We used in-gel SHAPE to study the structures of these mutants in an attempt to derive structure-function correlations that could inform optimized vector RNA design. In-gel SHAPE of both dimeric and monomeric species of RNA revealed a previously unreported direct interaction between the U5 region of the HIV-1 leader and the downstream gag sequences. Our data suggest a structural equilibrium exists in the dimeric viral RNA between a metastable structure that includes a U5–gag interaction and a more stable structure with a U5–AUG duplex. Our data provide clarification for the previously unexplained requirement for the 5′ region of gag in enhancing genomic RNA packaging and provide a basis for design of optimized HIV-1 based vectors.Clinical usage of lentiviral vectors is now established and increasing but remains constrained by vector titer with RNA packaging being a limiting factor. Lentiviral vector RNA is packaged through specific recognition of the packaging signal on the RNA by the viral structural protein Gag. We investigated structurally informed modifications of the 5′ leader and  It is packaging   and U5 (nts 103–122) elements of the HIV-1 leader and gag sequences  were observed in our structural models. We chose to interrogate the RNA structures present and to derive our structural models in the absence of the Gag protein as the timing of Gag's role in the packaging process is not certain and as this approach enabled us to separate and probe individual RNA structures under native conditions. We then applied further techniques to assess the relevance of our in vitro-derived structures to the viral lifecycle. As HIV-1 genetic variation is high, phylogenetic analyses are routinely used to assess whether structures modelled using in vitro analyses are evolutionarily conserved  from template pCCL-eGFP  or corresponding mutants described below using primers Fw 5′-TAATACGACTCACTATAG GGTCTCTCTGGTTAGACCAGATCTG-3′, Rv(416nt) 5′-CTTTCCCCCTGGCCTTAACC-3′, Rv(690nt) 5′- CTTGCTGTGCGGTGGTCT-3′. PCR products were gel extracted and purified using the NEB Monarch extraction kit (#T1020S). The PCR product was eluted in a 15 μl final volume. In vitro transcription was carried out using the MEGAScript T7 transcription kit . Reactions contained extracted PCR product (150 ng), 1x transcription buffer, 10 mM each ATP, TTP, CTP and GTP, and 4 μl of T7 Polymerase enzyme mix in a final volume of 40 μl. The mix was incubated at 37°C for 4h and the product was subsequently treated with 6 U Turbo DNase for 1 h at 37°C. The RNA product was purified by lithium chloride precipitation, resuspended in RNase-free water and stored at −80°C.A 416 or a 690 nucleotide PCR fragment including the sequences between TAR and the minimal or extended 2), 130 mM KCl and 50 mM sodium cacodylate pH 7.5]. The mixture was incubated at 37°C for 30 min. 60 μl of Orange G native loading dye , 5%  orange G dye) were added and 20 μl of the treated RNA were loaded in each lane of a 20 cm × 18.5-cm 4% (w/v) acrylamide/bis 19:1 gel prepared with 1× TBM . Gel electrophoresis was set at 90 V for 1h and 100 V for four additional hours. One fragment corresponding to the RiboRuler Low Range RNA ladder (RbLR ladder)  and one RNA sample were excised and stained for 10 min with 1.3 μM ethidium bromide in 1× TBM and visualized under UV to detect the position of the dimeric and monomeric RNAs. The ethidium bromide-stained gel fragments were aligned with the remaining unstained gel, from which gel pieces corresponding to the dimeric and monomeric RNA were excised using a scalpel. The expected size of the monomeric RNA containing the gag sequences is 690 nt and should be observed between the second and third bands of the RbLR ladder, which correspond to 800 nt and 600 nt, respectively. Similarly the expected size of the dimeric RNA containing the gag sequences is 1380 nt and should be observed above the top band of the RbLR ladder which corresponds to 1000 nt. Both the dimeric and monomeric gel pieces were divided into two equal parts, with one fragment incubated in 1× TBM containing 10% (v/v) DMSO, and the other one in 1× TBM containing 10% (v/v) DMSO and 10 mM N-methylisatoic anhydride (NMIA). The 4 gel pieces were incubated at room temperature for 1 h and were then washed three times in 1× TAE (40 mM Tris-acetate and 1 mM EDTA pH 8.3). The gel pieces were minced and the RNA electroeluted using the BIO-RAD model 422 Electro-Eluter at 40 mA for 1h 30min. RNA was precipitated with 300 mM sodium acetate pH5.5 and 2.5× volume of ethanol and recovered by centrifugation, washed with 70% ethanol and resuspended in 10 μl of water. RNA concentration was determined by spectrophotometry and gel bands for dimerization analysis were quantified using ImageJ (https://imagej.nih.gov/ij/).45 μg of RNA were resuspended in 160 μl of renaturation buffer  in a microfuge tube, heated at 85°C for 5 min and slow-cooled to room temperature by placing the heat block insert containing the microfuge tube onto the bench. The RNA was subsequently refolded in 40 μl of 5x refolding dimerization buffer  were added to each sample and were incubated at 55°C for 50 min. RNA was degraded with 200 mM NaOH at 95°C for 5 min and cooled on ice for 5 min. The samples were then treated with 200 mM HCl and were precipitated as described above. The cDNA produced was combined with two sequencing ladders produced using the Thermo Sequenase Cycle Sequencing Kit  and the NED™ and PET® labelled Rv primers (Applied Biosystems) according to the manufacturer's instructions. Sequencing ladders were precipitated with 300 mM sodium acetate and 2.5× volume of ethanol and recovered by centrifugation, washed with 70% ethanol and resuspended in 30 μl of water. RNA samples were mixed with the sequencing ladders in a 1:1:1 concentration ratio and were fractionated by fragment analysis in an Applied Biosystems 3730xl analyser. Data were analysed with SHAPEFinder software (https://rna.urmc.rochester.edu/) where RNA secondary structure is predicted using SHAPE data and free energy constraints. Structures were drawn using XRNA (http://rna.ucsc.edu/rnacenter/xrna/).1000 ng of each RNA sample were each resuspended in 12 μl of 2.1 mM Tris pH 8.0, 42 μM EDTA. 5 nmol of 6FAM™-labelled reverse (Rv) primer (Applied Biosystems) was added to the dimeric and monomeric NMIA-treated samples and 5 nmol of VICsoftware  and normgag and U5–gag interactions respectively. A control scrambled oligonucleotide sequence 5′ TCGAGTGCCCGAAGGATAGCTA 3′; (https://blast.ncbi.nlm.nih.gov/Blast.cgi) to ensure that there were no predicted interactions outside of the targeted binding site on the 690 nt WT RNA sequence for o1 and o2, and no predicted interactions for the scrambled oligonucleotide. Oligonucleotides were resuspended in water at a concentration of 10 pmol/μl. 1500 ng of the 690 nt WT RNA fragment were incubated with or without each individual oligo, at a 1:1 molar ratio, in a 1.5 ml microcentrigue tube in 10 μl volume of 1×  renature buffer (as above). Tubes were incubated to 95°C for 5 min and pulsed in the microcentrifuge to recover evaporated content. Reactions were then slow-cooled on the bench to 30°C. 2.5 μl of refolding buffer (as above) was subsequently added and the reactions were incubated at 37°C for 1h, Products were electrophoresed on ethidium bromide-containing 1% agarose gels in 1× TBM alongside the RbLR ladder  at 70 V for 1h. Bands were visualized using UV transillumination. The monomeric and dimeric RNA bands were subsequently analysed and quantified using densitometry (ImageJ: https://imagej.nih.gov/ij/index.html).Antisense oligonucleotides_o1, 5′ ATCTTGTCTAAAGCTTCC 3′; and_o2, 5′ GTTCTAGCTCCCTGCTTGCCC 3′; were designed to target the putative polyA–GCTA 3′;  was alsohttps://www.agilent.com/store/primerDesignProgram.jsp) was used to create primer sequences for site directed mutagenesis (SDM). Primers were as follows:Agilent QuikChange Primer Design Program . The 2790 bp PCR product was cloned into the pCR™Blunt II-TOPO® vector  as per manufacturer′s instructions and was subsequently used to transform Stbl3 Escherichia coli competent cells . Mutagenesis was performed using the Site-Directed-Mutagenesis (SDM) XLII Agilent kit (#200521) using the primers presented above. Mutations were verified by Sanger sequencing. pCCL-eGFP BglG and MS2 transfer vectors and the resulting mutant pCR™Blunt II-TOPO® vectors were digested with NdeI and MfeI-HF and the mutated 1081 bp NdeI–MfeI fragment was ligated back into the digested 6729 bp NdeI–MfeI pCCL-eGFP BglG and MS2 backbones to produce the mutant transfer vectors presented in Figure A 2790 bp fragment from the WT pCCL-eGFP BglG and MS2 transfer vectors  containi6 293T cells maintained in Dulbecco's modified Eagle's medium (DMEM)  supplemented with 10% fetal bovine serum  (cDMEM) were seeded in 10 cm2 dishes in a final volume of 10 ml cDMEM. Fresh cDMEM was added 18 h later and cells were transfected 2–3 h later. For each transfection plate, 500 μl of 250 mM CaCl2 containing a total of 25 μg DNA in a 1:1:1:3 molar ratio of pMDLg/pRRE : pRSV-Rev :pMD2.G : pCCL-eGFP transfer vector was added dropwise to 500 μl of 2× HEPES Buffered Solution pH 7  whilst bubbling air through it. Precipitates were incubated at room temperature for 30 min before being added dropwise across the plate. 5 ml of fresh cDMEM were added 16 h post transfection, cells were harvested at 40 h post transfection, aliquoted into cryovials, and stored at –80°C. Transfection efficiency was assessed by flow cytometry detecting eGFP expressing cells. The minimum transfection level for continuation of experiments was 95%.Production of third generation lentiviral vectors was performed as previously described . Briefly5 293T cells were seeded per well of a six-well plate in a final volume of 2 ml. Frozen viral vector supernatants were thawed and used to transduce 293T cells 18 h post-seeding in 500 μl cDMEM containing 8 μg/ml polybrene. Lentiviral vectors were titrated 48 h post-transduction by flow cytometry assessment of eGFP positive cells. Titres were calculated using wells with transduction efficiencies ranging between 4% and 25%. The transduction efficiency of each lentiviral vector was calculated by multiplying the number of seeded cells by the percentage of eGFP positive cells and by the vector dilution factor and divided by the volume of the transduction culture. Transduction units were defined as the number of functional viral particles in the supernatant that were capable of transducing a cell and thus successfully driving the expression of the eGFP transgene.Viral vector transduction efficiency was titrated as described previously . BrieflyIntracellular RNA from transfected cells was extracted using the Qiagen RNeasy kit (#74004) as described by the manufacturer. Extracellular RNA extraction from lentiviral particles was performed as previously described .g for 2 min. Reverse transcription was performed using the High-Capacity cDNA Reverse Transcription kit™  as per the manufacturer’s instructions. cDNAs were then quantified by qPCR using a common set of primers Fw 5′-GAATTCTGCAGTCGACGGTA-3′, Rv 5′-TCCAGAGGTTGATTGCGA-3′ and probes targeting either the unique sequence of BglG—5′-FAM-TCGATCGGGATTGTTACTG-BHQ1-3′ or MS2—5′-HEX-CATGGGTGATCCTCATGCCGAT-BHQ2-3′; sequences contained in the backbone of all transfer vectors. The final reaction conditions were 750 nM BglG probe or 250 nM MS2 probe, 500 nM each primer, 1xTaqMan™ Fast Advanced Master Mix  and 3 μl cDNA in a 10 μl reaction volume. Standard curves were built using plasmids containing a single copy of the BglG and MS2 unique sequences. Samples with Ct values 30–35 were rejected to ensure measurements were restricted to the linear part of the standard curve and samples were considered negative when Ct values were higher than 35.Relative packaging efficiency was measured using a previously published competitive RT-qPCR assay developed as per the MIQE guidelines . Brieflyhttps://imagej.nih.gov/ij/).RNA visualization was achieved as previously described  using a https://www.proteinsimple.com/documents/HIV-1_Gagp24_Specification_Sheet_D10-1145-001_Rev_A.pdfp24 quantification was achieved using the ELLA simplex immunoassay (ProteinSimple) according to the manufacturer′s instructions. Aliquots of frozen harvested supernatant were thawed and diluted 500x in 0.5% Triton X Lysis buffer (ProteinSimple). 