Radiomics signature and widely invasive histology were significantly associated with distant metastasis on multivariate analysis (p < 0.01 and p = 0.003). The classifier using the results of the multivariate analysis showed an AUC of 0.93. The radiomics signature from thyroid ultrasound is an independent biomarker for noninvasively predicting distant metastasis of FTC.We aimed to evaluate whether radiomics analysis based on gray-scale ultrasound (US) can predict distant metastasis of follicular thyroid cancer (FTC). We retrospectively included 35 consecutive FTCs with distant metastases and 134 FTCs without distant metastasis. We extracted a total of 60 radiomics features derived from the first order, shape, gray-level cooccurrence matrix, and gray-level size zone matrix features using US imaging. A radiomics signature was generated using the least absolute shrinkage and selection operator and was used to train a support vector machine (SVM) classifier in five-fold cross-validation. The SVM classifier showed an area under the curve (AUC) of 0.90 on average on the test folds. Age, size, widely invasive histology, extrathyroidal extension, lymph node metastases on pathology, nodule-in-nodule appearance, marked hypoechogenicity, and rim calcification on the US were significantly more frequent among FTCs with distant metastasis compared to those without metastasis ( Follicular thyroid carcinoma (FTC) is the second most common thyroid cancer and constitutes approximately 10–15% of all thyroid malignancies ,3,4,5.Distant metastasis may be the initial presentation of the disease or it may occur after the initial treatment for cancer. Even if distant metastasis is not clinically evident at the time of diagnosis, early recognition and aggressive treatment of FTCs with high risk of distant metastasis would be invaluable in planning an appropriate treatment strategy. Old age, invasive histological classification, large tumor size (>4 cm), extensive vascular invasion, and lymph node metastasis are independent risk factors for distant metastasis of FTCs ,5,6,7,8.Radiomics, which extracts high dimensional features from medical images, has recently emerged and received great attention as an innovative tool in cancer research ,11. ReseKnowing which predictors are associated with distant metastasis in patients diagnosed with FTC may influence preoperative planning, may prevent an unnecessary second operation, and may be useful in determining the need for radioiodine therapy. To our knowledge, there have been no published studies aimed at predicting distant metastasis in FTC based on radiomics analysis. We hypothesized that the substantial objective information extracted from US images, distinguishable through visual interpretation, can predict distant metastasis in FTC. Moreover, if a noninvasive, objective method such as radiomics is effective, it may be possible to achieve personalized management strategies.The purpose of our study was to determine whether a radiomics signature based on preoperative gray-scale US could predict distant metastasis in FTC.Our institutional review board approved this retrospective study. The informed consent was waived. We retrospectively searched our institutional database to identify patients with surgically confirmed FTC who underwent preoperative thyroid US between January 1998 and December 2016 and had follow-up surveillance data. The exclusion criteria were as follows: (1) nodule diameter <10 mm, as the region of interest (ROI) method has lower accuracy and current guidelines do not recommend fine needle aspiration (FNA) for nodules with a diameter <10 mm ,18; (2) We extracted clinical characteristics from electronic medical records including age at diagnosis, sex, type of surgery , and the time of diagnosis of distant metastasis. We also retrospectively collected pathologic findings based on final pathologic reports after surgery including tumor size, histologic classification according to the extent of invasiveness , the extrathyroidal extension , or gross ), and lymph node metastasis.All patients underwent preoperative thyroid US using a HDI5000 , Logic 700  or an iU22 system  with a commercially available 5- to 12-MHz linear-array transducer before surgery. All scans were performed by one of seven radiologists with 2 to 17 years of experience in thyroid US.Two radiologists  retrospectively reviewed preoperative US and assessed image features. Radiologists were blinded to the presence of distant metastasis and clinicopathologic variables when reviewing US. Discordant cases were discussed to reach a consensus. According to the Korean thyroid imaging reporting and data system (K-TIRADS), all thyroid nodules were evaluated for internal content, echogenicity, shape, orientation, margin, calcification, presence of halo, and vascularity ,19. NoduA study process is summarized in A total of 60 radiomics features were extracted using an open-source radiomics software, PyRadiomics. The features were grouped into first order (17 features), 2D shape (3 features), gray-level cooccurrence matrix , and gray-level size zone matrix features . The histogram-based features were computed from 32-bin histograms calculated over the intra-tumoral intensity range. The GLCM features assess textural information and reflect intra-tumoral heterogeneity. They were computed using a 2D histogram with 32 bins. A total of eight matrices corresponding to eight 2D directions with an offset of one were computed and then averaged to yield a single matrix. The averaged matrix was used to compute the GLCM features. The GLSZM features are also texture features assuming that a tumor consists of many gray-level zones with different sizes. They were computed using a 16 × 256 matrix, where the first dimension was binned gray level and the second dimension was the size of the gray level zone. Further details of the radiomics features are publicly available.Due to the lack of external validation data, we applied five-fold cross-validation to separate our data into training and test sets to reduce the risk of overfitting. Models were built using the training set only and tested on a left-out test set. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) method from the training set. The selected features were used as inputs to train a support vector machine (SVM) using the linear kernel. The trained classifier was further tested on a left-out test fold. A radiomics signature was computed as the probability of the SVM classifier output for each nodule from the test-fold. In detail, the SVM classifier with a linear kernel was constructed using the selected features. The parameters of SVM were weights that determined the decision hyperplane to separate two groups . A signed distance was computed from features from the given sample to the hyperplane, which was further transformed using the sigmoid function to yield a probability value. The output probability value was assigned as the radiomics signature. The whole procedure was performed with a MATLAB command “fticsvm” with the prior uniform option. As we adopted five-fold cross-validation, we repeated the feature selection, model training, and testing steps five times, each leaving out a different test fold. The performance of the classifier models was assessed based on the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. The software code for the whole procedure is given in the t-test.FTCs with and without distant metastasis were compared based on patient demographic information. Categorical variables were analyzed using the Chi-square test, while continuous variables were analyzed using Student’s p-values. Multivariate logistic regression analysis was performed using variables that showed statistically significant differences on univariate analysis to identify independent predictors of distant metastasis of FTC using the same five-fold cross-validation. A p-value < 0.05 was considered statistically significant. A final SVM classifier was built using significant variables from the multivariate analysis, and the performance was assessed using the same five-fold cross-validation.Univariate analysis was performed to evaluate the association between distant metastases of FTC and radiomics signature, US findings, and clinicopathologic variables. The correlation between continuous variables was assessed with Pearson’s correlation analysis, that between binary variables was assessed with Matthew’s correlation analysis, and that between binary and continuous variables was assessed with point biserial correlation analysis. The univariate association of radiomics signature was evaluated using the same five-fold cross-validation with averaged correlation and The clinicopathologic characteristics of the study population are shown in n = 14), followed by both bone and lung (n = 12), lung alone (n = 7), and multiple sites (n = 2), that is bone, lung, and brain (n = 1) and bone, lung, and kidney (n = 1). Patients with distant metastasis were older than those without metastasis (p < 0.0001). The mean pathologic tumor size was larger in FTCs with distant metastasis than without metastasis (p = 0.033). Widely invasive histology, lymph node metastasis, and extrathyroidal extension were more frequent in patients with FTC who had distant metastasis than in those who did not (p < 0.01). Of the 134 patients without distant metastasis, a median follow-up period was 5.7 years (range: 3.3–8.3 years).Of the 35 patients with distant metastases, the initial presentation of 24 patients was distant metastases