Vaccine development utilizing various platforms is one of the strategies that has been proposed to address the coronavirus disease 2019 (COVID-19) pandemic. Adjuvants are critical components of both subunit and certain inactivated vaccines because they induce specific immune responses that are more robust and long-lasting. A review of the history of coronavirus vaccine development demonstrates that only a few adjuvants, including aluminum salts, emulsions, and TLR agonists, have been formulated for the severe acute respiratory syndrome-associated coronavirus (SARS-CoV), Middle East respiratory syndrome-related coronavirus (MERS-CoV), and currently the SARS-CoV-2 vaccines in experimental and pre-clinical studies. However, there is still a lack of evidence regarding the effects of the adjuvants tested in coronavirus vaccines. This paper presents an overview of adjuvants that have been formulated in reported coronavirus vaccine studies, which should assist with the design and selection of adjuvants with optimal efficacy and safety profiles for COVID-19 vaccines. Coronaviruses (CoVs) are single-stranded RNA viruses characterized by club-like spikes that can potentially cause severe respiratory disease in humans , 2. The e.g., toll-like receptors  vaccines, DNA vaccines, and mRNA vaccines , 6, 7. Aeceptors . Nucleicotection . Howeverotection . In prevotection Table 1.otection  and haveotection . When cootection , 31, adjotection . Among totection , 18, whiotection , 16, 24.RS-CoV-2 . Other aThe SARS-CoV-2 is a novel strain of the coronavirus, and very little is known about its epidemiology and pathogenesis. Therefore, extreme cautions should be taken when considering vaccine formulations that can achieve the desired efficacy and safety profiles. The selection of adjuvants should consider the magnitude, affinity, isotype, and durability of antibodies that are critical for coronavirus vaccine developments . It shou1, increased high affinity viral neutralizing antibodies, and the generation of long-lasting memory B cells in mice  were the first adjuvants used in licensed human vaccines. They are still the most widely used because of their wide-spectrum ability to strengthen immune responses and their excellent track record of safety –41. In l in mice , 17–19.  in mice , 12, 14  in mice Table 1 y titers . It shouy titers , aluminuy titers , aluminuy titers , and Imjy titers , which iy titers , 18, 21,y titers . However1, when compared with the more balanced antibody production in the nonadjuvanted DIV vaccine  vaccines  compared protein . Therefo protein .2a) with no clear Th2 responses (IgG1)  , 59.Toll-like receptors (TLRs), a category of pattern-recognition receptors, are critical to pathogen recognition. This allows for rapid activation of innate immunity, and subsequently, effective adaptive immunity. TLR agonists have been extensively studied as vaccine adjuvants , 61. CpG+ T cells, optimal protection against coronavirus probably involves the synergistic effect of CD8+ T cells  administration −sn−glycero−3−phosphoethanolamine):DDA  multilamella liposomes rather than linear mixed, a high potency of immunostimulatory activity was observed , Natural Science Foundation of Liaoning Province (No. 20180550597), LiaoNing Revitalization Talents Program (XLYC1807113), Dalian Science and Technology Innovation Fund (No. 2020JJ25CY015), and Fundamental Research Funds for the Central Universities (DUT19TD12).WZ was employed by NCPC Genetech Biotechnology Co., Ltd. LS was employed by Shanghai Zerun Biotechnology Co., Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Rationale: Previous studies have reported on the role of extracellular acidity in the metastasis of numerous cancers. However, the involvement of long noncoding RNA (lncRNA) in the extracellular acidity-induced cancer metastasis of pancreatic cancer (PC) remains unclear.Methods: Different expression levels of lncRNAs in PC cells under normal and acidic conditions were compared by RNA sequencing (RNA-seq). The effects of antisense lncRNA of metastasis suppressor 1 (MTSS1-AS) on acidic PC cells were assessed by gain- and loss-of-function experiments. Fluorescence in situ hybridization, RNA immunoprecipitation, RNA pull-down, Western blot, luciferase reporter, and Chromatin immunoprecipitation assays were employed to determine the regulatory mechanisms of MTSS1-AS in the acidity-induced metastasis of PC cells. The expression of MTSS1-AS and associated pathways were compared in PC samples and peritumoral normal tissues.Results: RNA-seq demonstrated that MTSS1-AS was significantly downregulated in pancreatic cells cultured with the acidic medium. The overexpression of MTSS1-AS remarkably inhibited the acidity-promoted metastasis of PC cells by upregulating the expression of its sense gene metastasis suppressor 1 (MTSS1). Mechanistically, MTSS1-AS scaffolded the interaction between E3 ubiquitin-protein ligase STIP1 homology and U-box containing protein 1 (STUB1) and transcription regulator myeloid zinc finger 1 (MZF1), leading to ubiquitination-mediated degradation of MZF1. Further, MZF1 inhibited the expression of MTSS1 by binding to the MTSS1 promoter. Thus, the acidity-reduced MTSS1-AS facilitated the stability of MZF1 and its inhibitory effect on MTSS1 transcription, thereby promoting the metastasis of PC cells under acidic conditions. Moreover, MTSS1-AS was transcriptionally repressed by the binding of MYC proto-oncogene (Myc) with initiator (Inr) elements of the MTSS1-AS promoter. Meanwhile, MTSS1-AS mutually repressed the expression of Myc by impairing the MZF1-mediated transcription activation of Myc, thereby forming a negative feedback loop between MTSS1-AS and Myc in acidic PC cells. In accordance with the experimental results, MTSS1-AS and MTSS1 were downregulated in PC and correlated with poor overall survival.Conclusions: The results implicated that a reciprocal feedback loop between Myc and MTSS1-AS contributed to the extracellular acidity-promoted metastasis of PC, and indicated that MTSS1-AS was a valuable biomarker and therapeutic target for PC. Alterations in lncRNAs disrupt various cellular processes, including cell differentiation, splicing or decay of RNAs, cell cycle progression, and epigenetic regulation within cancer cells In the present study, RNA sequencing (RNA-seq) was performed to analyse the expression of lncRNAs derived from PC cells cultured with normal (pH 7.4) or acidic (pH 6.8) medium. The results showed that lncRNA (RP11-532M24.1), an antisense RNA of metastasis suppressor 1 (MTSS1) designated as lncRNA-MTSS1-AS, significantly decreased in acidic PC cells. Studies demonstrated that the MTSS1 level reduced in numerous types of cancer and inhibited the migration and invasion of different cancer cells 125 seed implantation. The tissues were obtained from surgical resection of patients or biopsy of the palliative surgery patients. Part of the excised tissue specimens were fixed in 10% buffered formalin solution and then embedded in paraffin, and part of the samples were immediately frozen at liquid nitrogen after surgical resection. PC diagnosis in these individuals was based on original histopathology findings by two pathologists. The study was conducted in accordance with the Declaration of Helsinki. All samples were collected with the written informed consent of the patients, and the study was approved by the local Research Ethics Committee at the Academic Medical Centre of Huazhong University of Science and Technology.The clinical samples were obtained from Pancreatic Disease Institute of Union Hospital, Wuhan, China. We randomly selected 132 both PC and paired normal peritumoral (NP) tissue samples from patients without chemotherapy or radiotherapy before operation. Patients were undergoing surgical treatment according to the National Comprehensive Cancer Network (NCCN) guideline, including pancreatectomy, choledochojejunostomy, gastroenterostomy, and IRNA-seq analysis was performed by Sangon Biotech . Briefly, BxPC-3 cells were cultured in normal (pH 7.4) and acidic media (pH 6.8) for 24 h. Total RNAs were extracted from the two samples using TRIzol reagent . Purity of these samples were tested using Thermo Nanodrop 2000  and the concentration were quantified accurately using Qubit™ RNA BR Assay Kit (Invitrogen). Ribo-Zero™ rRNA Removal Kit  was used to remove rRNA of samples. Then the strand specific cDNA library was well prepared and was tested properly by Agilent 2100 Bioanalyzer System . RNA-seq was performed on the Illumina HiSeq 2500 platform and RNA-seq dataset was visualized using the Qubit 2.0 Fluorometer (Invitrogen). High-quality data reads were consistent with publicly available NONCODE database. Gene expressions were normalized to fragments per kilobase of exon model per million mapped reads (FPKM) by Expectation Maximization (RSEM).2. Some specific experiments were carried out with cells in exponential growth, cultured in acidic (pH 6.8) or normal (pH 7.4) medium for 24 or 48 h. To achieve media acidification, 20 mM Tris-(hydeoxymethyl)-aminomethane and 2-(N-morpholino)-ethane-sulfonic acid were utilized The human PC cell lines  were obtained from American Type Culture Collection , while human pancreatic duct epithelial (HPDE) cell line was purchased from BeNa Culture Collection . They were tested and authenticated for genotypes by DNA fingerprinting within 6 months. Cells were cultured in RPMI-1640  containing 10% fetal bovine serum  and 100 U/mL penicillin and 100 mg/mL streptomycin at 37 °C under 5% CO6 cells were fixed in 1% formaldehyde for 10 min at 37 °C and washed twice using ice cold PBS containing protease inhibitors. Subsequently, cells were scraped into conical tube and lysed with SDS lysis buffer supplemented with protease inhibitors. The chromatin DNA was sonicated and sheared to lengths between 200 and 1000 bp. After centrifugation, the supernatant was transferred to a new tube and diluted with ChIP dilution buffer containing protease inhibitors. Protein A agarose beads were removed to the diluted cell supernatant and agitated for 1 h at 4 °C to reduce the nonspecific background. The supernatant fraction was collected, and mixed with anti-MZF1 antibody  or anti-Myc  and incubated overnight at 4 °C with rotation. Normal mouse IgG was served as a control, and an anti-RNA pol II antibody (Millipore) was used as the positive control. Protein A agarose beads were added to the reaction mixture for 1 h at 4 °C with rotation to collect the antibody/histone complex. Supernatant contained unbound and non-specific DNA was removed after gentle centrifugation. Protein A agarose/antibody/histone complex was washed on a rotating platform with the low salt immune complex wash buffer, high salt immune complex wash buffer, LiCl immune complex wash buffer and TE buffer. Elution buffer was used to separate the complex from the antibody. DNA samples were recovered by phenol/chloroform extraction and ethanol precipitation, and then amplified by PCR and separated by 2% agarose gel electrophoresis for analysis. The ChIP-qPCR primer sequences are given in ChIP analyses were performed using the EZ-ChIP™ Kit  according to manufacturer's instructions. For each ChIP assay, 1 × 10in vitro with the effect of T7 RNA polymerase. To purify the amine-modified RNA immediately, binding buffer with isopropanol was added to the synthesis reaction and mixed well. After centrifuged and washed by wash buffer and elution buffer repeatedly, the purified RNA was contained in collection tube and precipitated by ethanol. The concentration of the RNA was adjusted with water to a final concentration of 0.2 μg/μL. Subsequently, amine-modified RNA was labeled with fluorescent dye and purified again. The probe identified MTSS1-AS was labeled with green fluorescence. After ethanol precipitation, the fluorescent dye-labeled probe was well prepared. Cells were grown on cover glass and fixed with formaldehyde/PBT for 25 min with gentle rocking. Then the