50 μl of the diluted samples were loaded on the ELLA cartridge (ProteinSimple) with 1 ml of the Wash Buffer (ProteinSimple) loaded in each designated well. Each run was 90 min duration and the integral software automatically calculates the p24 amount per well (pg/ml) using an inbuilt standard curve. Information about the HIV-1 Gag p24 ELLA simplex immunoassay and reagents used are available online at Pfx Supermix , , Site-Directed-Mutagenesis (SDM) XLII Agilent kit (#200521), DMEM , fetal bovine serum , 2× HEPES buffered solution pH 7 , Qiagen RNeasy kit , Turbo DNA-free™ kit , High-Capacity cDNA Reverse Transcription kit™ , 1xTaqMan™ Fast Advanced Master Mix , UltraPure™ TBE , RiboRuler High Range RNA Ladder , UltraPure™ 20× SSC , Hybond N+ nylon membrane , UltraHyb , anti-DIG-AP fragments antibody , DIG luminescent detection kit Phusion DNA polymerase  NEB Monarch extraction kit (#T1020S), MEGAScript T7 transcription kit , the RiboRuler Low Range RNA ladder , Reverse transcription mix , Thermo Sequenase Cycle Sequencing Kit , AccuprimeTM E. coli competent cells , pCCL-eGFP , pMDLg/pRRE : pRSV-Rev : pMD2.G Stbl3 https://imagej.nih.gov/ij/), RNAstructure software (https://rna.urmc.rochester.edu/), XRNA (http://rna.ucsc.edu/rnacenter/xrna/), SHAPEfinder software (https://github.com/drsuuzzz/ShapeFinder), Agilent QuikChange Primer Design Program (https://www.agilent.com/store/primerDesignProgram.jsp), Los Alamos HIV Sequence Database (http://www.hiv.lanl.gov/), BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi)ImageJ  using 690 nt long in vitro synthesized RNA fragments of the WT and S1 transfer vectors  was defined as the ratio of extracellular to intracellular WT gRNA divided by the ratio of extracellular to intracellular S1 gRNA derived from the same co-transfection plate. S1 RPE was measured to be 1.2, which was not statistically significantly different from WT , showing that a mutation that improves dimerization does not automatically improve packaging or infectivity.The role of the U5–AUG duplex in promoting a structure that favours dimerization e groups , 10, 39.d Figure , ii. Wilo Figure , i. In ao Figure , 41, thio Figure . The loowas used , where 2Overall, our data suggest that the mutations introduced into the U5 region affect not only the dimerization properties of HIV-1 derived lentiviral vector RNA, but also negatively influence viral infectivity by effects on genome packaging and possibly other undefined processes in the lifecycle. The S1 transfer vector was designed to increase lentiviral vector efficiency based on existing structural information linking stabilization of U5–AUG and increased dimerization.cis-acting elements included in the backbone of the third generation lentiviral transfer vector system have been extensively studied , which is the level that would be expected by random chance) and is not suggestive of a large-scale structural rearrangement. The gag sequences absent from the shorter RNA were found within self-contained structures in the longer RNA and no long-range interactions were predicted. Thus the structural features underlying the enhanced packaging ability of the longer RNA do not appear to be due to the structural influence of the 3′ sequences upon the major packaging signal region in the monomeric species.In gel-SHAPE is a technique that enables structural modelling of individual conformers within a mixed structural population, under native conditions and without using mutagenesis . Here, ichniques ,10,13,39gag sequences an unexpected structural rearrangement occurred. Whilst the presence of the U5–AUG motif in the dimeric RNA of the WT HIV-1 leader has been supported by several different groups (gag sequences there was no U5–AUG formation in the dimer. Instead a novel direct interaction was identified between the U5 region (nts 103–122) and a sequence of gag nucleotides that are not present in the shorter RNA  forming SL3 showed a reduced reactivity value. In contrast three of them, one from the loop region of SL3 and two from the lower part of the stem, were characterized by an increase in their measured NMIA reactivity. One base showed no change in reactivity.Overall the dimeric structure of the short monomer presented in Figure t groups , 10, 13,1 Figure  correspo1 Figure , gag corgag sequences stabilize the ψ element in the dimeric RNA and change the structures of the nucleotides immediately 5′ and 3′ of it. The longer gag-containing dimeric RNA structure was not characterized by the formation of SL4 despite the lack of formation of the U5–AUG duplex. Overall, addition of the gag sequences present in an optimal length transfer vector RNA had no effect on the monomeric structure of the 5′ region but results in two direct interactions between gag and the HIV-1 leader, including the polyA and U5 regions, that form in the dimeric RNA in vitro.Whilst the monomeric structure contained the extended SL3 helix the dimeric structure did not. This observation suggests that gag sequences, rather than the formation of the highly conserved and well documented U5–AUG helix. If these interactions are functionally important to HIV-1, then they would be expected to be structurally conserved amongst various naturally occurring HIV-1 isolates. To investigate the likelihood of the U5–gag interaction being of biological importance, sequence and structure conservation analysis was performed for nucleotides 103–122 (U5) and (433–452) (gag) by comparison of all HIV-1 Group M strains from the Los Alamos database sequence compendium database 2018 (http://www.hiv.lanl.gov/). In order to put our findings into context we performed the same analysis for the U5–AUG interaction (U5 nucleotides 105–115 and gag nucleotides 334–344). All three sequences involved are highly conserved, but do still contain some sequence variants. Our findings presented in Table gag, which is a longer interaction. All strains were able to form a substantial part of each predicted structure, with 55 out of 57 strains able to form at least 10 of 11 base-pairs of U5–AUG and 54 out of 57 strains able to form at least 16 of 18 base-pairs of U5–gag structural motifs. One out of the 57 was an outlier, capable of forming less than half of base-pairs in both the U5–AUG and U5–gag predicted structures (02_AG.PK.15.PK032). This virus was derived from an incompletely characterized patient source with a low viral load from an analysis of recombinant CRF02/A1 viral spread in Pakistan from a point source introduction and would need full sequence analysis to investigate if there are other sequence characteristics that might compensate structurally  and 3–452 , and as suggested by our proposed equilibrium mechanism. In vitro dimerization studies , 1.8 times more dimer for U5s than WT  and 1.3 times more dimer for LU5AUG than WT , confirming our hypothesis that U5–gag destabilization and U5–AUG strengthening leads to increased dimerization propensity and supporting the proposed structural equilibrium. Additionally, based on this suggested equilibrium we hypothesized that removal of the gag sequences from the WT RNA template should lead to an increase of in vitro dimerization propensity as consequentially WT RNA would only be able to adopt the more stable U5–AUG dimeric conformation. As shown in Figure gag sequences indeed increased the in vitro dimerization propensity of WT by 1.3 times (n = 7 for WT(+gag) and n = 3 for WT(–gag), P = 0.000005 by an independent two sample t test with a two-tailed distribution). Antisense DNA oligos targeting the putative polyA–gag (o1) and the U5–gag (o2) interactions were designed as a final approach to investigate the presence of these two suggested novel interactions in vitro. Specific antisense DNA oligonucleotide targeting of RNA sequences has been extensively used as a method of studying structural features, RNA–RNA and RNA–protein interactions within the HIV-1 genome in vitro , while incubation of the WT long RNA fragment with the o2 oligo led to a 25% reduction of dimerization propensity . A scrambled oligo that was predicted not to interact with the RNA was added at the same molar ratio, did not affect dimerization . These data further support the formation of the suggested polyA–gag and U5–gag structural interactions in vitro.As shown in Figure  virions . In vitrs Figure  were perin vitro , 45–47. n = 8 for U5s, P = 0.000000008 and n = 3 for LU5AUG, P = 0.006, Figure n = 12 for both WT and S1, P = 0.2 by an independent two sample t test with a two-tailed distribution), while U5s and LU5AUG were characterized by a twofold and a fourfold reduction in their functional titers . Lastly, ELLA p24 was performed to measure physical vector titer .To further investigate the properties of the designed transfer vector mutants, 293T cells were co-transfected with equal amounts of WT and mutant transfer vectors alongside the packaging plasmids to measure the RPE of all described U5–AUG mutants. RPE measurements revealed a two and a half fold and a fivefold reduction in the RPE of the U5s and LU5AUG mutants respectively  and U5 sequences and the gag sequence, that are adopted by the dimeric RNA in preference to the widely published U5–AUG helix. The U5–AUG helix is an important component of the generally accepted structure of the optimal packaging signal in the 5′ UTR and for the virus it may be of advantage to restrict generation of a competent packaging structure in favour of a structure optimizing a translation phenotype for the full length RNA until the optimal conditions (e.g. adequate amounts of Gag and Gag/Pol polyprotein) are available. A mechanism in which structures that act as higher-affinity packaging signals on the gRNA do not form, might favour translation initially. However a population of the full length RNA has at some stage to become packageable and the availability of lower-affinity signals dependent on higher concentrations of Gag to trigger the full packaging process would be a logical strategy. This cooperative nature of packaging dependent on Gag concentration has previously been observed in cell culture . Given the evidence that this region is important in encapsidation (Our results suggest that sequences that allow for a structural equilibrium between the newly identified U5–g Figure ,D suggesg Figure . Our in how here . The in sidation  we favougag gene. They raise the possibility of a stepwise thermodynamically favourable pathway beginning with a stable monomeric RNA and ending with formation of the mature genomic dimer. This improvement in our understanding of the structure of the vector RNA could be used to improve the basic design of the lentiviral vector backbone employed for clinical applications of gene therapy as well as to identify novel drug targets in the RNA genome. More structurally informed approaches to enhancing RNA packaging will now be feasible based on these results and may help to enhance vector efficiency.These novel structural findings which were shown to be supported by both phylogenetic and functional assessments help to consolidate disparate observations in the lentiviral packaging field and to uncover functional reasons for extended packaging signal sequences within the SHAPE data used for structural modelling are provided as gkab1206_Supplemental_FileClick here for additional data file.