and follicular cancers were diagnosed later. The remaining 11 patients developed distant metastasis during follow-up. Three patients were found to have metastases within six months after surgery. Eight patients developed distant metastasis one to three years after surgery. The sites of distant metastasis were bone alone (p < 0.05). FTCs with distant metastasis had higher incidences of K-TIRADS category 5 (high suspicion) and 4 (intermediate suspicion) compared to FTCs without distant metastasis (p < 0.0001).The US characteristics of the study population are presented in We used five-fold cross-validation, and thus, the selected features varied from fold-to-fold. Six features were selected in all five training folds. They were the minimum (first order), elongation and sphericity (shape), gray-level nonuniformity normalized, size zone nonuniformity, and small area low gray-level emphasis (GLSZM). The SVM classifier showed a high AUC of 0.90 on average on the test folds. The performance of SVM classifiers is shown in p < 0.001) (p < 0.0001) in radiomics signature between two groups . Among the clinicopathologic variables, age, size, widely invasive histology, lymph node metastasis, and extrathyroidal extension were associated with distant metastasis (p < 0.05). Among the US characteristics, orientation, echogenicity, rim calcification, and nodule-in-nodule appearance were associated with distant metastasis (p < 0.05). At multivariate analysis, only radiomics signature and widely invasive histology were independently associated with distant metastasis    and therctively) . Diagnostic performance of predictors using the results of multivariate analysis (radiomics signature and widely invasive histology) for predicting metastasis in FTC patients showed a high AUC of 0.93 on the test folds .The ICC for the six important features were 0.99, 0.96, 0.81, 0.82, 0.95, and 0.96 for the minimum (first order), elongation and sphericity (shape), gray level non-uniformity normalized, size zone nonuniformity, and small area low gray-level emphasis (GLSZM) features, respectively, showing excellent agreement. The ICC for all 60 radiomics features was 0.94 on average. The full list of ICC values is given in r < 0.2) or high p-value (p > 0.1)  . The corp > 0.1) . This coSince the introduction of radiomics, many studies concluded that it has potential power for analyzing thyroid cancers, including the differentiation between malignant and benign nodules or the prediction of prognosis or gene mutations in papillary thyroid carcinoma ,23,24,25At multivariate analysis, only widely invasive histology and radiomics signature were independent risk factors for distant metastasis in FTC, and these predictors achieved a high AUC value of 0.93 on the test folds. Widely invasive histology is classified as high-risk by the American Thyroid Association guidelines, which supports our results . As a hiIn fact, radiomics is usually performed using tomographic images, including CT, PET, or MRI, because these modalities can acquire 3D volume data and data acquisition can be standardized by setting scan parameters identically . ContrarOur results imply that radiomics goes beyond visual inspection of image. Although several suspicious US features were more common in FTCs with distant metastasis, they failed to show association with metastasis in multivariate analysis, but the radiomics signature remained as an independent marker for predicting distant metastasis. Our radiomics signature was derived from the selected features in the test fold within the cross-validation framework. That led to having a different number of features selected in each fold. However, six features were common in all five folds and many of them have interpretable implications that might lead to explaining the potential mechanism. The two shape features of elongation and sphericity were selected, and they imply that less elongated and anti-spherical nodules tend to be more malignant and thus could be metastatic based on widely accepted thyroid malignant US features . The texMachine learning classifiers require sufficient samples to be trained properly. We used a different set of features (on average 10 features across different folds) from 169 samples (134 FTC without metastasis and 35 FTC with metastatic cases) for the SVM classifier. A theoretic study pointed out that, if the features followed multivariate Gaussian distribution, the number of samples per class to apply SVM effectively should be greater than three times the number of features . In our There are several limitations to our study. First, this study was retrospective using data from a single institution. FTCs with distant metastasis are relatively rare in comparison to FTCs without metastasis. For statistical analysis, the enrollment periods between two groups were different but consecutive periods were used. This is a natural limitation of the disease. Second, using different US machines during the study period could have introduced machine-dependent bias. One study reported that their radiomics models were dependent upon the type of US machine due to innate differences in features such as brightness or contrast . Cases iIn conclusion, the radiomics signature from thyroid ultrasound is an independent biomarker for noninvasively predicting distant metastasis in FTC preoperatively.


Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses. Deep sequencing of mRNA represents a powerful tool in cancer genome research as well as in cell biology ,2,3. ExpWhile several solutions for analyzing RNA sequencing data using short reads (ca. 50–300 bp) are available , longer To extend existing pipelines for long sequencing reads to the RNA level, our approach was to integrate existing workflows for base calling, alignments, and indexing to enable the generation of an easily accessible data (text) file containing both gene counts and aligned RNA reads. The resulting analyses tool, Alignator, is readily available and easy-to-use for FASTQ files obtained from long RNA reads such as ONT as described below or PacBio  but is pTechnically, this workflow is applicable to any method that generates sequencing output in FASTQ files containing sequence information and quality metrics. To align long reads to a reference genome (FASTA file), the genome data obtained from a database  must be indexed first  as input and an Ensembl cDNA database (FA file) as a reference. All the steps can be performed using established R scripts and packages, which are integrated in a sophisticated manner. For example, Alignator allows using the same Ensembl cDNA file for alignment (via Rsubread) and annotation of the generated SAM file. Here, the reference genome is compressed into an index file using Buildindex, which is part of the Rsubread Bioconductor package   and RStudio (https://rstudio.com/), the Alignator R script (Alignator_v1-x.R) opens in RStudio and starts using the source button that opens the main window  file and two result files stored in the predefined output folder. The SAM file saves RNA reads that were successfully aligned to a reference sequence in a text-based format. When imported into genome viewer software, the file can be used for graphical representation of the mapping. Furthermore, it contains information, such as read IDs, deletions, insertions, mismatches, and error probability for each individual base. As a representative example, the SAM file in Here, we introduced an innovative and self-explanatory R-based tool, designated Alignator, for unbiased quantitative analyses of long RNA reads. Alignator enables users to directly create index files for any species for which a reference genome is publicly available. This only requires a local copy of a reference file that can be downloaded, e.g., from the Ensembl databases. Once the reference is compressed into an index file, it can immediately be used for analysis without further processing. The user interface and its output are easy to understand and intuitive. We successfully showed that the combination of the comprehensive AnnotatedData file and the compact GeneCounts file offers an easy way to obtain quantitative results on RNA abundance. This can be further used for downstream analyses of biological contexts, as shown here for LLC cells.The Alignator can be run on any hardware that supports R language and RStudio. A human cDNA database requires 3 to 4 Gb of RAM and about 2.3 Gb of free disk space. The MinION device and related flow cells  were acquired from Oxford Nanopore Technologies . All other chemicals were purchased from Sigma-Aldrich .RNA sequencing was performed with 500 ng poly-A enriched RNA isolated with the NEBNext Poly(A) mRNA Magnetic Isolation Module  using the Direct RNA sequencing kit . This procedure is feasible for high amounts of RNA (40–50 µg). For low amounts, the cDNA-PCR Sequencing kit  is recommended . Sample v/v) fetal calf serum  and maintained in a humidified incubator  at 37 °C and 5% CO2.Murine Lewis lung carcinoma (LLC) cells were purchased from ATCC . Cells were cultivated in RPMI  medium supplemented with 10% (https://nanoporetech.com/products/minion). For analyses in the R environment, we recommend using RStudio 1.3 and R 4.0.2, or higher versions of both (R Core Team (2020) is an open-source software package. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/). We used Rsubread included in the Bioconductor version 3.11 or higher [The MinKNOW 3.3.2 software is available to registered users from the Oxford Nanopore Technologies website (r higher .


