Protein-RNA interaction participates in many biological processes. So, studying protein–RNA interaction can help us to understand the function of protein and RNA. Although the protein–RNA 3D3D model, like PRIME, was useful in building 3D structural complexes, it can’t be used genome-wide, due to lacking RNA 3D structures. To take full advantage of RNA secondary structures revealed from high-throughput sequencing, we present PRIME-3D2D to predict binding sites of protein–RNA interaction. PRIME-3D2D is almost as good as PRIME at modeling protein–RNA complexes. PRIME-3D2D can be used to predict binding sites on PDB data (MCC = 0.75/0.70 for binding sites in protein/RNA) and transcription-wide (MCC = 0.285 for binding sites in RNA). Testing on PDB and yeast transcription-wide data show that PRIME-3D2D performs better than other binding sites predictor. So, PRIME-3D2D can be used to predict the binding sites both on PDB and genome-wide, and it’s freely available. Xie et al. report a new computational method PRIME-3D2D to predict binding sites of protein–RNA interaction by considering protein 3D structure and RNA 2D structure. It is freely available, performs better than other binding sites predictor and is as good as PRIME to model protein–RNA complex. RNA never acts alone, and it works with RNA-binding proteins (RBPs) or other molecules2. Protein–RNA interaction participates in several cellular processes like splicing, mRNA location, gene regulation4. In previous studies, a lot of RBPs were discovered without interaction partner information5. Studies on RNA secondary structurome uncover many RNA secondary structures in vivo or in vitro7 in which the conclusions imply that RNA secondary structure plays a significant role in protein–RNA interaction.In recent years, many noncoding RNAs8, PAR-CLIP (Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation)9, iCLIP 10, and eCLIP (enhanced Crosslinking and Immunoprecipitation)11, provide the protein–RNA interaction data on genome wide. These data have been collected by several databases for further analysis15. Besides, some computational methods have been proposed for predicting the protein–RNA interaction pairs. The sequence features , RNA secondary information or RNA 3D structure features were applied to these methods19 to predict the protein–RNA interaction. Different to these methods, some other methods were proposed based on networks23 to predict the interaction. In addition, the co-evolution methods were also introduced to predict the 3D protein–RNA complexes24 or interaction25. All these approaches do not take into account the RNA structurome data produced by NGS. However, NGS data can extend our knowledge of protein–RNA interaction on genome wide. Furthermore, we still lack the researches based on 3D information on genome wide because of the lack of a protein–RNA complexes27.Many studies have been carried out to investigate the protein–RNA interaction. High-throughput experimental techniques, such as HITS-CLIP (High Throughput Sequencing of RNA isolated by Crosslinking Immunoprecipitation)30, it is still difficult to obtain RNA-binding sites information for all proteins. Therefore, predicting binding sites by computational methods can compensate for this defect. Recently, many teams are working to predict protein–RNA-binding sites. Current methods for predicting protein–RNA interaction binding sites are divided into two major categories: predicting the RNA-binding sites on proteins45, and predicting the protein-binding sites on RNA51. These methods usually consider the sequence, structure or physicochemical characteristics of the given protein or RNA. The methods for predicting the binding sites of RNA on proteins are mainly divided into two categories: sequence-based and structure-based. The sequence-based approaches mainly use the sequence features of proteins and machine learning methods to identify RNA-binding residues on proteins. For example, BindN utilized the pKa value of side chain, hydrophobicity index and molecular mass of an amino acid as features33. Subsequently, Naive Bayes and the identity information of amino acid sequences were employed to predict binding sites on protein32. Other groups used PSSM evolutionary information to predict RNA-binding sites on proteins39. Subsequently, in 2010, several teams developed methods to predict RNA-binding sites on proteins. Such as Ma et al.37 predicted the binding sites of RNA on proteins by using the information of predicted secondary structures, the polarity-charge correlation physicochemical properties and the hydrophilic and hydrophobic properties of amino acids. NAPS used protein sequence characteristics to predict RNA/DNA-binding sites on protein38. PiRaNhA used the protein sequence with the position-specific scoring matrices, residue interface propensity, predicted residue accessibility and residue hydrophobicity characteristics to predict the binding sites on RNA-binding residues34. Meta-predictor was based on the features used in the other three prediction methods44 and RNABindR2.0 was based on sequence homology and SVM classifier36. Earlier structure-based methods used multiple scores to predict the RNA-binding sites on proteins42. Some research groups utilized protein structural features to predict RNA-binding sites53. The template-based approaches predicting the RNA-binding sites on protein were compared the target protein structure with the known complex structure45. Ren’s method was different from previous approaches. The surrounding patches were compared with template patches and the accumulated distances was used as structural features43.Obtaining the binding sites of protein–RNA interaction is very helpful in understanding its biological functional mechanism. Although high-throughput sequencing methods can obtain a large amounts of RNA-binding sites for specific proteins51. However, these methods currently train the model mainly for specific proteins, so they are not universal. In addition, the current binding site prediction tools can only predict the binding sites on the protein or RNA. Therefore, a universal model to predict protein–RNA-binding sites on both protein and RNA is needed.With the development of high-throughput sequencing methods, thousands of protein-binding sites on RNA have been discovered. Protein-binding site predictors based on sequencing data were also developed54 and LocARNA55 is introduced to model the 3D structure and predict the binding sites of protein–RNA interaction. For binding model predictions, benchmarked in 439 binary complexes (NRBC43927), the success rate of PRIME-3D2D is almost as good as PRIME for top 10 predictions27. For binding sites predictions, benchmarked in NRBC439, PRIME-3D2D obtains the MCC about 0.70. Comparing to the state-of-the-art methods, PRIME-3D2D outperforms other binding sites predictors on both PDB and genome wide data.In this study, PRIME-3D2D is introduced to expand the available structural data of protein–RNA interaction, for overcoming the lack of protein–RNA 3D structure. The protein–RNA 3D3D model is transformed into 3D2D model (the RNA 3D structure is replaced by RNA 2D structure). 3D2D score is introduced in searching templates to describe the binding mode of two complexes. First, the phase transition points are determined in all-to-all pairwise alignment for identifying the good template. Then, PRIME-3D2D based on TM-align56, the software BEAR converts the dot-bracket notation to BEAR encoding. In order to align the RNAs based on this novel representation, a BLOSUM like matrix is calculated from Rfam. In Supplementary Fig. 57. The mean SPS of RNA2dA is 0.94, which is almost as good as LocARNA. Besides, RNA2dA also achieves a comparable result with Beagle58.For investigating the effect of RNA secondary structure in RNA alignment, we developed RNA2dA, an RNA alignment approach combined RNA secondary structure and sequence. For RNA, a novel representation called BEAR encoding RNA secondary structure was chosen to represent the RNA. In this paper56. In this section, we can make a conclusion that more templates can be found by the alignment method combining sequence with secondary structure than RNA secondary structure information alone. We performed NRBC90 vs NRBC349 pairwise comparison of protein–RNA binary complexes in NRBC439 set. The similarity of binding mode is measured by interaction RMSD (iRMSD59). TM-align was employed to implement protein structure alignment, and LocARNA and RNA2dA (see Methods) were utilized for RNA alignment, which are corresponded to the approaches combining RNA sequence with secondary structure information and using secondary structure alone (bonus is set to 0 in RNA2dA).The previous study on RNA alignment indicated that approach using secondary structure alone performed worse than approach combining RNA sequence and secondary structure information in searching templatesIn Supplementary Fig. 60. This may be not the best way to describe the similarity of binary complexes, because we found that the success rate was not the highest (missing some correct models) after applying a cutoff in modeling27. In the current study, a scoring function, which combined the protein similarity and RNA secondary structure similarity (3D2D score), was used to measure the similarity of binary complexes. We performed all-to-all paired comparison in NRBC439 set and found that binding mode similarity correlates with the similarity of the participating protein and RNA, with different weights and different phase transition values. We found that the trend of the protein–RNA structural similarity with the binding mode is similar to that of the protein–RNA complexes27 and protein–protein complexes60.The small similarity value of two monomers (RNA and protein) was employed to measure the similarity of binary complexes on the previous studiesW values. The subfigure labeled “W = 1” is correspond to the similarity of binary complex described by TM-score. The subfigure labeled “W = 0” stands for the similarity of binary complex described by the RNA secondary structural identity alone. Other W values correspond to a combination of TM-score and RNA secondary structural identity. When W varies from 1 to 0, the noise signals (points above the iRMSD ≥ 10 Å) move to two sides of the figure. This movement results in a changing of phase transition point (cutoff). The smaller W values are, the bigger cutoff are. When the W is 0.2, the cutoff becomes big. At this time, the proportion of RNA is significant. It can be seen from the figure that the number of iRMSD ≤ 5 Å is relatively small, and the binding modes are not very similar. This phenomenon may be explained as RNA secondary structure is not always conserved61. This result also suggests that both the similarity of protein and RNA are needed in identifying a good template like previous study27. Overall, correlation of the protein–RNA structural similarity with the binding mode depends on the way of combining the similarity of monomers. For W = 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, the transition 3D2D score 0.4, 0.45, 0.5, 0.6, 0.65, 0.7 were determined respectively.The relationship between binding mode and the similarity of monomers are plotted in Fig. 27.In order to confirm the combination parameter of protein and RNA similarity score, a protein 3D structure/RNA secondary structure docking method was implemented in a program named PRIME-3D2D (3D/2D Protein–RNA Interaction ModEling). Figure 27, the success rate of predicting “acceptable” model almost reaches the highest value at top four. Different weights result in