Colorectal cancer (CRC) is the third most commonly diagnosed and the second cancer-related death worldwide, leading to more than 0.9 million deaths every year. Unfortunately, this disease is changing rapidly to a younger age, and in a more advanced stage when diagnosed. The DEAD-box RNA helicase proteins are the largest family of RNA helicases so far. They regulate almost every aspect of RNA physiological processes, including RNA transcription, editing, splicing and transport. Aberrant expression and critical roles of the DEAD-box RNA helicase proteins have been found in CRC. In this review, we first summarize the protein structure, cellular distribution, and diverse biological functions of DEAD-box RNA helicases. Then, we discuss the distinct roles of DEAD-box RNA helicase family in CRC and describe the cellular mechanism of actions based on recent studies, with an aim to provide future strategies for the treatment of CRC. It is estimated that more than 1.9 million new cases and 0.9 million deaths in 2020 . The risRNA helicases are highly conserved enzymes that play critical roles in RNA metabolism by using adenosine triphosphate (ATP) to bind and modulate RNA structures and ribonucleoprotein (RNP) complexes . RNA hel2All DEAD-box helicase family members contain a profoundly conserved helicase core that is flanked by domains of variable sequences of amino (N)- and carboxy (C)-terminal ends. The core element possesses the binding sites for ATP and RNA. The variable auxiliary domains are responsible for the various functions of these proteins, because they interact with RNA or protein components of substrates.2.1The conserved helicase core is composed of two recombinase A (RecA)-like domains named domain 1 and domain 2, which form the ATP- and RNA-binding clefts and contribute to ATP binding and hydrolysis, RNA binding and duplex unwinding. These two domains contain at least 12 profoundly conserved sequence motifs, with motifs Q, I, Ia, Ib, Ic, II/DEAD, and III in the N-terminal domain  and IV, IVa, V, Va, and VI in the C-terminal domain . Among them, the motifs Q, I, II, and VI are involved in ATP binding and hydrolysis, the motifs of Ia, Ib, Ic, IV, IVa, and V are participated in RNA recognition, and the motifs of III and Va are involved in the communication between RNA and ATP binding Figure 1The two RecA-like domains are connected by flexible central hinge region that allows changing their orientation to each other. Therefore, DEAD-box helicases can switch their conformations between “inactive open”, wherein both domains move freely as for each other in the absence of ATP and RNA, and “active closed”, wherein a compact helicase core structure is formed when collaboratively binds with ATP and RNA. The alteration of conformations plays a vital role for the functions of DEAD-box helicases , 88.DEAD-box RNA helicase family proteins are specific for ATP to catalyze RNA duplex unwinding and RNA-induced hydrolysis of the triphosphate. This effect is mediated by the particular interaction between the base-pairing face of the adenine nucleotide and a conserved glutamine of the Q motif. However, RNA binds over the two helicase core domains opposite the ATP-binding site. The RNA-binding motif consists of a highly and positively charged surface cleft that can bind single-stranded RNA (ssRNA) with five or more nucleotides. The RNA-binding sites only contact the sugar phosphate backbone of the RNA substrates, which makes the DEAD-box helicases function as general RNA chaperones targeting various RNAs , 88.2.2The helicase core of the DEAD-box helicases is flanked by variable N- and C- terminal regions. The length varies from a few to several hundred amino acids. The auxiliary domains can interact with particular RNAs or protein cofactors, which contribute to the function variety in this protein family . The len3DEAD-box RNA helicases are multifunctional proteins and involved in a wide range of cellular processes, including transcription, translation, pre-mRNA splicing, RNA degradation, microRNA (miRNA) biogenesis, gene regulation, rRNA processing and ribosome biogenesis . The cel3.1The DDX1 refers to the Dead-Box 1 protein which is predominantly located to the nucleus of normal cells, while relocation of DDX1 from the nucleus to the cytoplasm is observed in specific situations , 93. Imp3.2in vitro. However, they are inefficient helicases and their helicase activities are largely dependent on the binding partners , EIF4A2  and EIF4A3 . The sequences of these proteins share high homology. EIF4A1 and EIF4A2 are cytoplasmic-localized and there is approximately 90% sequence identity at the amino acid level between them . EIF4A3 partners . For exapartners . Howeverpartners .EIF4A1 is thought to function primarily as an important component of eIF4F complex, which also contains the mRNA cap-binding protein eIF4E and the scaffolding protein eIF4G. EIF4A1 functions in regulating the initiation of cap-dependent translation that is the most vital step of protein production . The 40SAlthough the protein sequences of EIF4A1 and EIF4A2 are highly similar, they are functionally distinct. EIF4A1 is essential for translation initiation, while EIF4A2 is not necessary for this process. The binding partners of both proteins are divergent. EIF4A1 predominantly binds to eIF4G, whereas EIF4A2 preferentially binds to cNOT7, a member of CCR4-NOT complex, which plays an important role in miRNA-mediated translational repression , 94.EIF4A3 is a core component of the exon junction complex (EJC)  . Physiol3.3DDX3 contains two genes DDX3X and DDX3Y, which locate on the X- and Y-chromosome, respectively . BecauseAlthough DDX3Y protein is ubiquitously expressed in mouse, it is widely expressed in many tissue types but not translated, except in testis in human. Human DDX3Y is also found in leukemia and lymphoma cells, but not in normal B lymphocytes , 102. St3.4DDX5 (p68) and DDX17 (p72) share the greatest homology among the members of DEAD-box RNA helicase family, sharing approximately 90% homology across the conserved helicase core. However, the N- and C-terminal regions of both proteins are highly divergent (about 60% and 30% sequence similarity respectively). DDX5 and DDX17 can interact with each other to form heterodimers. It appears that they have several partially redundant functions although they also have distinct roles . Both prThey exhibit RNA-dependent ATP hydrolase activity and RNA helicase activity as well as RNA annealing activity. Both proteins are involved in multiple cellular processes, including pre-mRNA splicing/alternative splicing, biogenesis of miRNA and ribosomes, regulation of transcription as a transcription factor (coactivators or cosuppressors), NMD and interaction with long noncoding RNAs . DD3.5DDX6 is located to the processing bodies (P-bodies) in somatic, stem and tumor cells . It is e3.6Nucleolar DDX10 is involved in early stages of ribosome biogenesis as a component of the small subunit processome and it is essential for 18S ribosomal RNA (rRNA) production , 30. How3.7DDX21 is predominantly localized to nucleolus. In the nucleolus, DDX21 is associated with ribosome biogenesis, which is largely through regulation of rDNA transcription by interacting with small nucleolar RNAs (snoRNAs) to regulate rRNA processing and modification. This process is dependent on its ADP-ribosylation . InteresDDX50  is a paralogue of DDX21, sharing 55.6% amino acid identity . Similar3.8DDX27 is a nucleolar protein and it regulates the maturation of rRNA, which is required for ribosome biogenesis and the translation of genes , 41. DDX3.9DDX39A  and DDX39B  show a high degree of homology, with 90% in amino acid identity in humans . Both pr3.10DDX42  and DDX46  are enriched in nucleus especially Cajal bodies and associated with human U2 snRNP, which participates in early spliceosome assembly . The 17S3.11DDX54 is distributed in nuclear speckles . It inte3.12DDX56 is a nucleolar protein and participates in ribosome biogenesis and ribosome assembly , 69. Fur3.13DDX58 encodes RIG-I, which is mainly localized to cytoplasm. DDX58 is firstly identified as a pattern recognition receptor in host innate immunity that recognizes cytosolic viral nucleic acids to activate the IFN signaling and proinflammatory cytokines, resulting in the promotion of host antiviral immune response . DDX58 c3.14DDX60 is an IFN-inducible cytoplasmic helicase and plays an important role in human antiviral activity. As an upstream factor of RIG-I, DDX60 not only activates RIG-I signaling in a ligand-specific manner but also is involved in RIG-I-independent viral RNA degradation. This indicates that DDX60 is a sentinel for cytoplasmic antiviral response . Additio4The majority of DEAD-box proteins play tumor-promoting roles in CRC. DDX60 seems to have the tumor-suppressing role in CRC. However, we observed that DDX3X and DDX58 exert both tumor-promoting and tumor-suppressing roles in CRC.4.14.1.1DDX1 protein was highly expressed in human CRC specimens. However, its level was not correlated with patients’ pathological grade and clinical stage . Consist4.1.2DDX2 contains two subtypes, including DDX2A (EIF4A1) and DDX2B (EIF4A2). Li et al.  found thBoth mRNA and protein levels of EIF4A2 was upregulated in CRC tumors. High EIF4A2 predicted patients’ poor survival. Knock-down or small-molecule inhibitor