We show that DENV2 NS5 arginine 888, located within the carboxy-terminal 18 residues, is completely conserved in all flaviviruses and interacts specifically with the top-loop of 3′SL in the 3′UTR which contains the pentanucleotide 5′-CACAG-3′ previously shown to be critical for flavivirus RNA replication. We present virological and biochemical data showing the importance of this Arg 888 in virus viability and de novo initiation of RNA polymerase activity in vitro. Based on our binding studies, we hypothesize that ternary complex formation of NS5 with 3′SL, followed by dimerization, leads to the formation of the de novo initiation complex that could be regulated by the reversible zipping and unzipping of  Aedes mosquito. The four DENV serotypes (DENV 1–4) along with Zika virus (ZIKV), Yellow fever virus (YFV), West Nile virus (WNV), tick-borne encephalitis virus (TBEV), and the zoonotic Usutu virus (USUV) are members of the flavivirus genus belonging to the family Flaviviridae , which is transmitted by the iviridae . Despiteiviridae .upstream of ATG region) that is complementary to a sequence within the 3′UTR involved in genome circularization and is flanked by the AU-rich region that has been suggested to form a cis-acting conformationally tuned RNA element termed 5′UAR-flanking stem (UFS) (The ∼11k nucleotide (nt) positive-strand RNA genome of DENV contains a long open reading frame (ORF) that encodes a polyprotein that is post-translationally cleaved by viral and host proteases into three structural proteins  and seven nonstructural proteins . The ORF is flanked by 5′ and 3′ untranslated regions (UTR) that are 100 and 450 nt long, respectively, which contain multiple secondary structure elements . The twoem (UFS) . The secem (UFS) . The firem (UFS) . The 3′Sem (UFS) , as wellem (UFS) . In addiem (UFS) .ter18) residues (882–900) of DENV2 NS5 contain a nuclear localization signal (NLS) sufficient for its recognition as a cargo by the host importin α nuclear transport protein for translocation to the nucleus  and has been identified as a potential RNA binding residue  and carboxy-terminal RNA-dependent RNA polymerase (RdRp) , and can nucleus . Althoug nucleus . In addi nucleus . The Cteiral NS5 , except  partner . Here, R residue .It is widely accepted that recruitment of NS5 by 5′SLA acting as a promoter initiates negative-strand RNA synthesis . In contSupplemental Fig. 1). Residue R888 interacts with residue Y838  in one of the molecules in the unit cell of DENV3 NS5 crystal structure where the carboxy-terminal end is ordered via the formation of a dimeric interaction (662DD” to “GAA” (referred to as GAA mutant) was included as negative control. BHK-21 cells transfected with Y838F IVT RNA showed approximately two- to threefold lower viral RNA synthesis (P < 0.0001) and extracellular RNA (P = 0.019) levels to that of wild-type (WT) (P = 0.045) and 72 h post-transfection (P = 0.042) (P < 0.0001), extracellular RNA (P = 0.004), and infectious virus production  compared to WT  . On the ed to WT B. SimilaP = 0.03; We next examined the NS5 subcellular localization of its Y838F, R888K mutants in comparison to WT NS5 by confocal microscopy. In agreement with a previous report , NS5 of Overall, our data show that NS5 carboxy-terminal residues Y838 and R888 play a critical role in DENV2 replication independent of R888's role as a determinant of NS5 subcellular localization .Escherichia coli  and elongation assays in vitro ,c. The rhia coli A. The NShia coli B,C,E. Thhia coli B. Compar662DD → GAA RdRp defective mutant protein, and a combined D146A + GAA mutant in the polymerase activity assays. The MTase inactive D146A mutation led to a significant decrease in both dni/elongation and primer-dependent elongation activities of NS5 polymerase by ∼65% and 40%, respectively, suggesting that MTase is in cross-talk with the RdRp activity through intramolecular signaling between the two domains which could potentially influence NS5's affinity for RNA substrate. The polymerase inactive GAA mutation completely abolished the dni/elongation activity as expected; however, it still retained ∼10% of the elongation activity in vitro. The D146A + GAA mutant completely abolished both dni/elongation and elongation activities. Taken together the in vitro polymerase assays indicate that the NS5 residues Y838 and R888 play a role in RNA replication.Since NS5 is a multifunctional protein where the amino-terminal MTase domain has been shown to modulate the carboxy-terminal RdRp and vice versa , we inclThe precise role of Y838 and R888 of NS5 is not known but the dni/elongation polymerase activity assay results suggest that they play a role in RNA binding events associated with viral RNA replication. To examine this further, we investigated the interactions of the WT and mutant NS5 proteins with the conserved RNA secondary structural features in the 5′UTR and 3′-UTR regions focusing on the 70 nt 5′SLA and 79 nt 3′SL, respectively A. The seKd) in Kd value. The Kd value of WT NS5 with 5′SLA is in the micromolar range and is in a similar Kd range measured for ZIKV full-length NS5 binding with ZIKV 5′SLA . In addition, the addition of yeast tRNA at vast molar excess (>100-fold) did not affect WT NS5's ability to bind 3′SL. Taken together, the binding we demonstrate between NS5 and 3′SL is specific and dependent on the R888 residue.5′SLA and 3′SL RNA were generated by in vitro transcription as described previously. The purity of RNA samples was analyzed by agarose gel electrophoresis as shown in KV 5′SLA . MutatioKV 5′SLA . When weP < 0.0001) (P < 0.05), suggesting an additional association of NS5 to the 5′SLA attached beads facilitated by 3′SL most likely in the form of a 3′SL–NS5 complex.In order to examine the interactions of 5′SLA and 3′SL in NS5 in solution using coimmunoprecipitation, we labeled 5′SLA with biotinylated cytidine by adding it to its 3′ end with T4 RNA ligase. The biotinylated 5′SLA was immobilized onto the streptavidin Sepharose beads and incubated with NS5 at a 2:1 molar ratio of NS5 to 5′SLA to form the 5′SLA–NS5 complex. We found that removal of excess NS5 by stringent washing followed by the addition of various amounts (molar ratio of 3′SL to 5′SLA ranges from 0:1 to 4:1) of unlabeled 3′SL resulted in a significant decrease of SLA-bound NS5 at high 3′SL concentration ( 0.0001) A. Intere 0.0001) B  demonstrated that the single alanine substitution did not impact NS5 stability or solubility.After establishing that R888 may be crucial for dni/elongation of polymerase and also demonstrating that it dramatically decreased binding affinity for 3′SL, we carried out further coimmunoprecipitation studies to assess complex formation between the NS5 R888A mutant and 3′SL. A biotinylated cytidine was added to the 3′ end of the 79 nt 3′SL RNA molecule with T4 RNA ligase in order to immobilize it on the streptavidin Sepharose beads. Binding of NS5 WT, R888A, or GAA to the immobilized 3′SL was assessed after stringent wash steps to remove nonspecific binding. The NS5 proteins bound to the beads were then analyzed by SDS-PAGE followed by western blot detection using in-house NS5 5M1 antibody . Direct Supplemental Fig. 3; To further evaluate the stable binding of NS5 to 3′SL in solution and compare it with the R888A mutant, dynamic light scattering (DLS) and gel filtration analysis of the protein–RNA complex were carried out. Taken together, our results from Co-IP and DLS suggested that the R888A mutation abolished the formation of the NS5–3′SL complex.del) or the side-loop (3′SL-SLdel) of 3′SL. The mfold  and 3′SL-SLdel (lane 17) were almost depleted at a 4:1 molar ratio suggesting the formation of a complex with WT NS5  residues of DENV NS5 protein played a crucial role in RNA replication, but the precise mechanism remained unclear at that time  to host importin α nuclear transport protein . The carboxy-terminal region of NS5 is often not observed in NS5 crystal structures as it is considered to be a highly dynamic part of the protein located within the thumb subdomain  in de novo initiation/elongation polymerase activity compared with primer-dependent polymerase elongation activity  that is critical for NS5 recruitment and replication  and at the 3′UTR via the top-loop of 3′SL (this study). Translation will be blocked by 5′ UTR–NS5 interaction (this includes SLA and SLB) (In hepatitis C virus (HCV), a on state . Based oon state , we propon state  stabilizand SLB)  thus perand SLB) , the 5′ and SLB) . (2) Nexand SLB) . As mentand SLB)  which suand SLB) B. (3) Asand SLB) , we posiand SLB)  in the dand SLB)  and entedni complex mediated by both 5′SLA and 3′SL RNA that requires further biophysical validation that is beyond the scope of the current study. Given the highly conserved property of both R888 and 3′SL, our findings also pave the way toward the development of a pan-flavivirus therapeutic solution targeting the critical de novo initiation step in RNA replication.Overall, we demonstrate in this work for the first time that DENV2 NS5 residue R888 which is conserved in all flaviviruses specifically recognizes the 3′SL top-loop, which is essential for de novo initiation of genome replication. We also propose a highly plausible model depicting the