Viral RNA-host protein interactions are indispensable during RNA virus transcription and replication, but their detailed structural and dynamical features remain largely elusive. Here, we characterize the binding interface for the SARS-CoV-2 stem-loop 3 (SL3) cis-acting element to human TIA1 protein with a combined theoretical and experimental approaches. The highly structured SARS-CoV-2 SL3 has a high binding affinity to TIA1 protein, in which the aromatic stacking, hydrogen bonds, and hydrophobic interactions collectively direct this specific binding. Further mutagenesis studies validate our proposed 3D binding model and reveal two SL3 variants have enhanced binding affinities to TIA1. And disruptions of the identified RNA-protein interactions with designed antisense oligonucleotides dramatically reduce SARS-CoV-2 infection in cells. Finally, TIA1 protein could interact with conserved SL3 RNA elements within other betacoronavirus lineages. These findings open an avenue to explore the viral RNA-host protein interactions and provide a pioneering structural basis for RNA-targeting antiviral drug design. Here the authors reveal the binding interface for the SARS-CoV-2 SL3 RNA element to human TIA1 protein and find that disruptions of the identified viral RNA-host protein interactions with designed ASOs reduce SARS-CoV-2 infection in cells. Due to the RNA nature of their genomes, it is anticipated that RNA viruses would co-opt host RNA-binding proteins (RBPs) to perform their particular functions during transcription and replication5. For instance, several host RBPs have been identified that affect the recruitment of plus viral strands for replication, play important roles during the switch from translation to replication, control viral RNA synthesis, affect the stability of viral RNAs, and participate in multiple events during infection3. Therefore, a deeper understanding of the interactions between viral RNAs and host RBPs will provide new insights into the virus life cycle and help the development of novel antiviral strategies.There are hundreds of different RNA viruses that trigger infectious diseases in the world, such as the ongoing COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)9. For example, Flynn et al.7 identified 309 host proteins that bind the SARS-CoV-2 RNA during active infection by mass spectrometry (ChIRP-MS) and provided a comprehensive catalog of functional SARS-CoV-2 RNA-host protein interactions. Schmidt et al.9 identified up to 104 human proteins that directly and specifically bind to SARS-CoV-2 RNAs in infected human cells by using RNA antisense purification coupling with mass spectrometry, and linked protein-RNA interactome to cellular pathways relevant to SARS-CoV-2 infections. Kamel et al.8 identified comprehensively the RBPs involved in SARS-CoV-2 infection through a multi-omic approach and revealed that SARS-CoV-2 infection profoundly remodels the cellular RNA-bound proteome. Overall, the molecular functions of the related genes encoding proteins identified as binding partners for viral RNA include mRNA processing stability and localization, P-body/stress granule assembly, translation, and many more6. Some representative host proteins that have been individually validated as proviral factors include dead-box helicase 5 (DDX5), T-cell intracellular antigen-1 (TIA1), and insulin like growth factor 2 mRNA binding protein 16. Nonetheless, which segment of the SARS-CoV-2 RNA genome serves as the binding site for a particular host protein remains largely elusive.During the last few years, in order to understand and combat SARS-CoV-2, various ‘omics’ technologies have been applied to discover the proviral host factors that are required for the completion of the SARS-CoV-2 life cycle12. Previous studies12 have demonstrated that those cis-acting elements are essential for viral transcription and replication through complicated RNA–RNA and RNA–protein interactions. Particularly, the 5′-UTR of the SARS-CoV-2 RNA genome is highly structured and harbors several stem-loop (SL) elements  conserved among the betacoronavirus lineage, which has been validated by chemical probing, NMR chemical shift experiments, and computational predictions19. Moreover, the secondary structures of the isolated SL cis-acting elements are in good agreement with the full-length construct16, implying their ability to fold independently. Furthermore, compared to the high mutation rates in the coding regions, those SL elements are thought to be highly sequence and structure conserved in different virus variants12. Therefore, those properties make the SL cis-acting elements attractive targets for development of novel antivirals, such as the RNA-targeting antivirals, against the rapidly evolved RNA viruses and the possible future coronavirus outbreaks.Generally, the genome of positive-sense RNA viruses, including SARS-CoV-2, encode the information essential for their life cycle in two aspects. Apart from the common coding regions for the translation of proteins that hijack the host cell machinery and assemble new viral particles, the single-stranded RNA genome could fold back onto itself to form the so-called cis-acting elements in the 5′- and 3′-untranslated regions (UTR)20 that plays multifunctional regulatory roles in the gene expression layers, such as transcription, alternative splicing, and translation of mRNAs, and in the physio(patho)logical-associated events, such as cell stress, apoptosis, viral infections, etc21. TIA1 contains three N-terminal RNA recognition motifs (RRMs) connected by flexible linkers and interacts mainly with the target RNA segments via the second and third RRMs23. Structural studies24 indicated that both RRM2 and RRM3 adopt the canonical βαββαβ RRM fold composed of a four-stranded anti-parallel β-sheet covered on one side by two α-helices  targeting the SL2/3 RNA element and efficient depletion of the TIA1 protein by siRNA knocking down could dramatically reduce SARS-CoV-2 infects in human cells. Thus, those points further emphasize the vital function role of cis-acting RNA elements (such as SL2/3) and their cognate host RBPs (such as TIA1) in the SARS-CoV-2 life cycle. However, the molecular mechanism and structural details of the binding between the SL2/3 RNA element of SARS-CoV-2 and host TIA1 protein remain unclear, which largely hinders further efforts to develop desirable RNA-targeting antiviral drugs.TIA1 is a ubiquitous RBPsee Fig. . Previou29. In another prior work by Park et al., a 3D structural model of the SARS-pseudoknot was constructed, then a novel ligand that dramatically inhibits the −1 ribosomal frameshifting of SARS-CoV has been identified by structure-based virtual screening30. Here, combined with molecular modeling, free energy calculations, electrophoretic mobility shift assays (EMSA) and in vivo ASO assays, we identified the binding mode of the SL3 RNA element and host TIA1 protein, constructed the putative 3D structure for the binding complex and evaluated various mutagenic point mutations on the key nucleotides (nts) at the binding interface. We expected those knowledges could provide new insights into viral mechanisms during infection and lay down the foundation for effective RNA-targeting antiviral drug design.As a complement to expensive experimental structural characterizations, computational predictions for the 3D structures of cis-acting RNA elements and their binding complexes with host proteins could provide adequate information for virtual screening of novel RNA-targeting antivirals before accurate but time-consuming determination of related atomic details. For instance, computational models for 5′-UTR regions have been used for in silico ligand screening to inhibit SARS-CoV-2 replication19 have demonstrated the secondary structures of SARS-CoV-2 SL2 and SL3 RNA elements and their ability to fold independently. As shown in Fig. 31 to predict 3D structures of SL2 and SL3 RNA elements. Our previous work indicated that the coarse-grained IsRNA2 model enables de novo modeling RNA 3D structures with a comparable performance to the atomic model but at much less cost31. For the SL2 element, the predicted 3D structure adopts nearly the same global fold as the NMR solution structure of SARS-CoV SL232 (Protein Data Bank (PDB) id: 2l6i), which shares the identical sequence with SARS-CoV-2 SL2, and the heavy-atom root-mean-square deviation (RMSD) between two structures is 2.2 Å . The RRMs of a single protein can contribute differentially to the overall RNA binding, in terms of both affinity and specificity. For TIA1 protein, early experiments24 indicated that its preferred target is U-rich sequences predominantly directed by RRM2. Its RRM1 is thought to have little intrinsic RNA binding affinity and contribute trivially to RNA binding in the context of RRM1,2,3, while the RRM2,3 may bind cooperatively to