Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type. The involvement of RNAs in a wide range of biological processes, such as transcription, translation, neurogenesis, and the biogenesis and function of non-coding RNAs (ncRNAs) has been discussed in multiple studies . Basic cin vitro or in vivo methods can be classified into two main categories: i) RNA-centric methods used to study proteins associated with a specific RNA; ii) protein-centric methods used to identify RNAs interacting with a specific protein  as well as non-interacting RNA-protein pairs (which will henceforth be referred to as “negative dataset”). We reported an overview of the major methods employed for the construction of RPI negative sets as well as a summary table reporting assumptions and outlines of such strategies.Datasets currently considered as benchmarks for training, cross-validating or testing RPI prediction models include RPI369 and RPI2241 , RPI488 The RPI2241 and RPI369 datasets were obtained from PRIDB , a databThe RPI488 dataset is a structure-based dataset, derived from PDB complexes and specifically incorporating lncRNA-protein interactions. In order to generate the dataset, 18 ncRNA-protein complexes were downloaded from the PDB and 726 lncRNA-protein pairs were collected from them. In order to derive both a positive and a negative dataset, a distance cutoff of 5Å was used. Also, redundant sequences (sequence identity greater than 90% for both protein and lncRNA sequences) were excluded by using CD-HIT . FollowiThe RPI1807 dataset was derived by integrating the Nucleic Acid Database (NDB)  and the The overlap between RPI datasets is reported in NPInter2.0 is a database that integrates experimentally-validated functional interactions between ncRNAs and other biomolecules , collected both from literature mining and from multiple databases. Although newer releases of the database exist (up to NPInter v4.0), NPInter v2.0 is the most widely used dataset for the development of prediction models. The dataset contains a total of 201,107 ncRNA interactions from 18 organisms, excluding interactions involving tRNAs and rRNAs.Interactions were derived from manual annotation of articles published between 2008 and 2013 and include both experimentally-validated interactions as well as binding sites identified by genome-wide techniques . The autSeveral datasets have been derived from NPInter2.0 by selecting subsets of interactions with characteristics of interest. More specifically, the most widely used non-structure-based dataset for the development and testing of RPI prediction models is a subset of this database (namely NPInter10412) first assembled by via the ChIRP-seq technique, as well as interactions involving circular RNAs. Additionally, disease associations were added to the database.A fourth release of NPInter was published in 2019 that increases the amount of high-throughput interactomes available data. NPInter4.0  includestaxa. Lastly, RNAInter4.0 is a recent resource that integrates experimentally validated and computationally predicted RNA interactions from literature-mining and databases . It provUltimately, despite remarkable advances in experimental techniques, the development of large and reliable RPI datasets is still the main bottleneck for training ML models. Hence, we would also like to stress the importance of redundancy control within data, since its presence may cause a leakage of information between training and test set during model training, resulting in untruthful prediction performance.The lack of reliable datasets of non-interacting RNA-protein pairs is a major concern in the development of computational methods for RPI prediction. Indeed, it is not trivial to conclusively state that a given protein does not interact with a given RNA molecule (absence of evidence does not constitute evidence of absence). Indeed, various papers have demonstrated the critical effect of negative dataset composition on the performance of Machine Learning and Deep Learning models . AdditioThe most often used  method tAn interesting, albeit not widely used, technique to construct negative samples is the FIRE (FInding Reliable nEgative samples) method . The corAn additional approach that circumvents the requirement to create a negative dataset is PU learning, a binary classification method that can be applied when only positive (P) and unlabeled (U) data are available. For example, PRIPU trains a biased SVM on only positive and unlabelled examples , 2017.Some of the most often employed strategies for the construction of a negative dataset are listed in If on the one hand, the choice of the right training dataset is critical, on the other hand the choice of the right algorithm for RNA-RBP interaction prediction is also important, considering that some predictors were developed for a specific class of RNAs, such as lncRNA or circRNA. We will therefore review the latest methods for RNA-RBP interaction prediction. Some methods predict interactions between proteins and a specific class of RNA molecules, such as circRNAs  and lncRLong non-coding RNAs (lncRNAs) are a class of RNA molecules that have attracted strong interest in recent years due to their abundance and their role in many physiological and pathological processes . Since mMost models are developed to predict lncRNA-protein interactions irrespective of the species, which can result in the introduction of noise and negatively affect performance.To address this and other limitations, https://github.com/nmt315320/CRBPDL).Circular RNAs or circRNAs are non-coding RNA molecules which can bind RBPs and are involved in multiple regulatory processes . CRBPDL In this section, we discuss a class of ncRNA-RBP interaction predictors not designed for a specific RNA type. A recent computational method developed in this field, called EDLMFC, uses an Ensemble Deep Learning framework with Multi-scale Features Combination  Wang et. EDLMFC For the prediction of RNA-RBP interactions, several methods have been developed in order to find the potential binding sites in RNA or in RBP sequences. One of them is from in vivo (via in vivo click selective 2′-hydroxyl acylation and profiling experiments (icSHAPE)   that wericSHAPE) , GraphPricSHAPE)  and DeepicSHAPE) , using tRNAs can also interact with other RNAs and several studies have shown these interactions to be crucially involved in the regulation of gene transcription, cell metabolism, and other key cellular functions . DespiteIn the last few years several studies have explored the RNA interactions landscape, given the crucial role that RNA-RBPs and RNA-RNA networks play in cell biology. Despite the advances made so far, novel experimental methods for the identification of binding sites (such as HITS-CLIP and PAR-CLIP) are still time-consuming and cost-intensive. That is why computational approaches represent a complementary strategy to guide experimental work. In this review, we provide an overview of the most recent prediction methods. We summarize recent advances in the algorithms developed to solve specific tasks, such as circRNA- or lncRNA-RBPs interaction predictions or, more generally RNA-RBPs interactions. Besides, we highlight how the development of a larger dataset of interactions is crucial to increase performance. Lastly, despite the fact that many methods rely only on sequence information, among the ones analyzed, those that obtain the best performances tend to include a variety of different biological features. Performance comparison of the described methods shows how the inclusion of structure information contributes to improving the accuracy and efficiency of the models. Only one of the described methods uses both RNAs and proteins structural information as input features for the predictive model because if, on the one hand, a large number of reliable protein structures is available, on the other hand, RNA structures are mainly obtained through computational prediction. RNA structure uncertainty could add noise to the model, resulting in untruthful prediction performances. The prediction of protein structure has reached satisfactory levels of performance thanks to the development of AlphaFold . Convers


Escherichia coli hisL and trpL pause sites as models, we describe here the relation of pause sites with upstream RNA structures suspected to stabilize pausing. We find that the transcription factor NusA influences the pause half-life at leuL, pheL and thrL pause sites. Using a mutagenesis approach, we observe that transcriptional pausing is affected in all tested pause sites, suggesting that the upstream RNA sequence is important for transcriptional pausing. Compensatory mutations assessing the presence of RNA hairpins did not yield clear conclusions, indicating that complex RNA structures or transcriptional features may be playing a role in pausing. Moreover, using a bioinformatic approach, we explored the relation between a DNA consensus sequence important for pausing and putative hairpins among thousands of pause sites in E. coli. We identified 2125 sites presenting hairpin-dependent transcriptional pausing without consensus sequence, suggesting that this mechanism is widespread across E. coli. This study paves the way to understand the role of RNA structures in transcriptional pausing.Transcriptional pausing occurs across the bacterial genome but the importance of this mechanism is still poorly understood. Only few pauses were observed during the previous decades, leaving an important gap in understanding transcription mechanisms. Using the well-known  RNA is involved in many crucial biological processes, such as amino acid transportation, ribosomal-subunit assembly, long non-coding RNA folding, riboswitch activity and intron splicing. All these functions are achievable thanks to the formation of RNA structures, which may be acquired cotranscriptionally . To alloTranscriptional pausing can be categorized into four distinct groups, such as elemental, backtrack, hairpin-stabilized and regulator-stabilized pauses ,7,15,16.Escherichia coli histidine and tryptophan operon leader peptides, namely hisL and trpL pause sites. Both pause sites were shown to rely heavily on cotranscriptionally folded RNA hairpins stabilization . To. To46].  ~3-fold  or ~4.4- ~3-fold , suggesthisL pause half-life. Using this approach, we first tested a previously reported mutation  UTP (11 kBq/µL) were added and incubated 10 min at 37°C. Samples were then diluted twice in buffer and were filtered through Sephadex G-50 columns (Sigma-Aldrich). Complete elongation of transcripts was started by the addition of all NTP (25 µM) and heparin (0.9 µg/µL) to allow single round conditions. NusA transcription factor (75 nM) was added to the reaction when required. For each timepoint, a aliquot was taken and the reaction was stopped in chilled formamide (60%). Transcription products were resolved on 8% polyacrylamide gels and quantified using QuantityOne software (Bio-Rad).All transcription reactions were performed in 20 mM Tris-HCl pH 8.0, 100 mM KCl, and 20 mM MgClt-test [p-value below 0.05. Each pause site was analysed at least twice. A sequencing ladder was used to precisely assess the position of the paused transcript. Sample series handling and treatments were managed using homemade Python 3.8.5 scripts (available upon request).To monitor transcriptional pausing, we quantified the signal intensity in a rectangle area corresponding to the paused transcript PT, 54 nt) and including the pause escaped transcripts  over time using the QuantityOne software (Bio-Rad). Thereafter, the pause half-life was obtained by fitting the proportion of paused transcript over the escaped transcripts to an exponential decay model using the R library nlstools (version 1.0.2) . Half-li nt and iConsidering that pause coordinates of two of the three datasets were provided using the MG1655 reference genome (Larson and Vvedenskaya), the pause sequences from position −49 to +1 were extracted from the W3110 reference genome for Imashimizu, and aligned on the MG1655 (NC_000913.3) genome to compare pause positions between the three different datasets . We alsoFor every pause site, the RNA structure was predicted using the sequence from positions −30 and −11 upstream of each pause site. The distribution of ΔG was compared to a group of 100 control datasets, each consisting of26222random MG1655 genomic sequences. The size of the control datasets was chosen considering the combined sizes of the three sample datasets. Predicted RNA structures characterized by energies falling between 0 and −1 kcal/mol were assigned to an unstructured group. The second group  was assigned to the presence of weak structures. The third group  was assigned to the presence of stable structures. Finally, the last group  was assigned to hyperstable structures.hisL pause. For instance, the screen was done to identify pauses having the consensus sequence, an RNA stem of 5 bp, a loop of 8 nt and a distance of 10 nt between the stem and the pause site.The identification of transcriptional pauses within the third group  was performed using criteria matching the Click here for additional data file.