formation of a stabilized 2.BHK-21 cells  were maintained in RPMI 1640 medium (Gibco) supplemented with 10% [v/v] fetal bovine serum (FBS) and 1% [v/v] penicillin–streptomycin (P/S) at 37°C with 5% COFull-length DENV2-3295 infectious cDNA clone (GenBank accession: EU081177) used in this study has been previously described . pWSK29 The full-length cDNA clones (WT and NS5 mutants) were linearized by SacI and purified using the phenol-chloroform method. RNA was then in vitro transcribed from the linearized plasmid using the T7 mMESSAGE mMACHINE Kit (Ambion). The in vitro transcribed RNA was transfected into BHK-21 cells using the previously described electroporation conditions to examine the replication profile of the viruses over 96 h . SupernaFor extracellular viral gRNA quantification, viral RNA from the supernatants was extracted by the QIAamp Viral RNA Extraction Kit (Qiagen) according to the manufacturer's instructions. Viral load in the supernatants was obtained by real-time RT-qPCR in Bio-Rad real-time thermal cycler CFX96 using iTaq Universal SYBR Green One-Step Kit (Bio-Rad) using primers 5′-CAGGCTATGGCACTGTCACGAT-3′ and 5′-CCATTTGCAGCAACACCATCTC-3′ targeting the DENV2 envelope protein region . AbsolutTotal RNA was isolated from cell lysates using the TRIzol (Invitrogen) extraction method and subjected to cDNA synthesis using the ImProm-II Reverse Transcription System (Promega) according to manufacturer's instructions. Quantification of intracellular viral gRNA was conducted by real-time PCR using iQ SYBR Green Supermix (Bio-Rad). PCR product of DENV2-3295 E gene was used to generate the standard curve for quantification of viral genome copy. The values of intracellular viral RNA genome copies were normalized to actin expression and reported as genome copy per µg of total RNA.5 BHK-21 cells were seeded into a 24-well plate and incubated overnight at 37°C in 5% CO2. Viral supernatants were serially diluted with serum-free RPMI 1640 media and inoculated onto the cell monolayer for 1 h. Virus inoculums were then removed, and cells were overlaid with 0.8% carboxylmethyl cellulose (CMC) and further incubated for 4–5 d. Infected cells were fixed with 3.7% formaldehyde and stained with 1% crystal violet for plaque visualization.A total of 2 × 10Transfected cells on coverslips were fixed with ice-cold methanol at −20°C for 15 min. Antibodies against DENV NS5  and 79 nt-long 3′SL (5′-AGATCCTGCTGTCTCCTCAGCATCATTCCAGGCACAGAACGCCAGAAAATGGAATGGTGCTGTTGAATCAACAGGTTCT-3′) RNAs were generated by in vitro transcription using the MEGAscript Kit (Life Technologies) following the product manual. The DNA templates were PCR amplified from a minireplicon construct followed by purification by gel extraction using the QIAquick Gel Extraction Kit. The synthesized RNAs were purified by the lithium chloride precipitation method following the MEGAscript Kit (Life Technologies) manual, followed by analysis on a 1.5% agarose gel with 1× TAE buffer under 100V for 1 h. GelRed (Biotium) was added into agarose at 1:10,000 v/v right before gel casting for detection of RNA using the Chemidoc Imaging system.Supplemental Fig. 5A) was used as a template for the in vitro polymerase de novo initiation/elongation assay. The minireplicon RNA was synthesized and capped with m7G(5′)ppp(5′)A cap structure analog (New England Biolabs) by in vitro transcription using the MEGAscript Kit (Life Technologies) following the product manual. RNA purification and agarose gel electrophoresis were carried out as described above.A minireplicon RNA containing 5′ and 3′-UTR and part of the capsid coding sequence  or side-loop (6 nt 5′-AGAAAA-3′) from a template DNA construct which is part of the 3′UTR was done by overlap PCR. After successful construction of 3′SL-TLBiotinylation of the purified 5′SLA and 3′SL RNA was carried out using the Thermo Scientific Pierce RNA 3′ End Biotinylation Kit. A single biotinylated cytidine (bis)phosphate nucleotide was ligated to the 3′ terminus of the single RNA strand by T4 RNA ligase followed by purification steps to remove the enzyme and other components that remained in the ligation reaction according to the product manual.The secondary structures for the 5′ SLA and 3′SL were predicted with mfold web server  and the The Y838A, Y838F, R888A, R888K, D146A, G662DD → GAA, and D146A + GAA mutations on DENV2 NS5 were done using the QuikChange II XL Site-Directed Mutagenesis Kit (Agilent Technologies) following the product manual. Primer sequences used here are available upon request. Sequences of the resulting mutants were confirmed by DNA sequencing service provided by first BASE.E. coli cells were resuspended in His Buffer A  and lysed with 0.1 volume of 10× FastBreak Cell Lysis Reagent (Promega), followed by DNase treatment and clarified by centrifugation. The supernatant was filtered by a 0.45-µm syringe filter unit and loaded onto a HisTrapFF column and eluted by a linear gradient of imidazole from 10 to 500 mM. Fractions containing His-tagged protein were bagged into a SnakeSkin  membrane tubing together with TEV for His tag cleavage and concomitantly dialyzed against His buffer A at 4°C for 18 h or overnight. The cleavage mixture was reloaded onto the HisTrapFF column to separate any possible un-cleaved NS5 protein. The flow-through sample from the second run of His affinity chromatography was concentrated and loaded onto a HiPrep 26/60 Sephacryl S-200 HR column  which was equilibrated in S200 Buffer  for further purification by size exclusion chromatography. The purity of elution fractions was confirmed by SDS-PAGE.The NS5 coding sequence was constructed in pProEX-HTb vector with an amino-terminal His6 tag followed by a TEV cleavage site. Expression and purification of the recombinant full-length NS5 of DENV2 have been described previously . E. coliSupplemental Fig. 5A), 20 µM ATP, 20 µM GTP, 20 µM UTP, 5 µM BBT-CTP (Jena Bioscience), in a buffer containing 50 mM Tris-HCl, pH 7.5, 10 mM KCl, 1 mM MgCl2, 0.3 mM MnCl2, 0.001% Triton X-100, and 10 µM cysteine. The elongation assay reaction consists of 100 nM DENV2 NS5, 100 nM poly(U) RNA template as shown in Supplemental Figure 5B, 3 µM BBT-ATP (Jena Bioscience), in a buffer containing 50 mM Tris-HCl, pH 7.5, 10 mM KCl, 0.5 mM MnCl2, 0.001% Triton X-100, and 10 µM cysteine.Both the de novo initiation/elongation assay  and the elongation assay  have been described previously . Briefly2, 1.5 M DEA, pH 10) supplemented with 25-nM calf intestinal alkaline phosphatase  to remove the pyrophosphate (PPi) from the BBT group. The amount of fluorescent BBT group was measured from a 384-well flat-bottom, black plate (Corning) by the Tecan Spark 10M microplate reader at excitation and emission wavelengths of 422 and 566 nm, respectively. Each tested condition was recorded from triplicate reactions.Both assay reactions were incubated at 37°C for exactly 1 h, terminated by addition of 10 µL 2.5× STOP buffer  has been described previously . Brieflyg for 1 min, the beads were boiled in SDS loading buffer and analyzed by SDS-PAGE and western blot detection using the in-house NS5 antibody 5M1  preequilibrated in buffer 0  and incubated at 4°C for 30 min with gentle rotation followed by blocking with 2 mM biotin in buffer 0. An amount of 25 pmole of WT NS5 or mutant was added to the RNA tagged beads which were reequilibrated with buffer 1  and incubated for 1 h with gentle rotation at 4°C. After being washed with buffer 1 and precipitated by centrifugation at 150body 5M1 .H of the particle which is defined as the radius of a sphere having the same diffusion rate as the particle was calculated by the Stokes–Einstein equation:B is the Boltzmann coefficient, T is the absolute temperature, η is solvent viscosity, D is the diffusion coefficient which is determined from the auto-correlation function describing the fluctuation of scattered light. The scattering profile was analyzed by Zetasizer Nano software (version 6.01) using the non-negative least-squares algorithm to deconvolute the correlation curve to intensity-weighted size distribution.Dynamic light scattering (DLS) was used to generate the intensity-weighted size distribution of WT NS5, NS5 R888A mutant, 3′SL RNA, and 1:1 molar ratio mixture of WT NS5 or R888A with 3′SL. An amount of 15 µL of sample in DLS buffer  was loaded into a fluorescence quartz cuvette (Hellma) and measured by Zetasizer NanoS  equipped with a backscatter detection system at 173° at 25°C. WT NS5 at 1.3 and 0.65 mg/mL, R888A mutant at 1.94 and 0.65 mg/mL, 3′SL RNA at 1.45 and 0.72 mg/mL were measured multiple times independently to eliminate concentration effect on the estimation of particle size. The hydrodynamic radius Rt-test with two-tailed distribution and two-sample method assuming unequal variances. Two-way ANOVA with Bonferroni correction was performed to determine significant differences between groups for the intracellular and extracellular viral RNA kinetics data. P-values ≤0.0001, ≤0.001, ≤0.01, ≤0.05 were represented by [****], [***], [**], [*], respectively; P-values >0.05 were not considered statistically significant.The statistical significance of difference between the two groups was evaluated using Student's Supplemental material is available for this article.