sample was permeabilized using Triton X-100 for 20 min and washed using PBT/50% hybridization buffer. Labeled probe was hybridized overnight with cell samples at 55 °C in a humid environment. Stained results were observed by using LSM 5 Pascal Laser Scanning Microscope . Probe sequence is given in FISH analysis was conducted using the FISH Tag™ RNA Multicolour  and MAXIscript®  kits. The probe synthesis, labelling and purification procedures were following manufacturer's instructions. Total RNA was extracted from the PC cells using TRIzol reagent (Invitrogen). RNAs were amplified by qRT-PCR using primers containing a T7 promoter sequence specific for antisense of MTSS1-AS. After purification by PCR Clean Up Kit , the amplified product was transcribed into indicated RNA in vitro using the MAXIscript™ T7 Transcription Kit . Briefly, DNA template was mixed with T7 RNA polymerase, rNTPs, and transcription buffer, and the reaction mixture was incubated for 10 min to 1 h at 37 °C. The newly synthesized RNA was labeled with biotinylated cytidine bisphosphate using Pierce™ RNA 3' End Desthiobiotinylation Kit . Lysates of BxPC-3 and PANC-1 cells were prepared using standard lysis buffers in Pierce™ Magnetic RNA-Protein Pull-Down Kit (Thermo Fisher Scientific). Streptavidin magnetic beads were bound to labeled RNA in RNA capture buffer firstly, and were then mixed with protein lysate, 50% glycerol and protein-RNA binding buffer for 1 h at 4 °C with agitation. After washing and elution, the RNA-binding protein complexes were heated and eluted for 10 min in boiling water. Retrieved proteins were detected by Western blot analysis. The antisense RNA was used as the negative control.Total RNA was extracted from the PC cells using TRIzol reagent (Invitrogen), and was amplified by qRT-PCR using primers containing a T7 promoter sequence specific for MTSS1-AS or antisense of MTSS1-AS. Amplified product was purified using PCR Clean Up Kit (Beyotime), and then transcribed into indicated RNA RIP assays were performed using the Magna RIP™ RNA-Binding Protein Immunoprecipitation Kit (Millipore) according to the manufacturer's instructions. Firstly, cell lysates were prepared using cell lysis buffer containing protease inhibitors and RNase inhibitors. Subsequently, magnetic beads were pre-incubated with an anti-MZF1 antibody (Santa Cruz Biotechnology) or IgG for 30 min at room temperature and washed by RIP wash buffer. The lysates were then immunoprecipitated with the bead-bound antibodies at 4 °C overnight in RIP immunoprecipitation buffer included with 0.5 M EDTA and 5 μL RNase inhibitor. Magnetic beads were reserved and washed repeatedly after centrifugation. To purify the RNA bound with protein, proteinase K buffer was used to re-suspend magnetic beads. Supernatant was removed, and the immunoprecipitate was mixed with salt solution I, salt solution II, precipitate enhancer and absolute ethanol overnight at -80 °C. Finally, the samples were centrifuged at 14,000 rpm for 15 min at 4 °C, and washed with 80% ethanol. Pellets were re-suspended in 10 to 20 μL of RNase-free water for analysis by qRT-PCR.Cell lysates were prepared using Cell lysis buffer for IP (Beyotime) and adjusted to a final concentration of 1 μg/μL. 5% of cell lysates were retained as input group for direct Western blot analysis. For Co-IP group, protein A agarose beads were removed to the diluted cell supernatant and agitated for one hour at 4 °C to reduce the nonspecific background. Subsequently, cellular extracts were mixed overnight at 4 °C on a rotating platform with control IgG  or following primary antibodies (4 μg/mL): rabbit anti-MZF1 (Santa Cruz Biotechnology), rabbit anti-STUB1 . Protein A/G PLUS-Agarose (Santa Cruz Biotechnology) was then added and the mixture was incubated for a further 2 h at 4 °C with agitation. The agarose was then separated, rinsed with lysis buffer and equivalent volumes of each sample were assessed by Western blot analysis.MTSS1-AS-specific probes for Northern blot analysis were generated using a DIG RNA Labeling Kit  according to the manufacturer's instructions. Total RNA was extracted from the PC cells using standard TRIzol methods, and amplified using specific primers of MTSS1-AS by qRT-PCR. To obtain DIG-labeled DNA probes, the prepared DNA template was denatured in boiling water for 10 min and then incubated with DIG-High prime overnight at 37 °C. Total RNA was extracted again, and proceeded electrophoresis in denatured agarose gel for 2 h with formaldehyde gel electrophoresis buffer. To test the integrity of RNA, band signals were visualized by ChemiDoc™ XRS Molecular Imager System . The prepared RNA samples were then transferred to nylon membrane  overnight using saline sodium citrate buffer. Membrane was stored for 2 h at 80 °C to fix RNA. DIG-labeled DNA probes were denatured in boiling water for 5 min and then hybridized with RNA on the membrane overnight at 55 °C on a rotating platform. Saline sodium citrate buffer contained with 0.1% SDS was used to wash the membrane for 15 min at 55 °C twice. Probes/MTSS1-AS complex on the membrane was incubated and stabilized on a rotating platform with the blocking solution, anti-digoxigenin-AP solution, wash buffer, detection buffer, and CSPD. Anti-digoxigenin-AP was utilized for capture of complex and CSPD was used to visualize probes. Images were collected by ChemiDoc™ XRS Molecular Imager System (Bio-Rad).Immunohistochemistry was conducted using a previously described method 6 cells/mouse) lines were injected into nude mice through the tail vein. After 4 weeks, the lungs and liver of the mice were removed and stained with H&E for enumeration of metastases. No blinding approach was used during this study. The Animal Research Committee of the Academic Medical Center at Huazhong University of Science and Technology  approved all aspects of this study. Care and handling of the animals were in accordance with the guidelines for Institutional and Animal Care and Use Committees.Male BALB/c nude mice  were obtained from HFK Bio-Technology, Beijing, China. For metastatic models, stable BxPC-3  curve analysis was used to evaluate the ability of biomarkers to forecast the mortality risk of PC. Arithmetic mean was used to divide high group and low group of RNA expression. An estimate of variation was performed within each group of data. The data meet the assumptions of the tests, respectively. P < 0.05 was considered to indicate statistical significance.SPSS 24.0 was used for data analysis. Data were presented as means ± standard deviation (SD). Student's Although lncRNAs have been extensively researched in relation to tumorigenesis and progression, the changes in lncRNAs in the acidic microenvironment of cancer cells were unclear. In this study, the RNA-seq analysis was performed on the changes in the expression of lncRNA transcript in PC cells cultured in normal (pH 7.4) and acidic media (pH 6.8). The results revealed a prominently downregulated lncRNA, RP11-532M24.1  on the MTSS1 promoter Figure G. FollowThese results suggested that MZF1 was an inhibitor of MTSS1 transcription, while MTSS1-AS decreases the binding of MZF1 to the MTSS1 promoter. Therefore, acidity-mediated repression of MTSS1-AS promoted the binding between MZF1 and the MTSS1 promoter, thereby decreasing the expression of MTSS1 and promoting the metastasis of PC cells.The relationship between MTSS1-AS and MZF1 was further investigated to fully understand the mechanism by which MTSS1-AS affected the interaction between MZF1 and the MTSS1 promoter. Studies showed that STIP1 homology and U-box-containing protein 1 (STUB1) was responsible for the ubiquitination-mediated degradation of MZF1 A luciferase reporter plasmid containing the promoter region of MTSS1-AS was transfected into BxPC-3 and PANC-1 cells to investigate the mechanisms by which the expression of MTSS1-AS was regulated in acidic microenvironments. In luciferase reporter assays, the luciferase activity obviously decreased in cells cultured in acidic medium, indicating that the transcriptional activity of MTSS1-AS was inhibited under acidic conditions Figure A. MYC prStudies demonstrated that Myc transcription was upregulated by liver kinase B1 loss-mediated expression of MZF1, and the MZF1/Myc axis was responsible for migration and invasion in lung adenocarcinoma cells in vivo and then stably transfected with lentivirus-containing negative control (Lv-NC), or MTSS1-AS (Lv-MTSS1-AS), or co-transfected with Lv-MTSS1-AS and lentivirus-containing Myc (Lv-Myc). Subsequently, these cells were injected into nude mice via the tail vein. Long-term acidic cultured PC cells caused more visible lung and liver metastases in mice compared with the normal group. Meanwhile, the mice in the Lv-MTSS1-AS group had decreased visible lung and liver metastases compared with the Lv-NC group when injected with PC cells cultured under acidic conditions for a long term, which were recovered in co-transfection with Lv-Myc group , vascular infiltration (P = 0.0298), and distant metastasis (P = 0.0108)  . The Kap P = 0.028, and di P = 0.028, and di P = 0.028, and di P = 0.028, and di P = 0.028, and di P = 0.028, and di P = 0.028, and di P = 0.028, and diAlthough numerous studies revealed that acidic extracellular microenvironments induced the migration and invasion of cancer cells, the underlying mechanism was not fully understood cis-activating SATB2 transcription via mediating histone H3 lysine 4 tri-methylation deposition, and DNA demethylation of the promoter region of SATB2 Recent studies indicated that antisense lncRNA regulated the expression of the sense gene through various mechanisms, such as providing a physical barrier against posttranscriptional interactions, triggering RNA interference, and so forth Since MTSS1-AS is mainly located in the nuclei of PC cells, it was hypothesized that MTSS1-AS might regulate MTSS1 transcription by influencing the binding between the transcription factor and promoter area of MTSS1 gene. Online software catRAPID and JASPAR were used to predict the protein potentially interacting with MTSS1-AS and MTSS1 promoter. The interaction analysis of catRAPID and JASPAR suggested a superior combination potential of MZF1 on MTSS1-AS and MTSS1 promoter. MZF1 has been shown to serve as a transcription factor targeting excision repair cross-complementation group 1, which was involved in the development of drug resistance of human ovarian cancer Then, the potential mechanism by which MTSS1-AS regulated the interaction between MZF1 and MTSS1 promoter was thoroughly investigated. The results showed that the overexpression and depletion of MTSS1-AS remarkably increased or decreased the expression of MZF1 at the protein level but not at the mRNA level, indicating that MTSS1-AS regulated the expression of MZF1 at the posttranscriptional level. Numerous studies revealed that lncRNAs interacted with protein and regulated its stability. For instance, hypoxia-induced lncRNA-AC020978 directly interacted with pyruvate kinase M2 (PKM2) and enhanced the protein stability of PKM2, promoting the proliferation and glycolysis of non-small cell lung cancer cells Bio-information analysis was used to identify the putative transcriptional factor that regulated the expression of MTSS1-AS to further clarify the mechanism by which extracellular acidity affected the expression of MTSS1-AS. Numerous studies found that Myc activated transcription through E-box Myc binding sites but represses transcription through a mechanism dependent on initiator (Inr) elements The formation of the feedback loop was essential in cancer progression Together, this study implicated an extracellular acidity-triggered a reciprocal feedback loop between Myc and MTSS1-AS, which was involved in the metastasis of PC by regulating the expression of MTSS1. It suggested that MTSS1-AS was a potentially valuable biomarker and therapeutic target for PC.Supplementary figures and tables.Click here for additional data file.