various cutoffs  obtained by PRIME27 and slightly better than that of PRIME2.062  was applied to identify good templates for binding sites prediction.In Fig. In order to evaluate our binding prediction approach PRIME-3D2D, we made a comparison with the current binding site prediction methods on PDB and genome scale data sets. The results show that PRIME-3D2D performed better than the current existing methods.64, in which the authors benchmarked the softwares for predicting RNA-binding sites on proteins. From Fig. On PDB scale, PRIME-3D2D(PDB) was compared with the RNA-binding site predictors on independent testing sets . These three independent testing sets and the results of other RNA-binding site predictors were grabbed from the review article46 on yeast transcriptome predicted the binding sites on RNA of yeast transcriptome, but the MCC is lower than that in PDB wide. At first, RNAs of the yeast transcriptome are employed as the targets and NRBC439 is used as the templates. However, PRIME-3D2D does not work well 66 according to the blocks from Rfam67. In the sequence alignment procedure, the scoring matrix of RNA2dA is different with Beagle’s substitution matrix (Matrix of Bear-encoded RNAs), which used Percent Accepted Mutations (PAM). The scoring matrixes of RNA2dA were generated with different weights by combining RNA sequence similarity and RNA secondary structure similarity. Finally RNA2dA was benchmarked on BRAliBase II57.In order to test the effect of RNA sequence and secondary structure in searching protein–RNA complex templates, an RNA alignment method considering RNA sequence with secondary structure is needed. Beagle56. The BEAR representation is same as Beagle.BEAR is a representation of RNA secondary structure which is first introduced in ref. 67 are used to construct RNA blocks. RNA block is defined as multiple sequence alignment fragments with the length of alignment greater than five nucleotide without insertions and deletions. The RNA sequences of blocks are extracted from the multiple sequence alignment. And the secondary structures are extracted from the lines labeled “SS_cons” in seed alignment of Rfam. Then RNA is converted to BEAR encoding with BEAR software. After these steps, we obtain the blocks in BEAR coding.Seed alignments of Rfam66 of generating protein BLOSUM matrix is modified to calculate the RNABLOSUM matrix at first. Redundancy of RNA with BEAR encoding are then removed with different RNA secondary structure identity (SSI) (with BEAR representation). With different cutoffs x, various RNABLOSUM matrixes labeled with RNABLOSUMx are calculated.The C source codeIn order to consider RNA sequence information, we combine the RNABLOSUMx matrix and NUC.4.4 matrix as follows:Score  = RNABLOSUMx (BEAR (i), BEAR (j)) + NUC.4.4  * bonusftp://ftp.ncbi.nih.gov/blast/matrices/. The bonus indicates the effect of RNA sequence in RNA2dA. If the bonus is set as 0, RNA2dA will only use RNA secondary structure to align RNA. In RNA2dA, the gap open penalty is set to 10. And the gap extend penalty is set to 2. The alignment is accomplished by Needleman–Wunsch algorithm.Score  is the scoring matrix of RNA2dA. BEAR (i) and BEAR (j) are the BEAR representation of nucleotide i and nucleotide j. NUC.4.4 is the scoring matrix for RNA sequence. The matched nucleotides will be scored to 5 and the mismatched nucleotides will be scored to −4. NUC.4.4 was downloaded from 57. Like dealing alignments in Rfam, sequence and secondary structure are extracted from the BraliBase II. Sum-of-pairs (SPS), that is defined as the fraction of correctly aligned nucleotide pairs within one alignment is used as quality measurement.RNA2dA was benchmarked and compared with LocARNA on BraliBase IIThe differences between PRIME and PRIME-3D2D are mainly in: 1. PRIME requires the 3D structure of the RNA as input, whereas PRIME-3D2D requires the 2D structure of the RNA; 2. PRIME is used for prediction complex structure. When applied to the PDB scale, the PRIME-3D2D model can predict both the complex structure and the binding site; on the genome scale, PRIME-3D2D is used to predict the binding site of proteins on RNA; 3. When searching templates in RNA, the algorithm is different. SARA/RMalign is used for RNA alignment in PRIME1.0/PRIME2.0, and LocARNA is used for structural alignment in PRIME-3D2D; 4. The finally complex structure scores are different. PRIME1.0/2.0 use the lower score between protein and RNA as the complex structure score, but PRIME-3D2D employs a linear combination between protein and RNA structure scores.27 was downloaded from http://www.rnabinding.com/PRIME.html. NRBC439 is used as the template library, in which all-to-all alignment of protein–RNA complex structure is conducted like PRIME27. To compare LocARNA with RNA2dA and predict the new complex structure based on the known complex structures, NRBC439 is split into two parts. 80% with an older deposit date are designated as the templates (NRBC349), and 20% with a newer deposit date are designated as targets (NRBC90), which is consistent with the division method in the PRIME1.0 and PRIME2.0. The binding site in NRBC439 is defined by distance ≤4.5 Å between any heavy atom of the protein and any heavy atom of the RNA.NRBC439Saccharomyces cerevisiae (S. cerevisiae) from http://ouyanglab.jax.org/seqfold/instructions.html#Prerequisites. We predicted the RNA secondary structure by following the tutorial of SeqFold68, which converts the high-throughput RNA structure information to structure preference profile (SPP). After getting the SPP file, SeqFold will determine that the base is on a single-strand or double-strand, then the RNA secondary structure will be predicted. The predicted secondary structure of each transcript was in CT format, so we then used the RNA structure69 to convert the CT format to dot-bracket format. We assume that the secondary structural state of RNA remains the same before and after binding to the protein. To obtain the native protein-binding sites file, we downloaded the BED file corresponding to the 20 yeast proteins from CLIPdb13, which include the information of protein-binding sites on RNA, and the 3D structures of these 20 proteins were downloaded from PDB website and ModBase database70. The CDS file of S.cerevisiae was downloaded from the Ensemble database. If the binding site is included in the BED file and included in the CDS file, we take out the transcripts, respectively. According to the BED and the transcript files, the native binding sites files were defined.To obtain the RNA secondary structures, at first, we downloaded the sequence file in FASTA format of 27. The interaction RMSD (iRMSD)59 is used to characterize the binding mode of complexes of different monomersNRBC90 vs NRBC349 pairwise alignments were performed by two approaches to detect the ability of templates searching. The first approach is LocARNA, which combines RNA sequence and RNA secondary structure information to align RNAs. It was developed for RNA local or global structural comparison. It is currently one of the most effective and convenient methods for RNA secondary structure alignment. This method can classify RNAs according to RNA secondary structure. Hence, this method can be used to align the secondary structure of the target RNA to the known secondary structure of RNA, to find a known RNA similar to the target. The parameter of LocARNA was set to run global alignment. The second approach is RNA2dA (bonus is set to 0). For the structural alignment of protein, we chose TM-align following the previous studyW * TM-score + (1 − W) * RNA SSI). The combination parameter (weight) is optimized in benchmarking.In NRBC439 set, all-to-all pairwise alignments were performed by LocARNA and TM-align for RNAs and proteins, respectively. In order to get a normalized score to measure the RNA similarity, we define the SSI like sequence identity. The similarity of the complex is defined as the linear combination of TM-score and SSI, called 3D2D score .After the model is built, the ligand RMSD (RMSD of RNA C3’ atoms) between the model and the native structure is calculated. The quality of the model is measured by ligand RMSD. Like PRIME1.0 and PRIME2.0, an “acceptable” prediction is defined as the model with ligand RMSD ≤10 Å.In addition to building the 3D structural model of protein–RNA interaction, PRIME-3D2D can be used to predict binding sites in both protein and RNA sequences on PDB scale and predict binding sites in RNA sequence on genome wide Fig. . PredictThe performance in binding sites prediction is evaluated by sensitivity (SN), specificity (SP), precision (PRE), accuracy (ACC), Matthews Correlation Coefficient (MCC), Strength, F-measure, and AUC, which are defined as follows:Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


Cells carry sensors that are primed to detect invading viruses. To avoid being recognized, coronaviruses express factors that interfere with host immune sensing pathways. Previous studies revealed that a coronavirus endoribonuclease (EndoU) delays activation of the host sensor system, but the mechanism was not known. Here, we report that EndoU cleaves a viral polyuridine sequence that would otherwise activate host immune sensors. This information may be used in developing inhibitors that target EndoU activity and prevent diseases caused by coronaviruses. Coronaviruses (CoVs) are positive-sense RNA viruses that can emerge from endemic reservoirs and infect zoonotically, causing significant morbidity and mortality. CoVs encode an endoribonuclease designated EndoU that facilitates evasion of host pattern recognition receptor MDA5, but the target of EndoU activity was not known. Here, we report that EndoU cleaves the 5′-polyuridines from negative-sense viral RNA, termed PUN RNA, which is the product of polyA-templated RNA synthesis. Using a virus containing an EndoU catalytic-inactive mutation, we detected a higher abundance of PUN RNA in the cytoplasm compared to wild-type−infected cells. Furthermore, we found that transfecting PUN RNA into cells stimulates a robust, MDA5-dependent interferon response, and that removal of the polyuridine extension on the RNA dampens the response. Overall, the results of this study reveal the PUN RNA to be a CoV MDA5-dependent pathogen-associated molecular pattern (PAMP). We also establish a mechanism for EndoU activity to cleave and limit the accumulation of this PAMP. Since EndoU activity is highly conserved in all CoVs, inhibiting this activity may serve as an approach for therapeutic interventions against existing and emerging CoV infections. Coronaviruses (CoVs) are positive-sense RNA viruses that replicate in the cytoplasm of infected cells. The positive-sense virion RNA is translated to generate the viral replication machinery, which then replicates the positive-sense RNA into negative-sense, genomic RNA and subgenomic RNAs (sgRNAs). The negative-sense RNAs then function as templates for synthesis of positive-sense genomic RNA and sgRNA , 2. This712Here, we investigate the mechanism used by one CoV IFN antagonist, the nonstructural protein 15 (nsp15), which is an endoribonuclease designated EndoU. EndoU is highly conserved in all known CoVs , 20. End2227In this study, we show that CoV EndoU activity limits the abundance and length of the polyuridine (polyU) extension on 5′-polyU-containing, negative-sense (PUN) RNAs for both the beta-CoV mouse hepatitis virus strain A59 (MHV-A59) and the alpha-CoV PEDV. Importantly, we found that the PUN RNAs can act as PAMPs recognized by MDA5. Overall, we propose a mechanism for EndoU, which is to cleave polyU sequences from PUN RNAs, thus limiting the formation of a PAMP and impeding the ability of MDA5 to activate the innate immune response to infection.