HNADOCK: a nucleic acid docking server for modeling RNA/DNA-RNA/DNA 3D complex structures

Rational Design Problematics of Peptide Nucleic Acids as SARS-CoV-2 Inhibitors

The use of viral protein inhibitors has shown to be insufficiently effective in the case of highly variable SARS-CoV-2. In this work, we examined the possibility of designing agents that bind to a highly conserved region of coronavirus (+)RNA. We demonstrated that while the design of antisense RNAs is based on the complementary interaction of nitrogenous bases, it is possible to use semirigid docking methods in the case of unnatural peptide nucleic acids. The transition from N-(2-aminoethyl)glycine chain to a more conformationally rigid piperidine-containing backbone allowed us to significantly increase the affinity of structures to the target RNA.

circKDM1A suppresses bladder cancer progression by sponging miR-889-3p/CPEB3 and stabilizing p53 mRNA

Circular RNAs (circRNAs) play crucial biological functions in various tumors, including bladder cancer (BCa). However, the roles and underlying molecular mechanisms of circRNAs in the malignant proliferation of BCa are yet unknown. CircKDM1A was observed to be downregulated in BCa tissues and cells. Knockdown of circKDM1A promoted the proliferation of BCa cells and bladder xenograft growth, while the overexpression of circKDM1A exerts the opposite effect. The dual-luciferase reporter assay revealed that circKDM1A was directly bound to miR-889-3p, acting as its molecular sponge to downregulate CPEB3. In turn, the CPEB3 was bound to the CPE signal in p53 mRNA 3'UTR to stabilize its expression. Thus, circKDM1A-mediated CPEB3 downregulation inhibits the stability of p53 mRNA and promotes BCa malignant progression. In conclusion, circKDM1A functions as a tumor suppressor in the malignant proliferation of BCa via the miR-889-3p/CPEB3/p53 axis. CircKDM1A may be a potential prognostic biomarker and therapeutic target of BCa.

Transcriptional reprogramming by mutated IRF4 in lymphoma

Disease-causing mutations in genes encoding transcription factors (TFs) can affect TF interactions with their cognate DNA-binding motifs. Whether and how TF mutations impact upon the binding to TF composite elements (CE) and the interaction with other TFs is unclear. Here, we report a distinct mechanism of TF alteration in human lymphomas with perturbed B cell identity, in particular classic Hodgkin lymphoma. It is caused by a recurrent somatic missense mutation c.295 T > C (p.Cys99Arg; p.C99R) targeting the center of the DNA-binding domain of Interferon Regulatory Factor 4 (IRF4), a key TF in immune cells. IRF4-C99R fundamentally alters IRF4 DNA-binding, with loss-of-binding to canonical IRF motifs and neomorphic gain-of-binding to canonical and non-canonical IRF CEs. IRF4-C99R thoroughly modifies IRF4 function by blocking IRF4-dependent plasma cell induction, and up-regulates disease-specific genes in a non-canonical Activator Protein-1 (AP-1)-IRF-CE (AICE)-dependent manner. Our data explain how a single mutation causes a complex switch of TF specificity and gene regulation and open the perspective to specifically block the neomorphic DNA-binding activities of a mutant TF.

RNA 3D Structure Prediction: Progress and Perspective

Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding

DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences.

Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents

Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. We examined the performance of five molecular docking programs (AutoDock 4, AutoDock Vina, DOCK 6, rDock, and RLDock) for their ability to model ribosomal-ligand interactions with oxazolidinones. Eleven ribosomal crystal structures with oxazolidinones as the ligands were docked. The accuracy was evaluated by calculating the docked complexes' root-mean-square deviation (RMSD) and the program's internal scoring function. The rankings for each program based on the median RMSD between the native and predicted were DOCK 6 > AD4 > Vina > RDOCK >> RLDOCK. Results demonstrate that the top-performing program, DOCK 6, could accurately replicate the ligand binding in only four of the eleven ribosomes due to the poor electron density of said ribosomal structures. In this study, we have further benchmarked the performance of the DOCK 6 docking algorithm and scoring in improving virtual screening (VS) enrichment using the dataset of 285 oxazolidinone derivatives against oxazolidinone binding sites in the S. aureus ribosome. However, there was no clear trend between the structure and activity of the oxazolidinones in VS. Overall, the docking performance indicates that the RNA pocket's high flexibility does not allow for accurate docking prediction, highlighting the need to validate VS. protocols for ligand-RNA before future use. Later, we developed a re-scoring method incorporating absolute docking scores and molecular descriptors, and the results indicate that the descriptors greatly improve the correlation of docking scores and pMIC values. Morgan fingerprint analysis was also used, suggesting that DOCK 6 underpredicted molecules with tail modifications with acetamide, n-methylacetamide, or n-ethylacetamide and over-predicted molecule derivatives with methylamino bits. Alternatively, a ligand-based approach similar to a field template was taken, indicating that each derivative's tail groups have strong positive and negative electrostatic potential contributing to microbial activity. These results indicate that one should perform VS. campaigns of ribosomal antibiotics with care and that more comprehensive strategies, including molecular dynamics simulations and relative free energy calculations, might be necessary in conjunction with VS. and docking.

Identification of Human miRNA Biomarkers Targeting the SARS-CoV-2 Genome

SARS-CoV-2 poses a great challenge toward mankind, majorly due to its evolution and frequently occurring variants. On the other hand, in human hosts, microRNA (miRNA) plays a vital role in replication and propagation during a viral infection and can control the biological processes. This may be essential for the progression of viral infection. Moreover, human miRNAs can play a therapeutic role in treatment of different viral diseases by binding to the target sites of the virus genome, thereby hindering the essential functioning of the virus. Motivated by this fact, we have hypothesized a new approach in order to identify human miRNAs that can target the mRNA (genome) of SARS-CoV-2 to degrade their protein synthesis. In this regard, the multiple sequence alignment technique Clustal Omega is used to align a complement of 2656 human miRNAs with the SARS-CoV-2 reference genome (mRNA). Thereafter, ranking of these aligned human miRNAs is performed with the help of a new scoring function that takes into account the (a) total number of nucleotide matches between the human miRNA and the SARS-CoV-2 genome, (b) number of consecutive nucleotide matches between the human miRNA and the SARS-CoV-2 genome, (c) number of nucleotide mismatches between the human miRNA and the SARS-CoV-2 genome, and (d) the difference in length before and after alignment of the human miRNA. As a result, from the 2656 ranked miRNAs, the top 20 human miRNAs are reported, which are targeting different coding and non-coding regions of the SARS-CoV-2 genome. Moreover, molecular docking of such human miRNAs with virus mRNA is performed to verify the efficacy of the interactions. Furthermore, 4 miRNAs out of the top 20 miRNAs are identified to have the seed region. In order to inhibit the virus, the key human targets of the seed regions may be targeted. Repurposable drugs like carfilzomib, bortezomib, hydralazine, and paclitaxel are identified for such purpose.

HDOCK update for modeling protein-RNA/DNA complex structures

Protein-nucleic acid interactions are involved in various cellular processes. Therefore, determining the structures of protein-nucleic acid complexes can provide insights into the mechanisms of the interactions and thus guide the rational drug design to modulate these interactions. Due to the high cost and technical difficulties of solving complex structures experimentally, computational modeling such as molecular docking has been playing an important role in the study of molecular interactions. In order to make it easier for researchers to obtain biomolecular complex structures through molecular docking, we developed the HDOCK server for protein-protein and protein-RNA/DNA docking (accessed at http://hdock.phys.hust.edu.cn/). Since its first release in 2017, HDOCK has been widely used in the scientific community. As nucleic acids may include single-stranded (ss) RNA/DNA and double-stranded (ds) RNA/DNA, we now present an updated version of HDOCK, which offers new options for structural modeling of ssRNA, ssDNA, dsRNA, and dsDNA. We hope this update will better help the scientific community solve important biological problems, thereby advancing the field. In this article, we describe the general protocol of HDOCK with emphasis on the new functions on RNA/DNA modeling. Several application examples are also given to illustrate the usage of the new functions.