Oncogenic fusion genes as the result of chromosomal rearrangements are important for understanding genome instability in cancer cells and developing useful cancer therapies. To date, the mechanisms that create such oncogenic fusion genes are poorly understood. Previously we reported an unappreciated RNA-driven mechanism in human prostate cells in which the expression of chimeric RNA induces specified gene fusions in a sequence-dependent manner. One fundamental question yet to be addressed is whether such RNA-driven gene fusion mechanism is generalizable, or rather, a special case restricted to prostate cells. In this report, we demonstrated that the expression of designed chimeric RNAs in human endometrial stromal cells leads to the formation of JAZF1-SUZ12, a cancer fusion gene commonly found in low-grade endometrial stromal sarcomas. The process is specified by the sequence of chimeric RNA involved and inhibited by estrogen or progesterone. Furthermore, it is the antisense rather than sense chimeric RNAs that effectively drive JAZF1-SUZ12 gene fusion. The induced fusion gene is validated both at the RNA and the genomic DNA level. The ability of designed chimeric RNAs to drive and recapitulate the formation of JAZF1-SUZ12 gene fusion in endometrial cells represents another independent case of RNA-driven gene fusion, suggesting that RNA-driven genomic recombination is a permissible mechanism in mammalian cells. The results could have fundamental implications in the role of RNA in genome stability, and provide important insight in early disease mechanisms related to the formation of cancer fusion genes. Fusion genes resulting from chromosomal translocations are important for understanding cancer mechanisms and developing anti-cancer therapies. Fusion gene are presumed to occur prior to fusion RNA expression. However, studies have reported the presence of fusion RNAs in individuals who were negative for chromosomal translocations. The observation, that fusion RNA could be present prior to fusion gene formation, raises the provocative hypothesis that fusion RNA, or any cellular RNA with sequence compositions resembling that of fusion RNA, could act as a template to mediate genomic rearrangement which leads to the final gene fusion. In this report, we demonstrated that the expression of designed chimeric RNAs in human endometrial stromal cells leads to the formation of JAZF1-SUZ12, a cancer fusion gene found in endometrial stromal sarcomas. The process is specified by the sequence of chimeric RNA involved and inhibited by estrogen or progesterone. Furthermore, it is the antisense rather than sense chimeric RNAs that effectively drive JAZF1-SUZ12 gene fusion. The results could have fundamental implications in the role of RNA in mammalian genome stability, provide important insight in early disease mechanism, as well as developing gene editing technology via mechanisms native to mammalian cells. One of the most prominent genetic alterations in cancer is gene fusion resulting from chromosomal rearrangements . Fusion 4 to 1 in 105 cells that expressed chimeric RNAs in our experiments were estimated to carry the induced gene fusion [In our previous report , we expee fusion . Nonethee fusion ,16.While these striking results support the provocative model that specific RNA can drive the formation of gene fusions in mammalian cells, one fundamental question yet to be answered is whether such an RNA-driven mechanism is generalizable, or rather, a special case unique to prostate cells. In this report, we provide the important evidence that RNA-driven gene fusion is not a mechanism restricted to prostate cells, but permissible to another mammalian cell type when proper requirements are met. This principle is demonstrated using JAZF1-SUZ12 fusion gene in human endometrial stromal cells (hESC), which represents a particularly important experimental system as it is from this model that “the cart before the horse” hypothesis was originally proposed ,12. The Previously we demonstrated that the expression of short chimeric RNA can induce TMPRSS2-ERG gene fusion in human prostate cells in a sequence dependent manner . To deteThe chimeric RNA sequences were designed according to our previous publication . BrieflyIt is important to point out that the induced JAZF1-SUZ12 fusion transcripts, which contain only annotated exon sequences, cannot arise from the sequence of the expression plasmids. This is because that the chimeric RNA sequences encoded in the plasmids are designed to target the introns  and contTo test whether the designed chimeric RNAs specify a pair of genes to undergo fusion in a sequence-specific manner, and whether induced gene fusion is cell type-specific, we cross-compared the effect of a chimeric RNA known to induce TMPRSS2–ERG fusion (antisense-5)  to that One important observation from our previous study using TMPRSS2-ERG fusion in prostate cell as the model is that it is the antisense, rather than sense chimeric RNAs, that effectively drive gene fusion . This diOne plausible explanation for this pronounced disparity between sense and antisense chimeric RNAs in fusion induction is “transcriptional conflict”. It is known that both the JAZF1 and SUZ12 gene are actively transcribed in hESC cells . During Previously we have shown that chimeric RNA-induced TMPRSS2-ERG fusion in prostate cells requires testosterone , a male To investigate whether the induced JAZF1-SUZ12 fusion transcript is the result of JAZF1-SUZ12 gene fusion at the DNA level via genomic rearrangement, we continued to propagate and enrich the transfected hESC population by dividing and testing the subpopulations for JAZF1-SUZ12 fusion transcript continuously for 60 days  containing 10% fetal bovine serum , 1% penicillin/streptomycin , 1% ITS premix  and 1.5g/L Sodium Bicarbonate  in a 5% CO4cells/well and 1 ml/well of culture medium. Transfection was performed using 293fectin reagent  according to manufacturer’s protocol. Briefly, 1μg of a particular plasmid was first diluted in 50μl of the serum-free DMEM followed by immediate mixing by pipetting and 3 μl of transfection reagent was diluted in another 50μl of the serum-free DMEM in a separate tube. Both tubes were incubated at room temperature for 5 minutes followed by mixing together in one tube and another round of incubation of 20 minutes. The DNA/transfection reagent mixture was then added drop wise to a well containing hESC cells in 1ml medium.Twenty hours prior to transfection, hESC cells were seeded in 12-wells plate  with a density of 3.5x10For enough numbers of cells for RNA preparation and RT-PCR, each experiment was done in 4 wells of 12 well plate.Estrogen (E2) (β-Estradiol #E8875-1G) and Progesterone (P) (#P8783-1G) was purchased from Sigma Aldrich. Concentrated stock of 2mM was prepared in 100% ethanol  and then aliquoted into 1ml per tube and stored at -20°C.For treating cultured cells, concentrated E2 or P stock was diluted as 10x working solutions  with the appropriate complete culture medium and used immediately. Six hours post transfection, 111μl of fresh 10x E2 or P working solutions was added to each well of 12-wells plate containing 1ml medium and transfected cells.Total RNA from cultured cells was extracted using RiboPure RNA isolation Kit according to manufacturer’s instructions . Briefly, cells were homogenized in 1000μl of Tri-reagent (provided) and were then lysed with 200μl of chloroform. The sample was then vortexed and incubated at room temperature for 5 min followed by centrifugation at 12,000 x g for 10 min at 4°C to separate the mixture into a lower, red, organic phase, an interphase and a colorless, upper, aqueous phase. Upper aqueous phase was then collected and 200μl of ethanol was added. Each sample was then passed through the filter assembly resulting in the binding of the nucleic acids to the filter. The filter assembly was then rinsed with wash buffer and total RNA then eluted in a new tube for further analysis.For detection of residual genomic and plasmid DNA, eluted RNA was subject to PCR reaction with primers specific to intron regions of house-keeping gene GAPDH, and with primers specific to plasmid transfected. Total RNA was converted to cDNA only if it is validated as free of DNA contamination.1 μg of total RNA was used for each reverse transcription reaction according to manufacturer instruction . RNA was converted to cDNA either with Oligo dT primer (for induced fusion transcripts) or with random hexamers (for chimeric RNAs expressed by U6 promoter). After the addition of dNTPs, the mixture was denatured at 65°C for 5 minutes. This was followed by the addition of a master-mix containing 1× superscript buffer, 10 mM DTT, 5 mM Magnesium chloride, RNaseOUT and Superscript III reverse transcriptase. Reactions were carried out at 50°C for 50 minutes and then terminated by incubation at 85°C for 5 minutes. cDNA was then treated with RNase-H for 20 minutes at 37°C to degrade RNA in DNA/RNA hybrid. 5 μl of cDNA was used as template for each subsequent PCR reaction.PCR for induced fusion RNAs was done with a standard three-step protocol using REDTaq DNA polymerase  according to manufacturer instruction.Reaction was set as follows:PCR reaction:    Forward primer: 1.0μl     Reverse primer: 1.0μl     10x reaction buffer: 5.0μl     dNTPs: 1.0μl     DMSO: 1.5μl     cDNA: 5.0μl (from 20μl stock prepared from 1 μg RNA)    Autoclaved Milli-Q water: 35.5μl    REDTaq DNA polymerase: 1.0μl     Total volume: 50μl                                                    JAZF1-SUZ12:PCR conditions for three-round nested PCR for     1st round: PCR with JAZF1 ex-3 F1 and SUZ12 ex-3 R1 on 4μl of cDNA    Pre-denaturation 94°C, 4 min    Denaturation 94°C, 30 sec    Annealing 57°C, 45 sec    Extension 72°C, 60 sec    Repeat Denaturation-Annealing-Extension cycle 35 times for induced fusion RNA    Final Extension 72°C, 5 minJAZF1 ex-3 F2 and SUZ12 ex-3 R2 on 3μl of 1st round product, PCR conditions same as 1st round.    2nd round: PCR with JAZF1 ex-3 F3 and SUZ12 ex-3 R2 on 3μl of 2nd round product, Annealing temperature is 52°C.    3rd round: PCR with     For genomic breakpoint mapping, PCRs were done with a standard two-step protocol using REDTaq DNA polymerase  according to manufacturer instruction.    