UNASSIGNED: Bluetongue (BT) poses a significant threat to the livestock industry, affecting various animal species and resulting in substantial economic losses. The existence of numerous BT virus (BTV) serotypes has hindered control efforts, highlighting the need for broad-spectrum vaccines.
UNASSIGNED: In this study, we evaluated the conserved amino acid sequences within key non-structural (NS) proteins of BTV and identified numerous highly conserved murine- and bovine-specific MHC class I-restricted (MHC-I) CD8+ and MHC-II-restricted CD4+ epitopes. We then screened these conserved epitopes for antigenicity, allergenicity, toxicity, and solubility. Using these epitopes, we developed in silico-based broad-spectrum multiepitope vaccines with Toll-like receptor (TLR-4) agonists. The predicted proinflammatory cytokine response was assessed in silico using the C-IMMSIM server. Structural modeling and refinement were achieved using Robetta and GalaxyWEB servers. Finally, we assessed the stability of the docking complexes through extensive 100-nanosecond molecular dynamics simulations before considering the vaccines for codon optimization and in silico cloning.
UNASSIGNED: We found many epitopes that meet these criteria within NS1 and NS2 proteins and developed in silico broad-spectrum vaccines. The immune simulation studies revealed that these vaccines induce high levels of IFN-γ and IL-2 in the vaccinated groups. Protein-protein docking analysis demonstrated promising epitopes with strong binding affinities to TLR-4. The docked complexes were stable, with minimal Root Mean Square Deviation and Root Mean Square Fluctuation values. Finally, the in silico-cloned plasmids have high % of GC content with > 0.8 codon adaptation index, suggesting they are suitable for expressing the protein vaccines in prokaryotic system.
UNASSIGNED: These next-generation vaccine designs are promising and warrant further investigation in wet lab experiments to assess their immunogenicity, safety, and efficacy for practical application in livestock. Our findings offer a robust framework for developing a comprehensive, broad-spectrum vaccine, potentially revolutionizing BT control and prevention strategies in the livestock industry.

The insulin-degrading enzyme (IDE) is a zinc-dependent metalloendopeptidase that belongs to the M16A metalloprotease family. IDE is markedly expressed in the brain, where it is particularly relevant due to its in vitro amyloid beta (Aβ)-degrading activity. The subcellular localization of IDE, a paramount aspect to understand how this enzyme can perform its proteolytic functions in vivo, remains highly controversial. In this work, we addressed IDE subcellular localization from an evolutionary perspective. Phylogenetic analyses based on protein sequence and gene and protein structure were performed. An in silico analysis of IDE signal peptide suggests an evolutionary shift in IDE exportation at the prokaryote/eukaryote divide. Subcellular localization experiments in microglia revealed that IDE is mostly cytosolic. Furthermore, IDE associates to membranes by their cytoplasmatic side and further partitions between raft and non-raft domains. When stimulated, microglia change into a secretory active state, produces numerous multivesicular bodies and IDE associates with their membranes. The subsequent inward budding of such membranes internalizes IDE in intraluminal vesicles, which later allows IDE to be exported outside the cells in small extracellular vesicles. We further demonstrate that such an IDE exportation mechanism is regulated by stimuli relevant for microglia in physiological conditions and upon aging and neurodegeneration.

ATP-sensitive potassium (K-ATP) channels are ubiquitously expressed on the plasma membrane of cells in several organs, including the heart, pancreas, and brain, and they govern a wide range of physiological processes. In pancreatic β-cells, K-ATP channels composed of Kir6.2 and SUR1 play a key role in coupling blood glucose and insulin secretion. A tryptophan residue located at the cytosolic end of the transmembrane helix is highly conserved in eukaryote and prokaryote Kir channels. Any mutation on this amino acid causes a gain of function and neonatal diabetes mellitus. In this study, we have investigated the effect of mutation on this highly conserved residue on a KirBac channel (prokaryotic homolog of mammalian Kir6.2). We provide the crystal structure of the mutant KirBac3.1 W46R (equivalent to W68R in Kir6.2) and its conformational flexibility properties using HDX-MS. In addition, the detailed dynamical view of the mutant during the gating was investigated using the in silico method. Finally, functional assays have been performed. A comparison of important structural determinants for the gating mechanism between the wild type KirBac and the mutant W46R suggests interesting structural and dynamical clues and a mechanism of action of the mutation that leads to the gain of function.