Metastasis is the main cause of mortality in cervical cancer (CC). Circular RNAs (circRNAs) have been demonstrated to play a crucial role in carcinoma biology. However, the expression and function of circRNAs in cervical cancer metastasis are still unclear.In the present study, we identified a circRNA with an N6-methyladenosine (m6A) modification, circCCDC134, whose expression was increased in CC tissues by circRNA-Seq and qPCR. CircCCDC134 upregulation in CC was fine-tuned by ALKBH5-mediated m6A modification, which enhanced its stability in a YTHDF2-dependent manner. The functional experiments illustrated that circCCDC134 enhanced tumour proliferation and metastasis in vitro and in vivo. For the comprehensive identification of RNA-binding proteins, circRNA pull-down and mass spectrometry (ChIRP-MS), chromatin immunoprecipitation-seq (Chip-seq), RNA binding protein immunoprecipitation (RIP) and luciferase reporter assays were used to perform mechanistic investigations.The results revealed that circCCDC134 recruited p65 in the nucleus and acted as a miR-503-5p sponge to regulate the expression of MYB in the cytoplasm, ultimately stimulating HIF1A transcription and facilitating CC growth and metastasis. Conclusion: These findings indicate that circCCDC134 is an important therapeutic target and provide new regulatory model insights for exploring the carcinogenic mechanism of circCCDC134 in CC.The online version contains supplementary material available at 10.1186/s13046-022-02462-7. Cervical cancer (CC) remains the fourth most common gynaecological malignancy worldwide, with approximately 604,000 new cases and 342,000 deaths each year . The cliCircular RNAs (circRNAs), as a special type of noncoding RNA, have a closed circular structure without 5′ and 3′ ends and are not degraded by RNA exoenzymes . Based oMultiple studies have revealed that the N6-methyladenosine (m6A) modification is an important regulator of the production and function of mRNAs and noncoding RNAs . M6A-depIn the present study, a circRNA-Seq analysis was performed to identify that hsa_circ_0008806 (a circular RNA derived from CCDC134 termed circCCDC134) is significantly upregulated in CC. Further analyses indicated that m6A modification of circCCDC134 enhanced its stability and facilitated HIF1A transcription, promoting CC tumour growth and metastasis processes by acting as a recruited p65 in the nucleus and as a miR-503-5p sponge to regulate the expression of MYB in the cytoplasm. Thus, this study reveals that circCCDC134 is a novel potential biomarker and therapeutic target in CC.Fresh CC tissues were provided by the Cancer Hospital of the Chinese Academy of Medical Sciences. Information regarding the clinical samples is shown in Supplementary Table The human epidermal cell (HUCEC) and cervical cancer cell  lines were provided by the Chinese Academy of Sciences Cell Bank . All cells were cultured in RPMI-1640 containing 10% foetal bovine serum medium . All cells were cultured in a humidified incubator at 37 °C and 5% carbon dioxide.Small interfering RNAs (siRNAs) targeting the junction sequence of circCCDC134 were designed and synthesized by GenePharma . The cells were transfected with these siRNAs with siRNA-mate . Forty-eight hours post-infection, the cells were collected and processed for various assays. Lentivirus plasmids of shRNA were constructed by Genechem Company  and packaged using pMD2. G and psPAX2  into the cell. For circCCDC134 overexpression (EX-circCCDC134), the sequence was cloned into the pLCDH-ciR vector and packaged using pMD2. G and psPAX2. Information regarding the sequences is shown in Supplementary Table The total RNA extraction was performed using RNA-easy Isolation Reagent . qRT–PCR was performed using a HiScript III 1st Strand cDNA Synthesis Kit  and ChamQ™ Universal SYBR® qPCR Master Mix  according to the manufacturer’s instructions. For immunohistochemistry, the expression levels of ALKBH5 and HIF1A were measured by immunohistochemical staining with antibodies against ALKBH5 (1:200)  and HIF1A (1:200) . The total RNA extraction, qRT–PCR analysis and immunohistochemistry were performed as previously described . InformaME180 cells reached 70% confluence in six-well plates, and the cells were treated with 5 μg/ml actinomycin D and collected at the designated time point. For the RNase R treatment, the total RNA and 2.5 U/μg RNase R  were incubated at 37 °C for 20 min. After the actinomycin D or RNase R treatment, the RNA expression levels of circCCDC134 and CCDC134 mRNA were analysed by qRT–PCR.RNA-FISH was conducted using a Ribo fluorescence in situ hybridization kit  in accordance with the manufacturer’s directions. circCCDC134 and hsa-miR-503-5p FISH probes were designed and synthesized by RiboBio. In brief, the cells were seeded, fixed with 4% paraformaldehyde and incubated with a hybridized 5 mM probe at 37 °C overnight. All images were visualized and obtained under a confocal microscope (Philips).6 cells were used for the RNA fractionation assays. RNA from the nucleus and cytoplasm was separated by a Cytoplasmic & Nuclear RNA Purification Kit  following the manufacturer’s instructions.In total, 1 × 10The circRNA pull-down assay was performed with a TRAP (tagged RNA affinity purification) kit  according to the manufacturer’s instructions. In brief, we constructed a plasmid with circCCDC134 and MS2. We also constructed a plasmid with MS2-CP-Flag, which was fused with a mCherry tag (MS2-CP-FlagmCherry). CC cells were transfected with these two plasmids, and circCCDC134 was precipitated by pulldown using anti-Flag antibodies. Lysates derived from cells without the MS2 tagging system were used as controls. The cell lysates were incubated with Protein A/G beads overnight at 4 °C. Then, the RNA and bound proteins were eluted; RNA was analysed by qPCR; and the bound proteins were analysed by label-free mass spectrometry (MS) and Western blot assays.The RNA immunoprecipitation (RIP) assay was performed with a RIP kit  according to the manufacturer’s instructions. In brief, magnetic beads were incubated with 10 μg of antibodies against ALKBH5 , YTHDF2 , p65  and normal IgG  overnight at 4 °C. Methylated RNA immunoprecipitation was performed with a MeRIP kit  according to the manufacturer’s instructions, and an anti-N6-methyladenosine (m6A) antibody  was used for MeRIP. The Western blot and RIP assays were performed as previously described [The ChIP assay was performed withaCUT&Tag Assay Kit according to the manufacturer’s instructions. In brief,chromatin solution was added to 10 μg of NF-κB, p65  or Myb antibodies  andincubatedovernight at 4 °C with slow rotation. The ChIP assays were performed as previously described [6 ME180 cells (shRNA circCCDC134 or empty vector) in 0.1 mL of phosphate-buffered saline were injected into the tail vein of nude mice. After 4 weeks of feeding, the animals were euthanized, and the lung tissues were removed for further processing.The animal experiments were approved by the Animal Center of the Institute of National Cancer Center/Cancer Hospital, CAMS & PUMC and followed the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Four-week-old female nude mice  were subcutaneously injected with ME180 cells stably expressing shRNA circCCDC134 or an empty vector. The tumour size was measured every 7 days. At the end of the experiment (after 4 weeks), the mice were euthanized by an intraperitoneal injection of 100 mg/kg pentobarbital sodium , and the xenograft tumours were resected and weighed. To measure cell metastasis in vivo, a lung metastasis model was used. Briefly, 1 × 10P-values < 0.05 indicated statistical significance.All statistical analyses were performed with GraphPad Prism v. 8.01 . Student’s t-test was used to compare values between the test and control groups. The data generated in this study are available upon request from the corresponding author.p < 0.05) . We found hundreds of circRNAs that varied between the CC tissue and adjacent normal tissue. We detected 21 upregulated circRNAs with a 2-fold change (http://www.cuilab.cn/sramp/)  Fig. B, and thTo explore this further, we first performed RNA pull-down assays and a mass spectrometry analysis (ChIRP-MS) to screen circCCDC134-interacting proteins and comprehensively identify RNA-binding proteins. The results of ChIRP-MS  and HCC94 cells with circCCDC134 