Ab initio predictions for 3D structure and stability of single- and double-stranded DNAs in ion solutions

The three-dimensional (3D) structure and stability of DNA are essential to understand/control their biological functions and aid the development of novel materials. In this work, we present a coarse-grained (CG) model for DNA based on the RNA CG model proposed by us, to predict 3D structures and stability for both dsDNA and ssDNA from the sequence. Combined with a Monte Carlo simulated annealing algorithm and CG force fields involving the sequence-dependent base-pairing/stacking interactions and an implicit electrostatic potential, the present model successfully folds 20 dsDNAs (≤52nt) and 20 ssDNAs (≤74nt) into the corresponding native-like structures just from their sequences, with an overall mean RMSD of 3.4Å from the experimental structures. For DNAs with various lengths and sequences, the present model can make reliable predictions on stability, e.g., for 27 dsDNAs with/without bulge/internal loops and 24 ssDNAs including pseudoknot, the mean deviation of predicted melting temperatures from the corresponding experimental data is only ~2.0°C. Furthermore, the model also quantificationally predicts the effects of monovalent or divalent ions on the structure stability of ssDNAs/dsDNAs.

To determine 3D structures and quantify stability of single- (ss) and double-stranded (ds) DNAs is essential to unveil the mechanisms of their functions and to further guide the production and development of novel materials. Although many DNA models have been proposed to reproduce the basic structural, mechanical, or thermodynamic properties of dsDNAs based on the secondary structure information or preset constraints, there are very few models can be used to investigate the ssDNA folding or dsDNA assembly from the sequence. Furthermore, due to the polyanionic nature of DNAs, metal ions (e.g., Na⁺ and Mg²⁺) in solutions can play an essential role in DNA folding and dynamics. Nevertheless, ab initio predictions for DNA folding in ion solutions are still an unresolved problem. In this work, we developed a novel coarse-grained model to predict 3D structures and thermodynamic stabilities for both ssDNAs and dsDNAs in monovalent/divalent ion solutions from their sequences. As compared with the extensive experimental data and available existing models, we showed that the present model can successfully fold simple DNAs into their native-like structures, and can also accurately reproduce the effects of sequence and monovalent/divalent ions on structure stability for ssDNAs including pseudoknot and dsDNAs with/without bulge/internal loops.

FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures

Knowledge of RNA three-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, the high-accuracy predictions of RNA 3D structures are still limited to the RNAs with short lengths or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of non-redundant CG fragment ensembles regardless of the sequences, building CG 3D structure ensemble through fragment assembly, identifying top-scored CG structures through a specific CG scoring function, and rebuilding the all-atom structures from the top-scored CG ones. Extensive examination against different types of RNA structures indicates that FebRNA consistently gives the reliable predictions on RNA 3D structures, including pseudoknots, three-way junctions, four-way and five-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available on the Web site: https://github.com/Tan-group/FebRNA.

Probing the mutation independent interaction of DNA probes with SARS-CoV-2 variants through a combination of surface-enhanced Raman scattering and machine learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution has been characterized by the emergence of sets of mutations impacting the virus characteristics, such as transmissibility and antigenicity, presumably in response to the changing immune profile of the human population. The presence of mutations in the SARS-CoV-2 virus can potentially impact therapeutic and diagnostic test performances. We design and develop here a unique set of DNA probes i.e., antisense oligonucleotides (ASOs) which can interact with genetic sequences of the virus irrespective of its ongoing mutations. The probes, developed herein, target a specific segment of the nucleocapsid phosphoprotein (N) gene of SARS-CoV-2 with high binding efficiency which do not mutate among the known variants. Further probing into the interaction profile of the ASOs reveals that the ASO-RNA hybridization remains unaltered even for a hypothetical single point mutation at the target RNA site and diminished only in case of the hypothetical double or triple point mutations. The mechanism of interaction among the ASOs and SARS-CoV-2 RNA is then explored with a combination of surface-enhanced Raman scattering (SERS) and machine learning techniques. It has been observed that the technique, described herein, could efficiently discriminate between clinically positive and negative samples with ∼100% sensitivity and ∼90% specificity up to 63 copies/mL of SARS-CoV-2 RNA concentration. Thus, this study establishes N gene targeted ASOs as the fundamental machinery to efficiently detect all the current SARS-CoV-2 variants regardless of their mutations.