of EIF4A2 inhibited the invasion, migration and stemness, and elevated oxaliplatin sensitivity in CRC cells . What’s 4.1.3DDX5 (p68) protein was overexpressed and post-translationally modified (predominantly by sumoylation modification) mainly by the small ubiquitin-like modifier-2 (SUMO-2) on a single site (K53) in CRC , 122. DuThe high expression of DDX5 was associated with poor prognosis . InteresPumilio RNA-binding family member 1 (PUM1) interacted with DDX5 in 3’-UTR and positively regulated its mRNA expression in CRC cells . DDX5 exDDX17 and DDX5 share the greatest homology among the DDX family members and they can form heterodimers to modulate various physiological and pathological processes . Similar4.1.4DDX6 was overexpressed in tumor tissues of CRC , 131. Do4.1.5in vitro, but also prevented tumor growth and lung metastasis in nude mice , which was analyzed by the TIMER database  and positively regulated its expression, and then affected their downstream target gene POLR1B, the critical components of Pol I RNA polymerase complex, resulting in promotion of CRC cell proliferation, invasion, migration, and suppression of cell apoptosis  in vivo  , through the potential binding sites, and positively regulated circ_0084615 expression. Circ_0084615 promoted CRC cell proliferation, migration, invasion and angiogenesis as well as tumor growth in murine xenograft model via miR-599/ONECUT2 pathway, which increased the expression of ZEB2, Vimentin and VEGFA, but decreased E-cadherin expression   secreted exosomes cricN4BP2L2 could bind to EIF4A3 and positively regulate its expression, and subsequently caused the activation of PI3K/AKT/mTOR axis in CRC cells, which promoted CRC cells stemness and oxaliplatin resistance  (Figure4.1.11In vitro, DDX54 was demonstrated to promote the proliferation, migration, invasion and EMT of CRC cells through activating p65 and AKT pathway (DDX54 was overexpressed in CRC tissues, and high level of DDX54 was associated with tumor stage and distant metastasis as well as shorter overall survival in patients with CRC. DDX54 knockdown significantly inhibited tumor growth in subcutaneously injected nude mice . In vitr pathway .4.1.12DDX56 expression was upregulated in CRC tumor cells and mainly localized to their cytoplasm. Its upregulation was positively correlated with the amplification of DDX56. High DDX56 expression was correlated with lymphatic invasion and distant metastasis and was an independent poor prognostic factor in CRC patients . Knockdo4.2+, and CD8+ T cells than that with low DDX60 tumors (DDX60 expression was downregulated in CRC cancerous tissues compared with normal tissues. And DDX60 expression was positively related to better survival probability of CRC patients. CRC tumors with increased DDX60 showed more proinflammatory phenotypes, such as increased infiltration of dendritic cells, CD40 tumors . In CRC 0 tumors . This su4.3Strikingly, a portion of the DEAD-box proteins have controversial roles in CRC. For instance, DDX3X and DDX58 could function as both tumor-promoting and tumor-suppressing roles based on different molecular pathways. Hence, elucidating the dual role of these DEAD-box proteins can better understand their functions in CRC.4.3.1Most papers indicated that DDX3X acted as a tumor promoter in CRC Figure 8in vitro and enhanced tumor metastasis in vivo by inhibiting E-cadherin and activating β-catenin signaling via suppressing the MAPK pathway (However, Su et al.  and Shen pathway , 82 (Fi4.3.2+ T cell survival and effector function in tumor microenvironment and attenuated CD8+ T cell-mediated antitumor immunity (+ T cell survival and cytotoxicity by restricting STAT5 activation via competitive sequestering of HSP90 (+ tumor-infiltrating CD8+ T cells was positively correlated with compromised CD8+ T cells’ survival and cytotoxicity as well as poor prognosis in CRC patients (DDX58 is an important immune-related gene that is closely associated with immune cell infiltration and tumor prognosis . Liu et immunity . Mechaniof HSP90 . The frepatients  (FigureIn contrast, Deng et al.  demonstr5Although the DEAD-box helicases have conserved helicase core, they perform different cellular functions and act via diverse mechanisms, which may be related to their variable auxiliary domains, cellular localization, and binding partners. Growing evidence has found that the DEAD-box helicases perform an crucial role in CRC. Current studies have demonstrated that most of these proteins, such as DDX1, DDX2, DDX5, DDX17, DDX6, DDX10, DDX21, DDX27, DDX39B, DDX46, EIF4A3, DDX54, DDX56, are upregulated and have tumor-promoting roles in CRC. DDX60 is downregulated in CRC tissues and lower levels of DDX60 are associated with poor prognosis in patients. DDX60 seems to play tumor-suppressing role in CRC via modulating tumor microenvironment; however, its exact function and mechanism need further study. It’s interesting that some DEAD-box helicases, including DDX3X and DDX58, exhibit opposite effects on CRC. Current studies revealed that they employ different mechanism during the divergent function, however, it is still confusing whether the controversial roles result from experimental limitations or other undiscovered mechanisms.What’s more, the DEAD-box helicases also play vital roles in CRC treatment. For example, EIF4A2 inhibitor improved oxaliplatin sensitivity . DDX27 kBZ: Conceptualization, Project administration, Supervision, Writing – original draft. XC: Writing – original draft. QL: Writing – original draft. QC: Writing – original draft. SY: Supervision, Writing – review & editing.


Escherichia coli. We showed that the resulting riboregulator can trans-activate gene expression by interacting with a cis-repressed messenger RNA. We characterized the system with a fluorescent reporter and with an antibiotic resistance marker, and we modeled this novel posttranscriptional mechanism. This first reported example of a circular RNA regulating gene expression in E. coli adds to an increasing repertoire of RNA synthetic biology parts, and it highlights that topological molecules can play a role in the case of prokaryotic regulation.RNAs of different shapes and sizes, natural or synthetic, can regulate gene expression in prokaryotes and eukaryotes. Circular RNAs have recently appeared to be more widespread than previously thought, but their role in prokaryotes remains elusive. Here, by inserting a riboregulatory sequence within a group I permuted intron-exon ribozyme, we created a small noncoding RNA that self-splices to produce a circular riboregulator in  We desin Figure  [1].Figure 1cis-repressed mRNA is trans-activated by the circular sRNA, which induces a conformational change in the 5Diagram of group I and PIE ribozymes , 18 and E. coli DH5α cells (Life Tech). Riboregulator circRAJ31 . Plasmids were sequenced (GATC Biotech) using primer M13-Fw/M13-Rv (pIDTSmart plasmids) or VF2/VR (pSB4K5-derived plasmids). Unless otherwise noted, all primers were purchased from IDT and chemicals from Sigma-Aldrich.All cloning was performed using 31 Table  was syntble repA , with a ts Table , as wellrs Table . A lineaE. coli BL21(DE3) cells. Three colonies of each sample were grown overnight in LB medium, then refreshed at 1 : 200 in 20 mL, grown for 4 h in the presence of 125 μM IPTG and 35 μg/mL chloramphenicol, spun down, and resuspended in 400 μL TE buffer. 400 μL cold methanol was added, and cells were disrupted with 0.1 μm beads, followed by acidic phenol-chloroform extraction (Ambion) and ethanol precipitation with 50 μg/mL glycogen (Ambion) and 0.3 M sodium acetate and then resuspended in water. RNA was treated with DNase I (NEB), and acidic phenol-chloroform extraction was repeated. For in vitro transcribed RNA, a TranscriptAid T7 high-yield transcription kit (Life Tech) was used following the manufacturer’s instructions. Reverse transcription polymerase chain reaction (RT-PCR) was performed using GoTaq Green MM (Promega). Primer-specific RT was performed using circRT-Rv and M-MuLV-Reverse Transcriptase (Life Tech). Cycling conditions for PCR were as follows: 2 min at 95°C, then 35 cycles of 15 s at 95°C, 30 s at 58°C, and 30 s at 72°C, and finally 5 min at 72°C. Primers  were used at 150 nM. Products were then run on a 2% gel for 1.5 h at 85 V and sequenced using primer circRT-Rv after purification.RNA was produced in 32P-labelled circRAJ31 of complementary polarity. Band intensities were quantified with ImageJ2 [Two-dimensional (2D) urea-polyacrylamide gel electrophoresis (urea-PAGE) was run with RNA extracted as above. The first dimension was run in 5% polyacrylamide, the second dimension in 7.5% polyacrylamide, and both in TBE buffer with 8 M urea for 1.5 h at 200 V. 2D electrophoresis begins with an electrophoresis in the first dimension to separate the RNA molecules according to their mass. The gel is then rotated 90 degrees, so the molecules are separated again in the second dimension. Circular RNAs can then be identified because they lie out of the diagonal in the gel. This is because topological species migrate in a manner different than that of linear species. For northern blot, the RNA gel was electroblotted to a nylon membrane with positive charge and then immobilized through covalent linkage by UV light. The hybridization was done with 106 counts/min of  ImageJ2 .E. coli DH5αZ1 cells [2, 2 