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Mitochondrial turnover in the form of mitophagy is emerging as a central process in maintaining cellular function. The degradation of damaged mitochondria through mitophagy is particularly important in cells/tissues that exhibit high energy demands. Skeletal muscle is one such tissue that requires precise turnover of mitochondria in several conditions in order to optimize energy production and prevent bioenergetic crisis. For instance, the formation of skeletal muscle (i.e., myogenesis) is accompanied by robust turnover of low-functioning mitochondria to eventually allow the formation of high-functioning mitochondria. In mature skeletal muscle, alterations in mitophagy-related signaling occur during exercise, aging, and various disease states. Nonetheless, several questions regarding the direct role of mitophagy in various skeletal muscle conditions remain unknown. Furthermore, given the heterogenous nature of skeletal muscle with respect to various cellular and molecular properties, and the plasticity in these properties in various conditions, the involvement and characterization of mitophagy requires more careful consideration in this tissue. Therefore, this review will highlight the known mechanisms of mitophagy in skeletal muscle, and discuss their involvement during myogenesis and various skeletal muscle conditions. This review also provides important considerations for the accurate measurement of mitophagy and interpretation of data in skeletal muscle.

Centrioles are microtubule-based cell organelles present in most eukaryotes. They participate in the control of cell division as part of the centrosome, the major microtubule-organizing center of the cell, and are also essential for the formation of primary and motile cilia. During centriole assembly as well as across its lifetime, centriolar tubulin display marks defined by post-translational modifications (PTMs), such as glutamylation or acetylation. To date, the functions of these PTMs at centrioles are not well understood, although pioneering experiments suggest a role in the stability of this organelle. Here, we review the current knowledge regarding PTMs at centrioles with a particular focus on a possible link between these modifications and centriole's architecture, and propose possible hypothesis regarding centriolar tubulin PTMs's function.

The wide application of molecular beacon probes in specific DNA detection, especially in the fast prototyping of pathogen DNA detection kits in point-of-care diagnostics, has been hindered by the nonflexible choice of target sequences and the unstable fluorophore output. We developed an in vitro DNA detection system consisting of a pair of dCas9 proteins linked to split halves of luciferase, named the Paired dCas9 (PC) reporter. Co-localization of the reporter pair to a ~46 bp target sequence defined by two single guide RNAs (sgRNAs) activated luciferase which subsequently generated highly intensified luminescent signals. Combined with an array design and statistical analyses, the PC reporter system could be programmed to access sequence information across the entire genome of the pathogenic Mycobacterium tuberculosis H37Rv strain. These findings suggest great potential for the PC reporter in effective and affordable in vitro nucleic acid detection technologies. In this article we highlighted the systems design from our previous research work on the PC reporter (Zhang et al, 2015) with a focus on methodology.

Introduction Plasma D-lactate levels can be considered a surrogate indicator of MG flux. There are commercial methods specifically designed to cover pathological ranges (mmol/l in sepsis or leaky gut) that are not suitable to encompass the metabolically relevant ranges. Material and methods We modified and optimized the D-lactate Colorimetric Assay kit MAK058 from Sigma adding an ultracentrifugation step, kinetic reading and increasing reaction temperature. Results and conclusions We present a modified, optimized and validated method to measure serum D-lactate using a commercial kit to increase the sensitivity of the method to 2 orders of magnitude lower as well as minimizing interferences so that it may be used by researchers to explore the D-lactate pathway in metabolic disorders at a low cost (colorimetric method and a plate reader) and with a much higher specificity.

Cytokinesis is a mechanism that separates dividing cells via constriction of a supramolecular structure, the contractile ring. In animal cells, three modes of symmetry-breaking of cytokinesis result in unilateral cytokinesis, asymmetric cell division, and oriented cell division. Each mode of cytokinesis plays a significant role in tissue patterning and morphogenesis by the mechanisms that control the orientation and position of the contractile ring relative to the body axis. Despite its significance, the mechanisms involved in the symmetry-breaking of cytokinesis remain unclear in many cell types. Classical embryologists have identified that the geometric relationship between the mitotic spindle and cell cortex induces cytokinesis asymmetry; however, emerging evidence suggests that a concerted flow of compressional cell-cortex materials (cortical flow) is a spindle-independent driving force in spatial cytokinesis control. This review provides an overview of both classical and emerging mechanisms of cytokinesis asymmetry and their roles in animal development.

N6,2'-O-dimethyladenosine (m6Am) is a reversible modification widely occurred on varied RNA molecules. The biological function of m6Am is yet to be known though recent studies have revealed its influences in cellular mRNA fate. Precise identification of m6Am sites on RNA is vital for the understanding of its biological functions. We present here m6AmPred, the first web server for in silico identification of m6Am sites from the primary sequences of RNA. Built upon the eXtreme Gradient Boosting with Dart algorithm (XgbDart) and EIIP-PseEIIP encoding scheme, m6AmPred achieved promising prediction performance with the AUCs greater than 0.954 when tested by 10-fold cross-validation and independent testing datasets. To critically test and validate the performance of m6AmPred, the experimentally verified m6Am sites from two data sources were cross-validated. The m6AmPred web server is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/m6am, and it should make a useful tool for the researchers who are interested in N6,2'-O-dimethyladenosine RNA modification.

To date, over 150 different RNA modifications have been identified, playing crucial roles in biological processes and disease pathogenesis. Thanks to the advancement of high-throughput sequencing technologies employed for transcriptome-wide mapping, a bunch of RNA modification databases have emerged as an exciting area, which promotes further investigation of the mechanisms and functions of these modified ribonucleotides. This article introduces the high-throughput sequencing technique developed for transcriptome-wide mapping of RNA modifications, as well as the procedures and main techniques of building these databases from the developers' perspective. It also reviews existing RNA modification databases in terms of their main functions, species, the number of sites they collected, the annotations, and the tools they provided. From the view of users, we further analyze and compare these databases in terms of their functions. For instance, these databases can be applied to record chemical structures and biosynthetic pathways, or unravel the epi-transcriptome comprehensively, or only investigate specific features of RNA modifications. Additionally, the limitations of the existing approaches are discussed, and some future suggestions are offered.

