Importin-α serves as an adaptor linking importin-β to proteins carrying a nuclear localization sequence (NLS). During interphase, this interaction enables nuclear protein import, while in mitosis it regulates spindle assembly factors (SAFs) and controls microtubule nucleation, stabilization and spindle function. Here, we show that human importin-α1 is regulated during the cell cycle and is phosphorylated at two sites (threonine 9 and serine 62) during mitosis by the major mitotic protein kinase CDK1–cyclin B. Mutational analysis indicates that the mitotic phosphorylation of importin-α1 inhibits its binding to importin-β and promotes the release of TPX2 and KIFC1, which are then targeted like importin-β to the spindle. Loss of importin-α1 or expression of a non-phosphorylated mutant of importin-α1 results in the formation of shortened spindles with reduced microtubule density and induces a prolonged metaphase, whereas phosphorylation-mimicking mutants are functional in mitosis. We propose that phosphorylation of importin-α1 is a general mechanism for the spatial and temporal control of mitotic spindle assembly by CDK1–cyclin B1 that acts through the release of SAFs such as TPX2 and KIFC1 from inhibitory complexes that restrict spindle assembly. Summary: Phosphorylation of importin-α1 by cyclin-dependent kinase 1 provides spatial and temporal control of mitotic spindle assembly through the release of spindle-assembly factors from inhibitory complexes during mitosis. The mitotic spindle, composed mainly of microtubules (MTs) and MT-associated proteins (MAPs), functions to distribute the duplicated genome to daughter cells during cell division . SpindleXenopus egg extracts (C. elegans (KPNA2) recognizes classical NLSs for nuclear import and is likely to be the major regulator of SAFs in mitosis (Importin-α contains an importin-β binding (IBB) domain in its N-terminus and two nuclear localization sequence (NLS)-binding sites (major and minor) in its C-terminus. It serves as a linker between importin-β and NLS-containing cargo to form a trimeric complex that is transported into the nucleus . Importiextracts  and it i elegans . During  elegans . Seven i mitosis .Spindle assembly also requires the activation of mitotic protein kinases, notably CDK1–cyclin B1, which phosphorylates key proteins involved in nuclear envelope breakdown, chromatin condensation and spindle formation . CDK1–cyIn this work, we have found that importin-α1 is regulated during the cell cycle and is phosphorylated during mitosis by CDK1–cyclin B1 at two sites. These modifications negatively regulate its binding to importin-β and NLS-containing SAFs, thereby releasing the active SAF to promote mitotic spindle assembly. These results identify a mechanism for the induction of mitotic spindle assembly by CDK1–cyclin B1 through the control of inhibitory complexes with importin-α1.Fig. S1A). This result was also confirmed in experiments where cells where subjected to a G1/S or M phase block and release, followed by western blotting analysis, which showed that the levels of importin-α1 correlated with those of the mitotic marker phosphorylated histone H3 .To investigate the functions of importin-α1 during the cell cycle, we first analyzed the expression levels of importin-α1 in human HeLa cells and we found that the protein is expressed 2- to 3-fold more highly in mitosis than in interphase A. When cFig. S2). The specificity of immunofluorescence was confirmed by knockdown of endogenous importin-α1 after exposure to siRNA. In importin-α1-knockdown cells, we found that mitotic spindles were significantly shortened, and the mean intensity of the spindle MTs was reduced . Furthermore, we observed that two SAFs, the kinesin 14 family motor protein KIFC1, also known as HSET , greatly reduced the upshifted bands of mitotic importin-α recognized by I1784 in Phos-tag™ blotting  or mitosis (with nocodazole) followed by release into the cell cycle. Western blot analysis of SDS-PAGE gels incorporating Phos-tag™ showed that importin-α1 was phosphorylated when cells entered mitosis and that dephosphorylation occurred when the cells exited mitosis A. By usiblotting B,C, indiblotting D, indicaIn vitro translated (IVT) importin-α1 was incubated with asynchronous or mitotic HeLa extracts with the addition of various kinase inhibitors. The results showed that the pan-CDK inhibitors roscovitine and purvalanol A, as well as the CDK1 inhibitor RO3306, effectively blocked mitotic phosphorylation of IVT importin-α1, while the PLK1 inhibitor BI2536, the Aurora kinase inhibitor ZM447439 and the phosphoinositide 3-kinase (PI3K) inhibitor LY294009 did not affect the mitotic phosphorylation of IVT importin-α1 . These results suggest that the phosphorylation of importin-α1 at T9 and S62 by CDK1–cyclin B1 in mitosis reduces the binding of importin-α1 to importin-β and its substrates. Consistent with these results, in asynchronous HeLa cells, GFP–importin-α1 2D had a similarly reduced interaction with importin-β and its substrates, while there was no significant difference among the WT, T9A, S62A and 2A mutants .To test whether the phosphorylation of importin-α1 affects its interaction with importin-β and its NLS-containing partners, lysates of mitotic HeLa cells transiently expressing GFP–importin-α1, GFP–importin-α1 2A and GFP–importin-α1 2D were immunoprecipitated using an anti-GFP antibody, followed by western blotting using anti-importin-β, -KIFC1, -TPX2, -NuMA and -GFP antibodies. We found that the non-phosphorylated double-mutant GFP–importin-α1 2A had a stronger binding affinity with importin-β, KIFC1, TPX2 and NuMA, while GFP–importin-α1 and the phosphorylation-mimicking double-mutant GFP–importin-α1 2D had reduced interactions with importin-β and those three SAFs in mitosis A. The siFig. S3D). These results indicate that phosphorylation at T9 and S62 by CDK1–cyclin B1 can attenuate the interaction between importin-α1 and its substrates in an NLS-dependent manner in mitosis. Consistent with these results, Myc–importin-α1 2D had similarly reduced interaction with importin-β and its substrates, while there was no significant difference among the WT, T9A, S62A and 2A mutants in asynchronous HeLa cells .To test whether the phosphorylation of importin-α1 at T9 and S62 affects its interaction with NLS-containing substrates, lysates of mitotic HeLa cells co-expressing GFP–NT, a typical NLS-containing sequence from NuMA , were inFig. S4A–C) progressed to anaphase normally. However, once anaphase was initiated, cells exited mitosis on schedule regardless of whether they were expressing GFP-importin-α1 2A, T9E or S62E mutants . We also found that the nuclear localization of GFP–importin-α1 2D was reduced compared with that of the WT and non-phosphorylated mutant 2A in interphase, presumably because it disrupts the interaction of importin-α1 with nuclear transport complexes, whereas no alteration of the spindle localization of importin-α1 was observed in mitosis . Taken together, these data demonstrate that the phosphorylation of importin-α1 at T9 and S62 by CDK1–cyclin B1 is specific and functional in mitosis but not in interphase.To test whether the phosphorylation of importin-α1 is important for mitotic spindle assembly, HeLa cells were transfected with GFP–importin-α1 plasmids for 24 h and then were prepared for live-cell imaging. The results showed that the cells expressing GFP–importin-α1 T9A, S62A and 2A had a significantly prolonged metaphase, while those expressing WT, the phosphorylation-mimicking double-mutant 2D  or the sUpon expression of GFP–importin-α1 WT, 2A and 2D in cells, we found that the non-phosphorylated mutant 2A affected mitotic spindle assembly in cells with shortened spindle formation and weakened the MT intensity, while the WT and the phospho-mimicking double-mutant 2D mutant had no such effects . These rKnowing that the knockdown of importin-α1 resulted in shortened spindles, reduced intensity of MTs, impaired localization of SAFs on spindle and prolonged metaphase arrest, we carried out knockdown-and-rescue experiments to further investigate the role of phosphorylation of importin-α1 in mitotic spindle assembly. HeLa cells were exposed to siRNA and siRNA-resistant GFP–importin-α1, GFP–importin-α1 2A and GFP–importin-α1 2D plasmids for 72 h. Through measuring the relative MT fluorescence intensity and the pole-to-pole length of the metaphase spindles, we found that the non-phosphorylated mutant 2A was completely unable to restore the shortened spindle caused by siRNA, while WT and 2D almost restored the spindle length to a normal level A,B. MoreFig. S6A,B). Thus, phosphorylation of importin-α1 appears to promote the recruitment of RanBP2 to the spindle but is not required for the importin-β-restricted targeting of HURP to the spindle. In summary, these results show that the phosphorylation of importin-α1 induces the release of NLS-containing SAFs to load onto the mitotic spindle, thereby promoting their function in mitotic spindle assembly.Because the phosphorylation of importin-α1 promotes its release from importin-β, it was of interest to find out the effect of the phosphorylation of importin-α1 on the localization of importin-β, which is targeted to the spindle during mitosis . By compIn eukaryotes with an open mitosis, the controlled boundary between the nucleoplasm and the cytoplasm breaks down at prometaphase, dispersing soluble nuclear proteins and exposing the chromosomes to cytoplasmic proteins. Importin-α1–importin-β dimers bind to NLS-containing SAFs during mitosis and this interaction restricts their functions. The spatial control of these interactions is critical for the proper formation of the mitotic apparatus during which MTs need to be specifically nucleated and stabilized only in the region of spindle