Reaction was set as follows:PCR reaction:    Forward primer: 1.0μl     Reverse primer: 1.0μl     10x reaction buffer: 5.0μl     dNTPs: 1.0μl     DMSO: 1.5μl     Genomic DNA: 200 ng    Autoclaved Milli-Q water: to make final volume 50.0 μl    REDTaq DNA polymerase: 1.0μl     Total volume: 50μl                                                 2μl from the above reaction (1st round PCR) was used as template for the 2nd round PCR.PCR amplified cDNA bands were excised from the gel and eluted using QIAquick Gel Extraction Kit . The eluted bands were then cloned to pGEM-T vector  following manufacturer instruction. Sanger sequencing was performed using the service of Eurofins Genomics.4cells/well and 1 ml/well of culture medium. Transfection was performed using 293fectin reagent  as described earlier. Following overnight incubation, cells were then treated with 0.25 μg/ml α-amanitin for various time periods . Cells were then revived in fresh medium without α-amanitin for 4 days and RT-PCR was performed for JAZF1-SUZ12 fusion.Twenty hours prior to transfection, hESC cells were seeded in 12-wells plate  with a density of 3.5x10S1 FigJAZF1 exon-3 and SUZ12 exon-3 as described in JAZF1 exon-3) and 3R2 (dark blue arrow in SUZ12 exon-3). Sanger sequencing chromatograms also confirmed that the induced fusion transcript contains JAZF1 exon-3, SUZ12 exons-2 and exons-3, and that these exons were joined by splicing at the annotated splice sites as would be expected of mature endogenous JAZF1-SUZ12 fusion mRNA. We sequenced each of the thirteen upper bands shown in Induced fusion transcripts were converted to cDNA using oligo dT primers and PCR was performed using nested primers targeting (TIF)Click here for additional data file.S2 FigJAZF1 exon-3) and 3R2 (dark blue arrow in SUZ12 exon-3). The results confirmed that the lower induced fusion transcript contains JAZF1 exon-3, SUZ12 exons-2 and exons-3, and that these exons were joined by splicing at the annotated splice sites as would be expected of mature endogenous JAZF1-SUZ12 fusion mRNA. We sequenced each of the thirteen lower bands shown in Similar to what is described in (TIF)Click here for additional data file.S3 FigJAZF1 gene and 50 nts targeting SUZ12 gene, and expressed either in the sense or antisense orientation using the U6 promoter. Plasmid expressing sense chimeric RNA contains the same DNA sequences as the plasmid expressing antisense chimeric RNA except that the U6 promoter is placed in the opposite direction. ‘ts’ represents the transcriptional stop sequence “TTTTTT” for U6 promoter.The chimeric RNAs are designed with 50 nts targeting (TIF)Click here for additional data file.S4 FigThe hESC cells were treated with different concentrations  of either estrogen (E2) or progesterone (P) or both (E2 + P) for three days. RT-PCR was performed for FOXO1A  and PR  respectively. The results indicate that hESC cells responded to estrogen and progesterone moderately at 10nM and more robustly at 100nM or 1μM.(TIF)Click here for additional data file.S5 FigJAZF1-SUZ12 gene fusions are of low frequency events. To increase the percentage of positive cells in the population, hESC cells were transfected consecutively five times with ‘asJS-8’, and then divided into sub-populations. Half of the cells from each sub-population were harvested for RT-PCR assays to detect the presence of induced fusion transcript, whereas the other half was used for continuous propagation of the population. The sub-population containing the highest intensity of induced fusion transcripts (circled in red) was used for the next round of division, RT-PCR assays, and continuous propagation.The induced (TIF)Click here for additional data file.S6 FigJAZF1 intron-3 (~54 kb) and 10 reverse primers (dark blue) spacing across SUZ12 intron-2 (~2.7 kb) are shown. Each vertical line represents a target location by a nested primer set. The locations of identified genomic breakpoint ‘x’ and ‘y’ are marked by dark green lines. The specific primers that initially identified the genomic breakpoint ‘x’ and ‘y’ are labeled as red.The locations of 56 forward primers (light blue) spacing across (TIF)Click here for additional data file.S7 FigJAZF1 intron-3 joined to ~450 bp of SUZ12 intron-1. The locations of the primers used to amplify the fusion DNA product are marked as the light blue and dark blue arrows in JAZF1 intron-3 and SUZ12 intron-1, respectively. The pGEM-T plasmid was used for cloning the genomic DNA PCR product.hESC cells transfected with asJS-8 were enriched as described in (TIF)Click here for additional data file.S8 FigJAZF1 intron-3 joined to ~483 bp of SUZ12 intron-1. The genomic breakpoint between JAZF1 intron-3 and SUZ12 intron-1 is indicated by the dashed vertical lines that contains a two nucleotide ‘AA’ insertion. The locations of the primers used to amplify the fusion DNA product are marked as the light blue and dark blue arrows in JAZF1 intron-3 and SUZ12 intron-1, respectively. The pGEM-T plasmid was used for cloning the genomic DNA PCR product.hESC cells transfected with asJS-8 were enriched as described in (TIF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S3 Text(DOCX)Click here for additional data file.S4 Text(DOCX)Click here for additional data file.


The analytical performances of the proposed RNA aptasensor were better than the previously reported aptasensors for CRP. The proposed aptasensor was also applied for the determination of CRP in the human plasma samples. The obtained results indicated that there were no statistically significant differences between the responses of the proposed RNA aptasensor and an enzyme-linked immunosorbent assay kit (ELISA). The analytical performances of the proposed RNA aptasensor described in this paper are better than previously reported aptasensors for CRP determination.C-reactive protein (CRP) is one of the biomarkers related to coronavirus disease 2019 (COVID-19). Therefore, it is crucial to develop a highly sensitive, selective, and cost-effective biosensor for the determination of CRP. In this study, we designed an electrochemical aptasensor. For this purpose, the surface of a carbon screen-printed electrode was first modified with a carbon nanofiber-chitosan (CNFs-CHIT) nanocomposite. After that, the amino-terminal RNA aptamer probes were linked to the amino groups of CHIT via glutaraldehyde as the cross-linker. Finally, methylene blue (MB) as a redox probe was self-assembled on the surface of the aptasensor. The obtained results indicated that the CNFs-CHIT nanocomposite increased the surface coverage of the aptamer up to 5.9 times. The square-wave voltammetry was used for the measurement of CRP concentration in the linear range of 1.0–150.0 pM. The obtained results indicated that the signal had a logarithmic relationship with the concentration of CRP. The limit of detection (LOD) was obtained to be 0.37 pM. The dissociation constant (K Sepsisis a potentially fatal response of the body to an infection that can lead to death in the framework of many diseases like coronavirus disease 2019 (COVID-19) . Th. ThMB) wol cm−2) . TherefoFaradic) was obtained for the anodic peak current of the CSPE/CNFs-CHIT-GLU-RNA aptamer-MB. As shown, the total charge QTotal includes the non-Faradic and Faradic charge:Non-Faradic) from QTotal (the charge below the red area), the QFaradic can be obtained  on the modified electrode was also investigated. Since every CRP aptamer probe sequence contains 44 phosphodiester groups and a phosphodiester group interacts with an MB, the ΓMB can be converted to ΓAptamer [A is Avogadro’s number (6.022 × 1023 molecules mol−1). The aptamer surface coverage ) or 1.35 × 1013 molecules cm−2 (by using Equation (6)),which was greater than previously reported for the CRP aptamer probe [Aptamer on the CSPE/CHIT-GLU-RNA aptamer-MB was also obtained with the same method used for the CSPE/CNFs-CHIT-GLU-RNA aptamer-MB , respectively. The value of Γer probe . The valtamer-MB –C. The vAptamer value up to 5.9 times.s) for MB was also calculated for the CSPE/CNFs-CHIT-GLU-RNA aptamer-MB using Laviron’s formula for the surface controlled electrochemical system  [−1), F is the Faraday constant , R is the universal gas constant (8.31 J K−1 mol−1), and T is the temperature (298 Kelvin). The average value of Ks for MB was found to be 1.83 s−1, suggesting that the electron transfer of the self-assembled MB on the proposed aptasensor has good reversibility.The heterogeneous electron transfer rate constant (Kα = 0.5) . This isThe influence of the volume of the CNFs-CHIT (A), the concentration of the immobilized RNA aptamer on the amount of the self-assembled MB (B), and the incubation time on the response of the CSPE/CNFs-CHIT-GLU-RNA aptamer-MB to 20.0 pM CRP (C) were investigated  by usingAs can be seen in The effect of the incubation time was also studied C. As canCRP) decreased logarithmically as the CCRP increased from 1.0 to 150.0 pM (The SWV method was applied for the measurement of the various concentrations of CRP A. As sho150.0 pM B. The co−1 cm−2. The limit of detection (LOD) was found to be 0.37 pM (3σ/S), where σ is the standard deviation of the blank measurements for four different aptasensors  and S the slope of The sensitivity was 9.0 μA pML), the dissociation constant (Kd), and the maximum number of binding sites (Imax) for the CRP-RNA aptamer were also obtained using the Langmuir adsorption systems equation [The value of the Langmuir isotherm constant (Kequation . Equatiomax and 1/KL × Imax can be obtained from the slope (0.043) and intercept point (−0.04) in max, KL, and Kd (1/KL) were found to be 23.25 µA, 1.07 pM−1, and 0.93 pM, respectively. The value of Kd is lower than the previously reported RNA aptasensor for CRP [The values of 1/I for CRP , indicat−1 [−1 mol−1, and T is the temperature (298.15 Kelvin).Moreover, the Gibbs free energy for the desorption band of the CRP-RNA aptamer was found to be −179.6 kJ mol−1 . This isThe analytical performance of the CSPE/CNFs-CHIT-GLU-RNA aptamer-MB was compared with other aptasensors for CRP measurement . It is oThe selectivity of the CSPE/CNFs-CHIT-GLU-RNA aptamer-MB was also studied . As can The reproducibility of the CSPE/CNFs-CHIT-GLU-RNA aptamer-MB was evaluated by three newly fabricated aptasensors . The relHuman serum samples were prepared based on the previous report. Details are included in the supplementary data section. Serum samples 40.0 μL in amount were added to 156.0 µL PBS and mixed for 15 min. Then the solution was divided into two samples. After that, 2 µL CRP solution with different concentrations  was added to each of the two solutions and mixed for 15 min. The CRP concentration was 2.0 pM for the first diluted serum sample and 10.0 pM for the second one. The CRP concentration in the real sample was calculated by using the calibration curve B. FigureWhile the CSPE/CNFs-CHIT-GLU-RNA aptamer-MB demonstrated excellent analytical performance in terms of sensitivity, stability, and selectivity, its response time is slower than the immunosensors. Therefore, our future plans include designing a new aptamer probe to decrease the response time of the sensor.In this study, an electrochemical RNA aptasensor was introduced with a strategy based on the association of the CNFs, CHIT, and MB to measure CRP concentration. The investigations indicated that the CNFs-CHIT nanocomposite increased the aptamer surface coverage dramatically. The proposed RNA aptasensor for the CRP measurement had a wider linear-response range and a lower LOD, and an affinity. The obtained results also demonstrated that the proposed RNA aptasensor has good stability, reproducibility, and selectivity. Moreover, there were not any significant differences between the responses of the proposed RNA aptasensor and a CRP-ELISA kit in human serum samples. The analytical performances of the proposed RNA aptasensor were not only better than the aptasensors previously reported for CRP, but also better than the immunosensors previously reported for CRP . This is