Enzymatic modification of the 5′-cap is a versatile approach to modulate the properties of mRNAs. Transfer of methyl groups from S-adenosyl- l -methionine (AdoMet) or functional moieties from non-natural analogs by methyltransferases (MTases) allows for site-specific modifications at the cap. These modifications have been used to tune translation or control it in a temporal manner and even influence immunogenicity of mRNA. For quantification of the MTase-mediated cap modification, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) provides the required sensitivity and accuracy. Here, we describe the complete workflow starting from in vitro transcription to produce mRNAs, via their enzymatic modification at the cap with natural or non-natural moieties to the quantification of these cap-modifications by LC-QqQ-MS.

Protein synthesis is an energy consuming process characterised as a pivotal and highly regulated step in gene expression. The net protein output is dictated by a combination of translation initiation, elongation and termination rates that have remained difficult to measure. Recently, the development of ribosome profiling has enabled the inference of translation parameters through modelling, as this method informs on the ribosome position along the mRNA. Here, we present an automated, reproducible and portable computational pipeline to infer relative single-codon and codon-pair dwell times as well as gene flux from raw ribosome profiling sequencing data. As a case study, we applied our workflow to a publicly available yeast ribosome profiling dataset consisting of 57 independent gene knockouts related to RNA and tRNA modifications. We uncovered the effects of those modifications on translation elongation and codon selection during decoding. In particular, knocking out mod5 and trm7 increases codon-specific dwell times which indicates their potential tRNA targets, and highlights effects of nucleotide modifications on ribosome decoding rate.

Pseudourine (Ψ) is the most abundant cellular RNA modification, present in tRNA, rRNA, snRNA, mRNA, long noncoding RNA (lncRNA), and others. Ψ sites and fractions are dynamically regulated in stress response and across development stages. Although high throughput Ψ sequencing methods based on N-Cyclohexyl-N'-(2-morpholinoethyl)carbodiimide (CMC) reaction are available for Ψ detection transcriptome-wide, a simple method for the analysis of specific, targeted Ψ sites and their fraction quantitation is needed to better investigate Ψ function. Here, we describe an RT-PCR and gel electrophoresis based method that can sensitively and quantitatively assess Ψ at single-nucleotide resolution in mRNA/lncRNA, termed CMC-RT and ligation assisted PCR analysis of Ψ modification (CLAP). The principle of the CMC-method is the reverse transcription stop induced by the CMC-Ψ adduct. In CLAP, CMC reaction is first carried out with the RNA sample. Reverse transcription using a non-processive RT produces two cDNA products for each RNA transcript, one with the 3' end at the Ψ site, the other read-through product from the unmodified RNA. Using splint oligonucleotide assisted site-specific ligation, these two cDNA products are then visualized on a gel as two distinct PCR products in the same lane corresponding to the Ψ-modified and unmodified target site. CLAP validates Ψ sites identified by high throughput sequencing, quantifies Ψ levels in mRNA and lncRNA, and enables convenient and rapid investigation on the function and mechanism of the Ψ modification.

The 5-methyluridine (m5U)modification plays important roles in a series of biological processes. Accurate identification of m5U sites will be helpful to decode its biological functions. Although experimental techniques have been proposed to detect m5U, they are still expensive and time consuming. In the present work, a support vector machine based method, called iRNA-m5U, was developed to identify the m5U sites in the Saccharomyces cerevisiae transcriptome. The performance of iRNA-m5U was validated based on different datasets. The accuracies obtained by iRNA-m5U is promising, indicating that it holds the potential to become an useful tool for the identification of m5U sites.

N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.

Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals. National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative.

Situation awareness (SA) has received much attention in recent years because of its importance for operators of dynamic systems. Electroencephalography (EEG) can be used to measure mental states of operators related to SA. However, cross-subject EEG-based SA recognition is a critical challenge, as data distributions of different subjects vary significantly. Subject variability is considered as a domain shift problem. Several attempts have been made to find domain-invariant features among subjects, where subject-specific information is neglected. In this work, we propose a simple but efficient subject matching framework by finding a connection between a target (test) subject and source (training) subjects. Specifically, the framework includes two stages: (1) we train the model with multi-source domain alignment layers to collect source domain statistics. (2) During testing, a distance is computed to perform subject matching in the latent representation space. We use a reciprocal exponential function as a similarity measure to dynamically select similar source subjects. Experiment results show that our framework achieves a state-of-the-art accuracy 74.32% for the Taiwan driving dataset. National Research Foundation (NRF) This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative.

To evaluate the performance of the classic machine learning algorithms and the effectiveness of various features, the iterative algorithms (i.e., support vector machine (SVM), and least-squares SVM (LS-SVM)) and non-iterative algorithms (i.e., random forest (RF) and naive bayes (NB)) for six feature schemes were performed to classify the ECG recordings. The ECG recordings were initially filtered with a 0.1 Hz − 12 Hz band pass filter. Then 80 features, including 48 time domain, 18 frequency domain, 12 time–frequency and two principle component analysis (PCA) features, were extracted to construct six feature schemes. The RF, SVM, LS-SVM and NB were employed to discern a binary-classification task (i.e., normal and AF ECG recordings) and a tri- classification task (i.e., the normal, AF and ST change ECG recordings) for the six feature schemes. The results revealed that time domain, frequency domain features and PCA features can provide relatively reliable feature combinations to the RF and SVM. In addition, the RF yielded the highest F1-scores (0.8908 and 0.7535) for the binary-classification task and the tri-classification task than the SVM, LS-SVM and NB.

The standard 12-lead electrocardiogram (ECG) records the heart’s electrical activity from electrodes on the skin, and is widely used in screening and diagnosis of the cardiac conditions due to its low price and non-invasive characteristics. Manual examination of ECGs requires professional medical skills, and is strenuous and time consuming. Recently, deep learning methodologies have been successfully applied in the analysis of medical images. In this paper, we present an automated system for the identification of normal and abnormal ECG signals. A multi-channel multi-scale deep neural network (DNN) model is proposed, which is an end-to-end structure to classify the ECG signals without any feature extraction. Convolutional layers are used to extract primary features, and long short-term memory (LSTM) and attention are incorporated to improve the performance of the DNN model. The system was developed with a 12-lead ECG dataset provided by the Kaohsiung Medical University Hospital (KMUH). Experimental results show that the proposed system can yield high recognition rates in classifying normal and abnormal ECG signals.

