-base target RNA sequences that differ from the wild-type site by either one base (one target), two bases (two targets), three bases (eight targets), four bases (three targets), five bases (one target) or six bases (one target) Fig. . RNA tarTo assay binding to these targets, we generated an array of 1900 RNA elements , the target sequence was the most enriched RNA sequence  appeared to tolerate combinatorial variation better than C-terminal repeats (5–8). For example, target RNA sequences were highly enriched in several triple variants with two N-terminal substitutions, whereas they were not in triple variants with two C-terminal substitutions  with QFR (specific to C) in repeat 1 and VFQ (specific to A) in repeat 2. QFR is a newly identified TRM code for binding to C, with the QFR-C pair highly enriched and specific in our screens with TRM mutations in repeats 1 and 3; similarly, the VFQ-A pair was enriched and specific for repeat 2 Figs. . We clonWe purified the design 1 variant (with changes to two TRM combinations but only one change in RNA base recognition) and design 2 variant (with both two TRM combinations changed and two RNA base changes in the target) in order to test their in vitro binding in the electrophoretic mobility shift assay. Design 2 showed sequence specificity, binding to its target RNA sequence but not to the wild-type RNA sequence. Specific binding occurred with an approximate Kd for this pair of ~45 nM (95% CI 33–62 nM). Heterogeneity of the shifted bands may indicate dissociation during electrophoresis or non-specific interactions. Design 1 did not bind to either its target or the wild-type RNA sequence in the gel shift assay. Both the design 1 and design 2 PUF variant proteins showed binding in a yeast three-hybrid plate assay, although the results of the dilutions indicated that the design 1 variant had about a 10-fold weaker signal than the design 2 variant Fig. . For desE. coli48. For example, Cheong and Hall30 were unable to produce the soluble protein of human Pumilio-1 when they mutated the residue in repeat 7 or 3 that forms a stacking interaction with the base. Though future studies would be needed to examine the correlation between the three-hybrid selections and affinity, the values we determined in the biochemical assays generally corroborated behavior in the yeast selections.We attempted to purify PUF variants with three or more mutated repeats , but these proteins, containing nine or more altered residues, were insoluble. Others have also observed that variant PUF proteins have been difficult to obtain in soluble form from The PUF domain’s modular architecture of eight repeats and its affinity and specificity for binding to an 8-mer RNA sequence make it attractive for engineering a protein to bind to an arbitrary RNA sequence. We elucidated the RNA-binding preferences of nearly all possible TRM combinations for each of the repeats of the human Pumilio-1 PUF domain. By calculating scores both for binding interaction and for specificity, we obtained base-specific recognition patterns for each repeat as a resource for the rational design of PUF domains Fig. . For som34 showed binding when present in a few, but not all, repeat locations. We identified novel TRM combinations that worked well for RNA base recognition in each repeat of the PUF domain. However, context effects apparent from our selections complicate the application of PUF proteins in rational design. Variations among the specificity code might be minimized by using a scaffold derived from a single repeat, or pairs of repeats, in series, rather than a naturally occurring PUF protein. Such derivatives could be assessed using assays described here.Many base-specific TRM combinations identified for one repeat are not generic across all repeats, suggesting that distinct codes should be considered depending on the repeat, and the protein being engineered. For example, previously described C-specific codes 28. In addition, crosstalk appeared to occur between N- and C-terminal repeats. For example, while double variants with substitutions in only one terminus worked well in recognition of a new RNA target, the addition of one or two substitutions at the other terminus of the domain often resulted in failed designs. This phenomenon might be related to PUF domain structure, with basic concave and acidic convex surfaces critical for RNA binding and structural stability27. Mutations in both N- and C-terminal repeats might lead to the partial unfolding of the domain due to changes in surface acidity. Another possibility is that two PUF domains may bind to a single RNA sequence in an antiparallel fashion. Gupta et al.49 reported that two PUF domains can co-occupy a single intact NRE RNA with cooperative binding, and that this phenomenon can be found in other non-canonical PUF proteins 50. Thus, beyond a focus on designing specificity for each individual repeat location in an engineered PUF domain, crosstalk between repeat locations should be considered to maximize affinity.We found that N-terminal locations in a PUF domain were more robust for tolerating combinatorial mutations than C-terminal locations, as observed previously28 scored the prevalence of TRM combinations at each PUF repeat in 94 Pumilio-1 homologues, inferring the abundance of natural TRM combinations from the sequence alignments. Their aligned data are broadly consistent with our high-throughput screening results. It is striking that the C-terminal repeats of the PUF domain can be rationally designed to bind other RNA sequences, yet are highly conserved in their specificity during evolution. As has been noted28, the observation that a wide range of PUF proteins maintain similar C-terminal TRMs and a UGU sequence at the 5’ end of the binding site implies that this region executes biological roles beyond RNA binding that constrain the protein’s evolutionary divergence.Campbell et al.51 found that stacking amino acids in the C. elegans FBF-2 PUF domain contribute to the protein’s specificity for its RNA sequence. The structural analysis supports the idea that different stacking arrangements can lead to different specificities for RNA recognition51. Campbell et al.28. provide additional examples of the role of stacking interactions. By comparing base-specific recognition patterns across repeats, we found that stacking interactions at different positions influence specificity. For example, SWD specifically recognized guanine in repeats 5, 6, and 7, while SGD was specific for guanine in repeats 1 and 3. These results support the idea that the stacking residue in each repeat may function in combination with neighboring amino acids to specify an RNA base. Koh et al.51 tested whether the identity of the stacking residue contributes to specificity for the neighboring 3′ base and found that the effect is limited. Our data suggest a similar conclusion. For example, with mutation of SNE to NPG in repeat 7, the substitution from asparagine to proline in the stacking residue did not alter the preference for U at the neighboring 3′ base (base 3)  use their eight repeats to bind to RNA sequences with nine or ten bases by allowing one base to be turned away from the RNA-binding surface55. Occasionally, base flipping can occur to accommodate simultaneous occupancy of the binding pockets. Wang et al.36 suggest that PUF proteins likely exist with greater flexibility to allow base flipping to accommodate different modes of binding to achieve overall binding affinity. With these possibilities in mind, a multi-stage process should be considered for engineering PUF domains to bind to a target RNA. Initially, PUF designs having TRM combinations in each repeat specific for the target RNA can be used in screens to recover RNA targets that bind. Then, this information can be incorporated to determine additional design features for improved binding. The addition of machine learning to the screening results may enable features critical for PUF-RNA binding to be readily exploited.Finally, our findings indicate a balance between individual binding specificity and total binding affinity. In the evolution of PUF proteins, TRM combinations have been selected to increase or decrease individual repeat specificity while maintaining the total binding affinity needed for biological function56) into the NotI site of pACT2 (ref.46). Unique restriction sites for cloning the protein-coding sequence and the RNA element were added to the construct. With the Pumilio-1 PUF domain cloned into NcoI and SacI sites of pAIO3H, either the wild-type p38α-NREa or each of its possible single-base substitutions variants were cloned into the XmaI and SphI sites of the RNA expression module, generating 25 distinct pAIO3H plasmids. For the randomized libraries, we used 32 primers (four primers for each PUF repeat), with each primer containing a random NNK (K = G or T) for each of the three TRM residues. In addition, we introduced to each primer a two-base code in the form of synonymous changes in codons 11 and either 14 or 15, which are adjacent to the TRM residues, to specify