formation, and proteins have to be recruited to the spindle for the regulation of chromosome segregation. The release of SAFs from inhibitory complexes with importin-α1–importin-β is directed by chromosomes through chromatin-generated Ran-GTP, which exists at a high concentration in the vicinity of chromosomes and a lower concentration farther away . When thIn this report, we have shown that the key partner and regulator of SAFs containing an NLS motif, importin-α1, is controlled during mitosis through phosphorylation at two sites by CDK1–cyclin B1, providing a general mechanism for the temporal and possibly spatial regulation of SAFs during mitosis. Activation of CDK1–cyclin B1 specifically during prometaphase/metaphase provides a temporal control, while concentration of CDK1–cyclin B1 to sites such as unattached kinetochores, chromatin, spindle MTs and centrosomes  could prWe have shown that importin-α1 protein is highly expressed in mitosis, a finding that is consistent with cell cycle regulation of importin-α1 mRNA levels , whereasFig. S3F). Importin-α1 consists of a flexible N-terminal IBB domain (in which T9 is located) containing an auto-inhibitory motif that regulates the binding of importin-α1 with an NLS, and an elongated domain comprising 10 Arm repeats containing major and minor NLS-binding sites. The two domains are connected by a presumably disordered loop in which S62 is located. The efficient interaction of importin-β and NLS-containing substrates via these two domains is important for importin-α1 to act as a regulator for NLS cargoes  according to the manufacturer's instructions. E. coli expressing GST–importin-β was purified using glutathione–Sepharose-4B  according to the manufacturer's instructions. The proteins were desalted using an Amicon ultral-4 centrifugal filter (Millipore) and were resuspended in PBS buffer .The cDNA of importin-α1 was cloned into pEGFP-C2 (Clontech), pET-28a (Novagen) and pcDNA3.1-Myc (Invitrogen) to generate GFP-, His-, and Myc-tagged importin-α1. The pEGFP-C2-importin-α1 and pcDNA3.1-Myc-importin-α1 plasmids were used as templates for site-directed mutagenesis to generate the mutations at T9A, S62A, T9E, S62E, 2A and 2D. The cDNA of importin-β was cloned into pGEX4T-1 (Novagen) to generate GST-tagged importin-β. pET28a-KIFC1 887-end was kindly provided by Shuli Zhang and pEGFP-NT was a kind gift from Qinying Liu . The following antibodies were used for immunofluorescence (IF) and western blotting (WB): rat monoclonal anti-importin-α1 , rat polyclonal anti-importin-α1 , mouse monoclonal anti-importin-α , mouse monoclonal anti-importin-β , rabbit polyclonal anti-importin-β (raised against GST-tagged full-length importin-β and used at 1:1000 for WB), anti-histone H3 phospho-S10 , anti-α-tubulin , anti-GFP , anti-KIFC1 , rabbit polyclonal anti-TPX2 , anti-NuMA , anti-Ran  and anti-Myc . Kinase inhibitors were used at the following concentrations: 10 µM purvalanol A(Sigma-Aldrich), 9 µM Ro-3306 (Sigma-Aldrich), 0.1 µM BI2536 (Axon Medchem), 2 µM ZM447439(Selleck), 0.5 μM LY294009 (Selleck).2. Cells were tested routinely to ensure they were free from Mycoplasma. Transient transfections were carried out in cells using Lipofectamine 2000 (Invitrogen) with the indicated constructs. For G1/S arrest, the cells were synchronized by performing a double-thymidine blockade. Briefly, cells were incubated with 2 mM thymidine (Sigma-Aldrich) for 16 h, followed by washing and releasing cells into fresh medium for 9 h, and then incubating with 2 mM thymidine for another 16 h. The cells were then released into normal medium and harvested at different time points or into medium containing 100 ng/ml nocodazole for 12 h to harvest the cells in mitosis. The cells in mitosis were then released into normal medium and harvested at different time points. For monastrol arrest, double-thymidine blocked cells were released into fresh medium for 9h, then treated with 50 μM monastrol (Sigma-Aldrich) for 3 h.Authenticated HeLa cells were obtained from the ATCC and were maintained in DMEM supplemented with 10% fetal bovine serum, and were cultured in an incubator at 37°C in the presence of 5% CO2+-Phos-tag™ in a resolving gel. After electrophoresis, the gel was soaked in transfer buffer with 1 mM EDTA for 10 min and then in transfer buffer without EDTA for another 10 min before transfer onto a PVDF membrane using a wet-tank method followed by western blotting.Phos-tag™ is a phosphate-binding reagent that is used for the separation, purification and detection of phosphorylated proteins . We used3, 10 mM β-glycerophosphate, 10 µg/ml leupeptin, 0.7 µg/ml pepstatin A, 1.5 µg/ml aprotinin and 1 mM PMSF). For GFP-tagged precipitations, 5 µg of rabbit anti-GFP antibody (raised in the laboratory in rabbit using His-tagged GFP) was incubated with 15 µl of prewashed Protein-A–Sepharose beads  at 4°C for 1 h. The beads were then washed three times with immunoprecipitation buffer. Mitotic HeLa cells transfected with GFP-tagged plasmids were lysed on ice for 30 min in immunoprecipitation buffer. The lysates were then centrifuged at 13,200 rpm for 15 min to collect the supernatants to be incubated with beads at 4°C for 1 h. The beads were then washed five times with immunoprecipitation buffer and then were analyzed by western blotting. For endogenous protein precipitations, the indicated antibodies and non-transfected HeLa cells were used. The experiments were carried out as stated above.To determine the interaction between proteins, immunoprecipitation was carried out from cell lysates using immunoprecipitation buffer , the cells were resuspended in an equal volume of pre-cooled EBS buffer and incubated in an ice bath for 10 min. The resuspended cell pellets were sonicated in an ice bath and then were centrifuged at 16,560 g for 30 min at 4°C. The supernatants were collected and transferred into new pre-cooled 1.5 ml centrifuge tubes and were centrifuged again at 16,560 g for 15 min at 4°C. The asynchronous and mitotic supernatants were collected and transferred into new pre-cooled 1.5 ml centrifuge tubes. The concentrations of these extracts were measured with a spectrophotometer and then were stored in liquid nitrogen.HeLa cells were grown in 15 cm cell culture plates. Asynchronous cells and mitotic cells were collected. After being washed in pre-cooled EBS buffer  and then was made up to 25 µl with dH2O, followed by incubation at 30°C for 90 min. The in vitro-translated (IVT) proteins were then used immediately or stored at −20°C for later use.Importin-α1 DNA was subcloned into pcDNA3.1-Myc (which contains a T7 promoter) and then was transcribed and translated in rabbit TNT reticulocyte lysate using a TNT Quick Coupled Transcription/Translation Kit (Promega). Next, 500 ng of the pcDNA-importin-α1 WT or mutant DNA was mixed with 20 µl of TNT lysate and 1 µl of -labeled IVT protein were incubated at 30°C for 30 min in buffer containing 50 mM Tris-HCl pH 7.5, 100 µM DTT, 100 µM ATP and 100 µM MgCl2. The reactions were terminated by the addition of 2× sample buffer and equimolar MnCl2. The samples were boiled at 100°C for 3 min, followed by centrifugation for 5 min at 15,870 g. The samples were subjected to Phos-tag™ SDS-PAGE and conventional SDS-PAGE. The gels were then dried on a gel dryer for 2 h. [35S]-labeled proteins were detected by autoradiography of the dried gel after overnight exposure at −80°C.To determine the kinase activity in HeLa cell extracts that could phosphorylate recombinant importin-α1 proteins, 2 μl of radiolabeled IVT protein was incubated with 20 µg of cell extracts in a 10-µl kinase reaction containing 50 mM Tris-HCl pH 7.5, 100 µM DTT, 100 µM ATP and 100 µM MgClFor radiolabeled proteins separated by SDS-PAGE, the gels were dried onto 3-mm chromatography paper at 80°C for 2 h under vacuum. The gels were exposed to X-omat film (Kodak) overnight at −80°C or longer. Radiating bands were viewed after the films were developed.in vivo, chemically synthesized siRNAs were used. The siRNA sequence were as follows: negative control siRNA, 5′-UUCUCCGAACGUGUCACGU-3′; importin-α1 siRNA 1#, 5′-CCAAGAUUAUUCUGGUUAU-3′ (U→C mutations for siRNA resistance are indicated in bold). Where stated, anti-Luc (luciferase) siRNA (Dharmacon) was also used as a negative control.To knockdown importin-α1 For KPNA2 siRNA-resistant plasmids, we generated point mutations in GFP–importin-α1 using site-directed mutagenesis as stated above. Next, 100 pmol of siRNA and 1 μg of resistant plasmids were transfected into HeLa cells using 5 μl of Lipofectamine 2000 (Invitrogen) for 72 h. The cells were then collected for western blotting or immunofluorescence and microscopy.HeLa cells grown on coverslips were washed with PBS buffer and then were fixed with precooled methanol for 6 min on ice. After rehydration in TBS and blocking with 3% BSA for 30 min, the cells were incubated with primary antibodies overnight at 4°C. Next, the cells were washed with TBS three times and were incubated with secondary antibodies for 1 h at room temperature. Finally, after washing with TBS, the cells were stained with 1 μg/ml of DAPI and were observed under a DeltaVision microscopy imaging system. The spindle length and fluorescence intensity were measured by Volocity software (Perkin Elmer). Statistical quantifications were performed using GraphPad Prism5 software.HeLa cells were grown in glass-bottomed dishes and were transfected with the indicated siRNA for 72 h or plasmids for 24 h. The cells were then placed in a DeltaVision live cell station for automated time-lapse imaging. Images were captured every 3 min under a fluorescence microscope. The average time from nuclear envelope breakdown (NEBD) to sister chromosome separation of cells was calculated and statistically analyzed using GraphPad Prism5 software.