In breast mass detection, there are many different sizes of masses in the image. However, when the existing target detection model is directly used to detect the breast mass, it is easy to appear the phenomenon of misdetection and missed detection. Therefore, in order to improve the detection accuracy of breast masses, this paper proposed a target detection model D-Mask R-CNN based on Mask R-CNN, which is suitable for breast masses detection. Firstly, this paper improved the internal structure of FPN, and modified the lateral connection mode in the original FPN structure to dense connection. Secondly, modified the size of the anchor of RPN to improve the location accuracy of breast masses. Finally, Soft-NMS was used to replace the NMS in the original model to reduce the possibility that the correct prediction results may be eliminated during the NMS process. This paper used the CBIS-DDSM dataset for all experiments. The results showed that the mAP value of the improved model for detecting breast masses reached 0.66 in the test set, which was 0.05 higher than that of the original Mask R-CNN.

The suppression of artifact noise in computed tomography (CT) with a low-dose scan protocol is challenging. Conventional statistical iterative algorithms can improve reconstruction but cannot substantially eliminate large streaks and strong noise elements. In this paper, we present a 3D cascaded ResUnet neural network (Ca-ResUnet) strategy with modified noise power spectrum loss for reducing artifact noise in low-dose CT imaging. The imaging workflow consists of four components. The first component is filtered backprojection (FBP) reconstruction via a domain transformation module for suppressing artifact noise. The second is a ResUnet neural network that operates on the CT image. The third is an image compensation module that compensates for the loss of tiny structures, and the last is a second ResUnet neural network with modified spectrum loss for fine-tuning the reconstructed image. Verification results based on American Association of Physicists in Medicine (AAPM) and United Image Healthcare (UIH) datasets confirm that the proposed strategy significantly reduces serious artifact noise while retaining desired structures.

Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. Manual stage classification is a labor-intensive process and usually suffered from many subjective factors. Recently, more and more computer-aided techniques have been applied to this task, among which deep convolutional neural network has been performing well as an effective automatic classification model. Despite some comprehensive models have been developed to improve classification results, the accuracy for clinical applications has not been reached due to the lack of sufficient labeled data and the limitation of extracting latent discriminative EEG features. Therefore, we propose a novel hybrid manifold-deep convolutional neural network with hyperbolic attention. To overcome the shortage of labeled data, we update the semi-supervised training scheme as an optimal solution. In order to extract the latent feature representation, we introduce the manifold learning module and the hyperbolic module to extract more discriminative information. Eight subjects from the public dataset are utilized to evaluate our pipeline, and the model achieved 89% accuracy, 70% precision, 80% sensitivity, 72% f1-score and kappa coefficient of 78%, respectively. The proposed model demonstrates powerful ability in extracting feature representation and achieves promising results by using semi-supervised training scheme. Therefore, our approach shows strong potential for future clinical development.

Skin cancer is one of the most common and dangerous cancer that exists worldwide. Malignant melanoma is one of the most dangerous skin cancer types has a high mortality rate. An estimated 196,060 melanoma cases will be diagnosed in 2020 in the USA. Many computerized techniques are presented in the past to diagnose skin lesions, but they are still failing to achieve significant accuracy. To improve the existing accuracy, we proposed a hierarchical framework based on two-dimensional superpixels and deep learning. First, we enhance the contrast of original dermoscopy images by fusing local and global enhanced images. The entire enhanced images are utilized in the next step to segmentation skin lesions using three-step superpixel lesion segmentation. The segmented lesions are mapped over the whole enhanced dermoscopy images and obtained only segmented color images. Then, a deep learning model (ResNet-50) is applied to these mapped images and learned features through transfer learning. The extracted features are further optimized using an improved grasshopper optimization algorithm, which is later classified through the Naïve Bayes classifier. The proposed hierarchical method has been evaluated on three datasets (Ph2, ISBI2016, and HAM1000), consisting of three, two, and seven skin cancer classes. On these datasets, our method achieved an accuracy of 95.40%, 91.1%, and 85.50%, respectively. The results show that this method can be helpful for the classification of skin cancer with improved accuracy.

Background: A combination of fluticasone propionate and formoterol fumarate (FLUT/FORM; flutiform®) in a single aerosol inhaler has been developed. This study investigated the efficacy and safety of the combination compared to its individual components administered concurrently (FLUT+FORM). Methods: Patients aged 12 years or over (N=210) with mild to moderate-severe persistent, reversible asthma were randomized in a 1:1 ratio to 12 weeks of twice daily treatment with one of two doses of FLUT/FORM or FLUT+FORM in an open-label, parallel group, multicentre study in Europe. Total daily doses were FLUT/FORM: 200/20μg or 500/20μg; FLUT+FORM: 200/24μg or 500/24μg. The primary objective was to show non-inferiority of FLUT/FORM compared to FLUT+FORM based on post-dose forced expiratory volume in 1 second (FEV1) on Day 84. Results: FLUT/FORM had comparable efficacy to FLUT+FORM in the treatment of asthma with mean FEV1 30 to 60 minutes post-dose on Day 84 of 2.6L in both groups (per protocol groups; least squares mean difference: -0.03 L; 95%CI: -0.148, 0.081; p=0.004). The lower limit of the 95% CI was above the pre-defined non-inferiority threshold of ≥-0.2L. Analysis of other pulmonary function tests, patient reported outcomes, rescue medication use, asthma exacerbations and quality of life questionnaires were also comparable. The safety profiles of the two study groups were similar overall. Conclusions: Fluticasone/formoterol combination therapy has comparable efficacy to its individual components administered concurrently. The safety and tolerability profile of fluticasone/formoterol combination therapy is similar to that of its individual components.