the identity of the RNA base in the cognate binding site. Each of the PCR products that included the randomized TRMs and the RNA base-specifying synonymous changes was cloned into one of the 25 pAIO3H plasmids with the matching RNA mutation, thereby allowing the identification of the protein-RNA partners by sequencing the region encoding the TRM and adjacent residue. For the targeted library, we ordered an oligo pool that contains 2000 fragments (Twist Bioscience) and incorporated them into the construct through Gibson assembly57.A centromeric vector designated pAIO3H was created by cloning the NotI-RNA module-NotI fragment from p3HR2 -HIS3, ura3:: (lexA-op)-lacZ, LexA-MS2 coat (N55K). We collected transformants from plates containing SC-Leu media37. For the three-hybrid selection, we plated the transformants onto plates containing SC-Leu media and plates with SC-Leu-His + 0.5 mM 3-AT media. Colonies were grown for four days. We collected cells and extracted their plasmids .Library plasmids were transformed according to a previous protocolThe region including each PUF repeat was amplified through sequential reactions. Internal PCR was carried out through primers with sequences that anneal to each repeat (primers 623–654) and external PCR was carried out to add an Illumina sequencing adapter (primers 419–422), which is provided in Supplementary Data Base calls were converted to fastq format and demultiplexed using Illumina’s bcl2fastq/2.16.0.10 tolerating one mismatched base in barcodes (edit distance (ED) < 2). Read1 and read2 were merged using SeqPrep/2016. Low quality reads were filtered based on the exact match of the first 10 bp common sequence in the plasmid. Reads were first filtered by their internal common sequence, with those that matched this sequence retained and those that did not discarded. We then divided the matched reads into eight groups and assigned a repeat identifier to each matched read based on the unique common sequence in each repeat. The filtered reads were then trimmed to a 16 bp-sequence. The first base of this sequence indicates RNA identity (A/G/U/C) and the following 15 bp indicate the amino acid sequence from position 12 through position 16. Based on the amino acids in position 12, 13, and 16, we assigned a PUF variant identifier to that read. Sequencing read counts corresponding to a given PUF variant were equal to the sum of read counts from all trimmed reads matching that variant.We determined the frequency of each variant in both input pool and post-selection pool by comparing its sequencing reads from both pools. The log2 change in the frequency from input to selection pool serves as a measure of binding activity for each PUF variant, designated as a PUF domain–RNA interaction score in this assay. For those PUF variants only shown in the input pool, their interaction score is calculated by adding a count of 1 (pseudocount) in the post-selection pool. A specificity score is assigned to each PUF variant calculating the difference between its highest and second-highest interaction score. For Fig. 59 to summarize the recognition patterns. For regions with fewer than 10 variants, we used one sequence logo to summarize all information. For regions with more than 10 variants, we manually separated these variants into several subgroups based on the properties of the amino acids  in the three positions and generated separate sequence logos to summarize the patterns of these subgroups.For PUF variants with interaction scores >0, we carried out unsupervised hierarchical clustering analysis based on the scores against all four RNA bases. PUF variants with similar RNA specificity were clustered together. We focused on regions of variants with specificity >4 and generated sequence logosWe designed the RNA oligo array with 1900 RNA elements; 900 of them comprised 20 copies of wild-type sequence and 55 copies of each of the 16 target sequences. For the remaining 1000 RNA sequences, we programmed the percentage of A, C, G, and U in each location of the 8-base RNA sequences based on a combination of the 16 targets, such that for each location, the programmed base had a relatively high probability to match the target bases and a low probability to match the off-target bases. We used the code: base_1 = sample, size = 1, prob = c); base_2 = sample, size = 1, prob = c); base_3 = sample, size = 1, prob = c); base_4 = sample, size = 1, prob = c); base_5 =  sample, size = 1, prob = c); base_6 = sample, size = 1, prob = c); base_7 = sample, size = 1, prob = c); base_8 = sample, size = 1, prob = c).Cells were grown overnight in YPD medium at 30 °C. The cultures were diluted to an OD600 of 0.05, and 3 additional 10-fold serial dilutions were made. The cells were spotted onto plates with one of three different media: synthetic complete without leucine (SC-L); synthetic complete without leucine and histidine and with 0.5 mM 3-AT (SC-L-H + 0.5 mM 3-AT); and synthetic complete without leucine and histidine and with 2 mM 3-AT (SC-L-H + 2 mM 3-AT).E. coli cells  by heat shock at 42 °C for 45 s. The resulting GST fusion proteins were expressed in BL-21 E. coli cells, which were grown at 37 °C to O.D. 0.6, chilled on ice, and incubated at 16 or 18 °C overnight after the addition of IPTG to 400 µM final concentration. Cells were harvested by centrifugation, washed with PBS, and stored at -80 °C overnight. Protein purification was performed using B-PER™ Complete Bacterial Protein Extraction Reagent  following the manufacturer’s instructions and the addition of 1 mM EDTA, 1 mM DTT, and 1x Complete™ Protease Inhibitor . Cell extracts were purified using either Pierce™ Glutathione Chromatography Cartridges   or Pierce™ GST Spin Purification Kit  . Glutathione agarose was washed with Bind/Wash buffer  and cell lysates mixed with Bind/Wash buffer at 1:1 ratio were loaded onto Glutathione columns by syringe or incubated while rotating at 4 °C for 1 h. Bound protein was washed three times using Bind/Wash buffer and on-column or on-resin cleavage was performed by adding Elution buffer  containing Turbo3C (HRV3C) Protease . Purified proteins were analyzed using SDS-PAGE and protein concentrations were determined using the Bradford assay.The wild-type and PUF variants were subcloned into the pGEX-6P-1 plasmid to generate an N-terminal GST tag using the Gibson assembly method. Briefly, the vector backbone was digested with EcoRI-HF  and BamHI-HF  enzymes, and the insert with complementary overhang sequences was amplified using Phusion polymerase and cleaned up using the Zymo DNA Clean & Concentrator™ Kits . Gibson assembly was performed according to the manufacturer’s instructions  and used directly to transform One Shot™ TOP10 Chemically Competent UrGrUrArArArUrArGrUrCrUrGrCrArU). The 20-mer oligoribonucleotides were labeled with [γ-32P]-ATP and T4 polynucleotide kinase  using standard conditions. The labeled RNAs were denatured in 1X EMSA buffer  without BSA and tRNA by heating the sample for 30 s at 80 °C and cooling it down on ice for 2 min60. About 10 nM labeled, denatured RNA was incubated with 0–1 μM protein in 14-μl reactions in 1× EMSA buffer. Reactions were incubated on ice for 1 h. After the addition of 5 µl 4× loading buffer , samples were separated on a 1.2% agarose gel for 1 h at 100 V at room temperature using 1× TBE as running buffer. The agarose gel was dried, exposed to a phosphorimager screen overnight and radioactive bands were detected using the Typhoon FLA 9500 biomolecular imager 61. The Kd value is the concentration of PUF at which 50% of the RNA is in a complex with the protein. It was determined by first using ImageJ software to quantify the signal in each RNA band. Background signals from blank regions of the gel were subtracted from the signal intensities obtained from the bands. The fraction of RNA bound was determined from the background-subtracted signal intensities using the expression: bound/(bound + unbound). The fraction of RNA bound in each reaction was plotted versus the concentration of PUF protein. We then used Prism Software to perform non-linear regression and obtain a value for Kd and its 95% CI.20-mer Oligoribonucleotides were ordered from IDT. The NRE was flanked by six upstream and six downstream bases (e.g., wild-type RNA is rGrGrUrArArGrFurther information on research design is available in the supplementary infoPeer Review Filesupplementary data 1supplementary data 2supplementary data 3Description of additional supplementary filesReporting Summary