The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only  The limited availability of high-resolution 3D RNA structures for model training limits RNA secondary structure prediction. Here, the authors overcome this challenge by pre-training a DNN on a large set of predicted RNA structures and using transfer learning with high-resolution structures. As a result, the knowledge of the RNA secondary structure is essential for modeling RNA structures and understanding their functional mechanisms. As such, many experimental methods have been developed to infer paired bases by using one-dimensional or multiple-dimensional probes, such as enzymes, chemicals, mutations, and cross-linking techniques coupled with next-generation sequencing3. However, precise base-pairing information at the resolution of single base pairs still requires high-resolution, three-dimensional RNA structures determined by X-ray crystallography, nuclear magnetic resonance (NMR), or cryogenic electron microscopy. With 4 having experimentally determined structures5, it is highly desirable to develop accurate and cost-effective computational methods for direct prediction of RNA secondary structure from sequence.RNA secondary structure is represented by a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Stacking these base pairs forms the scaffold driving the folding of RNA three-dimensional structures6. Comparative sequence analysis determines base pairs conserved among homologous sequences. These methods are highly accurate7 if a large number of homologous sequences are available and those sequences are manually aligned with expert knowledge. However, only a few thousand RNA families are known in Rfam8. As a result, the most commonly used approach for RNA secondary-structure prediction is to fold a single RNA sequence according to an appropriate scoring function. In this approach, RNA structure is divided into substructures such as loops and stems according to the nearest-neighbor model9. Dynamic programming algorithms are then employed for locating the global minimum or probabilistic structures from these substructures. The scoring parameters of each substructure can be obtained experimentally10  or by machine learning . However, the overall precision  appears to have reached a “performance ceiling”6 at about 8018. This is in part because all existing methods ignore some or all base pairs that result from tertiary interactions19. These base pairs include lone (unstacked), pseudoknotted (non-nested), and noncanonical  base pairs as well as triplet interactions20. While some methods can predict RNA secondary structures with pseudoknots  and others can predict noncanonical base pairs , none of them can provide a computational prediction for both, not to mention lone base pairs and base triplets.Current RNA secondary-structure prediction methods can be classified into comparative sequence analysis and folding algorithms with thermodynamic, statistical, or probabilistic scoring schemes28 and SPOT-Contact29 with deep-learning neural network algorithms such as Residual Networks (ResNets)30 and two-dimensional Bidirectional Long Short-Term Memory cells (2D-BLSTMs)32. SPOT-Contact treats the entire protein “image” as context and used an ensemble of ultra-deep hybrid networks of ResNets coupled with 2D-BLSTMs for prediction. ResNets can capture contextual information from the whole sequence “image” at each layer and map the complex relationship between input and output. Also, 2D-BLSTMs proved very effective in propagating long-range sequence dependencies in protein structure prediction29 because of the ability of LSTM cells to remember the structural relationship between the residues that are far from each other in their sequence positions during training. Similar to protein contact map, a RNA secondary structure is a two-dimensional contact matrix, although its contacts are defined differently . However, unlike proteins, the small number of nonredundant RNA structures available in the Protein Data Bank (PDB)5 makes deep-learning methods unsuitable for direct single-sequence-based prediction of RNA secondary structure. As a result, machine-learning techniques are rarely utilized. To our knowledge, the only example is mxfold33 that employs a small-scale machine-learning algorithm (structured support vector machines) for RNA secondary-structure prediction. Its performance after combining with a thermodynamic model makes some improvement over folding-based techniques. However, mxfold is limited to canonical base pairs without accounting for pseudoknots.The work presented in this paper is inspired by a recent advancement in the direct prediction of protein contact maps from protein sequences by Raptor-X34) with automated annotation of secondary structure was released. While this database is large enough for us to employ deep-learning techniques, the annotated secondary structures from the comparative analysis may not be reliable at the single base-pair level. To overcome this limitation, we first employed bpRNA to train an ensemble of ResNets and LSTM networks, similar to the ensemble used by us for protein contact map prediction by SPOT-Contact29. We then further trained the large model with a small database of precise base pairs derived from high-resolution RNA structures. This transfer-learning technique35 is used successfully by us for identifying molecular recognition features in intrinsically disordered regions of proteins36. The resulting method, called SPOT-RNA, is a deep-learning technique for predicting all bases paired, regardless if they are associated with tertiary interactions. The new method provides more than 53Recently, a large database of more than 100,000 RNA sequences  as the only input, we trained many two-dimensional deep-learning models with various combinations in the numbers and sizes of ResNets, BLSTM, and FC layers with a layout shown in Fig. We trained our models of ResNets and LSTM networks by building a nonredundant set of RNA sequences with annotated secondary structure from bpRNAmentclass2pt{minimmentclass2pt{minimmentclass2pt{minim40 against the training data (TR0 and TR1) with an e-value cutoff of 10. To examine the consistency of the models built, we performed 5-fold cross-validation by combining TR1 and VL1 datasets. The results of cross-validation on training data (TR1+VL1) and unseen TS1 for the ensemble of the same top 5 models are shown in Table The models obtained from the bpRNA dataset were transferred to further train on base pairs derived from high-resolution nonredundant RNA structures with TR1 (training set), VL1 , and TS1 (test set) having 120, 30, and 67 RNAs, respectively. The TS1 set is independent of the training data (TR0 and TR1) as it was obtained by first filtering through CD-HIT-EST at the lowest allowed sequence-identity cutoff  on the structured RNA train set TR1) and validated by VL1 and tested by TS1. The performance of the ensemble of five models by direct learning on VL1 and TS1 is shown in Table  and valientclass1pt{minimaFigure entclass1pt{minimaP value t test. Also, we calculated the ensemble defect (see the “Methods” section) from the predicted base-pair probabilities for SPOT-RNA, CONTRAfold, and CentroidFold on TS1. The ensemble defect metric describes the deviation of probabilistic structural ensembles from their corresponding native RNA secondary structure, where 0 represents a perfect prediction. The ensemble defect for SPOT-RNA was 0.19 as compared with 0.24 and 0.25 for CONTRAfold and CentroidFold, respectively, showing that the structural ensemble predicted by SPOT-RNA is more similar to target structures in comparison with the other two predictors.The results presented in Fig. L Our method was trained for RNAs with a maximum length of 500 nucleotides, due to hardware limitations. It is of interest to determine how our method performs in terms of size dependence. As the maximum sequence length in TS1 was 189, therefore, we added 32 RNAs of sequence length from 298 to 1500 to TS1 by relaxing the resolution requirement to 4 Å and including RNA chains complexed with other RNAs (but ignored inter-RNA base pairs). The reason for relaxing the resolution to 4 Å and including RNA chains complexed with other RNAs because there were not many high-resolution and single-chain long RNAs in PDB. Supplementary Fig. The above comparison may be biased toward our method because almost all other methods compared can only predict canonical base pairs, which include Watson–Crick (A–U and G–C) pairs and Wobble pairs (G–U). To address this potential bias, Table 34 (available at https://github.com/hendrixlab/bpRNA) was used to obtain base pairs in pseudoknots from both native and predicted secondary structures. Table Base pairs associated with pseudoknots are challenging for both folding-based and machine-learning-based approaches because they are often associated with tertiary interactions that are difficult to predict. To make a direct comparison in the capability of predicting base pairs in pseudoknots, we define pseudoknot pairs as the minimum number of base pairs that can be removed to result in a pseudoknot-free secondary structure. The program bpRNAi, j] in the absence of  and ). Triplets refer to the rare occasion of one base forming base pairs with two other bases. As shown in Supplementary Table Noncanonical pairs, triplets, and lone base pairs are also associated with tertiary interactions other than pseudoknots. Here, lone base pairs refer to a single base pair without neighboring base pairs  from TS1 with one high performance and one average performance, respectively. For both the examples, SPOT-RNA is able to predict noncanonical base pairs (in green), pseudoknot base pairs, and lone pair (in blue), while mxfold and IPknot remain unsuccessful to predict noncanonical and pseudoknot base pairs.Secondary structure of RNAs is characterized by structural motifs in their layout. For each native or predicted secondary structure, the secondary-structure motif was classified by program bpRNAentclass1pt{minimaP value t test. Ensemble defect on TS2 is the smallest by SPOT-RNA . Here, we did not compare the performance in pseudoknots because the number of base pairs in pseudoknots  in this dataset is too small to make statistically meaningful comparison.To further confirm the performance of SPOT-RNA, we compiled another test set (TS2) with 39 RNA structures solved by NMR. As with TS1, TS2 was made nonredundant to our training data by using CD-HIT-EST and BLAST-N. Figure 43 was compared with the native structure in Fig. 44 and Synthetic Construct Hatchet Ribozyme 45 are compared with their respective native secondary structure in Fig. 45. Thus, their monomeric native structures are obtained by replacing the swapped stand by its original stand. SPOT-RNA is able to predict both the stems and pseudoknot (in Blue) with an overall F1 score of 0.90 for Glutamine II Riboswitch. For Hatchet Ribozyme, SPOT-RNA is able to predict native-like structure with F1 score of 0.74 although it has missed noncanonical and pseudoknot base pairs.In addition, we found a total of 6 RNAs with recently solved structures  that are not redundant according to CD-HIT-EST and BLAST-N to our training sets (TR0 and TR1) and test sets (TS1 and TS2). The prediction for a synthetic construct RNA 46, Mango Aptamer 47, and Adenovirus Virus-associated RNA 48. SPOT-RNA achieves F1 score of 0.57, 0.41, and 0.63 on Pistol Ribozyme, Mango Aptamer, and adenovirus virus-associated RNA, respectively. For this level of performance, it is more illustrative to show a one-dimensional representation of RNA secondary structure Performance comparison on these 6 RNAs with 12 other secondary-structure predictors is shown in Fig. This work developed RNA secondary-structure prediction method purely based on deep neural network learning from a single RNA sequence. Because only a small number of high-resolution RNA structures are available, deep-learning models have to be first trained by using a large database of RNA secondary structures (bpRNA) annotated according to comparative analysis, followed by transfer learning to the precise secondary structures derived from 3D structures. Although the slightly noisy data in bpRNA lead to an upbound around 96entclass1pt{minimaOne advantage of a pure machine-learning approach is that all base pairs can be trained and predicted, regardless if it is associated with local or nonlocal (tertiary) interactions. By comparison, a folding-based method has to have accurate energetic parameters to capture noncanonical base pairs and sophisticated algorithms for a global minimum search to account for pseudoknots. SPOT-RNA represents a significant advancement in predicting noncanonical base pairs. Its F1 score improves over CycleFold by 47To further confirm the performance, SPOT-RNA was applied to 39 RNA structures determined by NMR (TS2). Unlike X-ray structures, structures determined by NMRs resulted from minimization of experimental distance-based constraints. These 39 NMR structures, smaller with average length of 51 nucleotides, have only a total of 21 base pairs in pseudoknots. As a result, they are much easier to predict for all methods  E5-2630v4 CPU is 540 s, which is faster than Knotty (2800 s) but slower than IPknot (1.2 s), ProbKnot (13 s), and pkiss (112 s). However, our distributed version can be easily run on multiple CPU threads or on GPUs. For example, by running SPOT-RNA on a single Nvidia GTX TITAN X GPU, the computation time for predicting all 62 RNAs would be reduced to 39 s. Thus, SPOT-RNA can feasibly be used for genome-scale studies.40 and HHblits49 as well as direct coupling analysis among homologous sequences50 are the key input vectors responsible for the recent improvement in highly accurate prediction. Thus, one expects that a similar evolution-derived sequence profile generated from BLAST-N and direct/evolution-coupling analysis would further improve secondary-structure prediction for nonlocal base pairs in long RNAs, in particular. Indeed, recently, we have shown that using evolution-derived sequence profiles significantly improves the accuracy of predicting RNA solvent accessibility and flexibility39. For example, the correlation coefficient between predicted and actual solvent accessibility increases from 0.54 to 0.63 if a single sequence is replaced by a sequence profile from BLAST-N38. However, the generation of sequence profiles and evolution coupling is computationally time consuming. The resulting improvement (or lack of improvement) is strongly depending on the number of homologous sequences available in current RNA sequence databases. If the number of homologous sequences is too low (which is true for most RNAs), it may