These studies rely on the assumption that RNA quality is consistent between disease and neurologically normal cases; however, previous studies have suggested that RNA quality may be affected by neurodegenerative disease. Here, we compared RNA quality in human post-mortem brain tissue between neurologically normal cases (n = 14) and neurodegenerative disease cases  in regions affected by pathology and regions that are relatively devoid of pathology. We identified a statistically significant decrease in RNA integrity number (RIN) in Alzheimer’s disease tissue relative to neurologically normal tissue . There were no statistically significant differences between neurologically normal cases and Parkinson’s disease or Huntington’s disease cases. Next, we investigated whether total RNA quality affected mRNA quantification, by correlating RIN with qPCR threshold cycle (CT). CT values for all six genes investigated were strongly correlated with RIN ; this effect was only partially mitigated by normalization to RPL30. Our results indicate that RNA quality is decreased in Alzheimer’s disease tissue. We recommend that RIN should be considered when this tissue is used in gene expression analyses.Gene expression studies of human  It is critical to obtain high-quality RNA from human post-mortem brain tissue, in order to enable transcriptomic analyses in health and disease  is associated with oxidative stress, which can damage RNA species ; PD: substantia nigra (SN); HD: caudate nucleus (CN)] and one relatively unaffected region: cerebellum (CB). RNA was extracted from these same regions in neurologically normal cases for comparison of RNA quality.The aim of this research was to determine whether RNA quality in human Post-mortem human brain tissue was obtained from the Neurological Foundation Human Brain Bank at the University of Auckland. Human tissue was donated to the Brain Bank with consent from donors’ families and its use in this project was approved by the University of Auckland Human Participants Ethics Committee (011654). Tissue from 14 neurologically normal cases, 10 AD cases, 11 PD cases, and 9 HD cases was included in the study. Details of diagnosis, age, sex, cause of death, and post-mortem delay are available in The brain regions used in this study were: CB ; HP ; SN ; and CN . The CB was chosen as a region that is relatively devoid of pathology in these neurodegenerative diseases . All other regions were chosen as areas with high pathology in their respective diseases. Disease cases used only displayed disease-specific pathology to avoid confounding results with mixed pathologies or other comorbidities.Brains were processed and stored according to previously published protocols . BrieflyTotal RNA was extracted from fresh-frozen human brain tissue sections using the RNeasy Mini Kit (QIAGEN) according to manufacturer’s instructions with pre-processing using a recently published protocol . Tissue ® SYBR® Green RT-qPCR SuperMix-UDG with Rox (Life Technologies), 1.2 μL primer mix , and 2.8 μL DPEC-treated water. Reactions were prepared in clear qPCR 384-well plates  sealed with adhesive covers. All amplifications were carried out in triplicate. The qPCR cycling conditions were as follows: 2 min at 50°C to incubate UDG, 2 min at 95°C to activate DNA polymerase, followed by 40 cycles of denaturing at 95°C for 15 s, and annealing and extension at 60°C for 60 s. The melt curve protocol followed with 15 s at 95°C, 15 s at 60°C, and 15 s at 95°C. CT was determined automatically using QuantStudio 12K Flex Software v.1.3.Total RNA was diluted to a concentration of 25 ng/μL prior to cDNA synthesis to ensure that total RNA input was consistent across samples. Total RNA was DNase-treated prior to cDNA synthesis . 225 ng of total RNA from each sample was used to generate cDNA using the High-Capacity RNA-to-cDNA Kit (Applied Biosystems), which includes a combination of random octamers and oligoDT to minimize the impact of partial RNA degradation on downstream assays. cDNA was diluted 1 in 5 in DPEC-treated water before subsequent use for qPCR on a 7900HT Fast Real-Time PCR machine (Applied Biosystems). The reaction volume was 10 μL. Each reaction included: 1 μL cDNA, 5 μL PlatinumPOLR2A) we used two primer pairs of differing amplicon length (73 bp vs. 131 bp) to determine whether shorter amplicon length mitigated the effect of moderate RNA degradation on mRNA quantification (r2 > 0.99). All primers produced melt curves with single peaks indicating amplification of a single, specific product. All no template control reactions and no reverse transcription control reactions produced CT values that were “undetermined” or <5 CT higher than all template reactions. ΔCT  were determined using a mixed effects model approach to analyze repeated measures data, because there were missing values. The Sidak method was used to correct for multiple comparisons. Unless stated otherwise, values reported represent mean values for each group and region. *Power 3  indicatep = 0.0350) and region (p = 0.0142) had a statistically significant effect on RIN. However, there was not a statistically significant interaction between the effects of disease and region (p = 0.3914). Sidak’s test for multiple comparisons found that the mean RIN was statistically significantly lower in AD HP than in neurologically normal HP (p = 0.0380). The difference in mean RIN between AD CB and neurologically normal CB did not reach statistical significance (p = 0.1845). In contrast, there was no effect of disease or region on RIN in HD . RIN was also unchanged in either the SN or CB in PD . Furthermore, RIN did not differ by region in normal cases (p = 0.1724). Therefore, these results suggest that RNA extracted from AD tissue is significantly degraded compared to RNA from neurologically normal tissue.To determine whether RIN is affected by disease, comparisons in RIN values of RNA extracted from affected and unaffected brain regions were made between neurologically normal and disease cases . A mixedp = 0.3948) and all other comparisons remained insignificant , as this is a commonly used cut-off for acceptable RNA quality . In thisnificant . This reTo establish whether RNA yield is altered in affected brain regions, comparisons in RNA yield per input tissue weight extracted from affected and unaffected brain regions were made between normal and disease cases . No signT for six genes commonly used in qPCR studies of human brain tissue were made across pooled normal and AD HP cases , one gene displayed a weak or moderate negative correlation depending on the primer pair used (POLR2A), and two genes showed no correlation (PPIB and GFAP). Therefore, RIN is correlated with CT for some genes even after normalization to RPL30 using the ΔCT method and removal of cases with low RIN values, indicating that degraded RNA affects results in downstream applications relying on quantification of RNA signal. This suggests that cases must be matched for RIN when analyzing disease differences, or that RIN must be considered as a co-variate during statistical analysis. Similar correlations were found for POLR2A using primers with both shorter (73 bp) and longer (131 bp) amplicon lengths. Note that no significant correlations were observed when this normalization was performed in the CB  and this effect was lost when cases with RIN < 5 were excluded. Nonetheless, we show that RIN strongly correlates with RT-qPCR Cn = 74 AD) was much larger than both the current study and the studies mentioned above (n = 7 – 26 AD). Therefore, it cannot be ruled out that the smaller studies were underpowered to detect small decreases in RNA quality in the AD CB.Our finding of decreased RNA quality in the AD brain is consistent with a previously published study . We did In contrast to AD, we found no significant difference in RNA quality in PD or HD in either affected regions or the CB. This result is also consistent with previous studies that reported no changes in RNA quality in cortical tissue  or CB C in theseT). We found that RIN showed strong, negative correlations for all six genes investigated, including commonly used housekeeping genes. This result is consistent with correlations between RIN and CT observed in bovine tissue  tissue samples with RIN < 5 are excluded from analyses; (b) RIN is considered when selecting matched cases; (c) ΔCT correlations between genes of interest and RIN are reported; and (d) RIN is included as a co-variate during statistical analyses.In conclusion, the current study has demonstrated the importance of considering RNA degradation when quantifying gene expression in human The original contributions presented in the study are included in the article/Post-mortem human brain tissue was obtained from the Neurological Foundation Human Brain Bank at the University of Auckland. Human tissue was donated to the Brain Bank with consent from donors’ families and its use in this project was approved by the University of Auckland Human Participants Ethics Committee.This study was reviewed and approved by the University of Auckland Human Participants Ethics Committee (011654). BH and BR contributed to the conceptualization, methodology, analysis, writing, and editing. RP contributed to the methodology, analysis, and editing. RF contributed to the resources and editing. MC contributed to the resources, analysis, and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The digitization and automation of diagnostics and treatments promise to alter the quality of health care and improve patient outcomes, whereas the undersupply of medical personnel, high workload on medical professionals, and medical case complexity increase. Clinical decision support systems (CDSSs) have been proven to help medical professionals in their everyday work through their ability to process vast amounts of patient information. However, comprehensive adoption is partially disrupted by specific technological and personal characteristics. With the rise of artificial intelligence (AI), CDSSs have become an adaptive technology with human-like capabilities and are able to learn and change their characteristics over time. However, research has not reflected on the characteristics and factors essential for effective collaboration between human actors and AI-enabled CDSSs.Our study aims to summarize the factors influencing effective collaboration between medical professionals and AI-enabled CDSSs. These factors are essential for medical professionals, management, and technology designers to reflect on the adoption, implementation, and development of an AI-enabled CDSS.We conducted a literature review including 3 different meta-databases, screening over 1000 articles and including 101 articles for full-text assessment. Of the 101 articles, 7 (6.9%) met our inclusion criteria and were analyzed for our synthesis.We identified the technological characteristics and human factors that appear to have an essential effect on the collaboration of medical professionals and AI-enabled CDSSs in accordance with our research objective, namely, training data quality, performance, explainability, adaptability, medical expertise, technological expertise, personality, cognitive biases, and trust. Comparing our results with those from research on non-AI CDSSs, some characteristics and factors retain their importance, whereas others gain or lose relevance owing to the uniqueness of human-AI interactions. However, only a few  studies have mentioned the theoretical foundations and patient outcomes related to AI-enabled CDSSs.Our study provides a comprehensive overview of the relevant characteristics and factors that influence the interaction and collaboration between medical professionals and AI-enabled CDSSs. Rather limited theoretical foundations currently hinder the possibility of creating adequate concepts and models to explain and predict the interrelations between these characteristics and factors. For an appropriate evaluation of the human-AI collaboration, patient outcomes and the role of patients in the decision-making process should be considered. From a global perspective, many health care systems face comprehensive challenges that affect how care is delivered to society. In this regard, several factors increasingly strain care structures, processes, and the actors involved. For instance, demographic changes and the overall aging of society raise age-related health issues and demands ,2 and