−/− bone marrow-derived macrophages (BMDMs) as measured by immunofluorescence . We quantified the number of nsp2/3 foci and dsRNA foci from 50 individual cells. We found that, while the numbers of nsp2/3-labeled replication complexes were not significantly different (Right), the total number of dsRNA foci per cell was elevated in EndoUmut-infected cells (Left). Median fluorescent intensity of the individual dsRNA foci was also brighter in EndoUmut-infected cells  in the abundance of the reads from the EndoUmut virus-infected samples  . We then samples A. These  samples .−/− BMDMs, C57BL/6 BMDMs, and AML12 cells with either wild-type or EndoUmut virus, and performed the anti-dsRNA immunoprecipitation experiment. We used random hexamers as primers for complementary DNA (cDNA) synthesis, which allows for generation of cDNA from both positive- and negative-sense RNA, and then evaluated the abundance of cDNA by qPCR. We consistently detected elevated levels of viral RNA immunoprecipitated by the dsRNA antibody from EndoUmut virus-infected cells as compared to the levels detected in wild-type virus-infected cells  and detected a 60-fold increase in IFNAR−/− BMDMs (Left). To determine whether the polyA tail on the positive-sense RNA was similarly reduced by EndoU activity, we used either random hexamers or oligo-dT primers for reverse transcription and determined that the abundance of polyA tails on positive-sense RNA does not differ between wild-type and EndoUmut infections (Middle and Right). We concluded that EndoU activity reduces the abundance of negative-sense RNA that contains polyU extensions.Previous studies showed that the 5′ end of the CoV negative-sense RNA contains polyU extensions , and tha27/− BMDMs D, Left. To determine whether EndoU reduces the lengths of the polyU extensions on the PUN RNA, we completed a nested PCR to obtain polyU-containing PCR products with a minimum predicted size of ∼100 base pairs (bp) A. We detSince EndoU is conserved among CoVs, we sought to determine whether EndoU reduces the abundance and length of the PUN RNAs in the alpha-CoV porcine epidemic diarrhea virus (PEDV). Although the EndoU domains of MHV and PEDV exhibit only about 50% overall amino acid similarity, the catalytic histidines are 100% conserved . We showSince EndoU both reduces PUN RNA abundance and suppresses host MDA5 activation, we hypothesized that CoV PUN RNA is a PAMP. To directly test this hypothesis, we measured IFN stimulation following introduction of PUN RNAs derived from MHV-A59 into AML12 cells. PUN RNA was synthesized by T7 in vitro transcription of digested plasmids that contained sequences representing the 5′ end or 3′ end of the viral genome A. The PUTo determine whether the polyU sequence contributed to the robust IFN stimulation of the PUN RNA, we transcribed PUN RNA containing either 12 uridines (N5) or no uridines (N5.NoU) at the 5′ end. We found that removing the 12 uridines from the PUN RNA significantly decreased the ability of that RNA to induce IFNβ1 expression C. Also, Previous studies documented that MHV-A59 infection induces IFN through MDA5 signaling , 11. To 2, the RNA is degraded over time  genome encodes a long polyU stretch on the positive-sense RNA, but the polyU region is flanked by highly structured RNA, which may limit immune stimulation . FurtherOur study also raises interesting questions about how the CoV polyA tail is generated during positive-sense RNA synthesis. Studies from influenza virus revealed that a short polyU sequence within a unique RNA stem−loop mediates a stuttering mechanism used to polyadenylate the positive-sense RNA . In contOur study reveals that the CoV endoribonuclease activity is distinctly different from three other previously documented viral ribonucleases. Influenza PA-X is an endoribonuclease that selectively degrades host mRNA by hijacking host RNA splicing machinery . PA-X inThe current outbreak of 2019-nCoV and the associated morbidity and 2% mortality highlight the importance of developing effective vaccines against CoVs –57. One In summary, this study provides evidence for a mechanism used by the CoV EndoU to cleave a viral RNA PAMP, which would otherwise be recognized by MDA5. EndoU activity delays recognition by the host innate immune sensors, and thus is a highly conserved virulence factor and a potential target for antiviral and vaccine strategies.l-glutamine, 1% sodium pyruvate, 1% nonessential amino acids, and 1% penicillin/streptomycin. Differentiated BMDMs were maintained in bone marrow macrophage media containing DMEM  supplemented with 30% L929 cell supernatant, 20% FBS, 1% l-glutamine, 1% sodium pyruvate, and 1% penicillin/streptomycin. Methods for generation of BMDMs are described in Deng et al.  were cultured in Dulbecco’s modified Eagle’s medium (DMEM)/F-12  supplemented with 1% penicillin/streptomycin, 10% fetal bovine serum (FBS), Insulin, Transferrin, and Selenium , and Dexamethasone . L929 cell line was gifted from Francis Alonzo, Loyola University of Chicago, Maywood, IL, and maintained in DMEM  supplemented with 10% FBS, 1% g et al. . Porcineg et al. . Infecti−/− BMDMs, C57BL/6 BMDMs, or AML12 cells were infected with wild-type or EndoUmut virus at a multiplicity of infection (MOI) of 1. At indicated times postinfection, RNA was isolated with RNeasy kit . One microgram of RNA was mixed with dsRNA antibody  or mouse anti-Beta actin , RNA binding buffer . After overnight incubation, protein G beads  were added and incubated rotating for 4 h at 4 °C. Protein G beads were precipitated with magnets, then washed with cold binding buffer. RNA was purified off beads with RNeasy Kit and reverse-transcribed into cDNA with RT2 First Strand kit . The qPCR was performed as described below.IFNARhttps://usegalaxy.org/). Reads were groomed, clipped, and mapped with Hisat2 to the wild-type MHV strain A59 (GenBank accession no. AY910861) or host genome (GRCm38 Ensembl build of the C57BL/6J). The number of reads at individual nucleotides was calculated by plotCoverage. RNA-seq data have been deposited and are available in the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database (accession no. GSE144886).For RNA-seq, RNA was processed by University of Chicago Genomics Facility. The cDNA sequencing libraries were generated with TruSeq Stranded Total RNA with Ribo-zero extraction (Illumina), then sequenced on Illumina HiSeq4000. RNA-seq reads were analyzed with Galaxy’s online platform . The qPCR reaction is 1) 95 °C for 10 min, 2) 95 °C for 15 s, 3) 60 °C for 1 min, and a repeat of steps 2 and 3 for 40 cycles.SI Appendix, Table S1.Cells were infected with wild-type or EndoUmut virus at an MOI of 1. At indicated times postinfection, RNA was isolated with RNeasy Kit. For strand-specific cDNA, 500 ng of RNA was reverse-transcribed with Omniscript  with a reaction temperature of 50 °C to reduce self-primed cDNA synthesis . For negSI Appendix, Table S1. For polyU length sequencing, sequencing libraries were generated with custom amplicon primers with nextera XT indexes, and the amplicons were sequenced on an Illumina Miseq V2 500 with paired-end 250-bp reads. Reads were analyzed and mapped to viral genomes with Galaxy’s online platform.PolyU length nested PCRs were performed with PFU Ultra Polymerase  with an annealing temperature of 60 °C. The cDNA was generated as described above. For negative-sense RNA, the cDNA primer was 5′-GAATTCTGGTGGTGCTGATGAAC-3′ for MHV and 5′-GCAGCATTGCTCTTTGGTG-3′ for PEDV. For positive-sense RNA, the cDNA primer was 5′-GGGGATCCGCGGTTTTTTTTTT-3′. A PCR was performed with set 1 primers for PolyU length. Then the product was diluted 1:1,000 and utilized in a PCR with set 2 primers for PolyU length. PCR products were separated on a 10% polyacrylamide gel and stained with SYBR Green II dye . Primers for cDNA synthesis and PCRs are listed in SI Appendix, Table S2. Plasmids were digested with HindIII-HF , then purified with Wizard Gel and PCR Purification Kit . RNA was in vitro-transcribed using a T7 RNA polymerase  and purified by LiCl precipitation. Five picomoles of RNA or Poly I:C  was transfected into AML12 cells using Lipofectamine 2000 . At 8 hpt, RNA was isolated using RNeasy kit and qPCR for IFNβ1 , and 18s ribosomal RNA (rRNA)  was performed as described above.The pCAGGs constructs were generated containing a T7 promoter, the viral genome segment, and a HindIII cleavage site. Viral sequences are listed in Ifih1 gene. Sequence used for targeting Ifih1 was 5′-ATGGACGCAGATGTTCGTGG-3′. The cDNA versions of guide RNA were annealed and inserted into a pLentiCRISPRv2-puro (Addgene 52961) cassette between flanking BsmBI sites. Transducing particles (TPs) were generated by transfecting HEK-293T/17 cells with pLentiCRISPRv2-puro, pPax2, and pHEF-VSV-G and collecting supernatant. TPs were centrifuged at 1,000 × g for 10 min at 4 °C then filtered through a 0.45-μM filter (Millipore Sigma). AML12 cells were transduced with TPs, then incubated for 24 h at 37 °C in 5% CO2. Transduced AML12 cells were then selected with 1 μg/μL puromycin (InvivoGen) for 96 h. Puromycin-selected cells were then grown and cloned into a monoclonal population. Knockdown of MDA5 was determined by Western blot using rabbit anti-MDA5  and mouse anti-actin .A modified CRISPR/Cas9 protocol, based on the GeCKO system , was use2. Reactions were incubated at 30 °C for indicated time, and reactions were stopped by addition of RNA Gel Loading Buffer  and incubation at 95 °C for 5 min. Reaction products were immediately loaded into a 10% polyacrylamide gel with Tris-Borate-ethylenediaminetetraacetic acid (EDTA) buffer or a 1% agarose gel with Tris-Acetate-EDTA buffer, and bands were separated by electrophoresis. Gels were then stained with SYBR Green II dye and visualized with a ChemiDoc XRS+ imager (Bio-Rad), and processed with Image Lab software (Bio-Rad).Cleavage of RNA substrates was performed according to Kang et al. . PurifieRNA-seq data have been deposited and are available in the NCBI GEO database (accession number: GSE144886).Supplementary File