overexpression (EX-circCCDC134). The qPCR results showed that Si-circCCDC134-1 and Si-circCCDC134-2 could significantly inhibit circCCDC134 expression in CC cells (ME180 and SiHA cells)  [To clarify the abnormal expression of genes related to CC metastasis and circCCDC134 disorder, mRNA-seq of CC  . By combMany studies have demonstrated that circRNAs play regulatory roles by binding proteins and sponging miRNAs. In the above experiments, ChIRP-MS was performed to determine whether circCCDC134 could interact with binding proteins, and the results showed that circCCDC134 cooperated with multiple nuclear proteins. Among the binding proteins, we found that circCCDC134 could interact with p65, which is a very important transcription factor that promotes CC metastasis  and intehttp://www.circbank.cn/) and the circRNA database (https://circinteractome.nia.nih.gov/) were used. Among these data, only hsa-miR-503-5p was low in CC tissues and may be sponged by circCCDC134  and RFS  associated with these 14 genes in CC based on TCGA databases. The analysis revealed that MYB  was used. Interestingly, HIF1A was an important MYB target gene  revealed that circCCDC134 contained no poly-A tail; 3. RNase R and actinomycin D showed that circCCDC134 was more stable than linear CCDC134; and 4. according to the FISH and RNA fractionation assays, the fluorescence signal of circCCDC134 was found in both the nucleus and cytoplasm. Additionally, we revealed upregulated expression of circCCDC134 in CC metastasis tissues and cells using qRT–PCR. Our data indicate that the expression of circCCDC134 was significantly high in CC tissues, especially in metastatic tissues. The FISH and RNA fractionation assays suggested that circCCDC134 could play a regulatory role in both the nucleus and the cytoplasm. Then, we focused on the mechanism underlying the association between circCCDC134 and CC metastasis in terms of dysregulated expression and regulatory mechanisms.N6-methyladenosine is the most abundant and reversible internal modification in mRNAs and circRNAs; recent studies have shown that m6A modification participates in circRNA expression. ALKHB5 is a member of the AlkB family and can remove m6A methylation from target RNAs and lead to lower m6A levels . An incrRecent studies have revealed that circRNAs can interact with RBPs and function as miRNA sponges to promote cancer metastasis. For instance, hsa_circ_0003258 promotes prostate cancer metastasis by complexing with IGF2BP3 and sponging miR-653-5p , which iOur study was not free of limitations. First our results need to be validated in larger CC patient cohorts in terms of the overall survival prediction of circCCDC134. Second, we focused only on the roles of circCCDC134 in tumour proliferation and metastasis. More detailed studies are necessary to explore the impact of circCCDC134 on other malignant biological behaviours of CC cells, including angiogenesis and immune escape. Therefore, these unresolved limitations need to be addressed in future studies.In summary, our findings highlight the attractive value of m6A demethylases in the expression of circCCDC134 and the key role of circCCDC134 in enhancing HIF1A transcription in the cytoplasm and nucleus. These findings shed light on novel molecular mechanisms and provide new insight into developing effective therapeutic strategies for CC metastasis.Additional file 1: Supplementary Table 1. Information concerning the clinical samples.Additional file 2: Supplementary Table 2. Sequence information.Additional file 3: Supplementary Fig. 1. (A and B): Melting curve and separation curve display the primers for circCCDC134 designed for the qRT–PCR experiments. (C): RNA fractionation assays showed that circCCDC134 was expressed in both the nucleus and cytoplasm of SiHA cells. (D and E): The mass spectrometry results of circCCDC134-binding proteins. (F): PPI analysis based on mass spectrometry data revealed that the ALKBH5 and YTHDF2 proteins could bind circCCDC134-MS2. Gene-specific m6A qPCR to detect the m6A methylation status of circCCDC134 was performed and the results indicated that an reduction of m6A methylation in the ALKBH5 overexpression group of (G): SiHA and (H): ME180 cell lines. (I): Patients with high ALKBH5 expression had a much longer overall survival in CC.Additional file 4: Supplementary Fig. 2. (A): The expression of EFNA1 was lower in CC tissues. : The expression of KLF2, PER1 and COL5A3 was higher in CC tissues. The survival analysis based on these genes in CC based on TCGA databases revealed that EFNA1, KLF2, PER1 and COL5A3 expression was closely associated with CC overall survival.Additional file 5: Supplementary Fig. 3. (A): Transfection of the miR-503-5p mimic resulted in a decrease in proliferation, migration and invasion ability in ME180 cells, and the function of miR-503-5p was rescued by the reintroduction of circCCDC134. (B): According to the miRDB, miRTarBase and TargetScan database analysis, 90 target genes were found. (C): Combined with the si-circCCDC134 mRNA-seq data, 14 target genes were analysed. (D): MYB was found to be the key target gene of miR-503-5p and was closely related to CC OS and RFS. (E): The qPCR results showed that the expression of MYB was rescued by transfection with sh-circCCDC134 and anti-miR-503-5p or transfection with circCCDC134 and miR-503-5p mimics.Additional file 6: Supplementary Fig. 4. The survival analysis of 14 genes in CC based on TCGA databases. (A): Patients with high ANK3 expression had a much longer OS in CC. (B-G): Patients with low ANLN, CD2AP, KPNA3, NUFIP2, SEC24A or TLL1 expression had a better OS in CC. (H-M): OS analysis revealed that MYB CDK17, CCND2, ZBTB34, TSC22D2, TMEM245 and ZNF449 expression is not correlated with patient survival in CC.Additional file 7: Supplementary Fig. 5. The RFS of 14 genes in CC based on TCGA databases. (A): Patients with high ANK3 expression had a much longer RFS in CC. (B-G): Patients with low ANLN, CD2AP, KPNA3, NUFIP2, SEC24A or TLL1 expression had a better RFS in CC. (H-M): The RFS analysis revealed that MYB CDK17, CCND2, ZBTB34, TSC22D2, TMEM245 and ZNF449 expression is not correlated with RFS in CC.


Oryza sativa subsp. Indica. Furthermore, the reliability of the improved-tdMDA-NGS method was also asserted in HeLa cell lines, showing a significant fold difference in comparison with the existing traditional method of circRNA sequencing. Among the identified circular RNAs, a significant percentage from both rice (∼58%) and HeLa cell lines (∼84%) is found to be matched with the previously reported circular RNAs, suggesting that the improved-tdMDA-NGS method can be adapted to detect and characterize the circular RNAs from different biological systems.Covalently closed circular RNAs are neoteric to the eukaryotic family of long non-coding RNAs emerging as a result of 5′–3′ backsplicing from exonic, intronic, or intergenic regions spanning the parental gene. Owing to their unique structure and stability, circular RNAs have a multitude of functional properties such as micro-RNA and protein sponges, direct and indirect modulators of gene expression, protein translation, and many unproven activities apart from being potential biomarkers. However, due to their low abundance, most of the global circular RNA identification is carried out by high-throughput NGS-based approaches requiring millions of sequencing reads. This lag in methodological advancements demands for newer, more refined, and efficient identification techniques. Here, we aim to show an improved version of our previously reported template-dependent multiple displacement amplification (tdMDA)-NGS method by superimposing the ribosomal depletion step and use of H minus reverse transcriptase and RNase H. Implication of tdMDA using highly replicative Phi29 DNA polymerase after minimizing the linear and ribosomal RNA content further intensifies its detection limit toward even the abysmally expressing circular RNA at a low NGS depth, thereby decreasing the cost of identifying a single circular RNA. A >11-fold and >6-fold increase in total circular RNA was identified from the improved-tdMDA-NGS method over the traditional method of circRNA sequencing using DCC and CIRI2 pipelines, respectively, from  The circularization of the RNA molecules has been in the limelight for many decades , while iCircRNAs are 1–3% of