Viral load monitoring in human immunodeficiency virus type 1 (HIV-1) infection is often performed using reverse transcription quantitative PCR (RT-qPCR) to observe response to treatment and identify the development of resistance. Traceability is achieved using a calibration hierarchy traceable to the International Unit (IU). IU values are determined using consensus agreement derived from estimations by different laboratories. Such a consensus approach is necessary due to the fact that there are currently no reference measurement procedures available that can independently assign a reference value to viral reference materials for molecular in vitro diagnostic tests. Digital PCR (dPCR) is a technique that has the potential to be used for this purpose. In this paper, we investigate the ability of reverse transcriptase dPCR (RT-dPCR) to quantify HIV-1 genomic RNA without calibration. Criteria investigated included the performance of HIV-1 RNA extraction steps, choice of reverse transcription approach and selection of target gene with assays performed in both single and duplex format. We developed a protocol which was subsequently applied by two independent laboratories as part of an external quality assurance (EQA) scheme for HIV-1 genome detection. Our findings suggest that RT-dPCR could be used as reference measurement procedure to aid the value assignment of HIV-1 reference materials to support routine calibration of HIV-1 viral load testing by RT-qPCR.

Sensitive detection of viral nucleic acids is critically important for diagnosis and monitoring of the progression of infectious diseases such as those caused by SARS-CoV2, HIV-1, and other viruses. In HIV-1 infection cases, assessing the efficacy of treatment interventions that are superimposed on combination antiretroviral therapy (cART) has benefited tremendously from the development of sensitive HIV-1 DNA and RNA quantitation assays. Simian immunodeficiency virus (SIV) infection of Rhesus macaques is similar in many key aspects to human HIV-1 infection and consequently this non-human primate (NHP) model has and continues to prove instrumental in evaluating HIV prevention, treatment and eradication approaches. Cell and tissue associated HIV-1 viral nucleic acids have been found to serve as useful predictors of disease outcome and indicators of treatment efficacy, highlighting the value of and the need for sensitive detection of viruses in cells/tissues from infected individuals or animal models. However, viral nucleic acid detection and quantitation in such sample sources can often be complicated by high nucleic acid input (that is required to detect ultralow level viruses in, for example, cure research) or inhibitors, leading to reduced detection sensitivity and under-quantification, and confounded result interpretation. Here, we present a step-by-step procedure to quantitatively recover cell/tissue associated viral DNA and RNA, using SIV-infected Rhesus macaque cells and tissues as model systems, and subsequently quantify the viral DNA and RNA with an ultrasensitive SIV droplet digital PCR (ddPCR) assay and reverse transcription ddPCR (RT-ddPCR) assay, respectively, on the Raindance ddPCR platform. The procedure can be readily adapted for a broad range of applications where highly sensitive nucleic acid detection and quantitation are required.

Sensitive PCR detection of viral nucleic acids plays a critical role in infectious disease research, diagnosis and monitoring. In the context of SARS-CoV-2 detection, recent reports indicate that digital PCR-based tests are significantly more sensitive than traditional qPCR tests. Numerous factors can influence digital PCR reaction sensitivity. In this review, using a model for human HIV infection and the Raindance ddPCR platform as an example, we describe technical aspects that contribute to sensitive viral signal detection in DNA and RNA from tissue samples, which often harbor viral reservoirs and serve as better predictors of disease outcome and indicators of treatment efficacy.

Cell migration depends on the dynamic organization of the actin cytoskeleton and assembly and disassembly of focal adhesions (FA). However the precise mechanisms coordinating these processes remain poorly understood. We previously identified the estrogen-related receptor α (ERRα) as a major regulator of cell migration. Here, we show that loss of ERRα leads to abnormal accumulation of actin filaments that is associated with an increase in the level of inactive form of the actin-depolymerizing factor cofilin. We further show that ERRα depletion decreases cell adhesion and promotes defective FA formation and turnover. Interestingly, specific inhibition of the RhoA-ROCK-LIMK-cofilin pathway rescues the actin polymerization defects resulting from ERRα silencing, but not cell adhesion. Instead we found that MAP4K4 is a direct target of ERRα and down-regulation of its activity rescues cell adhesion and FA formation in the ERRα-depleted cells. Altogether, our results highlight a crucial role of ERRα in coordinating the dynamic of actin network and focal adhesion through the independent regulation of the RhoA and MAP4K4 pathways.

Asparagines in proteins deamidate spontaneously, which changes the chemical structure of a protein and often affects its function. Current prediction algorithms for asparagine deamidation require a structure as an input or are too slow to be applied at a proteomic scale. We present NGOME-Lite, a new version of our sequence-based predictor for spontaneous asparagine deamidation that is faster by over two orders of magnitude at a similar degree of accuracy. The algorithm takes into account intrinsic sequence propensities and slowing down of deamidation by local structure. NGOME-Lite can run in a proteomic analysis mode that provides the half-time of the intact form of each protein, predicted by taking into account sequence propensities and structural protection or sequence propensities only, and a structure protection factor. The detailed analysis mode also provides graphical output for all Asn residues in the query sequence. We applied NGOME-Lite to over 257,000 sequences in 38 proteomes and found that different taxa differ in their predicted deamidation dynamics. Spontaneous protein deamidation is faster in Eukarya than in Bacteria because of a higher degree of structural protection in the latter. Predicted protein deamidation half-lifes correlate with protein turnover in human, mouse, rat, C. elegans and budding yeast but not in two plants and two bacteria. NGOME-Lite is implemented in a docker container available at https://ngome.proteinphysiologylab.org.

As ZIKV continues to spread, many “unknowns” remain and research is needed to advance the understanding of this important pathogen. Viral RNA dependent-RNA polymerases (RdRp) are validated targets for inhibitors of the replication of several viruses. Several studies have set up in vitro enzymatic assays of the RdRp of the Zika virus for testing of candidate inhibitors. While most of these studies use short synthetic polymers, we have shown in a previous work that the Zika polymerase domain is capable of a de novo synthesis of the viral genome using the natural viral RNA as template. Here we have studied the role of the sequences at the 3′end of the minus-strand RNA in the initiation of the RNA synthesis by the Zika isolated RdRp. Our results strongly suggest that the region containing the 105 first nucleotides from the 3′ end of the minus-strand RNA is important for initiation of the positive RNA synthesis. This indicates that this region displays all the primary and secondary structures to be efficiently recognized by the recombinant RdRp in vitro. Moreover, we show that the 46 nucleotides are sufficient to initiate RNA synthesis. In addition, the ZIKV polymerase domain poorly replicated the RNA of other RNA viruses and appeared highly selective for its own RNA.