Sequencing of miRNAs isolated from exosomes has great potential to identify novel disease biomarkers, but exosomes have low amount of RNA, hindering adequate analysis and quantification. Here, we have assessed several steps in developing an optimized small RNA (sRNA) library preparation protocol for next-generation sequencing (NGS) miRNA analysis from urinary exosomes.A total of 24 urinary exosome samples from donors were included in this study. RNA was extracted by column-based methods. The quality of extracted RNA was assessed by spectrophotometric quantification and Bioanalyzer software analysis. All libraries were prepared using the CleanTag small RNA library preparation protocol and the effect of our additional modifications on adapter-dimer presence, sequencing data and tagged small RNA library population was also analyzed.Our results show that good quality sequencing libraries can be prepared following our optimized small RNA library preparation protocol from urinary exosomes. When the size selection by gel purification step was included within the workflow, adapter-dimer was totally removed from cDNA libraries. Furthermore, the inclusion of this modification step within small RNA library protocol augmented the small RNA mapped reads, with an especially significant 37% increase in miRNA reads, and the gel purification step made no difference to the tagged miRNA population.This study provides researchers with an optimized small RNA library preparation workflow for next generation sequencing based exosome-associated miRNA analysis that yields a high amount of miRNA mapped reads without skewing the tagged miRNA population significantly. Over recent years changes in circulating microRNAs (miRNAs) levels have been associated with a broad range of pathological processes. Their analysis offers various advantages which make them a potential goldmine in identification of novel biomarkers: 1) they can be found in non- or minimally invasive specimens; 2) they are relatively stable in clinical samples as regards RNase digestion, temperature variation and multiple freeze–thaw cycles; (3) they are involved in pathway regulation, showing tissue and cell-specific expression profiles [ they can they areOur group has previously reported an association between exosomal miRNAs and albuminuria in hypertension, and renal damage in systemic lupus erythematosus , 9. ExosHowever, there is a limiting factor, the lower amount of small RNA (sRNA) present in exosomes than in tissues, cell cultures or biofluids like plasma makes it difficult to obtain good quality sRNA libraries for NGS analysis. Thus, previous studies presented sRNA library protocols modified to low RNA template input to avoid adapter-dimer formation, for example with an extra adapter dilution, using chemical modified adapters or with an additional gel purification step , 18, butThe objective of this study is to provide researchers with an improved sRNA library preparation workflow for NGS analysis of miRNAs from urinary exosomes. We tested the effect of low RNA input and an additional purification step on adapter-dimer formation, sRNA mapped reads and tagged library population.A total of 24 samples of urinary exosomes from donor patients were analyzed and raw data are included in the BioProject PRJNA590749,  and centrifuged at 20,000g for 45 min at 4 °C to eliminate large microvesicles . The supernatant was spun in an ultracentrifuge at 121,000g for 70 min at 4 °C, obtaining exosome-depleted supernatant. Exosome pellets were treated with DTT to eliminate protein complexes, washed with sterile RNase-free PBS and ultracentrifuged again at 121,000g for 70 min . Exosome pellets from 50 mL urine were suspended in 100 μL of sterile RNase-free PBS and immediately processed to extract RNA, as described below.Exosomes were isolated from urine specimens using a combination of centrifugation, ultracentrifugation, and DTT treatment, as previously described . In brie® Technologies, Inc., Santa Clara, CA, USA). A RNA 6000 Pico chip run was performed afterwards for analysis and quantification of RNA eluates. The extracted RNA was stored at − 80 °C until further analysis.Total RNA was extracted from exosome pellets in 100 μL of exosome suspension using a Total exosome RNA and protein isolation kit  according to the manufacturer’s instructions, and stored at − 80 °C. Total RNA was quantified with NanoDrop ND-1000 spectrophotometer , and 2100 Bioanalyzer .sRNA transcripts were converted into barcoded cDNA libraries. Library preparation was performed with CleanTag Small RNA library preparation  followed by sRNA-Seq on the Illumina HiSeq 2000 platform . This sRNA library kit contains chemically modified adapters and reagents to convert sRNA to corresponding cDNA libraries for NGS, suppressing adapter-dimer formation, which is optimized for low total RNA template input . LimitedAfter PCR pre-amplification, the cDNA constructs were loaded onto the ABI 3730  for DNA fragment analysis by capillary electrophoresis according to manufacturer’s protocol. This method measures the relative size of DNA fragments with very high resolution and reproducibility, by capillary electrophoresis of fluorescent labelled DNA fragments, using internal fluorescent size standards .Electropherograms obtained were analyzed with GeneMapper software 5.0 . The size selection of amplified cDNA libraries are described in the section below. The cDNA libraries were qPCR-quantified using a KAPA library quantification kit by LightCycler 480 II . A final concentration of 20 nM per library was used to generate the two pools, with or without the size selection step (24 samples per pool). Experiments were designed containing 24 samples which would be barcoded, pooled equally, and then loaded onto one lane of a flow cell. Libraries were prepared individually and barcoded with reverse primers during the PCR step which contained Illumina-compatible indices #1–24. The sequence libraries obtained were subjected to the Illumina sequencing pipeline, passing through clonal cluster generation on a single-read flow cell by bridge amplification on the cBot  and 50 cycles were sequenced by synthesis on the HiSeq 2000 .To test whether size selection was a necessary step to obtain higher quality sequencing data, 5 µL of PCR products together with 5 µL TBE-Urea Sample Buffer were loaded onto TBE-Urea gel 15%) in a vertical electrophoresis chamber, XCell Sure Lock Mini Cell , at 180 V and 13 mA for 4 h at room temperature. Next, gels were stained at 1× dilution for 20 min at dark  and washed three times afterwards. To ensure the correct size of extracted bands, we developed and included in the gel three amplicons with different known sizes as markers  corresponding to adapter-dimer, miRNAs and other sRNA species, respectively. Bands of samples, approximately 138 bp to 152 bp in size were cut out and passed on to gel extraction with 100 µL of Tris–EDTA in individual eppendorfs, then stored for 30 min at − 80 °C until frozen solid. Next, they were quickly thawed for 5 min at 95 °C in a thermal block, to ensure optimal recovery to PCR products. This freeze-rapid thaw approach greatly increases yield by allowing ice crystals to break apart the acrylamide matrix. Homogenates were centrifuged at 13,000 rpm for 2 min at room temperature to remove gel debris and collect supernatants containing clean libraries. Afterwards, the libraries were re-amplified in 10 cycles of PCR amplification with QIAGEN Multiplex PCR Kit , obtaining a volume of 60 µL for each library. Finally, the cDNA constructs were purified and concentrated to 25 µL final volume with the QIAquick PCR Purification Kit . A brief work-flow chart figure to side by side compare the tradition method and the optimized Small RNA Library Preparation method was included , and reads without detectable adaptors were excluded from the data set. Sequence length distribution and base call accuracy were calculated with FastQC high throughput sequence data quality control software . This checking was performed before and after read cleaning. Trimmed reads were selected within desired ranges (18–26 bp and 24–33 bp) and aligned with Bowtie 2 v.0.12.7 , againstThe small RNA library prep kit allowed us to obtain the corresponding libraries for next-generation sequencing from urinary exosomes. However, the modified adapters used did not prevent adapter-dimer formation from exosome samples Fig. . ElectroAfter CleanTag protocol optimization with an additional size selection step, we observed adapter-dimer formation in the gel before band extraction in non-purified (NP) samples 1 and 2, but no presence in purified (P) samples 1 and 2 Fig. a. As eleWhen analyzing these two workflows with and without using a gel purification step, the total raw reads were statistically comparable, with slightly increase in non-purified samples (1.3%) Fig. a. HoweveIn addition, when we compared the effect of gel purification on miRNA mapped reads alone, we found that purified samples had a significant 37% increase compared to non-purified ones (p = 0.008), and they represented 33% of the total raw reads compared to 19% of non-purified ones (p = 0.011) Fig. c. From t2 = 0.96, p < 0.0001 after log2 transformation  types. As with the miRNA population, the other ncRNA did not change in distribution, obtaining similar percentage between ncRNA types Fig. a, b. In This study presents an optimized Small RNA library preparation workflow for NGS-based exosome-associated miRNA analysis. We completely eliminated adapter-dimer formation and the sRNA mapped read count was increased, overall for miRNAs, without changing tagged miRNA population.® Small RNA Sample Prep Kit and NEBNext Multiplex Small RNA Library Prep Set always implemented PAGE gel size-selection [The low quantity of RNA present in exosomes is a limiting factor for researchers. In response to this, a variety of small RNA sample preparation kits have implemented several modifications in their workflows to avoid adapter-dimer formation and increase sRNA enrichment in samples with low RNA template input. As an example, to optimize sequencing performance the CleanTag Small RNA library prep kit builds libraries using specific adaptors designed for small amounts of starting material, suppressing adapter-dimer formation and eliminating the need for gel purification . TruSeq®election , 25. Manelection , 27, henOur results showed that libraries from urinary exosomes could be prepared using CleanTag Small RNA library prep, although it failed to prevent adapter-dimer formation entirely. We obtained high cDNA libraries with a high amount of adapter dimer and low cDNA libraries with high or similar adapter-dimer. This fact indicates the need for a size selection step to purify the libraries for sRNA sequencing. When we analyzed the effect of gel purification on sRNA reads, we were able to demonstrate that including size selection within workflow efficiently tags sRNA and produces significantly more valuable reads and adapter dimer reduction compared to the protocol without gel purification. The robustness of our study lies in the analysis of results obtained from 24 individual cDNA libraries from urinary exosomes, which are more specific and sensitive for testing changes than working with library pools.Human exosomes samples contain low abundance of sRNAs, and a unique or enriched set of miRNA compared to other EV subpopulations and cells , 29. IdeThese findings prove that gel purification is specific for isolating the miRNA population from the urinary exosomes. This is because we were able to manually and carefully cut the band to between 145 and 160 nt, corresponding to miRNAs from the gel and avoid any other smaller or larger RNA populations in the samples. Further studies that analyzed exosome miRNA from other tissues it would be more universal. However, we think that whether this optimized protocol yields a higher amount of miRNA mapped reads without affecting the tagged miRNA population in urinary samples (biofluid with lower RNA input than plasma or plasma exosomes), could be applicable across other biofluids. We found purification by PAGE gel to be a very precise and specific method, particularly suitable for small sample size projects where miRNAs are the main focus.Our results present an optimized NGS-based library preparation workflow for exosome-associated miRNA analysis. Firstly, adapter-dimer formation was totally eliminated from urinary exosome cDNA libraries. Secondly, this study provides evidence that within our small RNA library preparation workflow, the size selection step itself obtained high miRNA mapped reads from urinary exosomes without skewing the tagged miRNA population. Therefore, in order to prepare sRNA libraries for NGS-analysis from samples with low RNA input, it is important to carefully analyze the effect of their adapted library preparation workflow, to further validate the NGS-data obtained.Additional file 1: Table S1. Sequence Read Archive (SRA) and BioSample accession IDs of the samples analyzed.