In silico prediction and experimental evaluation of potential siRNAs against SARS-CoV-2 inhibition in Vero E6 cells

Objective

The acute cases of pneumonia (COVID-19) were first reported from China in December 2019, and the pathogen was identified as SARS-CoV-2. Currently, many vaccines have been developed against this virus by using multiple genes, applying different platforms, and used for the vaccinations of the human population. Spike protein genes play an important role in host cell attachment and viral entry and have been extensively used for the development of vaccine and antiviral therapeutics. Short interfering RNA is also known as silencing RNA and contribute a significant role to regulate the expression of a specific gene. By using this technology, virus inhibition has been demonstrated against many viral diseases.

Methods

In this work, we have reported the Insilico prediction, designing, and experimental validation of siRNAs antiviral potency against SARS-CoV-2-S-RBD. The siDirect 2.0 was selected for siRNAs prediction, and secondary structure was predicted by RNAfold while the HNADOCK was used for molecular docking analysis and specific binding of siRNAs to the selected target. We have used and evaluated four siRNAs for cellular toxicity and determination of antiviral efficiency based on the Ct value of q-real-time PCR in Vero E6 cells.

Results

Based on the experimental evaluation and analysis of results from generated data, we observed that there is no cytotoxicity for any tested siRNAs in Vero E6 cells. Total four siRNA were filtered out from twenty-one siRNAs following the strict selection and scoring criteria. The better antiviral efficiency was observed in 3rd siRNAs based on the Ct value of q-real-time PCR. The results that emerged from this study encouraged us to validate the efficiency of these siRNAs in multiple cells by using alone and in a combination of two or more siRNAs to inhibit the SARS-CoV-2 proliferation.

Conclusion

The Insilico prediction, molecular docking analysis provided the selection of better siRNAs. Based on the experimental evaluation only 3rd siRNA was found to be more effective than others and showed better antiviral efficiency. These siRNAs should also be evaluated in other cell lines either separately or in combination against SARS-CoV-2 to determine their antiviral efficiency.

Effect of insilico predicted and designed potential siRNAs on inhibition of SARS-CoV-2 in HEK-293 cells

Objectives

The COVID-19 was identified for the first time from the sea food market, Wuhan city, China in 2019 and the pathogenic organism was identified as SARS-CoV-2. Currently, this virus has spread to 223 countries and territories and known as a serious issue for the global human community. Many vaccines have been developed and used for immunization.

Methods

We have reported the insilico prediction, designing, secondary structure prediction, molecular docking analysis, and in vitro assessment of siRNAs against SARS-CoV-2. The online bioinformatic approach was used for siRNAs selection and designing. The selected siRNAs were evaluated for antiviral efficacy by using Lipofectamine 2000 as delivery agent to HEK-293 cells. The MTT assay was used for cytotoxicity determination. The antiviral efficacy of potential siRNAs was determined based on the Ct value of q-RT-PCR and the data analysis was done by Prism-GraphPad software.

Results

The analyzed data resulted in the selection of only three siRNAs out of twenty-six siRNAs generated by online software. The secondary structure prediction and molecular docking analysis of siRNAs revealed the efficient binding to the target. There was no cellular toxicity observed in the HEK-293 cells at any tested concentrations of siRNAs. The purification of RNA was completed from inoculated cells and subjected to q-RT-PCR. The highest Ct value was observed in siRNA 3 than the others. The results offered valuable evidence and invigorated us to assess the potency of siRNAs by using alone or in combination in other human cells.