Patients with COVID-19 shed SARS-CoV-2 RNA in stool, sometimes well after their respiratory infection has cleared. This may be significant for patient health, epidemiology, and diagnosis. However, methods to preserve stool, and to extract and quantify viral RNA are not standardized. We test the performance of three preservative approaches at yielding detectable SARS-CoV-2 RNA: the OMNIgene-GUT kit, Zymo DNA/RNA shield kit, and the most commonly applied, storage without preservative. We test these in combination with three extraction kits: QIAamp Viral RNA Mini Kit, Zymo Quick-RNA Viral Kit, and MagMAX Viral/Pathogen Kit. We also test the utility of ddPCR and RT-qPCR for the reliable quantification of SARS-CoV-2 RNA from stool. We identify that the Zymo DNA/RNA preservative and the QiaAMP extraction kit yield more detectable RNA than the others, using both ddPCR and RT-qPCR. Taken together, we recommend a comprehensive methodology for preservation, extraction and detection of RNA from SARS-CoV-2 and other coronaviruses in stool. While the analysis of SARS-CoV-2 RNA in stool samples has led to important insights regarding the disease, quantification is currently challenging. Here the authors use patient samples to benchmark preservation, extraction and quantification methods to optimise detection of viral RNA. Coronaviridae family1 that causes coronavirus disease 2019 (COVID-19). This disease has spread rapidly across the globe and remains a public health threat2. COVID-19 is typically considered a respiratory disease, with primary symptoms including cough, sore throat, congestion, anosmia, and dyspnea. However, gastrointestinal (GI) symptoms are also recognized as manifestations of the disease4. Further, patients shed viral RNA in their stool up to 70 days after disease onset, well after they have cleared the infection from their respiratory tissues5. While transmission of SARS-CoV-2 typically occurs through the respiratory tract, some reports indicate the presence of infectious viral particles in patient stool7. Whether these are truly infectious and have ramifications for public health remains to be definitively demonstrated. However, from an individual patient health perspective, SARS-CoV-2 antigen is found to persist in the GI tract. Further, there is a preliminary hypothesis that persistent SARS-CoV-2 RNA and protein antigen in the GI tract may promote evolution of host humoral immunity to variants of the virus8. Relatedly, prolonged viral RNA shedding in stool may indicate a superior immune response8. Finally, from an epidemiological perspective, researchers monitor SARS-CoV-2 load in sewage as a proxy for the burden of disease within a population9. Taken together, monitoring the fecal shedding of SARS-CoV-2 is vital to fully understanding this pathogen and its effect on patient health in addition to informing public health measures. Therefore, a standardized method to handle and process samples for accurate quantification of viral RNA in stool is critical. Notably, the proposed method should allow for external validity and harmonization of data across studies.Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is an RNA virus from the 10. Further, stool also contains RNases that can rapidly degrade unprotected RNA. Therefore, it is critical that we use appropriate preservatives that protect RNA in stool and employ extraction methods that effectively recover RNA without co-eluting inhibitors or contaminants. In the absence of a comprehensive, standardized protocol, existing studies of SARS-CoV-2 RNA in stool employ methods that have not yet been optimized. Further, the variability in techniques used across studies makes meta-analysis difficult, hindering our overall understanding of the disease. While there is heterogeneity, the majority of existing studies collect and store stool without any preservative13, dilute stool in phosphate-buffered saline (PBS) at the time of RNA extraction, and employ the QIA-Amp Viral RNA Kit from Qiagen for RNA isolation. Unfortunately, the efficiency of these strategies in preserving and extracting SARS-CoV-2 RNA is unknown and has not yet been systematically analyzed. Finally, after RNA extraction, the detection and quantification of RNA by RT-qPCR has elements that have yet to be standardized. While the primer/probe sets used are generally consistent, classifying samples as positive for the presence of SARS-CoV-2 RNA has often been based on arbitrary thresholds set in the absence of a relevant standard curve16. These experimental inconsistencies and the lack of a clearly validated experimental pipeline contribute significantly to heterogeneity in detection and quantification of viral RNA in stool. To overcome these challenges, we sought to test a variety of accessible and common methods for the preservation, extraction, and detection of viral RNA from stool samples, and present here an optimized pipeline.Accurately quantifying fecal shedding of SARS-CoV-2 RNA is challenging. Stool is a heterogeneous sample matrix that contains numerous PCR inhibitors that impede downstream processes like reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) for quantifying RNAIn the current study, we present data comparing the performance of three different stool preservatives, three nucleic acid extraction kits, and two PCR-based assays for detecting fecal SARS-CoV-2 RNA. Based on these data, we recommend a pipeline for collecting and processing stool samples for the detection of SARS-CoV-2 RNA. Finally, we validate this standardized pipeline using patient samples collected from a clinical trial. Altogether, our findings here will guide the field toward a more standardized method of robustly measuring the fecal burden of SARS-CoV-2 RNA both in clinical and research settings.17 and the German Centre for Infection Research (DZIF)18, preliminary studies have revealed that not all of them are at reliable concentrations19. Therefore, we tested two synthetic RNA preparations: one from the American Type Culture Collection (ATCC) and one from the United States National Institute of Standards and Technology (NIST) with listed concentrations of 105–106 and 106 copies/μL, respectively. We chose these positive controls since they are easily accessible to other laboratories and are from reliable sources. A five-point tenfold dilution series from a starting concentration of 104 going down to 100 copies/μL was tested in duplicate droplet digital PCR nes Fig. ; efficie22, while RT-qPCR is a more accessible platform for nucleic acid detection, we employed both techniques across the study to be widely informative. In both assays, we used the one-step format that combines the reverse transcription and amplification steps in a single reaction for a simpler protocol.We found that primer/probe sets targeting E, N1, and N2 performed comparably in both the ddPCR and RT-qPCR assays based on accuracy of detection with respect to theoretical input concentration and efficiencies of RT-qPCR reactions Fig. . Given t23, and (c) stool preserved in the Zymo DNA/RNA Shield Collection Kit  that is explicitly rated for RNA preservation and virus inactivation.We tested three different strategies to identify suitable methods of stool preservation for detection of SARS-CoV-2 viral RNA: (a) stool stored without any preservative and resuspended in PBS (PBS), (b) stool preserved in the OMNIgene-GUT tube , a commonly used preservation kit in microbiome studies24, (b) QiaAMP Viral RNA Mini Kit , a column-based protocol that is used in many studies of fecal SARS-CoV-2 RNA13, and (c) Quick-RNA Viral Kit , another column-based protocol that is rated to be compatible with the ZY Stool Collection Kit. All three of these extraction kits are scalable to a high-throughput format and therefore easily adaptable to clinical laboratories and other large-scale efforts.In parallel, we also tested how these preservation methods interact with three different extraction kits—(a) MagMAX Viral/Pathogen Nucleic Acid Isolation Kit , a magnetic bead-based protocol that has been successfully used with respiratory samples25. We spiked in synthetic SARS-CoV-2 RNA from ATCC (CoV-2 RNA) at two concentrations (103 and 104 copies/μL of preserved stool sample) in this standardized stool sample and tested the combination of stool preservation and extraction kits to benchmark their performances across multiple target RNA concentrations . We observe the same performance trend with samples spiked with 103 concentration of CoV-2 RNA extracted using QA . Across both stool preservatives, MM and ZV outperform QA , or a combination thereof.Notably, in the PBS preservative we detected SARS-CoV-2 RNA at roughly three orders of magnitude lower in eluates extracted from stool spiked with 10Betacoronavirus, in the subgenus Embecovirus, sharing this taxonomy with other human pathogens (HCoV-HKU1 and HCoV-OC43). BCoV and SARS-CoV-2 share a common structural architecture and are both positive stranded RNA viruses. Further, BCoV can be procured as an over-the-counter attenuated vaccine. Prior to stool-based testing, we evaluated the performance of the ddPCR and RT-qPCR assays with the recommended primer/probe set to detect the M gene in BCoV RNA. Toward this, we used RNA extracted directly from the attenuated BCoV vaccine prepared in PBS in the absence of stool. We found both the ddPCR and RT-qPCR assays reliably tracked a seven-point tenfold dilution of the RNA extracts, and the RT-qPCR reaction efficiency of targeting the M gene is 97–100% , a virus that belongs to the same genus as SARS-CoV-2, In this experiment, we recovered the target BCoV RNA even in PBS, albeit at lower concentrations compared to other preservation