pyrimidine-rich RNA sequences23. To explore the interactions between SARS-CoV-2 SL2/3 RNA elements and human TIA1 protein in detail, we purified recombinant TIA1  RNA sequences  of TIA1 with SL2 + SL3, SL2 and SL3 from EMSA experiments, respectively. The Kd of TIA1 with full SL2 + SL3 is 0.43 ± 0.05 µM, which is consistent with published Kd values of TIA1 with other oligonucleotide substrates24 , but that for SL2 is dramatically increased to 2.47 ± 0.37 µM , demonstrating the HP is required for TIA1 binding. Moreover, the 7-nt HP was mutated to all cytosines (SL3-C7) or uridines (SL3-U7) to check sequence preference. EMSA results showed SL3-C7 variant has a lower binding affinity (Kd = 1.95 ± 0.44 µM) but SL3-U7 variant has a higher binding affinity with TIA1 (Kd = 0.54 ± 0.03 µM) than wild-type SL3 , indicating the 5-nt TL is also essential for TIA1 binding. Considering that three successive uridines  are present in the TL and the strong U-rich preference of RRM224, we speculated that SL3 interacts with TIA1 RRM2 through those three uridines. To validate this assumption, we replaced those 3 Us to 3 Cs (SL3-TL-C3) or 3 Gs (SL3-TL-G3) and repeated the measurements of their binding ability with TIA1. Expectedly, either SL3-TL-C3 or SL3-TL-G3 variant has a lower binding affinity than WT SL3  to −6.6 kcal/mol for SL3-GC. Intriguingly, superior stability of the stem inhibits the TIA1 binding and results in a weaker binding affinity Kd = 5.65 ± 1.70 µM , an ASO-HP targeting the HP, an ASO-TL targeting the 3′-TL, and an ASO-TRS targeting the transcriptional regulatory sequences37 (TRS). In agreement with the previous study18, we observed decreased SARS-CoV-2 RNA yield (to ~52.4%) in Huh7.5.1 cells (human liver cancer cell line) transfected with ASO-SL2/3 in comparison with the scramble control , as well as hydrophobic interactions  located in the RNP-2 and RNP-1 sub motifs of RRMs interact directly with RNA bases U77, U78, U67, and A68 through π–π stacking, respectively. Additionally, U67 forms two H-bonds via N3–H3 and O2 atoms with Trp272 and Lys274, respectively  and the identified residues that interact with SL3 RNA element are all conserved  were estimated through free energy perturbation (FEP) calculations. Up to now, FEP is regarded as the most rigorous and reliable method in estimating binding affinity changes, which has also achieved high accuracy in characterizing vital residues and their mutational effects for many protein–protein, protein–ligand, and protein–DNA bindings, as compared with experiments45. As shown in Fig. Gcalc = 3.62 ± 0.33 kcal/mol for U67C and ΔΔGcalc = 3.84 ± 0.58 kcal/mol for U67G mutations, and are predicted to decrease the binding affinities. Likewise, mutations for nts U76 (except for U76C), U77, and U78 are also all unfavorable, such as U77 (U78) with the least binding free energy change ΔΔGcalc = 2.29 ± 0.91 kcal/mol  at U77C (U78C) mutation. The aforementioned U’s particular H-bonds may partially account for those disfavors, including U67@N3–H3…Trp272@O  and A68G  were predicted to have trivial influence on binding affinity. On the other hand, the predicted binding free energy changes for A68U and A69G mutations are ΔΔGcalc = −1.84 ± 0.63 and −3.07 ± 0.65 kcal/mol, respectively, which indicates an enhanced binding affinity between those two SL3 variants and TIA1 protein.The putative 3D structure of SL3 and TIA1 binding complex proposed above suggests that six nts  play important roles in directing SL3 binding to TIA1 protein. Therefore, exhaustive mutations were introduced for each of those six nts and the relative binding free energy changes  for some representative mutations were measured from ESMA experiments  binding free energy changes is observed  and A69C (Kd = 0.42 ± 0.04 µM) variants have an enhanced binding affinity to TIA1 protein than the wild type (Kd = 0.78 ± 0.09 µM). Inspection of the detailed interactions before and after mutations in MD simulations reveal that an extra H-bond was formed between side chain of Arg233 at TIA1 RRM3 and SL3 U68/C69 variant  coronavirus that triggered the MERS outbreak in 201247. In addition to the identical SL3 shares by SARS-CoV-2 and SARS genomes, multiple sequence alignment12 indicates that SL3 RNA elements are well conserved among different species within genus Betacoronavirus  and Kd = 0.30 ± 0.02 µM (MERS) were observed . Then, both the 7-nt HP and 5-nt 3′-TL were found to be essential for SL3 in its binding with TIA1 protein in a sequence-dependent manner, in which truncations of loops or sequence alterations in loop regions by cytosines/guanines obviously decrease the binding affinity. And disruptions of the SL3 RNA and TIA1 binding with specific ASOs designed apparently decreased the bona fide SARS-CoV-2 RNA yields in Huh7.5.1 cells. After that, a 3D model for the binding complex between SL3 RNA element and TIA1 protein was constructed by template-based approach and MD simulations. Along with the electrostatic interactions between positively charged residues and the negatively charged RNA backbone, the aromatic stacking, specific H-bonds, and hydrophobic interactions collectively direct the specific binding of SL3 to TIA1 protein. Moreover, for six identified nts  at the binding interface, exhaustive single nucleotide mutations were introduced (in silico mutagenesis) and their relative binding free energy changes were calculated. Specifically, FEP calculation and subsequent experiments demonstrated that the SL3 A68U (~1.7-fold) and A69C (~1.9-fold) variants have an enhanced binding affinity to TIA1 protein than the wild type. Finally, due to sequence conservation of the cis-acting elements, we found that the human TIA1 protein also interacts with other SL3 elements from betacoronavirus RNA genomes, such as the MERS and SARS coronavirus, in comparable binding affinities. In conclusion, those presented data provide a pioneering structural basis to understand the viral RNA-host protein interactions for SARS-CoV-2 (which might be extended to other betacoronavirus infections).Particularly, though interactions between viral RNA and host RBPs are indispensable during RNA virus transcription and replication, which RBPs and how they interact with viral RNA genomes remain largely unclear. Here, we systematically investigated the binding interactions between the SARS-CoV-2 cis-acting RNA elements and human TIA1 protein through a combined theoretical and experimental approach including molecular modeling, free energy calculations, EMSA, and ASO assays. To the best of our knowledge, this is the first time that a detailed study for the binding interface between the cis-acting element in the 5′-UTR of SARS-CoV-2 RNA genome and a particular human protein (namely TIA1) was performed. We first confirmed that the SL3 element of SARS-CoV-2 RNA genome can bind to TIA1 protein RRM2_3 truncation with high affinity  and are associated with the regulation of viral replication, subgenomic mRNA production and translation. Therefore, development of novel antivirals targeting those cis-acting RNA elements is particularly attractive and several pioneering works have been performed recently62. To this end, our study here and previous works63 provided putative 3D structural information for future structure-based drug designs. For instance, since SL3 element binding to TIA1 protein requires notable conformation changes both in protein domain arrangement and RNA 3D structure adaptation, drug-like compounds that either disrupt RNA 3D structure adaptations or interfere with the viral RNA-host protein interactions are promising.Due to the RNA nature of its genome, SARS-CoV-2 can rapidly evolve and adapt towards vaccines and drugs by slightly altering their core protein-coding sequences31 was employed to predict the 3D structures for SARS-CoV-2 SL2 and SL3 RNA elements. With the sequences and secondary structures of SL2 and SL3 elements  simulations with 10 replicas possessing temperatures from 200 to 425 K were run in coarse-grained representations to sample the 3D conformational space. After that, a clustering process based on the top 10% snapshots with the lowest potential energies was used to obtain the candidate structures. Finally, the centroid structure of the largest cluster followed by all-atom reconstruction and energy minimization presented the predicted 3D conformation.The IsRNA2 model67 (version 2020.6) to further relax the predicted 3D structures. For each simulated system, a water box with at least 1.5 nm distant from the surface of the RNA or RNA & TIA1 complex was used to solvate the systems with 50 mM NaCl ions added. The Amberff14SB57 force field was used for protein and RNA58 parameters. Water molecules were described by TIP3P model68 and Li and Merz 12-6 ion parameters69 were used. The periodic boundary conditions were applied in all three dimensions. The particle mesh Ewald (PME) method was used to compute the long-range electrostatic interactions while the vdW interactions were truncated at 1.5 nm. The LINCS algorithm70 was adopted to constrain the H-bonds to allow an integration timestep of 2 fs. Before MD productions, an energy minimization, 100 ps NVT, and 1 ns NPT simulation with a temperature of T = 300 K and pressure of 1 atm was executed sequentially to equilibrate the simulation box. To avoid unexpected structural deviations in the beginning, a further 15 ns MD simulation with gradually weakening position restraints on C1′ atoms of RNA molecule and Cα atoms of protein was subsequently performed in the NPT ensemble. After that, series of MD simulations (1000/1500/2500 ns) for isolated RNA and RNA&TIA1 complex were conducted in the NPT ensemble with velocity-rescaled Berendsen thermostat71. Three duplicate simulations were run for each concerned system. A summary of the simulation systems performed in this study was given in Supplementary Table 72 with a 0.3 nm RMSD cutoff to obtain the most probable conformation. The analysis of occupancies of key interactions between RNA and protein was done by VMD73. The 3D structure models were rendered by UCSF Chimera74 programs.All-atom MD simulations were conducted using Gromacs24 (PDB id: 5ith), the binding complex between RNA fragment 76UUU78 and RRM2 was achieved straightforward through replacing the thymine by uracil. For complex of RRM3 and HP of SL3 element, we selected the crystal structure of the RNA-binding domain of U1A spliceosomal protein complexed with an RNA hairpin as a template38 (PDB id: 1urn). Due to highly structural similarity between different canonical RRMs, the TIA1 RRM3 extracted from the apo state23 (PDB id: 2mjn) could consistently align to U1A protein with RMSD 1.7 Å over Cα atoms. Then, the RNA segment 9GCAC12 in the U1A/RNA complex was chosen as a feasible template for fragment 66CUAA69 of SL3 RNA in the binding complex. The second step is to predict the possible 3D structures for SL3 RNA element in its bound form. In addition to sequence and secondary structure , while those for RRM3 . Then, the flexible linker between RRM2 and RRM3 was recovered by Modeller75. As a result, two possible 3D binding conformations (Conf1 and Conf2) were constructed for subsequent refinement in MD simulations. The final step is to further optimize the binding complex through MD simulations. After balance of the simulation box and release of the position restraints on C1’ (RNA)/Cα (protein) atoms, an extra 100 ns MD simulation with distance restraints (kb = 800 kJ/mol/nm2) on 14 particular atom pairs between RNA and TIA1 was performed. Those atom pair restraints account for key interactions between nucleotide/residue derived from the aforementioned templates in step 1, such as U67@C4-Tyr206@Cδ1, A68@C8-Phe242@Cζ, U76@O2-Arg164@Nη2, U77@O4-Asn169@Nδ2, U78@C4-Phe140@Cδ1, etc. Then, a 2500 ns MD production without any restraints was run to further relax the initial binding complex. For each possible 3D binding conformation in step 3, three duplicated simulations were performed. For each of those six runs (2 conformations × 3 runs), snapshots extracted from the last 1500 ns MD trajectories (7500 snapshots recorded in the duration of 200 ps) were clustered based on gromos method72 with a 0.3 nm RMSD cutoff to obtain the most probable conformation and the centroid structure of the largest cluster was chosen. After comprehensive consideration of the profiles of RMSDs  has been determined by X-ray diffractionsee Fig.  as input42. We estimated the free energy changes for single nucleotide mutation in both the bound state (SL3 and TIA1 complex) 76 manner based on the Amberff14SB force field58 (Supplementary Data α = 0.3) were used. For each mutation, at least five independent runs starting from different conditions were performed for sufficient sampling and at least 270 ns (1.5 ns × 18 windows × 5 runs × 2 states) simulation time was generated, which result in reasonable convergence in the free energy calculations. After MD simulations completion, the Gromacs bar77 analysis tool was used to estimate the free energy changes based on the last 1 ns simulation per window.The binding affinity changes, due to point mutations of key RNA nts at the interface, between the SL3 RNA element and TIA1 RRM2_3 complex were calculated by the FEP methodBamH I/Hind III (Thermo Fisher), then ligated into a BamH I/Hind III-digested Pet28a-6×His-SUMO vector76 to obtain the Pet28a-6 × His-SUMO-Htia1 RRM1-3 construct.For the 6 × His-SUMO-TIA1 RRM1-3 1-274aa) construct, the CDS of TIA1 RRM1-3 was amplified from a human cDNA template using specific primers (Supplementary Table 4aa constBamH I/Hind III. The resultant fragment was ligated into the BamH I / Hind III-digested Pet28a-6 × His-SUMO vector to produce the Pet28a-6×His-SUMO-TIA1 RRM2_3 construct.For the 6 × His-SUMO-TIA1 RRM2_3 (93-274aa) construct, the fragment of TIA1 RRM2_3 was amplified from the Pet28a-6 × His-SUMO-TIA1 RRM1-3 plasmid and digested with Escherichia coli strain BL21 (DE3) cells. Cells were grown in Luria–Bertani (LB) at 37 °C until an OD600nm = 0.6–0.8 was reached. Expression of recombinant proteins was typically induced with 0.5 mM IPTG and grown at 16 °C overnight.pET28a-6×His-SUMO-TIA1 RRM1-3 and pET28a-6 × His-SUMO-TIA1 RRM2_3 constructs were transformed into For purification of TIA1 RRM2_3, the induced bacterial cells were harvested by centrifugation and re-suspended in ice-cold lysis buffer  and disrupted with a high-pressure homogenizer (JNBIO). After centrifugation and filtering with a 0.4 µm filter, the cleared lysate was supplemented with 20 mM imidazole and loaded on a HisTrap HP column . The column was washed with 25 mL wash buffer  and eluted with gradient elution buffer from 80 to 200 mM imidazole . The peak fractions containing the recombinant 6 × His-SUMO-Htia1 RRM2_3 proteins were pooled and incubated with SUMO protease at 4 °C overnight for the 6 × His-SUMO tag removal. Then the fractions were concentrated by 10 kDa molecular weight cut-off centricon (Thermo Fisher), and loaded onto a HiLoad 26/600 Superdex 200 pg column  to separate 6 × His-SUMO tags from the TIA1 RRM2_3 proteins. The gel filtration buffer contains 10 mM Sodium phosphate buffer pH 6.5, 50 mM NaCl, and 2 mM DTT. The peak fractions containing TIA1 RRM2_3 were dialyzed overnight with the dialysis buffer . The purity of the purified TIA1 RRM2_3 proteins was detected on a sodium dodecyl sulfate (SDS) polyacrylamide gel. The TIA1 RRM2_3 proteins were finally frozen by liquid nitrogen and stored at −80 °C. Purification of TIA1 RRM1-3 used the same protocol.78. Synthesized 5′ end Cy3-labeled RNAs  software.The EMSA was performed based on previous protocol with minor modifications5 cells per well for 16 h, then 1.5 μl 100 μM ASOs were transfected with 1.5 μl Lipofectamine RNAiMAX  on a final concentration of 0.3 μM for 12 h. Cells were washed with opti-MEM and incubated with SARS-CoV-2 at an MOI = 0.05 for 1 h, then cells were washed with opti-MEM and supplemented with maintenance medium. At 24 hpi, supernatants were collected and viral RNA in the cell supernatants were extracted by using Direct-zol RNA MiniPrep kit  according to the manufacturer’s instructions. SARS-CoV-2 viral RNA levels were measured by qPCR.All ASOs were synthesized by Synbio technologies. The sequences of ASOs are listed in Supplementary Table Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA–RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA–RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA–RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA–RNA correlations and networks consisting of miRNA–RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery. The past two decades have seen remarkable progress in cancer research and treatment. However, despite significant progress, cancer still affects millions of people and ranks as a leading cause of death in the world . In partIn our previous studies ,8, we haMotivated by the increasing amount of miRNA expression data, several studies have been conducted recently to construct ceRNA networks in cancer. Zhu et al. , for exaAs an extension of our previous studies ,8, we haThe RNAs of 4 biotypes  obtained after removing those with low-expressions are shown in While our method uses For a fair comparison, three methods with different features were evaluated in the same way. We partitioned the data sets randomly into training and test data sets with a ratio of 7:3. We used the training data set to optimize the hyperparameters of each model using a grid search with 5-fold cross-validation. We repeated the whole process of the data partition, training, and testing 10 times for the evaluation of the methods.p-value of the log–rank test in each type of cancer. As shown in the table, several lncRNAs and pseudogenes are included