introduce more noise than the signal to prediction as demonstrated in protein secondary structure and intrinsic disorder prediction52. Moreover, synthetic RNAs will not have any homologous sequences. Thus, we present the method with single-sequence information as input in this study. Using sequence profiles and evolutionary coupling as input for RNA secondary-structure prediction is working in progress.This work has used a single RNA sequence as the only input. It is quite remarkable that relying on a single sequence alone can obtain a more accurate method than existing folding methods in secondary-structure prediction. For protein contact map prediction, evolution profiles generated from PSIBLAST33. Moreover, most thermodynamic methods simply ignore noncanonical base pairs and many do not even account for pseudoknots. mxfold, for example, employs a pseudoknot-free thermodynamic method to combine with its machine-learning model. Thus, balancing the performance for canonical, noncanonical, and pseudoknots will require a careful selection of appropriate scoring schemes. A simple integration may lead to high performance in one type of base pair at the expense of other types of base pairs. Nevertheless, we found that if we simply keep only the base pair with the highest predicted probability in predicted triple interactions, SPOT-RNA would be improved by another 3Another possible method for further improving SPOT-RNA is to employ the predicted probability as a restraint for folding with an appropriate scoring function. Such a dual-approach method will likely improve SPOT-RNA as folding optimization may have a better capability to capture nonlocal interactions between WC pairs for long RNAs, in particular as shown in Supplementary Fig. 53. Moreover, improvement in predicting secondary structural motifs 34, which consists of 102,348 RNA sequences with annotated secondary structure. Sequences with sequence similarity of more than 80% were removed by using CD-HIT-EST37. About 8039. After removing sequence similarity, 14,565 sequences remained. RNA sequences with RNA structures from the PDB5 available in this dataset were also removed as we prepared separate datasets based on RNAs with PDB structure only5. Moreover, due to hardware limitations for training on long sequences, the maximum sequence length was restricted to 500. After preprocessing, this dataset contains 13,419 sequences. These sequences were randomly split into 10,814 RNAs for training (TR0), 1300 for validation (VL0), and 1,305 for independent test (TS0). Supplementary Table 34 (available at https://github.com/hendrixlab/bpRNA).The datasets for initial training were obtained from bpRNA-1m (Version 1.0)5. Sequences with similarity of more than 8037. After removing sequence similarity, only 226 sequences remained. These sequences were randomly split into 120, 30, and 76 RNAs for training (TR1), validation (VL1), and independent test (TS1), respectively. Furthermore, any sequence in TS1 having sequence similarity of more than 8040 to further remove potential sequence homologies with training data with a large e-value cutoff of 10. This procedure further decreased TS1 from 69 to 67 RNAs.The datasets for transfer learning were obtained by downloading high-resolution  for all the sets are listed in Supplementary Table http://sparks-lab.org/jaswinder/server/SPOT-RNA/ and https://github.com/jaswindersingh2/SPOT-RNA.The secondary structure of all the PDB sets was derived from their respective structures by using DSSR34. A stem is defined as a region of uninterrupted base pairs, with no intervening loops or bulge. A hairpin loop is a sequence of unpaired nucleotides with both ends meeting at the two strands of a stem region. An internal loop is defined as two unpaired strands flanked by closing base pairs on both sides. A bulge is a special case of the internal loop where one of the strands is of length zero. A multiloop consists of a cycle of more than two unpaired strands, connected by stems. The distribution of different secondary-structure types in TR1, VL1, and TS1 (excluding multiplet base pairs) is shown in Supplementary Table 34.For the classification of different RNA secondary-structure types, we used the same definitions as previously used by bpRNA29.We employed an ensemble of deep-learning neural networks for pretraining. The ensemble is made of 5 top-ranked models based on their performance on VL0 with the architecture shown in Fig. 30. The initial convolution layer is followed by 59 activation function and the layer normalization technique60 were used. A dropout rate of 2561. In some models, we used dilated convolutions that are reported to better learn longer-range dependencies62. For the dilated convolutional layers, the dilation factor was set to The architecture of each model consists of ResNet blocks followed by a 2D-BLSTM layer and a fully connected (FC) block. An initial convolution layer for pre-activation was used before our ResNet blocks as proposed in He et al.mentclass2pt{minim32. The output from the final ResNet block was activated (with ELU) and normalized  before being given as an input to the 2D-BLSTM. The number of nodes in each LSTM direction cell was The next block in the architecture was a 2D-BLSTMmentclass2pt{minim63 and trained by using the ADAM optimization algorithm64 with default parameters. All models were trained on Nvidia GTX TITAN X graphics processing unit (GPU) to speed up training65. We trained multiple deep-learning models, based on the architecture shown in Fig. Each model was implemented in Google’s Tensorflow framework v1.12).1263 and35 involves further training a large model that was trained on a large dataset for a specific task to some other related task with limited data. In this project, we used our large dataset bpRNA for initial training, and then transfer learning was employed by using the small PDB dataset as shown in Fig. L is the length of the RNA sequence. All of the labels used during the transfer learning were derived from high-resolution X-ray structures in the PDB. Some approaches in transfer learning freeze weights for specific layers and train for other layers. Here, we trained all the weights of the models without freezing any layer, as this provided better results. Previous work on protein molecular recognition features (MoRFs) prediction36 also showed that using transfer learning by retraining through all of the weights provides a better result than freezing some of the layers during retraining.Transfer learningDuring transfer learning on TS1, we used the same hyperparameters  that were used for the TS0-trained models. All the models were validated for VL1, and based on the performance of these models on VL1, the 5 best models were selected for the ensemble. The parameters of these models are shown in Supplementary Table L L is the length of the RNA sequence and 4 corresponds to the number of base types . In one-hot encoding, a value of 1 was assigned to the corresponding base-type position in the vector and 0 elsewhere. A missing or invalid sequence in residue value of −1 was assigned in one-hot encoded vector.The input to SPOT-RNA is an RNA sequence represented by a binary one-hot vector of size L L L 28. The input is standardized to have zero mean and unit variance (according to the training data) before being fed into the model.This one-dimensional  66 was also used. MCC is calculated asRNA secondary-structure prediction is a binary classification problem. We used sensitivity, precision, and F1 score for performance measure where sensitivity is the fraction of predicted base pairs in all native base pairs  is given bywhere TN denotes true negatives. MCC measures the correlation between the expected class and the obtained class. Moreover, a precision-recall (sensitivity) curve is used to compare our model with currently available RNA secondary-structure predictors. To show the statistical significance of improvement by SPOT-RNA over the second-best predictor, a paired L is the length of the sequence. The smaller the value of ED is, the higher the structural similarity between predicted base-pair probability and target structure.where 33 (available at https://github.com/keio-bioinformatics/mxfold), ContextFold16 (available at https://www.cs.bgu.ac.il/negevcb/contextfold/), CONTRAfold14 (available at http://contra.stanford.edu/contrafold/), Knotty24 (available at https://github.com/HosnaJabbari/Knotty), IPknot23 (available at http://rtips.dna.bio.keio.ac.jp/ipknot/), RNAfold11 (available at https://www.tbi.univie.ac.at/RNA/), ProbKnot22 (available at http://rna.urmc.rochester.edu/RNAstructure.html), CentroidFold15 (available at https://github.com/satoken/centroid-rna-package), RNAstructure12 (available at http://rna.urmc.rochester.edu/RNAstructure.html), RNAshapes13 (available at https://bibiserv.cebitec.uni-bielefeld.de/rnashapes), pkiss13 (available at https://bibiserv.cebitec.uni-bielefeld.de/pkiss), and CycleFold27 (available at http://rna.urmc.rochester.edu/RNAstructure.html). In most of the cases, we used default parameters for secondary-structure prediction except for pkiss. In pkiss, we used Strategy C that is slow but thorough in comparison with Strategies A and B that are fast but less accurate. For CONTRAfold and CentroidFold their performance metrics are derived from their predicted base-pair probabilities with threshold values from maximizing MCC.We compared SPOT-RNA with 12 best available predictors. We downloaded the stand-alone version of mxfoldFurther information on research design is available in the Supplementary InformationReporting Summary


Macrolides are antimicrobial agents that can be used to treat a variety of infections. Allergic reactions to macrolides occur infrequently but can include minor to severe cutaneous reactions as well as systemic life-threatening reactions such as anaphylaxis. Most reports of allergic reactions occurred in patients without prior exposure to a macrolide. Cross-reactivity among macrolides may occur due to the similarities in their chemical structures; however, some published literature indicates that some patients can tolerate a different macrolide. Most published reports detailed an allergic reaction to erythromycin. Desensitization protocols to clarithromycin and azithromycin have been described in the literature. The purpose of this article is to summarize macrolide-associated allergic reactions reported in published literature. An extensive literature search was conducted to identify publications linking macrolides to hypersensitivity reactions. One of the most common causes of medication allergies among adults and children is antibiotics . These aThe structure of macrolides consists of a large lactone ring that varies in size from 12 to 18 atoms. Sugar molecules are attached to the lactone ring with glycosidic bonds. Macrolide antibiotics are classified based on the number of atoms in the lactone ring; 14-membered lactones (erythromycin and clarithromycin), 15-membered lactones (azithromycin), ketolide (telithromycin) and 18-membered lactone (fidaxomicin). All clinically available macrolides, with the exception of erythromycin and fidaxomicin, are either synthetically or semi-synthetically generated. Natural macrolides have instability in the gastric environment, resulting in undesired pharmacokinetic properties, such as incomplete absorption resulting in decreased bioavailability [All macrolides except for fidaxomicin block bacterial protein synthesis by binding reversibly to the 23S ribosomal RNA (rRNA) in the 50S-subunit of prokaryotic ribosomes . FidaxomAllergic reactions to macrolides are relatively less common compared to other classes of antibiotics  4]. Mac. Mac4]. Macrolides are a well-established class of antibiotics. They exhibit bacteriostatic activity against a wide range of gram-positive, gram-negative, and atypical bacteria . ErythroCorynebacterium diphtheriae, although antitoxin is the primary treatment. Erythromycin is also the drug of choice for the treatment of infants with pneumonia caused by Chlamydia trachomatis. Azithromycin is the drug of choice for the treatment of trachoma, urethritis, and cervicitis caused by Chlamydia trachomatis and chancroid caused by Haemophilus ducreyi. Azithromycin with or without rifampin is also one of the regimens of choice for the treatment of Legionella pneumophila, which can cause serious atypical pneumonia. Clarithromycin is the drug of choice for the treatment of Helicobacter pylori as part of a combination regimen with amoxicillin and omeprazole. Clarithromycin in addition to amikacin is the regimen of choice for the treatment of infections caused by Mycobacterium fortuitum and Mycobacterium chelonae. Erythromycin and azithromycin are the drugs of choice for the treatment of infections caused by Bartonella henselae (cat scratch fever), Bartonella quintana (trench fever), Campylobacter jejuni (diarrhea), Chlamydia trachomatis (conjunctivitis and urethritis), and Ureaplasma urealyticum (urethritis). Azithromycin and clarithromycin are the drugs of choice for the treatment of infections caused by Mycobacterium avium complex as part of a combination regimen with ethambutol and rifabutin or monotherapy for primary or secondary prophylaxis. Lastly, all three macrolides are the drugs of choice for the treatment of pertussis (whooping cough) caused by Bordetella pertussis and atypical pneumonia caused by Chlamydophila pneumonia and Mycoplasma pneumoniae.Macrolides are the drugs of choice for the treatment of various atypical bacteria . ErythroStreptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis is on the rise . Sw. SwMycobllularae  [100]. Rortuitum  [102]. IMacrolides are infrequently reported to cause various types of allergic reactions, with cutaneous reactions being the most common. Macrolides are similar in chemical structure, and limited reports have demonstrated cross-reactivity. Strategies to overcome hypersensitivity reactions, such as desensitization, or allergy testing for cross-reactivity to another macrolide have been utilized with successful outcomes. Consequently, if a patient has a severe hypersensitivity reaction to a macrolide, then the benefit versus the risk must be evaluated for using an agent in this class. In clinical practice, it may be more convenient and safer to change to an alternative class of medications if the option is available. However, if a macrolide must be used in a patient with a confirmed history of a severe allergic reaction, then allergy testing should be employed to investigate if the patient reacts to other agents in the macrolide class. If so, a desensitization may be conducted while the patient is monitored closely for signs and symptoms of an allergic reaction.