methods Fig. . FurtherAlongside efforts to extract BCoV RNA from spiked stool samples, each user also extracted RNA directly from the BCoV vaccine without any stool sample. This allows us to evaluate whether the extraction kits interact differently with encapsulated RNA and also serves as a positive control for the extractions. Notably, we find that all extraction kits perform comparably and reliably extract RNA from the BCoV vaccine  and BCoV (1:10 dilution) from our previous analysis to challenge the sensitivity of the combinations of preservation, extraction, and detection techniques tested here.While the NIST stool samples are a useful, standardized preparation, this processed, pooled, and diluted standardized stool sample is limited in its representation of regular clinical specimens. Therefore, we next tested the combinations of preservatives and viral RNA extraction kits using undiluted and unprocessed stool samples from healthy donors, spiked with the SARS-CoV-2 RNA and BCoV standards. We picked the lower concentrations of both the SARS-CoV-2 RNA  and the other on a vegetarian diet (Veg)  by 0.381 log10 units (or ~2.4 times more) .From this data set, we picked instances of paired OG and ZY viral loads determined from samples collected from the same patient at the same time. Out of 240 such samples from 98 independent COVID-19 outpatients, 122 stool samples did not yield a detectable amount of target RNA in either preservative and were left out of further analysis. Taking the 118 paired samples from which we detected the viral RNA targets in at least one of the preservatives, we plotted their logT test; P = 5.49E−6). This roughly threefold difference in stool biomass tracks closely with the threefold difference we observe above in the performance of the two kits. Therefore, likely, the two kits preserve and yield comparable amounts of detectable SARS-CoV-2 RNA, when accounting for the amount of input stool.While the ZY preservative may be more effective at protecting RNA, it is also possible that the ZY collection kit ends up with more stool compared to the OG kit. In order to address this question, we estimated how much of the sample from either of these kits is actually composed of stool. To this end, we randomly selected paired samples collected in the OG and ZY tubes from the biobank of stool samples collected from COVID-19 outpatients enrolled in the aforementioned clinical trial of Peginterferon Lambda-1a. Specifically, each of these pairs was collected from the same patient at the time of enrollment in the study. We took two biopsy punches from each of these ten stool samples and measured their wet weight. Next, we dried these samples on a heat block for 72 h and measured their dry weight. The percentage of dry weight to wet weight represents the proportion of patient stool biomass in the original sample. We found that 31.4 ± 1.6% of sample weight in the ZY preservative corresponds to stool biomass, compared to 13.6 ± 3.3% of sample weight in the OG preservative  and 1000 mg of stool to ZY (containing 9 mL of buffer). We suspect that this difference in stool input we observe in the clinical samples may be the effect of the format of the two kits. Specifically, the OG kit is composed of a specific receptacle of defined volume to collect stool, while the ZY kit is just a standard collection tube with a proprietary buffer , yielding more detectable target RNA. However, other pair-wise comparisons of SARS-CoV-2 RNA yields across kits are comparable , we also report the detected copies of N1 gene per gram of stool to normalize the copies of SARS-CoV-2 RNA to the amount of stool placed into the preservative by patients. This does not alter our conclusions regarding the best extraction kit, but given the differing input of stool in various preservative options, we believe that reporting detected copies per gram of stool where possible will best harmonize reported viral loads of SARS-CoV-2 in feces  provide a roadmap for a rigorous evaluation of LoBFinally, it is important to be able to quantify the viral RNA load in stool as a potential indicator of the state and prognosis of infection in patients. To this end, while ddPCR provides a powerful platform capable of determining the absolute concentration of RNA, we recognize that this may be cost prohibitive and inaccessible. Therefore, additionally, we demonstrate here experimental strategies that enable the adoption of the more accessible RT-qPCR assay to enable the accurate detection and relative quantification of viral load in samples  protocol #42043  and stored at −80 °C without any preservatives. Finally, samples and data from patients were collected as per Stanford IRB protocol #55619  Compared with Standard Supportive Care in Outpatients with Mild COVID-19).We used two types of non-clinical stool samples in this study. The first set of samples were acquired from the United States NIST13. They are then resuspended in PBS in a 1:5 ratio (w:v) prior to RNA extraction. As a proxy for these samples, we added 1000 mg of stool to 5 mL of PBS and describe these as PBS-preserved samples throughout this study. Separately, we also stored samples in the OG  and ZY  according to the manufacturers’ instructions. Briefly, we added 500 mg of stool to the OG tube containing 2 mL of preservative to prepare the OG samples and 1000 mg stool to the ZY kit with 9 mL of buffer to prepare the ZY samples. Given shortages in the supply of the ZY kit, we also resorted to recreating this kit in house using 9 mL of the DNA/RNA Shield buffer  in a 15 mL centrifuge tube . Subsequently, where listed, we spiked in synthetic SARS-CoV-2 RNA from ATCC  at a final concentration of either 103 or 104 copies/µL of preserved stool sample. For samples spiked with the control BCoV, we prepared BCoV by resuspending one vial of lyophilized Zoetis Calf-Guard Bovine Rotavirus-Coronavirus Vaccine  in 3 mL of PBS to create an undiluted reagent as per the manufacturer’s instructions. We then added 60 µL of either undiluted or diluted BCoV (1:10 in PBS) to 3 mL of preserved stool sample. To create an extraction blank control, an equivalent volume of PBS was added to samples labeled 'None'. All samples were then stored in 1.5 mL DNA LoBind tubes  and immediately frozen at −80 °C.In most studies, stool samples are collected and stored without a preservative26. Briefly, study subjects were requested to collect samples in both the OG and ZY tubes according to the manufacturer’s instructions, and samples were stored at room temperature for up to 7 days before being processed into cryovials and frozen at −80 °C until subsequent use. We directly used OG- and ZY-preserved samples in the subsequent extraction steps. Where mentioned, samples were spiked with 10 μL of attenuated BCoV vaccine per 500 μL of preserved stool sample after thawing an aliquot for extraction.Clinical samples were collected and stored from patients participating in an interventional study of Peginterferon Lambda-1ag for 2 min to remove solids from the sample. We then processed 200 µL of supernatant according to the manufacturer’s instructions for QA  and ZY . For supernatant processed using MM , we followed the manufacturer’s instructions with the following exception: samples were processed in 1.5 mL DNA LoBind tubes rather than 1.5 mL deep-well plates. We eluted RNA from each sample in 60 µL of the elution buffer included in each kit. The eluted RNA was stored in a 96-well plate at −80 °C.We spun down 600 µL of each preserved stool sample at 10,000 × We assembled the RT-qPCR reaction using a Biomek FX liquid handler by adding 5 µL of eluted RNA to 5 µL of TaqPath 1-Step RT-qPCR CG mastermix , 8.5 µL of nuclease-free water , and 1.5 μL of primer/probe mix. The primer/probe mix was composed of 200 nM each of forward primer, reverse primer, and probe  with sequences summarized in Supplementary Data 37, and the recommended associated checklist is included in Supplementary Data Cq) value using the Design and Analysis software . Standard curves for quantification were generated using a five-point tenfold dilution of the SARS-CoV-2 ATCC standard from 104 to 100 copies/µL of template. We calculated the concentration of RNA using a linear regression of the standard curve. We established LoB on a plate-by-plate basis; specifically, we turned to the specific plate that an experimental sample was assayed on and picked the lowest Cq among the following controls run in the same plate: the y-intercept of the line of best fit from the standard curve, none (no RNA or BCoV spiked) stool samples, water, and elution buffers from the RNA extraction kits as listed in the relevant experiments. RNA concentrations from reactions with Cq values below the LoB were defined as Undetermined. The concentration of RNA from technical duplicate RT-qPCR reactions were averaged. If one of the two technical duplicate reactions failed to amplify within the range of the standard curve, the viral concentration from that sample was treated as Undetermined.Our RT-qPCR analysis is guided by the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines38 