This study compared the association between working hours and self-rated health (SRH) according to sex, socioeconomic status, and working conditions. In all, 25,144 participants were selected from the Korea National Health and Nutrition Examination Survey (KNHANES), conducted from 2010 to 2018. The risks of poor SRH, according to working hours, were investigated using multiple logistic regression. Both short and long working hours were associated with poor SRH. Men working short hours and women working long hours were at risk of poor SRH. Workers with fewer than nine years of education were at risk of poor SRH when working short hours, whereas workers with more than nine years of education were at risk when working long hours. Similarly, simple laborers were at risk of poor SRH when working short hours, while managers and professional workers were at risk when working long hours. When working for short hours, paid employees were at risk of poor SRH. Workers with a non-fixed work schedule showed no risk of poor SRH when working long or short hours. In conclusion, workers working short hours with low education and workers working long hours with high education were at risk of poor SRH. Working conditions were significantly related to the association between SRH and working hours. The 2018 report by the Organization for Economic Cooperation and Development (OECD) indicated that the Republic of Korea (hereinafter Korea) had the third-longest number of working hours (1933 h per year) among countries in the OECD, following Mexico 2148 h) and Costa Rica (2121 h) 8 h and C, poor slSelf-rated health (SRH) was verified as a predictor of mortality ,13 and wHowever, several studies showed that socioeconomic status (SES) may be related to the association between working conditions and health. According to a study, the increase in type 2 diabetes based on long working hours was greater in individuals with low SES . In anotThus, to determine how personal, social, and work traits influence SRH, we investigated the trends in SRH according to working hours with stratification of sex, SES, and working conditions, employing the survey data representative of workers in Korea.This cross-sectional study was based on the Korea National Health and Nutrition Examination Survey (KNHANES) conducted from 2010 to 2018, which included health behavioral status, SES, and working conditions as variables. The Korea Centers for Disease Control and Prevention conducted these surveys. In KNHANES, the sample design and size are estimated to represent the entire Korean population, so that the results can be employed to represent the overall population in Korea in each survey year . The ratParticipants were asked about their average working hours per week, including night work and extra working time; weekly working hours were grouped into four categories: <20 h, 20–39 h, 40–59 h, and ≥60 h. Participants were asked to evaluate their health using a five-point scale; “very good,” “good,” and “fair” were defined as good SRH, whereas “very poor” and “poor” were defined as poor SRH.SES  and working conditions (type of work and work schedule) were categorized for stratification. Household income was classified into four quartiles based on national statistics. Education was classified into four levels according to the Korean school system: “fewer than six years (elementary school),” “fewer than nine years (junior high school),” “fewer than 12 years (high school),” and “more than 12 years (college).” The occupation was classified into six categories: simple labor (Major Group 9: elementary occupations), craft, plant and machine operators, and assemblers (Major Group 7: craft and related trade workers and Major Group 8: plant and machine operators and assemblers), skilled agricultural, forestry, and fishery , service and sales , clerk , and manager and professional  based on the International Standard Classification of Occupations. Korea assigns more than 18 months of mandatory military duty to all male citizens (approximately 20–24 years old). Thus, Major Group 0: armed forces  was excluded due to heterogeneity in age and gender.A previous study indicated that SRH was closely related to health behavioral factors (smoking and drinking), body mass index (BMI), and chronic diseases . We selep-value <0.05 was considered statistically significant. The chi-square test was used to assess the relationships between participants’ working hours and other variables. Multiple logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) to estimate the trends of poor SRH by working hours while adjusting for age, disease, smoking, drinking, and obesity. Sex, household income quartiles, education, occupational classification, type of work, and work schedule were stratified. SPSS software version 25  was used for statistical analyses. A After excluding the unemployed, individuals aged below 20 years and above 65 years, professional soldiers, and individuals who did not answer the questionnaire, a final sample of 25,144 participants was obtained . Table 1After adjusting for several demographic and health-related factors, statistically significant ORs were estimated between long or short working hours and SRH. In summary, poor SRH was related to long and short working hours in the total population. Both men and women working long hours were at risk of poor SRH, were the magnitude was greater among women. Only men were at risk of poor SRH when working for short hours. Workers with low education were at risk of poor SRH when working short hours, whereas workers with high education were at risk of poor SRH when working long hours. Additionally, occupations associated with either high or low educational level showed an identical relation. Paid employees were at risk of poor SRH when working short hours as compared to employers or self-employed workers. Compared with non-fixed workers, workers with fixed work schedules were at risk of poor SRH when working short or long hours.Several studies indicated the adverse effects of long working hours on SRH, which is attributed to high job demands accompanied by long working hours ,17,18,19The result that women working long hours showed poorer SRH than men working long hours can be explained by the “double burden” of household and working duties of women ,28. ThisHigh educational level and related occupations  were associated with poor SRH when working long hours, which can be attributed to the lack of leisure-time physical activity. This shortage is related to poor health  and SRH Paid employees working short hours showed poor SRH as opposed to employers or self-employed workers; this can be attributed to the differences in autonomy in modulating working hours. Employers or self-employed workers desiring short work can modulate their working hours at will; however, the short working hours of some employees would be involuntarily decided by the employers. Despite the fact that non-fixed work schedules were widely researched ,37, an aThis study has several limitations. Firstly, owing to its cross-sectional design, the causality between working hours and SRH could not be fully explained; the probability of reverse causality cannot be negligible. Secondly, due to recall bias, which inevitably occurred due to a retrospectively conducted survey, the participants’ answers could be inaccurate to a certain extent. Finally, these findings cannot be extrapolated to all participants due to differences in the reliability of self-rated health according to SES, ethnicity, gender, and age . NeverthIn conclusion, long and short working hours were associated with poor SRH. Men working short hours were at risk of poor SRH, while women working short hours were not, which is attributed to the gender inequalities of wage and housework. Highly educated workers’ long working hours and less-educated workers’ short working hours were associated with poor SRH, indicating that workers’ educational level is a distinct factor in predicting SRH according to working hours. Occupations associated with either high or low educational levels had the same involvement, thus supporting the predictability of educational level. Employers or self-employed workers did not have any risk of poor SRH with short working hours, whereas paid employees did. The SRH of non-fixed workers did not worsen with long or short working hours in contrast to fixed workers; this implies that both short and long working hours are associated with workers’ health, but the magnitudes differ according to sex, SES, and working conditions. Further research is needed to clarify the causal association between working hours and self-rated health.


We observed that a high percentage of β-globin LV viral genomic RNAs were incomplete toward the 3′ end in packaging cells and in released vector particles. The incomplete vector genomes impeded reverse transcription in target cells, limiting stable gene transfer to HSPCs. By combining three modifications to vector design and production  there was a 30-fold increase in vector titer and a 3-fold increase in vector infectivity in HSPCs. These approaches may improve the manufacturing of β-globin and other complex LVs for enhanced gene delivery and may facilitate clinical applications.Lentiviral vectors (LVs) commonly used for the treatment of hemoglobinopathies often have low titers and sub-optimal gene transfer efficiency for human hematopoietic stem and progenitor cells (HSPCs), hindering clinical translation and commercialization for   •Vector genomes are truncated in a length-dependent manner during packaging•Truncated RNAs cannot be reverse transcribed, impeding titer and infectivity•Protein kinase R inhibits virion formation for bidirectional lentiviral vectors•Three strategies to improve lentiviral vector titer by 30× and infectivity by 3× In this article, Kohn and colleagues investigated the mechanisms which lead to poor vector production and infectivity of long lentiviral vectors. This is a major problem for many clinical gene therapy trials. This work substantially adds to the mechanistic understanding of the problems associated with complex lentiviral vector production, and the combination of three modifications results in a significant gain in titers and infectivity. In multiple clinical trials, autologous hematopoietic stem cell (HSC) transplantation in combination with gene therapy has produced clinical benefits with desirable long-term engraftment of gene-corrected HSCs and stable gene expression . DespiteSCD is one of the most common monogenic disorders worldwide, with >250,000 new patients each year . SCD is Small-molecule transduction enhancers have been reported to improve the transduction efficiency of HSPCs . NonetheAlthough the main reason for poor infectivity and low titer has been attributed to the increased proviral length, the effects of complex proviral construct on lentiviral life cycle have not been fully explored. Therefore, a systematic study of the lentiviral life cycle was conducted to compare a “well-behaved” simple vector, EFS-ADA (4 kb), and a “poorly performing” complex β-globin vector, Lenti/βAS3-FB (8.9 kb). Five steps of the lentiviral life cycle were examined: (1) viral RNA production in packaging cells, (2) viral RNA incorporation into virions, (3) physical vector particle formation, (4) reverse transcription, and (5) integration.Our results showed that the viral RNAs of long complex vectors in packaging cells were predominantly incomplete at the 3′ end and failed reverse transcription in target cells at the first-strand transfer step, thus reducing the amount of vector DNA available for integration. Shortening the vector length and packaging with HIV-1 Tat expression plasmids improved the viral RNA production for the complex vectors. In addition, we observed that the Lenti/βAS3 vector with its β-globin gene expression unit in reverse orientation yielded fewer virion particles (measured by p24 Gag) than were produced with forward-oriented vectors and even with empty vectors lacking any genome. Knocking out protein kinase R (PKR) in HEK293T packaging cells increased virion production with a proportionate increase in virion RNA released into medium, improving titer and infectivity. These findings may be applied for clinical gene therapy not only with β-globin vectors, but also with many complex vectors with low titer and infectivity.ADA gene transfer and expression  orientation. Both vectors consist of a pCCL backbone and differ only by the internal promoters and transgene cassettes  B.8 TU/mL, the average VCN of Lenti/βAS3-FB was 0.88 ± 0.23 versus 3.51 ± 0.83 for EFS-ADA in three independent donors, resulting in a 4-fold difference