in the top miRNA–RNA pairs, which corroborates the assertion that miRNAs play an important role in cancer through the interaction with lncRNAs and pseudogenes as well as with mRNAs [We performed the univariate Cox regression analysis with respect to th mRNAs ,21. If tp-value < 0.01 in the log–rank test .With the miRNA–RNA pairs, we constructed star-shaped networks centered on common miRNAs, and selected the networks with C-index > 0.6, and adjusted p-value in the log–rank test. MIR145, which is present in the potential prognostic network biomarker of BLCA, is known as a potential biomarker of cancer migration and invasion [invasion . MIR645 invasion . MIR760 invasion ,26. MIR1invasion .p-value in the log–rank test in LUAD. The network_MIR4666A  , which pIn the Cancer Genome Atlas (TCGA), we selected the data sets which have at least 50 tumor samples with lymph node metastasis (LNM) and 10 normal samples. Distant metastasis (DM) was not included in the selection criteria due to the small number of samples with distant metastasis. Among the 33 cancer data sets of TCGA, 10 cancer data sets satisfied the selection criteria: urothelial bladder carcinoma (BLCA), breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head-neck squamous cell carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), and thyroid carcinoma (THCA).The tumor samples in the selected cancer data sets were classified into four types based on the Tumor, Node, Metastasis (TNM) stage index in the clinical supplement data of TCGA.Samples with no metastasis (nonM): T stage of 1–4, N stage of 0, and M stage of 0Samples with lymph node metastasis only (LNM_only): T stage of 1–4, N stage of 1–3, and M stage of 0Samples with distant metastasis only (DM_only): T stage of 1–4, N stage of 0, and M stage of 1Samples with both lymph node metastasis and distant metastasis (LNM&DM): T stage of 1–4, N stage of 1–3, and M stage of 1We obtained RNA-seq gene expression data from the Genomic Data Commons (GDC) data portal . After f project , we clas project  in the R project .n normal samples, we computed the Pearson correlation coefficient (PCC) using Equation C-insurvMisc . The cutrocedure , and selp-value < 0.01 were sorted in increasing order of p-values. Starting with the miRNA–RNA pair with the smallest p-value, we combined up to 15 miRNA–RNA pairs with common miRNAs. The combined miRNA–RNA pairs form star-shaped networks centered at common miRNAs.The miRNA–RNA pairs with an adjusted i, we computed the risk score of the star-shaped networks using Equation (j denotes a miRNA–RNA pair in a network. j in sample i. j.For every patient sample Equation . In EquaEquation , j denotp-value < 0.01 were selected as potential prognostic biomarkers.The risk score was used in classifying patient samples into two groups, the high-risk group and the low-risk group. Again, the cutp function was used to determine an optimal threshold for classification. Finally, the networks with a C-index > 0.6 and adjusted So far, many computational methods for predicting prognosis in cancer have focused on survival rates without considering metastasis. There are a few methods developed for predicting lymph node metastasis, but few attempts have been made to predict distant metastasis mainly due to the difficulty of the problem and the small number of publicly available samples with distant metastasis. We developed a new method for predicting both lymph node metastasis and distant metastasis using differential correlations of miRNAs and their target RNAs in cancer, which were derived from a large amount of RNA-seq data and clinical data. Testing our method on several types of cancer demonstrated that differential correlations of miRNAs and their target RNAs are much more powerful than gene expressions in predicting distant metastasis as well as lymph node metastasis. With the differential correlations of miRNAs and their target RNAs, we found network biomarkers for predicting the prognosis of cancer patients. The network biomarkers derived from metastasis analysis were more predictive of survival rates than correlations of individual miRNA–RNA pairs or gene expressions of individual genes. The results of our study showed that network biomarkers based on correlations of genes could be more powerful than typical molecular biomarkers of individual genes in predicting prognosis as well as metastasis. The method developed in this study, and its results will be useful in selecting treatment options for cancer patients and a target of anti-cancer drug discovery.


The integration of individually replicating genes into a primitive chromosome is a key evolutionary transition in the development of life, allowing the simultaneous inheritance of genes. However, how this transition occurred is unclear because the extended size of primitive chromosomes replicate slower than unlinked genes. Theoretical studies have suggested that a primitive chromosome can evolve in the presence of cell-like compartments, as the physical linkage prevents the stochastic loss of essential genes upon division, but experimental support for this is lacking. Here, we demonstrate the evolution of a chromosome-like RNA from two cooperative RNA replicators encoding replication and metabolic enzymes. Through their long-term replication in cell-like compartments, linked RNAs emerged with the two cooperative RNAs connected end-to-end. The linked RNAs had different mutation patterns than the two unlinked RNAs, suggesting that they were maintained as partially distinct lineages in the population. Our results provide experimental evidence supporting the plausibility of the evolution of a primitive chromosome from unlinked gene fragments, an important step in the emergence of complex biological systems. The integration of genes into a chromosome is a fundamental genetic organization in all extant life. The assembly of unlinked genes during prebiotic evolution was likely a major evolutionary transition toward the development of a complex cell. Decades of theoretical studies have suggested a plausible evolutionary pathway to a primitive chromosome from replicating RNA molecules that harbor cooperative genes within a protocell structure. However, demonstrating the evolution of a primitive chromosome in an experimental setup is challenging. We previously developed a cooperative RNA replication system in which two types of RNAs co-replicate using their self-encoded replication and metabolic enzymes. Using this system, in the present study, we demonstrate the evolution of a linkage between the two cooperative RNA replicators in compartments. An evolved “linked” RNA harbored the entire region of both genes, accumulated distinct mutations, and retained the ability to replicate using the two proteins translated from itself. These experimental findings support a prebiotic evolutionary scenario, in which unlinked genes assembled into a single genomic structure. All extant cells have chromosomes that harbor multiple genes and ensure their organized inheritance. In the early evolution of life, such a genome organization may have been absent; unlinked RNA molecules encoding different functions or genes cooperated with each other for replicating the entire system –6. The sTheoretical studies have suggested that the presence of primitive cell-like compartments (protocells) could have facilitated the selection of a primitive chromosome over individual RNA replicators –11. The Previously, we constructed an experimental cooperative RNA replication system consisting of two RNAs and a reconstituted translation system . One of In the present study, we examined whether a linked RNA could evolve through the long-term replication of the two cooperative RNAs in water-in-oil droplets. We conducted two successive long-term replication experiments by gradually increasing RNA concentrations, since higher RNA concentrations raise the likelihood of generating linked RNAs through recombination or ligation, which could be facilitated by the RNA polymerase –15 or ocEscherichia coli [rep gene), which becomes active upon association with EF-Tu and EF-Ts in the translation system. NDK-RNA (752 bp) encodes NDK (ndk gene), which provides a substrate for RNA replication by converting cytidine diphosphate (CDP) into cytidine triphosphate (CTP). Therefore, Rep- and NDK-RNAs cooperatively replicate through the translation of their encoded enzymes. During replication, mutations occur, and occasional recombination generates a parasitic RNA that loses the gene region of either Rep- or NDK-RNA but replicates by exploiting replicase and NDK translated from other RNAs  RNA replication by incubating the cooperative RNA replication system at 37°C for 4 or 6 h in water-in-oil droplets, (2) diluted the droplet population typically 5-fold, and (3) induced fusion and division of droplets through vigorous mixing . In stepIt