BACKGROUND: Long noncoding RNAs represent a large class of transcripts with two common features: they exceed an arbitrary length threshold of 200 nt and are assumed to not encode proteins. Although a growing body of evidence indicates that the vast majority of lncRNAs are potentially nonfunctional, hundreds of them have already been revealed to perform essential gene regulatory functions or to be linked to a number of cellular processes, including those associated with the etiology of human diseases. To better understand the biology of lncRNAs, it is essential to perform a more in-depth study of their evolution. In contrast to protein-encoding transcripts, however, they do not show the strong sequence conservation that usually results from purifying selection; therefore, software that is typically used to resolve the evolutionary relationships of protein-encoding genes and transcripts is not applicable to the study of lncRNAs.
RESULTS: To tackle this issue, we developed lncEvo, a computational pipeline that consists of three modules: (1) transcriptome assembly from RNA-Seq data, (2) prediction of lncRNAs, and (3) conservation study-a genome-wide comparison of lncRNA transcriptomes between two species of interest, including search for orthologs. Importantly, one can choose to apply lncEvo solely for transcriptome assembly or lncRNA prediction, without calling the conservation-related part.
CONCLUSIONS: lncEvo is an all-in-one tool built with the Nextflow framework, utilizing state-of-the-art software and algorithms with customizable trade-offs between speed and sensitivity, ease of use and built-in reporting functionalities. The source code of the pipeline is freely available for academic and nonacademic use under the MIT license at https://gitlab.com/spirit678/lncrna_conservation_nf .

Protein interactions play a crucial role among the different functions of a cell and are central to our understanding of cellular processes both in health and disease. Here we present Galaxy InteractoMIX (http://galaxy.interactomix.com), a platform composed of 13 different computational tools each addressing specific aspects of the study of protein-protein interactions, ranging from large-scale cross-species protein-wide interactomes to atomic resolution level of protein complexes. Galaxy InteractoMIX provides an intuitive interface where users can retrieve consolidated interactomics data distributed across several databases or uncover links between diseases and genes by analyzing the interactomes underlying these diseases. The platform makes possible large-scale prediction and curation protein interactions using the conservation of motifs, interology, or presence or absence of key sequence signatures. The range of structure-based tools includes modeling and analysis of protein complexes, delineation of interfaces and the modeling of peptides acting as inhibitors of protein-protein interactions. Galaxy InteractoMIX includes a range of ready-to-use workflows to run complex analyses requiring minimal intervention by users. The potential range of applications of the platform covers different aspects of life science, biomedicine, biotechnology and drug discovery where protein associations are studied.

Knowledge management tools that assist in systematic review and exploration of scientific knowledge generally are of obvious potential importance in evidence based medicine in general, but also to the design of therapeutics based on the protein subsequences and fold motifs of virus proteins as considered here. Rapid access to bundles (clusters) of related elements of knowledge gathered from diverse sources on the Internet and from growing knowledge repositories seem particularly helpful when exploring less obvious therapeutic targets in viruses (for which knowledge new to the researcher is important), and when using the following concept. Subsequences of amino acid residue sequences of proteins that are conserved across strains and species are (a) more likely to be important targets and (b) less likely to exhibit escape mutations that would make them resistant to vaccines and therapeutic agents. However, the terms \"conserved\" and even \"highly conserved\" used by authors are matters of degree, depending on how distant from SARS-CoV-2 they wished to go in comparing other sequences. The binding site to the human ACE2 protein as virus receptor and human antibody CR3022 binding site on the spike glycoprotein are rather variable by the criteria used in the present and preceding studies. To look for more strongly conserved targets, open reading frames of SARS-CoV-2 were examined for extremely highly conserved regions, meaning recognizable across many viruses and organisms. Most prominent is a motif found in SARS-CoV-2 non-structural protein 3 (Nsp3). It relates to a fold called type called the macro domain and has remarkably wide distribution across organisms including humans with significant homologies involving three especially conserved subsequences (a) VVVNAANVYLKHGGGVAGALNK, (b) LHVVGPNVNKG, and (c) PLLSAGIFG. Careful study of the variations of these and of the more variable sequences between and around them might provide a finer \"scalpel\" to ensure inhibition of a vital function of the virus without impairing the functions of related host macro domains.