in transduction between the two LVs despite using the same MOIs. We subsequently used Lenti/βAS3-FB and EFS-ADA as examples of complex and simple LVs to study different steps of the lentiviral life cycle, identify the blocks that restrict the performance of complex vectors, and develop strategies to improve the titer and infectivity of complex vectors.To characterize the gene transfer efficiency of these vectors in clinically relevant target cells, prestimulated human bone marrow (BM) CD34+ HSPCs from healthy donors were transduced at three doses of TUs, and the integrated VCN were measured by ddPCR 12 days after transduction. Lenti/βAS3-FB displayed less-efficient gene transfer to human BM CD34+ cells than EFS-ADA , especially at higher doses C. At theTo quantify RNA species at different stages of transcription, we designed multiple ddPCR primers and probes along the viral RNA. Because transcription of vector plasmids starts at the 5′ LTR driven by the CMV promoter, RNA is transcribed in the following order: R/U5, primer binding site (PBS), transgene, and U3/R. R/U5 primers and probe were used to quantify initial RNA, PBS primers and probe were used to quantify intermediate RNA, and U3/R primers and probes were used to quantify complete RNA A. RNA waAs shown in We further quantified the viral RNA content in vector particles to assess whether the RNA truncation was also observed in vector particles. EFS-ADA and MND-GFP displayed minor decreases in the RNA content from 5′ to 3′, whereas Lenti/βAS3-FB showed a substantial decrease in the level of complete RNA, resulting in 42.9-fold lower complete RNA in Lenti/βAS3-FB than in EFS-ADA D. Lenti/The R/U5 primer and probes for quantification of initial RNA may additionally pick up readthrough transcripts through the 3′ LTR and introduce a systematic enrichment of R/U5 sequences. To investigate the extent of transcription readthrough, we quantified the abundance of the sequences from the 3′ LTR U5 region to the SV40 polyadenylation site of the plasmid backbone by high-throughput RNA sequencing of the viral RNA D. The re2 = 0.81, p < 0.01)   A. The co < 0.05) B.Figure The decrease in complete RNA in complex LVs could be caused by the presence of specific sequences, which lead to discrete termination of vector transcripts, such as cryptic polyadenylation signals or splice signals. Alternatively, there may be progressive termination of transcription in a length-dependent manner, consistent with a previous study that demonstrated that inefficient transcription of the HIV provirus is associated with premature termination of viral RNA, attributed to poor processivity of RNA polymerase II (RNAP II) . To idenViral RNA was extracted from unconcentrated viral supernatants packaged from three independent experiments, and sequencing library preparations were produced and sequenced by the UCLA Technology Center for Genomics and Bioinformatics. Two β-globin LVs of different lengths, Globe-AS3-FB (6.4 kb) and Lenti/βAS3-FB (8.9 kb), and the EFS-ADA LV (4.2 kb) were analyzed. Reads were aligned to the vector genome and plotted as the percentage of maximum reads versus mapping position of the vector genome C. All LVWe next investigated the reverse transcription kinetics of Lenti/βAS3-FB and EFS-ADA in target cells to elucidate the inhibitory effect of truncated RNA. The steps of reverse transcription, locations of ddPCR primers and probes, and the reverse-transcribed products are illustrated in A primitive human hematopoietic myeloid progenitor cell line, KG1a, was chosen as the target cells because of its relative resistance to LV transduction, similar to that of primary human CD34+ HSPCs . A contrAs expected, all three vectors displayed similar kinetics for the production of early reverse-transcribed products, because equal amounts of initial RNA were used to infect cells D. IntereWe next sought methods to improve titer and infectivity of the complex β-globin vector by rescuing the viral RNA truncation. Because the level of complete RNA decreases with increasing vector length, we hypothesized that shortening the vector can rescue RNA production, thereby increasing titer and infectivity. Shortening the vector successfully rescued the RNA production and increased titer and infectivity in CD34+. Mini-G showed 27-fold more complete RNA than Lenti/βAS3-FB  B. The peTat is an accessory protein encoded in the genomes of wild-type HIV-1 that plays a pivotal role in transcription activation and elongation. Upon binding to the Tat-responsive element (TAR) hairpin from the 5′ end of the HIV transcript, Tat recruits the transcriptional elongation factor pTEFb to phosphorylate the C-terminal domain of RNAP II, thereby enhancing the processivity of the polymerase . We hypo∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001). Tat significantly increased the percentage of complete RNA in DL- βAS3 , confirming that Tat was necessary for efficient transcription elongation from LV with intact 5′ HIV LTR in the packaging plasmid  C. AlthouWe tried to further increase the level of complete RNA by manipulating the location of the Rev-response element (RRE). The HIV-1 Rev protein binds to the RRE to facilitate nuclear export of viral RNA . Moving Although EFS-ADA and Lenti/βAS3-FB had similar levels of initial and Intermediate RNA in 293T packaging cells, Lenti/βAS3-FB had substantially reduced initial and intermediate RNA species in viral supernatant compared with EFS-ADA, as shown in PKR in 293T cells via CRISPR-Cas9, generated an isogenic PKR−/− cell clone, and confirmed the allelic disruption by TIDE sequencing and protein expression by western blot -activated protein kinase that protects cells against viral infections . VectorsPKR−/− 293T cells. roUBC (4.2 kb) consists of a reverse-oriented GFP expression cassette driven by the Ubiquitin C (UBC) promoter.Two forward-oriented vectors bearing expression cassettes in the sense direction, MND-GFP and EFS-ADA, and two reverse-oriented vectors bearing expression cassettes in the anti-sense direction, Lenti/βAS3-FB and roUBC, were packaged in either parental 293T cells or PKR restored the p24 production by both reverse-oriented vectors    . These data strongly suggest that PKR was activated and inhibited the p24 production in reverse-oriented vectors. The increase in titer of forward-oriented vectors was likely caused by overcoming the antiviral effect of PKR recognizing single-stranded viral RNA species from these vectors, such as HIV TAR (Knocking out < 0.001) E. In add< 0.001) G. Notabl HIV TAR .PKR−/− cells in human BM CD34+ cells. We observed an increase in infectivity of both EFS-ADA and Lenti/βAS3-FB LVs that were packaged in PKR−/− cells compared with the LVs packaged in parental 293T cells   H.PKR−/− cells), we were able to increase the copies of complete RNA from the Mini-G vector compared with Lenti/βAS3-FB packaged without Tat in PKR-replete cells by 149.9-fold and to improve the percentage of Complete RNA to 79.4%. These strategies resulted in a 29.3-fold increase in titer , Lenti/βAS3-FB (-HS4), CoreGA-AS3-FB, Core-AS3-FB, UV-AS3, Mini-G, roUBC, MND-GFP, and a version of EFS-ADA designed to not generate any full-length LV genomes by inserting the BGH-ADA upstream of the 3′ LTR  Carbona.LVs were packaged by transient transfection of 293T cells with fixed amounts of HIV Gag/Pol, Rev, and VSV-G expression plasmids and equimolar amounts of each of the different vector transfer plasmids using TransIT-293  as described in the To determine the titers of the LVs, the HT-29 human colon carcinoma cell line was transduced with different dilutions of the LVs and harvested ∼60 h after transduction. Genomic DNA was extracted using the PureLink Genomic DNA Mini Kit . VCN was measured by droplet digital PCR (ddPCR), as described previously in All human samples have been used following UCLA IBC protocol 2019-126-001. Human CD34+ HSPCs were isolated from healthy donor BM aspirates using Ficoll-Hypaque gradient separation and a CD34 MicroBead Kit . The processed cells were cryopreserved in liquid nitrogen with BAMBANKER .6 cells/mL in non-tissue culture-treated plates coated with RetroNectin . The cells were prestimulated for 24 h in X-Vivo 15  with 1× L-glutamine-penicillin-streptomycin , 50 ng/mL stem cell factor (SCF), 50 ng/mL thrombopoietin (TPO), 50 ng/mL Flt3L, and 20 ng/mL IL-3 . Prestimulated cells were transduced with concentrated viral supernatants, and 24 h after transduction cells were collected for in vitro myeloid differentiation cultures, as described below.CD34+ HSPCs were thawed at 37°C and plated at 1 × 102. Cells were maintained in culture for 12 days with addition of fresh BBMM and cytokines every 4 days. After 12 days, the cells were harvested for genomic DNA extraction and VCN analysis by ddPCR as described above.Transduced human BM CD34+ HSPCs were cultured in basal BM medium  with recombinant human cytokines of 5 ng/mL interleukin-3, 10 ng/mL interleukin-6, and 25 ng/mL cKit ligand (hSCF) (PeproTech) at 37°C, 5% CO6 293T cells or 140 μL unconcentrated viral supernatant 3 days after transfection for vector packaging. Cellular RNA was extracted using the RNeasy Plus Mini Kit , and RNA from the viral supernatant was extracted using the QIAmp Viral RNA Mini Kit (QIAGEN). Equal masses of RNA were treated with DNase I (Invitrogen) to remove any traces of genomic or plasmid DNA contamination and then reverse transcribed using random primers, M-MLV reverse transcriptase, and RNAseOUT Recombinant Ribonuclease Inhibitor , following the manufacturer's protocol. Amplification of R/U5, PBS, and U3/R regions by ddPCR was conducted to quantify the initial, intermediate, and complete viral RNA. The cycling conditions were 95°C for 10 min for one cycle (94°C for 30 s and 60°C for 1 min) for 40 cycles, 10 min at 98°C for one cycle, and a 12°C hold.RNA was extracted from either 2 × 10p24 antigen concentration in vector supernatants were measured by the UCLA/CFAR (Center for AIDS Research) Virology Core using the Alliance HIV-1 p24 Antigen ELISA Kit , following the manufacturer's manual.Total RNA was extracted from raw viral supernatant using the QIAmp Viral RNA Mini Kit (QIAGEN). Viral RNA was treated with Turbo DNase (Invitrogen) and sent to the UCLA Technology Center for Genomics and Bioinformatics Core for downstream library preparation. A cDNA library was prepared using the KAPA RNA HyperPrep Kit with RiboErase  following the manufacturer's manual without rRNA depletion to avoid bias. In brief, RNA was fragmented into ∼400-bp fragments; first-strand cDNA was synthesized with random priming followed by second-strand synthesis. Paired end reads (150 bp) were sequenced on a HiSeq 3000 . The reads were aligned to the designed vectors via a Burrows-Wheeler Aligner (version 0.7.17) using parameters “bwa mem -t 16 -k 100 -A 1 -B 30 -O 10 -E 10.” Bigwig files were generated on the bam files using the bamCoverage function from deepTools. The numbers of reads for each location were extracted from the bigwig files into R, and the average of the upstream and downstream 450 bp were calculated as the coverage score. The coverage scores were then normalized to the maximum value for each sample.Descriptive statistics, such as number of observations, mean, and standard error are reported and presented graphically for quantitative measurements. Unpaired t tests were used to compare between vectors for outcome measures, such as titers, VCN, copies, and percentage of initial/intermediate/complete RNA. In the case of normality assumption violation, nonparametric Wilcoxon rank-sum tests were used. Evaluation of infectivity of vectors was done by comparing the slopes of respective regression lines. Pearson's correlation was used to correlate the titer of the LVs with the proviral length. For all statistical investigations, tests for significance were two tailed. A p value of less than the 0.05 significance level was considered to be statistically significant. All statistical analyses were carried out using statistical software SAS version 9.4  and GraphPad Prism version 8.3.0 .GSE158252.The RNA sequencing data can be accessed via the GEO repository number GEO: J.H., R.P.H., and D.B.K. conceived and designed all experiments. M.P. and M.M. provided advice on portions of the experiments. J.H. executed and analyzed all experiments. J.H., K.T., C.T., B.A., and J.Q. helped execute portions of the experiments. R.P.H. provided research materials. R.P.H. and D.B.K. designed the experiments. F.M and X.W. performed bioinformatics analysis. D.B.K. provided financial and administrative support. J.H. and D.B.K. wrote the manuscript. J.H. and D.B.K. approved the final manuscript.