should be noted that compartmentalization in this experimental procedure is transient because RNA molecules in different droplets can be mixed in every step of droplet fusion. More sustained compartmentalization could be achieved if compartments undergo growth instead of fusion, as implemented in previous theoretical studies –11. AlthWe measured Rep- and NDK-RNA concentrations after every replication step using quantitative PCR after reverse transcription (RT-qPCR) with sequence-specific primers. We also measured parasitic RNA concentrations in some rounds using native polyacrylamide gel electrophoresis (PAGE), as their sequences were unknown.In a previous long-term replication experiment, we maintained RNA concentrations below a certain level (1 nM) by changing the dilution rate every round . This warep and ndk genes linked in the order 5′-rep-ndk-3′   . In thisp-ndk-3′ . If the l rounds , with avl rounds . We also-rep-3′) , but bar-rep-3′) . These rEncouraged by the first experiment, we performed a second long-term replication experiment with one of the mutant Rep- and NDK-RNAs obtained at round 46 of the first experiment. These mutant Rep- and NDK-RNAs accumulated six and one mutations, respectively . The repIn the second long-term replication experiment with the e2R0 pair, we changed the dilution rate from 5- to 100-fold when the RNA concentration reached around 100 nM and successfully continued the replication of both Rep- and NDK-RNAs for 79 rounds . Parasitrep-ndk-3′  at round 79 of the second long-term replication experiment and analyzed their sequences. All analyzed clones harbored the entire region of both rep and ndk genes in the 5′-rep-ndk-3′ order, confirming that the obtained long RNAs were the linked products of Rep- and NDK-RNAs (hereafter referred to as RepNDK-RNA). We found two major ways by which Rep- and NDK-RNAs were linked. In 45% of the RepNDK-RNA clones, Rep- and NDK-RNAs were simply connected end-to-end without deletion or insertion. In 26% of the clones, a single C was inserted between the connected Rep- and NDK-RNAs. At the NDK-RNA site around the linkage, we also detected G3A and U12G in 83% and 87% of the RepNDK-RNA clones, respectively , which was found in 26% of the 47 clones. For comparison, we also chose a pair of unlinked Rep- and NDK-RNAs containing only the most common mutation sets at round 79. The selected Rep-RNA contained U963C, U1349C, and A1604G, found in 55% of the Rep-RNA clones, whereas the selected NDK-RNA contained A240G, U465C, G498A, and U582C, found in 32% of the NDK-RNA clones. The replication of these Rep- and NDK-RNAs (e2R79) was similar to that of the ancestral Rep- and NDK-RNA pair (e2R0) with slightly improved NDK-RNA replication .RepNDK-RNA was expected to replicate by expressing both encoded replicase and NDK. To test this, we incubated the RepNDK-RNA clone in a reconstituted translation system containing fluorescent-labeled lysyl-tRNA. The newly synthesized proteins were analyzed by sodium dodecyl sulfate (SDS)-PAGE, followed by fluorescent imaging. We found that bands corresponding to both the replicase subunit and NDK were detected for the RepNDK-RNA . The proAlthough the linked RepNDK-RNA clone replicated less efficiently than the pair of unlinked Rep- and NDK-RNAs, the linked RNA may have a relative selective advantage at low RNA concentrations because it can replicate on its own. We examined this possibility by incubating a set of Rep-, NDK-, and RepNDK-RNA clones mixed at low (1 nM each) or high (10 nM each) concentrations with a translation system in water-in-oil droplets at 37°C for 4 h . As expeWe also investigated the effect of the three dominant amino acid substitutions  in the replicase  on RepNDThe integration of distinct genetic information on a primitive chromosome has been considered a major evolutionary transition in the development of life –4. In thAlthough the accumulation of unique mutations in RepNDK-RNA indicated their maintenance in the population, at round 79, the linked RNA replicated less efficiently than the pair of Rep- and NDK-RNAs , possiblin vitro RNA replication systems using different RNA replicases [It should be noted that some theoretical studies assumed the putative RNA world, where RNA (ribozyme) acts as both a catalyst and a carrier of genetic information ,11. Thusplicases  needs toplicases –18 or wiplicases –31. TherWe also developed a new method that allows the long-term replication of cooperative RNAs, even if the RNA concentrations increase tentatively and parasitic RNA emerges. Our previous study suggested that maintaining a relatively low RNA concentration was crucial for sustained replication of the cooperative system, and continuous RNA replication was achieved only by controlling the dilution rate at every replication round and maintaining the two cooperative RNAs below 1 nM . HoweverThe evolution of a linked RNA from multiple RNAs that encode different genes may also be useful for developing an artificial replicable RNA genome. To the best of our knowledge, RepNDK-RNA is the first artificial RNA genome that replicates based on the translation of more than one encoded protein. The manual expansion of an RNA genome by introducing new genes is challenging, as it typically disrupts RNA structures and impairs its ability to undergo replication and translation. Our previous study connected two RNAs based on the predicted RNA structures of linked RNAs while maintaining their replication coupled with the translation of one encoded protein . Howeverin vitro transcription with T7 RNA polymerase (Takara). The cDNAs of Rep-RNA variants containing specific amino-acid substitutions and the control Rep-RNA were subjected to PCR amplification for truncation of 40 bp at the 3′ end before in vitro transcription. All transcribed RNAs were purified using the RNeasy Mini Kit (QIAGEN).Two plasmids, each encoding Rep- or NDK-RNA “e1R0,” were obtained in the previous study as the plasmids encoding Rep- or NDK-RNA “Evo” . The othEscherichia coli translation system [Escherichia coli. For all proteins except ribosomes, the re-purification procedure was the same as that used for removing tRNA from EF-Tu, described in our previous study [The composition of the reconstituted translation system was as described previously  (based on system ) except us study . Ribosomus study  and thenThe remaining NDK activity in each protein component before and after the purification step was estimated as the activity to convert adenosine diphosphate (ADP) into adenosine triphosphate (ATP). First, each protein component (twice the concentration of that in the translation system) was incubated with 2.5 mM ADP and 1.25 mM CTP in a reaction buffer (100 mM Hepes-KOH (pH 7.6), 70 mM glutamic acid potassium salt, 0.375 mM spermidine, and 11 mM magnesium acetate) at 37°C for 2 h. Then, an aliquot of the reaction was used to determine the amount of synthesized ATP using ATP Assay Kit (Colorimetric/Fluorometric) (Abcam). The assay was performed at 37°C, and the fluorescence intensity was measured using Mx3005P Real-Time PCR System (Agilent Technologies) as the indicator of ATP synthesis .The experiments were performed as described previously  with sevIn some rounds of the long-term replication experiments, the water phase containing RNA was collected from the droplets (100 μL) by centrifugation . The recovered phase was mixed with diethyl ether (40 μL) and centrifuged  to remove the diethyl ether phase. Then, RNA was purified with the RNeasy Mini Kit (QIAGEN) and subjected to 8% polyacrylamide gel electrophoresis in 1×TBE buffer. The fluorescence intensities of bands corresponding to parasitic RNAs were quantified using ImageJ (NIH) after staining with SYBR Green II (Takara). The concentrations of parasitic RNAs were determined from the intensities based on a dilution series of a standard parasitic RNA (s222 ).Escherichia coli, the sequences of randomly selected plasmids were analyzed. Similarly, RepNDK-RNA was reverse transcribed with primer 10, PCR-amplified with primers 9 and 10, size-selected, and cloned using the same method. The 5′ and 3′ untranslated regions of the RepNDK-RNA clones could not be retrieved to distinguish them from Rep- and NDK-RNAs in the same RNA population. The sequence information of the linkage region was obtained only for the RepNDK-RNA clones. To detect putative elongated RNAs with rep and ndk genes linked in this order  and/or the RepNDK-RNA clone (10 or 1 nM) was incubated with the translation system in water-in-oil droplets at 37°C for 4 h. The concentration of each RNA clone was determined by RT-qPCR as described above. Primers 5 and 6, 15 and 16, and 17 and 18 were used for the RepNDK-RNA clone, the Rep-RNA clone, and the NDK-RNA clone, respectively .First, a truncated Rep-RNA (10 nM) containing a selected amino-acid substation was incubated at 37°C for 12 h in