Staphylococcus aureus, an opportunistic human and animal pathogen, produces EVs whose RNA content is still poorly characterized. Here, we investigated in depth the RNA content of S. aureus EVs. A high-throughput RNA sequencing approach identified RNAs in EVs produced by the clinical S. aureus strain HG003 under different environmental conditions: early- and late-stationary growth phases, and presence or absence of a sublethal vancomycin concentration. On average, sequences corresponding to 78.0% of the annotated transcripts in HG003 genome were identified in HG003 EVs. However, only ~5% of them were highly covered by reads (≥90% coverage) indicating that a large fraction of EV RNAs, notably mRNAs and sRNAs, were fragmented in EVs. According to growth conditions, from 86 to 273 highly covered RNAs were identified into the EVs. They corresponded to 286 unique RNAs, including 220 mRNAs. They coded for numerous virulence-associated factors , ribosomal proteins, transcriptional regulators, and metabolic enzymes. Twenty-eight sRNAs were also detected, including bona fide RsaC. The presence of 22 RNAs within HG003 EVs was confirmed by reverse transcription quantitative PCR (RT-qPCR) experiments. Several of these 286 RNAs were shown to belong to the same transcriptional units in S. aureus. Both nature and abundance of the EV RNAs were dramatically affected depending on the growth phase and the presence of vancomycin, whereas much less variations were found in the pool of cellular RNAs of the parent cells. Moreover, the RNA abundance pattern differed between EVs and EV-producing cells according to the growth conditions. Altogether, our findings show that the environment shapes the RNA cargo of the S. aureus EVs. Although the composition of EVs is impacted by the physiological state of the producing cells, our findings suggest a selective packaging of RNAs into EVs, as proposed for EV protein cargo. Our study shedds light to the possible roles of potentially functional RNAs in S. aureus EVs, notably in host-pathogen interactions.The roles of bacterial extracellular vesicles (EVs) in cell-to-cell signaling are progressively being unraveled. These membranous spheres released by many living cells carry various macromolecules, some of which influence host-pathogen interactions. Bacterial EVs contain RNA, which may serve in communicating with their infected hosts.  These sractions . The funractions . Althougractions , the forbacteria .Staphylococcus aureus commonly colonizes the skin or nasal tract of vertebrates, without causing disease . Then, the culture supernatant fraction was concentrated around 100-fold using the Amicon ultrafiltration systems (Millipore) with a 100kDa filter, and ultra-centrifuged for 120 min at 150,000 × g to eliminate the soluble proteins. Next, the suspended pellet was applied to a discontinuous sucrose gradient (8–68%) and ultra-centrifuged at 100,000 × g for 150 min. Fractions containing EVs were recovered and washed in TBS  for final ultra-centrifugation at 150,000 × g (120 min). At last, EVs were suspended in cold TBS and kept at −80°C until use.Cultures were submitted to EVs isolation and purification, as previously described , 2020. I10 and 1011 particles per ml were analyzed. For this, samples were applied to glow-discharged copper EM grids (FF200-Cu) for 30 s, followed by excess solution removal with filter paper. The same process was repeated with 2% uranyl acetate, and samples were observed with a Jeol 1400 transmission electron microscope (JEOL Ltd.), operating at 120 kV.Negative staining electron microscopy was performed as previously described  to invesNanoparticle Tracking Analysis (NTA) using an sCMOS camera and a Blue488 laser (Nano Sight NS300) was performed to assess EVs size and concentration. For that, samples were diluted into TBS to achieve optimal concentration and submitted to a constant flux generated by a syringe pump (speed 50), at 25°C. Results were retrieved from 5 × 60 s videos recorded with camera level at 15 and threshold at 5, while other parameters were adjusted as necessary.RNA extraction was carried out as similar as possible for both cell and EV samples. Bacterial RNA extraction was performed from 10 ml culture pellet. The samples were mixed with glass beads in 300 μl lysis buffer  and 400 μl phenol (acid buffered at pH 5.0) at 65°C. Mechanical lysis was accomplished with 2 cycles of 30 s in Precellys at 6,500 rpm. For EV sample RNA extraction, particles isolated from the equivalent of 800 ml bacterial culture were mixed with 300 μl of lysis buffer and 400 μl phenol at 65°C, the same volumes used for cell RNA extraction. Since EVs lack the thick layer of peptidoglycan (PGN) found in the bacterial cell wall, mechanic lysis was not necessary and was achieved with lysis buffer. EV and EV-producing cell samples were incubated for 10 min at 65°C, being homogenized by vortex every minute. Next, samples were centrifuged during 10 min 13,000 rpm, 4°C, and the upper phase was recovered to a new tube. All samples were mixed with additional 400 μl of phenol at 65°C, and the previous steps were repeated. Then, 400 μl of phenol:chloroform 1:1 was added, followed by two times addition of 400 μl pure chloroform, repeating the step of upper phase recovery, mixture and centrifugation . Subsequently, 1.5 volumes of ice-cold 100% ethanol and 10% volume of NaAc were added and the mix was stored at −20°C overnight. Samples were centrifuged at 13,000 rpm for 30 min at 4°C, and the pellets were washed twice with 1 ml of cold 70% ethanol. Finally, the pellets were dried with a SpeedVac concentrator for 2 min and dissolved in RNase-free water. The quality and quantity of the RNAs were verified by Nano Drop, agarose gel, and Bioanalyzer (Agilent). Samples were kept at −80°C until use. No RNase treatment was applied to bacterial cell or to EV samples before RNA extraction.The RNA samples were sent to ViroScan3D®  for DNA removal, ribosomal RNA depletion and RNA sequencing. Total RNA samples were submitted to a DNase treatment with RNase-Free DNase Set (Qiagen) according to manufacturer’s instructions. Then, the samples quantified using the Quantifluor RNA system (Promega), and qualified using RNA Nano Chip on Bioanalyzer 2100 (Agilent) for the EV-producing cell samples, and on the SS RNA system on Fragment Analyzer (AATI) for the EVs samples. RNA samples were then submitted to the standard protocol Ovation Universal Prokaryotic RNA-Seq, Nugen, Anydeplete rRNA, library preparation. RNA quantity used for library preparation are displayed in The reads were cleaned and trimmed with Trim-Galore  using thA list of differentially expressed RNAs was obtained by EdgeR  embeddedp 0.05 was set as a threshold for significative categories.Subcellular location prediction was performed with SurfG+ . ClusterA timepoint clustering study was conducted with the R package maSigPro  on highlS. aureus cultures at late-stationary growth phases (12 h) in the absence of vancomycin. EV RNAs were purified as mentioned above. Around 1.5 μg of RNAs was treated with DNAse I  according to manufacturer’s instructions. cDNA synthesis was performed with the high capacity cDNA Reverse Transcription kit (Applied Biosystems). The primers used for quantitative PCR (qPCR) are listed in Reverse transcription quantitative PCR (RT-qPCR) was used to validate RNA-seq results. EVs were isolated from the cell-free supernatants of three new independent S. aureus HG003 releases EVs with variable diameters depending on the growth phase. A sublethal concentration of vancomycin, however, does not impact the EV morphology, concentration, or diameter.Extracellular vesicles secreted by HG003 were isolated from the cell-free supernatants of bacterial cultures at early- and late-stationary growth phases , as well as in the absence (V-) and presence (V+) of a sublethal concentration of vancomycin (0.5 μg/ml). For that purpose, we used centrifugation, filtration, and density gradient ultracentrifugation, the standard method for EV isolation and purification at high purity . EV homoTotal RNA was extracted from HG003 EVs to investigate their compositions. The quality of the RNA preparations was checked and validated, and the samples sequenced. RNA-seq data were compared between the different growth conditions, and with those obtained from parental HG003 cells that produced the EVs in each condition . Around 2649 ± 238 RNAs annotated in the HG003 genome were identified in the EVs according to growth conditions, with an average count of over five reads per RNA in each condition, whereas 3120 ± 35 annotated RNAs were identified within the EV-producing cells  and S4. For the EV-producing cells, the median values of mRNAs, tRNAs, and residual rRNAs coverages were between 95 and 100%, and were 74 and 92% for the sRNAs , implyinS. aureus HG003 (p < 0.05) in the EVs compared to the EV-producing bacteria . Five copies of the 16S and 23S rRNAs were also detected, implying that our rRNA depletion procedure was incomplete. Finally, 28 annotated potential sRNAs were detected within EVs, and among the 50 or so bona fide sRNAs defined for the HG003 strain  of all detected RNAs with ≥90% coverage were common to all the EV samples (n = 1761). The shared RNAs among the EVs included mRNAs expressing virulence factors (Atl and Spa), metabolic enzymes  and transcriptional regulators , as well as RNAs involved in translation  than at 12 h  and tRNAThr (SAOUHSC_T00054), with fold changes greater than 16. Among the differentially abundant RNAs according to the growth phase, 32 were detected both in presence and absence of vancomycin, with similar fold changes highlighting their reproducible variations into EVs across different environmental conditions  in EV RNA abundance between the experimental conditions were also detected . Among tnditions .n = 136) and in presence of vancomycin (n = 147), which was expected since bacterial transcription differs qualitatively and quantitatively when facing different growth conditions  and cluster 3 with 18 RNAs  contained more abundant transcripts over time in both EVs and EV-producing cells, whereas cluster 6 with 14 RNAs  contained less abundant transcripts at 12 h than at 6 h in both. Interestingly, the three other clusters grouped RNAs that showed opposite expression profiles over time in EVs and EV-producing cells. Cluster 1 with 10 RNAs , and cluster 2 with 24 RNAs  grouped, similarly, more abundant transcripts at 12 h in EVs, and less abundant transcripts at 12 h in EV-producing cells. On the contrary, cluster 5 with 17 RNAs  grouped less abundant transcripts in EVs and more abundant transcripts in EV-producing cells at 12 h. Altogether, this analysis highlighted that the transcript expression pattern could temporally differ between EVs and EV-producing cells.To evaluate the influence of the growth phase on EV and EV producing cell RNA composition, a negative binomial-based approach, with the R package maSigPro  was applS. aureus EV RNA cargo, however, is lagging behind. Here, we report the first exploratory work on EVs released by S. aureus HG003, the characterization of its EV RNA cargo under different conditions, and an indepth transcriptomic comparison between the EVs and the EV-producing cells.Extracellular vesicles are universal carriers of macromolecules including extracellular RNAs all along from bacteria, archaea, and fungi to protists. Recent investigations on bacterial EV biogenesis, release, and trafficking showed their functional importance for bacterial communication and survival . InformaS. aureus EV production, the selected conditions were the early- and late-stationary growth phases, with or without a sublethal concentration of vancomycin that does not impact growth. Differences were observed regarding EV sizes. EVs derived from the late-stationary growth phase were larger than those collected during the early-stationary phase. This can be due to cell-wall morphology and peptidoglycan structure that are characteristics of the growth stage in S. aureus  that harbor around 73% of the annotated transcripts including up to 86% of mRNAs according to growth conditions . Of these, a large portion of mapped RNAs corresponded to mRNAs. These results are consistent with RNA-Seq data obtained with similar criteria from OMVs in nditions . A recenepletion . On the as tRNAs . Neverthide long . The predegraded . Since odegraded , the S. bacteria .psmβ1, 135 nucleotides; agrD, 141 nucleotides), and some sRNAs . Yet, these highly covered EV RNAs also comprise long transcripts, including 15 mRNAs with lengths >2,000 nucleotides . 67% of these RNAs are organized into 42 gene clusters that are co-transcribed in S. aureus , immunoglobulin-binding protein (Sbi), immunodominant staphylococcal antigen B (IsaB), δ hemolysin (Hld) encoded by the multifunctional sRNA RNAIII, and the PSMβ1 phenol-soluble modulin, as well as several iron acquisition systems. Besides, most tRNA species are detected within the EVs, as well as residual rRNAs and 28 annotated sRNAs, including bona-fide RsaC involved in S. aureus oxidative stress adaptation and nutritional immunity , mRNAs  can be found at: BSRL, BF, SC, YL, VA, and EG conceived and designed the experiments. VA and EG supervised the study. BSRL and SC performed the experiments. AN performed computational analysis. BSRL, BF, SC, VRR, AN, and EG analyzed the data. VA, YL, and EG contributed to funding acquisition. BSRL and EG wrote the original draft. All authors contributed to data interpretation, drafting the manuscript, critically revising the manuscript, and approving its final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In vitro spermatogenesis, which produces fertile spermatozoa, has been successfully performed using an organ culture method from murine tissue. Here, we provide a dataset of time-course microarray transcriptome data of in vitro cultured neonate murine testes and age-matched in vivo-derived testes. The dataset presented here is related to the article titled “Transcriptome analysis reveals inadequate spermatogenesis and immediate radical immune reactions during organ culture in vitro spermatogenesis” published in Biochemical and Biophysical Research Communications in 2020 in vitro culture testes and in vivo testes at each time point. A schematic representation of the data generation is shown in https://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE147982. The link between each GEO sample accession and the original mouse is provided in GSE147982. The distributions of signal intensity of raw and preprocessing data are reported in RNA was extracted from 22.1Acr-Gfp transgenic mice (C57BL/6 strain) Acr-Gfp transgenic mice were bred with female ICR, C57BL/6 , or ICRxC57BL/6 F1. Thus, male mice of Acr-Gfp (+/+) or (+/-) background were used for analysis. Mice were housed in the TSRI Specific Pathogen Free (SPF) facility with a 14-hour light cycle at 24 ± 1°C and 55 ± 5% air conditions and were given hard pellet food  and acidified water (pH 2.8–3.0) ad libitum.2.2Acr-Gfp transgenic mice. The extracted testes were decapsulated and cut into 4-8 fragments by forceps under a microscope in the culture medium ), supplemented with 40 mg/mL AlbuMAX (Thermo Fisher Scientific Inc.). The testis fragments were then patterned onto a 1.5% (w/v) agarose gel block (about 10 mmW × 10 mmD × 5 mmH) that was pre-soaked for at least two days in advance and then half-submerged in the culture medium in a well of a 12 well tissue-culture plate . The testes fragments were covered with the Polydimethylsiloxane (PDMS)-ceiling chip 2 at 34°C with once a week medium replacement.Testes were extracted from 7 dpp male 2.3in vivo-derived testes were maintained in RNA later (Thermo Fisher Scientific Inc.) on ice for at least 3 h until RNA extraction. The in vivo-derived testes and in vitro cultured testes were homogenized in TRIzol Reagent (Thermo Fisher Scientific Inc.) with the PT1300D polytron homogenizer , and the aqueous phase was collected according to the manufacturer's instructions. RNA was extracted from the corrected aqueous phase using the NucleoSpin RNA , according to the manufacturer's instructions. The quality of the RNA was confirmed by NanoDrop  and RNA 6000 nano kit of Bioanalyzer . RNA samples with high RNA integrity values (> 9) were used in the microarray analyses.Fresh 2.4in vitro transcription method and the resulting cRNAs were biotinylated using the Illumina TotalPrep RNA Amplification Kit . The biotinylated cRNA was hybridized to MouseWG-6 v2.0 Expression beadchip . Signal intensity was measured using the Illumina BeadArray reader .Total RNA was amplified by 2.5lumiExpresso function implemented in the lumi package of R.Raw intensity binary data (idat files) were converted to text data using BeadStudio  without background correction and normalization. Raw intensity text data were subjected to background correction, variance-stabilizing transformation (VST), and quantile normalization using 2.6lmFit function implemented in the limma package of R. Then, p-values of empirical Bayes moderated t-statistics test between in vitro cultured- and in vivo-derived-testes at each time point were computed by contrasts.fit and eBayes functions implemented in the limma package of R, followed by p-values adjustment by Benjamin-Hochberg method. The probes were considered to be differentially expressed genes if their adjusted p-value was less than 0.05.The probes whose detection p-value was greater than 0.01 in any of the samples were removed. A linear model was fitted to the expression data for each probe using the 2.7Scripts and data for R analysis used in this paper are available on GitHub (https://github.com/RIKEN-CFCT/ivs_data_in_brief).The authors have no conflicts of interest to declare.