The prebiotic synthesis of ribonucleotides is likely to have been accompanied by the synthesis of noncanonical nucleotides including the threo-nucleotide building blocks of TNA. Here, we examine the ability of activated threo-nucleotides to participate in nonenzymatic template-directed polymerization. We find that primer extension by multiple sequential threo-nucleotide monomers is strongly disfavored relative to ribo-nucleotides. Kinetic, NMR and crystallographic studies suggest that this is due in part to the slow formation of the imidazolium-bridged TNA dinucleotide intermediate in primer extension, and in part because of the greater distance between the attacking RNA primer 3′-hydroxyl and the phosphate of the incoming threo-nucleotide intermediate. Even a single activated threo-nucleotide in the presence of an activated downstream RNA oligonucleotide is added to the primer 10-fold more slowly than an activated ribonucleotide. In contrast, a single activated threo-nucleotide at the end of an RNA primer or in an RNA template results in only a modest decrease in the rate of primer extension, consistent with the minor and local structural distortions revealed by crystal structures. Our results are consistent with a model in which heterogeneous primordial oligonucleotides would, through cycles of replication, have given rise to increasingly homogeneous RNA strands. Studies of the path from the prebiotic world to the RNA World have long contended with the chemical complexity of RNA. Eschenmoser proposed a comprehensive synthetic approach, suggesting that the origin of RNA might be elucidated by a systematic exploration of the properties of its chemical analogues and structural relatives ,2. Such l-threofuranose sugars and, therefore, has one fewer atom in its repeating unit than RNA (Eschenmoser and colleagues proposed and synthesized a promising candidate in this search: threose nucleic acid (TNA). The TNA backbone is composed of phosphodiester-linked 3′→2′ α-than RNA . The posthan RNA . Threosethan RNA , B. Prebthan RNA . TNA synthan RNA  4). Str. Strl-ththan RNA  despite To address the question of the replicative capacity of TNA, we have previously reported the enzymatic copying of DNA to TNA and vice versa, using both wild type and engineered polymerases . HoweverHere, we examine the behaviour of TNA in nonenzymatic template-directed primer extension reactions from both a kinetic and a structural perspective. We find that activated TNA monomers alone are not effective substrates for multi-step nonenzymatic primer extension reactions, and even single activated threo-nucleotides in an otherwise all-RNA context are incorporated with poor efficiency. Through a series of kinetic and co-crystallization studies, we analyse the origins of these experimental observations. Our results suggest that the reactive intermediate in primer extension, an imidazolium-bridged dinucleotide composed of 3′-3′ linked threo-nucleotides, is both generated very slowly and once formed, binds to the template with suboptimal geometry for primer 3′-OH attack. In contrast we find that a single TNA nucleotide at the primer terminus or at the +1 template site only modestly slows the rate of primer extension, consistent with crystallographic observations that such substitutions have relatively minor structural consequences. Taken together, we propose that structural constraints explain the diminished ability of TNA to engage in nonenzymatic template-directed primer extension, and we discuss the consequences of these observations for the plausibility of the involvement of TNA in the origin of primitive cells.3N•3HF. Oligonucleotides were HPLC purified on an Agilent ZORBAX Eclipse-XDB C18 column using 25 mM triethylammonium bicarbonate in H2O (pH 7.5) with gradient elution from 0% to 30% acetonitrile over 40 mins. The oligonucleotides were collected, lyophilized, desalted, and concentrated as appropriate for primer extension and crystallization experiments. Oligonucleotides were characterized by LC–MS (see SI for details).Native, LNA-containing, and TNA-containing RNA oligonucleotides used for primer extension and crystallographic studies were custom-synthesized by Exiqon Inc.  or IDT Inc. , or were synthesized in-house on an Expedite 8900 DNA/RNA synthesizer. TNA phosphoramidites were synthesized as previously reported ,17. Oligl-threofuranosyl-3′-monophosphate (tGMP and tCMP) , triethylamine  and 2,2′-dipyridyldisulfide  under stirring at room temperature. The reaction was allowed to proceed for 4 h, followed by precipitation of the crude product, supernatant removal and product purification by reverse phase flash chromatography. For detailed experimental procedures, see Supporting Information 1.4.The activated monomers 2AIptG and 2AIptC were synthesized essentially as previously reported for the corresponding activated ribonucleotides . Brieflynd tCMP) ,19 and 22. The primer extension reaction was initiated by adding the indicated activated monomers  from 200 mM stock solutions to a final concentration of 40 mM in the reaction. At the indicated time points, 1 μl aliquots were removed and quenched with 29 μl of 90% (v/v) aqueous formamide containing 15 mM EDTA. Samples were heated at 95°C for 2 min, cooled to room temperature, and analysed on 18% (19:1) denaturing PAGE gels containing 7 M urea. Gels were imaged using a Typhoon 9410 scanner. The resulting gel images were analysed using ImageJ software.Primer (1 μM) and template (1.5 μM) were pre-mixed in 15 μl of a solution containing 200 mM HEPES pH 8.0 and 200 mM MgClRNA samples (0.5 mM duplex or hairpin in water) were heated to 80°C for 5 min, slowly cooled to room temperature (20°C), and incubated at 4°C overnight prior to crystallization. In the self-complementary RNA duplexes, the overhanging 5′-termini were composed of locked 5-Me-C residues, in order to rigidify the template in the A-form conformation and facilitate crystallization. Nucleic Acid Mini Screen Kits, Natrix (Hampton Research) and Nuc-Pro-HTS (Jena Bioscience) were used to screen crystallization conditions at 18°C using the sitting-drop method. An NT8 robotic system and Rock Imager  were used for crystallization screening and monitoring the crystallization process. Optimal crystallization conditions are listed in the Supporting Information .Crystals were mounted and soaked for 2 min in a drop of mother liquor solution which contained 30% glycerol as a cryo-protectant, then transferred to liquid nitrogen. Diffraction data were collected at a wavelength of 1 Å under a liquid nitrogen stream at −174°C on Beam line 821 or 822 at the Advanced Light Source in the Lawrence Berkeley National Laboratory (USA). The crystals were exposed for 1 s per image with a 1° oscillation angle. The distances between detector and the crystal were set to 200–300 mm. Raw diffraction data was processed using HKL2000  and appll-threofuranosyl guanosine 3′-monophosphate (tGMP) to its corresponding 3′-phosphoro-2-aminoimidazolide (2AIptG) following the standard procedure for the preparation of activated ribo-nucleotides (−1). In contrast, primer extension reactions containing the activated ribo-nucleotide monomer 2AIpG yielded >95% extended product within one hour, and the observed rate constant for disappearance of unreacted primer was 12 h−1   ,27  . NonenzymCmCmCGACUUAAGUCG-3′  bound to the template  monomer with a short partially self-complementary RNA 7-mer  stem-loop structures  . The two4 template (1.3 h−1) was roughly ten-fold slower than on a (rC)4 template (12 h−1). . −1, compared to 11 h−1 for the all-RNA template , a decrease comparable to that seen for a TNA residue at the end of the primer. Thus, a TNA:RNA base-pair seems to significantly slow primer extension, irrespective of whether the TNA residue is in the primer or the template. However, subsequent extension to the +3 and +4 products was not observably inhibited, and almost complete conversion of the primer to fully extended +4 products was observed after 24 hours.In a prebiotic situation in which both ribo- and threo-nucleotides were present, templates in which isolated threo-nucleotides were interspersed in an otherwise RNA oligonucleotide might be common. However, the effect of an isolated threo-nucleotide in an otherwise all-RNA template has not been previously investigated. We therefore synthesized two new template oligonucleotides (5′-AAGGG(t)GCCAGUCAGUCUACGC-3′), one all-RNA and one containing a single threo-guanosine nucleotide (tG) at the +1 position Figure . When the Figure . Howeverexo conformation, and the glycosidic torsion angle χ of tG is –111°, similar to the tA residue seen at the end of the primer. However, the noncovalently bound CMP is poorly ordered, suggesting a significant degree of conformational heterogeneity. The electron density of the monomer's sugar and phosphate is poorly resolved, yielding only a rough estimate of 4 Å for the distance between the 3′-OH of primer and the P atom of the CMP  contains a single tG residue at the appropriate position to serve as the template to bind a free CMP monomer Figure . BroadlyP Figure .Superimposing the TNA-T1 structure over the all-RNA complex 5VCI, we find the two complexes are very similar in overall conformation , suggesting that even if 2AI-activated TNA monomers do form the intermediate and bind the template, primer extension would be very slow.Our crystallographic findings show that both TNA monomers and the TNA imidazolium-bridged dinucleotide do bind to the template by Watson-Crick base pairing, and the threose sugar is consistently in the C4′-Our results cast doubt upon the viability of TNA as a self-replicating polymer, at least in the context nonenzymatic template-directed primer extension with phosphorimidazolide-activated threo-nucleotide monomers. The copying of a C4 template region with 2AI activated tG monomers was extremely inefficient no matter whether the primer ended in a ribo- or a threo-nucleotide, and no matter whether the template region was composed of a ribo- or a threo-nucleotides. Since even copying in an essentially all TNA context was ineffective, the inability of 2AIptG monomers to mediate efficient multi-step primer extension cannot simply reflect the local structural context of the reaction. Instead, the more likely explanation follows from our observation that formation of an imidazolium-bridged tG-p2AIp-tG dinucleotide is inefficient, such that the low levels of this key intermediate lead to poor primer extension irrespective of the local structural context.endo conformation; as a consequence the RNA primer/template/intermediate complex is preorganized so as to favour rapid primer extension. Our experiments show that in this context, RNA copying has a significant kinetic advantage over alternative nucleic acids including not only TNA, but also ANA and DNA . In contrast, the kinetic defect associated with primer extension with threo-nucleotide monomers is context dependent. The most likely explanation for the slow polymerization of the arabino-substrate is the suboptimal local conformation of the arabino-nucleotide backbone, because the rate of formation of the arabino-intermediate in solution is comparable to that of the RNA counterpart. However, the slow nonenzymatic copying rates observed with TNA appears to be due to both slow formation of the dinucleotide intermediate and the unfavorable local conformation. Our results suggest that the major route for the incorporation of threo-nucleotides into a growing chain would be through reaction of the primer with a mixed TNA-RNA imidazolium-bridged intermediate, for which the kinetic defect is only about 4-fold relative to ribonucleotide incorporation. Nevertheless this suggests that in the presence of, for example, an equimolar mixture of activated threo- and ribonucleotides, RNA synthesis would predominate. RNA has an exceptional double helical structure with the ribose sugars in the 3′- and DNA . Our expAtomic coordinates and structure factors for the reported crystal structures have been deposited with the Protein Data bank under accession numbers 6U7Y, 6U7Z, 6U89, 6U8F, 6U8U.gkaa1215_Supplemental_FileClick here for additional data file.