the total poly(A) RNA population in the transcriptomic pool , and ideOryza sativa subsp. Indica was grown and maintained in a green house under controlled conditions of a 32°C temperature, 70–80% humidity, and a 16/8 hours day–night cycle. Old leaf materials  were collected and ground to fine powder using liquid nitrogen in a pre-chilled mortar–pestle. RNA was extracted using RNAiso Plus  reagent by following the method as mentioned previously  supplemented with 10% fetal bovine serum  and 1% penicillin–streptomycin  antibiotics. The cells were harvested in 1 ml of RNAiso Plus  using a cell scrapper upon reaching 70–80% confluent state. The collected HeLa cells were passed four to five times through a 1 ml syringe in order to lyse the cells, and total RNA was isolated using RNAiso Plus  using the manufacturer’s protocol.HeLa cells were obtained from NCCS Pune, India, and were maintained under an atmosphere of 37°C, 5% COO. sativa subsp. Indica and HeLa cells, 4 U Turbo DNase  was used at 37°C for 30–45 min. The reaction was heat-inactivated using 0.01 mM ethylenediaminetetraacetic acid for 10 min at 70–75°C, followed by phenol/chloroform purification. The quantity and quality of the RNA were assessed using NanoDrop ND-1000 spectrophotometer  readings, followed by running 1.5% MOPS agarose gel electrophoresis. After ensuring the RNA quality, 10 μg of DNA-depleted RNA was subjected to rRNA depletion using a RiboMinus™ Plant Kit for RNA-Seq  and RiboMinus™ Eukaryote Kit v2  for rice and HeLa cells, respectively, by adhering to the manufacturer’s instructions. The rRNA-depleted RNA was subjected to RNase R  digestion at 37°C for 20 min.For every 10 μg of total RNA extracted from both The entire enriched RNA was reverse-transcribed with random primers and oligo-dT primers separately using a RevertAid H minus first strand cDNA synthesis kit according to the manufacturer’s instructions . The converted complementary DNA (cDNA) was treated with 0.5 μL of RNase H  in 20 µL reaction at 37°C for 20 min, followed by enzyme inactivation at 65°C for 10 min.For eliminating template-independent amplification (TIA), circular pBluescript (pBKSII+) and water were used as a positive control and negative control, respectively, for phi29 DNA polymerase amplification. They were incubated with either an exo-resistant random hexamer (NNNN*N*N) (ERRH) having two phosphothioarate bonds at the 3′end or an exo-resistant random pentamer (/5Sp18/NNN*N*N) (ERRP) having the 5′end blocked by the C18 spacer apart from two phosphothioarate bonds at the 3′end at two different final concentrations  at either 28°C or 30°C for 16, 18, or 21 h with other reagents and protocols as followed previously .Enrichment of circRNA and its Reverse Transcription Section) from both rice and HeLa cells was used separately as a template for tdMDA by incubating with 30 µM exo-resistant random pentamer at 28°C for 21 h along with other reagents as mentioned in The ss cDNA produced (Enrichment of circRNA and its Reverse Transcription Section) and 250 ng of circRNA-derived td-MDA products (Abolishing Template Independent Amplification (TIA) by Phi29 DNA Polymerase Section) from both rice and HeLa cells were used for library preparation using an Illumina-compatible NEBNext® Ultra™ II Directional RNA Library Prep Kit  and an Illumina-compatible NEXTflex Rapid DNA sequencing Bundle , respectively, as per the manufacturer’s instructions at Genotypic Technology Pvt. Ltd., Bangalore, India. The prepared sequencing libraries were purified with JetSeq beads  and quantified using a Qubit fluorometer , and their fragment size distribution was analyzed on a 2200 TapeStation . They were sequenced in an Illumina platform [150 nt paired end (PE)] at Genotypic Technology Pvt. Ltd., Bangalore, India, as mentioned previously  alignment for the clean reads as per the CIRI2 manual. The processed reads from both rice and HeLa cells were aligned with their respective reference genome [ensembl plant release 29 (https://hgdownload.soe.ucsc.edu/downloads.html#human) for HeLa cells]. A minimum of one supporting read having the backsplice junction out of any one sample was considered for circRNA identification in both DCC and CIRI2 pipelines.The processed enriched circRNA (25–100 ng) . Further, the PCR products were gel-eluted by the silica matrix, cloned into pGEM-T Easy vector , and Sanger sequenced , for detection of the backsplice junction corresponding to each circRNA. qRT-PCR was also performed with two different cDNAs (Enrichment of circRNA and its Reverse Transcription Section) for checking the expression level of circRNAs.Randomly, two non-canonical circRNAs were selected and validated from a set of circRNAs under study by designing divergent primer pairs . DivergeAt first, ERRH was used with the circular pBKSII+ plasmid (positive control) and no template (water control) at its designated final concentration of 50 µM and incubated with phi29 DNA polymerase at 30°C for 16 h. There was high-molecular-weight amplification with pBKSII+, but no band was seen in the water control, suggesting td-amplification . HoweverO. sativa subsp. Indica .A total of 34,299,487 and 38,618,983 PE raw reads were obtained from biological triplicates of rice RNA (∼10 million PE reads/sample) using traditional RNA-Seq and i-tdMDA-NGS, respectively, which were processed at Phred25 to remove low-quality, adapter-specific, and other unwanted reads, resulting in retaining of 31,261,868 (91.14%) and 36,086,992 (93.44%) trimmed PE reads . Similarhttps://hgdownload.soe.ucsc.edu/downloads.html#human). The unaligned reads from both rice and HeLa cell lines were used in DCC and CIRI2 computational pipelines for circRNA identification.On the other hand, 48,688,532 and 29,834,900 PE raw reads were trimmed at phred25 to get 48,599,461 (99.82%) and 28,803,581 (96.54%) PE reads , respectA total of 408 and 2,517 circRNAs were detected from the processed unaligned reads in rice by traditional RNA-Seq and i-tdMDA-NGS, respectively . Almost Similarly, 8,499 and 710 circRNAs were identified in HeLa cell lines using traditional RNA-Seq and i-tdMDA-NGS, respectively , which wUsing the DCC pipeline set in the paired-dependent mode, 6,368 and 70,908 circRNAs were identified from rice-processed reads (the same as used for CIRI2) using traditional RNA-Seq  and i-tdMDA-NGS , respectively , which wOn the other hand, DCC analysis of HeLa RNA in the paired-dependent mode with the same number of processed reads gives 8,958 and 34,213 circRNAs from traditional RNA-Seq  and i-tdMDA-NGS , respectively , presentMoreover, rice RNA processed reads from both the methods were also analyzed in the paired-independent plus stranded mode using the DCC computational pipeline. In this case, the total number of identified circRNAs increased to 17,509 and 179,783 from traditional RNA-Seq and i-tdMDA-NGS, respectively , equallyEnrichment of circRNA and its Reverse Transcription. The divergent primer, Osi_DC_02  with an additional G nt at its junction when compared with the circRNA, osi_circ_2-187437-187904_(-)  . However7904_(-) . As expeved cDNA . Carefulved cDNA , which w samples .A-45°C yielded the anticipated ∼267 bp amplicon with random-primed cDNA but not with oligo-dT-derived cDNA (β-actin primer (TA-55°C) (β-actin primer) apart from primer dimer amplification in triplicates   from botTA-55°C) . The divTA-55°C) . Moreoveplicates , which eplicates  and a coplicates .de novo circRNA identification till date (CircRNA research has grown manifold in the past decade. Recently, a lot of research is focused on functional aspects of circRNAs and the role in biomarker development . Howeverill date . This, will date  or rRNA-ill date  or combiill date . Howeverill date  as previill date . Moreoveill date . CircRNAHere, in this article, we have introduced a new method of circRNA identification which is actually a refined version of our previously published method . The conWe formulated a new method, i-tdMDA-NGS, for circRNA identification which is superior over the current traditional circRNA-Seq in detecting a large number of circRNAs at a much-reduced cost. However, we aim to validate and authenticate the existence of a larger number of circRNAs identified by our method in near future by including other experiments such as Northern blotting, the RNase protection assay, and so forth.