Synthetic biology seeks to probe fundamental aspects of biological form and function by construction  rather than deconstruction . In this sense, biological sciences now follow the lead given by the chemical sciences. Synthesis can complement analytic studies but also allows novel approaches to answering fundamental biological questions and opens up vast opportunities for the exploitation of biological processes to provide solutions for global problems. In this review, we explore aspects of this synthesis paradigm as applied to the chemistry and function of nucleic acids in biological systems and beyond, specifically, in genome resynthesis, synthetic genetics , and the elaboration of orthogonal biosystems and components. In this review, we seek to provide an overview of current synthetic biology approaches to engineering, controlling, and expanding the central dogma via use of synthetic approaches for the reassignment of the informational units and exploration of alternative chemistries for their ability to support life or provide expanded functionalities.Arguably the biggest impact of such synthetic approaches has come from the advances enabled by the ever-increasing efficiency and scale of the classic phosphoramidite DNA synthesis of Caruthers . Togethe2Mycoplasma mycoides to a Mycoplasma capricolum recipient cell, which then developed into M. mycoides progeny; this experiment conclusively established the primacy of genotype in determining phenotype  orthogonal (x) to the tRNAs and aaRSs in the target organism , (b) engineered for the orthogonal aaRSx to load a defined unnatural amino acid (uAA) (X) onto the tRNAx, and (c) engineered so that the tRNAx recognizes a stop codon, thus in effect reassigning the stop codon to the uAA. GCE has undergone many improvements and expansions with regard to the types of amino acids that are suitable for incorporation, resulting in widespread applications (Two other powerful applications of whole-genome synthesis (or rewriting) are genetic code expansion (GCE) and reassignment, both of which have the ultimate aim of converting cellular protein translation into a general system for the sequence-defined synthesis of artificial polymers. GCE originally relied on amber suppression, based on the pioneering work of Schultz and colleagues . This stications . Howeverications  or the rications  can alleEscherichia coli genome by iterative mutagenesis and the deletion of release factor 1 (RF1)  was used to remove all amber codons from the  1 (RF1)  to free  1 (RF1) , an adva  . By 2017 system) . The SCR system) , 62. The system)  to test  system) . The eff system) .3Genome resynthesis provides an opportunity for the wholesale reconfiguration of biological systems, for example, in the pursuit of orthogonal biosystems, i.e., systems that are sufficiently decoupled from natural or host organism biology that they can function and evolve independently . In lighOrthogonal genetic elements such as T7 RNA polymerase have been used for decades, often merely for the sake of convenience in the absence of cross-reactivity with the host chassis; the same motivation sparked the application of an orthogonal hypermutation-enabling DNA polymerase in yeast . A broad3.1Because virtually all strategies for the prevention of unintentional environmental proliferation  can theoA key step toward the achievement of semantic orthogonality was the development of orthogonal ribosomes (o-ribs). These allow translation of dedicated orthogonal messenger RNAs (mRNAs) (not translated by the wild-type ribosome), which in turn frees up the o-rib for engineering without toxicity effects. This strategy has enabled the evolution of o-ribs with enhanced amber suppression , 116 or E. coli genome can aid in both genetic code expansion and the establishment of orthogonality. Going beyond single (amber) codon removal and synthetic auxotrophy by establishing cell dependence on uAAs (a) replacing two isoacceptor serine codons, TCG and TCA, and the TAG stop codon with the synonymous codons AGC, AGT, and TAA, respectively, and (b) concurrently removing the corresponding serine tRNAs and RF1, three new codons were freed up for genetic code expansion. This gave rise to an organism (Syn61Δ3) with a compressed 61-codon genetic code ] constitute a promising alternative . Indeed,a) The unnatural nucleotide bases and their activated triphosphates need to be available in the cell at sufficiently high levels without causing toxicity ; (b) DNA and RNA polymerase(s) must enable their replication and transcription with high efficiency and fidelity; and (c) the ribosome must be able to correctly translate the new UBP-containing codons to incorporate a defined amino acid into the growing polypeptide chain, which also requires the presence of UBP-tRNAs and cognate aaRSs capable of loading UBP-tRNA specifically with the appropriate uAA  amplifications of appropriate templates , 68, 126E. coli polymerase or introduction of specific polymerases, such as the Klenow fragment of E. coli DNA polymerase I, that are capable of replicating the d5SICS:dNaM pair in vitro  range from 5 μM for dGTP to 37 μM for dTTP . In the E. coli relies on direct administration of the triphosphates to the growth medium and uptake via specialized transporters like the nucleoside triphosphate transporter of the algae Phaeodactylum tricornutum (PtNTT2). This strategy enabled the import of d5SICS and dNaM triphosphates and allowed for the propagation of a single UBP over several generations in vivo, generating the first semisynthetic organism with an expanded genetic alphabet (a) supplementation of the medium with potassium phosphate, which acts as a competitive inhibitor for triphosphate-degrading phosphatases (b) chemical modification of the unnatural nucleotides to increase phosphatase resistance but retain polymerase recognition ; and (c) transporter engineering to reduce toxicity and increase expression (and thus nucleotide import) via removal of an N-terminal signal peptide and chromosomal integration  cheegration .Thalassiosira pseudonana (TpNTT2) imports triphosphates  more efficiently and is less toxic for E. coli than PtNTT2.A very promising approach for selection and optimization of nucleoside triphosphate transporters was developed recently for dTTP uptake . TranspoDrosophila melanogaster has a variant with broader substrate specificity, which can phosphorylate all four natural and various unnatural nucleosides, including analogs of d5SICS and dNaM , provided that the corresponding unnatural ribonucleoside triphosphates were added to the media in the presence of the engineered nucleotide transporter  accepts various unnatural nucleobases as substrates, including 5SICS and NaM , as wellnsporter . Furthers absent . This re3.2.3In the early 1990s, the Benner group first demonstrated successful translation of iso-C and iso-G UBPs in vitro, observing incorporation of the uAA iodotyrosine with yields of 90%, exceeding those resulting from amber suppression . Thus, tE. coli by Romesberg and colleagues  and that, through continuous growth and evolution in the presence of 5CU, became dependent on it for growth. Due to the close similarity of T and 5CU, it is likely that this approach is only a first step to full genetic isolation -RNA or αP-S (phosphorothioate), can occur in nature  . However3.3.1XNA aptamers have been discovered for a range of XNA chemistries through the same process used for DNA and RNA aptamers, systematic evolution of ligands by exponential enrichment (SELEX) . In the P-alkyl phosphonate nucleic acids (phNAs) have been evolved into aptamers  approximately sixfold. Similarly, FANA modifications at G2 and U8 in the catalytic core and full FANA-substitution of the RNA-target binding arms, in combination with two additional flanking TNA nucleotides, yielded a modified X10–23 with improved catalytic rate, biostability, and in vivo gene-silencing activity —ASOs, sometimes with flanking chemical modifications to make biostable gapmers, are part of the established ASO technology toolbox (P-S)-DNA stretch crucial for RNase H recognition. The only XNA chemistry found to be able to elicit RNase H cleavage on a bound RNA is FANA (see above), but it is not as nuclease resistant as αP-S-DNA (O-(2-methoxyethyl) (MOE), locked or bridged nucleic acids (LNAs/BNAs), and constrained ethyl nucleic acid (cEt), including fully modified (αP-S)-MOE-RNA and phospho-rodiamidate morpholino oligomer (PMO) ASOs, are used in the development of novel antisense medicines , siRNAs with several chemical modifications that confer nuclease resistance, and DNA-based—usually phosphorothioated  and phosphoryl guanidine oligonucleotides (PGOs)  or even 3.3.4.P-S) can also affect decoding efficiency, especially on the level of translation  could increase cellular function and information content in several ways. Alternative backbone chemistries, when synthesized inside the cell, could be designed to form specific XNA aptamers, XNAzymes, or ASOs (see above) that affect cellular regulation, e.g., by modifying protein interactions or modulating gene expression. XNA backbones  are likely toxic, while those that diverge sufficiently to be fully orthogonal require such extensive cellular re-engineering as to be currently impractical. It would therefore seem that chemical, steric, or stereochemical orthogonality, i.e., orthogonal genetic systems based on XNA or mirror chemistries, may find more immediate utility in in vitro applications and in the context of the emerging field of synthetic biomaterials and cells , 90. Syn4By expanding the central dogma and extending the boundaries of life both at the informational and at the chemical level, the concepts outlined in this review promise not only to augment our abilities for biological engineering, but also to extend them to studying and understanding biological function and evolution at a deeper level by opening up fundamental aspects of the informational organization and chemical makeup of life for investigation.