mM MgSO4, 10 μM FeSO4, 0.8% glycerol, 0.2% casamino acids, 1 μg/mL thiamine, 20 μg/mL uracil, and 30 μg/mL leucine and was adjusted to pH 7.4 with NaOH. Three single colonies were grown overnight in LB medium. The next morning, for each condition, 1 mL of M9 medium was inoculated with 5 μL of overnight culture and then grown for 6 h with shaking at 37°C. 5 μL of refreshed culture was then used to inoculate 195 μL of M9 medium with antibiotics and appropriate inducers . Three technical replicates were grown for each sample. Fluorescence measurements were then made in a TECAN Infinite F-500 fluorometer, with 4 cycles/h of shaking with incubation at 37°C followed by measurement of OD600 and fluorescence . For each sample, fluorescence over OD600 after background subtraction (corresponding to M9 medium) was taken for the time points closest to For fluorescence assays, plasmids were cotransformed in Z1 cells . Plates D600=0.5 . AutofluE. coli DH5αZ1 cells were picked and grown overnight in LB. In the morning, they were diluted to 1 : 1000 in LB, grown for 2 h, serially diluted, and plated on various chemicals. All plates contained 35 μg/mL kanamycin, 100 μg/mL ampicillin, and 100 μg/mL spectinomycin, as well as 35 μg/mL chloramphenicol, 100 ng/mL aTc, and 250 μM IPTG if appropriate. Plates were then incubated for 20 h at 37°C, and colonies were counted. For liquid cultures, cells were grown as for fluorescence measurements, refreshed in M9 medium without inducers, and then inoculated into 96-well plates with chemicals as appropriate .For activation of chloramphenicol resistance assays, three single colonies of cotransformed E. coli cells expressing the sRNA circRAJ31. Convergent primers amplify the unspliced product, whilst divergent primers amplify the circular spliced product if it exists , we took a fragment of our previously engineered sRNA RAJ31 (RAJ31min) . This frthe loop . The pre Figures . The div Figures . Moreove Figures . The gelircRAJ31 . We quanFigure 2in vitro. (a) Sequence scheme of the riboregulator encased in the PIE ribozyme, which produces a circular RNA molecule after self-splicing. Left: diagram of RT over a circular RNA template, showing the creation of a cDNA with multiple copies of the riboregulator due to a rolling cycle, until the RNA is cleaved by the RNase H subunit of the transcriptase. Right: diagram of RT over a linear RNA template, showing the creation of a cDNA with a single copy of the riboregulator. The RT is initiated with primer circRT-Rv (labelled B). (b) Diagram of PCR over the nascent cDNA with appropriate primers. On the one side, PCR with divergent primers  only yields a product if the RNA template is circular. On the other hand, PCR with convergent primers  only yields a product if the RNA template includes intron 2 . Note that primers A and B hybridize in the riboregulator region, whilst primer C does in the intron 2 region. (c) Agarose gel of RT-PCR with primers described above of total RNA extracted from E. coli cells expressing circRAJ31 or the C873U mutant. The first lane corresponds to Invitrogen 100 bp DNA ladder. Lanes labelled 1 and 2 correspond to duplicates of the RT reaction. The last lane corresponds to PCR without doing RT (control). Top: there is a product in the case of both circRAJ31 and C873U mutant with primers B and C, indicating that there is always a fraction of noncircularized RNA. Bottom: there is a clear product in the case of circRAJ31 with primers A and B, indicating that circularization has taken place. In the case of the C873U mutant, the product is difficult to see. (d) Sequencing chromatogram of the RT-PCR product (with primer B) from circRAJ31 produced in vivo. This shows the alignment between the theoretical circular RNA sequence and the actual sequence. The sequencing result covering exons 1 and 2 shows that the splicing and ligation happen at the expected site. See also the gel and sequencing result of RT-PCR from RNA produced in vitro with T7 polymerase in Figure E. coli cells expressing circRAJ31. The first lane corresponds to Thermo Sci. RiboRuler low-range 100 bp RNA ladder. Other lanes correspond to extractions in triplicate from different colonies. A lane marked with the asterisk was used for subsequent 2D analysis. (f) On the top, 2D urea-PAGE  of total RNA extracted from E. coli cells expressing circRAJ31. On the bottom, radioactive hybridization with a probe targeting circRAJ31. Linear RNAs show along a diagonal in 2D, whilst circular RNAs show off this diagonal.Detection of the engineered circular riboregulator cis-repressed reporter gene in E. coli. We placed the sRNA circRAJ31 under the control of the inducible promoter PLtetO1 [LlacO1 [E. coli DH5αZ1 cells, which overexpress the LacI and TetR repressors, providing a tight control of transcription with isopropyl-β-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc), respectively. High fluorescence was observed only in the presence of both IPTG and aTc, showing that the riboregulator circularized by the T4-PIE ribozyme can activate gene expression . IPTG induces production of the mRNA and aTc of the ribozyme RNA, which releases the circular riboregulator after self-splicing. (b) Scheme of the control systems constructed: the toehold mutant (to inactivate interaction), the C873U mutant , and the linear riboregulator RAJ31min (without the T4-PIE ribozyme scaffold). (c) Characterization of systems based on circRAJ31, the native system, and its controls. See also the effect of different concentrations of IPTG and aTc in Figure trans-activate a cis-repressed cat gene, which codes for chloramphenicol resistance (CamR). (e) Characterization of circRAJ31 controlling growth of E. coli on chloramphenicol plates (35 μg/mL), with appropriate inducers of gene expression , with appropriate inducers of gene expression  Scheme of the construction used: the riboregulator circRAJ31 can E. coli cells expressing this splicing-deficient mutant. However, in vitro transcription of this system showed that significant amounts of circular sRNA, as well as aberrant splicing products, are produced . Such dysfunctionality is known to occur when ribozymes are destabilized and then adopt different conformational states  [Because the T4-PIE ribozyme generates a heterogeneous population of circular and linear sRNAs, we decided to investigate if the unspliced version of the sRNA could still activate gene expression. For that, we constructed a mutant (C873U) known to have a very low efficiency of self-splicing (1.5% of wild type) , 27. Thefolding) , 29. In folding) . If so, Anabaena), and the mean time for exon ligation (Tetrahymena). In addition, E. coli BL21(DE3) cells, which had a doubling time ; atop, it is difficult to estimate from in vivo concentrations. Indeed, the growth rate imposes an effective degradation for all macromolecules in the cell. Second, we obtained To better understand the dynamical behavior of the system, we constructed a simple mathematical model. By denoting t 20 min  , a certain degree of speculation might be attributed to our calculations. However, irrespective to the parameterization, this model is useful to describe the mode of action of the engineered riboregulatory system.Each of the sRNAs in the heterogeneous population will activate sfGFP in a different manner. By denoting egulator , we wrote stable . Accordicat gene, which confers chloramphenicol resistance; Figure cat expression in the absence of the riboregulator  of chloramphenicol  of chloramphenicol, the differences between growth curves of systems expressing or not the riboregulator were reduced  [in vivo (circRAJ31) and that this circular molecule is capable, by directly interacting with the corresponding mRNA, of activating gene expression of a fluorescent reporter and of an antibiotic resistance gene  [In this work, we have shown that a functional circular regulatory RNA can be engineered in  RAJ31) , which wl growth ). As thel growth , 33. Oneout 90%) . Anotherout 90%)  or even out 90%) . Further , which out 90%) . In thisout 90%) ) to incrcis  or in t