SV for isolation of US, PS, and CS transcripts from a single population of infected CD4+ T-cells and mass spectrometric identification of their in vivo protein interactomes. Analysis revealed 212 proteins differentially associated with the unique RNA classes, including preferential association of regulators of RNA stability with US and PS transcripts and, unexpectedly, mitochondria-linked proteins with US transcripts. Remarkably, >80 of these factors screened by siRNA knockdown impacted HIV-1 gene expression. Fluorescence microscopy confirmed several to co-localize with HIV-1 US RNA and exhibit changes in abundance and/or localization over the course of infection. This study validates HyPR-MSSV for discovery of viral splice variant protein interactomes and provides an unprecedented resource of factors and pathways likely important to HIV-1 replication.HIV-1 generates unspliced (US), partially spliced (PS), and completely spliced (CS) classes of RNAs, each playing distinct roles in viral replication. Elucidating their host protein ‘interactomes’ is crucial to understanding virus-host interplay. Here, we present HyPR-MS HIV-1 uses the alternative splicing of a single primary RNA transcript to produce three major classes of viral RNA variants: unspliced (US), partially spliced (PS), and completely spliced (CS). The individual variants perform distinct roles during HIV-1 replication through dynamic interactions with specific viral and host proteins . These pHIV-1 gene expression is traditionally divided into two phases referred to as ‘early’ and ‘late.’ Early gene expression involves translation of auxiliary proteins Tat and Rev as well as the accessory protein Nef from CS transcripts. Tat and Rev localize to the nucleus where Tat facilitates viral transcription and Rev mediates nuclear export of intron-retaining US and PS transcripts. Late gene expression is marked by translation of the US transcript to synthesize Gag and Gag-Pol capsid proteins and translation of PS transcripts to generate Envelope glycoproteins as well as the Vpu, Vpr, and Vif immunomodulatory factors.cis- and trans-regulatory elements  -mediated nuclear export pathway through the activities of Rev and a nt (RRE) . Rev mulnt (RRE) . This isnt (RRE) . These bnt (RRE) . Additiont (RRE) . HoweverIn the current study, we describe a novel strategy for efficient isolation of the three major HIV-1 splice variant classes from a single population of natively infected CD4+ T-cells and define distinct US, PS, and CS in vivo RNA-protein interactomes using mass spectrometry. We identify over 200 proteins differentially associated with the US, PS, and CS HIV splice variant pools, 102 of which are previously unkown HIV-1-host interactors. Gene-specific siRNA knockdown (KD) of 121 host proteins indicated more than 80 are effectors of HIV-1 RNA regulation. We further demonstrate, using fluorescence microscopy, several instances of identified host protein co-localization with HIV RNA and changes to the single-cell abundance and/or subcellular distribution of several identified host proteins over the course of HIV-1 infection. Collectively, we detail a powerful new approach for probing virus-host interactions and use it to expose new host factors with apparent roles in the HIV-1 replication cycle.Hybridization Purification of RNA-Protein Complexes Followed by Mass Spectrometry), a strategy to identify the in vivo protein interactomes of specific viral RNAs, lncRNAs, and mRNAs  derived from a single primary transcript and isolated from a single-cell population. Applied here, we purified the three major classes of HIV-1 splice variants  from a single population of infected Jurkat CD4+ T-cells, then identified and characterized their protein interactomes using mass spectrometry.We recently described HyPR-MS  (multiplicity of infection [MOI] of ~1 infectious unit per cell). The Jurkat cell line was chosen because it is a well-characterized CD4+ T-cell line previously confirmed to support replication of the HIV-1is study . To isolis study . Cell lyis study .GAPDH) was >100-fold  of 5%, we identified 212 proteins that differentially interacted with one or more of the HIV splice variant classes: 101, 93, and 68 proteins in the US, PS, and CS captures, respectively  term enrichment algorithms , we evalSV were targeted for siRNA KD -based detection of the host proteins /IF single-cell imaging to show host factor subcellular localization relative to US RNA and viral Gag proteins –3. HeLa FISH/IF was performed on HeLa cells 48 hr post-infection to localize US RNA, Gag, and each host protein. Analysis by single-cell fluorescence microscopy showed consistent co-localization of US HIV-1 RNA with 11 of the proteins –3, with We quantified the frequency of each co-localization phenotype for 17–52 cells per antibody , excludiSV  were observed to accumulate at bright US RNA nuclear puncta, suggesting that they associate with US RNA at or near sites of de novo transcription –3. Of thSV , and shoSV . By contSV , and MBOSV , localizpression . Interespression –3 and MBpression –3.The FISH/IF single-cell analyses of US HIV RNA, Gag, and host proteins also allowed for tracking of host factor responses to infection . For exaSV host factor changes, we plotted single-cell measurements of total Gag and total US HIV RNA and used the resulting inflection point to discriminate cells in ‘early’ and ‘late’ stages of HIV gene expression (2 = 0.690) (To further track HyPR-MSpression . We measpression . In genepression . For exa= 0.690) , while M−5)  . In all,−5) . Three o−11 and median decrease=-27%; p=7×10−6, respectively)  ratios relative to US RNA abundance and looked for statistically significant differences in early and late cells . HNRNPR ctively) . In all,ctively) .SV not only modulate HIV-1 gene expression  ensure the relevance of protein interactors by only pursuing interactions that occur in cells , (2) ensure versatility of the technique for broad applications wherein it is useful to differentiate between one or more RNA splice variants for comparative RNA-capture proteomics, and (3) determine, for the first time, the protein interactomes for the three major HIV splice variant classes. The first two goals were achieved by configuring HyPR-MSSV to sequentially deplete specific classes of HIV RNA from the same pool of natively infected cell lysates using three independently targeted sets of short (~30 nt) biotinylated capture oligos  but also the specific class of HIV-1 splice variant with which it associates  . The cel2. This cell line was authenticated by morphology and are G418 resistant.HEK293T cells are human embryonic kidney cells and were obtained from ATCC (CRL-11268). The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 1% L-glutamine-penicillin-streptomycin at 37°C in 5% COHuman 293 T-cells obtained from ATCC were modified to stably express YFP-tagged F-actin (ACT-YFP) and were cultured in DMEM supplemented with 10% fetal bovine serum, 1% L-glutamine, and 1% penicillin-streptomycin.2.HeLa cells obtained from ATCC were cultured in DMEM media supplemented with 10% fetal bovine serum and 1% L-glutamine-penicillin-streptomycin at 37°C in 5% COAll cell lines were validated as mycoplasma-negative using a PCR assay developed by 6 HEK293T cells were plated in 10 cm tissue-culture-treated dishes in 10 mL media, then transfected using polyethylenimine with 1 µg of DNA plasmid expressing the G envelope glycoprotein from vesicular stomatitis virus (VSV-G) and 9 µg of plasmid DNA encoding the full-length NL4-3 molecular clone of HIV-1 bearing inactivating mutations in env, vpr, and either (1) expressing a CFP reporter from the nef reading frame (HIV-1 E-R-CFP) (2.5 × 10E-R-CFP)  or (2) eE-R-CFP) . At 24 h8 Jurkat cells in 25 mL RPMI, 25 mL viral inoculum (HIV-1 E-R-CFP) in DMEM, and polybrene (concentration 10 μg/mL) were combined and incubated in a rotating roller bottle. After 3 hr, culture volume was increased to 300 mL using RPMI media and incubated at 3 rpm for 45 hr. Infection was confirmed to be >90% by visualizing CFP expression via epifluorescence microscopy. Cells were centrifuged at 1500 rpm for 10 min, washed three times with phosphate buffered saline (PBS), then cross-linked by resuspending in 0.25% formaldehyde and incubated at room temperature for 10 min. Cross-linked cells were washed once with PBS, then resuspended in 100 mM Tris-HCl, and incubated at room temperature for 10 min to quench formaldehyde. Cells were washed twice more in 1xPBS, pelleted by centrifugation, and frozen at −80°C.1 × 106 cells/mL. Cells were lysed by frequent vortexing for 10 min, keeping the cells on ice between vortexes.Jurkat cell pellets were resuspended on ice in lysis buffer  to a final cell concentration of 5 × 107 cells. Three biological replicates of each splice variant capture were conducted for this analysis. The HIV-1 US, PS, and CS RNAs were each purified from the Jurkat cell lysate by three sequential and separate hybridization and capture events: US followed by PS followed by CS HIV RNA. The amounts of biotinylated COs and streptavidin-coated magnetic beads for each hybridization and capture are listed in Each capture