A Comprehensive Review of Animal Models for Coronaviruses: SARS-CoV-2, SARS-CoV,and MERS-CoV

The recent outbreak of coronavirus disease(COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) has already affected a large population of the world. SARS-CoV-2 belongs to the same family of severe acute respiratory syndrome coronavirus(SARS-CoV) and Middle East respiratory syndrome coronavirus(MERSCoV). COVID-19 has a complex pathology involving severe acute respiratory infection, hyper-immune response, and coagulopathy. At present, there is no therapeutic drug or vaccine approved for the disease. There is an urgent need for an ideal animal model that can reflect clinical symptoms and underlying etiopathogenesis similar to COVID-19 patients which can be further used for evaluation of underlying mechanisms, potential vaccines, and therapeutic strategies. The current review provides a paramount insight into the available animal models of SARS-CoV-2, SARS-CoV, and MERS-CoV for the management of the diseases.     

Stop codon read-through of mammalian MTCH2 leading to an unstable isoform regulates mitochondrial membrane potential

Stop codon read-through (SCR) is a process of continuation of translation beyond a stop codon. This phenomenon, which occurs only in certain mRNAs under specific conditions, leads to a longer isoform with properties different from that of the canonical isoform. MTCH2, which encodes a mitochondrial protein that regulates mitochondrial metabolism, was selected as a potential read-through candidate based on evolutionary conservation observed in the proximal region of its 3′ UTR. Here, we demonstrate translational read-through across two evolutionarily conserved, in-frame stop codons of MTCH2 using luminescence- and fluorescence-based assays, and by analyzing ribosome-profiling and mass spectrometry (MS) data. This phenomenon generates two isoforms, MTCH2x and MTCH2xx (single- and double-SCR products, respectively), in addition to the canonical isoform MTCH2, from the same mRNA. Our experiments revealed that a cis-acting 12-nucleotide sequence in the proximal 3′ UTR of MTCH2 is the necessary signal for SCR. Functional characterization showed that MTCH2 and MTCH2x were localized to mitochondria with a long t_1/2 (>36 h). However, MTCH2xx was found predominantly in the cytoplasm. This mislocalization and its unique C terminus led to increased degradation, as shown by greatly reduced t_1/2 (<1 h). MTCH2 read-through–deficient cells, generated using CRISPR-Cas9, showed increased MTCH2 expression and, consistent with this, decreased mitochondrial membrane potential. Thus, double-SCR of MTCH2 regulates its own expression levels contributing toward the maintenance of normal mitochondrial membrane potential.     

CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

Characterization of a genetically engineered elastin-like polypeptide for cartilaginous tissue repair

Elastin-like polypeptides (ELPs) are artificial polypeptides with unique properties that make them attractive as a biomaterial for tissue-engineered cartilage repair. ELPs are composed of a pentapeptide repeat, Val-Pro-Gly-Xaa-Gly (Xaa is any amino acid except Pro), that undergo an inverse temperature phase transition. They are soluble in aqueous solution below their transition temperature (T(t)) but aggregate when the solution temperature is raised above their T(t). This study investigates the rheological behavior of an un-cross-linked ELP, below and above its T(t), and also examines the ability of ELP to promote chondrogenesis in vitro. A thermally responsive ELP with a T(t) of 35 degrees C was synthesized using recombinant DNA techniques. The complex shear modulus of the ELP increased by 3 orders of magnitude as it underwent its inverse temperature phase transition, forming a coacervate, or gel-like, ELP phase. Values for the complex shear moduli of the un-cross-linked ELP coacervate are comparable to those reported previously for collagen, hyaluronan, and cross-linked synthetic hydrogels. Cell culture studies show that chondrocytes cultured in ELP coacervate maintain a rounded morphology and their chondrocytic phenotype, characterized by the synthesis of a significant amount of extracellular matrix composed of sulfated glycosaminoglycans and collagen. These results suggest that ELPs demonstrate great potential for use as in situ forming scaffolds for cartilaginous tissue repair.     

A Bayesian framework for combining gene predictions

MOTIVATION: Gene identification and gene discovery in new genomic sequences is one of the most timely computational questions addressed by bioinformatics scientists. This computational research has resulted in several systems that have been used successfully in many whole-genome analysis projects. As the number of such systems grows the need for a rigorous way to combine the predictions becomes more essential. RESULTS: In this paper we provide a Bayesian network framework for combining gene predictions from multiple systems. The framework allows us to treat the problem as combining the advice of multiple experts. Previous work in the area used relatively simple ideas such as majority voting. We introduce, for the first time, the use of hidden input/output Markov models for combining gene predictions. We apply the framework to the analysis of the Adh region in Drosophila that has been carefully studied in the context of gene finding and used as a basis for the GASP competition. The main challenge in combination of gene prediction programs is the fact that the systems are relying on similar features such as cod on usage and as a result the predictions are often correlated. We show that our approach is promising to improve the prediction accuracy and provides a systematic and flexible framework for incorporating multiple sources of evidence into gene prediction systems.