and the digital MIQE guidelines , 1.1 µL of 20× Custom ddPCR Assay Primer/Probe Mix , and 6.6 µL of nuclease-free water . The Supermix, reverse transcriptase, and DTT are from the One-Step RT-ddPCR Advanced Kit for Probes . We then processed the assembled reactions on a QX200 AutoDG Droplet Digital PCR System to partition samples into droplets of roughly 1 nL using default settings. Amplification was performed on a BioRad T100 thermocycler using the following thermocycling program: 50 °C for 60 min, 95 °C for 10 min, 40 cycles of 94 °C for 30 s and 55 °C for 1 min, followed by 1 cycle of 98 °C for 10 min and 4 °C for 30 min with ramp speed of 1.6 °C/s at each step40.Our ddPCR analysis is guided by the Droplet Digital PCR Applications Guide on QX200 machines (BioRad)We thresholded the samples to ascertain the value at which a droplet was considered positive by applying a multistep process that used the following positive and negative controls included on each plate: ATCC SARS-CoV-2 RNA, RNA extracted directly from attenuated BCoV vaccine prepared in PBS, water, and elution buffers. First, we set the threshold between the mean positive and negative amplitudes of these controls to minimize detected copies in the negative controls and to reflect the expected RNA concentration of the positive samples. We then calculated the difference between the mean negative amplitude and the threshold amplitude in the negative control reactions and added it to the mean negative amplitude for each sample. Positive and negative examples of the final thresholded data are included in Supplementary Fig. 41.A step-by-step protocol describing the pipeline from sample preparation to quantification and analysis can be found at Protocol ExchangeWe first recorded the weight of one 1.5 mL DNA LoBind microcentrifuge tube per sample. Next, we took two biopsy punches using the Integra Miltex Biopsy Punches with Plunger System  from each of the relevant stool samples and transferred these to microcentrifuge tubes corresponding to the respective samples. The tubes were then weighed, and the respective wet weight was calculated upon subtracting the weight of the empty tube. Next, they were incubated on a heat block at 100 °C for 72 h and reweighed. The dry weight was calculated by subtracting the weight of the empty tube.10-transformed data prior to rounding, and statistical significance was assessed at α = 0.05. Unless otherwise stated, we performed the paired T tests in all comparisons. Linear regressions and paired T tests were performed using the stats (version 4.0.0) and rstatix (version 0.7.0) packages using default parameters and commands. No custom code was used. Regressions were plotted using the ggpubr package (version 0.4.0).We performed statistical analyses using R (version 4.0.0). All statistical analyses were two-sided, performed on the logFurther information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Description of Additional Supplementary FilesPeer Review FileReporting Summary


Saccharomyces cerevisiae lacks sequence homology with retroviral Gag, but is functionally related. In addition to capsid assembly functions, Ty1 Gag promotes Ty1 RNA dimerization and cyclization and initiation of reverse transcription. Direct interactions between Gag and retrotransposon genomic RNA (gRNA) are needed for Ty1 replication, and mutations in the RNA-binding domain disrupt nucleation of retrosomes and assembly of functional virus-like particles (VLPs). Unlike retroviral Gag, the specificity of Ty1 Gag-RNA interactions remain poorly understood. Here we use microscale thermophoresis (MST) and electrophoretic mobility shift assays (EMSA) to analyze interactions of immature and mature Ty1 Gag with RNAs. The salt-dependent experiments showed that Ty1 Gag binds with high and similar affinity to different RNAs. However, we observed a preferential interaction between Ty1 Gag and Ty1 RNA containing a packaging signal (Psi) in RNA competition analyses. We also uncover a relationship between Ty1 RNA structure and Gag binding involving the pseudoknot present on Ty1 gRNA. In all likelihood, the differences in Gag binding affinity detected in vitro only partially explain selective Ty1 RNA packaging into VLPs in vivo.A universal feature of retroelement propagation is the formation of distinct nucleoprotein complexes mediated by the Gag capsid protein. The Ty1 retrotransposon Gag protein from  Pseudoviridae) family contains well-studied LTR retrotransposons present in many eukaryotic genomes  primer PR3 [5′-TCAGGTGATGGAGTGCTCAG-3′], 0.1 mM EDTA, were incubated at 95 °C for 3 min, at 37 °C for 10 min and at 55 °C for 2 min, next reverse transcribed at 50 °C for 50 min using Superscript III Reverse Transcriptase . Sequencing ladders were carried out using plasmid pBDG433 as a template, primer PR3 labeled with WellRed D2 (ddA) or IRD-800 (ddT), and a Thermo Sequenase Cycle Sequencing kit  as recommended by the manufacturer. The following cycling parameters were used: 96 °C/10 s; 24 cycles: 96 °C/20 s, 55 °C/20 s, 72 °C/20 s; 72 °C/1 min. cDNA samples and sequencing ladders were purified using ZR DNA Sequencing Clean-up Kit  and analyzed on a GenomeLab GeXP Analysis System . Raw data from capillary electrophoresis were processed using SHAPEfinder software [p-value was calculated separately for each nucleotide across the samples .RNA samples (5 pmol) were denatured by heating at 90 °C for 2 min in water followed by incubation for 3 min on ice. Next, binding buffer (30 mM HEPES, pH 6.0, 10 mM DTT, 2.5 mM MgClsoftware , and thesoftware . All rea


Association of mRNAs with RNA-binding proteins (RBPs) in the cytoplasm regulates their fate from their translation, storage to decay. Identification of novel RBPs and understanding the relevance of known mRNA-protein interactions still remains an exciting thrust area of RNA biology. Understanding how physiological cues experienced by the cell lead to changes in RNA-protein interactions, eventually leading to altered proteome, is an active field of research. RNA-protein complexes assembled in RNA granules (P-bodies and stress granules) play a key role in determining mRNA fate.The aim of our topic was to highlight studies focusing on above aspect of RNA Biology. This has been accomplished through eight articles focusing on different aspects of RNA-protein interactions.Behari et al., report the conserved RNA-binding activity of Inositol-5-phosphate-4-kinase (PIP4K2A) which was reported earlier in Plasmodium and further confirmed in Drosophila and Toxoplasmi. Interestingly the RNA-binding activity is independent of its kinase activity. The authors propose that the RNA-binding activity of PIP4K2A may be important for posttranscriptional gene control in Plasmodium propagation as well as in multicellular host organism. Further, in the context of solid tumors, Glaß et al., identify three mRNA targets of oncofetal IGF2 mRNA-binding protein (IGF2BP1). These transcripts are stabilized by IGF2BP1 and are involved in processes associated with hallmarks of cancer. Finally, de Vries et al., identify the role of P23 protein in macrophage inflammation response. P23 is a Hsp90 co-chaperone that demonstrates poly(A) RNA-binding activity. P23 contributes towards macrophage migration and phagocytic activity by stabilizing Kif15, a motor protein belonging to the kinesin family.The role of RBPs in disease-related topics has been addressed in three articles. Fernandez and Buchan review the scaffolding role of RNAs in the functioning of mRNPs in both the cytoplasm and the nucleus. An exciting aspect of this discussion is the emerging role of mRNAs as decoys in regulating mRNP assembly dynamics. Tweedie and Nissan highlight the role of stress granules (SGs) in response to infections by bacteria, fungi and protozoa. The assembly of SGs could affect host immune response and alter the outcome of microbial infection.Two review articles in this topic provide insights into RNA granule assembly and function. Paturi and Deshmukh review the role of Dicer in the RNA-induced silencing complex (RISC) by orchestrating complex protein interaction network. Based on the observation that there is more than one distinct Dicer paralog in insects and plants as compared to single paralog in other eukaryotic organism, they discuss the role of Dicer in RNAi and other defense processes. Qin et al., on the other hand identify the role of miR-490-3p in silencing CDK1 in a liquid-liquid phase separation (LLPS) dependent manner in colon cancer cell lines.The role of RBPs in non-coding RNA biology has been addressed by two articles. Bheemireddy et al. address RNA-protein interactions during translation initiation using computational methods. The authors inspect the interaction of 30S ribosome subunit with mRNAs and identify ribosomal proteins S13, S19, and S20 as sensors that respond to changes induced by the interactions of the 30S subunit with the mRNA.Finally, Overall, the collection of articles in this topic advances our understanding of the role of different RNA-binding proteins in health and disease.