replicate used 5 × 10The beads for the US, PS, and CS RNA captures were individually resuspended in a 3× bead volume of release buffer. The appropriate amount of release oligonucleotide  was adde2, and 0.2% LiDS to remove the proteins. The RNA was then extracted from the samples using TriReagent per manufacturer’s protocol and precipitated in 75% ethanol, with 2 μL of GlycoBlue, at −20°C for at least 2 hr. The RNA was pelleted by centrifugation at 20,800 g and 4°C for 15 min, the pellet was washed with 75% ethanol, centrifuged at 20,800 g and 20°C for 15 min, then resuspended in 15 μL of nuclease free water; 10 μL of the purified RNA was used for reverse transcription  per the manufacturer’s protocol. The procedures described here were also performed on HIV-1 E-R-CFP virus inoculum for isolation and analysis of a semi-purified standard of the US HIV RNA. The isolated RNA was quantified by NanoDrop analysis, serially diluted, reverse transcribed, and then used for a standard calibration curve for qPCR analysis.Two percent by volume of each release sample was incubated overnight at 37°C with 1 mg/mL proteinase K, 4 mM CaClThe 20 μL reverse transcription product was diluted with 20 μL of nuclease free water and analyzed using sequence-specific qPCR primers and probes  and Roch2O:ACN, 0.1% formic acid.Ninety-eight percent by volume of each capture sample was processed using an adapted version of eFASP  for puriThe samples were analyzed using an HPLC-ESI-MS/MS system consisting of a high-performance liquid chromatography  set in line with an electrospray ionization (ESI) Orbitrap mass spectrometer . A 100 μm id × 365 μm od fused silica capillary micro-column packed with 20 cm of 1.7 μm diameter, 130 Å pore size, C18 beads (Waters BEH), and an emitter tip pulled to approximately 1 μm using a laser puller (Sutter Instruments) was used for HPLC separation of peptides. Peptides were loaded on-column with 2% acetonitrile in 0.1% formic acid at a flow-rate of 400 nL/min for 30 min. Peptides were then eluted at a flow-rate of 300 nL/min over 120 min with a gradient from 2% to 30% acetonitrile, in 0.1% formic acid. Full-mass profile scans were performed in the FT orbitrap between 375 and 1500 m/z at a resolution of 120,000, followed by MS/MS HCD scans of the 10 highest intensity parent ions at 30% relative collision energy and 15,000 resolution, with a mass range starting at 100 m/z. Dynamic exclusion was enabled with a repeat count of one over a duration of 30 s. The Orbitrap raw files were analyzed using MaxQuant (version 1.5.3.30)  and searTo determine the differential interactomes of the HIV-1 splice variants, pairwise comparisons  were statistically analyzed with the Student’s t-test and a permutation-based FDR (5% threshold) using Perseus software . Protein2 in a humidified incubator. Approximately 5 × 103 cells were plated in 24 wells of a 96-well culture plate and incubated 24 hr; then the media was replaced with 125 μL of anti-biotic-free DMEM; 0.875 μL of DarmaFECT transfection reagent in 25 μL of Opti-MEM was mixed with 25 μL of Opti-MEM containing 4.4 pmol of gene-specific siRNA (The virus used for determining the effect of gene-specific siRNA KD on early and late HIV-1 gene expression was a two-color fluorescent HIV-1 reporter virus (E-R-Gag-CFP mCherry/nef). This virus expresses mCherry in the nef ORF and three copies of CFP, in tandem, between the matrix and capsid ORFs of Gag, in a similar but expanded manner as previously done . This viic siRNA , incubatImaging experiments were performed on a Nikon Ti-Eclipse inverted wide-field epifluorescence deconvolution microscope (Nikon Corporation). Images were collected using an Orca-Flash 4.0 C11440 (Hamamatsu Photonics) camera and Nikon NIS Elements software (version 4.20.03) using Nikon 4x/0.13 (Plan Apo) objective lense and the following excitation/emission filter set ranges (wavelengths in nanometers): 418-442/458-482 (CFP), 490-510/520-550 (YFP), and 555-589/602-662 (mCherry).Images were processed and analyzed using FIJI/ImageJ2 . For eacHEK293T cells, with and without gene-specific siRNA KD, were lysed in 1x radioimmunoprecipitation assay buffer , 1% Triton X-100, 1% sodium deoxycholate) and sonicated. Samples were then boiled for 10 min in 2x dissociation buffer , run on SDS-PAGE 10% polyacrylamide gels, and transfered to nitrocellulose membranes (0.2 μM pore size). Immunoblotting was performed as previously described  using thHeLa cells cultured in DMEM in eight-well Ibidi plates were infected with HIV-1 attenuated virus (E-R-CFP), described above, by adding polybrene to each well at a final concentration of 2 μg/mL followed by the virus inoculum. Media was exchanged with fresh media 24 h.p.i. The cells were fixed 48 h.p.i. by washing with PBS, incubating with 3.7% formaldehyde for 10 min at room temperature, then washing three times with PBS. Cells were then made permeable by incubating with 0.2% Triton X-100 for 15 min at room temperature and washing three times with PBS. Endogenous RNases were then deactivated by incubating with 0.1% diethyl pyrocarbonate (DEPC) in PBS for 15 min, removing the solution then incubating again with fresh 0.1% DEPC in PBS for 15 min; the cells were then washed three times with PBS and stored at 4°C.All IF steps were conducted at room temperature. Blocking buffer  was added to each well, incubated for 30–60 min, then removed. Cells were then incubated with fresh blocking buffer containing the appropriate primary antibodies at designated concentrations  for 60 mThe FISH protocol was conducted using Stellaris designed hybridization probes  and StelThe performance of each primary antibody was dependent on the order that the FISH and IF protocols were performed. For some protein/antibody pairs  the IF signal was superior if the IF was conducted prior to FISH. For other antibodies  the IF signal was superior if the IF was conducted after FISH. For G3BP1 and IGF2BP3, either order was fine. If a Gag primary antibody was used, it was added at the same time as the host protein primary antibody. The protocols for each procedure remained consistent, the order in which they were done was only reversed.Single-cell imaging experiments were performed on a Nikon Ti-Eclipse inverted wide-field epifluorescence deconvolution microscope (Nikon Corporation). Images were collected using an Orca-Flash 4.0 C11440 (Hamamatsu Photonics) camera and Nikon NIS Elements software (version 4.20.03) using Nikon 60x  or 100×  objective lenses and the following excitation/emission filter set ranges (wavelengths in nanometers): 405/470 (DAPI), 430/470 (CFP), 490/525 (AlexaFluor488), 585/610 (CAL Fluor Red 590), 645/705 (AlexaFluor647). Images were generally acquired in z-stacks containing various numbers of images along the z-axis of the cells. Images were processed and analyzed using FIJI/ImageJ2 . All z-f2 values were calculated using the chart tools in Excel  was quantified using FIJI/ImageJ2 . Nuclearin Excel .For determining host protein expression and distribution changes, outliers were determined, as described above, for host protein expression values in early and late cells in four categories: nuclear, cytoplasmic, total cellular, and nuc/cyto ratio. To determine if ‘early’ and ‘late’ cells showed statistically significant differences in host protein expression, a Student’s t-test, excluding outliers, was used to determine a p-value. The percent change in each category was calculated using the mean values for each category within early and late cells.Statistical methods are described in the appropriate ‘Materials and Methods’ section or figure captions for all data analyses. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:SV (Hybridization Purification of RNA-Protein Complexes Followed by Mass Spectrometry for splice variants), and its application to identify cellular RNA binding proteins that interact with each of three major HIV-1 RNA species: unspliced RNA, partially spliced RNA, and completely spliced RNA. The authors also present a comprehensive study of subcellular localization, co-localization with other viral factors, and results of knockdowns of identified host factors to further reveal their potential roles in HIV-1 RNA biology. This technical tour-de-force will serve as a valuable resource, and should prompt further mechanistic studies of host factors that influence RNA trafficking and HIV-1 infection.The manuscript describes a new method to identify proteins that interact with different splice variants of HIV-1 RNA, called HyPR-MSDecision letter after peer review:SV" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Detlef Weigel as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Identifying HIV-1 RNA splice variant protein interactomes using HyPR-MSThe reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.SV (Hybridization Purification of RNA-Protein Complexes Followed by Mass Spectrometry for splice variants). The method was developed to identify cellular RNA binding proteins that bind to each of three major HIV-1 RNA species: unspliced RNA, partially spliced RNA, and completely spliced RNA. The authors present a comprehensive study of subcellular