Conclusion

The data generated from this study indicates the significance of in silico prediction and narrow down the potential siRNA' against SARS-CoV-2, and molecular docking investigation offered the effective siRNAs binding with the target. Finally, it is concluded that the online bioinformatics approach provided the prediction and selection of siRNAs with better antiviral efficacy. The siRNA-3 was observed to be the best for reduction of viral RNA in cells.

Dissecting and predicting different types of binding sites in nucleic acids based on structural information

The biological functions of DNA and RNA generally depend on their interactions with other molecules, such as small ligands, proteins and nucleic acids. However, our knowledge of the nucleic acid binding sites for different interaction partners is very limited, and identification of these critical binding regions is not a trivial work. Herein, we performed a comprehensive comparison between binding and nonbinding sites and among different categories of binding sites in these two nucleic acid classes. From the structural perspective, RNA may interact with ligands through forming binding pockets and contact proteins and nucleic acids using protruding surfaces, while DNA may adopt regions closer to the middle of the chain to make contacts with other molecules. Based on structural information, we established a feature-based ensemble learning classifier to identify the binding sites by fully using the interplay among different machine learning algorithms, feature spaces and sample spaces. Meanwhile, we designed a template-based classifier by exploiting structural conservation. The complementarity between the two classifiers motivated us to build an integrative framework for improving prediction performance. Moreover, we utilized a post-processing procedure based on the random walk algorithm to further correct the integrative predictions. Our unified prediction framework yielded promising results for different binding sites and outperformed existing methods.

RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources

Background

RNA regulates a variety of biological functions by interacting with other molecules. The ligand often binds in the RNA pocket to trigger structural changes or functions. Thus, it is essential to explore and visualize the RNA pocket to elucidate the structural and recognition mechanism for the RNA-ligand complex formation.

Results

In this work, we developed one user-friendly bioinformatics tool, RPocket. This database provides geometrical size, centroid, shape, secondary structure element for RNA pocket, RNA-ligand interaction information, and functional sites. We extracted 240 RNA pockets from 94 non-redundant RNA-ligand complex structures. We developed RPDescriptor to calculate the pocket geometrical property quantitatively. The geometrical information was then subjected to RNA-ligand binding analysis by incorporating the sequence, secondary structure, and geometrical combinations. This new approach takes advantage of both the atom-level precision of the structure and the nucleotide-level tertiary interactions. The results show that the higher-level topological pattern indeed improves the tertiary structure prediction. We also proposed a potential mechanism for RNA-ligand complex formation. The electrostatic interactions are responsible for long-range recognition, while the Van der Waals and hydrophobic contacts for short-range binding and optimization. These interaction pairs can be considered as distance constraints to guide complex structural modeling and drug design.

Conclusion

RPocket database would facilitate RNA-ligand engineering to regulate the complex formation for biological or medical applications. RPocket is available at http://zhaoserver.com.cn/RPocket/RPocket.html.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04349-4.

Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement

Macromolecules, such as RNAs, reside in crowded cell environments, which could strongly affect the folded structures and stability of RNAs. The emergence of RNA-driven phase separation in biology further stresses the potential functional roles of molecular crowding. In this work, we employed the coarse-grained model that was previously developed by us to predict 3D structures and stability of the mouse mammary tumor virus (MMTV) pseudoknot under different spatial confinements over a wide range of salt concentrations. The results show that spatial confinements can not only enhance the compactness and stability of MMTV pseudoknot structures but also weaken the dependence of the RNA structure compactness and stability on salt concentration. Based on our microscopic analyses, we found that the effect of spatial confinement on the salt-dependent RNA pseudoknot stability mainly comes through the spatial suppression of extended conformations, which are prevalent in the partially/fully unfolded states, especially at low ion concentrations. Furthermore, our comprehensive analyses revealed that the thermally unfolding pathway of the pseudoknot can be significantly modulated by spatial confinements, since the intermediate states with more extended conformations would loss favor when spatial confinements are introduced.