Nucleic acids help clarify variation in species richness of insects having different metamorphosis modes, a biological conundrum. Here we analyse nucleic acid contents of 639 specimens of aquatic insects collected from four high mountain streams of Sierra Nevada in southern Spain to test whether the allocation to RNA or DNA content differs during ontogeny between juvenile insects undergoing direct (hemimetabolous) or indirect (holometabolous) metamorphosis. The results show that RNA content as a function of body mass was negatively correlated to insect body length in four out of six and three out of six of the holometabolan and hemimetabolan taxa, respectively. Although no significant differences in RNA content were found between holometabolans and hemimetabolans, the significant interaction between body length and metamorphosis mode for RNA and RNA:DNA indicates a strong ontogenetic component to RNA allocation. In addition, our finding of lower DNA content in holometabolans relative to hemimetabolans agree with the analysis of empirical genome data in aquatic and terrestrial insects, and extend to this class of arthropods the “growth rate-genome size-nutrient limitation” hypothesis that differences in allocation between RNA and DNA may reflect fundamental evolutionary trade-off of life-history strategies associated with high growth rates (and RNA content) in holometabolans at the expense of diminished genome sizes. This radical transformation from juvenile to adult has long been studied from the standpoint of morphology, physiology, and regulation of development, while recent studies have made progress elucidating the molecular and hormonal mechanisms driving moulting and metamorphosis3. However, many questions have been neglected regarding how the mode of metamorphosis relates to the biochemical composition and in turn affects key life-history traits and ecological fitness.In pterygote insects juveniles undergo greater or lesser morphological differences with respect to adult forms by undergoing one of two modes of metamorphosis: either indirect metamorphosis (holometaboly) or direct (hemimetaboly). Of all known living insects, 80.7% are holometabolans, 19.2% are hemimetabolans, and 0.1% are non-pterygote ametabolans Archaeognatha and Zygentoma5. In the field of biological stoichiometry, the growth-rate hypothesis (GRH) states that organisms with high specific growth rates demand large amounts of phosphorus (P) to build the RNA needed to sustain rapid protein synthesis and growth6. The GRH holds for many invertebrates, helping to explain biochemical differences among and within species8. Recent work has demonstrated differences more pronounced than previously thought regarding the development of holometabolans in comparison to hemimetabolans in terms of P content variability, and presumably in RNA content9. Because holometabolous insects grow almost twice as much in length at each moult as hemimetabolous do10, we might expect the former to have considerably more RNA.At the core of Biology as a science lies the idea that each organism’s life-history strategies require biochemical changes and shifts in elemental components13. However, profound causal links between RNA and DNA content have been cited by one research team as an explanation, among others14 (the “bulk DNA” or the “selfish DNA”), for the variation in the genome size of organisms15. These latter researchers hypothesised that the evolutionary mechanism by which intracellular allocation of P from DNA to RNA would accelerate growth rates but reduce the genome size in fast-growing cladocerans compared to long-lived copepods facing chronic P limitation (“growth rate-genome size-nutrient limitation” hypothesis). In our work, we extend this hypothesis by predicting that large differences in nucleic acid (NA) investment and allocation should accompany the ontogenetic development of insects having different modes of metamorphosis. Specifically, we expect to find an overall lower DNA content in holometabolous compared to hemimetabolous taxa. In addition, we predict significant body length × metamorphosis mode effects on RNA and RNA:DNA ratio as an indication of a varying RNA investment over the ontogeny of holo vs. hemimetabolans. To test these predictions, we compared the NA content of 639 specimens of aquatic nymphs or larvae belonging to six holometabolan and six hemimetabolan taxa in four basins of Sierra Nevada National Park in Spain .The relation between RNA and DNA content has been used widely as a potential indicator of growth in numerous organismsP < 0.001, Table Our results showed that RNA content was not significantly different between holometabolans and hemimetabolans  lower DNA content in holometabolans relative to hemimetabolans, and (2) significant body length × metamorphosis mode effects on RNA and RNA:DNA ratio as an indication of pronounced variations in nucleic acid allocation throughout the ontogeny between metamorphosis modes.3. It has been suggested that the main adaptive benefit of indirect metamorphosis of holometabolans is the decoupling between growth and differentiation17. In this process, holometabolous larvae with high RNA undergo fewer moults while gaining and accumulating the nutrients and energy for maximum growth in preparation for pupation. Although growth rate is among the most important life-history traits presumably driving evolutionary fitness4, rapid growth might be costly. For example, the lower metabolic cost of growth (i.e. the amount of energy required to synthesise a unit of biomass) in holometabolans can give rise to cells vulnerable to stressors18. Our findings, however, suggest a fundamental trade-off between growth and reduced genome size. We found that holometabolans exhibited lower DNA content. This is consistent with their lower genome sizes  relative to hemimetabolans in the comparison between these two groups using the insect-genome dataset by Gregory16, which comprises both aquatic and terrestrial insects  from DNA to RNA under selection for rapid growth can lead to genome streamlining in eukaryotes21.The striking ontogenetic differences found in NA allocation in this study coincide with the divergent evolutionary dynamics in the juvenile development of insects showing contrasting life histories Fig. . In gene22. In addition, differences in the allometric patterns of NA allocation between metamorphosis modes might provide a valuable perspective to examine how mixed communities might respond when confronted to nutrient constraints. We might expect early stages of holometabolans with higher demands for P-rich RNA to be particularly sensitive to food quality, as it has been previously shown for copepods23.The observation of substantial ontogenetic differences in NA allocation between RNA and DNA not only provides a mechanistic basis for understanding the eco-evolutionary trade-offs driving metamorphosis diversity, but has far-reaching implications across broad spheres of the ecosystem. Because RNA is directly linked to body-mass production, increased expression of rDNA to produce RNA is directly coupled to secondary production, a process at the foundation of food webs24 sought a scientific explanation for why insects undergo metamorphosis, observing that natural selection might operate in juvenile stages of insects with different immature and life-cycle types. Later, in-depth biomolecular information has established a more consistent relation between biochemical composition and evolutionary fitness. That is, differences in nucleic allocation between RNA and DNA vary ontogenetically but, taken together, may reflect fundamental evolutionary pressure towards rapid growth in holometabolans at the expense of diminished genome sizes relative to hemimetabolans. Given the pivotal role that aquatic insects play in freshwater as well as terrestrial food webs, the evolutionary trade-off between RNA and DNA allocation is essential as it influences key life-history traits such as growth rate. In the challenge to establish generalities underlying living systems, the interrelation of body biochemical composition and life-history traits holds considerable promise. According to our results, such an approach provides a suitable mechanistic basis for understanding the ecological fitness and the eco-evolutionary trade-offs driving the success in insects of different metamorphosis modes facing current accelerated environmental changes.In the nineteenth century, Lubbock2) taken on each station and date and distributed randomly in proportion to the occurrence of major stream habitats . All samples from each station were pooled and individuals representing six hemimetabolous and six holometabolous taxa sorted specifically for the analysis of nucleic acids  or genus level , except for Lepidostomatidae, Limnephilidae, Brachycentridae, and Simuliidae that were identified to the family level. Although taxonomic resolution in the identification varied, taxa identified at the species and genus level represented the majority of the samples in this study. In addition, morphologically similar animals were selected for all supraspecific taxa in order to represent similar morphospecies for each taxon. When possible, up to 20 individuals per taxa that covered the full size spectrum available for each taxon were sorted into 10-mL vials containing RNAlater , and transported inside a cooler to the laboratory. There, all insect samples were stored at − 80 °C until prepared for analysis. Before processing the insects, we measured body length to the nearest half millimetre under a stereoscopic microscope and verified the insect’s identity. In total, 639 individuals of 12 different taxa (six hemimetabolans and six holometabolans) were measured and analysed for NA content.Samples of insects for the analyses of NAs were collected in four basins of Sierra Nevada National Park, southern Spain ; RNase DNasefree ; N-lauroysarcosine ; Tris-EDTA buffer (Q-biogene). Fluorescence measurements were performed using a FLUOstar Optima fluorometer  and black solid flat-bottom microplates . The plate was scanned with a 0.2-s well measurement time, making 10 measurements per well, before and after RNase digestion (30 min under dark conditions at 37 °C). Fluorescence measurements were converted into RNA and DNA concentrations (pg) by using standard curves for RNA  and DNA , and expressed as a percentage of body dry mass (%RNA and %DNA).NAs were measured using a microplate fluorimetric high-range assay Ribo-Green assay  after N-laurylsarcosine extraction and RNase digestion, as described in Gorokhova & Kyle16 in our analysis. The dataset covers a variety of insect groups (including 140 families and 20 orders of hemimetabolous and holometabolous insects) with a representation of most functional feeding groups, life cycles, and trait-based morphologies, comprising a total of 336 hemimetabolous and 999 holometabolous insect records.To test the generality of our hypothesis that DNA size varied between insect metamorphosis modes across taxa and environments , we incorporated the Animal Genome Size Database by GregoryW test) and could not be transformed to fit a normal distribution, so differences in NAs were tested using generalized linear mixed effects models (GLMM). Models included body length and metamorphosis mode as fixed factors, and insect taxa nested within order as random factors to account for variability within taxa subgroups. The significance of the interaction between body length and metamorphosis mode was used to test whether NA allocation to RNA differed during the ontogenetic development of animals. To examine whether insect genome size  varied between holo- and hemimetabolans using Gregory’s genome size dataset, a GLMM was also used with metamorphosis mode set as a predictor and taxa nested within order as a random variable. Before performing the models, the data were standardized (Deconstand function in R) to provide meaningful estimates of main effects in models with interaction terms27 and the best GLMM was selected according to deviance information criteria28. GLMM analyses were conducted using the ‘glmer’ function in the package ‘lme4’29. Finally, because NA data for taxon subsets were normally distributed after a log-transformation, linear-regression models were used to test the relationship between RNA and body length for each taxon. All statistical analyses were made in R30.Testing for differences in NAs between metamorphosis modes, we found that data were not normally distributed (Shapiro–Wilk’s Supplementary Information.