localization, co-localization with other viral factors, and results of knockdowns of identified host factors to reveal potential roles in HIV-1 RNA biology. This is a technical tour-de-force, the experiments are rigorously performed, and the results are presented in detail. A limitation is the lack of virology , but as a methodology paper this is valuable resource, and the results will lead to further studies that will add to improved understanding about host factors that influence RNA trafficking and function in HIV-1 infection.Knoener et al. describe a new method for identifying proteins that interact with different splice variants of HIV-1 RNA, called HyPR-MSEssential revisions:1) The probe used for CS RNA is within the 3' exon that is present in all 3 RNA species  yet the analysis was performed in a pairwise fashion (CS vs. PS and US vs. CS). Were there host factors that bound to all 3 RNA species? if so, please indicate them and provide an explanation for why this may be so. Are there any RNA binding proteins that have been shown previously to interact non-specifically to RNA that could potentially be discounted?2) Did the authors note that several factors were omitted from their identified protein list that have been previously shown to have an effect on HIV-1 RNA or were packaged into virus particles? Factor that comes to mind include DHX9 (RHA), DDX3, eIF4E, PABP, and nuclear cap binding complex, which are missing from the analysis and have been shown to be important in HIV biology. It would be informative if the authors would comment on why factors previously shown to be important were not found in their study?3) A previous study that examined host factors that bind to unspliced HIV-1 RNA (5'UTR) was not cited, although some of the factors in that paper and this work are overlapping  . In a similar vein, the authors should briefly discuss how their HIV RNA-protein interactome compare to other studies that used segments of the genome as bait or where affinity handles were engineered in.SV studies were performed in Jurkat cells while the validation experiment were carried out in 293T or He La cells. While this is acceptable given the questions being asked, the different cell lines might differentially express other factors that influence the activity of the protein being KO or IF. The authors should clarify whether they consider this a limitation of the study and what interpretations it might impact.4) The HYPER-MS5) Figure 2B shows that the largest group of cellular proteins that preferentially interact with US viral transcripts belong to carboxylic acid and coenzyme metabolic pathways. This is an interesting observation that the authors did not validate or discuss a in terms of a potential virological function. Although we will not require it, the authors could consider performing similar KO/FISH experiments to better understand how a subset of those metabolic enzymes regulate HIV gene expression. Essential revisions:1) The probe used for CS RNA is within the 3' exon that is present in all 3 RNA species  yet the analysis was performed in a pairwise fashion (CS vs. PS and US vs. CS). Were there host factors that bound to all 3 RNA species? if so, please indicate them and provide an explanation for why this may be so. Are there any RNA binding proteins that have been shown previously to interact non-specifically to RNA that could potentially be discounted?We thank the reviewers for this important comment. Indeed, we identified over 900 host factors associated with all three splice variant pools. We agree that including these factors and discussing their significance and specificity to HIV-1 RNA would be valuable. To this end, we made the following changes to the revised manuscript:1) We constructed a list of 926 proteins identified in at least two biological replicates for all three splice variant capture types . This list and the data are included as a tab named “Common Protein Interactors” in Supplementary file 2.2) We mined the literature for prior studies that used PolyA-tail capture strategies to identify “common” cellular RNA-associated proteins and then annotated our “Common Protein Interactors” tab in Supplementary file 2 to highlight which of the proteins identified in our HIV-1 study are most likely to represent “non-specific” mRNA-associated proteins.3) We also added a tab named “Differential Protein Interactors” to Supplementary file 2 that highlights which of the 212 proteins identified as differential HIV-1 splice variant interactors in our study were previously identified as more general RNA-associated proteins in the studies noted above.4) Finally, we modified the text to discuss these data in the Results and Discussion sections (see below).“In all, 926 proteins were identified in at least two biological replicates of all three HIV splice variant captures (Supplementary file 2). […] Furthermore, several common interactors matched proteins previously identified in RNA capture screens that had used partial segments of HIV-1 RNA as bait, including 27 of 32 proteins identified by Kula, et al., 32 of the 41 identified by Marchand, et al., and 93 of the 121 identified by Stake, et al. (Supplementary file 2) .”“Over 900 proteins were detected in the interactomes of all three HIV splice variant classes (Supplementary file 2); many of these are likely “general” RNA regulatory factors as they were found associated with cellular polyadenylated RNAs in prior studies . However, many of these common interactors included RNA regulatory proteins already demonstrated to play important roles in HIV-1 replication   or were identified in prior pull-down studies .”2) Did the authors note that several factors were omitted from their identified protein list that have been previously shown to have an effect on HIV-1 RNA or were packaged into virus particles? Factor that comes to mind include DHX9 (RHA), DDX3, eIF4E, PABP, and nuclear cap binding complex, which are missing from the analysis and have been shown to be important in HIV biology. It would be informative if the authors would comment on why factors previously shown to be important were not found in their study?We agree that we did not include sufficient description or discussion of known HIV-1 RNA host co-factors and regret this deficiency. Many of the known factors noted by the reviewers ) were identified as common protein interactors; and they, among other known host regulatory factors, are now each featured in Supplementary file 2 . We also added a more thorough discussion of these proteins and other known HIV-1 RNA effectors to the Results and Discussion sections (see below).“These common interactors also featured several proteins previously implicated in HIV-1 replication including host factors regulating RNA transport and translational initation  , known HIV splicing factors  , RNA nuclear export and transport proteins   and proteins implicated in HIV-1 virus particle assembly , STAU2, UPF1, and VPS4) . Furthermore, several common interactors matched proteins previously identified in RNA capture screens that had used partial segments of HIV-1 RNA as bait, including 27 of 32 proteins identified by Kula, et al., 32 of the 41 identified by Marchand, et al., and 93 of the 121 identified by Stake, et al. (Supplementary file 2)”.“However, many of these common interactors included RNA regulatory proteins already demonstrated to play important roles in HIV-1 replication   or were identified in prior pull-down studies .”3) A previous study that examined host factors that bind to unspliced HIV-1 RNA (5'UTR) was not cited, although some of the factors in that paper and this work are overlapping  . In a similar vein, the authors should briefly discuss how their HIV RNA-protein interactome compare to other studies that used segments of the genome as bait or where affinity handles were engineered in.We apologize for not citing the Stake et al. paper  considering its importance to our study. This was a mistake that occurred during editing and we have remedied the oversight. We also appreciate the reviewers’ request that we better compare our findings to those of others that used HIV RNA baits in different contexts. To do so, we now cross-reference both our differential and common protein interactor lists in Supplementary file 2 to the lists of proteins identified by Stake et al. and two additional studies  and indicate which proteins were common among them. We also added discussion of these studies to the Results and Discussion sections (see below).“Furthermore, several common interactors matched proteins previously identified in RNA capture screens that had used partial segments of HIV-1 RNA as bait, including 27 of 32 proteins identified by Kula, et al., 32 of the 41 identified by Marchand, et al., and 93 of the 121 identified by Stake, et al. (Supplementary file 2) .”