Recognition of plausible therapeutic agents to combat COVID-19: An omics data based combined approach

Coronavirus disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has become an immense threat to global public health. In this study, we performed complete genome sequencing of a SARS-CoV-2 isolate. More than 67,000 genome sequences were further inspected from Global Initiative on Sharing All Influenza Data (GISAID). Using several in silico techniques, we proposed prospective therapeutics against this virus. Through meticulous analysis, several conserved and therapeutically suitable regions of SARS-CoV-2 such as RNA-dependent RNA polymerase (RdRp), Spike (S) and Membrane glycoprotein (M) coding genes were selected. Both S and M were chosen for the development of a chimeric vaccine that can generate memory B and T cells. siRNAs were also designed for S and M gene silencing. Moreover, six new drug candidates were suggested that might inhibit the activity of RdRp. Since SARS-CoV-2 and SARS-CoV-1 have 82.30% sequence identity, a Gene Expression Omnibus (GEO) dataset of Severe Acute Respiratory Syndrome (SARS) patients were analyzed. In this analysis, 13 immunoregulatory genes were found that can be used to develop type 1 interferon (IFN) based therapy. The proposed vaccine, siRNAs, drugs and IFN based analysis of this study will accelerate the development of new treatments.

An in silico analysis of effective siRNAs against COVID-19 by targeting the leader sequence of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a retrovirus having genome size of around 30 kb. Its genome contains a highly conserved leader sequence at its 5' end, which is added to all subgenomic mRNAs at their 5' terminus by a discontinuous transcription mechanism and regulates their translation. Targeting the leader sequence by RNA interference can be an effective approach to inhibit the viral replication. In the present study an in-silico prediction of highly effective siRNAs was performed to target the leader sequence using the online software siDirect version 2.0. Low seed-duplex stability, exact complementarity with target, at least three mismatches with any off-target and least number of off-targets, were considered as effective criteria for highly specific siRNA. Further validation of siRNA affinity for the target was accomplished by molecular docking by HNADOCK online server. Our results revealed four potential siRNAs, of which siRNA having guide strand sequence 5'GUUUAGAGAACAGAUCUACAA3' met almost all specificity criteria with no off-targets for guide strand. Molecular docking of all predicted siRNAs (guide strand) with the target leader sequence depicted highest binding score of -327.45 for above-mentioned siRNA. Furthermore, molecular docking of the passenger strand of the best candidate with off-target sequences gave significantly low binding scores. Hence, 5'GUUUAGAGAACAGAUCUACAA3' siRNA possess great potential to silence the leader sequence of SARS-CoV-2 with least off-target effect. Present study provides great scope for development of gene therapy against the prevailing COVID-19 disease, thus further research in this concern is urgently demanded.

MiR193a Modulation and Podocyte Phenotype

Apolipoprotein L1 (APOL1)-miR193a axis has been reported to play a role in the maintenance of podocyte homeostasis. In the present study, we analyzed transcription factors relevant to miR193a in human podocytes and their effects on podocytes’ molecular phenotype. The motif scan of the miR193a gene provided information about transcription factors, including YY1, WT1, Sox2, and VDR-RXR heterodimer, which could potentially bind to the miR193a promoter region to regulate miR193a expression. All structure models of these transcription factors and the tertiary structures of the miR193a promoter region were generated and refined using computational tools. The DNA-protein complexes of the miR193a promoter region and transcription factors were created using a docking approach. To determine the modulatory role of miR193a on APOL1 mRNA, the structural components of APOL1 3’ UTR and miR193a-5p were studied. Molecular Dynamic (MD) simulations validated interactions between miR193a and YY1/WT1/Sox2/VDR/APOL1 3′ UTR region. Undifferentiated podocytes (UPDs) displayed enhanced miR193a, YY1, and Sox2 but attenuated WT1, VDR, and APOL1 expressions, whereas differentiated podocytes (DPDs) exhibited attenuated miR193a, YY1, and Sox2 but increased WT1, VDR, APOL1 expressions. Inhibition of miR193a in UPDs enhanced the expression of APOL1 as well as of podocyte molecular markers; on the other hand, DPD-transfected with miR193a plasmid showed downing of APOL1 as well as podocyte molecular markers suggesting a causal relationship between miR193a and podocyte molecular markers. Silencing of YY1 and Sox2 in UPDs decreased the expression of miR193a but increased the expression of VDR, and CD2AP (a marker of DPDs); in contrast, silencing of WT1 and VDR in DPDs enhanced the expression of miR193a, YY1, and Sox2. Since miR193a-downing by Vitamin D receptor (VDR) agonist not only enhanced the mRNA expression of APOL1 but also of podocyte differentiating markers, suggest that down-regulation of miR193a could be used to enhance the expression of podocyte differentiating markers as a therapeutic strategy.

