The Dynamic Landscapes of Circular RNAs in Axolotl, a Regenerative Medicine Model, with Implications for Early Phase of Limb Regeneration.

Circular RNAs (circRNAs) are of relevance to regenerative medicine and play crucial roles in post-transcriptional and translational regulation of biological processes. circRNAs are a class of RNA molecules that are formed through a unique splicing process, resulting in a covalently closed-loop structure. Recent advancements in RNA sequencing technologies and specialized computational tools have facilitated the identification and functional characterization of circRNAs. These molecules are known to exhibit stability, developmental regulation, and specific expression patterns in different tissues and cell types across various organisms. However, our understanding of circRNA expression and putative function in model organisms for regeneration is limited. In this context, this study reports, for the first time, on the repertoire of circRNAs in axolotl, a widely used model organism for regeneration. We generated RNA-seq data from intact limb, wound, and blastema tissues of axolotl during limb regeneration. The analysis revealed the presence of 35,956 putative axolotl circRNAs, among which 5331 unique circRNAs exhibited orthology with human circRNAs. In silico data analysis underlined the potential roles of axolotl circRNAs in cell cycle, cell death, and cell senescence-related pathways during limb regeneration, suggesting the participation of circRNAs in regulation of diverse functions pertinent to regenerative medicine. These new observations help advance our understanding of the dynamic landscape of axolotl circRNAs, and by extension, inform future regenerative medicine research and innovation that harness this model organism.

Data mining and molecular dynamics analysis to detect HIV-1 reverse transcriptase RNase H activity inhibitor.

HIV-1 is a deadly virus that affects millions of people worldwide. In this study, we aimed to inhibit viral replication by targeting one of the HIV-1 proteins and identifying a new drug candidate. We used data mining and molecular dynamics methods on HIV-1 genomes. Based on MAUVE analysis, we selected the RNase H activity of the reverse transcriptase (R.T) enzyme as a potential target due to its low mutation rate and high conservation level. We screened about 94,000 small molecule inhibitors by virtual screening. We validated the hit compounds' stability and binding free energy through molecular dynamics simulations and MM/PBSA. Phomoarcherin B, known for its anticancer properties, emerged as the best candidate and showed potential as an HIV-1 reverse transcriptase RNase H activity inhibitor. This study presents a new target and drug candidate for HIV-1 treatment. However, in vitro and in vivo tests are required. Also, the effect of RNase H activity on viral replication and the interaction of Phomoarcherin B with other HIV-1 proteins should be investigated.

Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction.

The attachment of a virion to a respective cellular receptor on the host organism occurring through the virus-host protein-protein interactions (PPIs) is a decisive step for viral pathogenicity and infectivity. Therefore, a vast number of wet-lab experimental techniques are used to study virus-host PPIs. Taking the great number and enormous variety of virus-host PPIs and the cost as well as labor of laboratory work, however, computational approaches toward analyzing the available interaction data and predicting previously unidentified interactions have been on the rise. Among them, machine-learning-based models are getting increasingly more attention with a great body of resources and tools proposed recently.In this chapter, we first provide the methodology with major steps toward the development of a virus-host PPI prediction tool. Next, we discuss the challenges involved and evaluate several existing machine-learning-based virus-host PPI prediction tools. Finally, we describe our experience with several ensemble techniques as utilized on available prediction results retrieved from individual PPI prediction tools. Overall, based on our experience, we recognize there is still room for the development of new individual and/or ensemble virus-host PPI prediction tools that leverage existing tools.

A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.

Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature selection. We also tested the performance of these baseline models against the previously available tools to ensure their predictive power is acceptable. Here, the Pearson coefficient provides the best performance with respect to the baseline model as measured by AUPR; a drop of 0.003 in AUPR while achieving a 73.3% (from 686 to 183) reduction in the number of tripeptides features for random forest. The results suggest our correlation coefficient-based feature selection approach, while decreasing the computation time and space complexity, has a limited impact on the prediction performance of virus-host PPI prediction tools.

First case with RANBP2 biallelic mutation and severe acute necrotizing encephalopathy phenotype.

Familial acute necrotizing encephalopathy (ANE) is a rapidly progressive encephalopathy that can occur after common viral infections at different stages of life. The clinical findings of 2 siblings diagnosed with ANE were shared and the whole-exome-sequencing study of the index case was performed. It was confirmed by the Sanger method. We found the RANBP2 gene p.I656V variant homozygous in the index case. We found the variant in the parents as heterozygous. We argue that biallelic mutations in the RANBP2 gene may result in ANE with early onset and severe prognosis by increasing penetrance.

ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor.

Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of AdVs as vectors in therapeutic applications. For an adenoviral infection to occur, attachment of the viral ligand to a cellular receptor on the host organism is a prerequisite and, in this sense, it is a criterion to decide whether an adenoviral infection can potentially happen. The interaction between any virus and its corresponding host organism is a specific kind of protein-protein interaction (PPI) and several experimental techniques, including high-throughput methods are being used in exploring such interactions. As a result, there has been accumulating data on virus-host interactions including a significant portion reported at publicly available bioinformatics resources. There is not, however, a computational model to integrate and interpret the existing data to draw out concise decisions, such as whether an infection happens or not. In this study, accepting the cellular entry of AdV as a decisive parameter for infectivity, we have developed a machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host. For this purpose, we used the sequence data of the known receptors of AdVs, we identified sets of adenoviral ligands and their respective host species, and eventually, we have constructed a comprehensive adenovirus-host interaction dataset. Then, we committed interaction predictions through publicly available virus-host PPI tools and constructed an AdV infection predictor model using SVM with RBF kernel, with the overall sensitivity, specificity, and AUC of 0.88 ± 0.011, 0.83 ± 0.064, and 0.86 ± 0.030, respectively. ML-AdVInfect is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts. We anticipate our approach led to ML-AdVInfect can be adapted in making predictions for other viral infections.

Preclinical Molecular Signatures of Spinal Cord Functional Restoration: Optimizing the Metamorphic Axolotl (Ambystoma mexicanum) Model in Regenerative Medicine.

Regenerative medicine offers hope for patients with diseases of the central and peripheral nervous system. Urodele amphibians such as axolotl display an exceptional regenerative capacity and are considered as essential preclinical model organisms in neurology and regenerative medicine research. Earlier studies have suggested that the limb regeneration ability of this salamander notably decreases with induction of metamorphosis by thyroid hormones. Metamorphic axolotl requires further validation as a negative control in preclinical regenerative medicine research, not to mention the study of molecular substrates of its regenerative abilities. In this study, we report new observations on the effect of experimentally induced metamorphosis on spinal cord regeneration in axolotl. Surprisingly, we found that metamorphic animals were successful to functionally restore the spinal cord after an experimentally induced injury. To discern the molecular signatures of spinal cord regeneration, we performed transcriptomics analyses at 1- and 7-days postinjury (dpi) for both spinal cord injury (SCI)-induced (experimental) and laminectomy (sham) groups. We observed 119 and 989 differentially expressed genes at 1- and 7-dpi, respectively, while the corresponding mouse orthologous genes were enriched in junction-, immune system-, and extracellular matrix-related pathways. Taken together, our findings challenge the prior notions of limited regenerative ability of metamorphic axolotl which exhibited successful spinal cord regeneration in our experience. Moreover, we report on molecular signatures that can potentially explain the mechanistic substrates of the regenerative capacity of the metamorphic axolotl. To the best of our knowledge, this is the first report on molecular responses to SCI and functional restoration in metamorphic axolotls. These new findings advance our understanding of spinal cord regeneration, and may thus help optimize the future use of axolotl as a preclinical model in regenerative medicine and integrative biology fields.

In Silico Analysis of a Highly Mutated Gene in Cancer Provides Insight into Abnormal mRNA Splicing: Splicing Factor 3B Subunit 1K700E Mutant.

Cancer is the second leading cause of death worldwide. The etiology of the disease has remained elusive, but mutations causing aberrant RNA splicing have been considered one of the significant factors in various cancer types. The association of aberrant RNA splicing with drug/therapy resistance further increases the importance of these mutations. In this work, the impact of the splicing factor 3B subunit 1 (SF3B1) K700E mutation, a highly prevalent mutation in various cancer types, is investigated through molecular dynamics simulations. Based on our results, K700E mutation increases flexibility of the mutant SF3B1. Consequently, this mutation leads to i) disruption of interaction of pre-mRNA with SF3B1 and p14, thus preventing proper alignment of mRNA and causing usage of abnormal 3' splice site, and ii) disruption of communication in critical regions participating in interactions with other proteins in pre-mRNA splicing machinery. We anticipate that this study enhances our understanding of the mechanism of functional abnormalities associated with splicing machinery, thereby, increasing possibility for designing effective therapies to combat cancer at an earlier stage.

Comparison of protein expression profile of limb regeneration between neotenic and metamorphic axolotl.

The axolotl (Ambystoma mexicanum) salamander, a urodele amphibian, has an exceptional regenerative capacity to fully restore an amputated limb throughout the life-long lasting neoteny. By contrast, when axolotls are experimentally induced to metamorphosis, attenuation of the limb's regenerative competence is noticeable. Here, we sought to discern the proteomic profiles of the early stages of blastema formation of neotenic and metamorphic axolotls after limb amputation by employing LC-MS/MS technology. We quantified a total of 714 proteins and qRT-PCR for selected genes was performed to validate the proteomics results and provide evidence for the putative link between immune system activity and regenerative potential. This study provides new insights for examination of common and distinct molecular mechanisms in regeneration-permissive neotenic and regeneration-deficient metamorphic stages at the proteome level.