Development of nanocarriers has opened new avenues for the delivery of therapeutics of various pharmacological activities with improved targeting properties and reduced side effects. Niosomes, non-ionic-based vesicles, have drawn much interest in various biomedical applications, owing to their unique characteristics and their ability to encapsulate both hydrophilic and lipophilic cargoes. Niosomes share structural similarity with liposomes while overcoming limitations associated with stability, sterilization, and large-scale production of liposomes. Different methods for preparation of niosomes have been described in the literature, each having its own merits and a great impact on the sizes and characteristics of the formed niosomes. In this article, procedures involved in the thin-film hydration method, a commonly used method for the preparation of niosomes, are described in detail, while highlighting precautions that should be considered for consistent and reproducible construction of niosomes.

Mass spectrometry (MS) based omics data analysis require significant time and resources. To date, few parallel algorithms have been proposed for deducing peptides from mass spectrometry-based data. However, these parallel algorithms were designed, and developed when the amount of data that needed to be processed was smaller in scale. In this paper, we prove that the communication bound that is reached by the \emph{existing} parallel algorithms is $\Omega(mn+2r\frac{q}{p})$, where $m$ and $n$ are the dimensions of the theoretical database matrix, $q$ and $r$ are dimensions of spectra, and $p$ is the number of processors. We further prove that communication-optimal strategy with fast-memory $\sqrt{M} = mn + \frac{2qr}{p}$ can achieve $\Omega({\frac{2mnq}{p}})$ but is not achieved by any existing parallel proteomics algorithms till date. To validate our claim, we performed a meta-analysis of published parallel algorithms, and their performance results. We show that sub-optimal speedups with increasing number of processors is a direct consequence of not achieving the communication lower-bounds. We further validate our claim by performing experiments which demonstrate the communication bounds that are proved in this paper. Consequently, we assert that next-generation of \emph{provable}, and demonstrated superior parallel algorithms are urgently needed for MS based large systems-biology studies especially for meta-proteomics, proteogenomic, microbiome, and proteomics for non-model organisms. Our hope is that this paper will excite the parallel computing community to further investigate parallel algorithms for highly influential MS based omics problems., Comment: 20 pages, 6 figures, preprint

Drug resistance and inability to distinguish between cancerous and non-cancerous cells are important obstacles in the treatment of cancer. Zinc oxide nanoparticles (ZnO NPs) is now emerging as a crucial material to challenge this global issue due to its tunable properties. Developing an effective, inexpensive, and eco-friendly method in order to tailor the properties of ZnO NPs with enhanced anticancer efficacy is still challenging. For the first time, we reported a facile, inexpensive, and eco-friendly approach for green synthesis of ZnO-reduced graphene oxide nanocomposites (ZnO-RGO NCs) using garlic clove extract. Garlic has been playing one of the most important dietary and medicinal roles for humans since centuries. We aimed to minimize the use of toxic chemicals and enhance the anticancer potential of ZnO-RGO NCs with minimum side effects to normal cells. Aqueous extract of garlic clove was used as reducing and stabilizing agent for green synthesis of ZnO-RGO NCs from the zinc nitrate and graphene oxide (GO) precursors. A potential mechanism of ZnO-RGO NCs synthesis with garlic clove extract was also proposed. Preparation of pure ZnO NPs and ZnO-RGO NCs was confirmed by powder X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and dynamic light scattering (DLS). The in vitro study showed that ZnO-RGO NCs induce two-fold higher cytotoxicity in human breast cancer (MCF7) and human colorectal cancer (HCT116) cells as compared to pure ZnO NPs. Besides, biocompatibility of ZnO-RGO NCs in non-cancerous human normal breast (MCF10A) and normal colon epithelial (NCM460) cells was higher than those of pure ZnO NPs. This work highlighted a facile and inexpensive green approach for the preparation of ZnO-RGO NCs with enhanced anticancer activity and improved biocompatibility.

Objectives: This literature review aims to summarize the current knowledge regarding the genome plasticity observed within the genome of the Leishmania parasite, and to discuss how genome plasticity contributes to the adaptation of the parasite and to development of a drug resistant state.Materials and Methods: The search terms “Leishmania” and “genome plasticity”, were used to search the PubMed database for relevant papers, published between the years 2000 and 2020.Results: Aneuploidy within the Leishmania genome allows for drug resistance and adaptation to the environment. In addition copy number variation promotes the up regulation of genes conferring drug resistant capabilities to the parasite. Conclusion: Drug-resistant Leishmania mutants display differential patterns of chromosomal somy when compared to wild-type strains. Highlighting a role for mosaic aneuploidy in the development of drug resistance. Leishmania parasites in the amastigote life cycle stage display differential gene copy numbers compared to parasites in the promastigote life cycle stage. Suggesting that copy number variation contributes to parasite adaptation to the environment. *This paper was published by Scientific Scholar and has been archived here.*

Major variations in venom composition can occur between juvenile and adult venomous snakes. However, due to logistical constraints, antivenoms are produced using adult venoms in immunising mixtures, possibly resulting in limited neutralisation of juvenile snake venoms. Daboia russelii is one of the leading causes of snakebite death across South Asia. Its venom is potently procoagulant, causing stroke in prey animals but causing in humans consumptive coagulopathy—a net anticoagulant state—and sometimes death resulting from hemorrhage. In this in vitro study, we compared the venom activity of—and antivenom efficacy against—six 2-week-old D. russelii relative to that of their parents. Using a coagulation analyser, we quantified the relative coagulotoxicity of these venoms in human, avian, and amphibian plasma. The overall potency on human plasma was similar across all adult and neonate venoms, and SII (Serum Institute of India) antivenom was equipotent in neutralising these coagulotoxic effects. In addition, all venoms were also similar in their action upon avian plasma. In contrast, the neonate venoms were more potent on amphibian plasma, suggesting amphibians make up a larger proportion of neonate diet than adult diet. A similar venom potency in human and avian plasmas but varying selectivity for amphibian plasma suggests ontogenetic differences in toxin isoforms within the factor X or factor V activating classes, thereby providing a testable hypothesis for future transcriptomics work. By providing insights into the functional venom differences between adult and neonate D. russelii venoms, we hope to inform clinical treatment of patients envenomated by this deadly species and to shed new light on the natural history of these extremely medically important snakes.