Escherichia coli has not been determined. Here, we report genome-wide identification and verification of the aminoglycoside Kanamycin B binding to Escherichia coli RNAs. Immobilized Kanamycin B beads in pull-down assays were used for transcriptome-profiling analysis (RNA-seq).The aminoglycosides are established antibiotics that inhibit bacterial protein synthesis by binding to ribosomal RNA. Additional non-antibiotic aminoglycoside cellular functions have also been identified through aminoglycoside interactions with cellular RNAs. The full extent, however, of genome-wide aminoglycoside RNA interactions in Over two hundred Kanamycin B binding RNAs were identified. Functional classification analysis of the RNA sequence related genes revealed a wide range of cellular functions. Small RNA fragments  or small mRNA was used to verify the binding with Kanamycin B in vitro. Kanamycin B and ibsC mRNA was analysed by chemical probing.Escherichia coli.The results will provide biochemical evidence and understanding of potential extra-antibiotic cellular functions of aminoglycosides in The online version contains supplementary material available at 10.1186/s12864-023-09234-3. Actinomycetes, encode resistance factors that encompass all of the major mechanisms that confer antibiotic resistance to self-protect the producer form the antibiotic. These include target site modification , drug modification  and efflux pumps [Pseudomonas aeruginosa and in Escherichia coli (E.coli) [E.coli the transcriptional SOS stress response is induced directly by the fluoroquinolones [Vibrio cholerae, the aminoglycosides do not trigger the SOS response in E.coli [The aminoglycosides are effective broad-spectrum antibiotics that primarily inhibit protein synthesis by binding to the 30S ribosomal subunit at the A site in the decoding region of 16SrRNA leading to inhibition of translocation and mistranslation of mRNA –6. The pux pumps . These rux pumps –10. Aminux pumps –14. Aminux pumps , HIV traux pumps  and autoux pumps . Aminoglux pumps  and can ux pumps . The amiux pumps –23. Amin(E.coli) . In E.coinolones , Beta-lainolones  and triminolones  antibiotinolones . Althougn E.coli , 30.The development of in vitro selection methods, systematic evolution of ligands by exponential enrichment (SELEX) allows the selection of RNA aptamer domain for a specific small molecule –34. SELEE. coli by using immobilized Kanamycin B beads and transcriptome-profiling analysis (RNA-seq). We identified over two hundred RNAs that bind to Kanamycin B through the Kanamycin B pull-down assay. Functional classification analysis was performed on the RNA sequence related genes. We further verified the binding of RNA with Kanamycin B in vitro. We performed chemical probing of ibsC mRNA with Kanamycin B. Confirmation of cellular RNA binding by Kanamycin B is consistent with the notion that Kanamycin B might participate in wider biological processes or mimic an existing interactions, in addition to their function as ribosomal antibiotics.Although a variety of non-antibiotic cellular functions of aminoglycosides have been described, and aminoglycoside binding aptamers have been identified by SELEX, few studies that investigate cellular RNAs that bind Kanamycin B have been reported so far. Such cellular RNAs that bind to Kanamycin B may be potential functional sites for Kanamycin B that reveal further novel cellular effects of Kanamycin B. Here we report the genome-wide identification of Kanamycin B binding to RNA in E. coli RNA, followed by transcriptome-profiling analysis (RNA-seq). Previous studies suggested that addition of 1 μM Kanamycin B had no effect on E. coli cell growth and was thus a reasonable concentration of Kanamycin B to use [E. coli str. K-12 substr. MG1655 genome.In order to investigate potential cellular functions for Kanamycin B and identify Kanamycin B binding cellular RNAs, immobilized Kanamycin B Sepharose beads were used to select against total B to use . Cells wTo investigate the reproducibility of the three biological repeat experiments, principal component analysis (PCA) of the RNA-Seq data was used. The result showed that PC1 and PC2 had values of 32.04% and 18.42%, respectively, and accounted for 50.46% of the principal components. The PCA revealed consistency between the three biological replicates of total RNA or Kanamycin B binding RNA from samples without or with 1 μM Kanamycin B . The RNA sequences that were within the selection criteria from the three biological repeats are clustered as heatmap enrichment Fig. . Under tE. coli is known [E. coli was calculated. This analysis shows that the functions of the pull down RNA largely fall into cell death (42.11% and 31.58% for 0 and 1 μM Kanamycin B respectively) and post-transcriptional gene silencing by RNA groups (62.5% for 0 and 1 μM Kanamycin B)  to predict RNA motif. No main common covariance motif was found although we noticed shared stem loops with different specific sequences. In the study of in vitro selection of unnatural RNA against Kanamycin B , they alE. coli have been identified [Bacterial biofilms are complex multicellular aggregates that form in response to environmental challenges. There have been reports that some aminoglycoside antibiotics induce bacterial biofilm formation . Thirty-entified . Ten of Small metabolite molecules have been reported to regulate gene expression by binding to untranslated RNA , 57. AmiE.coli K-12 derived JM109 strain grown in LB to log phase with the Trizol method . DNase I  was used for RNA purification. The RNA quality was assessed by formaldehyde agarose gel electrophoresis and was quantitated spectrophotometrically (NanoDrop 2000).Total RNA was extracted from 2O and twice washed with coupling buffer . After a second wash with coupling buffer, the agarose was mixed 1:2 with 10 mM Kanamycin B solution in coupling buffer. The reaction was protected from light and incubated overnight at 4 °C on an H5600 rotator (Labnet). Afterwards, unreacted Kanamycin B was washed, away with ~ 2 volumes of coupling buffer. Finally, quenching buffer was added , and incubated for 20 min. A non-derivatized column (mock), consisting of NHS-Activated Agarose that had been treated with coupling buffer without Kanamycin B.Kanamycin B was immobilized on Pierce™ NHS-Activated Agarose (Thermo Scientific™). For this, 0.05 g dry agarose was swollen in MilliQ-H2, 0.1% NP40), respectively. For depletion of RNAs able to bind the affinity matrix, the RNA was first incubated for 30 min with 1 CV of the mock column. After negative selection, unbound RNAs were added to 1 CV Kanamycin B-coupled agarose and incubated for 30 min at room temperature. Next, the column was washed with 20 CV binding buffer and specific bound RNAs were eluted by 5 mM Kanamycin B in 1 × binding buffer. The RNA precipitated with an equal volume of isopropanol, 1/300 volume of Glycoblue, 0.1 volume of 3 M NaOAc pH 5.2. RNA pellet was washed in 1 ml 70% EtOH, and pellets were resuspended in 10 μl H2O.RNA was folded by heating the mixture to 95 °C for 5 min and placed on ice water for 5 min, the volume was adjusted to 1 column volume  with 1 × binding buffer . The library preparations were constructed according to the manufacturer′ s protocol. Three biological replicates of each library were sequenced on an Illumina HiSeq X Ten  by AZENTA . RNA-seq data were uploaded to the Sequence Read Archive of the National Center for Biotechnology Information .E. coli str. K-12 substr. MG1655, genome assembly (NCBI: NC_000913.3) by using bowtie2 v2.4.2 [Firstly, raw reads in the fastq format were filtered to remove reads containing adaptors, reads containing poly-N and low quality reads using Trim Galore . At the 2 v2.4.2 .P value < 0.05 were defined as enriched.The DEseq2(1.28.0)  package E.coli, containing information about its genome, gene regulation, and metabolic pathways. All enriched genes were assigned a functional category based on available databases .PCA was analysed with the plotPCA function in DESEQ2 package. The scores of the first and second principal components were plotted into two-dimensional space to represent the spatial relationships within the repeat samples for visualization .E. coli JM109 using specific primers with the promoter sequence (TAATACGACTCACTATAGG) for T7 RNA polymerase at the 5′-end. PCR amplified using Phanta Max Super-Fidelity DNA Polymerase (Vazyme) according to the supplier’s instructions. After ethanol precipitation, the DNA template was used for in vitro transcription. In vitro transcription was carried out in 40 mM Tris- HCl (pH 7.9), 20 mM MgCl2, 0.01%(v/v) TritonX-100 and 2 mM sperdine at 37 °C for 4 h. The RNAs was were purified by denaturing polyacrylamide gel electrophoresis (PAGE), and isolated from the gel by crush-soaking in 300 mM NaOAC and 1 mM EDTA. After precipitation with ethanol, the molarity of RNAs was determined by spectrophotometric measurement using NanoDrop 1000 Spectrophotometer (Thermo Scientific). Purified RNA was labeled with fluorescein- 5-thiosemicarbazide as previously described [2) at room temperature. MST experiments were conducted in triplicate on a Monolith NT.115 system (NanoTemper Technologies) [The RNAs were produced by in vitro transcription using the appropriate DNA templates and T7 RNA Polymerase (T7 RNA Polymerase was produced in our laboratory). The corresponding DNA templates were prepared by PCR amplifying the genomic DNA of escribed . The Fluologies) , 52.2), and RNA was probed by 2 μl of freshly prepared 65 mM N-methylisatoic anhydride  in DMSO at 25 °C for 2.5 h [The ibsC RNA with 3′ linkers transcribed by T7 RNA polymerase was subjected to SHAPE analysis on titration of Kanamycin B. A series of concentrations of Kanamycin B were used with approximately 20 pmol of RNA in 1 × binding buffer (40 mM HEPES pH 7.4, 100 mM KCl, 2.5 mM MgClor 2.5 h . ControlAdditional file 1: Table S1. Fold change and gene functions of 215 enrichment genes in 0 μM Kanamycin B pull down assay.Additional file 2: Table S2. Fold change and gene functions of 230 enrichment genes in 1 μM Kanamycin B pull down assay.Additional file 3: Table S3. 134 overlapped enrichment genes in 0 μM Kanamycin B and 1 μM Kanamycin B pull down assay.Additional file 4: Fig. S1. Binding curve generated by MST for binding of Kanamycin B to 24 enriched RNA with Kanamycin B measured by MST.Additional file 5: Fig. S2. Electropherogram of NMIA-modification on ibsC RNA on Kanamycin B titration.Additional file 6: Fig. S3. Comparison of a secondary structure of RNA against Kanamycin B. a stem loop of ibsC. b stem loop of spf. c stem loop of dsrA. d stem loop of the 16 s rRNA A-site model.