RNA therapeutics involve the use of coding RNA such as mRNA as well as non-coding RNAs such as small interfering RNAs (siRNA), antisense oligonucleotides (ASO) to target mRNA, aptamers, ribozymes, and clustered regularly interspaced short palindromic repeats-CRISPR-associated (CRISPR/Cas) endonuclease to target proteins and DNA. Due to their diverse targeting ability and research in RNA modification and delivery systems, RNA-based formulations have emerged as suitable treatment options for many diseases. Therefore, in this article, we have summarized different RNA therapeutics, their targeting strategies, and clinical progress for various diseases as well as limitations; so that it might help researchers formulate new and advanced RNA therapeutics for various diseases. Additionally, U.S. Food and Drug Administration (USFDA)-approved RNA-based therapeutics have also been discussed. The Human Genome Project (HGP) has revealed vast information about the human genome and has greatly enhanced its role in the development of biomedical research.  As a res1) RNAs that inhibit RNA activity. This includes the use of siRNAs, antisense RNAs, 2) RNAs that target proteins such as RNA aptamers, 3) RNAs that reprogram genetic information including trans-splicing ribozyme, 4) RNAs that encode therapeutic proteins (mRNAs), and 5) DNA modifying CRISPR guide RNAs (gRNAs)  .via the interferon pathway. Thus, the hybridized siRNA is processed by T1 ribonuclease to remove the single-stranded 5′ GGG overhang. (RNA interference (RNAi) involves the use of siRNAs to control gene function. These are long double-stranded RNA (dsRNA) molecules with a characteristic 3′ overhang.  SiRNAs averhang.  The chemverhang.  To achieverhang.  A list oFamilial hypercholesterolemia, which is characterized by a higher level of low-density lipoprotein-cholesterol (LDL-C), increases the risk of atherosclerotic cardiovascular disease (CD).  The LDL The metastasis and invasion are major processes that lead to the proliferation of cancers. Many cancer drugs require their long-term use to be effective and often produce unwanted side effects. SiRNA therapeutics are being tested for the prevention or treatment of various cancers. KRAS mutation is the most well-known alteration in various cancers. The study (NCT01188785) of siG12D-LODER, a biodegradable polymeric matrix enclosing a siRNA against KRASG12D, has demonstrated that the polymer was able to target the tumor and reduce its progression.  Further Protein kinase N3 (PKN3) is a downstream effector of the phosphoinositide-3-kinase (PI3K) pathway and its inhibition in vascular and lymphatic endothelial cells suppresses tumor progression and lymph node metastasis.  Atu027, Rhesus macaques. It demonstrated no pathological or dose-related microscopic findings however, stimulated a mild to moderate inflammatory response and mild hemolytic effect in the female mice while minimal to moderate infiltration of mononuclear cells in Rhesus macaques.  are characterized by increased overproduction of oxalate in the liver due to the lack of metabolic enzymes.  OverprodThe chronic viral infection of hepatitis B virus (HBV) can develop into chronic hepatitis, cirrhosis, and hepatocellular carcinoma (HCC) and covalently closed circular DNA (cccDNA) integrated into the nucleus of hepatocytes persistently produces transcripts that serve as templates for the translation of HBV early antigen (HBeAg) and surface antigens (HBsAg) proteins, which are important in the production of new viral particles. Therefore, HBeAg and HBsAg are targeted for new therapies.  ARC-520 Acute kidney injury (AKI) is generally treated by supportive treatments due to the absence of approved therapeutic agents. P53 is known to play an important role in ischemia reperfusion-induced AKI and has emerged as the pivotal regulator of apoptosis and p53 inhibition in several ischemia-reperfusion injury models has demonstrated to enhance renal function post-injury and improve histology. A clinical study evaluating QPI-1002  has shown that intravenous injection was safe and well-tolerated by AKI patients.  FurthermThe beta-thalassemia or hereditary hemochromatosis are described by iron accumulation due to dysregulation in the hepcidin-ferroportin axis, regulated by transmembrane protease matriptase-2 that is encoded by the TMPRSS6 gene, causes organ damage and severe clinical complications.  In the pASOs are 18–30 base pairs, single-stranded RNA/DNA molecules designed to specifically inhibit mRNA function. They bind to a specific mRNA with Watson-Crick base-pairing, impairing mRNA translation or degrading mRNA through RNase H.  Apart frin vitro and localize in the retinal layers in mice and rabbits. In monkeys, the administration was favorably tolerated, correcting the splicing defect, demonstrating efficacy and safety  retinal dystrophy is linked with juvenile blindness or severe visual impairment due to intronic mutation in various genes including centrosomal protein 290 (CEP290).  QR-110, d safety .in vitro and used not only for detection, inhibition but also for the characterization of specific targets. (Aptamers are single-stranded nucleic acid (DNA or RNA) molecules that bind and inhibit proteins.  The abiltargets.  Various targets.  have entAMD, a common macular disease is a multifactorial disorder and a prominent cause of visual impairment and vision loss. It is characterized by the presence of drusen in the early stage of neovascularization and atrophy in the late stage.  A study Von Willebrand disease results from a defect in the vWF and is the most common inherited disorder causing mucocutaneous bleeding and excessive bleeding after trauma or invasive procedures. The vWF is responsible for regulating primary hemostasis and functions as a coagulation factor VIII carrier and  therefor®, NOX-H94 aptamer targeted hepcidin, a 25 amino acid peptide, and demonstrated a significant increase in Hb levels and increased red cell and reticulocyte hemoglobin in multiple myeloma and low-grade lymphoma patients. , a chemically stabilized anti-VEGFR-1 ribozyme in conjunction with carboplatin and paclitaxel in advanced solid tumor participants has shown to be safe  with goo+ cells showed a significant increase in CD4+ lymphocyte indicating that cell-delivered gene transfer is as much reliable in terms of maintaining safety and activeness of the ribozyme.  delivered through autologous CD34ibozyme.  Regardleibozyme.  Thereforpression .in vitro transcribed (IVT) mRNA could promote the expression of the encoded protein in the muscle, allowing mRNA manipulation to transitorily express proteins that have been suppressed in various diseases to resemble natural mRNA.  is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with a high fatality rate in older adults, especially those with comorbidities.  ThereforA vaccine candidate, mRNA-1273 contains a lipid nanoparticle–encapsulated, nucleoside-modified mRNA vaccine encoding the SARS-CoV-2 spike (S) glycoprotein stabilized in its prefusion conformation to enhance immune response. The study of mRNA-1273 in nonhuman primates has shown that the vaccine was able to stimulate antibody and T-cell responses.  A dose-eRabies virus glycoprotein (CV7201) coding prophylactic mRNA vaccine has also demonstrated to stimulate the production of antibodies against a viral antigen in human beings without safety concerns and with good tolerability.  In other+ and CD8+ T cell responses against vaccine antigens  patients in conjunction with pembrolizumab was well-tolerated without dose-limiting toxicities and also induced clinical responses in combination with pembrolizumab and neoantigen-specific T cells.  The Lipoantigens .165) in the formation of new blood vessels and improving cardiac function post-myocardial infarction in swine. . .  In the f (T2DM). .CRISPR-CRISPR-associated protein 9 (Cas9) system is a bacterial defense mechanism that employs the guide RNA (gRNA) to mediate DNA endonuclease Cas9 to introduce site-specific breaks in target DNA. This ability of DNA editing has led CRISPR-Cas9 to be used as RNA-therapeutic in genome modification for biological and therapeutic applications by synthesizing single-guide RNA (sgRNA).  Many stuin vivo and possible off-target effects. , 5-methylcytosine (m5C), pseudouridine (Ψ), 5-hydroxymethylcytosine (hm5C), and N1-methyladenosine (m1A) have been demonstrated to regulate mRNA stability. (6A modification involve methyltransferase like 3 (METTL3), METTL14, WTAP, and KIAA1429, m5C modification involve NOP2/Sun RNA methyltransferase 2 (NSUN2), and pseudouridine modification involve pseudouridines synthases (PUSs), while RBPs that can carry m6A modification involve IGF2BPs, FMRP, G3BP1, PRRC2A, YT521-B homology (YTH) domain-containing proteins , and HuR, m5C modification involve Y-box binding protein 1 (YBX1) and m6A erasers such as α-ketoglutarate-dependent dioxygenase alkB homolog 5 protein (ALKBH5) remove the methyl group from m6A and converts it into adenosine. (6-methyladenosine can suppress circRNA immunity by marking it as self RNA (Many different RNA modifications, biological and chemical have been documented so far. RNA modifications, such as N6-methyladenosine (mability.  (Huang eability.  The coorability.  The writenosine.  The prevenosine.  allow olenosine.  The stabenosine.  The addienosine.  CircRNAsself RNA .In recent years, RNA-based therapeutics evolved into a potential possible intervention strategy in various diseases. Generally, RNA therapeutics are divided into different categories based on their mode of action and molecule used, such as siRNA-based, miRNA-based, antisense oligonucleotides, aptamers, and mRNA vaccines. Many RNA therapeutics have been developed for various diseases and promising results obtained in many clinical studies have led to their approval for the treatment of various diseases. However, the major hurdle in the development of RNA therapeutics in many diseases is the lack of suitable delivery systems. Therefore, much work is needed in the development of delivery systems with higher targeting ability and less toxicity .