TRF5 deletion reduces Mtr4 recruitment and increases RNA abundance for mRNAs specifically showing high Trf5 binding.During nuclear surveillance in yeast, the RNA exosome functions together with the TRAMP complexes. These include the DEAH-box RNA helicase Mtr4 together with an RNA-binding protein (Air1 or Air2) and a poly(A) polymerase (Trf4 or Trf5). To better determine how RNA substrates are targeted, we analyzed protein and RNA interactions for TRAMP components. Mass spectrometry identified three distinct TRAMP complexes formed in vivo. These complexes preferentially assemble on different classes of transcripts. Unexpectedly, on many substrates, including pre-rRNAs and pre-mRNAs, binding specificity is apparently conferred by Trf4 and Trf5. Clustering of mRNAs by TRAMP association shows co-enrichment for mRNAs with functionally related products, supporting the significance of surveillance in regulating gene expression. We compared binding sites of TRAMP components with multiple nuclear RNA binding proteins, revealing preferential colocalization of subsets of factors.  During nuclear surveillance in yeast and human cells, the RNA exosome functions together with the TRAMP complexes. Here, the authors defined the protein composition of the TRAMP complexes and identified specific RNA binding sites for the different TRAMP components. In consequence, quality control or “surveillance” of aberrant nuclear RNAs is an essential feature of eukaryotic gene expression. In addition, all stable RNA species undergo post-transcriptional maturation, including 3′ processing. A key component of both RNA surveillance and processing pathways is the exosome; an essential, multi-subunit complex that is highly conserved among eukaryotes.The transcription, processing, and packaging of eukaryotic RNAs offers many opportunities for errors -knuckle, putative RNA-binding proteins9, whereas Trf4 and Trf5 are noncanonical poly(A) polymerases. Together, they add a short oligo(A) tail, significantly shorter than poly(A) tail added by the canonical polymerase (Pap1)10. This is presumed to provide a single-stranded “landing pad” that makes the RNA a better substrate for 3′-end degradation. It was previously suggested that TRAMP recruitment to substrates would be driven by Air1/2 through their RNA-binding activities11 (reviewed in ref. 12). In addition, Mtr4 includes a distinctive “arch” or “kow” domain, consisting of a β-barrel stalk inserted into the typical DExH core15. The arch domain is specifically bound by ribosomal biogenesis factors carrying an arch-interacting motif (AIM), enabling direct Mtr4 recruitment to pre-ribosomal RNAs (rRNAs)16. The arch was also proposed to function as a docking platform for Trf4 and Air2, acting independently of the helicase activity of the DExH core.The purified exosome shows weak activity in vitro, indicating that the rapid and processive activity inferred in vivo requires cofactors. In budding yeast, the Trf4/5–Air1/2–Mtr4 polyadenylation (TRAMP) complexes are major cofactors for the nuclear exosome and critical for nuclear RNA surveillance activity19. Budding yeast appears to lack NEXT and PAXT homologs, but the TRAMP complexes are present in both the nucleolus and nucleoplasm. The Air1/2 and Trf4/5 pairs show some functional redundancy, since the single mutants are viable, whereas double mutants are inviable or severely growth impaired, depending on strain background20. Structural and functional analyses have focused on the Trf4–Air2 TRAMP complex25. However, both the Air1/2 and Trf4/5 pairs have very significantly diverged in sequence , but diverge in both the N- and C-terminal regions. These sequence divergences are much greater than most duplicated gene pairs in yeast, suggesting that the functions of the different forms of the yeast TRAMP complex may also have diverged and developed distinct specificities in vivo.Human cells express at least three complexes that each contain MTR4 and a zinc-finger protein. These include TRAMP, which is predominately nucleolar in humans, as well as NEXT (Nuclear EXosome Targeting) and PAXT (Poly(A) eXosome Targeting) complexes, which are nucleoplasmicnce Fig. . The N-t26, which requires the presence Air1 or Air2 to adenylate its substrate in vivo6. However, a catalytically inactive Trf4 mutant (Trf4-DADA) can support degradation of most Trf4 targets and rescue the lethality of a Δtrf4 Δtrf5 double mutant, indicating that Trf4 can target RNAs to the exosome independently of adenylation20. In strains depleted of Mtr4, TRAMP substrates are both stabilized and hyper-adenylated27. This shows that Trf4/5 can be recruited to target RNAs and activated independent of Mtr4 and the exosome.The two C-terminal zinc knuckles of Air2  mediate the interaction with the central domain of Trf4Here, we aimed to determine which TRAMP complexes form in vivo, how they bind to different substrate classes, and how they cooperate with the exosome. To this end, we characterized TRAMP protein–protein and protein–RNA interactions via mass spectrometry (MS) and CRAC (ultraviolet (UV)-cross-linking analyses), respectively. Unexpectedly, the results indicate major roles for Trf4 and Trf5 in TRAMP targeting and recruitment, and a specific role for Trf5 in messenger RNA (mRNA) stability. In contrast, Air1 and Air2 appear to be highly redundant.29 were calculated for each protein recovered , Trf4 + Air2 (TRAMP4-2), and Trf5 + Air1 (TRAMP5-1) . Purifications were initially performed under high salt conditions (1 M NaCl) to recover only stable interactions. Associated proteins were identified by MS and subjected to label-free quantification. iBaq scores-1) Fig. . Each prAIR1 was deleted in the strain expressing Trf5-HTP. In the air1∆ strain, Air2 was well recovered with Trf5-HTP, indicating competition for Trf5 association and redundancy between Air1 and Air2 . This suggests that the Trf5–Air1 complex binds jointly to Mtr4, and that this interaction is not fully recapitulated by Trf5–Air2, making the complex less stable.To further characterize factors binding to Trf4 and Trf5, these purifications were repeated at 150 mM NaCl, with and without the inclusion of RNase treatment 34, and these interactions were largely RNase sensitive.Nab3 and Nrd1, subunits of the Nrd1–Nab3–Sen1 complex, were recovered with Trf4 but not Trf5, consistent with the reported presence of a Nrd1-interacting motif in Trf4 but not Trf536. Curiously, recovery of the small nucleolar ribonucleoprotein (snoRNP) proteins with Trf5 was enhanced in the air1∆ strain, which has increased Trf5–Air2 association, possibly indicating that Air2 contributes to snoRNP binding. A subset of ribosome synthesis factors was recovered, showing greater interaction with Trf5 than Trf4 and, particularly, with Air1 relative to Air2, for which none showed >2-fold enrichment  were strongly recovered with Trf4, Air1, and Air2, consistent with the reported involvement of Trf4 in snoRNA maturationent Fig. . This su16. The exosome was also very poorly recovered   Mtr4 was under PGAL control, allowing its depletion on glucose medium . We compared strains in which the endogenous gene was HTP tagged for Air1, Air2, Trf4, Trf5, and Mtr4, as well as the exosome exonucleases Rrp44 and Rrp6. The Mtr4 arch domain is implicated in substrate recruitment39. In contrast, the major ncRNA classes, CUTs, SUTs, and XUTs, were strongly recovered with Air2 and Trf4, relative to Air1 plus Trf5. Notably, protein-coding genes appeared to be major targets of Trf5, which showed the strongest recovery compared to the other factors.Comparison of RNA target classes recovered with each protein showed clear differences Fig. . NotablyTRAMP targets are expected to be subject to oligoadenylation, so we specifically analyzed cDNAs that carry additional, 3′-terminal non-templated A residues Fig. . These a16. Higher correlations between different TRAMP components were observed for oligo(A) RNAs than considering all reads  across ITS1 relative to the AIR2 strain , which is stabilized in binding to many exosome substrates, but this also did not show clearly elevated binding to A2–A3. Northern blot analysis showed strong accumulation of cleaved A2–A3 fragment in trf5∆ compared to WT, confirming the role of Trf5 in degradation of this RNA  or replaced by Trf4 C-terminal domain (Trf5-CTD4), or in which a 5-aa, AIM-like sequence was replaced by 5 Ala (Trf5-5xA). In CRAC analyses, these constructs all showed inefficient cross-linking, resulting in poor signal-to-noise ratios and variable results  or pA sites . All mRNAs were divided into five bins of equal length and the distribution of each factor was determined for every bin. These data were used to cluster genes with related patterns of factor distribution  binding factors Pab1 and Nab2 were closely correlated and distinct from most other factors. However, Nab2, which has been implicated in RNA surveillanceors Fig. . Similarors Fig.  or all gFor TRAMP components, Trf5 was better correlated with Air1 than with Trf4 or Air2, while Trf4 and Air2 were most highly correlated, consistent with the analyses of individual genes. The TRAMP components were also correlated with the exosome, as expected, although it was notable that Rrp6 was more correlated with Trf5, while Rrp44 correlated better with Trf4. Nab3 showed higher correlation with Air2 and Trf4 than Air1 and Trf5, consistent with Trf4 being recruited by Nrd1–Nab3 on some targets, including TSS-proximal ncRNA transcripts.41, also showed correlations with TRAMP factors. A number of other proteins, showed notably close correlations, including the pre-mRNA-binding protein Npl3 with Air1. It may be relevant that Air1 was initially identified through an interaction with Npl39. To follow-up this observation, Air1 CRAC peaks across the genome were selected using a peak-calling algorithm and used as a reference point to align reads from pre-mRNA-binding proteins and TRAMP components . However, greater increases were measured in qPCR, probably reflecting differences in normalization, which is complicated in surveillance mutants that potentially affect all RNA species.mRNAs were stratified by ranking in Trf5 association, based on reads per kilobase per million mapped reads (RPKMs) across the genome. Bins of 200 mRNAs were compared between RAC Fig.  or RNA aRAC Fig. . StrikinRAC Fig.  relativetrf5∆ strains showed a clear deficit in reads close to the poly(A) site in the absence of Trf5 , Trf4 and Air2 (TRAMP4-2), or Trf5 and Air1 (TRAMP5-1). Substantial differences in RNA binding were observed, indicating that TRAMP4-1, TRAMP4-2, and TRAMP5-1 each exhibit substrate specificities.Saccharomyces cerevisiae genome (SGD) (www.yeastgenome.org) lists median abundances (copies per cell) for TRAMP components as: Air1 (1851 ± 466); Air2: (1750 ± 420); Trf4 (2659 ± 411); Trf5 (1329 ± 608). If all components are in TRAMP complexes, this suggests very approximate abundances of: TRAMP4-1 (600); TRAMP4-2 (1700); TRAMP5-1 (1300). These values are consistent with the conclusion that Trf4 interacts with Air1 and Air2, whereas Trf5 binds only Air1.We note that, based on 14 different publications, 40, or by specific interactions of AIM-containing proteins with the arch domain of Mtr416.TRAMP5-1 preferentially targeted the ITS1 spacer region of 35S pre-rRNA, a characterized exosome substrate for which no AIM domain ribosome synthesis factor has been