RNA has emerged as the prime target for diagnostics, therapeutics and the development of personalized medicine. In particular, the non-coding RNAs (ncRNAs) that do not encode proteins, display remarkable biochemical versatility. They can fold into complex structures and interact with proteins, DNA and other RNAs, modulating the activity, DNA targets or partners of multiprotein complexes. Thus, ncRNAs confer regulatory plasticity and represent a new layer of epigenetic control that is dysregulated in disease. Intriguingly, for long non-coding RNAs  structural conservation rather than nucleotide sequence conservation seems to be crucial for maintaining their function. LncRNAs tend to acquire complex secondary and tertiary structures and their functions only impose very subtle sequence constraints. In the present review we will discuss the biochemical assays that can be employed to determine the lncRNA structural configurations. The implications and challenges of linking function and lncRNA structure to design novel RNA therapeutic approaches will also be analyzed. The HUMAN GENOME project has transformed our understanding of the basic unit of genetic information with RNA emerging as a versatile regulator of central cellular processes . The nonSubstantial effort has now been directed toward dissecting the function of lncRNAs. In the cardiovascular system, lncRNAs were reported to play key roles in physiology and disease and targeting lncRNAs for novel therapeutic interventions has been explored . Here wecis-and trans-regulatory activity have been described  is a high-throughput enzymatic probing method that measures the structural properties of isolated polyadenylated transcript pools that are renatured ormation .Frag-Seq (fragmentation sequencing) is an enzymatic method that uses a nuclease P1 to specifically cleave single-stranded RNA. High-throughput sequencing then analyses the fragments generated. This workflow provides an “RNA accessibility profile” that is likened to the DNase hypersensitivity assays on chromatin . Noteworin vitro and in vivo as DMS can easily penetrate the cell membrane. DMS-seq that combines DMS methylation with NGS was recently performed in vivo (The dimethyl sulfate (DMS) is a base specific reagent that can bind and alter the methylation state of unpaired adenosine and cytosine nucleotides . DMS foo in vivo .in vivo. Subsequently, RNA is isolated from cells and the methylation sites are determined by employing gene specific primers for the reverse transcription reaction. Sequencing of the DMS derived fragments can be used to assess the cellular conformation of the RNA. Based on this method, structural models of elements within Xist were developed  can interrogate the RNA structure both tructure . Severaltructure . In SHAPde novo discovery and direct validation of new functional motifs  was the first to combine the SHAPE protocol with NGS. Initially performed and reported to define the HIV-1 RNA genome, SHAPE-MaP is a highly sensitive technique that allowed rapid, l motifs .in vivo. It revealed translational regulatory mechanisms in E. coliin vivo (In-cell SHAPE-Seq is a modification of the SHAPE-Seq technique that combines the SHAPE-seq with gene expression measurements to elucidate the association of RNA structure and function in vivo .in vivo click SHAPE sequencing) uses the in-cell SHAPE chemical NAI-N3 followed by selective chemical enrichment of NAI-N3-modified RNA that provides an improved signal-to-noise ratio  was recently developed to determine both RNA structure and interactions sitivity . Using tsitivity . Encourain vitro .in vivo is extremely challenging as they are highly heterogenic with regions with well-defined base-pairing, others without base-pairing and regions with multiple structures. Additionally, lncRNAs may stretch across thousands of nucleotides, they are expressed in low abundance and tend to be part of multicomponent complexes (Table 1).Structure determination of lncRNA omplexes . NeverthIn vivo SHAPE data identified 33 regions in Xist that form well-defined secondary structures linked by structurally variable and dynamic regions.This is a very long lncRNA  controlling X chromosome inactivation. It spreads across the entire chromosome while triggering stable epigenetic modifications through recruitment of the PRC2 complex and enrichment for the H3K27me3 repressive chromatin modification . In vivoin vitro, an intricate structure of three independently folding modules was revealed. Phylogenetic analysis and computational 3D modeling demonstrated a defined tertiary architecture that can form autonomously in the absence of protein partners  are 3,700 and 1,200 nucleotides in length, respectively.  complex .in vitro. In parallel, RNase V1 enzymatic probing was performed. It was shown that SRA consists of four distinct domains with a variety of secondary structures (The human steroid receptor RNA activator (SRA) is an 870 nucleotide lncRNA that is derived from a gene encoding both lncRNA and protein coding transcripts. The structure of SRA was experimentally interrogated using SHAPE and DMS chemical probing ructures . More imructures .in vitro using chemical probing with SHAPE and DMS reagents  and a 55 nucleotide stretch at the 3′ end exhibiting high reactivity suggesting low probability of structure. Genetic deletion of this specific 11 nucleotide fragment demonstrated that the AGIL motif is essential for mouse embryonic stem cell differentiation to cardiomyocytes through binding of the zing-finger protein CNBP/ZNF9  to the DNA, can be guided in the immediate vicinity of the proto-adjacent motif NGG by an RNA molecule (sgRNA) consisting of a small 20 nucleotide long variable sequence and an adaptor transactivating RNA. Precise insertions, deletions, or base substitutions can be introduced at a DSB site  in prima disease . A modif disease  or an ac disease  dCas9 anin vivo are critical to determine lncRNA modules and structural domains. RNA structure determination can be coupled with comparative genomics analysis that will take into consideration the positional conservation and the fact that lncRNAs may rely on short elements rather than long stretches of conserved sequences. Genetic studies that can target precisely these structural domains while maintaining the expression of the lncRNA (Once specific lncRNAs are identified, technologies that can define the lncRNA structure e lncRNA  will dele lncRNA  can provAZ initiated the study, designed its structure, and wrote the manuscript. AA provided conceptual advice and revised the manuscript. KS designed the review structure, provided conceptual advice, and revised the manuscript. All authors read 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.


As they may affect various human biological processes, short-lived lncRNAs may be useful indicators of the degree of stress on chemical exposure. In the present study, we identified four short-lived lncRNAs, designated as OIP5-AS1, FLJ46906, LINC01137, and GABPB1-AS1, which showed significantly upregulated expression following exposure to hydrogen peroxide (oxidative stress), mercury II chloride , and etoposide (DNA damage stress) in human HepG2 cells. These lncRNAs may be useful indicators of chemical stress responses. The levels of these lncRNAs in the cells were increased because of chemical stress-induced prolongation of their decay. These lncRNAs were degraded by nuclear RNases, which are components of the exosome and XRN2, and chemical exposure inhibited the RNase activities within the cells.Long noncoding RNAs (lncRNAs) are non-protein-coding transcripts >200 nucleotides in length that have been shown to play important roles in various biological processes. The mechanisms underlying the induction of lncRNA expression by chemical exposure remain to be determined. We identified a novel class of short-lived lncRNAs with half-lives ( These lncRNAs play a wide range of structural and regulatory roles in various cellular processes, including chromatin remodeling, imprinting, transcription, translation, and epigenetic regulation4.Large numbers of intergenic, intronic, and cis-antisense long noncoding RNAs (lncRNAs), non-protein-coding transcripts >200 nucleotides in length, have been identified in mammals and shown to be transcribed in a tissue- and developmental stage-specific manner5. HOTAIR interacts with the catalytic subunit of polycomb repressive complex 2 (PRC2) and enhancer of zeste homolog 2 (EZH2) via its 5′ end7, while the 3′ end interacts with the histone demethylase lysine-specific histone demethylase 1 A (LSD1)7. Another lncRNA, nuclear-enriched abundant transcript 1 (NEAT1), has been shown to an essential structural component of paraspeckles10. The interaction of NEAT1 with splicing factor proline- and glutamine-rich (SFPQ) was shown to be involved in both the repression and activation of gene expression in a manner depending on the context of the promoter sequence and interactions with other transcription factors14. The lncRNA, Growth arrest-specific 5 (GAS5), is upregulated under conditions of serum starvation or treatment with translation inhibitors that induce growth arrest16, and has been shown to function as a riborepressor for the glucocorticoid receptor (GR) by binding to its DNA-binding domain and acting as a decoy glucocorticoid response element (GRE)17. The regulatory role of GAS5 for the apoptosis-related genes, cellular inhibitor of apoptosis protein 1 (cIAP2) and serum/glucocorticoid regulated kinase 1 (SGK1), is controlled by its RNA degradation pathway18. Although previous studies have demonstrated a number of important roles of lncRNAs, little is known regarding changes in their expression levels on exposure to chemical stressors.For example, the lncRNA, HOX transcript antisense RNA (HOTAIR), has been reported to play roles in the regulation of gene expression via a mechanism involving epigenetic modification20. Studies using the 5′-bromouridine immunoprecipitation chase-deep sequencing (BRIC-seq) genome-wide method for determining RNA stability23 showed that well-known regulatory lncRNAs, such as HOTAIR, NEAT1, and GAS5, have short half-lives (t1/2 ≤ 4 hours), while housekeeping lncRNAs, such as rRNAs, small nucleolar RNAs (snoRNAs), and small Cajal body-specific RNAs (SCARNAs), have long half-lives (t1/2 > 4 hours)20. These observations suggest that the half-lives of lncRNAs are correlated with their functional characteristics, and that the short-lived lncRNAs may include a disproportionate number of those with regulatory functions. Therefore, as yet unidentified short-lived lncRNAs may affect biological processes in humans, and they may therefore be useful as indicators of the degree of chemical stress in cells24.The half-lives of lncRNAs were reported to show a wide range of variation, comparable to those of mRNAs in mouse neuro-2a neuroblastoma cells and human HeLa Tet-off cellsin vitro model of human chemical detoxification, is easy to handle, and provides a reproducible human model system. Four lncRNAs, designated as OIP5-AS1, FLJ46906, LINC01137, and GABPB1-AS1, which showed upregulation of expression in response to chemical stressors, were identified in HepG2 cells and these lncRNAs were shown to be degraded by nuclear RNases.The present study was performed to identify novel short-lived lncRNAs that show changes in expression on exposure to chemical stressors in HepG2 cells. This cell line is a useful t1/2 ≤ 4 hours) in HeLa Tet-off cells were first selected according to the following