RNAExtraction and Storage (RNAES) protocol that eliminates requirements for instrumentation, expensive materials, and preserved cold chain. Through an iterative process, we optimized viral lysis and RNA binding to and elution from glass fiber membranes included in simple RNAES packets. Efficient viral lysis was achieved with a nontoxic buffer containing sucrose, KCl, proteinase K, and carrier RNA. Viral RNA binding to glass fiber membranes was concentration dependent across seven orders of magnitude (4.0–10.0 log10 copies/μL) and significantly increased with an acidic arginine binding buffer. For the clinical evaluation, 36 dengue virus (DENV)-positive serum samples were extracted in duplicate with the optimized RNAES protocol and once in an EMAG instrument (bioMérieux). DENV RNA was successfully extracted from 71/72 replicates (98.6%) in the RNAES protocol, and real-time RT-PCR cycle threshold (TC) values correlated between extraction methods. DENV RNA, extracted from clinical samples, was stable when stored on dried RNAES membranes at ambient temperature for up to 35 days, with median eluate RNA concentration decreasing by 0.18 and 0.29 log10 copies/μL between day 0 and days 7 and 35, respectively. At a cost of $0.08/sample, RNAES packets address key limitations to available protocols and may increase capacity for molecular detection of RNA viruses.RNA extraction is essential for the molecular detection of common viral pathogens. However, available extraction methods and the need for ultra-cold storage limit molecular testing in resource-constrained settings. Herein, we describe the development of an economical IMPORTANCE RNA extraction methods and ultra-cold storage requirements limit molecular testing for common viruses. We developed a simple, flexible, and economical method that simultaneously addresses these limitations. At $0.08/sample, the new RNAExtraction and Storage (RNAES) protocol successfully extracted viral RNA from acute-phase sera and provided stable, ambient-temperature RNA storage for 35 days. Using this approach, we expect to improve RNA virus detection and outbreak response in resource-constrained settings. RNA viruses are the largest group of human viral pathogens and the most common cause of emerging infectious disease outbreaks , 2. Clin3–8–8–Arboviruses comprise the subset of RNA viruses transmitted by infected arthropod vectors such as mosquitoes and ticks. These have resulted in large, recent outbreaks caused by the introduction of viruses into naïve populations  –18 or re–19–RNAExtraction and Storage (RNAES) protocol for use with serum and plasma in resource-constrained settings. This economical method utilizes a biosafe viral lysis buffer and capillary flow across an RNA binding membrane in simple packets to yield RT-PCR-compatible RNA and provide stable RNA storage at ambient temperatures. The protocol was developed and optimized using contrived DENV-positive clinical samples and purified arboviral RNA. Clinical evaluation was then performed on a set of 36 acute-phase samples from confirmed dengue cases in Paraguay.In this study, we developed a simple TC) values (indicating increased RNA yield) by real-time reverse transcriptase PCR (rRT-PCR) . To eliminate potential SDS inhibition of downstream molecular testing, sucrose buffer was chosen as the lysis buffer for further experiments. To further enhance RNA recovery and prevent degradation, varying amounts of poly-A carrier RNA and proteinase K were added to the lysis buffer. In side-by-side comparisons, carrier RNA (2.5 μg/sample) and proteinase K (5.0 μg/sample) significantly increased RNA recovery . Higher concentrations of carrier RNA  and proteinase K  in the lysis mixture did not enhance RNA recovery. In addition, the impact of lysis incubation on RNA recovery at room temperature was tested at various time durations from 10 to 60 minutes. Samples were stable for up to 1 hour in lysis mixture, but longer incubation times did not result in increased RNA recovery after 10-minute incubation at room temperature (Table S4).Of the four experimental lysis buffers evaluated -NaCl, and sucrose buffer), SDS-NaCl and sucrose solutions performed similarly, yielding earlier DENV cycle threshold . To further evaluate the impact of arginine binding buffer, DENV, CHIKV, and OROV RNA were added to RNAES packets in either sucrose lysis buffer or arginine buffer (plus ethanol in both cases). Arginine buffer demonstrated lower mean TC values and decreased variability in TC values with each membrane .Evaluation of arginine buffer as a viral lysis buffer with contrived DENV samples resulted in worse RNA recovery compared to sucrose buffer . The arginine buffer was subsequently integrated into the final RNAES protocol as a binding buffer mixed with ethanol . This al10 copies/mL. When extracted in duplicate with the RNAES protocol , DENV RNA was detected in 71/72 replicates (98.6%), and DENV multiplex rRT-PCR TC values correlated with results following EMAG extraction  and 35 days (n = 5 samples). RNA was eluted on day 0 or dried on the RNA binding membrane after the glycine wash and eluted on day 7 or day 35. DENV RNA concentration in the eluates was calculated in the DENV multiplex rRT-PCR. Concentrations in the eluates did not differ significantly across time points  with an estimated cost of USD $8.00 per sample, plus a one-time instrument cost of USD $115,000. Clinical samples were collected during two large DENV outbreaks in Paraguay . Notably, the range of viral loads in samples that were available for extraction in the current study represents 92.7% of all DENV-positive samples with quantifiable viral loads from these two large outbreaks  with varying sucrose concentrations (50 to 300 mM) and solution pH values . Optimal lysis was obtained with a solution of 150 mM sucrose ; 50 mM Tris-HCl, pH 7.0; and 100 mM KCl (both from MilliporeSigma).Four experimental lysis buffers were evaluated: deionized water, STET , SDS-NaCl  , and a sPoly-A carrier RNA  and proteinase K  were evaluated as additional components to the lysis mixture. Further experiments used 25 μL/reaction of lysis mixture containing 17.5 μL of lysis buffer, 2.5 μL (2.5 μg) carrier RNA, and 5 μL (5.0 μg) proteinase K. Following preparation, lysis mixture was used immediately or stored at 4°C until use.Arginine and glutamine amino acid buffers were initially prepared as described . BuffersDENV-positive clinical samples were collected as part of a study to detect and characterize arboviral infections in Asunción, Paraguay in collaboration with the Instituto de Investigaciones en Ciencas de la Salud, Universidad Nacional de Asunción (IICS-UNA) . This stSamples were selected for the RNAES protocol clinical evaluation that had quantifiable DENV viral loads in the DENV multiplex rRT-PCR , 43 and n = 10) and 35 (n = 5) postextraction. Six extraction packets were prepared, with duplicate packets for each time point. On day 0, DENV RNA was completely extracted, eluted off two membranes, and run in the DENV multiplex rRT-PCR to establish a baseline. For the other time points, DENV RNA was extracted with the packets through the glycine wash step on day 0 and transferred to empty 1.5-mL tubes to air dry at ambient temperature for 1–3 hours. After drying, tubes were closed and stored in airtight plastic bags with desiccant packets. RNA was eluted from dried membranes with 50 μL TE buffer on days 7 and 35 following extractions. Eluates were run in the DENV multiplex rRT-PCR for comparison with day 0 results. A four-point standard curve was included on each run to calculate DENV-1 RNA concentration at each time point . DENV RNA for this portion of the study was a 135-base synthesized DENV-2 RNA oligonucleotide containing the DENV multiplex rRT-PCR target sequence . For CHIKV and OROV, previously extracted (EMAG) genomic RNA was used. Contrived clinical samples were prepared by spiking negative human serum or plasma (MilliporeSigma) with DENV-positive serum of known concentrations. Aliquots of contrived specimens were prepared and stored at −80°C until use.Eluates from optimization and analytical evaluation experiments were tested in a single-reaction multiplex rRT-PCR for ZIKV, CHIKV, and DENV or a single-plex rRT-PCR for OROV, both performed as previously described , 45. For42–TC values were compared by unpaired Student’s t test. The t tests, linear regression, and the Bland-Altman plot were performed using GraphPad Prism . Graphs were prepared with GraphPad and Excel.Basic statistical analyses were performed in Excel .


C. eleganshomolog of DPYSL2, UNC-33, is also found to be critical for axonal outgrowth and synapse formation. Herein, we study the effects of environmental stressors such as increasing temperatures and pathogens on the expression of GFP driven by theunc-33promoter. Results indicate that neuronal GFP expression was lower inC. elegansexposed to these prenatal stressors, making this the first report denoting an environmental regulation of theunc-33 promoter. This study provides insight intounc-33and the regulation of its expression in relation to temperature and infection.Environmental factors such as prenatal stress are hypothesized to contribute to the development of schizophrenia. Lee and colleagues determined rats exposed to prenatal stress exhibited decreased levels of only one protein, DPYSL2, in their prefrontal cortex and hippocampus. DYPSL2, a protein seen to be inactivated in schizophrenic patients, is important for neuronal development. The Schizophrenia is a complex and chronic psychiatric disorder that is characterized by positive symptoms, negative symptoms, and cognitive impairment, some of which presents themselves as hallucinations and delusions, lack of emotional responsivity, and difficulty processing information respectively (Farah 2018). The development of schizophrenia is multifactorial in which non-genetic components are involved in conjunction with the effect of genes (Sullivan 2005). While there are no single causal molecular players or environmental factors leading to schizophrenia, prenatal stressors such as nutritional deficiency, maternal stress, and viral-bacterial infections have been shown to increase vulnerability to the disorder .et al.2020). The release of the inflammatory stress mediator could potentially compromise the blood-brain barrier and cause inflammation leading to neuropsychiatric disorders .Psychosocial maternal stress during key periods of neurodevelopment leads to the increased risk of activating