Intrauterine growth restriction (IUGR) impairs neonatal weight and causes multiple organ dysplasia. IUGR not only threatens human health but is also a significant constraint to the development of animal husbandry. However, the molecular mechanism underlying IUGR remains to be further elucidated. tRNA-derived small RNA (tsRNAs) is a regulative non-coding RNA, which has recently been reported to correlate with the onset and progression of several diseases. In this study, we investigated the tsRNAs expression profiles of IUGR pigs. A tsRNAs dataset for multiple organs in normal and IUGR pigs was generated, including muscle, liver, spleen and intestine. We further analyzed the characteristics of tsRNAs in different organs of pigs, and KEGG pathway analysis was performed to investigate possible pathways involved. This dataset will provide valuable information for further exploring the molecular mechanism of IUGR formation. IUGR remains an intractable public health concern worldwide and a major problem restricting animal husbandry development3. As a multiparous mammalian animal, pigs have exhibited a naturally high incidence of IUGR4. IUGR permanently negatively affects mortality, postnatal growth and development for newborns. Available research shows that IUGR piglets exhibit abnormal development features, including disrupted muscle development6, immune dysfunction7, insulin resistance8, abnormal glucolipid metabolism9 and other diseases. These abnormal physiological changes involve the dysplasia of multiple tissues and organs. Hence, exploring the molecular mechanisms of multiple organs is vitally essential to deepen the understanding of IUGR.Intrauterine growth restriction (IUGR) broadly refers to a fetus’s slow growth and development caused by adverse factors, which usually induces low weight of newborns and multiple hypoplastic organs10. tsRNAs are produced by specific nuclease cutting different sites of parental tRNA. Several nucleases, such as angiogenin, Dicer, RNase P, RNase Z, and RNase L, have been shown to cleavage tRNAs11. tsRNAs can be categorized into several subtypes, including tRF-1, tRF-2, tRF-3, tRF-5, tiRNA-3, tiRNA-5 based on the break site of parental tRNAs12. In early studies, tsRNAs were considered solely a tRNA degradation product13. Many studies suggests that this novel ncRNA has several important functions, including ribosome biogenesis regulation14, intergenerational inheritance15, RNA silencing16, and translational regulation17. tsRNAs are widely involved in various biological processes through the above mechanisms, such as cell proliferation, migration, apoptosis, differentiation, and cell cycle19.In epigenetics, non-coding RNA-dependent mechanisms are essential for gene expression regulation. Recently, a tRNA-derived small non-coding RNA (tsRNAs) have been identified by high-throughput RNA sequencingRecently, the role of tsRNAs in the occurrence and development of diseases has attracted significant attention. However, studies about tsRNA associated with the occurrence of IUGR are still lacking. Thus, the present study aimed to characterize the expression profiles of tsRNAs in muscle, liver, spleen and intestine in the IUGR pigs model. A flow chart of this study is shown in Fig. 1. The piglets were raised following standard commercial practice. Body weight measurements were taken at 1, 23, 26, 30, and 37 days  piglets. They were divided into two groups according to birthweight: Normal piglets  and IUGR piglets . The body weight of IUGR piglets was significantly lower than the weight of normal piglets. IUGR is commonly defined as a birth weight less than two standard deviations below the normalays Fig. . At 37 days Fig. . LongissTissue samples were ground in liquid nitrogen. The number of samples per group was three. We selected the three lightest piglets in the IUGR group and the three heaviest piglets in the normal group to extract RNA. Each sample was added 1 ml RNAiso reagent  to extract the total RNA according to the manufacturer’s instructions. Isolated total RNA was measured concentrations and purities using the NanoDrop 2000 spectrophotometer . The absorbance ratio of the sample at 260 nm and 280 nm is used to evaluate RNA purity. RNA samples with a ratio between 1.8 and 2.0 are used for sequencing analysis. RNA modifications are abundant in tsRNAs and interfere with small RNA-seq library construction. Before library preparation, the detailed processing flow of total RNA is as follows: 3′ - aminoacyl (charged) deacylation to 3′ –OH for 3′ adaptor ligation, 3′ -cP  removal to 3′ -OH for 3′ adaptor ligation, 5′ -OH (hydroxyl group) phosphorylation to 5′ -P for 5′ adaptor ligation, m1A and m3C demethylation for efficient reverse transcription. The total RNA from each sample was pretreated and then utilized to prepare the tsRNA-seq library. NEBNext® Multiplex Small RNA Library Prep Set for Illumina  was used for library construction. Library construction steps were carried out as follows. Firstly, the RNA was ligated to 3′ and 5′ small RNA adapters. Next, cDNA was synthesized from the ligated RNA using Illumina’s proprietary reverse transcription (RT) primers and amplification primers. Subsequently, PCR amplification was performed to generate fragments ranging in size from 134–160 bp, which were extracted and purified from the polyacrylamide gel electrophoresis (PAGE) gel. Finally, the completed libraries were quantified using the Agilent 2100 Bioanalyzer to determine the concentration and quality of the libraries. The purified libraries were mixed in equal amounts and then used for sequencing.The libraries were denatured with 0.1 M NaOH to generate single-stranded DNA molecules and diluted to a loading volume of 1.3 ml and loading concentration of 1.8pM. Diluted libraries were loaded onto reagent cartridges and forwarded to sequencing run on Illumina NextSeq500 system using NextSeq 500/550 V2 kit , according to the manufacturer’s instructions. For standard small RNA sequencing on Illumina NextSeq instrument, the sequencing type is 50 bp single-read.20. The reference genome used was Sscrofa11.1. tRNA sequences of mitochondria were predicted with tRNAscan-SE21 software. Raw sequence were trimmed 5′, 3′ -adaptor sequence and discarded reads (length < 14nt or length > 40nt) to generate trimmed reads using Cutadapt. Trimmed reads were aligned to mature tRNA sequences, allowing onely one mismatch, and then reads that did not map were aligned to precursor tRNA sequences, allowing one mismatch with Bowtie software.Raw sequence data in FASTQ format generated from the Illumina NextSeq500 sequencing platform were used for further analysis. First, the FastQC v0.11.7) was used to assess the quality scores of sequencing reads ; N: the total number of reads mapped onto all of the mature or precursor tRNA. The obtained counts and CPM data of tsRNA were used for subsequent analysis.The expression level of each tsRNA is evaluated using sequencing counts and is normalized as counts per million of total reads (CPM). The count and CPM of tsRNAs can be calculated with the following formula:Based on the tsRNAs expression level (CPM), we evaluated Spearman’s correlations coefficients between 24 samples Fig. . PrincipWe further analyzed the characteristics of tsRNAs identified in four tissues of pigs. Figure P-value < 0.05. Differentially expressed tsRNAs in four tissues between normal and IUGR groups were visualized according to fold change and P-value. The red circle represents up-regulated tsRNAs, and blue circle indicates down-regulated tsRNAs , and miRanda was set with a maximum binding free energy of less than −20. Those genes predicted jointly in miRanda and TargetScan were used as the target genes of tsRNAs for the Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. All up-regulated and down-regulated tsRNAs in four tissues were performed KEGG pathway enrichment analysis, respectively. The top 10 pathways for each tissue are shown in Fig. P-values < 0.05 (*) represent significant difference. P-values < 0.01 (**) represent highly significant difference.Results in Fig. 22 and PRJNA80065423. The tRNA sequences, results of differential expression analysis and the functional enrichment analysis are stored in figshare24.The RNA-Seq raw data were deposited in the NCBI Sequence Read Archive (SRA) database under the accession number PRJNA974817P): Q = −10log10(P). Q30 means the incorrect base calling probability to be 0.001 or 99.9% base calling accuracy. Table Raw data were obtained by Illumina platform. FastQC software v0.11.7) was used to assess quality scores of raw data for each samples. Quality score Q was used to predict the probability of base-calling error (1.7 was uPearsons correlation coefficient analysis was performed on all 24 samples. Strong correlations were seen between samples from the same tissue type Fig. . Princip