A Novel Noncoding RNA dsr11 Involved in Heat Stress Tolerance in Deinococcus radiodurans

Deinococcus radiodurans is an extremely resistant bacteria that has evolved masterful strategies to enable survival under various environmental stress conditions. Heat stress is a major environmental stress factor that can cause denaturation of proteins, membrane disruption, and oxidative stress. Previous studies have examined the mechanisms of the heat stress response by analyzing changes in protein levels; however, little is known about the role of small noncoding RNAs (ncRNAs), which are known to play important regulatory functions in bacteria during various environmental stress response. The ncRNA dsr11 of D. radiodurans was previously identified by RNA-seq and Northern blot. In this study, we showed that the transcription level of dsr11 was up-regulated 4.2-fold under heat stress by qRT-PCR analysis. Heat tolerance assay showed that deleting dsr11 significantly inhibited the viability under high temperature conditions. To assess the influence of dsr11 on the D. radiodurans transcriptome, 157 genes were found differentially expressed in the knock-out mutant by RNA-seq experiment. Combining RNA-seq and in silico analysis, we found that trmE (tRNA modification GTPase) and dr_0651 (arginase) were likely to be the direct targets of dsr11. Further microscale thermophoresis results demonstrated that dsr11 can directly bind to the mRNA of trmE and dr_0651. Our results indicated that dsr11 can enhance the tolerance to heat stress of D. radiodurans by binding to trmE and dr_0651 mRNA. Overall, these results extend our understanding of ncRNA regulation and provide new insights into the heat stress response in D. radiodurans.

Gold-Hybridized Zinc Oxide Nanorods as Real-Time Low-Cost NanoBiosensors for Detection of virulent DNA signature of HPV-16 in Cervical Carcinoma

Detection of host integrated viral oncogenes are critical for early and point-of-care molecular diagnostics of virus-induced carcinoma. However, available diagnostic approaches are incapable of combining both cost-efficient medical diagnosis and high analytical performances. To circumvent this, we have developed an improved IDE-based nanobiosensor for biorecognition of HPV-16 infected cervical cancer cells through electrochemical impedance spectroscopy. The system is fabricated by coating gold (Au) doped zinc oxide (ZnO) nanorods interfaced with HPV-16 viral DNA bioreceptors on top of the Interdigitated Electrode (IDE) chips surface. Due to the concurrently improved sensitivity and biocompatibility of the designed nanohybrid film, Au decorated ZnO-Nanorod biosensors demonstrate exceptional detection of HPV-16 E6 oncogene, the cancer biomarker for HPV infected cervical cancers. This sensor displayed high levels of sensitivity by detecting as low as 1fM of viral E6 gene target. The sensor also exhibited a stable functional life span of more than 5 weeks, good reproducibility and high discriminatory properties against HPV-16. Sensor current responses are obtained from cultured cervical cancer cells which are close to clinical cancer samples. Hence, the developed sensor is an adaptable tool with high potential for clinical diagnosis especially useful for economically challenged countries/regions.