“To our knowledge, all previous studies for discovery of HIV RNA protein interactors, with the exception of our prior study , utilized synthetic viral RNAs as bait added to cellular lysates  or viral constructs engineered to encode artificial RNA sequences for the purpose of RNA “tagging”  . […] Furthermore, this strategy can, in theory, be expanded to differentiate the protein interactomes of each individual protein-coding HIV-1 RNA, can be used to extract native RNA transcripts produced from any strain or infected cell type, and can easily be adapted to study other viruses or cellular RNA splice variants.”SV studies were performed in Jurkat cells while the validation experiment were carried out in 293T or He La cells. While this is acceptable given the questions being asked, the different cell lines might differentially express other factors that influence the activity of the protein being KO or IF. The authors should clarify whether they consider this a limitation of the study and what interpretations it might impact.4) The HYPER-MSSV technology, it is certainly our intention that the strategy next be applied to study more relevant R5-tropic HIV-1 founder strains primary CD4+ T cells and monocytes/macrophages.We appreciate the reviewers’ comment and now detail in both the Results and Discussion sections (see below) the reasons for selecting these cell lines for each application and acknowledge the limitations of these choices. Although using highly tractable cell systems was invaluable for establishing and validating the HyPR-MS+ T cell line previously confirmed to support replication of the HIV-1NL4-3 reporter virus used for this study .”“The Jurkat cell line was chosen because it is a well characterized CD4“The HEK293T cell line was selected here due to its extensive use in studies of HIV-1 expression and its compatibility with siRNA transfection experiments .”“HeLa cells were chosen based on their microscopy-conducive size and shape (large and flat) and their common use to study HIV-1 gene expression .”+ T cells and monocytes/macrophages using more relevant R5-tropic HIV-1 founder strains for elucidation of innate immune signaling host factors involved in HIV-1 RNA biology.”“It should be noted, the HyPR-MS interactome determination, siRNA knockdown screen and RNA-protein co-localization and expression analysis were completed using three different cell lines; Jurkat, 293T, and HeLa, respectively. […] To overcome some of these limitations, future work will include conducting similar screens in primary CD45) Figure 2B shows that the largest group of cellular proteins that preferentially interact with US viral transcripts belong to carboxylic acid and coenzyme metabolic pathways. This is an interesting observation that the authors did not validate or discuss a in terms of a potential virological function. Although we will not require it, the authors could consider performing similar KO/FISH experiments to better understand how a subset of those metabolic enzymes regulate HIV gene expression.This is an interesting point and we have added text to both the Results and Discussion sections (see below) that better highlight these findings and briefly discuss what has recently been discovered with regards to metabolic proteins having functions as RNA-binding proteins. We have also now added these proteins in Figure 4C so that readers can readily interrogate the lists. We agree that pursuing this category of proteins for further validation and functional analysis will be an important future avenue of study.“…and several have known roles in the carboxylic acid metabolic process, a GO term also over-represented in the US RNA interactome .”“Notably, many metabolic and enzymatic proteins were identified as US HIV RNA interactors . […] This suggests that metabolic proteins may play an important role in HIV gene expression, but further investigation is need to decipher the unique role of each.”


Since the characterization of mRNA in 1961, our understanding of the roles of RNA molecules has significantly grown. Beyond serving as a link between DNA and proteins, RNA molecules play direct effector roles by binding to various ligands, including proteins, DNA, other RNAs, and metabolites. Through these interactions, RNAs mediate cellular processes such as the regulation of gene transcription and the enhancement or inhibition of protein activity. As a result, the misregulation of RNA molecules is often associated with disease phenotypes, and RNA molecules have been increasingly recognized as potential targets for drug development efforts, which in the past had focused primarily on proteins. Although both small molecule–based and oligonucleotide-based therapies have been pursued in efforts to target RNA, small-molecule modalities are often favored owing to several advantages including greater oral bioavailability. In this review, we discuss three general frameworks (sets of premises and hypotheses) that, in our view, have so far dominated the discovery of small-molecule ligands for RNA. We highlight the unique merits of each framework as well as the pitfalls associated with exclusive focus of ligand discovery efforts within only one framework. Finally, we propose that RNA ligand discovery can benefit from using progress made within these three frameworks to move toward a paradigm that formulates RNA-targeting questions at the level of RNA structural subclasses. Additional RNA-cleavage mechanisms not mediated by ASOs have also been pursued. These include the development of siRNAs, which are double-stranded RNAs that recognize and induce cleavage of a target RNA via endogenous RNAi pathways. The first RNAi therapy, Patisiran, was approved in 2018 for hereditary transthyretin amyloidosis, an autosomal dominant neurodegenerative disease  RNA secondary structure motifs can be used as modules for ligand binding; (2) RNA-targeted small molecules may have distinct properties compared with protein-targeted molecules; and (3) RNA-targeted small molecules may look like typical drugs targeting proteins. For each framework, we will first discuss how it is generally conceived and implemented and then we will provide a critical evaluation in relation to other frameworks. Having focused our analysis mainly on major themes, we will not discuss important work that may not fit into the three main frameworks such as modulation of the RNA conformational landscape, which we discussed in a previous review article , or the in vitro experiments, the shape of a potential binding site is defined by the secondary structure motifs . The uniqueness of this shape originates from the size of the unpaired region and the identity of the unpaired bases and their neighbors. Therefore, to target an RNA with small molecules, it might be beneficial to focus on the unique set of secondary structure motifs it contains. This idea quickly matured into a more general approach to solving the problem of RNA recognition with small molecules.Early investigations in how aminoglycosides interact with RNA showed that recognition may depend on RNA shape and not on sequence . In the For example, the Hergenrother laboratory , 50, 51 B).The Disney laboratory took a large-scale approach to identify what secondary structure motifs are preferred by what molecules , 54, 55.Fig. 2in vitro assays. In a recent study, the multivalent approach was further expanded to produce oligomeric compounds composed of alternating bisamidinium and triaminotriazine moieties —they hypothesize that, in general, RNA-binding small molecules will have distinct structural properties compared with protein-binding small molecules. As such, there may exist a region of chemical space that is privileged or biased to interact with RNA  that utilizes RNA-biased libraries or scaffold-based synthesis (Framework 2) as the discovery method.Finally, we would like to discuss an aspect of RNA ligand research that, in our view, underlies all three frameworks discussed herein. This aspect is the tendency to treat RNA as a monolithic collection of biomolecules in the context of medicinal chemistry, and thus requiring (or not) umbrella RNA-centric methods, even as one simultaneously recognizes the structural diversity of RNA molecules in other contexts. This aspect of our current discourse leads to questions like what kinds of molecules can target RNA?, as opposed to what kinds of molecules target triple helices/pseudoknots? or what kinds of molecules target this particular cancer-related RNA fragment? While our attempts to find generalizable approaches at the level of RNA have led to important successes and remain important, it is likely that generalization may be more meaningful and more useful if tailored to a particular structural class of RNA, as routinely done for protein targets. We believe that efforts to target RNA will greatly benefit from approaches that embrace and capitalize on both the similarities between RNA molecules and their rich three-dimensional structure diversity but without being hindered by the former. Importantly, the strategies developed within the three frameworks have laid the groundwork for more detailed exploration of RNA-targeting with small molecules. As such, the field is poised to make significant progress that in time will lead to the discovery of several RNA-targeted life-saving medicines as exemplified by the recent FDA approval of Risdiplam for spinal muscular atrophy .The authors declare that they have no conflicts of interest with the contents of this article.