Coral reef predators are overwhelmingly sustained by offshore pelagic plankton sources rather than by reef-based sources. Coral reefs were traditionally perceived as productive hot spots in oligotrophic waters. While modern evidence indicates that many coral reef food webs are heavily subsidized by planktonic production, the pathways through which this occurs remain unresolved. We used the analytical power of carbon isotope analysis of essential amino acids to distinguish between alternative carbon pathways supporting four key reef predators across an oceanic atoll. This technique separates benthic versus planktonic inputs, further identifying two distinct planktonic pathways (nearshore reef-associated plankton and offshore pelagic plankton), and revealing that these reef predators are overwhelmingly sustained by offshore pelagic sources rather than by reef sources (including reef-associated plankton). Notably, pelagic reliance did not vary between species or reef habitats, emphasizing that allochthonous energetic subsidies may have system-wide importance. These results help explain how coral reefs maintain exceptional productivity in apparently nutrient-poor tropical settings, but also emphasize their susceptibility to future ocean productivity fluctuations. Ecosystem function relies on the movement and storage of energy and nutrients    is a new technology that profiles specific biochemical compounds, such as amino acids, and so provides additional resolution for distinguishing the energy sources of consumers. Some amino acids are essential (EAA); consumers cannot synthesize them de novo and must obtain them directly from their diet  to determine whether we could distinguish among the sources of benthic and planktonic inputs that are important in sustaining coral reef food webs and (ii) to quantify their contribution to key reef predators across an atoll seascape. We sampled a range of specialized consumers to represent the different potential planktonic  and benthic reef  energy pathways and key fishery target reef predators that occupy the upper trophic level of the food web, across both inner-lagoonal platform reef sites and outer-reef slopes of an oceanic atoll in the Maldives , lysine (Lys), phenylalanine (Phe), threonine (Thr), and valine ] from 72 samples of four species of grouper and 67 samples of eight specialized consumer species (as indicators of distinct carbon pathways) across inner and outer reef sites of an oceanic atoll. Ranges of Thr and Phe δ13C values were greatest , followed by Val (12.81‰), Leu (11.75‰), and Lys (10.5‰)  so we tested for temporal variation in their δ13CEAA values. Predator δ13CEAA values did not vary temporally, either when grouped together (year) or split by atoll habitat (year × atoll habitat) , so predator species from both years were combined for all analyses. δ13CEAA values showed no spatial differences in the specialized consumer species representing algae , detritus , nocturnal reef plankton , or diurnal reef plankton  sources, so samples from both inner and outer reef habitats were pooled for each species . Furthermore, no differences were observed among diurnal reef plankton consumer species (C. varilineata and C. xanthonota) or among pelagic plankton consumer species (Decapterus macarellus and Uroteuthis duvauceli), so they were combined, respectively, into “diurnal reef plankton” and “pelagic plankton” source groups .Predators were sampled twice . For specialized consumers where differences in δ13CEAA were evident, sources were kept distinct in further analysis. For the corallivorous butterflyfish, Chaetodon meyeri, a proxy for coral-derived carbon, δ13CEAA values differed significantly between inner and outer atolls, so these samples were analyzed by habitat . Overall, we observed strong differences in the δ13CEAA values among the specialized consumers representing source groups, indicating six isotopically distinct carbon pathways available to our predatory consumers . Hereafter, we refer to the specialized consumers by the source groups that they represent.Peacock grouper 13CEAA values, revealed distinct separation among the source groups , a multivariate technique used to visualize relationships and patterns among the δe groups , and the13CEAA fingerprints using linear discriminant analysis (LDA). LDA accounts for unique variations of δ13C values among the different EAAs within a single tissue type and is less rigid than Bayesian mixing models for examining diet contributions (We achieved greater quantitative separation between the source and predator groups by comparing their multivariate δy dimension LD2. The first linear discriminant (LD1) explained 79% of the data (2) explained 21% of the data, driven by Lys and Val  or the reef-associated plankton group . Overall, for both the full and the simplified LDA models, there were no differences in predator classifications spatially or interspecifically (fig. S3).There was excellent separation among source groups in the simplified model, and successful reclassification of the groups (to ensure good separation) was 100%. This confirmed that a combined reef-associated plankton group  and absence of the benthic algae and detritus groups led to more accurate within-group classifications of sources and better separation of the groups along the the data , driven  and Val . The boo and Val . ExceptiZ = −2.314, P = 0.029; C. miniata had the greatest reliance, then redmouth grouper Aethaloperca rogaa, peacock grouper C. argus, and last, slender grouper A. leucogrammicus. Coral-derived carbon also contributed to sustaining predator biomass  to the different predators in both atoll habitats, a Bayesian stable isotope mixing model was run . Median 13CEAA fingerprints of specialized consumers, we identified two isotopically distinct planktonic pathways (nearshore reef-associated plankton and offshore pelagic plankton) and two isotopically distinct benthic pathways . We show that the offshore pelagic plankton pathway predominates in supporting four key abundant species of predatory reef fish across an oceanic atoll and that secondary contributions from reef-based sources are coral-derived rather than from reef-associated plankton. Our results reveal an important energetic link between shallow reefs and offshore communities, highlighting the contribution of allochthonous inputs to coral reef food webs.Despite their oligotrophic setting, the presence and availability of planktonic inputs to coral reefs have long been recognized  and Indian Ocean squid (U. duvauceli), are found in deeper oceanic waters, and U. duvauceli come to the surface to feed at night  and soldierfish (Myripristis vittata) could be feeding on the same local reef-based plankton supported by common phytoplankton sources (34S) of these species, which help explain movement and habitat usage  and reef-associated plankton (soldierfish) resources  and the soldierfish  were distinct from one another, likely reflecting that fusiliers spend more time feeding off-reef. The δ34S values of the fusiliers overlapped with the pelagic scad (D. macarellus), despite their δ13CEAA values indicating reliance on different sources. Together, the δ13CEAA and δ34S data suggest that the more mobile scad and fusiliers may feed across a reef-pelagic gradient, with the δ34S values reflecting their habitat usage, while the δ13CEAA values reflect the sources most important in sustaining them and the carbon that they contribute to higher trophic levels.The diurnal and nocturnal reef-associated plankton were also isotopically similar to each other, suggesting that the fusiliers (A. leucosternon) is an herbivore (C. striatus) a detritivore , primarily sustained all predators. While recent research has highlighted this reef-pelagic linkage using stable isotopes  and serranids (Pseudanthias spp.), both frequently found in grouper stomach contents , run to account for this, revealed stricter pelagic source reliance across all the predators (97 to 100%); raw data estimates were more conservative. It is therefore highly unlikely that the strong pelagic signature arises from methodological discrepancies in δ13CEAA values. Last, although CSIA studies are increasing, very little is known about dietary incorporation rates of amino acids; there is variation among individual amino acids  than during the southwest monsoon (0.15 to 0.42 mg/m3)  is located in the center of the double chain of the archipelago on the eastern side. The atoll perimeter consists of an outer reef slope separated by deeper channels, while the atoll lagoon contains reef platforms  during tA. rogaa (redmouth), A. leucogrammicus (slender), C. argus (peacock), and C. miniata . No juveniles (<15 cm) were sampled to control for ontogenetic dietary changes.Groupers were chosen as representative reef predators as they are more site-attached, while other reef predators, e.g., snappers, emperors, and jacks, have larger home ranges involving long-distance movements so they may access resources from off the reef    , and C. xanthonota, yellowback fusilier     (13CEAA data where possible (see section S1), confirming them to be faithful indicators of the primary baseline carbon sources in their diets. Samples were collected using pole spears or from a local fish market .Six carbon energy pathways were identified, and specialized consumers were sampled as indicators on the basis of their carbon source reliance, accordingly: (i) benthic algae: All tissue sampling was carried out in compliance with UK Home Office Scientific Procedures  Act Requirements. The samples were collected under research permits (OTHR)30-D/INDIV/2016/515 and (OTHR)30-D/INDIV/2018/466 granted by the Republic of Maldives Ministry of Fisheries and Agriculture, and Newcastle University Animal Welfare and Ethical Review Body approved the project (project ID: 526).N-acetyl isopropyl ester (NAIP) derivatives of amino acids were prepared by following the protocol described by Corr et al. (2 (40°C), acetylation was achieved by adding a mixture of acetone:triethylamine:acetic anhydride (5:2:1) and heating for 10 min (60°C). Isolation of the NAIP derivatives was achieved using liquid-liquid separation with NaCl solution (saturated) and ethyl acetate. All organic phases were combined and dried under a very gentle stream of N2 (room temperature). Any residual water was removed with two successive 1-ml aliquots of DCM and evaporated under a very gentle stream of N2 (ice bath). Samples were then stored in a freezer until they could be screened.Muscle tissue was oven-dried at 50°C for 48 hours and then ground to a fine powder using a pestle and mortar. −1, then to 210°C at 2°C min−1, and then to 270°C at 8°C min−1. The injection mode was Cold on Column, and the injection volume was 1 μl with helium carrier gas at a flow rate of 2.00 ml/min.For screening, the derivatized amino acids were resuspended in ethyl acetate and analyzed using gas chromatography with an Agilent 7890 GC with flame ionization detection (GC/FID), fitted with a DB-35 column 30 m by 0.32 mm by 0.5 μm (Agilent), and an Agilent G4513A autosampler . The GC oven temperature was set to the following program: 70° (hold 2 min) to 150°C at 15°C min13C isotopic compositions of the amino acids were analyzed using a GC/IRMS. A Thermo Fisher Scientific  Delta V Plus IRMS was fitted with a Trace GC Ultra Oven, GC Isolink, and a ConFlo IV interface. The GC was fitted with a DB-35 column 30 m × 0.32 mm × 0.5 μm (Agilent). The oven was set as follows: 40° (hold 5 min) to 120°C at 15°C min−1, to 180°C at 3°C min−1, to 210°C at 1.5°C min−1, and then to 270°C at 5°C (hold 7 min).The δ2) were introduced into the IRMS instrument during the analysis giving rise to peaks with known δ13C values (13C:12C ratio relative to Pee Dee Belemnite). These reference pulses were used to calculate the analyte peaks in each chromatogram. Identification of the derivatized amino acids was achieved by matching the peak elution times with those from a mixed amino acid standard (derivatized) containing alanine (Ala), glycine (Gly), valine , leucine (Leu), norleucine (Nle), threonine (Thr), serine (Ser), proline (Pro), aspartic acid (Asp), glutamic acid (Glu), hydroxyproline (Hyp), phenylalanine (Phe), lysine (Lys), and tyrosine (Tyr).Pulses of reference gas . The correction factor calculation wasD. macarellus and U. duvauceli) were derivatized and analyzed in 2018, while all predators and the pelagic source samples were derivatized and analyzed in 2019 (table S3).All samples were derivatized at Newcastle University, UK, and all GC/FID work and GC/IRMS work were carried out at the National Environmental Isotope Facility Bristol, formerly known as the Bristol Node of the NERC Life Sciences Mass Spectrometry Facility, UK. All specialized consumer source samples (except for the pelagic consumers Rsample − Rstandard)/Rstandard], and R is the ratio of heavy to light isotope .Isotopic signatures were derived from five EAAs: leucine (Leu), lysine (Lys), phenylalanine (Phe), threonine (Thr), and valine . Stable isotope ratios are reported using the delta (δ) notation with measured values expressed in per mil (‰), where δ = [ was used. Some source groups were also sampled from both inner and outer atoll, so a one-way PERMANOVA determined whether there were spatial differences in their δ13CEAA values. Where two species were collected to represent the same carbon pathway, a one-way PERMANOVA determined whether there were interspecific differences in their δ13CEAA values. If there were none, then these samples were pooled into one representative source group. Last, as identifying the carbon pathways sustaining the predators relies on separation among the source groups, a one-way PERMANOVA was used to assess differences in δ13CEAA values among the final source groups. All PERMANOVAs were run on resemblance matrices based on Euclidean distance measures and with 999 permutations.We used PERMANOVAs [vegan . As a result, the EAM source groups were removed from all further analysis. To more finely resolve the reef contributions, the potential reef sources were simplified into coral (benthic) or reef-associated plankton . The traditional and bootstrapped LDAs were then repeated with these simplified source groups .C. meyeri), (ii) reef-associated plankton , and (iii) pelagic plankton (D. macarellus and U. duvauceli). Mean and SD values were calculated for each source group to represent source means in the mixing models.To capture and quantify the variable contributions of nearshore  versus offshore (pelagic plankton) sources to the different predators in both atoll habitats, a Bayesian stable isotope mixing model was run , and habitat (inner/outer) was a fixed factor. The model was run with two error terms , which help incorporate any variation in consumer digestibility or variation related to the sampling process .To ensure that the timing of the sampling and of the sample analysis did not bias our interpretations, all diet contribution analyses  were carried out with the measured raw δ


The Chinese Center of Disease Control and Prevention (CDC) executed an intensive investigation program. WHO named this novel strain of coronavirus as sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) aka coronavirus disease 2019 (COVID 19). The sufferer of this virus predominantly and typically shows the symptoms of acute respiratory infections ranging from barely noticeable cough and dyspnea to need of mechanical ventilation in excessively compromised individuals but it typically causes pneumonia thus producing pneumonia like symptoms.th leading cause of death in USA.According to World Health Organization (WHO) the ailments caused by viruses have always emerged in the past and posed masses to life threatening medical conditions. Considering the last two decades, a number of viral infections emerged as epidemic including severe acute respiratory syndrome coronavirus (SARS-CoV), H1N1 influenza, Middle East respiratory syndrome coronavirus (MERS-CoV) and when the timeline approaches the present days there is an outbreak of coronavirus once again with drastic dissemination all across the globe but this time it is the novel one, having a new strain and originating from China in the city of Wuhan with its 1MMAA: Concept, design and accountable for all work.SQ: Drafting and revision.KK: Critical analysis and final approval.