immune responses that could affect the fetus (Reynolds 2020). During fetal development, nutritional deficiency, maternal stress, and infections have been consistently associated with an increased risk of developing schizophrenia in the fetus (Reynolds 2020). It has been hypothesized that these stressors can cause immune activations, such as the release of cytokines, within the mother, that can alter neuronal development in the fetus . Epidemiological evidence obtained from two individual instances of famine, the Dutch Hunger Winter of 1944-1945 and the Chinese famine 1959-1961, has identified intrauterine exposure to nutritional deficits as one of the contributing factors for the development of schizophrenia . Through clinical and sociodemographic data analysis, it was observed that those conceived or in early gestation during the peak of the famine faced a 2-fold risk of developing schizophrenia .et al.2015). Lee and colleagues exposed pregnant rats to a series of stressors during a period analogous to a human’s critical period of brain development. Lee and colleagues conducted proteomic and behavioral analyses of prenatally stressed preadolescents and non-stressed controls . Through the evaluation of the prenatally stressed offspring it was revealed that out of an array of proteins isolated from the pre-frontal cortex and hippocampus, two regions often implicated in schizophrenia, the expression of Dihydropyrimidinase-like 2 (DPYSL2) was the only protein suppressed in these regions . In addition to the downregulation of DPYSL-2, schizophrenia-like behaviors were noted in the prenatally stressed offspring .Similar to humans, rodent offspring that were a product of maternal gestational stress have shown increased vulnerability to neurodevelopmental changes that may lead to schizophrenia . DPYSL2 also referred to as Collapsin response mediator protein 2 (CRMP2) is a protein coded by a gene found within chromosome region 8p21, which is involved in axonal elongation and dendritic pruning . Decreased levels of CRMP2 are associated with a decrease of dendritic spine density on pyramidal neurons which in turn could result in impaired neuronal activity associated with schizophrenia .Converging evidence in regard to DPYSL-2 and the risk of schizophrenia have led researchers to hypothesize that the development of the disorder could be related to decreased amounts of DPYSL2 . UNC-33 works with UNC-44 and UNC-119 to immobilize microtubules in mature neurons . UNC-33 has three isoforms identified by Tsuboi and colleagues as UNC-33L, UNC-33M, and UNC-33S. For proper UNC-33 function, the dimer formation between UNC-33M and UNC-33S must happen . UNC-33L is highly expressed in the nerve ring, as seen in research by Maniar and colleagues . The localization of the protein in the nerve ring is hypothesized to be a factor in axonal elongation in C. elegans.unc-33mutants display defects in axonal outgrowth and alterations to the localization of the UNC-33 proteins .unc-33mutations lead to defects in microtubule elongation, altered microtubule orientation, and significant microtubule sliding, which together produce early termination of axons and mislocalization of axonal proteins to dendrites Found in neurites, UNC-33, theunc-33promoter under conditions of increasing temperatures and pathogenic infection. To this end, we used a transgene containing a 2.7 Kb genomic fragment that is immediately upstream to the start site of theunc-33gene driving the expression of GFP . Initially, we examined the effects of temperature on synchronous larva 3 (L3)C. elegansgrown at 16°C or 25°C and quantified GFP fluorescence in neurons and glial cells. Analysis of the fluorescence ratio of neuronal to glial GFP of 30 nematodes per condition shows that at 25°C, neuronal expression of GFP is significantly decreased while the glial expression of GFP is increased. This finding suggests that the activity of theunc-33promoter in neurons is regulated by external factors and suppressed upon the exposure to a temperature of 25°C . To further investigate the effect of temperature on this promoter, we created a temperature tolerance curve by quantifying the GFP fluorescence in neurons and glial cells at temperatures that ranged from 16°C to 25°C. Quantification of neuronal to glial GFP ratios uncovered three temperature dependent responses. Specifically, neuronal expression of GFP driven by the transgene was significantly different at 16°C, 17-20°C, and 22- 25°C. Nematodes grown at temperatures of 16°C had significantly higher neuronal expression of GFP driven by theunc-33promoter compared to the other temperatures analyzed.C. elegansgrown at 17-20°C had an intermediate ratio of neuronal to glial GFP fluorescent and those incubated at 22- 25°C had the lowest neuronal expression of GFP. Together, the data show that the temperature range of 22°C to 25°C has the highest suppressing effect on the activity of theunc-33promoter in neurons. Moreover, results conveyed a negative trend, indicating that as temperature increases the ratio of neuronal to glial fluorescence decreases .To expand our understanding of Schizophrenia and investigate the role of environmental stressors regulating the expression of UNC-33/CRMP2/DPYSL2, we examined the activity of thePseudomonas aeruginosais a type of gram-negative bacteria that commonly inhabit soil and water.Pseudomonas aeruginosa, described as an opportunistic and resilient pathogen, affects humans and animals alike through its biofilm formation, quorum-sensing, and toxin and siderophore secretion . Thedaf-16gene is hypothesized to activateC. elegansimmunity upon the presence of pathogens such asPseudomonas aeruginosa. Through electron microscopy, it was found thatC. elegansexposed toPseudomonas aeruginosafor a duration of 24 or 48 hours presented negative effects such as intestinal enlargement and matrix accumulation . Herein we found that after quantifying the ratio of neuronal to glial fluorescence of nematodes cultivated at 16°C inPseudomonas aeruginosa PA14, there was a significantly lower expression of GFP driven by theunc-33 promoter in neurons . Conversely, at an incubation temperature of 25°C, nematodes showed no further suppression of neuronal GFP expression suggesting that temperature andPseudomonas aeruginosa PA14 may regulate the activity of the unc-33promoter through a similar mechanism .These findings led us to investigate whether bacterial infection, as a stressor, regulates the activity of the promoter controlling neuronal levels of UNC-33.unc-33promoter. These two different stressors seem to have the opposite effect on glial cells as compared with neurons, increasing the activity of theunc-33promoter; hence, the heightened level of GFP expressed in those cells. Even though another promoter driving GFP has not been investigated herein, this observation suggests that these stressors are not causing a general effect on transgene expression. Furthermore, it is intriguing to discover that two independent stressors, high temperature and pathogen exposure, have the same effect on theunc-33promoter driving the expression of GFP. Future experiments can investigate whether the reduction in promoter activation in neurons is due to epigenetic changes in the region of the genome. Gene expression can be regulated and altered by environmental conditions through epigenetics. Exposure to malnutrition during prenatal development causes epigenetic changes in animal models via DNA methylation . Additionally, researchers showed that in humans, prenatal stress due to famines led to epigenetic changes linking environmental stressors to an increased risk of developing schizophrenia . Periconceptional exposure to famine was associated with a 5.2 % reduction in DNA methylation . It is warranted to explore alternative avenues by which the activity of theunc-33promoter is modulated by external factors and potentially transmitted to subsequent generations via epigenetic changes.Taken together, these findings reflect that adverse environmental conditions, such as high temperatures and pathogen exposure, led to a decrease in neuronal GFP driven by theSynchronization of nematodesE. coliOP50.To synchronize nematodes to an L3 stage, adult gravid hermaphrodites from each strain were floated in DI water, transferred to 15mL conical tubes, and centrifuged for 1 minute at 8,000 x g at room temperature. After centrifugation, the supernatant was discarded, and 7 mL of alkaline bleach and 7 mL of water were added. The tube containing the pelleted worms and diluted alkaline bleach was incubated for 7 minutes on a rocker at room temperature. After incubation, the worm suspension was centrifuged for 1 minute at 8,000 x g at room temperature, and the supernatant was discarded. Next, the pellets were washed using 1 mL of M9 buffer and centrifuged for 1 minute at 8,000 x g at room temperature after each wash. A total of three washes were performed. After the final centrifugation, the supernatant was discarded, and the pellets containing eggs were pipetted onto their respective plates seeded withIncubation Conditions to study the effect of temperatureunc-33promoter, we incubated nematodes on plates seeded withE. coliOP50 at various temperatures within the experimental range of 16°C<X<25°C. To obtain L3 nematodes for imaging, we incubated embryos for 48 hours at 16 °C, 43.5 hours at 17 °C, 39 hours at 18 °C, 30 hours at 20 °C, 27.6 hours at 22 °C, 26.4 hours at 23 °C, and 24 hours at 25 °C prior to imaging.To analyze the effect of temperature on the activity of theIncubation Conditions to study the effect of bacterial infectionPseudomonas aeruginosaPA14 was used to seed NGM plates. Seeded plates were left to dry at 37°C for 24 hours. Once dried, synchronizedC. elegans embryos were plated on seeded plates and incubated at the appropriate temperature until an L3 stage is reached.An overnight culture ofThe use of fluorescence microscopy for quantificationFor imaging, eight nematodes were mounted onto a 2% agarose pad with 5µL of 100mM Levamisole. Nematodes were imaged using an Olympus Fluoview Confocal Laser Scanning Microscope (FV3000) and Z-stacks were obtained. To quantify fluorescence, the CellSens software was used to draw regions of interest (ROI) around glial cells and neurons. ROIs were converted to ‘Dynamic ROI over Z’ and the Mean Maximum Intensity Value was plotted as the ratio of Neuronal/Glial fluorescence.StatisticsStatistical analyses were performed using GraphPad Prism version 9.0. The error bars represent standard deviations and statistical differences were plotted as *p < 0.05, **p < 0.01, and ***p < 0.001. Statistical analysis of data from two groups was performed using the Mann-Whitney test and analysis of data from three or more groups was done using One way ANOVA with the post hoc Tukey's multiple comparisons test.Strainunc-4(e120)II; otIs117 [unc-33p::GFP +unc-4(+)]. IV strain was obtained fromC. elegansGenetics Center.OH438