the original translation system without CTP to induce the translation of the encoded replicase. Second, an aliquot of the reaction mixture was 5-fold diluted in the fresh mixture containing the translation system, 1.25 mM CTP, 30 μg/ml streptomycin, and 10 nM RepNDK-RNA clone, and subjected to further incubation. After 4 h incubation at 37°C, the amount of RepNDK-RNA replication was measured by sequence-specific RT-qPCR as described above.300 nM of Rep-, NDK-, or RepNDK-RNA was incubated at 37°C for 12 h in a translation system and FluoroTect GreenLys tRNA (Promega), with neither CTP nor CDP to preclude RNA replication. In this experiment, we used a translation system before excessive purification of protein components, as described previously , to enhaS1 FigA, B, C) Native PAGE of RNA mixtures during the long-term replication experiments shown in Figs ((TIF)Click here for additional data file.S2 FigA) Repeated RT-PCR using the primers that could detect 5′-rep-ndk-3′ for RNA samples in the first long-term replication experiments. The PCR products were analyzed by agarose gel electrophoresis. M1 and M2 are size markers. Asterisks indicate analyzed bands. (B) RT-PCR was performed three times in the same method for 0.1 nM RepNDK-RNA (P.C.) or a mixture of Rep- and NDK-RNAs . These RNAs were the representative clones obtained from the second long-term replication experiment. (C) RT-PCR was performed three times for the same P.C. and in the absence of RNA (N.C.). (D) Repeated RT-PCR for RNA samples in the second long-term replication experiments were performed and analyzed in the same method.((TIF)Click here for additional data file.S3 FigA) RNA samples in the long-term replication experiments were subjected to RT-PCR using the primers that could detect 5′-ndk-rep-3′. (B) The PCR products were analyzed by agarose gel electrophoresis for RNA samples of the first (e1) and second (e2) long-term replication experiment  Relative band intensities of the RT-PCR products, derived from the first (C) and second (D) long-term replication experiments, to M2 , in comparison with Rep- and NDK-RNA concentrations . Error bars indicate standard errors (n = 3). Dotted lines are plotted for visibility.Click here for additional data file.S4 FigA pair of Rep- and NDK-RNAs (10 nM each) was incubated with a translation system in water-in-oil droplets at 37°C for 4 h, and their replication was measured by RT-qPCR. e1R0, e2R0 (e1R46), and e2R79 represent the ancestral RNA clones in the first long-term replication experiment , the anc(TIF)Click here for additional data file.S5 FigChanges in Rep-RNA (red), NDK-RNA (blue), and parasitic RNA (black) concentrations during the long-term replication experiment. The experiment was initiated with Rep- and NDK-RNAs “e2R0” and conducted without high temporal dilution. The replication step was performed at 37°C for 4 h. RNA concentrations were measured by RT-qPCR (Rep- and NDK-RNAs) or based on native PAGE Click here for additional data file.S6 FigThe experiments were performed in two steps. (1) A Rep-RNA clone (10 nM) with the truncated 3′ end was incubated at 37°C for 12 h in the absence of CTP to translate the replicase without RNA replication. Rep-RNA was constructed based on the ancestral Rep-RNA (“None”) or those containing one of the three dominant non-synonymous mutations that correspond to “I200V”, “C246R”, and “C517R” amino acid substitutions. (2) The translated replicase was mixed with the RepNDK-RNA clone (10 nM) and CTP to induce replication at 37°C for 4 h, while stopping further translation by the addition of streptomycin. The amount of RepNDK-RNA replication was measured by sequence-specific RT-qPCR. Error bars indicate standard errors (n = 3).(TIF)Click here for additional data file.S7 FigA, B) The contamination level of NDK in each protein component before (A) and after (B) re-purification was measured as the increase in fluorescence using the ATP assay (see C) The contamination level of NDK in the mixture of all protein components before or after purification. The data were compared with the activity of 16 nM and 1.6 nM NDK. (D) 10 nM Rep-RNA (e1R0) was incubated in the presence or absence of 10 nM NDK-RNA (e1R0) with the purified translation system in water-in-oil droplets at 37°C for 4 h, and their replication was measured by RT-qPCR. Error bars indicate standard errors (n = 4).(ssay see . The pro(TIF)Click here for additional data file.S1 TableThe highlighted clones (Rep-RNA clone 6 and NDK-RNA clone 10) were used for further long-term replication experiments Figs  and S5.(TIF)Click here for additional data file.S2 Table(TIF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file. 11 Nov 2022Dear Dr Mizuuchi,Thank you very much for submitting your Research Article entitled 'Emergence of linkage between cooperative RNA replicators encoding replication and metabolic enzymes thorough experimental evolution' to PLOS Genetics.The manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the current manuscript. Based on the reviews, we will not be able to accept this version of the manuscript, but we would be willing to review a much-revised version. We cannot, of course, promise publication at that time.Should you decide to revise the manuscript for further consideration here, your revisions should address the specific points made by each reviewer. We will also require a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript.Perhaps sequencing the PCR products from Figures 2C, 3C, S2C, and S2E mentioned by reviewer #2 would remove doubts about their identities.plosgenetics@plos.org.If you decide to revise the manuscript for further consideration at PLOS Genetics, please aim to resubmit within the next 60 days, unless it will take extra time to address the concerns of the reviewers, in which case we would appreciate an expected resubmission date by email to Submission Checklist.If present, accompanying reviewer attachments are included with this email; please notify the journal office if any appear to be missing. 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All points should be backed up by data provided with the submission.Please be aware that our Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool.  PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.While revising your submission, please upload your figure files to the Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, use the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Juergen BrosiusAcademic EditorPLOS GeneticsKirsten BombliesSection EditorPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This is a very stimulating paper. It provides confirmation of theory, but it also enriches the picture with valuable results. It deserves to be published. Some important comments for a minor revision:1. Emphasize that this is transient compartmentation. Fusion-fission cycles do not fit the model of a growing protocell population, as analyzed theoretically. This is important because the latter provides the most stringent selection for cooperation. The system used in this paper is transient, not only dynamically but also, presumably, in the early history of replicators. This is fully okay, but needs to be explained somewhat.2. Chromosomes not only reduce the assortment load, but also automatically eliminate competition between the LINKED replicators. And we do see that the two unlinked genes are competing. Furthermore, linkage presumably ensures a balanced production of translation products., which is likely to advantageous. Discuss!3. Going through proteins is different from the assumptions of the theory of being at an RNA world stage. In the latter case theory found that chromosomes with multigene families appear, but this is due to a direct dosage effect when RNAs act as enzymes -- not only as information carriers, as in the present paper.3. This leads me to the big unknown: the translation products. The expressed proteins evolve because their genes evolve, but here we see only the analysis of the latter. Granted one cannot do everything in a paper, but please tocuh upon this important point in the Discussion. I hope you will do this missing work sometime later. Note that the RNA for the replicase component can affect its own replicative phenotype as template as well as through the catalytic phenotype of its protein product.So far we see a compound effect.4. Finally, it is appropriate to call attention to the fact that these replcators use a highly evolved, complex translation system provided by the experimenterm which can introduce unwanted side effects.But on the whole this is an important expperimental result.Reviewer #2: In this article, Ueda et al. explore a very interesting idea concerning the emergence of primordial genomes. As the authors mention in the introduction, emergence of genomes would have conferred unique advantages and disadvantages to