criteria20: (1) >200 nt; (2) highly and moderately expressed transcripts with RPKM (reads per kilobase million) >1 on RNA-Seq analysis in HeLa Tet-off cells; (3) searchable on NCBI; (4) no overlap with regions containing known protein-coding genes; and (5) excluding well-known short-lived lncRNAs, such as HOTAIR, NEAT1, and GAS5 , FLJ46906, long intergenic non-protein-coding RNA 1137 (LINC01137), and GABPB1 antisense RNA 1 (GABPB1-AS1) were observed following treatment with 100 μM hydrogen peroxide, 100 μM mercury II chloride, or 100 μM etoposide, respectively Fig. . TherefoThe degrees of change in expression of the four lncRNAs upregulated following treatment with hydrogen peroxide, mercury II chloride, and etoposide at various concentrations, i.e., OIP5-AS1, FLJ46906, LINC01137, and GABPB1-AS1, were examined Fig. . The lev28  were due to increases in their transcription rates or decreases in their decay rates by determining their transcription rates and half-lives using the 5-ethynyluridine (EU) pulse labeling methodt1/2 of OIP5-AS1 from 2.8 hours to >6 hours  resulted in increases in the urs Fig. , that ofurs Fig. , that ofurs Fig. , and thaurs Fig. . Therefo20, we examined the contributions to their nuclear degradation of the following nuclear components: exosome component 5 (EXOSC5) and exosome component 10 (EXOSC10), which are core (EXOSC5) or sub- (EXOSC10) components of the RNA exosome 3′-to-5′ exonuclease complex that plays a crucial role in the degradation of RNA in eukaryotic nuclei30 along with the trimeric nuclear exosome targeting (NEXT) subunit that directs a subset of noncoding short-lived RNAs for exosomal degradation31 and the trimeric poly(A) tail exosome targeting (PAXT) subunit that directs a subset of long and polyadenylated poly(A) RNAs for exosomal degradation32; MTR4, an ATP-dependent RNA helicase, which is a cofactor for the exosome complex involved in linking to RNA-binding protein adapters33. The EXOSC5, EXOSC10 and MTR4 are associated with NEXT or PAXT complexs. XRN2, a nuclear 5′-to-3′ exonuclease34 that is involved in the torpedo model of transcription termination35. HepG2 cells were transfected with siRNAs specific to each of these components and examined using the 5,6-dichloro-1-β-d-ribofuranosylbenzimidazole (DRB) chase method to determine the decay rates of the lncRNAs, OIP5-AS1, FLJ46906, LINC01137, and GABPB1-AS128 37, our observations suggest that these four lncRNAs may be useful as indicators of common cell stress in HepG2 cells. Indeed, HepG2 cells showed stronger responses to chemical insults than HeLa Tet-off cells and hiPSCs in the present study. In addition, the findings reported here indicated that these lncRNAs are degraded by nuclear RNases, i.e., exosome components and XRN2, and that the chemical stressors examined inhibited the activities of these RNases thus preventing degradation of the lncRNAs. The results of knockdown experiments in the present study indicated that GABPB1-AS1 was only degraded by XRN2, and not affected by components of the exosome, which explained its lower degree of upregulation compared to the other three lncRNAs. To our knowledge, this study is the first to show that nuclear RNases are responsible for the degradation of chemical stress-sensitive lncRNAs.Four short-lived lncRNAs, designated as OIP5-AS1, FLJ46906, LINC01137, and GABPB1-AS1, showed changes in expression in response to chemical stress in HepG2 cells , which is important for mitotic progression39. Moreover, OIP5-AS1 was shown to promote G2/M phase progression40. Thus, the chemical-induced upregulation of OIP5-AS1 in the present study may have been because OIP5-AS1 acted as a defense mechanism against chemical stress. Although the functions of the other three lncRNAs identified in the present study, FLJ46906, LINC01137, and GABPB1-AS1, have yet to be determined, our results indicated that the lncRNA degradation pathway plays an important role in regulating their levels in response to cellular stress. Further studies are required to elucidate the physiological functions of these lncRNAs.Several recent studies showed that OIP5-AS1 suppresses cell proliferation in human cells. The RNA-binding protein, Hu Antigen R (HuR), which enhances cell proliferation, was found to be associated with OIP5-AS1 and to stabilize protein-coding mRNAs, such as cyclin A241, and NEAT1 expression was reported to be induced in HeLa cells by influenza virus or herpes simplex virus infection. This resulted in excess paraspeckle formation and relocalization of the repressor of interleukin-8 (IL-8), SFPQ, from the promoter of IL-8 to the paraspeckles, leading to its transcriptional activation12. In addition, infection of HeLa cells with Salmonella was shown to markedly reduce the levels of the exosome/NEXT components, thus resulting in stabilization of the NEAT1 transcript, contributing to the expression of cellular immunity-related genes as part of the cellular defense mechanism against infection41.Pathogenic infections represent other types of cellular stressThe findings of this study will be useful in relating digital genomic information to cellular function, and further studies based on our results will elucidate the relationships between the novel lncRNAs identified here and RNA-binding proteins.Hydrogen peroxide and mercury II chloride were purchased from Wako . Etoposide was purchased from BioVision . Both of these chemicals were dissolved in dimethyl sulfoxide  and diluted in culture medium at a final concentration of 0.1% vol/vol.2.HepG2 cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) at 37 °C in a humidified incubator under an atmosphere of 5% COTotal RNA was extracted from cells with RNAiso Plus , in accordance with the manufacturer’s instructions. The isolated RNA was reverse transcribed into cDNA using PrimeScript RT Master Mix . The resulting cDNA was amplified using the primer sets listed in Table 28. To assess transcription rates, EU (400 μM) and the chemicals of interest were added to the culture medium and incubated for 2 hours. The cells were then harvested, and total RNA was isolated using RNAiso Plus (TaKaRa). EU-labeled RNAs were biotinylated and captured using a Click-iT Nascent RNA Capture kit (Thermo Fisher Scientific). To elute the EU-RNAs, magnetic beads were resuspended in 100 μl of buffer A . ISOGEN LS  was added to the mixture, and EU-labeled RNAs were isolated in accordance with the manufacturer’s instructions; these isolated RNAs were used in subsequent RT-qPCR assays. The total amount of EU-labeled RNA captured was divided by the amount of EU-RNA input. To assess degradation rates, we added EU (200 μM) to the culture medium and incubated the cells for 2 hours. The EU-containing medium was then replaced with EU-free medium containing the chemicals of interest, and cells were harvested at the indicated time points. Total RNA was isolated using RNAiso Plus (TaKaRa). EU-labeled RNAs were biotinylated and captured using a Click-iT Nascent RNA Capture Kit (Thermo Fisher Scientific). To elute the EU-RNAs, magnetic beads were resuspended in 100 μl of buffer A . ISOGEN LS  was added to the mixture, and EU-labeled RNAs were isolated in accordance with the manufacturer’s instructions; these isolated RNAs were used in the subsequent RT-qPCR assays.Analysis of RNA transcription and degradation rates was performed by EU pulse-labeling of RNA using a Click-iT Nascent RNA Capture kit , in accordance with the manufacturer’s instructions, with some modifications as described previouslyThe sequences of the siRNAs used are listed in Table d-ribofuranosylbenzimidazole  chase method28. Total RNAs were isolated in accordance with the manufacturer’s instructions at the indicated time points after addition of 20 μg/mL DRB; these isolated RNAs were used in the subsequent RT-qPCR assays.Analysis of RNA degradation rates in siRNA treatments was performed by the 5,6-dichloro-1-β-Supplementary information.


There is multiple evidence in the literature that a sub-threshold pre-pulse, delivered immediately prior to an electrical stimulation pulse, can alter the activation threshold of nerve fibers and motor unit recruitment characteristics. So far, previously published works combined monophasic stimuli with sub-threshold depolarizing pre-pulses (DPPs) with inconsistent findings—in some studies, the DPPs decreased the activation threshold, while in others it was increased. This work aimed to evaluate the effect of DPPs during biphasic transcutaneous electrical stimulation and to study the possible mechanism underlying those differences. Sub-threshold DPPs between 0.5 and 15 ms immediately followed by biphasic or monophasic pulses were administered to the tibial nerve; the electrophysiological muscular responses  were monitored via electromyogram (EMG) recording from the soleus muscle. The data show that, under the specific studied conditions, DPPs tend to lower the threshold for nerve fiber activation rather than elevating it. DPPs with the same polarity as the leading phase of biphasic stimuli are more effective to increase the sensitivity. This work assesses for the first time the effect of DPPs on biphasic pulses, which are required to achieve charge-balanced stimulation, and it provides guidance on the effect of polarity and intensity to take full advantage of this feature. Clinical and research environments use electrical stimulation (ES) widely. ES induces an artificial electrical field that depolarizes the nerve fibers near the active electrodes and, if the depolarization is large enough, it evokes action potentials in such fibers . AppliedA significant limitation of electrical stimulation is the difficulty to selectively activate target population fibers without trigger surrounding unwanted populations . One apppollicis brevis muscle was reduced with ramped DPPs of 100 ms duration and intensities below 40% of the excitation threshold, while higher intensities of the same DPPs increased the threshold 16]. For The delivery of stimulation pulses was randomized to avoid bias due to uncontrolled factors. A pause of 8 s between pulses was used to reduce possible fatigue effects and to allow full recovery of reflex mechanisms.The stimulation responses were monitored from the evoked potentials of the soleus muscle, which were recorded via surface EMG. Prior to the application of EMG electrodes, the skin was prepared with abrasive gel to reduce the skin-electrode interface impedance. A pair of recording electrodes was placed centrally above the muscle belly with 3-cm inter-electrode distance, while the reference electrode was placed on the anterior side of the leg, over the tibia Fig. a. The EMAll subjects were lying in prone position during the whole session to minimize the voluntary muscle activity, which could cause undesired modulation of responses , 20. ForThe datasets were post-processed in MATLAB . The areas of evoked M-waves were quantified for each pulse. For each subject, the values were normalized with reference to the average area of the responses evoked by cathodic-anodic biphasic pulses.A two-way ANOVA was conducted to compare the effects the DPP duration, polarity of the biphasic pulses, and DPP-polarity interaction on the evoked M-wave responses in the soleus muscle. Additionally, an ANOVA was performed to study the effect of short DPPs (0 to 1 ms) on anodic-cathodic biphasic pulses.The peak-to-peak (P2P) value was used to quantify the magnitude of the H-reflex. In this case, the data were normalized for each pulse configuration, based