The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons . They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results  in the survival analysis. The MGs that could not be identified by statistical analysis , as well as the MGs with the highest MS , were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein–protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical  Cancer metastasis is one of the main causes of cancer mortality, accounting for approximately 90% of cancer deaths . MetastaThe identification of genes that play key roles in metastasis is the beginning of its prevention. In many previous studies, differentially expressed genes (DEGs) were selected and utilized as the main strategy to identify metastasis markers. For example, Chen’s group determined 97 DEGs between primary lung cancers and lung cancer metastasized to the brain, and the involved biological functions and signaling mechanisms were identified . In addiUtilizing machine learning models could also be a good alternative to DEG approaches for characterizing metastasis markers. This is because cancer metastasis, as well as cancer itself, is intricately related to multiple biological events and numerous factors, and a machine learning model is able to deal with multiple factors in a combinatorial manner . Howeverp-value (EP) that was obtained by comparing it to the background distribution of the MS. As a result, three sets of MGs were characterized with three kinds of EP cutoffs, which were 0.001, 0.005, and 0.01. The results were evaluated in five ways, including (1) measuring AUCs of the models built using only the characterized MGs, (2) comparing them with known metastasis markers, (3) performing an enrichment test on processes associated with metastasis, (4) conducting a survival analysis, and (5) exploring evidence from the literature. The strategy overview is depicted in In this study, we devised an algorithm that specifies a set of significant metastasis markers based on the trained machine learning models. Specifically, a scoring function was designed that calculates the inner product between the feature importance and the AUC performance of the trained models. In this study, breast cancer was selected as the cancer to be analyzed, which is the most frequently diagnosed cancer in the world  and has Each sample of breast cancer in the TCGA database (BRCA) was given “ajcc_pathologic_m” information, indicating metastasis to other organs . HoweverThe expression profiles of more than 40,000 RNAs in the TCGA database include not only coding genes but also noncoding RNAs, and too many features in machine learning not only increase computational efforts but also degrade performance due to noise and redundancy ,16. ThusThe gene expression matrix was preprocessed using the following four techniques sequentially. First, gene expression was averaged per gene by mapping the ensemble IDs to gene symbols. Second, gene expression was averaged per participant by mapping the TCGA barcodes to participants. Third, log transformation was performed on every expression value to minimize outlier effects. Fourth, quantile normalization was applied to allow for an equal expression distribution for each participant  with their default parameters.Out of the various machine learning models, we decided to use eXtreme Gradient Boosting (XGBoost) to predict metastatic status, which is an ensemble model that has been intensively employed and has outstanding performance in biology fields ,19. An eWhen an XGBoost model is being trained, feature importance (FI) scores are generated. An FI for a certain feature presents the amount of decrease in performance when it is perturbed, which is assigned to every feature while a model is being trained. A feature with a high FI indicates that it plays an important role in discriminating the class. When an XGBoost model is tested, the area under the ROC curve (AUC) is generated, which is one of the most used performance metrics in machine learning approaches .MSn, where n = 1 to 19,177. The detailed results are presented in Algorithm 1.  Inner product between the feature importance and the AUC performance of the trained modelswhereIn this study, metastasis marker genes (MGs) were considered genes with the highest FI in the trained XGBoost models classifying metastasis status. This is because a high FI indicates that a gene plays an important role in discriminating metastasis status. To compute FIs, the 50 XGBoost models were constructed, each of which was trained with 80% of the randomly selected data and tested with the remaining 20% of the data. Here, we noticed that the AUCs of the 50 models varied from 0.494 to 0.692 . We belip-value (EP) that was obtained by constructing the background distribution. To this end, Algorithm 1 was performed 10 times on the data with shuffled metastasis status, allowing the background distribution to consist of 191,770 MSs , the Cap-value < 0.05), and there were three significant comparisons with a stricter p-value cutoff   . On averp-value < 0.05) . Similar to the previous results, the set of MGs with an EP of 0.005 showed better performance than the other two sets.DisGeNET is a discovery platform containing one of the largest publicly available collections of genes associated with human diseases, which integrates data from GWAS catalogs, animal models, and scientific literature. DisGeNET contains 1,134,942 gene–disease associations that have been identified between 21,671 genes and 30,170 diseases . For eac < 0.05)  and fourp-value < 0.05).We also performed enrichment tests on the terms in KEGG and Gene Ontology. To do this, we tried to find terms related to breast cancer metastasis but were unsuccessful. Thus, we performed enrichment tests on terms associated with cancer in KEGG and Gene Ontology, and the results are depicted in p-value < 0.01) on the log–rank tests , which were divided based on the median expression value of the corresponding MG. Among the 202 MGs, SPPL2C, KRT23, and RGS7 showed highly significant results , which means that they could not be revealed by statistical analysis  induces the EMT and promotes the proliferation, migration, and invasion of breast cancer cells. In addition, the overexpressing of GOLM1 markedly promotes the metastasis of breast cancer cells in vivo [t-test p-value: 0.805) was found to be modulated by MUC16, which promotes triple-negative breast cancer lung metastasis [t-test p-value: 0.546) consists of the CP2 transcription factor with TFCP2, which is known to be essential to the EMT process [t-test p-value: 0.209) is known to reduce cell proliferation and promote invasion, and it has also been found to be a blocker of the EMT induced by the TGFbeta1-ERK2 pathway [The 202 MGs identified with an EP cutoff of 0.005 produced the best performance in the multiple evaluations among the three sets of MGs. Among them, we noticed that the 75 genes failed to show statistical significance when a refer to . We exam in vivo . ELAVL1 tastasis . UBP1  cutoffs, and they were evaluated in several different ways, such as comparison with known metastasis markers, enrichment tests, survival analysis, and literature evidence. We noticed that the characterized MGs contained genes that could not be detected by t-tests, and we confirmed that they were also associated with breast cancer metastasis. We expect that the results of this study will be of great help in elucidating the mechanism of metastasis.In this study, XGBoost modeling was employed to characterize a set of breast cancer metastasis markers (MGs). A metastasis score was assigned to each gene by calculating the inner product between the FIs and the AUCs of the trained models. Then, three sets of MGs were characterized by applying three empirical