primordial replicators. They discuss these ideas briefly in the introduction and present the outline and objectives of the study in a clear and concise manner. The authors claim to demonstrate evolution of a ‘linked’ RNA genome, comprising of two (previously established) cooperative RNA replicators. The premise and relevance of the idea explored is relevant for audience interested in genetic and genomics research, particularly in the context of origin of life and evolution. However, I have multiple concerns and questions regarding the experimental methodology and data, as well as their description. Addressing these aspects is, in my opinion, necessary to improve the rigour of the manuscript, before it can be considered for acceptance in its current form.1. The random emergence and disappearance of linked RNAs implies that all of the linked products do not bring about any fitness advantage (which seems to me the main selling point of the manuscript). This observation is also reflected in the lower replication rate and translation efficiency of the linked RNAs. As the authors suggest, the linked RNAs will likely be more competitive at higher RNA dilutions. The manuscript would greatly benefit if some simple competition experiment between linked and unlinked RNA species were performed at increasing levels of dilution.2. For the relative RT-PCR band intensities of the linked products in Figures 2C, 3C, S2C, and S2E, at least three replicates should be performed if the authors want to display bars. Otherwise, I fear that the statistics are insufficient for this type of semiquantitative analysis and can, at most, only be used for a binary yes/no detection. In light of the seemingly random nature of the emergence of the RT-PCR products, repeated negative controls might also be necessary to rule out sample cross-contaminations or RT-artefacts such as template switching.3. Given the huge population of sequences in each reaction, it seems questionable to me to draw phylogenetic conclusions about the origin/maintenance of the linked RNAs from the mutation patterns of only a handful of clones (6 to 16). In my opinion, the authors should either confirm their findings using high-throughput sequencing experiments  or at least significantly increase the number of conventional clones they analyse.4. More quantitative details on the RNA concentrations is sorely needed throughout the manuscript. E.g. In lines 144, 172 and 175., the amount of RNA is described in very vague terms only, which affects the scientific rigour of the manuscript.5. What are the effects of the I200V, C246R, and C517R amino acid substitutions on the activity of Rep? Are they associated with a fitness advantage with respect to the replication of the linked genomes?Apart from these, I have some minor comments:- Figure S5. To me it seems unclear to which extend the arbitrary fluorescence increase is associated with residual NDK activity as there are no reference points (baseline / positive control). I.e. is the residual activity observed after re-purification still significant in particular if all proteins are combined again? Is there still detectable replication of Rep-RNA in this background without NDK-RNA?- sentence in line 295-297. Isn’t this counter to experimental findings, considering the emphasis authors place on the dilution step? 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  1 Aug 2023PGENETICS-D-22-01166R1 Emergence of linkage between cooperative RNA replicators encoding replication and metabolic enzymes through experimental evolution Dear Dr Mizuuchi, We are pleased to inform you that your manuscript entitled "Emergence of linkage between cooperative RNA replicators encoding replication and metabolic enzymes through experimental evolution" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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While numerous studies have described the transcriptomes of extracellular vesicles (EVs) in different cellular contexts, these efforts have typically relied on sequencing methods requiring RNA fragmentation, which limits interpretations on the integrity and isoform diversity of EV-targeted RNA populations. It has been assumed that mRNA signatures in EVs are likely to be fragmentation products of the cellular mRNA material, and the extent to which full-length mRNAs are present within EVs remains to be clarified.Using long-read nanopore RNA sequencing, we sought to characterize the full-length polyadenylated (poly-A) transcriptome of EVs released by human chronic myelogenous leukemia K562 cells. We detected 443 and 280 RNAs that were respectively enriched or depleted in EVs. EV-enriched poly-A transcripts consist of a variety of biotypes, including mRNAs, long non-coding RNAs, and pseudogenes. Our analysis revealed that 10.58% of all EV reads, and 18.67% of all cellular (WC) reads, corresponded to known full-length transcripts, with mRNAs representing the largest biotype for each group . We also observed that for many well-represented coding and non-coding genes, diverse full-length transcript isoforms were present in EV specimens, and these isoforms were reflective-of but often in different ratio compared to cellular samples.This work provides novel insights into the compositional diversity of poly-A transcript isoforms enriched within EVs, while also underscoring the potential usefulness of nanopore sequencing to interrogate secreted RNA transcriptomes.The online version contains supplementary material available at 10.1186/s12864-023-09552-6. Extracellular vesicles (EVs) are a heterogeneous group of membrane-delimited nanoparticles of cellular origin , 2, whicThe biological functions of RNAs are intrinsically tied to their sequence and structural features , 23. ForRecently, long-read nanopore sequencing has emerged as a powerful technology for long-read RNA profiling, allowing the detection of full-length transcripts and unambiguous mapping of isoform diversity, thereby offering a great advantage over traditional sequencing methods that require fragmentation of the starting RNA material –46. UsinTo investigate the sequence features of an EV-targeted transcriptome, we herein sought to apply nanopore long-read sequencing to RNA purified from whole cells (WC) or EVs of the human chronic myelogenous leukemia cell line K562. For this, we employed a poly-A priming approach utilizing a PCR-cDNA barcoding strategy, which enables one to sequence full-length transcripts generated through reverse-transcription of poly-A + material Fig. A. EVs weAmong the reads that passed quality control, a higher proportion of mappable reads were obtained for WC (~ 88%) comparted to EV (~ 56%) specimens, with unmappable reads presenting characteristics of repetitive sequences Figure SE. FurtheA deeper analysis of differentially expressed transcripts between WC and EV samples revealed a total of 280 and 443 transcripts that were respectively enriched within WC or EV samples, while 10,865 RNA were similarly detected in both specimen types  tool , we condrms Fig. A. By conens Fig. C. Converens Fig. C. In addens Fig. D, the leens Fig. E. AltogeExtracellular vesicles have gained prominence as novel mediators of the exchange of information between cells , 51. ThiWhile the transcripts that can be identified in EVs generally represent the RNAs found in parental cells, there is a marked specificity in the recruitment of particular RNA species to the EV population , 10, 65.Various reports have pointed to populations of fragmented coding and non-coding transcripts present in EVs , 33–38, Here, we show that long-read nanopore RNA sequencing is a robust means for investigating the transcriptomes trafficked by extracellular vesicles, demonstrating that these data that can be used to explore the sequence features of extracellular RNA. Indeed, we provide compelling evidence demonstrating the existence of full-length polyadenylated RNAs within EVs derived from human cells. Furthermore, these RNAs can exhibit differential expression relative to their parental cells, with some displaying isoform-specific enrichments.cis-regulatory elements driving RNA recruitment to sites of EV-biogenesis  or EV-depletion (i.e. cell-enriched) . Svist4get [https://github.com/josiegleeson/BamSlam) [align, correct and collapse were successively used for mapping FASTQ files to GENCODE v41, identifying splice junctions. Next, isoform quantifications were made with the quantify module, and the output count matrix was used to plot isoform structures and the percent usage of each isoform in each sample for a given gene. Finally, predictProductivity was utilized to predict isoform productivity with output definitions as PRO (productive), PTC (premature termination codon), NGO (no start codon), or NST (has start codon but no stop codon).The web-based integrated Differential Expression and Pathway analysis (iDEP) tool  was usedvist4get  was usedBamSlam) , requestBamSlam) . FLAIR mAdditional file 1: Figure S1. Library preparation and initial analyses. Figure S2. Gene ontology (GO) associations of EV and WC poly-A transcripts. Figure S3. Size distribution of EV-derived RNAs. Figure S4. Analyses of differentially expressied RNA isoforms.