Cell-free nucleic acids (cfNAs) such as short non-coding microRNA (miRNA) and circulating tumor DNA (ctDNA) that reside in bodily fluids have emerged as potential cancer biomarkers. Methods for the rapid, highly specific, and sensitive monitoring of cfNAs in biofluids have, therefore, become increasingly attractive as clinical diagnosis tools. As a next generation technology, we provide a practical guide for an amplification-free, single molecule Forster resonance energy transfer (smFRET)-based kinetic fingerprinting approach termed intramolecular single molecule recognition through equilibrium Poisson sampling, or iSiMREPS, for the rapid detection and counting of miRNA and mutant ctDNA with virtually unlimited specificity and single molecule sensitivity. iSiMREPS utilizes a pair of fluorescent detection probes, wherein one probe immobilizes the target molecules on the surface, and the other probe transiently and reversibly binds to the target to generate characteristic time-resolved fingerprints as smFRET signal that are detected in a total internal reflection fluorescence microscope. Analysis of these kinetic fingerprints enables near-perfect discrimination between specific binding to target molecules and nonspecific background binding. By accelerating kinetic fingerprinting using the denaturant formamide and reducing background signals by removing target-less probes from the surface via toehold-mediated strand displacement, iSiMREPS has been demonstrated to count miR-141 and EGFR exon 19 deletion ctDNA molecules with a limit of detection (LOD) of ~1 and 3 fM, respectively, as well as mutant allele fractions as low as 0.0001%, during a standard acquisition time of only ~10 s per field of view. In this review, we provide a detailed roadmap for implementing iSiMREPS more broadly in research and clinical diagnostics, combining rapid analysis, high specificity, and high sensitivity.

RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.

The cellular membrane has been identified to play a critical role in various biological processes including the assembly of biological systems. Membranes are complex, primarily two-dimensional assemblies with varied lipid compositions depending on the particular region of the cell. Supported lipid bilayers are considered as appropriate models for physio-chemical studies of membranes including numerous single molecule techniques. Atomic force microscopy (AFM) as a topographic technique is a fully appropriate single molecule technique capable of direct observation of molecular processes on membranes. However, reliable experimental AFM studies require the preparation of the bilayer with a sub-nanometer smooth morphology, which remains stable over long-time observation. Here we present the methodology, which allows one to prepare a smooth, stable, structurally homogeneous lipid bilayer without the presence of any trapped vesicles. We described the application of such lipid bilayers to probe time-dependent early stages of aggregation of monomeric amyloid proteins. Importantly, the proposed methodology can be extended to bilayers with various compositions, by incorporating different lipids for on-membrane aggregation study including cholesterol. Furthermore, this methodology development allowed us to monitor the aggregation of amyloid protein at its physiologically relevant low protein concentration. The flexibility of altering the membrane composition allows to identify the specific role of a particular lipid towards the aggregation kinetics, revealing the plausible mechanism of disease development.

The retinoblastoma gene product (pRb) is a chromatin-associated protein that can either suppress or promote activity of key regulators of tissue-specific differentiation. We found that twelve weeks after transfection of the exogenous active (ΔB/X and Δр34) or inactive (ΔS/N) forms of RB into the 10T1/2 mesenchymal stem cells and clonal selection not a single cell line did contain exogenous RB, despite being G-418 resistant. However, the consequences of the transient production of exogenous RB had different effects on the cell fate. The ΔB/X and Δр34 cells transfected with active form of RB showed elevated levels of inducible adipocyte differentiation (AD). On the contrary, the ΔS/N cells transfected with inactive RB mutant were insensitive to induction of AD associated with abolishing of expression of the PPARγ2. Additionally, the PPARγ2 promoter in undifferentiated ΔS/N cells was hypermethylated, but all except −60 position CpG became mostly demethylated after cells exposure to AD. We conclude that while transient expression of inactive exogenous RB induces long term epigenetic alterations that prevent adipogenesis, production of active exogenous RBs results in an AD-promoting epigenetic state. These results indicate that pRb is involved in the establishment of hereditary epigenetic memory at least by creating a methylation pattern of PPARγ2.

Malonyl-CoA is an energy-rich molecule formed by the ATP-dependent carboxylation of acetyl coenzyme A catalyzed by acetyl-CoA carboxylase. This molecule is an important precursor for many biotechnologically interesting compounds such as flavonoids, polyketides, and fatty acids. The yeast Saccharomyces cerevisiae remains one of the preferred cell factories, but has a limited capacity to produce malonyl-CoA compared to oleaginous organisms. We developed a new S. cerevisiae strain with a conditional allele of ACC1, the essential acetyl-CoA carboxylase (ACC) gene, as a tool to test heterologous genes for complementation. Yarrowia lipolytica is an oleaginous yeast with a higher capacity for lipid production than S. cerevisiae, possibly due to a higher capacity to produce malonyl-CoA. Measuring relative intracellular malonyl-CoA levels with an in-vivo biosensor confirmed that expression of Y. lipolytica ACC in S. cerevisiae leads to a higher accumulation of malonyl-CoA compared with overexpression of the native gene from an otherwise identical vector. The higher accumulation was generally accompanied by a decreased growth rate. Concomitant expression of both the homologous and heterologous ACC1 genes eliminated the growth defect, with a marginal reduction of malonyl-CoA accumulation., This work was supported by the Fundação para a Ciência e Tecnologia Portugal (FCT) through Project FatVal POCI-01-0145-FEDER-032506; CBMA was supported by the strategic program UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) and CEB by UIDB/04469/2020 funded by national funds through the FCTI.P. and by the ERDF through the COMPETE2020 - Programa Operacional Competitividade e Internacionalizacão (POCI). Humberto Pereira acknowledges FCT for the PhD scholarship, SFRH/BD/148722/2019. We thank Professors Jin Hou, Xiaoming Bao and colleagues for sending us the malonyl-CoA sensor, info:eu-repo/semantics/publishedVersion

The structural dynamics of the amyloid protein aggregation process are associated with neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease. High-speed atomic force microscopy (HS-AFM) is able to visualize the structural dynamics of individual aggregate species that otherwise cannot be distinguished. HS-AFM observations also detect impurities in the sample, and thus, experiments require relatively high sample purity. To derive valid information regarding the structural dynamics of the sample from the high-speed AFM images, a correction of the influence caused by the drift of the stage (scanner) from all frames is required. However, correcting the HS-AFM videos that consist of a large number of images requires significant effort. Here, using HS-AFM observation of α-synuclein fibril elongation as an example, we propose an HS-AFM image processing procedure to correct stage drift in the x-, y-, and z-directions with the free software ImageJ. ImageJ with default settings and our plugins attached to this article can process and analyze image stacks, which allow users to easily detect and show the temporal change in sample structures. This processing method can be automatically applied to numerous HS-AFM videos by batch processing with a series of ImageJ macrofunctions.