Leishmaniasis is a tropical neglected disease that imposes major health concerns in many endemic countries worldwide and requires urgent attention to the identification of new drug targets as well as drug candidates. In the current study, we propose homoserine kinase (HSK) inhibition as a strategy to induce pathogen mortality via generating threonine deficiency. We introduce a homology-based molecular model of leishmanial HSK that appears to possess all conserved structural as well as functional features in the GHMP kinase family. Furthermore, 200 ns molecular dynamics data of the enzyme in open and closed state attempts to provide the mechanistic details involved in the substrate as well as phosphate binding to this enzyme. We discuss the structural and functional significance of movements involved in various loops (motif 1, 2, 3) and lips (upper and lower) in the transition of leishmanial HSK from closed to open state. Virtual screening data of more than 40,000 compounds from the present investigation tries to identify a few potential HSK inhibitors that possess important features to act as efficient HSK inhibitors. These compounds can be considered an effective starting point for the identification of novel drug-like scaffolds. We hope the structural wealth that is offered in this report will be utilized in designing competent experimental and therapeutic interventions for leishmaniasis management. Graphical abstract.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route.
METHODS: Immunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis.
RESULTS: We found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC50 values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (- 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors.
CONCLUSIONS: Our system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.

Regulation of protein synthesis is an essential step of gene expression. This process is under the control of cis-acting RNA elements and trans-acting factors. Gemin5 is a multifunctional RNA-binding protein organized in distinct domains. The protein bears a non-canonical RNA-binding site, designated RBS1, at the C-terminal end. Among other cellular RNAs, the RBS1 region recognizes a sequence located within the coding region of Gemin5 mRNA, termed H12. Expression of RBS1 stimulates translation of RNA reporters carrying the H12 sequence, counteracting the negative effect of Gemin5 on global protein synthesis. A computational analysis of RBS1 protein and H12 RNA variability across the evolutionary scale predicts coevolving pairs of amino acids and nucleotides. RBS1 footprint and gel-shift assays indicated a positive correlation between the identified coevolving pairs and RNA-protein interaction. The coevolving residues of RBS1 contribute to the recognition of stem-loop SL1, an RNA structural element of H12 that contains the coevolving nucleotides. Indeed, RBS1 proteins carrying substitutions on the coevolving residues P1297 or S1299S1300, drastically reduced SL1-binding. Unlike the wild type RBS1 protein, expression of these mutant proteins in cells failed to enhance translation stimulation of mRNA reporters carrying the H12 sequence. Therefore, the PXSS motif within the RBS1 domain of Gemin5 and the RNA structural motif SL1 of its mRNA appears to play a key role in fine-tuning the expression level of this essential protein.

Genomic data sets are increasingly central to ecological and evolutionary biology, but far fewer resources are available for invertebrates. Powerful new computational tools and the rapidly decreasing cost of Illumina sequencing are beginning to change this, enabling rapid genome assembly and reference marker extraction. We have developed and tested a practical workflow for developing genomic resources in nonmodel groups with real-world data on Collembola (springtails), one of the most dominant soil animals on Earth. We designed universal molecular marker sets, single-copy orthologues (BUSCOs) and ultraconserved elements (UCEs), using three existing and 11 newly generated genomes. Both marker types were tested in silico via marker capture success and phylogenetic performance. The new genomes were assembled with Illumina short reads and 9,585-14,743 protein-coding genes were predicted with ab initio and protein homology evidence. We identified 1,997 benchmarking universal single-copy orthologues (BUSCOs) across 14 genomes and created and assessed a custom BUSCO data set for extracting single-copy genes. We also developed a new UCE probe set containing 46,087 baits targeting 1,885 loci. We successfully captured 1,437-1,865 BUSCOs and 975-1,186 UCEs across 14 genomes. Phylogenomic reconstructions using these markers proved robust, giving new insight on deep-time collembolan relationships. Our study demonstrates the feasibility of generating thousands of universal markers from highly efficient whole-genome sequencing, providing a valuable resource for genome-scale investigations in evolutionary biology and ecology.