Reconstitution into model membranes, such as liposomes, is a suitable approach to study lipid transporters in vitro and to investigate their key molecular features. In this review, we discuss the current approaches for reconstituting ATP-driven lipid transporters into large liposomes and common techniques used to study lipid transport in proteoliposomes. We also highlight the existing knowledge on the regulatory mechanisms that modulate the activity of lipid transporters, and finally, we address the limitations of the current approaches and future perspectives in this field.Lipid transporters play a crucial role in supporting essential cellular processes such as organelle assembly, vesicular trafficking, and lipid homeostasis by driving lipid transport across membranes. Cryo-electron microscopy has recently resolved the structures of several ATP-dependent lipid transporters, but functional characterization remains a major challenge. Although studies of detergent-purified proteins have advanced our understanding of these transporters,  Cellular membranes are vital structures that maintain cell integrity and perform critical functions such as organelle compartmentalization, signaling, and membrane trafficking. These complex structures are composed of a hydrophobic matrix consisting of a double layer of lipids ,2. Embed4 lipids per second [2+ or proteolytic cleavage [There are three types of lipid transporters: scramblases, flippases, and floppases . Scramblr second , therebycleavage . In contcleavage , phagocycleavage , and hoscleavage .Recent advances in structural biology and computational biology have made it possible to capture the structures of several lipid transporters in multiple functional states (reviewed in ). With tATP-dependent primary lipid transporters are essential membrane proteins that belong to either the P-type ATPase family or the ATP-binding cassette (ABC) transporter family. The P-type ATPase family can be divided into five major evolutionarily related subfamilies (P1-P5), each with distinct transport specificities. Among these subfamilies, P4-ATPases function as lipid flippases and are found exclusively in eukaryotes. The P-type ATPases have a similar structure, consisting of a membrane-spanning (M) domain and three cytosolic domains. These include an autocatalytic phosphorylation (P) domain, a nucleotide binding (N) domain, and an actuator (A) domain, which are responsible for the structural transfer of free energy release, derived from ATP hydrolysis. The amino and carboxyl termini of P-type ATPases face the cytosol, vary in length, and often accommodate regulatory domains or motifs. During substrate transport, P4-ATPases undergo conformational changes and the auto-phosphorylating and dephosphorylating of a conserved aspartate residue within a conserved signature sequence, hence the name ‘P-type’. P4-ATPases typically form heterodimeric complexes with an accessory subunit known as cell division control protein 50 (Cdc50), which are integral membrane proteins glycosylated in their exoplasmic domain. The interaction between Cdc50 proteins and individual P4-ATPases is crucial for endoplasmic reticulum exit and transporter activity .Members of the ABC transporter superfamily include ATP-dependent lipid transporters that function either as flippases, floppases or possibly as lipid importers/exporters. These transporters are found in bacteria, archaea and eukaryotes, and the wide diversity within this family is exemplified by differences in structure, mechanistic features, and substrates transported ,26–28. AThe mechanism by which P4-ATPases and ABC transporters flip-flop lipids is still not fully understood. The classical transport model for P4-ATPases is known as the ‘credit card model’ as an analogy to a credit card being swiped through a reader terminal by only contacting with the magnetic stripe. This model proposes that as phospholipids cross the membrane, only their headgroups gain access to the central cavity, while the hydrophobic hydrocarbon tails remain immersed in the hydrophobic core of the bilayer . Recent Based on recent advances in molecular dynamics simulations and structure determination of eukaryotic and bacterial ABC transporters, new transport models have been proposed that deviate from the canonical alternating access mechanism. Examples include the ‘outward-only’ mechanism with outward-occluded and outward-open conformations, proposed for lipid-linked oligosaccharide flopping by PglK, which does not invoke an inward-facing cavity to interact with the substrate . This baSaccharomyces cerevisiae has become an increasingly popular tool for the functional analysis of candidate lipid flippases. This is due to the ease of handling and the availability of numerous deletion mutants [Although ATP-driven lipid transporters have been extensively studied, much of our current understanding has come from cell-based studies. The first discovery of ATP-dependent lipid flippase activity dates back to the 1980s, based on pioneering work using spin-labeled lipid probes . Subsequ mutants .Cell-based assays to study lipid transport are typically based on short-chained nitrobenzoxadiazol (NBD)-labeled lipids . The method is based on the use of a soluble glucosyltransferase acting in the presence of an excess of nucleotide-bound radiolabeled sugars to convert the truncated substrate upon its appearance in the outer leaflet of the liposome. The change in radioactivity over time provides a quantitative measure of the rate at which PglK flips the truncated substrate , which cannot translocate from the donor liposomes to the proteoliposomes. During the exchange and translocation of the NBD-lipid, a decrease in the energy transfer between the NBD-lipid and the N-Rho-PE occurs, resulting in an increase in NBD fluorescence and enabling continuous measurement of lipid translocation C. This mTo date, only a small number of P4-ATPases and ABC transporters have been successfully reconstituted . Therefore, unidirectional lipid transport in these vesicles, even at small amplitudes, can lead to the accumulation of bilayer stress . This meetergent . To deteetergent . Alternaetergent .sn-2 position, while a long fatty acid chain is at the sn-1 position (As mentioned above, current cell-based and reconstitution assays commonly use lipid probes. These probes have a reporter group attached to a short fatty acid chain at the position . GUVs, rposition A. HoweveFinally, techniques such as measuring the zeta potential using the mechanisms of electrophoresis , nuclearAnother problem encountered in liposome reconstitution is sample heterogeneity, which can bias the quantitative analysis of lipid and protein content as well as protein activity. To overcome this challenge, a promising approach is to characterize the intrasample compositional variation at the single vesicle level. This approach is based on the visualization of single liposomes by microscopy, using fluorescently labeled lipids and reconstituted proteins with corresponding fluorescent labels. By using site-specific quenching of the fluorophores, it is possible to obtain a detailed characterization of the reconstituted proteoliposomes, including parameters such as size, tightness, lamellarity and the number of proteins per liposome as well as their orientation . In addiin vivo.Lipid transporters are inherently difficult to study. On the one hand, this can be attributed to the difficulties in handling the integral membrane proteins and their delicacy in production, purification, and characterization of their assembly with membrane lipids. On the other hand, sophisticated techniques are required to study membrane proteins at the molecular level. Despite these challenges, researchers continue to make progress in understanding the mechanisms underlying transporter function and in developing new techniques for studying these critical cellular components. In this review, we included current published reports on ATP-dependent lipid transporters and their functional analysis using liposomal reconstitution. Such functional assays, coupled with structural analysis and computational studies, including both molecular dynamics and coarse-grained simulations, will be essential in unravelling the inner working of these important transporter classes. In addition, we highlighted recent progress in uncovering key molecular and regulatory mechanisms of these transporters, the role of which remains to be further defined by systematic and detailed studies in model membrane systems and