Biomolecular condensates often consist of intrinsically disordered protein and RNA molecules, which together promote the formation of membraneless organelles in cells. The nucleation, condensation, and maturation of condensates is a critical yet poorly understood process. Here, we present single-molecule and accompanying ensemble methods to quantify these processes more comprehensively. In particular, we focus on how to properly design and execute a single-molecule nucleation assay, in which we detect signals arising from individual units of fluorescently labeled RNA-binding proteins associating with an RNA substrate. The analysis of this data allows one to determine the kinetics involved with each step of nucleation. Complemented with meso-scale techniques that measure the biophysical properties of ribonucleoprotein condensates, the methods described herein are powerful tools that can be adopted for studying any protein-RNA interactions that promote phase separation.

The design of hybrid nucleic acid nanomaterials capitalizes on the partitioning of architectural and functional roles between structurally diverse RNA modules and chemically robust DNA components. Selecting optimal combinations of RNA and DNA building blocks is the key to preparing stable polygonal RNA-DNA hybrid nanoshapes. Here, we outline a simple screening strategy by gel electrophoresis under native folding conditions to identify combinations of RNA and DNA modules that self-assemble to robust polygonal hybrid nanoshapes. As a proof of concept, we outline the preparation of RNA-DNA hybrid nanoshapes containing a set of different RNA architectural joints, including internal loop motifs and three-way junction (3WJ) folds. For each hybrid nanoshape, we demonstrate the selection process used to identify optimal DNA modules from a library of DNA connectors. The simple screening strategy outlined here provides a general robust method to identify and prepare RNA-DNA hybrid nanoshapes from diverse libraries of discrete nucleic acid building blocks.

Retroviral integration site targeting is not random and plays a critical role in expression and long-term survival of the integrated provirus. To better understand the genomic environment surrounding retroviral integration sites, we performed an extensive comparative analysis of new and previously published integration site data from evolutionarily diverse retroviruses from seven genera, including different HIV-1 subtypes. We showed that evolutionarily divergent retroviruses exhibited distinct integration site profiles with strong preferences for non-canonical B-form DNA (non-B DNA). Whereas all lentiviruses and most retroviruses integrate within or near genes and non-B DNA, MMTV and ERV integration sites were highly enriched in heterochromatin and transcription-silencing non-B DNA features (e.g. G4, triplex and Z-DNA). Compared toin vitro-derived HIV-1 integration sites,in vivo-derived sites are significantly more enriched in transcriptionally silent regions of the genome and transcription-silencing non-B DNA features. Integration sites from individuals infected with HIV-1 subtype A, C or D viruses exhibited different preferences for non-B DNA and were more enriched in transcriptionally active regions of the genome compared to subtype B virus. In addition, we identified several integration site hotspots shared between different HIV-1 subtypes with specific non-B DNA sequence motifs present at these hotspots. Together, these data highlight important similarities and differences in retroviral integration site targeting and provides new insight into how retroviruses integrate into genomes for long-term survival.Graphical AbstractSchematic comparing integration site profiles from evolutionarily diverse retroviruses. Upper left, heatmaps showing the fold-enrichment (blue) and fold-depletion (red) of integration sites near non-B DNA features (lower left). Lower right, circa plot showing integration site hotspots shared between HIV-1 subtype A, B, C and D virus.

The phylum Cnidaria is the oldest extant venomous group and is defined by the presence of nematocysts, specialized organelles responsible for venom production and delivery. Although toxin peptides and the cells housing nematocysts are distributed across the entire animal, nematocyte and venom profiles have been shown to differ across morphological structures in actiniarians. In this study, we explore the relationship between patterns of toxin expression and the ecological roles of discrete anatomical structures in Telmatactis stephensoni. Specifically, using a combination of proteomic and transcriptomic approaches, we examined whether there is a direct correlation between the functional similarity of regions and the similarity of their associated toxin expression profiles. We report that the regionalization of toxin production is consistent with the partitioning of the ecological roles of venom across envenomating structures, and that three major functional regions are present in T. stephensoni: tentacles, epidermis and gastrodermis. Additionally, we find that most structures that serve similar functions not only have comparable putative toxin profiles but also similar nematocyst types. There was no overlap in the putative toxins identified using proteomics and transcriptomics, but the expression patterns of specific milked venom peptides were conserved across RNA-sequencing and mass spectrometry imaging data sets. Furthermore, based on our data, it appears that acontia of T. stephensoni may be transcriptionally inactive and only mature nematocysts are present in the distal portions of the threads. Overall, we find that the venom profile of different anatomical regions in sea anemones varies according to its ecological functions.

A DNA G-quadruplex (G4) is a non-canonical four-stranded nucleic acid structure involved in many biological processes in mammals. The current knowledge on plant DNA G4s, however, is limited; whether and how DNA G4s impact gene expression in plants is still largely unknown. Here, we applied a protocol referred to as BG4-DNA-IP-seq followed by a comprehensive characterization of DNA G4s in rice (Oryza sativa L.); we next integrated dG4s (experimentally detectable G4s) with existing omics data and found that dG4s exhibited differential DNA methylation between transposable element (TE) and non-TE genes. dG4 regions displayed genic-dependent enrichment of epigenomic signatures; finally, we showed that these sites displayed a positive association with expression of DNA G4-containing genes when located at promoters, and a negative association when located in the gene body, suggesting localization-dependent promotional/repressive roles of DNA G4s in regulating gene transcription. This study reveals interrelations between DNA G4s and epigenomic signatures, as well as implicates DNA G4s in modulating gene transcription in rice. Our study provides valuable resources for the functional characterization or bioengineering of some of key DNA G4s in rice.