on the average value without DPP. In analogy to the M-wave assessment, a two-way ANOVA was conducted to assess the effects of the waveform  and pre-pulses  on the evoked reflexes.t tests were also performed to analyze specific differences in the responses. For all statistical tests, the significance level (α) was considered equal to 0.01.Welch N = 6) recorded from one subject (S13) during stimulation with different DPP durations. For the main measurements, the intensity was set to a level that elicited M-waves with a medium-range amplitude, which produced a consistent increase in the M-waves in all subjects when DPPs were added. Additionally, as shown in Fig. A total of ten valid datasets were acquired from nine volunteers. Figure F  = 39.25, p < 0.001; the polarity also has a significant effect on the evoked potentials, F  = 303.24, p < 0.001. The interaction between these factors, on the other hand, does not have a strong statistical effect on the stimulation outcome, F  = 2.50, p = 0.011. Table From the M-wave assessment, 1583 responses (including 2 polarities and 9 DPP durations) were computed. The two-way ANOVA shows a statistically significant influence of the DPP duration on the muscle response, F  = 1.91, p = 0.127.Although an enhancing effect was observed in both polarities, for anodic-cathodic pulses, the DPP-induced effect was not observed until durations higher than 1 ms. This delay is confirmed by the one-way ANOVA test on the responses of anodic-cathodic biphasic pulses with DPPs between 0 and 1 ms, which shows a non-significant influence of DPPs, t test shows that, without DPPs, M-waves elicited by monophasic cathodic stimuli are consistently bigger  than those following cathodic-anodic biphasic pulses , t (D.F. = 161.24) = 6.35, p ˂ 0.001. On the other hand, sole monophasic anodic stimuli produce zero or negligible responses  in comparison with cathodic-anodic biphasic pulses, t (D.F. = 137.50) = 38.72, p ˂ 0.001.The Welch F  = 19.32, p < 0.001) and the pre-pulse  = 35.10, p < 0.001) have a significant influence on the H-reflex amplitude. The data shows that, in general, when the pre-pulse has the same polarity as the stimulating phase, the peak-to-peak value of the response increases. On the other hand, cathodic pulses with an anodic pre-pulse showed a significantly smaller response, t (D.F. = 10.36) = 19.42, p = 0.001.The H-reflex behavior, in response to the addition of DPPs of 5 ms, was assessed in a single case study. The results show that a DPP prior to the stimulation pulse increases the H-reflex, except by DPPs with the inverse polarity on monophasic pulses Table . The ANOThe use of cathodic pre-pulses in our study led to opposing observations in comparison to most of the previously published work in this field, which rather describes a reduction of the nerve fiber excitability by DPPs , 6, 8, 9Grill and Mortimer initially explained the influence of DPPs to be based on conditioning of activation and inactivation gates of the sodium channels . FurtherIf we define “strong” as higher intensity and longer duration—high electrical charge—and “weak” as short and low-intensity pulses—low electrical charge—we can use this parameter, without consideration of the waveform, for comparison and interpretation of already published results in relation to findings in our study.All studies that have observed an excitatory effect of DPPs have been on the application of transcutaneous stimulation to depolarize subjacent nerve fibers, as in the distance of about 25 mm in our case. This implies that the current density in the tissue embedding the fibers are substantially smaller than close to the electrode surface . In addiInterestingly, the use of short (< 1 ms) cathodic DPPs prior to anodic-cathodic biphasic pulses did not significantly influence the responses. This observation suggests that the anodic phase reduces or reverses the effect of the depolarizing pre-pulse before the cathodic phase triggers an action potential. This is consistent with the state model of Armstrong, in which the transition between the states can occur in both directions.Finally, the application of anodic pre-pulses prior to monophasic cathodic pulses decreases the evoked neuromuscular response. This is consistent with the attenuation of the response when the polarity of a biphasic pulses is inverted from cathodic-anodic to anodic-cathodic (shown in Fig. To our knowledge, this is the first work that assesses the effect of depolarizing pre-pulses with biphasic pulses. The data show that, in transcutaneous NMES, depolarizing pre-pulses in the range of 0.5 to 15 ms enhance the activation of the nerve fibers in comparison to use of the same stimulation pulses without DPPs. Although our data suggest that the enhancing effect would increase proportionally with further increase of the DPP duration, other reports suggest that, with longer and higher intensity DPPs, the effect can change to the opposite and lead to smaller responses , 11.The most pronounced enhancing effect of DPPs is accomplished if the pre-pulse has the same polarity as the stimulating phase. At least for our setup, where stimulation intensities were kept below individual pain thresholds, the stimulating phase of biphasic pulses was the cathodic one, regardless the polarity order of the phases.The use of DPPs prior to biphasic stimuli could be applied to reduce the current intensity required for a desired neuromuscular response and, therefore, reduce potential stimulation related discomfort.


Copolymer PTTVBDT shows a low-lying HOMO energy level and ordered molecular-packing behavior. Furthermore, two terpolymers, PTTVBDT-TPD and PTTVBDT-DPP, display stronger absorption ability, alower-lying HOMO energy level, and preferred molecular orientation, due to the replacement TTV-monomer units with electron-deficient groups. Furthermore, bulk-heterojunction organic solar cells were fabricated using blends of the PTTVBDT-TPD, and PC61BM gave the best power conversion efficiency of 5.01% under the illumination of AM 1.5G, 100 mW·cm−2; the short circuit current (Jsc) was 11.65 mA·cm−2 which displayed a 43.8% improvement in comparison with the PTTVBDT/PC61BM device. These results demonstrate a valid strategy combining the two-dimensional molecular structure with random copolymerization strikes promising conjugated polymers to achieve highly efficient organic photovoltaics.A series of novel p-type conjugated copolymers, PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP, cooperating benzodithiophene (BDT) and terthiophene-vinylene (TTV) units with/without thieno[3,4- Development of novel semiconducting conjugated polymers and investigation ofstructure–property relationships always play a critical role in the organic electronic devices. Through rational molecular design, they will be tuned specifically to achieve desired optical and electronic properties for application in electronics and optoelectronics, such as field effect transistors (FETs) ,2, photoc]pyrrole-1,4-dione (DPP), in the 2-D conjugated polymers broadens the spectral absorption via the random polymerization dithiophene (BDT) has been considered as one of the most effective building-blocks for p-type conjugated polymers because it possesses a planar structure and BDT-based copolymers have high hole mobility and suitable electronic energy levels ,11,12. Trization . Taking c]pyrrole-4,6-dione (TPD) and DPP units both have been also widely used as building blocks and electron-withdrawing groups in high performance conjugated polymers due to their symmetric, rigidly fused, and coplanar structures pyrrole-1,4-dione (M3) dithiophene (M2)  and 3,6-one (M3)  were pre1H NMR and 13C NMR spectra were recorded in chloroform-d (CDCl3) solution at 400 MHz on Bruker DRX-400 spectrometer . All NMR spectra were calibrated by CDCl3 where 1H NMR chemical shifts of CDCl3 is 7.23 ppm; in addition, 13CNMR of CDCl3 is 77.0 ppm. Microwave assisted Stille cross-coupling reactions were performed in Anton Paar Monowave 300 microwave reactor . Ultraviolet–visible (UV–vis) absorption spectra in solution or solid thin film were recorded on a JASCO MD-2010 spectrometer . Gel permeation chromatography (GPC) was conducted at 40 °C using two Jordi DVB mixed-bed columns (250 mm (length) × 10 mm ; suitable for separating polymers with molecular weights from 1 × 102 to 1 × 107 g·mol−1) using THF as the eluent at a flow rate of 1.0 mL/min on a JASCO instrument  that was equipped with UV–vis and refractive index (RI) detectors connected in series. The work functions of materials were measured using an AC2 photoelectron spectrometer . Atomic force microscopy (AFM) images were captured using taping model in Digital Instruments Nanoscope III . The AFM samples were prepared by spin-coating the blend solution on a ITO/PEDOT:PSS substrate. The two-dimensional grazing incident X-ray diffraction (2D-GIXRD) samples were prepared by drop-casting from CB solution (10 g/L) on 10 mm × 10 mm Si wafers substrate and then thermal annealing at 120 °C, 20 min. 2D-GIXRD measurements were carried out on the wiggler beamline BL17A1 of the National Synchrotron Radiation Research Center , with a wavelength of 1.333 Å delivered from a superconducting wavelength-shifting magnet and a Si (111) triangular crystal monochromator (TCM) pyrrole-4,6(5H)-dione (M3). A mixture of 5  and N-bromosuccinimide  in 50 mL chloroform and 50 mL acetic acid stirred overnight at room temperature. Then, the solution was poured into water and extracted twice with 50 mL chloroform. The organic phase was combined and dried with MgSO4. After removal of the solvent, the residue was purified by flash column chromatography (eluent: hexane/CH2Cl2 = 6/1), yielding yellow solid M3 . 1H NMR  δ 7.63 , 7.05 , 3.61 , 1.64 , 1.22  , 0.84 ; 13C NMR  δ 162.12, 134.90, 133.66, 131.05, 129.66, 128.48, 116.69, 38.61, 31.74, 29.66, 29.12, 28.40, 26.92, 22.559, 14.05.1,3-Bis(5-bromothiophen-2-yl)-5-octyl-42dba3 (3 mol %)/P(o-tol)3 (6 mol %) were dissolved in anhydrous chlorobenzene and added into the reaction mixture via a syringe. The polymerization was carried out at 110 °C for 72 h. The raw product was dissolved by chloroform, then precipitated into methanol and collected through a Soxhlet thimble by filtration, which was then subjected to Soxhlet extraction with methanol, acetone, hexanes, methylene chloride and chloroform. The final polymer was recovered from chloroform fraction by rotary evaporation and dried in vacuum for 12 h at 60 °C.Monomers were first weighted into a 50 mL two neck round-bottom flask and then subjected to three successive cycles of vacuum followed by refilling with nitrogen. Then, PdPTTVBDT is synthesized according to the standard procedure with M1  and M2 , yielding PTTVBDT .PTTVBDT-TPD is synthesized according to the standard procedure with M1 , M2  and M3 , yielding PTTVBDT-TPD . Elemental analysis: found: S, 21.268%; H, 7.326%; C, 68.365%; N, 0.179%.PTTVBDT-DPP is synthesized according to the standard procedure with M1 , M2  and M4 , yielding PTTVBDT-DPP . Elemental analysis: found: S, 20.819%; H, 7.449%; C, 68.714%; N, 0.297%.H-thienopyrrole-4,6(5H)-dione 4, which is the microwave-assisted Stille cross-coupling reaction product of 1,3-dichloro-5-octyl-4H-thienopyrrole-4,6(5H)-dione 3 and tributyl(thiophen-2-yl)stannane.The synthetic routes of the monomers and copolymers are shown in 2dba3 and P(o-tol)3 as the catalyst and ligand, respectively. In the preparation of PTTVBDT-TPD and PTTVBDT-DPP, the feed molar ratio of M1 to the corresponding