Hepatitis delta virus (HDV) is the smallest known human virus and causes the most severe form of human viral hepatitis, yet it is still not fully understood how the virus replicates and how it interacts with many host proteins during replication. This review aims to provide a systematic review of all the host factors currently known to interact with HDV and their mechanistic involvement in all steps of the HDV replication cycle. Finally, we discuss implications for therapeutic development based on our current knowledge of HDV–host protein interactions. Hepatitis delta virus (HDV), a circular single-stranded RNA virus that uses hepatitis B virus (HBV) co-infection to propagate, causes the most severe form of human viral hepatitis. HDV is also the smallest known human virus, at ~1680 nt long, only producing one protein, hepatitis delta antigen (HDAg), of which there are two isoforms, HDAg-S and HDAg-L. Since HDV produces only one protein, and HDAg appears to be an RNA- and host-factor-binding protein with no known enzymatic function ,3,4. RecHDV virions are thought to mainly enter hepatocytes via the sodium taurocholate co-transporting polypeptide (NTCP) receptor found in the basolateral membrane of hepatocytes, using the preS1 domain of the HBV large surface antigen (HBsAg-L) . HoweverOnce inside the cell, HDV RNA is transported to the nucleus, facilitated by its interaction with HDAg. HDAg has a classical type 1 nuclear import signal (NLS) located at amino acids 66–75, which is required for nuclear entry of HDV RNPs using karyopherin (importin) 2α, a protein that is part of the nuclear pore complex and mediates import of cytoplasmic molecules into the nucleus . Once inThe HDV mRNA, that has an antigenome polarity, is transcribed from the HDV genomic RNA and is 5′ 7 mG capped and 3′ polyadenylated ,14. TherMany host proteins involved in mRNA splicing, such as arginine/serine-rich splicing factor (ASF), heterogeneous nuclear ribonucleoprotein L (hnRNP L), heterogeneous nuclear ribonucleoprotein D (hnRNP D), zinc finger protein 326 (ZNF326), CDC5L, SF3B155, SC35 and ELAV-like protein have been shown to interact with HDV RNA, HDAg or both. Arginine/serine-rich splicing factor (ASF) interacts with the right terminal stem-loop domain of the HDV genomic RNA in vitro, and with both polarities of HDV RNA in HeLa cells by various approaches including mass spectrometry of UV-crosslinked HDV ribonucleoprotein complex, RNA affinity chromatography and screening of a library of RNA binding proteins. The binding was confirmed by RT-PCR of co-immunoprecipitated HDV RNA . In the As the HDV mRNA is transcribed by host transcriptional machinery, it is recognized as a host transcript and able to be translated by host translational machinery. HDV has been found to interact with host translational machinery such as eukaryotic elongation factor 1A1 (eEF1A1), EIF3D, polyadenylate binding protein (PABP) and eukaryotic translation initiation factor 2 subunit 1. While PABP (found to interact with HDV using a combined proteomic–RNAi screen) helps direct the binding of translation factors to the poly-A-tail of mRNAs, eukaryotic translation initiation factor 2 subunit 1 (found to interact with HDV via proteomic screen) and eEF1A1 (found to interact with HDV RNA using mass spectrometry and confirmed with co-immunoprecipitation) aid the initiation and elongation, respectively, by delivering tRNAs to the 40S ribosomal subunit ,17,19. ERecently, Janus Kinase 1 (JAK1) was shown to be functionally involved in HDAg-S mediated HDV replication . This inOther host proteins have been found to post-translationally modify HDAg-S, including Casein Kinase II (CKII), PKR, PKC, PRMT1, SUMO1 and Ubc9 . More spHDAg has also been shown to interact with various proteins involved in chromatin remodelling, including the histones H1e, H2A and H4, and the histone-interacting proteins Ying-Yang 1 (YY1), CRB, p300, histone H1 binding protein (NASP), nucleolin (C23), nucleophosmin (B23), chromodomain helicase-DNA-binding protein 4 (CHD4) and BAZ2B. While these proteins interact with HDAg directly, they likely have a role in HDV RNA replication, similar to their role in histone modification and chromatin remodelling. HDAg-S was found to bind to histone H1e at the N-terminal 67 amino acids using tandem affinity purification and mass spectrometry; conversely, such an interaction barely occurs with HDAg-L. This binding was confirmed by mutation experiments, in which HDV replication was inhibited by N- or C-terminal deletion mutants of histone H1e, and was rescued by addition of wild-type histone H1e . Ying-YaHost RNAPII is co-opted by the circular HDV antigenome, which is used as a template for transcription of the linear, multimeric HDV genome using rolling-circle replication. RNAPII was first suggested as the host RNA polymerase that replicates the genome after α-amanitin, a known RNAPII inhibitor, was found to decrease the accumulation of genomic HDV RNA (and mRNA) in both cultured cells and nuclear extracts. Later, studies using co-immunoprecipitation, binding assays, mutagenesis and in vitro transcription experiments showed that RNAPII interacted with the terminal stem-loop domains of both polarities of HDV RNA ,34,35. TIn addition, the work of Yamaguchi et al., 2001  suggesteThere is much debate over which host RNA polymerase replicates the HDV antigenome from the genomic template. RNAPII has been found to interact with the genome, antigenome and mRNA (discussed above). Alpha-amanitin studies also showed a decrease in antigenomic RNA, supporting the role of RNAPII . HoweverMore recently, Verrier et al., 2020  performeHDV RNA has been shown to interact with the RNA binding proteins, NONO (previously known as P54nrb), SFPQ (previously known as PSF) and PSPC1, and the lncRNA, NEAT1, which are present in nuclear paraspeckles ,44,45. PFinally, during the rolling-circle replication of both the genome and the antigenome, a multimeric linear RNA is produced that is self-cleaved by the genomic and antigenomic ribozymes, leaving a 5′ hydroxyl and a 2′3′ cyclic phosphate at the 3′ end of the linear monomer. A host ligase is required for ligating the 5′ and 3′ end of the linear monomer, but no host ligase has been identified so far. Possible host ligases include DNA ligase 1, which has been shown to act as an RNA ligase in viroids, or C12orf29, the only known human enzyme catalysing ATP-dependent RNA ligation between a 5′ phosphate and a 3′ hydroxyl ,48.HDAg-L mRNA is created when the antigenomic RNA circle is edited by the double-stranded RNA specific editing enzyme Adenosine Deaminase RNA specific 1 (ADAR1) at the Amber-W site ,50,51,52In order to export HDV RNA out of the nucleus and into the cytoplasm, the HDAg-L, bound to the RNA circle as an RNP, interacts with the nuclear export signal-interacting protein (NESI) with its nuclear export signal (NES), which spans residues 198–210 . This chWithin the additional 19aa of HDAg-L is also an isoprenylation sequence, CXXX, used by host farnesyl transferase . The isoIt is not fully understood how HDV exits the cell and this is confounded by the controversial evidence that HBV envelope proteins are not necessarily required for HDV cell egress. In recent studies, HDV has been shown to be able to use non-HBV enveloped viruses, such as flaviviruses, hepaciviruses and vesiculoviruses, to form virions and exit the cell . As all Even if one focused just on cellular egress of HDV with the use of HBV as the helper virus, there is still more to be understood. Attempting to draw conclusions about HDV cellular egress from HBV cellular egress is not ideal, as research into HBV cellular egress is still ongoing and inconclusive. A recent paper suggests the involvement of the host gene tumour susceptibility gene 101 (TSG101) in HBV release, as knockdown of TSG101 in cells and transgenic mice suppressed HBV release. In addition, ubiquitination of TSG101 and Lys-96 in HBc by UbcH6 and NEDD4, respectively, was required for HBV egress . In addiHDV RNA has been shown to be recognized by the double-stranded binding protein, MDA5. Recognition of HDV by MDA5 leads to the induction of interferons beta and lambda . As statWhile many proteins have been found to interact with either the HDV RNA, HDAg or both, a fair amount of these proteins have been found with screens; therefore, it is unknown exactly how they interact with HDV during its replication cycle as many have not been confirmed with additional experimentation. In addition, many of the studies performed do not differentiate between the HDV mRNA, antigenome and genome.Despite recent advances about these proteins, there are still many aspects of the HDV replication cycle that remain a mystery, including the ligase that ligates the linear monomeric genome and antigenome, which RNA polymerase truly transcribes the HDV antigenomic RNA and how the host RNAPII, a DNA-dependent RNA polymerase, uses the HDV RNA circle(s) to replicate HDV.Knowing which host proteins interact with HDV during its replication cycle is important, not only for understanding the replication cycle but also as drug targets since HDV, by itself, has very few targetable proteins. Two available drugs, lonafarnib and Myrcludex B, do just that, targeting the host proteins farnesyltransferase and NTCP, respectively. However, while these drugs are able to prevent viral egress and entry, respectively, there are currently no licensed drugs that affect the in-cell replication cycle of HDV. In addition, as discussed before, the breakthrough of HDV infection during extended Myrcludex B treatment suggests that monotherapy may not be sufficient. Therefore, understanding which host proteins interact during the entire HDV replication cycle is of the utmost importance for treating HDV infections, even with drugs currently available.