Nonequilibrium work relations and fluctuation theorems permit us to extract equilibrium information from nonequilibrium measurements. They find application in single-molecule pulling experiments where molecular free energies can be determined from irreversible work measurements by using unidirectional  and bidirectional ) methods. However, irreversibility and the finite number of pulls limit their applicability: the higher the dissipation, the larger the number of pulls necessary to estimate  Thermodynamic properties of nucleic acids and proteins are commonly studied in bulk assays; however, since the advent of single-molecule technologies, molecular thermodynamics can be determined with unprecedented detail. Techniques such as fluorescence resonant energy transfer (FRET) and force spectroscopy techniques, such as laser optical tweezers, permit us to monitor reactions one molecule at a time ,2. By exTwo different kinds of experiments are usually performed: hopping and pulling experiments. In hopping experiments, the unfolding/folding and binding/unbinding reactions are investigated under equilibrium conditions ,13,14,15In pulling experiments, the optical trap is moved back and forth to mechanically unzip the molecule while a force-trap position curve is recorded. Some molecules exhibit a large hysteresis between the unfolding and folding trajectories, indicative of a high kinetic barrier. A useful approach to estimate the folding free energy from nonequilibrium experiments is the Crooks fluctuation theorem (CFT)  and its The CFT is a bidirectional method that uses the unfolding and folding trajectories to derive the folding free energy, l system . The 3WJE. coli cells (Invitrogen). Initially, T7 primers were used for PCR amplification  of the DNA containing the 3WJ sequence flanked by the handles. Next, RNA is synthesized by in-vitro transcription . The transcribed RNA contains the 3WJ sequence flanked by 527 and 599 bases at the 3’-end and 5’-end, respectively. Hybrid DNA-RNA handles are made by hybridizing the RNA strand with PCR-amplified complementary DNA sequences. Handles are labeled with a biotin (5’-end) and a tail of digoxigenins (3’-end) that specifically bind to streptavidin-coated (SA) beads and anti-digoxigenin-coated (AD) beads. Schematics of the experimental setup are shown in The molecular construction has been synthesized following the protocol described in reference . BrieflyW, for the unfolding and folding trajectories is defined as the area below the FDCs between Here, we have carried out non-equilibrium pulling experiments using a Mini-Tweezers optical setup  at room ng speed B. At theories in B. The wo0 and λ1 .In this section, we summarize the main formulae for determining the free-energy difference between the folded and the unfolded RNA, (em (CFT)  and its em (CFT) , are notem (CFT) ,27,28. Iem (CFT)  to extrang Mfold  and the ng Mfold .N) and misfolded (M) [N or M, and the ECFT must be applied. For simplicity, we will consider the N state, but the case for M is identical. Let N at U at N at T the temperature. N and U. N (U) in U (N) at M  equilibrium at the beginning of each process. In the ECFT, this condition is replaced by the more general partial equilibrium condition; at the beginning of each process, the system is partially equilibrated in a given subset of the conformational space. In the pulling experiments shown in lded (M) ,33. Therλ0 reads :1)ϕ→ϕ←Pd into M B. The wo pattern B. Equatiϕ→ϕ←Pd inn samples.A corollary of the CFT is the JE. Its extended version reads,For FDCs presenting high hysteresis B, the unEquation  and BAR;The Bennett’s acceptance ratio (BAR) method minimizes the statistical variance of the free-energy estimator given by the CFT. Bennet demonstrated that the function imator ΔG. RearranIntegrating over W give us the expected values of Taking logarithms and defining the form:u=kBT[zFP→(W)The value of estimate . Below, N), We have carried out pulling experiments at two different pulling speeds: 50 nm/s and 200 nm/s. The recorded FDCs show two different patterns B corresp)/(12kBT). For theN and M), select the N subset, estimate the fraction of trajectories, W, we have subtracted the energy contributions coming from the bead displacement, the stretching of the DNA-RNA handles, and the 3WJ single-stranded RNA (ssRNA). To do so, we have followed the methodology described in [p for persistence length and To determine ribed in ,35. Brieribed in . The ela handles ,39,40,41 handles . The beaeriments .W. We note that the unfolding work distributions are broader than the folding ones, suggesting that the transition state upon folding is closer to the native state. To characterize the work distributions, we have investigated their Gaussianity. To do so, we have defined a parameter R that relates the dissipated work, In n values are shown in the right plots of The biases, Equation ) for difAs mentioned before, crossing work values  and the Crooks fluctuation theorem (CFT) to determine the folding free energy of a native RNA three-way junction (3WJ), ries see B, which ries see , which iIn the presence of a misfolded structure, the standard JE and CFT must be corrected with the term tal data : the extby Mfold ,45; ΔG0=pendence  to obtai)kBT see , bold, iWe have applied the EJE to the unfolding and folding trajectories independently. In eference . Despitenm/s see . We remaWe have combined unfolding and refolding measurements in the ECFT (Equation ). This mEquation  is imposBennett’s acceptance-ratio (BAR) method provides a third estimator of Equation . AlthougThe RNA 3WJ was chosen because, despite its significant dissipation, there is a visible crossing point between unfolding and folding work distributions. This feature makes it an excellent candidate for comparing the different estimators.Regarding the estimators, bidirectional methods  and the Bennet acceptance ratio (BAR)) are more predictive because they combine data from unfolding and refolding in a unique formula. In contrast, the extended Jarzynski equality (EJE) uses the unfolding and folding work data separately. In general, when work distributions cross, it is advisable to take the crossing value as the free energy estimator. This includes the situation where dissipation is low (a few e energy  (see SecIt is important to remark that, in the linear and low dissipation regime, work distributions tend to be Gaussian. In this case, the variance We want to remark that Finally, we considered a new method of data analysis in which unfolding and folding trajectories are mixed to obtain full pulling cycles with ng speed  is fulfi


We evaluated the concordance of lymphoproliferative disorder (LPD) diagnoses made initially at Kamuzu Central Hospital (KCH) with a limited immunohistochemistry panel and telepathology consultation with University of North Carolina (UNC) at Chapel Hill and second review at UNC with an extensive immunohistochemistry panel available.The prospective KCH Lymphoma Study in Lilongwe has been enrolling patients with LPDs since 2013. Diagnoses are made by pathologists in Malawi and supported by US pathologists and Malawian clinicians at weekly telepathology conferences. Diagnostic tissue blocks are sent quarterly to UNC for secondary review and further confirmatory studies. Concordance of diagnosis from primary to secondary review was scored as: exact match (level 1), differences in granularity of the diagnosis (level 2), a change in classification but not in treatment (level 3), and a change to the diagnosis that would have altered the treatment course (Major Discordance). Cases with insufficient tissue for review at UNC were excluded from concordance evaluation.Three hundred eighty-eight adult patients were enrolled (June 2013-May 2019). Three hundred twenty-seven tissue blocks were sent to UNC for review and 292 LPDs diagnostic samples had sufficient quality for concordance review. The five most common LPDs were diffuse large B-cell lymphoma , Burkitt lymphoma , classical Hodgkin lymphoma , Multicentric Castleman disease , and low grade non-Hodgkin lymphoma . Level 1, 2 and 3 concordance represented 175 (60%), 49 (17%) and 50 (17%) cases, respectively, and a Major Discordance was identified in 18 cases (6%).With a limited, selected immunohistochemistry panel and telepathology consultation, 94% of cases were accurately classified in real-time, leading to appropriate therapy. The evaluation of concordance provides a measure of quality assurance of the telepathology program, confidence in diagnoses for involvement in interventional trials, and useful data for understanding the epidemiology of LPDs in Malawi.