donor and the acceptor monomer (TPD or DPP units) in the reaction is 4:5:1. In other words, 2,6-bis(trimethyltin)-4,8-bis(2-ethylhexyloxy)benzodithiophene (M2) was copolymerized with 80 mol % of the dibromo donors (M1) and 20 mol % of the dibromo acceptor, M3 and M4, to generate the corresponding copolymer PTTVBDT-TPD and PTTVBDT-DPP, respectively. The raw products were precipitated into methanol and collected through a Soxhlet thimble by filtration, which was then subjected to repeated Soxhlet extraction with methanol, acetone, hexane, and methylene chloride to remove the small molecules and oligomers, and finally with chloroform to collect the target compounds. Due to the presence of 2-ethylhexyl or n-octyl groups in the monomer units, all the copolymers are soluble in common organic solvents such as chloroform, THF, and chlorobenzene. More detailed synthetic procedures and characterization data of monomers and polymers are described in the Experimental Section and These three copolymers PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP were performed by Stille coupling polymerization in chlorobenzene (CB) at 110 °C using PdMn) of the resulting polymers, PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP, were 47.1, 110.3, and 120.8 kDa, with a polydispersity index (PDI) of 2.29, 2.81, and 2.49, respectively, as determined by gel-permeation chromatography (GPC) using THF as the eluent at 40 °C (shown in m/n) for PTTVBDT-TPD and PTTVBDT-DPP was 5.9 and 7.0, respectively.The number-average molecular weights  at 5% weight loss well beyond 350 °C  and absorption edge (λedge) were 536 and 635 nm, respectively, displaying broadened and red-shifted absorption attributed to the intermolecular interaction and aggregation in the solid state. Furthermore, by replacing TTV unit with electron acceptor TPD or DPP unit, a slightly broad and red-shift spectra of PTTVBDT-TPD with absorption edge measured at 647 nm was observed due to addition of the intramolecular charge transfer (ICT) interaction between the TPD and BDT. However, the absorption bands of the TPD containing polymers PTTVBDT-TPD are slightly broadened compared to PTTVBDT but are much narrower than for DPP-containing polymers PTTVBDT-DPP because TPD is a weaker acceptor than DPP [max at ca. 516–553 nm, implying that the presence of electron-rich spacer in TPD-based conjugated polymers leads to a weaker intramolecular charge transfer [The optical properties of the polymers were examined by ultraviolet–visible (UV–vis) absorption spectroscopy. The UV–vis spectra of the polymers in dilute chlorobenzene solution and in spin-coated thin films on glass substrates are shown in than DPP ,40. In atransfer . Therefog, opt.) of 1.95, 1.91 and 1.73 eV, respectively. It is worthy to mention that the two ternary copolymers PTTVBDT-TPD and PTTVBDT-DPP exhibit higher absorption coefficients than PTTVBDT in thin film due to the introduction of TPD and DPP units and higher molecular weight. These observations suggest that they can have great potential for more effective light-harvesting and achieve a higher short-circuit current in PSCs.In addition, the absorption edges of PTTVBDT, PTTVBDT-TPD and PTTVBDT-DPP thin films were located at 635, 647 and 791 nm, corresponding to optical band gaps  technique was used to understand the influence of the introduction of acceptors on crystallinity, π–π stacking, and polymer-packing orientation relative to the substrate in BDT-based PTTVs’ system. The neat polymer films were prepared by drop-casting on SiOd100) diffraction peaks in the 2θ range of 2°–5°. For a further precise comparison, PTTVBDT-TPD and PTTVBDT-DPP thin film possess weaker intensity and broader diffraction peaks with FWHM  = 1.10° and 1.13°, respectively, corresponding to average crystallite sizes of 6.2 and 6.1 nm, respectively; meanwhile, the lamellar d-spacing (d100) for PTTVBDT-TPD is also calculated as 20.9 Å and for PTTVBDT-DPP as 20.6 Å, indicating PTTVBDT-TPD and PTTVBDT-DPP have reduced lamellar d-spacing, lower degree of crystallinities and smaller average crystalline grains in (100) plane than PTTVBDT (a lamellar d-spacing of 22.4 Å with a grain size of 12.7 nm). The different size, shape, and position of alkyl chains in the chemical structures of PTTVBDT-derivatives might probably cause the difference in the lamellar d-spacing. Regardless, these results reveal that the PTTVBDT-based polymer exhibit ordered orientations which is a critical property in conjugated polymers for application in electronic devices, especially PTTVBDT-TPD and PTTVBDT-DPP, displaying a preferred molecular orientation. The hole mobility of these polymers was measured by space charge limit current (SCLC) method, as listed in −4 and 4.63 × 10−4 cm2·V−1·s−1, respectively, which is about 10 times higher than that (5.88 × 10−5 cm2·V−1·s−1) of PTTVBDT, implying the preferred molecular orientation in PTTVBDT-TPD and PTTVBDT-DPP is beneficial to the direction of charge transport in the SCLC method.The 2D diffraction patterns are further converted to the out-of-plane X-ray diffraction profile by fit-2D program, as shown in 0.5) as a function of the photon energy. Therefore, the work functions of PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP were found to be −4.93 eV, −5.04 eV, and −4.99 eV, respectively. For comparison, the HOMO value of P3HT is −4.69 eV under identical conditions. These results reveal that our series of conjugated copolymers have lower HOMO energies than rr-P3HT; this will be beneficial for the fabrication of PSCs with high Voc values. Furthermore, the lowest unoccupied molecular orbital (LUMO) energies were calculated using the optical band gap  and the HOMO energies according to the following equation: LUMO = HOMO + Eg, opt. (eV). Therefore, the energy-level diagrams of polymers derived from AC2 photoelectron spectroscopy and UV–vis absorption data are shown in The highest occupied molecular orbital (HOMO) energies were estimated from the ionization potentials of spin-coated films determined by UV photoelectron spectroscopy in air using an AC2 photoelectron spectrometer (Riken Keiki Co.) . The pho61BM (ca. 4.20 eV) should allow for efficient charge carrier generation to occur in the devices by overcoming Coulombic binding energy of the exciton, since it is normally accepted that the LUMO energies of the donor should be at least 0.3 eV higher than that of the acceptor in BHJ solar cells [By comparing with the HOMO of PTTVBDT, the HOMO energies of PTTVBDT-TPD and PTTVBDT-DPP are 0.11 and 0.06 eV, respectively, deeper due to the TTV unit being replaced to the stronger acceptor unit which reduces electron-donating ability. On the other hand, the LUMO level is primarily affected by the electron-deficient units in general donor-acceptor polymers. Hence, the order of the LUMO levels is PTTVBDT-DPP (−3.40 eV) < PTTVBDT-TPD −3.29 eV) < PTTVBDT (−3.00 eV), which not only is consistent with the acceptor strength of the introduced units but also agrees with the results of UV–vis absorption spectra. Moreover, the differences (>0.5 eV) between the LUMO energies of the polymers and PC eV < PTTar cells ,44.61BM/Ca/Al. The current density–voltage (J–V) curves of the PSC devices under AM 1.5G illumination with an intensity of 100 mW·cm−2 are plotted in Voc), short-circuit current (Jsc), fill factor (FF), power conversion efficiency (PCE), series resistance (Rs), and shunt resistance (Rsh), are listed in 61BM 5:4 w/w), 5.01% (PTTVBDT-TPD/PC61BM 1:1 w/w), and 4.39% (PTTVBDT-DPP/PC61BM 5:4 w/w).To compare the photovoltaic properties of copolymers, the bulk heterojunction PSCs were fabricated with an conventional device configuration of ITO/PEDOT:PSS/polymer:PCVoc value of PSCs is primarily proportional to the difference between the HOMO energy level of the donor polymer and the LUMO energy level of the fullerene acceptor, these devices prepared from the blends of PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP with PC61BM exhibited average Voc of 703, 740, and 723 mV, respectively, are in good agreement with the order of the HOMO energy levels of these polymers. Additionally, the Jsc of the devices based on blends of PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP with PC61BM were 8.10, 11.65, and 9.96 mA·cm−2, respectively.Since the Jsc, the monochromatic incident photon-to-electron conversion efficiency (IPCE) spectra of the optimized polymer/PC61BM devices were measured under AM 1.5G illumination with an intensity of 100 mW·cm−2 and shown in 61BM (>60% over the range 340–600 nm and peaking at ca. 85% at 370 nm), they not only possess the highest Jsc in all the solar cell devices but are potential materials for photo-detector devices. In addition, the IPCE plot of PTTVBDT-DPP/PC61BM displays the broadest IPCE region extended to 800 nm, indicating that the incorporation of the strongest electron-withdrawing DPP units into the polymer backbone effectively improves the Jsc value in solar cell devices by enhancing absorption ability in comparison with PTTVBDT/PC61BM.In order to investigate the improved 2/blend film/Ca/Al for the electron mobility was applied to verify the hole and electron mobility of the blend films. As listed in −4/8.37 × 10−4 and 4.23 × 10−4/5.69 × 10−4 cm2·V−1·s−1, respectively, which are one order higher than PTTVBDT blend film of 2.01 × 10−5/3.65 × 10−5 cm2·V−1·s−1. This result corresponds to improved series resistance and fill factor for devices based on PTTVBDT-TPD and PTTVBDT-DPP, in comparison with PTTVBDT. Furthermore, the balanced charge transport of PTTVBDT-DPP blend film is closer to 1 than the others, explaining why it exhibits the highest FF and the lowest Rs. Atomic force microscopy (AFM) was applied to verify the morphologies of the spin-coated films of polymers/PC61BM on top of ITO/PEDOT:PSS, which were prepared by using the same procedure and parameters for preparing the photoactive layers. As the phase images of the blend films were shown in 61BM blend films display very similar morphological properties. The root-mean-square (RMS) surface roughness of PTTVBDT blend films appears to be larger than that of the others, corresponding to the highest Rs due to the increased interfacial contact resistance.For photovoltaic devices, the charge mobility is a crucial factor for achieving highly efficient devices. Herein, the space-charge–limited current (SCLC) method with the device structures of ITO/PEDOT:PSS/blend film/Au for the hole mobility and ITO/SAM-NH61BM exhibited the best power conversion efficiencies of 3.10%, 5.01%, and 4.39%, respectively. This work demonstrates an effective strategy combining a two-dimensional molecular structure with random copolymerization strikes producing conjugated polymers to achieve highly efficient organic photovoltaic devices.In summary, we have successfully synthesized and characterized a novel p-type conjugated copolymer, PTTVBDT; cooperating BDT and TTV units via Stille polymerization. PTTVBDT shows a low-lying HOMO energy level and ordered molecular-packing behavior. Furthermore, by introducing different electron-withdrawing groups, two terpolymers, PTTVBDT-TPD and PTTVBDT-DPP; display stronger absorption ability, lower-lying HOMO energy level, and preferred molecular orientation, indicating the absorption ability, bandgaps, and molecular energy levels of the polymers can be further tuned by replacing TTV-monomer units with different acceptors. The polymer solar cells based on the blends of PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP with PC