Frailty is a state of increased vulnerability to poor resolution of homeostasis following a stress. Early diagnosis and intervention of frailty are essential to prevent its adverse outcomes. However, simple diagnostic criteria have not been established. The Questionnaire for Medical Checkup of Old-Old (QMCOO) is widely used for medical checkups of older adults in Japan. In our previous report, we developed a method to score the QMCOO and showed that frailty can be diagnosed with the highest accuracy when the score cutoff was set at 3/4 points. We aimed to validate the criteria in a larger cohort.Participants aged 65 years or over were recruited in the western region of Japan. They answered all the items of the Kihon Checklist (KCL) and the QMCOO. Based on the KCL score, they were diagnosed as robust (3 or lower), prefrail (4 to 7), or frail (8 or over). Then we tested the effectiveness to diagnose frailty using the QMCOO cutoff of 3/4 points. We also aimed to determine the score cutoff to separate robust and prefrail.7,605 participants  were recruited. 3,665 participants were diagnosed as robust, 2,448 were prefrail, and 1,492 were frail based on the KCL score. The diagnosis of frailty had a sensitivity of 84.0%, specificity of 82.5%, and accuracy of 82.8% with a QMCOO score cutoff of 3/4 points, suggesting its validity. To separate robust and prefrail, both the accuracy and the Youden index were the highest with the QMCOO cutoff of 2/3 points . All the questions of the QMCOO except Q12 (about smoking) were significantly related to prefrailty status after a logistic regression analysis.Diagnosis of frailty using the QMCOO score cutoff of 3/4 points was validated. Prefrailty could be diagnosed using the score cutoff of 2/3 points. Frailty is a state of increased vulnerability to poor resolution of homeostasis following a stress. Frailty is commonly observed in older adults, supposed to be a disorder of multiple interrelated physiological systems due to an accelerated decline in physiological reserve with aging . FrailtyExercise-based interventions could delay or improve frailty –6, and mThe Cardiovascular Health Study (CHS) criteria by Fried et al. define frailty as having three or more of the following phenotypes: unintentional weight loss, self-reported exhaustion, weakness, slow walking speed, and low physical activity . In a JaDiagnosis of frailty using questionnaires has been attempted. One is the Kihon Checklist (KCL), a self-reported questionnaire consisting of 25 items to screen the health and life status of older adults. The English version has been established, and all the items are described elsewhere . When a The Questionnaire for Medical Checkup of Old-Old (QMCOO) was established by the Ministry of Health, Labour and Welfare in Japan and has been officially used in the medical checkup of older adults in Japan. The QMCOO is self-reported by older adults. The QMCOO is aimed to assess the general health status of older adults, having 15 questions about 10 domains: health condition, mental health, eating behavior, oral function, body weight loss, physical function and falls, cognitive function, smoking, social participation, and social support. All the items of the QMCOO are described elsewhere . It has The QMCOO has seven questions in common with the KCL . The QMCOO has several other questions that are not identical but similar to those in the KCL. The QMCOO has fewer items than the KCL, taking less time and burden to complete for older adults. Since the usefulness of the KCL in diagnosing frailty has been validated, the QMCOO could be used to assess frailty, but the evidence is currently insufficient.In a previous cross-sectional study, we diagnosed frailty in community-dwelling older adults using the QMCOO. The cutoff value of 3/4 points was determined to maximize the Youden index; sensitivity, specificity, and accuracy were 76.3%, 88.1%, and 86.1%, respectively . HoweverWe also diagnosed robust and prefrail based on the KCL score and attempted to determine the cutoff for diagnosing prefrail using the QMCOO.This is a cross-sectional study of community-dwelling older adults. Participants were recruited in the western region of Japan: Yonago City (Tottori Prefecture), Kurayoshi City (Tottori Prefecture), Masuda City (Shimane Prefecture), and Taka Town (Hyogo Prefecture). Candidate participants were those aged 65 or over who had not been certified as requiring support or care by the long-term care insurance. We mailed the candidates a paper survey that included all of the QMCOO and KCL items, and participants answered all of them and returned them. Those who had participated in our previous study  were excThe scoring of the QMCOO was conducted as in the previous study . Each quEach question of the KCL was scored as 0 or 1, and the total was used as the score (0–25). Based on the previous study , a scoreThe group of 223 participants analyzed in our previous report  was rega2), “standard” (18.5 ≤ BMI < 25.0 kg/m2), and “obese” (BMI ≥ 25.0 kg/m2). Then we examined the relationship between body weight and the frailty status diagnosed by the QMCOO score. The ratio of frailty was also compared in the male and female groups. Furthermore, the participants were divided into three age groups , then the ratio of frailty was compared in the age groups. A logistic regression analysis was performed to examine the relationship between BMI and the frailty status.We divided the participants into three groups based on the body mass index (BMI): “lean” (BMI < 18.5 kg/mRobust (the KCL score is three or less) and prefrail (the KCL score is 4–7) participants were extracted from the validation cohort. The cutoff score of the QMCOO for diagnosing prefrail was determined using a receiver operating characteristic (ROC) curve. The point that maximized the Youden index was adopted as the cutoff. Subgroups of age and sex were also tested for QMCOO cutoff values. A logistic regression analysis was performed to examine which of the QMCOO items determined the prefrailty status.A t-test was used to compare the means of two groups, and a one-way analysis of variance (ANOVA) was used to compare the means of multiple groups. Comparisons of proportions were made with a chi-square test. The Pearson test was used to calculate and test the correlation coefficient between KCL and QMCOO.Logistic regression analysis was used to analyze the factors that affect frailty or prefrailty status. To examine the relationship between body weight and the frailty status, age, sex, and BMI were the explanatory variables, and the frailty status was the outcome. To examine which of the QMCOO items determine the prefrailty status, age, sex, BMI, and QMCOO items were the explanatory variables, and the prefrailty status was the outcome.P-values < 0.05 were considered significant. All the statistical analyses were performed using R 3.3.3 software .p = 0.900). The average age was 76.3 ± 6.9 years old in the validation cohort and significantly different from 77.4 ± 6.9 years old in the derivation cohort (p = 0.018). The average KCL score was 4.6 ± 3.9 in the validation cohort, and 4.2 ± 3.6 in the derivation cohort (p = 0.08). The average QMCOO score was 2.7 ± 2.1 in the validation cohort and 2.4 ± 2.1 in the derivation cohort (p = 0.012).The validation cohort consisted of 7,605 people that agreed to participate and were recruited for the present study. The cohort consisted of 3,458 males and 4,147 females, and the sex ratio did not significantly differ from the derivation cohort of 103 males and 120 females (p = 0.112). Height and body weight were significantly different between the robust, prefrail, and frail groups, but BMI was not.The characteristics of the validation cohort are shown in Table p < 0.001) 01) Fig. .In total participants, the ratio of frailty was significantly higher in the lean group and the obese group than in the standard group Fig. A. When aRobust  and prefrail  participants were extracted from the validation cohort . The KCL score and the QMCOO score showed a significant positive correlation , suggesting that those diagnosed as prefrail by the KCL tend to be diagnosed as prefrail by the QMCOO and that the cutoff has moderate reliability.We determined the cutoff as 2/3 points and assessed its validity. The kappa statistics was 0.483 , but unlike the KCL, does not include BMI itself. When considering the relationship between BMI and physical function, sarcopenia should also be considered. Sarcopenia is a progressive and generalized skeletal muscle disorder typically observed in older adults, requiring lower appendicular muscle mass or lower muscle quality for diagnosis in the EWGSOP2 criteria . Lower B2 , but in 2 [2 . In a st2 [2 [2 . In the Prefrailty was significantly associated with lower BMI but not with higher BMI Table . The scoWe also demonstrated that a QMCOO score cutoff of 2/3 points might help diagnose prefrailty. By picking up patients with a QMCOO score of 3 or more, it might be possible to diagnose and intervene in frailty at an earlier stage. All questions except Q12 (smoking) were significantly associated with the diagnosis  and Q2 (subjective satisfaction with daily life) are unique to the QMCOO, not included in the J-CHS, the KCL, and the five-item frailty screening index . The scoAn important limitation of our study is that we had very limited information about the participants. We used only data about the participants’ age, sex, height, body weight, and answers to the questionnaires, but other data were missing. We included age, sex, and BMI as the explanatory variables in the logistic regression analysis but could not consider other confounding factors that might affect the frailty/prefrailty status. Only those who had not been certified as requiring support or care by the long-term care insurance were recruited. However, older adults in general tend to have multiple comorbidities even if they are independent. As stated earlier, sarcopenia and diseases could cause body weight loss and lower gait speed, which are characteristics of frailty/prefrailty. Furthermore, other factors  should also be considered as explanatory variables in the analysis.Since the QMCOO will be used as a platform for frailty checkups for older adults in Japan, diagnosing frailty at the same time as medical checkups can contribute to medical care for older adults. The QMCOO could be used for screening, then older adults would be formally diagnosed as frail according to the J-CHS, which is supposed to be the standard. However, the present study has limitations. To establish the QMCOO as a diagnostic tool, further studies are needed on older adults with more information about their background. In addition, the KCL was used instead of the J-CHS criteria for the diagnosis of frail and prefrail in the present study, but further research using the J-CHS is needed. Furthermore, this is a cross-sectional study in four limited areas, and the QMCOO should be validated in other regions. Thus by accumulating evidence, the QMCOO might contribute to early diagnosis and intervention of frailty and prefrailty in the future.Diagnosis of frailty using the QMCOO score cutoff of 3/4 points was validated. Prefrailty could be diagnosed using the QMCOO score cutoff of 2/3 points. The QMCOO could be a screening tool for early diagnosis of frailty.