inRepresenting a promising and widely adopted technology to render processes and decisions more efficient, so-called clinical decision support systems (CDSSs) are software applications capable of catalyzing and informing the process of decision-making of medical professionals . AlthougOwing to technological innovations, health care technologies, including CDSSs, are increasingly enabled by artificial intelligence (AI) . The firConsidering the ambiguity of concerns regarding clinicians’ attitudes toward AI, the mentioned hindrances of CDSS adoption and the similarity between concerns associated with AI and CDSS , an AI-enabled CDSS might actually increase the relevance of perceptive and subjective factors for adoption and their interplay with technological characteristics. During the process of development and evaluation of AI-enabled CDSS, it became apparent that the potential benefits for clinical performance and treatment quality are maximized by human–AI collaboration, rather than by human-AI competition . Howevercollaborare which means work with. As the origin reveals, collaboration can be understood as a joint effort in which a common goal is pursued. From our perspective, collaboration is thus a successful interaction with an adaptive AI-enabled system. Under the assumption that both human and AI-enabled systems are not error-free, a human-AI collaboration is effective when errors are prevented. In this context, a key driver of such effective collaboration is that medical professionals perceive the system as trustworthy  for the tasks to be done and accept it. Trust is a complex psychological construct that is described as the will to make oneself vulnerable [To understand the dyadic relationship between humans and AI, it is necessary to understand key concepts and their interrelations. Although many researchers use the term interaction , literatlnerable . If a palnerable . NeverthThe objective of this study is to summarize the factors influencing effective collaboration between medical professionals and AI-enabled CDSSs. Capturing these factors is essential for medical professionals, management, and technology designers to reflect the adoption, implementation, and development of AI-enabled CDSSs ,49. FurtWe conducted a narrative review to summarize the current literature regarding our specific objectives . In the We included the databases PubMed, PsycInfo, and Business Source Complete for our literature review. PubMed indexes >5000 journals in the fields of medicine, health care, and related disciplines. We used PubMed, in particular, to gather information about the clinical effectiveness and implementation of AI-enabled CDSSs. PsycInfo contains >2000 journals from behavioral and social science research. We searched PsycInfo to examine the psychological dimensions of AI-enabled CDSSs and decisional processes. Finally, we scanned results from Business Source Complete, containing >1000 journals in the field of business sciences, to obtain insights regarding our objective from an economic and procedural perspective.. The second section reflected the interactional dimension of human-AI collaboration (trust* OR acceptance OR *agreement OR consent OR compliance OR congruency OR collaboration OR resistance). We included articles published in English over the last 10 years. To select relevant literature, 2 authors (MK and SW) independently screened titles and abstracts to exclude articles that did not involve AI-enabled technology  and those that were not related to health care or medicine. The inclusion and exclusion criteria were discussed in detail before the screening. In addition, to familiarize themselves with the procedure, an initial sample of 100 entries was screened. A high level of agreement was achieved, and disagreements were resolved through discussion between the 2 authors (MK and SW). In the remaining papers, only a few borderline cases were discussed until consensus was reached, and both the authors (MK and SW) finally came to the same result. In the full-text screening, articles that did not involve AI or AI-enabled systems (n=32), did not consider the interaction between the human actors and AI-enabled systems (n=15), did not distinguish between AI-enabled and non–AI-enabled CDSSs (n=6), did not involve CDSS (n=38) or the perspective of medical professionals (n=1), or appeared to be gray literature or opinion (n=2) were excluded. Detailed documentation of the exclusion process for full-text screening is provided in We combined 2 different sections of search terms (AND conditions). The first section represented the technologies associated with the objective of our research On the basis of our study selection, 7 studies were included in our final synthesis. From our perspective, this result stems from the fact that many studies of AI-enabled CDSSs (1) compare solely the diagnostic accuracies of human raters and those of AI-enabled systems and (2) focus on technological characteristics and the development of these systems, but do not discuss their effects on the interaction or collaboration between technology and human actors. Therefore, most  of the included studies reflected on the relevance of specific characteristics or factors by contemplating the objective from a meta-perspective .This study addresses different dimensions or steps in the development, implementation, and adoption of an AI-enabled CDSS. The technological characteristics of these systems, that is, the abilities and attributes of technology that are defined by their design , are desFurthermore, Tschandl et al  argue thIn addition, social , psychological , and cognitive characteristics  of a human actor affecting their interaction with technology, that is, human factors , appear Among the included studies, a human actor’s trust in an AI-enabled CDSS appeared to be another important factor that directly influenced the quality of collaboration and adoption of technology. For instance, Cabitza et al  argued tOf the 7 included studies, only 1 (14%) study mentioned the interrelation between an effective human-AI collaboration and primary clinical outcomes. Reviewing an AI-enabled CDSS for skin cancer diagnostics, Felmingham et al  mentioneOf the 7 included studies, only 1 (14%) study mentioned the theoretical foundations on which implications for practice are based explicitly. Jeng and Tzeng  derived Background section, the shape of an interaction between human actors and AI and their resulting interactional relationship can be considered a condition for successful collaboration. However, the foundation for evaluating an AI-enabled CDSS differs, in accordance with current research addressing non-AI CDSS [Our results show that only a few  studies have already broached the issue of individual factors influencing effective collaboration between a human actor and an AI-enabled CDSS. Although unique considerations with regard to these systems appear; for example, the important role of trust ,53, scar-AI CDSS . Studies-AI CDSS . As AI-e-AI CDSS , processWhen comparing our results to research concerning medical professionals’ interactions with non-AI CDSS, high correspondence can be noted. Khairat et al  mentioneFurthermore, other studies involving non-AI CDSSs have emphasized the essential role of trust in the effective interaction between humans and the system. Trust is a multidimensional construct. A lack of trust might result from reservations regarding the mathematical accuracy or appropriateness of a system or the purpose of a system in improving patient outcomes . As our Findings from our literature as well as ongoing research concerning non-AI patient decisional aid suggest that a stronger theoretical foundation for the interaction between human actors and CDSSs is important . FelmingOur study had some limitations. As some studies derived their conclusions about collaboration between AI-enabled CDSSs and human actors from studies of CDSSs not enabled by AI or assigned results from non-CDSSs to CDSSs, reasoning about interrelations between different technological characteristics and human factors is preliminary and requires further investigation. Although our results fit well with the current findings about the uniqueness and specific nature of human-AI interaction, very few  studies, of which most were narrative reviews, were included because of our innovative and novel objective as well as the specific context. This may be a result of our relatively narrow search, which could be extended by explicating the related constructs and prerequisites of trust. Explorative empirical studies based on suitable theoretical foundations might yield frameworks and models to structure future research on AI-enabled CDSSs, as our study primarily provides an orientation about relevant individual characteristics and factors. The consideration of environmental influences (eg, organizational policies or culture  and patiWe extracted the technological characteristics and human factors relevant for effective collaboration between medical professionals and AI-enabled CDSSs. Although most of the findings from previous research on non–AI-enabled CDSSs are in accordance with our results, the weighting of specific factors might change with AI-enabled systems. The adaptive and increasing human-like nature of AI-enabled CDSSs emphasizes the time sensitivity and reciprocity of decisional processes that should ultimately lead to an improvement in care. Cognitive biases may occur at any time during these processes, varying the effectiveness of collaboration. Explainability remains an essential prerequisite for interaction, and the expertise and personalities of medical professionals have come into focus. In addition, trust between humans and the system emerges as a central aspect of decisional support, whereas the interrelations among these facets still need to be investigated. Concepts such as shared decision-making justify the integration of patients’ demands and wishes, an important factor for medical care, and its role in human-AI collaboration is yet underrepresented. Currently, it is unclear how these concepts can be integrated into AI-enhanced decisional processes and to what extent medical decisions with the help of the CDSS are influenced by the subjective meaning and understanding of diagnoses or treatments by patients. In addition, as several studies have measured the effectiveness of collaboration by means of other parameters, primary and secondary patient outcomes should be considered in future research.As described earlier, modern health care structures are under increasing pressure. Involved medical professionals face immense workloads per capita, and the supply of personnel declines. Because these structures form the initial access points for most citizens in need of care and treatment, approaches that foster more efficient decision-making and treatment processes are becoming imperative to maintain comprehensive care. Thus, an AI-enabled CDSS represents an important and future-oriented measure that enables actors in the health care domain to improve resource allocation, make timelier and less stressful decisions, and cope with shortages in personnel, facilities, and expertise. However, the potential application of CDSSs and pursued benefits calls for investigations that shed light on how AI-enabled processes can be implemented within prevalent health care structures so that the associated risks and challenges, such as the oversimplification of individual patient data or the automated initiation of suboptimal or erroneous treatments, can be mitigated.