Recently there has been a continuing worldwide decrease in the quality of human spermatozoa, especially in spermatozoa motility and concentration. Many factors are involved in this decline, and great efforts have been made to rescue spermatogenesis; however, there has been little progress in the improvement of sperm quality. Chestnuts are used in traditional Chinese medicine; their major active components are chestnut polysaccharides (CPs). CPs have many biological activities but their effects on spermatogenesis are unknown. The current investigation was designed to explore the impact of CPs on spermatogenesis and the underlying mechanisms. We demonstrated that CPs significantly increased sperm motility and concentration , and improved seminiferous tubule development by increasing the number of germ cells after busulfan treatment. CPs dramatically rescued the expression of important genes and proteins  in spermatogenesis. Furthermore, CPs increased the levels of hormone synthesis proteins such as CYP17A1 and HSD17β1. All the data suggested that CPs improved the testicular microenvironment to rescue spermatogenesis. With CPs being natural products, they may be an attractive alternative for treating infertile patients in the future. At the same time, the deep underlying mechanisms of their action need to be explored. During the past three decades, a growing number of people have been suffering from reproductive health issues leading to infertility. Many factors are involved in infertility, but in the male, a decrease in the quality of spermatozoa is a key factor. Many studies have revealed that environmental pollution and ionizing radiation played vital roles in the deterioration of spermatozoa quality (spermatozoa motility and concentration) –4. ThereRecently, it has been found that polysaccharides have many biological functions. They pose anti-oxidative and anti-inflammatory functions through promoting the expression of antioxidant enzymes and decreasing the expression levels of inflammation factors –8, immunVASA, DAZL, and DMC1. VASA is a germ cell marker important for germ cell proliferation and differentiation, and VASA mutation results in the cessation of germ cell differentiation [DAZL serves as a gateway in oogenesis and spermatogenesis, and the abnormal expression of DAZL will affect the initiation of gametogenesis [DMC1 plays an important role in spermatogenesis, and its mutation leads to obstacles in male sterility [Spermatogenesis takes place in the seminiferous tubules of the testis; it includes spermatocyte proliferation, spermatogonial differentiation into spermatocytes, spermatid production, and sperm maturation. Many factors are involved in regulating spermatogenesis such as genes, hormones, and epigenetic regulators –24. The ntiation . DAZL seogenesis . DMC1 plterility .Hormones such as testosterone and estrogen play essential roles in regulating spermatogenesis . Many prAlthough CPs have been shown to be beneficial for human health, the effects on spermatogenesis and the underlying mechanisms are not understood. The aim of this study was to explore the means of CPs improve spermatogenesis and the underlying mechanisms.CPs alone did not change murine sperm motility , howeverFirst, testicular tissue transcriptomes were determined after busulfan and/or CPs treatments to search for gene expression patterns. Principal components analysis (PCA) showed that the busulfan and control groups were well separated, which suggested that the busulfan treatment produced profound effects on gene expression . The B+CVASA, CREM, SYCP3, DAZL, REC8, PGK2, DMC1, SYCP1, STRA8, and ZFP42. Most of the DEGs that were decreased in Control-vs-B while being increased in B-vs-B+CPs 0.10, were involved in spermatogenesis or reproduction, including STRA8, DAZL, SYCP1, SYCP3, TNP1, etc.  are involved in the production of these hormones. We determined these proteins by IHC and found that the levels of CYP17A1, and HSD17β1 were decreased by busulfan while elevated by B+CPs 0.10 –6C. The Chestnuts have been used as a traditional food and medicine for millennia in China due to their health advantages . CPs, keSubsequently, we explored the underlying mechanisms of CPs in rescuing mouse spermatogenesis. Gene expression within testicular samples was determined by RNA-seq analysis. It was interesting to notice that 9043 DEGs were decreased by busulfan while increased by CPs, and 4248 DEGs were increased by busulfan while decreased by CPs. GO enrichment analysis revealed that these 9043 DEGs, that were decreased by busulfan while being increased by CPs, were enriched in spermatogenesis related functional pathways. But the 4248 DEGs, those were increased by busulfan while decreased by CPs, were enriched in other functional pathways (non-sperm related groups). The data indicated that CPs may regulate gene expression machinery to improve spermatogenesis.Moreover, we explored the effect of CPs on hormone production pathways in mouse testes, because of the critical role that hormones play in spermatogenesis , 36, 37.In conclusion, the results of this study suggested that CPs improved spermatogenesis by increasing sperm motility and concentration. The underlying mechanisms involved CPs regulation of gene expression machinery to increase the expression levels of genes and proteins involved in spermatogenesis and to improve the testicular microenvironment to rescue spermatogenesis. A decrease in the quality of spermatozoa is a key factor that caused infertility. Therefore, looking for a feasible approach to protect fertilization capacity or to reverse any damage in testes is necessary. CPs being as natural products, they may be an attractive alternative method for treating infertile patients in the future.Male ICR mice were purchased from the Beijing Vital River Laboratory Animal Technology Co., Ltd . The mice were fed in constant temperature (22-23°C) rooms with a 12 h light: 12 h dark photoperiod and had free access to food (chow diet) and water throughout the period of study. All procedures used in this study were in accordance with the guidance of the Ethics Committee of Qingdao Agricultural University.Busulfan was used as a model compound to create an infertile animal model as it has been frequently used in recent published articles , 39. CPsSperm motility was analyzed during sample collection. Sperm were collected from the epididymis then cultured in DMEM/F12  with 10% FBS  medium and incubated at 37.5°C on a heated stage for 5 min, counting boards were prepared in advanced. Then samples were placed in a counting chamber  and sperTotal RNA was extracted from collected testis samples using a kit  and according to the manual instructions . SequencTotal RNA was reverse transcribed using the TransScript One-Step gDNA Removal Kit and cDNA Synthesis Kit . To evaluate the changes in mRNA level, we conducted quantitative real-time PCR (qRT-PCR) using transcribed cDNA. qRT-PCR was conducted using a Light Cycler real-time PCR instrument  using Light Cycler SYBR Green I Master Mix according to the manufacturer’s instructions. A reaction system volume of 24 μl was used in this study containing 2 μl cDNA, 12 μl of SYBR green master mix, 1.2 μl of primer mix including forward and reverse primer genes, and 8.8 μl of RNAase-free water. The PCR conditions of a previous study were followed . We quanCollected testes were fixed in 4% paraformaldehyde and kept in a refrigerator at 4°C overnight, then subsequently stored in different concentrations of dehydrating solutions. The dehydrated testicular samples were then embedded in paraffin and the resulting paraffin blocks were sectioned at 5 μm thickness following standard histological procedures. Sections were stained with H&E, following the procedure of a previous study , 50. TesTesticular proteins were extracted using the process described below. Samples were submerged in cold RIPA buffer and a crusher was used to treat the samples twice at a cold temperature. The protein lysates were centrifuged at 10000 rcf/min for 10 min then the supernatant was collected and added to SDS-loading buffer, and boiled for 5 min in water till the protein was completely denatured. Protein samples were mixed evenly before loading onto 10% SDS polyacrylamide electrophoresis gels and processed at 100V for 1.5 h; the specific time depended on the electrophoresis situation. The proteins on the gels were transferred to a polyvinylidene fluoride membrane. Subsequently, membranes were blocked with skimmed milk or 5% bovine serum albumin at a cold temperature for at least an hour, followed by 3 washes with Tris-buffered saline, 0.1% Tween 20 (TBST). We then used primary antibodies to incubate membranes in the refrigerator overnight at 4°C. On the second day, membranes were washed 3 times with TBST and incubated with the HRP-labeled secondary antibody  for 1 h at room temperature. After 3 washes with TBST, the membranes were imaged . The imaP < 0.05 as a significant difference, while P < 0.01 was a highly significant difference.All experiments were repeated at least 3 times and results were expressed as the mean ± SEM. SPSS software one-way analysis of variance (ANOVA) following by LSD multiple comparison test was used for data analysis and we defined Supplementary Figure 1Supplementary Tables