Circular RNAs with covalently linked ends are generated from many eukaryotic protein-coding genes when the pre-mRNA splicing machinery backsplices. These mature transcripts are resistant to digestion by exonucleases and typically have much longer half-lives than their associated linear mRNAs. Circular RNAs thus have great promise as sensitive biomarkers, including for detection of transcriptional activity. Here, we show that circular RNAs can serve as markers of readthrough transcription events in Drosophila and human cells, thereby revealing mechanistic insights into RNA polymerase II transcription termination as well as pre-mRNA 3' end processing. We describe methods that take advantage of plasmids that generate a circular RNA when an upstream polyadenylation signal fails to be used and/or RNA polymerase II fails to terminate. As a proof-of-principle, we show that RNAi-mediated depletion of well-established transcription termination factors, including the RNA endonuclease Cpsf73, results in increased circular RNA output from these plasmids in Drosophila and human cells. This method is generalizable as a circular RNA can be easily encoded downstream of any genomic region of interest. Circular RNA biomarkers, therefore, have great promise for identifying novel cellular factors and conditions that impact transcription termination processes.

Circular RNAs (circRNAs) have been studied extensively in the last few years, uncovering functional roles in a diverse range of cell types and organisms. As shown for a few cases, these functions may be mediated by trans-acting factors, in particular RNA-binding proteins (RBPs). However, the specific interaction partners for most circRNAs remain unknown. This is mainly due to technical difficulties in their identification and in differentiating between interactors of circRNAs and their linear counterparts. Here we review the currently used methodology to systematically study circRNA-protein complexes (circRNPs), focusing either on a specific RNA or protein, both on the gene-specific or global level, and discuss advantages and challenges of the available approaches.

Covalently closed circular RNAs (circRNAs) produced by back-splicing of exon(s) are co-expressed with their cognate linear RNAs from the same gene loci. Most circRNAs are fully overlapped with their cognate linear RNAs in sequences except the back-spliced junction (BSJ) site, thus challenging the computational detection, experimental validation and hence functional evaluation of circRNAs. Nevertheless, specific bioinformatic pipelines were developed to identify fragments mapped to circRNA-featured BSJ sites, and circRNAs were pervasively identified from non-polyadenylated RNA-seq datasets in different cell lines/tissues and across species. Precise identification and quantification of circRNAs provide a basis to further understand their functions. Here, we describe detailed computational steps to annotate and quantify circRNAs using a series of CIRCexplorer pipelines.

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed® database of citations and abstracts published in life science journals. The Entrez system provides search and retrieval operations for most of these data from 34 distinct databases. The E-utilities serve as the programming interface for the Entrez system. Custom implementations of the BLAST program provide sequence-based searching of many specialized datasets. New resources released in the past year include a new PubMed interface and NCBI datasets. Additional resources that were updated in the past year include PMC, Bookshelf, Genome Data Viewer, SRA, ClinVar, dbSNP, dbVar, Pathogen Detection, BLAST, Primer-BLAST, IgBLAST, iCn3D and PubChem. All of these resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.

Exonic circular RNAs (circRNAs) are RNA molecules that are covalently closed by back-splicing via canonical splicing machinery. Despite overlapping sequences, exon circularization generates RNA secondary structures through intramolecular base-pairing that are different from the linear transcript. Here we review factors that may affect circRNA structure and how structure affects circRNA function and regulation. We highlight considerations for RNA sequencing and expression measurement to ensure highly structured circRNAs are accurately represented by the data and discuss issues that need to be addressed in generating circRNAs to recapitulate their endogenous structures. We conclude our review by discussing experimental strategies on revealing the varied roles of RNA structure in circRNA biogenesis, function and decay.

Translation initiation of the viral genomic mRNA (vRNA) of human immunodeficiency virus-type 1 (HIV-1) can be mediated by a cap- or an internal ribosome entry site (IRES)-dependent mechanism. A previous report shows that Staufen1, a cellular double-stranded (ds) RNA-binding protein (RBP), binds to the 5’untranslated region (5′UTR) of the HIV-1 vRNA and promotes its cap-dependent translation. In this study, we now evaluate the role of Staufen1 as an HIV-1 IRES-transacting factor (ITAF). We first confirm that Staufen1 associates with both the HIV-1 vRNA and the Gag protein during HIV-1 replication. We found that in HIV-1-expressing cells, siRNA-mediated depletion of Staufen1 reduces HIV-1 vRNA translation. Using dual-luciferase bicistronic mRNAs, we show that the siRNA-mediated depletion and cDNA-mediated overexpression of Staufen1 acutely regulates HIV-1 IRES activity. Furthermore, we show that Staufen1-vRNA interaction is required for the enhancement of HIV-1 IRES activity. Interestingly, we find that only Staufen1 harboring an intact dsRNA-binding domain 3 (dsRBD3) rescues HIV-1 IRES activity in Staufen1 CRISPR-Cas9 gene edited cells. Finally, we show that the expression of Staufen1-dsRBD3 alone enhances HIV-1 IRES activity. This study provides evidence of a novel role for Staufen1 as an ITAF promoting HIV-1 vRNA IRES activity.

In infected cells, Epstein–Barr virus (EBV) alternates between latency and lytic replication. The viral bZIP transcription factor ZEBRA (Zta, BZLF1) regulates this cycle by binding to two classes of ZEBRA response elements (ZREs): CpG-free motifs resembling the consensus AP-1 site recognized by cellular bZIP proteins and CpG-containing motifs that are selectively bound by ZEBRA upon cytosine methylation. We report structural and mutational analysis of ZEBRA bound to a CpG-methylated ZRE (meZRE) from a viral lytic promoter. ZEBRA recognizes the CpG methylation marks through a ZEBRA-specific serine and a methylcytosine-arginine-guanine triad resembling that found in canonical methyl-CpG binding proteins. ZEBRA preferentially binds the meZRE over the AP-1 site but mutating the ZEBRA-specific serine to alanine inverts this selectivity and abrogates viral replication. Our findings elucidate a DNA methylation-dependent switch in ZEBRA’s transactivation function that enables ZEBRA to bind AP-1 sites and promote viral latency early during infection and subsequently, under appropriate conditions, to trigger EBV lytic replication by binding meZREs.