The sorting of RNA transcripts dictates their ultimate post-transcriptional fates, such as translation, decay or degradation by RNA interference (RNAi). This sorting of RNAs into distinct fates is mediated by their interaction with RNA-binding proteins. While hundreds of RNA binding proteins have been identified, which act to sort RNAs into different pathways is largely unknown. Particularly in plants, this is due to the lack of reliable protein-RNA artificial tethering tools necessary to determine the mechanism of protein action on an RNA in vivo. Here we generated a protein-RNA tethering system which functions on an endogenous Arabidopsis RNA that is tracked by the quantitative flowering time phenotype. Unlike other protein-RNA tethering systems that have been attempted in plants, our system circumvents the inadvertent triggering of RNAi. We successfully in vivo tethered a protein epitope, deadenylase protein and translation factor to the target RNA, which function to tag, decay and boost protein production, respectively. We demonstrated that our tethering system (1) is sufficient to engineer the downstream fate of an RNA, (2) enables the determination of any protein’s function upon recruitment to an RNA, and (3) can be used to discover new interactions with RNA-binding proteins.The online version contains supplementary material available at 10.1186/s13007-022-00907-w. MS2-MCP  and boxB-λN  have been used in yeast, Drosophila and other systems to tether a protein to a reporter RNA in order to study mRNA stability, splicing, localization, transport and translation . In. InSOC1 R’) Fig. . We founR’) Fig. B, C. TheR’) Fig. D, E. ThiSOC1 mRNA, and in each instance demonstrated the utility of the fused protein. Importantly, this system was capable of sorting the SOC1 mRNA into different fates, with CAF1a tethering leading to RNA decay and RPS6 tethering leading to enhanced translation. These proof-of-concept experiments prove that it is possible to synthetically tether a protein and enzymatic activity of the user’s interest to the SOC1 mRNA. This system can be used to dissect the molecular function of RNA-interacting proteins, using SOC1 as an endogenous reporter mRNA. To aid in the fusion of any protein of interest to FLAG-BD, we generated an AtUBQ10: FLAG-BD vector with a multiple cloning site to facilitate the insertion of the user’s protein of interest , there are four limitations of this system. First, there is the complicating factor of the natural biology of the endogenous BRN1 protein. The endogenous BRN1 protein likely naturally binds more than one mRNA, and we find evidence of this promiscuity in our RIP data  ecotype were grown at 22°C on Pro-Mix FPX soil in Conviron MTPS-120 growth chambers in long days (16 h light / 8 h dark) with 200 µmol/m2/s light. Mutant alleles have been described previously and are shown in Additional file XhoI and XbaI. Maps of the plasmids and sequences are shown in Additional file The FLAG-BD and BD + D transgenes were synthesized by Integrated DNA Technologies (IDT) and cloned into pEarleyGate100  using thBstBI and XhoI to remove 35S, gel purified and In-Fusion recombined with the AtUBQ10 amplicon.To swap the 35S and AtUBQ10 promoters, the AtUBQ10 (AT4G05320) promoter + 5’UTR sequence from pICSL12015  was direTo facilitate protein fusions to FLAG-BD, we synthesized the "5’BD" cloning vector containing an ATG codon + 1X FLAG-epitope tagged minimal BRN1 protein + flexible linker sequence (no Stop codon) via IDT, leaving the MCS that originated in pEarleyGate100 intact for future cloning of proteins to be tethered. The resulting AtUBQ10:ATG-FLAG-BD-linker-MCS plasmid is called pDCG019  was amplified from wt Col genomic DNA using primers displayed in Additional file BamHI and AvrII , 0.5 mM DTT, 1 mM PMSF, 1% Plant PIC (GoldBio protease inhibitor cocktail)) and homogenized for 15 min at 4°C. Lysates were clarified by centrifuging for 15 min at 4°C. Clarified lysates were reduced and denatured by boiling in 2X loading buffer at 95°C for 5 min, and then loaded onto 4%-20% gradient Tris–Glycine gels (BioRad). Proteins were separated at 200 V for 1 h. Protein was transferred from the gel to a PVDF membrane  using the BioRad semi-dry transblot for 35 min. Membranes were blocked for 1 h at room temperature in Odyssey/Intercept blocking buffer (LI-COR). Primary antibodies, which include anti-SOC1 (Agrisera), anti-PEP (Rockland), and anti-FLAG (Sigma Aldrich), were all diluted 1:2000 in Odyssey blocking buffer and incubated with blots overnight. The membranes were washed 5 times at room temperature with 1X PBS-T. The IR-800 Anti-rabbit secondary antibody (LI-COR) was diluted 1:5000 and incubated with membranes for 1 h. Membranes were washed 5 times at room temperature with 1X PBS-T, and then additional 2 times with 1X PBS. Blots were visualized using the Azure Sapphire Biomolecular Imager with exposure times ranging from 5 s to 5 min. Full images of un-cropped Western blots from all figures are shown in Additional file Leaf tissue was grounded in liquid nitrogen and thawed in lysis buffer , 0.5 mM DTT, 1 mM PMSF, 1% Plant PIC (GoldBio protease inhibitor cocktail). Lysates were pre-cleared with Dynabeads Protein G (Invitrogen) with rotation for 20 min at room temperature. Pre-cleared lysates were then incubated with the prepared IP beads for 90 min at 4°C with rotation. Beads were washed 3X in the washing buffer . After the final wash, 1 mL Trizol LS (Invitrogen) per sample was added, reverse crosslinking was performed at 55°C for 5 min and RNA was extracted following the manufacturer’s protocol.Before RNA-IP, 50 µl/IP of Dynabeads Protein G (Invitrogen) were washed in 1X PBS + 0.1% Tween, followed by incubation with 1 µg/IP FLAG antibody (Sigma) at room temperature for 90 min with rotation. For each sample, 0.5 g leaf tissue was crosslinked in formaldehyde and ground in liquid nitrogen. Proteins were extracted using 50 mM Tris–HCl pH 7.5, 150 mM NaCl, 5 mM MgClLeaf tissue was crosslinked in 1% formaldehyde under vacuum for 3 min for a total of 5 times. Crosslinking was stopped by the addition of 200 mM glycine, then washed in water 5 times. Leaf tissue was ground to fine powder in liquid nitrogen using mortar and pestle. The powder was suspended in lysis buffer ), then centrifuged for 10 min at 14,000 rpm at 4˚C. The supernatant was incubated with 50 µl of FLAG M2 magnetic beads (Sigma) at 4˚C for 3 h. The beads were then washed three times in cold TBS. The FLAG-IP was eluted twice with 50 µl of 0.1 M pH2.5 glycine and neutralized with 0.5 M Tris, 1.5 M NaCl pH8.0 solution.FLAG-IP elutions were reduced (10 mM TCEP) and alkylated (25 mM Iodoacetamide) followed by digestion with Trypsin at 37°C overnight. The digest was acidified with 1%TFA before being cleaned-up with C18 tip. The extracted peptides were dried down and each sample was resuspended in 10 μL 5% ACN/0.1% FA. 5 μL was analyzed by LC–MS with a Dionex RSLCnano HPLC coupled to an Orbitrap Fusion Lumos (Thermo Scientific) mass spectrometer using a 2 h gradient. Peptides were resolved using 75 μm × 50 cm PepMap C18 column (Thermo Scientific).Mascot . Mascot was set up to search against the provided sequences and the TAIR10 database. The digestion enzyme was set as trypsin. Mascot searched with a fragment ion mass tolerance of 0.60 Da and a parent ion tolerance of 10 ppm. Oxidation of methionine, carbamidomethylation of cysteine, and acetylation of N-terminal of protein were specified in Mascot as variable modifications.All MS/MS samples were analyzed using Scaffold (4.8.2 Proteome Software Inc.) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established under 1% FDR by the Peptide Prophet algorithm [lgorithm  with Scalgorithm . Proteinlgorithm .RNA was isolated using Trizol Reagent (Invitrogen) and RNA for RIP analysis was isolated using Trizol LS (Invitrogen) according to manufacturer instructions.5 µg of total RNA or the entire RIP RNA sample was DNase-treated using the Turbo DNA-free kit (Invitrogen). First-strand of cDNA synthesis, including the RIP RNA sample, was performed using an oligo-d(T) primer and Superscript IV reverse transcriptase (Invitrogen). For the detection of nascent RNAs, random hexamer primers were used for reverse transcription and one primer site is located in an intron. Primer sequences are shown in Additional file SOC1 3’UTR II Forward primer and ePAT anchor primer. For the ePAT TVN control sample, the ePAT control primer was used in place of the ePAT anchor primer. A nested PCR was performed by diluting the primary PCR 1:100 and repeating the PCR with the primer SOC1 3’UTR III Forward and the ePAT anchor primer. Amplicons were run on a 2% high resolution agarose gel and purified. Purified amplicons were TOPO TA cloned into pCR4-TOPO (Life Technologies) and transformed into E. coli. Plasmids from individual E. coli colonies were Sanger sequenced (Eton BioScience) and poly(A) tail length was analyzed in Rstudio. Primer sequences are shown in Additional file The length of the poly(A) tail was determined by ePAT assay, performed as in . Briefly100 µg of total RNA was enriched for small RNAs using the miRVana miRNA isolation kit (Life Technologies). 1 µg of enriched small RNA was used for library preparation with the TruSeq Small RNA Library Preparation Kit (Illumina). Multiplexed libraries were sequenced on an Illumina HiSeq 3000 at the Genome Technology Access Center in Washington University.UEA small RNA Workbench tool [Shortstack [Rstudio and ggplot2 were used to generate siRNA graphs.After sequencing, adapters were trimmed from raw sequences using fastx toolkit, t/rRNAs were removed and small RNAs were filtered to the 18–28 nt size range using nch tool  and the nch tool . Small Rortstack  with defAdditional file 1:Figure S1. FLAG-BD interacts with other mRNAs in addition to SOC1. Extended analysis of the experiment from Figure 1C for three additional mRNAs that have the identical 7-nt BRN1 binding site sequence. Each biological replicate is shown as a point. The bar represents the average and error bars represent the standard deviation between three biological replicates. Figure S2. IP control gels performed before Mass Spectrometry. (A) Coomassie blue stained SDS-PAGE gels of the mock-IP (left) and four biological replicates (R1-R4) of the FLAG-IP (right). IN = input protein sample, FT = flow through sample that did not interact with the beads or FLAG antibody. (B) Western blot of the samples in part 'A'. The PEPC protein is not detected in the mock-IP or FLAG-IPs (top). The FLAG-BD protein is detected in the FLAG-IPs but not mock-IP (bottom). Arrowheads denote the expected size of the proteins detected. Figure S3. Reduced expression of the BD+D transgene. qRT-PCR of the relative mRNA accumulation of the FLAG-BD and BD+D transgenes. AT2G20610 is a constitutively-expressed control gene. At least three biological replicates for each genotype were used (shown as points), the height of the bar represents their average and the error bars represent the standard deviation. The transgene structure and position of the RT-PCR primers is shown above. Figure S4. SOC1 protein accumulation in BD+D plants. Western blot displaying SOC1 protein levels with the BD+D transgene. The three wt Col and BD+D samples are biological replicates. PEP is an unrelated protein used as a loading control. Arrowheads mark the predicted size of the protein detected. Quantification of this Western blot is shown in Figure 4F. Figure S5. Plasmid maps and sequences. Vector maps and annotated plasmid sequences of (A) FLAG-BD, (B) BD+D, (C) BD+R and (D) a vector with multiple cloning site (MCS) to fuse any protein to AtUBQ10:FLAG-BD. Figure S6. Full Western blots from other figures. The full un-cropped Western blot images from Figures 1B, 2C, 5D and S4.Additional file 2:Table S1. Mass Spectrometry data of the 20 proteins identified in at least two biological replicates.Additional file 3:Table S2. Primer sequences and alleles used in this study.