identified. Mtr4 binding to ITS1 in the pre-rRNA was strongly reduced by loss of Trf5, whereas loss of Trf4, Air1, or Air2 had only modest effects. In contrast, TRAMP4-2 was more strongly associated with RNAPII transcripts, particularly mRNA 5′ ends, close to the TSS and with the CUT, SUT, and XUT ncRNAs. This implicated TRAMP4-2 in degradation of ncRNAs, including promoter-proximal ncRNAs generated by early termination of transcription, a model supported by co-localization with Rrp44 and Rrp6. Mtr4 exhibited lower binding to TSS-proximal regions when Trf4 was absent, with comparable reduction on deletion of the arch domain, but was little altered by loss of Air1 or Air2. In the absence of its paralog, the binding profiles of Air1 and Air2 across mRNAs are highly similar, strongly indicating that they are not directly responsible for the substrate specificity seen in the WT background. We conclude that substrate specificity is predominately provided by Trf4 and Trf5, while Air1 and Air2 appear largely interchangeable. This was unexpected, since it had previously been anticipated that RNA-binding specificity would largely be determined by Air1 and Air2, which are Zn-knuckle RNA BPs45. The structure of the Trf4–Air2–Mtr4 TRAMP complex shows the arch located opposite Trf4–Air222. N-terminal, low-complexity regions of Air2 and Trf4 are jointly responsible for binding to the DExH core of Mtr4 in vitro. The residues in Trf4 and Air2 that bind the Mtr4 helicase core, are conserved in Trf5 and Air1, respectively. The N terminus of Trf4 (aa 1–110) is absent from the TRAMP structure, but could conceivably extend to the arch domain of Mtr4. However, a more likely interaction is between Air2 and the arch. Indeed, in the crystal structure, the N terminus from the adjacent Air2 molecule in the crystal lattice was shown to contact the Mtr4 arch at the same sites as the AIM of Nop5322, through residues that are conserved in Air1. Association of Air2 and Air1 with the arch domain might require conformational changes in which the arch moves toward the helicase core, switching Mtr4 (and the TRAMP) to a closed conformation. We therefore postulate that recruitment of Mtr4 to most nuclear RNA surveillance targets is based on the specificity of Trf4 or Trf5 interactions with the target, followed by (largely redundant) binding of Air1 or Air2 to the arch domain.When bound to the exosome, Mtr4 is positioned at the entrance to the central lumen, such that RNAs can pass through the helicase domain directly into the exosome9. Air1–Npl3 binding is bridged by the arginine-methyl transferase Hmt1 and blocks arginine methylation on Npl3, potentially changing its functional properties.Comparison of TRAMP binding with other factors expected to interact with nuclear transcripts showed a range of concordances. We speculate that combinations of proteins binding to nascent transcripts act together to promote or disfavor binding by the surveillance machinery and rapid nuclear RNA degradation. Particularly notable was the correlation between the hnRNP-like, pre-mRNA binding protein Npl3 and Air1, which was initially isolated via interaction with Npl3Clustering of the most recovered ~2000 mRNAs, based on TRAMP factor occupancy, identified six clusters. Five of these showed statistically significant enrichment for functional classes of protein products, while the remaining class was enriched for types of transcript; introns with encoded features, notably snoRNAs; and unconfirmed protein products. Such functional enrichment strongly supports the significance of TRAMP factor binding in regulating gene expression.TRF5 on Mtr4 binding by CRAC and RNA abundance by RNAseq. When mRNAs were stratified by Trf5 association, mRNA species most highly bound by Trf5 in the WT, showed decreased binding by Mtr4 and increased total abundance in the trf5∆ strain. This is consistent with Trf5 promoting Mtr4 recruitment and degradation for many pre-mRNAs.A notable finding was the apparently specific binding of Trf5 to a cluster of mRNAs. To better understand the significance of this association, we assessed the effects of deletion of We also noted that clusters of mRNAs showed high p(A)-proximal association of Trf5 . Standard techniques were used to integrate C-terminal affinity tags and integration of galactose-driven genes. Strains used are listed in Supplementary Data All yeast strains are derived from  ~0.5) and used for CRAC or RNA extraction. Depletion was checked by western blotting.46. Yeast from 4OD of cells were loaded on NuPaGE Novex 4–12% gel, liquid transferred and incubated with rabbit anti-TAP antibody  rabbit anti-TAP antibody  and mouse anti-Pgk1 antibody (Thermo Fisher PA528612), followed by IRDye secondary antibodies (Licor) incubation : anti-mouse 680RD (926-68070) and anti-rabbit 800CW (926-32211) incubation. Membrane was visualized on Odyssey CLx scanner.Yeast was grown to exponential phase (OD 0.5). Proteins were extracted with a mild alkali treatment prior boiling in NuPaGE loading buffer47. Oligoadenylated RNAs were purified from 75 μg of total RNAs using polyA+ kit (Ambion). All outputs, along with 5 μg total RNA, were separated on a 10% acrylamide urea gel, stained with SybrSafe (Invitrogen) and liquid transferred to a HybondN+ membrane. Hybridization with a radiolabeled oligonucleotide (GCGTTGTTCATCGATGC) was performed with ultraHyb (Ambion) and signal detected using a Fuji FLA-5100 PhosphoImager.RNAs were extracted via a hot phenol, guanidium method48.Alignment were generated with Clustal Omega and visualized using MView 1.6349. Following digestion, samples were diluted with equal volume of 0.1% trifluoroacetic acid and spun onto StageTips as described50. Peptides were eluted and analyzed by liquid chromatography-tandem MS on a Q Exactive Mass Spectrometer (Thermo Fisher Scientific) coupled online, to an Ultimate 3000 RSLCnano Systems . Fourier transform mass spectrometry spectra were recorded at 70,000 resolution and the top 10 most abundant peaks with charge ≥2 and isolation window of 2.0 Thomson were selected and fragmented by higher-energy collisional dissociation51 with normalized collision energy of 27. The maximum ion injection time for the MS and MS2 scans was set to 20 and 60 ms, respectively, and the automatic gain control target was set to one E6 for the MS scan and to five E4 for the MS2 scan.Protein pull-downs were extracted from strains expressing the bait protein tagged with a C-terminal HisX6-Tev cleavage site-Protein A (HTP) tag. A non-tagged strain was used as a negative control. Protein purification has been made in 1 M NaCl conditions. For Trf4-HTP and Trf5-HTP, additional purification with lower stringency (150 mM NaCl) were performed in the presence or absence of RNase A. Extracts were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, trypsin-digested, as previously described52 version 1.6.1. 0 was used to process raw files and search was conducted against the Saccharomyces cerevisiae complete/reference proteome database , using the Andromeda search engine53. iBAQ (intensity-based absolute quantification) values calculated by MaxQuant are the (raw) intensities divided by the number of theoretical peptides , a normalized measure of molar abundance. We divided each yeast protein’s iBAQ value by the sum of all non-contaminant iBAQ valuesrol Fig. . All pro55 on yeast strains expressing the protein of interest carrying a C-terminal HTP tag, grown in SD medium to log phase and UV crosslinked  to covalently bind RNA to protein. RNA–protein complexes have been purified, and RNAs are partially digested to leave only the “footprint” of the protein or protein complex. Mircat and barcoded linkers (containing three random nucleotides) have been ligated on both 3′ and 5′ end, respectively. Proteins were then digested by proteinase K, RNAs were reverse transcribed, and PCR amplified. cDNA libraries were size fractionated on agarose gels and then subjected to next-generation sequencing using Illumina HiSeq (Edinburgh Genomics) or Illumina Miniseq (our laboratory). Illumina sequence data from this publication have been submitted to the GEO database (http://www.ncbi.nlm.nih.gov/geo/) and assigned the identifier GSE135526.CRAC was performed as describedYeasts were grown at 30 °C to OD 0.5 in minimal media and RNAs were extracted using a standard acidic hot phenol method. RNAseq libraries were prepared using a SENSE mRNA-seq Library Prep Kit V2 for Illumina (Lexogen), as recommended by the manufacturer. Two micrograms of RNA was denatured and used as input for poly(A)-tailed RNA purification through hybridization on oligodT magnetic beads. Purified poly(A) RNA were subjected to reverse transcription and ligation generating short cDNA fragments with linker sequences at either end. The library was converted to double-stranded DNA, purified, and PCR amplified (13 cycles). Samples were checked on bioanalyser and then sequenced using standard Illumina protocol on NextSeq .trf5∆) were carried out. For each sample, to ensure the level of residual genomic DNA (gDNA) in the RNA prep were negligible, qPCR was performed without reverse transcription on DNase-treated RNA (“no RT”). Primer’s linearity and efficiencies were tested by performing a standard amplification on serial dilution of WT gDNA in triplicates . Cycle threshold (Ct) values were averaged between triplicates for each RNA sample. Gene expression fold change were determined as reported56. For qPCR raw data, primer sequences and efficiencies, and fold change calculation on WT and trf5∆ strains, see Supplementary Data Yeasts were grown at 30 °C to OD 0.5 in minimal media and RNAs were extracted using a standard acidic hot phenol method. Five micrograms of 5 μg RNA was submitted to TURBO DNase treatment (Thermo), checked for quality, and reverse transcribed with Random decamers (Ambion) and Superscript III Reverse Transcriptase (Invitrogen) following the manufacturer’s protocols. After RNAse H (Thermo Scientific) treatment, cDNAs were diluted 20-fold. Real-time PCR amplifications were performed in a LightCycler480 system (Roche Diagnostics) in 384-well plates on 5 μL scale reactions using 2 μL diluted cDNAs and Brilliant III Ultra-fast SYBR Green qPCR Master Mix (Agilent) with primers listed in Supplementary Data 57 with parameters -x 1 –ao 4 -g and only reads containing the 3′ adapter were kept. Then, the sequences were collapsed: reads having identical ends and identical random nucleotides in the 5′ barcode were counted as one, allowing removal of PCR duplicates. CRAC pre-processed reads containing non-encoded oligo-A tailed were identified using a pipeline developed by Grzegorz Kudla41. Reads were then aligned to the Saccharomyces cerevisiae genome (SGD v64) using Novoalign  with genome annotation from Ensembl (EF4.74)58, supplemented with non-coding sequences as described in ref. 41, with parameters –r Random.CRAC sequencing data were quality filtered and adapter were trimmed using Flexbar 3.4.059 using genome database from Ensembl (EF4.74).Low-quality reads and 5′ extremities of reads were filtered out. Low-complexity sequences  were filtered out before alignment. Then, RNAseq reads were aligned with STAR60. pyReadCounters (pyCRAC) was used to count overlaps with genes and reads per million (RPMs) or RPM per kilobase (RPKM).Downstream analyses were performed using pyCRAC software (0.5.3)For correlation