Equine coronavirus (ECoV) was first identified in the USA and has been previously described in several countries. In order to test the presence of ECoV in China, we collected 51 small intestinal samples from donkey foals with diarrhoea from a donkey farm in Shandong Province, China between August 2020 and April 2021. Two samples tested positive for ECoV and full-length genome sequences were successfully obtained using next-generation sequencing, one of which was further confirmed by Sanger sequencing. The two strains shared 100% sequence identity at the scale of whole genome. Bioinformatics analyses further showed that the two Chinese strains represent a novel genetic variant of ECoV and shared the highest sequence identity of 97.05% with the first identified ECoV strain – NC99. In addition, it may be a recombinant, with the recombination region around the NS2 gene. To our knowledge, this is the first documented report of ECoV in China, highlighting its risk to horse/donkey breeding. In addition, its potential risk to public health also warrants further investigation. Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus. The genus Betacoronavirus consists of five subgenera: Embecovirus, Hibecovirus, Merbecovirus, Nobecovirus, and Sarbecovirus [Betacoronavirus, including severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 [Sarbecovirus, and Middle East respiratory syndrome coronavirus (MERS-CoV) in the subgenus Merbecovirus. The identification of close relatives of human coronaviruses from multiple wild animals suggested both the wide host spectrum of human coronaviruses and their animal origins [Coronaviruses are enveloped ss+RNA viruses, currently including four genera: ecovirus . There a origins . In part origins . For exa origins , and MER origins .Embecovirus currently contains five species: Betacoronavirus 1, China Rattus coronavirus HKU24, Human coronavirus HKU1, Murine coronavirus, and Myodes coronavirus 2JL24. Equine coronavirus (ECoV) is classified as a member of the species Betacoronavirus 1 along with human coronavirus OC43, bovine coronavirus, canine respiratory coronavirus, porcine haemagglutinating encephalomyelitis virus (PHEV), and a few camel coronaviruses. The main clinical signs of ECoV infection in adult horses include anorexia, lethargy, and fever. Some infected individuals may develop diarrhoea and mild colic signs, although less frequently. The first ECoV strain, NC99, was isolated from a diarrhoeic foal in the USA, and it was subsequently identified in Japan, France, Saudi Arabia, United Kingdom, and Ireland in foals, adult horses, and donkeys with respiratory diseases and diarrhoea, respectively [The subgenus ectively . In partectively , highligIn this study, a total of 51 small intestinal samples from donkey foals with diarrhoea were collected from a donkey farm in Shandong Province, China between August 2020 and April 2021. All samples were collected by dissection after the animals were killed, which were stored in liquid nitrogen and transported to our laboratory for analysis.Ct) values of 17.43 and 18.96, respectively, while Ct values of the remaining samples were greater than 35.A real-time quantitative PCR (RT-qPCR) assay was established targeting a specific gene region of 142 nt in length in the nucleocapsid (N) gene of ECoV and was performed as previously described . Two samCt value of 16.71 for 2021/464693 and 36.89 for 2020/7881. Transmission electron microscope examination was then operated for the cultured cells of ECoV_2021/464693. We observed several spherical virus particles with some pleomorphism inside the cells, and the virus particles were ∼100 nm in diameter with spikes , whose morphology is consistent with the Coronaviridae family.The two RT-qPCR positive samples were homogenated as a 10% suspension in RPMI1640 and filtrated with a 0.22 μm filter. The supernatant was inoculated onto confluent monolayers of human rectal adenocarcinoma (HRT-18G) cells in 6-well plates and incubated for 90 min at 37°C under a 5% CO2 atmosphere for adsorption. After washing twice with phosphate-buffered saline (PBS), 2 ml fresh RPMI1640 medium with 0.25 μg/ml TPCK trypsin were added as maintenance medium. After six days, the culture supernatant was inoculated with fresh HRT-18G cells. After three passages at six-day-intervals, RT-qPCR test for ECoV was performed, with Next-generation sequencing (NGS) was then performed to obtain the full-length genome of the two ECoV positive samples. In brief, 3 μg total RNA was extracted from each sample and was used for library construction. Ribosomal (r) RNA was removed by using Epicentre Ribo-zeroTM rRNA Removal Kit . Two sequencing libraries were generated using the rRNA-depleted RNA by NEBNext® UltraTM Directional RNA Library Prep Kit for Illumina®  following manufacturer’s recommendations. NGS was performed on the Illumina Hiseq 4000 platform and 150 nt paired-end reads were generated. RNA extraction, library construction, and NGS were performed by Novogene (Beijing).de novo assembled using Trinity 2.5.1 [Quality control was performed using Fastp 0.20.0 , and clety 2.5.1 . Blastx Embecovirus was performed using MAFFT (v7.450) [Multiple sequence alignment of the newly sequenced genome sequences and the representative coronaviruses from the subgenus (v7.450)  with theHowever, previously described ECoVs encode two types of NS2 gene, with the long form of 837 nt   and the To investigate the genetic relationships of the two Chinese strains, we performed phylogenetic analyses with a maximum likelihood approach using RAxML . PhylogeP values ranging from 1.141 × 10−98 for RDP to 4.297 × 10−21 for 3Seq. In addition, the breakpoints were estimated to be 21,523–22,209 nt by RDP. Phylogenetic analysis of the potential recombinant region showed that the two Chinese ECoV strains did not cluster with representative ECoVs. Alternatively, they fell within a lineage consisting of bovine CoVs, human coronavirus OC43, and PHEV (The potential genetic recombination around the NS2 gene region of ECoV_2021/464693 was also supported by different recombination detection methods embedded in the RDP program with In summary, we identified two ECoV positive samples from donkey foals with diarrhoea from China, from which we successfully obtained two full-length ECoV genomes. Genetic analysis revealed that they represent a novel variant of ECoV and may be a genetic recombinant. To our knowledge, this is the first documented report of ECoV from China, highlighting its expanded geographic range. Therefore, it is necessary to strengthen viral surveillance among horses and donkeys in China to prevent probable CoV outbreaks. In addition, the host spectrum of ECoV and whether ECoV is able to cause human infections warrant further investigation.Click here for additional data file.Click here for additional data file.