Long non-coding RNAs (lncRNAs) play multifaceted roles in modulating gene expression under both physiological and pathological processes. The dysregulation of lncRNAs has been increasingly linked with many human diseases, including a plethora of cancers. Mounting evidence indicates that lncRNAs are aberrantly expressed in hepatocellular carcinoma (HCC) and can regulate HCC progression, as well as metastasis. In this review, we summarize the recent findings on the expanding roles of lncRNAs in modulating various functions of HCC, and elaborate on how can lncRNAs impact HCC metastasis and progression via interacting with chromatin, RNA, and proteins at the epigenetic, transcriptional, and post-transcriptional levels. This mini-review also highlights the current advances regarding the signaling pathways of lncRNAs in HCC metastasis and sheds light on the possible application of lncRNAs for the prevention and treatment of HCC. The long non-coding RNAs (lncRNAs) are widely defined as a large class of regulatory transcripts with a length of more than 200 nucleotides and lacking obvious protein-coding potential . A largeHepatocellular carcinoma (HCC) is the fourth-leading cause of cancer death in the world, with 841,000 new cases and 782,000 deaths each year . TherefoSeveral recent reviews focus on the emerging role of lncRNAs in HCC ,7,8. TheLncRNAs are a large class of non-coding RNA transcripts, which are increasingly known to be important for the development and progression of chronic diseases. LncRNAs act as versatile regulators that can interact with RNA, DNA, or proteins to modulate gene expression at different molecular levels, including epigenetic modification, transcriptional, and post-transcriptional regulation . CurrentMounting evidence indicates that abnormal expression of lncRNAs can impact multiple steps of HCC progression, including HCC metastasis. A well-described example for functionally important lncRNA in HCC metastasis was termed HULC (highly up-regulated in liver cancer), which was related with highly specific up-regulation characterized in HCC tissues and associated with intrahepatic metastases, tumor node metastasis (TNM) stage, and HCC recurrence . AnotherAccumulating studies show that lncRNAs may exert a diverse range of functions to regulate gene transcription involved in epigenetic modifiers in HCC ,47. HistLiver tumor initiating cells (TICs), a small subset of cells in HCC, are considered to account for the origin, metastasis, and recurrence of HCC . Recent p = 0.033; p < 0.05 denotes significance) . S. S41]. SIntriguingly, some lncRNAs also act on the processing of mRNAs to influence their stabilities and translation processes. LncRNA-ATB can stabilize and increase the mRNA of Interleukin-11 (IL-11) through the crosstalk with lncRNA-mRNA, thus promoting the colonization of disseminated HCC cells in distant organs E 41]. I. I41]. IApart from the multiple aforementioned functions that allow for interactions of lncRNAs with miRNAs or mRNAs, lncRNAs also exert their biological functions by modifying proteins. Several studies have reported that lncRNAs may also be involved in the regulation of protein phosphorylation. A recent study revealed that lncRNA TSLNC8  exerts its tumor suppressive activity through the inactivation of the IL-6/STAT3 signaling pathway via physically interacting with TKT and STAT3, and thus inhibiting STAT3 phosphorylation and transcriptional activity in HCC . AnotherCurrently, signal pathways affecting cell proliferation, invasion, and metastasis in HCC have been extensively studied. Furthermore, the involvement of lncRNAs in key carcinogenic or metastatic signaling pathways have been closely implicated .Wnt/β-catenin signaling, which is the key mediator for the progression of malignancies , also plThe interactions of lncRNAs with other major signal pathways, such as STAT3, that are involved in the progression of HCC metastasis have been implicated ,64. For These studies highlight the diverse mechanisms by which lncRNAs can act as mediators of Wnt, JAK/STAT, PI3K/AKT, Hippo kinase, and other oncogenic signaling pathways. Nevertheless, how they affect the metastasis of HCC remains to be further elucidated, though the link between lncRNAs and signaling pathways has opened new window for the development of novel diagnostic and therapeutic applications in HCC.Tumor metastasis is a complex and dynamic process involving the reciprocal interplay between tumor cells and host stroma from the microenvironment . About 1In the pre-metastasis niche, lncRNA cox-2 prohibits immune evasion and metastasis of HCC by impeding the polarization of M1/M2 macrophages . The hepThus, summarizing the emerging link between lncRNAs and multi-step process of HCC metastasis will open up a new perspective on the role of lncRNA in modulating HCC metastasis, which may accelerate the progress of developing novel anti-metastasis strategies.Although various diagnostic and treatment strategies are available for HCC, including surgical resection, liver transplantation, radio-frequency ablation, radiation therapy, radioembolization, and targeted therapies , most HCMoreover, the possible role of lncRNA gene polymorphism in the risk of HCC was also explored, where data from a recent study demonstrated an inverse association of CASC8 gene polymorphisms, rs3843549, and rs13281615 with HCC progression and prognosis . CollectBesides the imminent use of lncRNAs as biomarkers for diagnosis and prognosis, the therapeutic targeting of lncRNAs is also being explored. One of the approaches toward influencing lncRNA function is by utilizing specifically designed siRNAs  against lncRNAs. Based on the mechanism of lncRNAs as molecular sponge of miRNAs. It is suggested that targeting diverse miRNAs using the artificial lncRNA could be a potential promising strategy for overcoming sorafenib resistance in HCC therapy . AlternaThe occurrence of HCC is a complex multi-gene and multi-step process. In the article above, we have highlighted the multifaceted regulatory mechanisms and signaling pathways of lncRNAs in HCC metastasis. LncRNAs execute a broad repertoire of functions involved in diverse molecule mechanisms, including epigenetic modification, transcriptional regulation, and post-transcriptional regulation. They are localized to specific cellular compartments, depending on their biological function. LncRNAs in the nucleus can function as versatile molecules via interacting with transcription factors or chromatin modifiers to regulate gene expression. Cytoplasmatic lncRNAs often act as regulators of post-transcriptional modulation, either acting as ceRNA or directly regulating mRNA stability. Additionally, we have summarized the current knowledge about the roles of lncRNA in regulating the tumor microenvironment and signal transduction pathways that can influence HCC metastasis, with the view of identifying novel strategies that may serve as future therapy for HCC.Current treatment options for HCC, especially advanced HCC with metastasis, are extremely limited. Lack of highly specific and sensitive detection systems and appropriate therapeutic targets for HCC remain a major clinical challenge. As a participant in the metastasis of HCC, the new function of lncRNAs is still being explored, and it is expected to become a new potential target for cancer therapy and function as potent biomarkers to facilitate early diagnosis. Therefore, the emerging correlation between lncRNAs and HCC metastasis have opened up a more profound awareness of lncRNA-based diagnostics and targeted therapeutics.