of binding analyses, low-complexity sequences were filtered out before alignment. Overlaps either with transcripts  or with a genome reference file divided into 50 nt windows, independently of transcripts, were calculated, averaged between two biological repeats, and used to calculate Pearson’s correlations between samples.trf5∆ strain, normalized coverage (RPM) was calculated at each position along the genome for all datasets and averaged between replicate RNAseq datasets . Log 2 enrichment was calculated after the addition of five pseudocounts to both the numerator and denominator as described61.Plot showing binding along single genes were generated using pyPileup (pyCRAC). Metagenomic plots were performed using homemade script using pyPileup on each individual transcript to count hits at each position. We obtain a table in which each row represents a transcript and each column represents the absolute position from 5′ end or 3′ end. The plot is summing up binding at each position allowing the display of a binding profile aligned either at 5′ end or 3′ end Fig. . To calck-medians, k = 6, Euclidean distance) and data displayed as heat maps. For data not included in the clustering analysis, pyBincollector output was sorted according to the clustered list and shown as heat map.We selected and combined the top 1000 protein-coding genes bound by each TRAMP component (2005 mRNAs were included). pyBinCollector (pyCRAC) was used to calculate binding distribution along transcripts, each one being divided into five bins from TSS to pA site. Binding across each bin was calculated as a fraction of total binding across individual gene (set to 1). For each transcript, we averaged the number of reads between two biological replicates to reduce the influence of experimental variation upon clustering analysis. We normalized the data for each gene. Air1, Air2, Trf4, Trf5, and Mtr4 coverage along transcripts were then clustered using cluster 3.0 (C Clustering Library version 1.52a, Low-complexity sequences trimmed dataset were used. The number of hits for each position of the genome was calculated excluding all non-RNA polymerase II transcripts and normalized to RPMs of RNA polymerase II transcripts. Clusters consisting of 15 continuous position with more than 50 hits across were detected. The highest position in each cluster was selected as “peak.” Air1 peaks were used as a reference file to which other protein peaks were aligned Fig. .Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


The outbreak of COVID-19 has brought great threat to human health. Its causative agent is a severe acute respiratory syndrome-related coronavirus which has been officially named SARS-CoV-2. Here we report the discovery of extremely low CG abundance in its open reading frames. We found that CG reduction in SARS-CoV-2 is achieved mainly through mutating C/G into A/T, and CG is the best target for mutation. Meanwhile, 5′-untranslated region of SARS-CoV-2 has high CG content and is capable of forming an internal ribosome entry site (IRES) to recruit host ribosome for translating its RNA. These features allow SARS-CoV-2 to reproduce efficiently in host cells, because less energy is consumed in disrupting the stem-loops formed by its genomic RNA. Notably, genomes of cellular organisms also have very low CG abundance, suggesting that mutating C/G into A/T occurs universally in all life forms. Moreover, CG is the dinucleotide related to CpG island, mutational hotspot and single nucleotide polymorphism in cellular organisms. The relationship between these features is worthy of further investigations. Its causative virus  has a genome of single-strand positive-sense RNA with approximately 30,000 nucleotides2. Based on its genome sequence, analyses have been conducted to characterize genomic features and to trace origin of the virus4. Meanwhile, many reports have been focused on developing efficient methods for detection6 and screening effective drugs for treatment8 against COVID-19.The outbreak of COVID-19 (coronavirus disease 2019) was listed as a public health emergency of international concern on 30 January 2020 and declared a global pandemic on 11 March 2020 by the World Health Organization. As of 15 June 2020, it has caused more than 7.8 million infection cases and over 430,000 deaths worldwide9. The viral RNA is much longer than host mRNAs. Such a long single-strand RNA will form multiple stem-loops through base-pairing between its adjacent segments. These stem-loops present an obstacle to the translation machinery, because they must be disrupted to expose coding information contained in them. The stability of a stem-loop structure is dependent on number of hydrogen bonds formed between bases in the stem part. Because C-G and T-A base-pairs are formed through three and two hydrogen bonds respectively, a viral RNA strand with high number of C and G bases will form more stable stem-loops than that with high number of T and A bases.Coronaviruses have some of the largest RNA genomes among all viruses. The single-strand genomic RNA of coronavirus has a cap-like structure at 5′-UTR (untranslated region) and a poly(A)-tail at 3′-UTR, both of which allow it to assume a structure similar to mRNA of host cells. After being released into cytoplasm of a host cell, the viral RNA is translated immediately to produce viral proteins by using the translation machinery of host cellsIn order to understand whether translation of coronavirus RNA is greatly affected by base composition, we analysed dinucleotide distribution and RNA stability of twenty-four coronavirus species. We found that open reading frames (ORFs) of SARS-CoV-2 have an extremely low abundance of CG dinucleotide. Moreover, the secondary structure formed by SARS-CoV-2 genomic RNA is less stable than many other coronaviruses. Therefore, it is suggested that SARS-CoV-2 is more efficient in reproduction than other coronaviruses, because less energy is consumed in disrupting the stem-loops formed by its genomic RNA.10. The genome of SARS-CoV-2  has 29.94% of A, 32.08% of T (T is used here instead of U for simplicity), 19.61% of G and 18.37% of C. Thus, the expected frequency of CG dinucleotide in viral genome is 3.60% (i.e. 19.61% × 18.37%). However, only 439 CGs are observed, which means the observed frequency of CG dinucleotide is 1.47% . Therefore, odds ratio of CG in SARS-CoV-2 is 0.41 (i.e. 1.47%/3.60%). Furthermore, odds ratio of CG in open reading frames (ORFs) of the virus is 0.39, being the lowest among 24 coronaviruses under survey 23 and (CG)31 respectively. We found that the odds ratio of (CG)23 in ORFs of SARS-CoV-2 is as low as 0.25, while that of (CA)23 and (CT)23 is as high as 1.54 and 1.92 respectively 31 and (TG)31 is 1.52 and 2.64 respectively 23 and (CT)23, and (CG)31 has been mutated into (AG)31 and (TG)31.DNA or RNA sequences are composed of four nucleotides, i.e. adenylate (A), thymidylate (T), guanylate (G) and cytidylate (C). They can also be considered polymers of 16 dinucleotides. Odds ratio is a value defined to indicate relative abundance of a nucleotide, which is the ratio of observed to expected frequency of a dinucleotidevey Fig. . Because23. They are TCG, CCG, ACG and GCG which code for serine, proline, threonine and alanine, respectively. Mutation of G at codon position 3 into T, C or A in all of them does not change the amino acid they encode. As for (CG)31, there are 16 codons having C at position 3. If this C is mutated into T, all 16 codons have the same meanings. And if it is mutated into A, 9 out of 16 codons still have the same meanings. Therefore, it is concluded that SARS-CoV-2 has evolved to reduce CG in ORFs mainly through mutating its G of (CG)23 and C of (CG)31 into A and T. Among them, C-to-T (i.e. C-to-U in RNA) occurs at a very high frequency probably because it is the simplest way to change a nucleotide (C becomes U after deamination). Besides, odds ratio of (CC)23 is much lower than that of (CA)23 and (CT)23 23. In fact, low odds ratio of (CC)23 is due to high frequency of C-to-T mutation at position 3, i.e. from (CG)31 into (TG)31 9. The translation of viral RNA is affected by two factors. One is that host ribosomes must be recruited to the 5′-UTR (untranslated region) of viral RNA for initiation of translation. The other is that stem-loops formed by ORFs of viral RNA must be disrupted to expose coding information during translation. In contrast to ORFs, 5′-UTR of coronaviruses have quite high odds ratios of CG  has very low virulence, because it has low stability in both 5′-UTR and TSS-to-end regions (Table  has very23 and C of (CG)31 into A or T ses Fig. . First, ses Fig. a. Secondses Fig. b. Third,ses Fig. c. As oddThen, if reducing hydrogen bonds is the goal of base mutation, why is CG but not GC, GG or CC taken as the target for mutation? An examination on number of silent mutations of each dinucleotide at various codon positions reveals that CG has the highest number (47) of silent mutations among these four dinucleotides Table . This ex18, we did not know why CG has such a low odds ratio in surveyed organisms. Now, after analysing cases in coronaviruses, we realize that low CG content in cellular organisms should also be the evolutionary consequence of increasing gene expression efficiency, because lowered CG content means reduced number of hydrogen bonds between DNA double strands (of the same length). Expression of a gene with low CG content saves energy not only in separating DNA double strands during transcription but also in disrupting stem-loops formed by mRNA during translation. Coincidently, CG is the very dinucleotide related to existence of CpG island, mutational hotspot, and single nucleotide polymorphism (SNP) in DNA sequences of cellular organisms. A CpG island is defined as a region of DNA with less methylated C, and this region generally contains actively expressed genes21. A mutational hotspot is defined as CG with methylated C, in which the methylated C is frequently mutated into T through deamination24. SNP refers to single nucleotide difference in genome sequences among individual organisms, which is observed most frequently at CG dinucleotide26. The relationship between CG reduction and these three important features of cellular DNA sequences is worthy of further investigations.It seems that the strategy of “reducing CG content to increase gene expression efficiency” has also been adopted by cellular organisms. As we have observed, CG in both ORFs and inter-genic regions of bacteria, archaea, fungi, plants and animals has an average odds ratio of 0.81, and that in introns of fungi, plants and animals is as low as 0.69. At time of our previous reportwww.ncbi.nlm.nih.gov). Odds ratios of dinucleotides were calculated using formulae developed by Karlin and Mrázek10 and by Wang et al.18 with self-compiled computer programs (C++ scripts are available upon request). Secondary structure and free energy of viral RNA is predicted using RNAstructure (version 5.7)27. SPSS software (version 17.0) was used to conduct independent-sample t-test for comparing difference in odds ratio of nucleotide between coronaviruses and cellular organisms, and to conduct correlation analysis between RNA stability and nucleotide composition in viral genomes.Genome sequences of coronaviruses were retrieved from GenBank (Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.