Global analysis of urinary extracellular vesicle small RNAs in autosomal dominant polycystic kidney disease.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is the most prevalent monogenic renal disease progressing to end-stage renal disease. There is a pressing need for the identification of early ADPKD biomarkers to enable timely intervention and the development of effective therapeutic approaches. Here, we profiled human urinary extracellular vesicles small RNAs by small RNA sequencing in patients with ADPKD and compared their differential expression considering healthy control individuals to identify dysregulated small RNAs and analyze downstream interaction to gain insight about molecular pathophysiology. METHODS: This is a cross-sectional study where urine samples were collected from a total of 23 PKD1-ADPKD patients and 28 healthy individuals. Urinary extracellular vesicles were purified, and small RNA was isolated and sequenced. Differentially expressed Small RNA were identified and functional enrichment analysis of the critical miRNAs was performed to identify driver genes and affected pathways. RESULTS: miR-320b, miR-320c, miR-146a-5p, miR-199b-3p, miR-671-5p, miR-1246, miR-8485, miR-3656, has_piR_020497, has_piR_020496 and has_piR_016271 were significantly upregulated in ADPKD patient urine extracellular vesicles and miRNA-29c was significantly downregulated. Five 'driver' target genes (FBRS, EDC3, FMNL3, CTNNBIP1 and KMT2A) were identified. CONCLUSIONS: The findings of the present study make significant contributions to the understanding of ADPKD pathogenesis and to the identification of novel biomarkers and potential drug targets aimed at slowing disease progression in ADPKD.

Network medicine in ovarian cancer: topological properties to drug discovery.

Network medicine provides network theoretical tools, methods and properties to study underlying laws governing human interactome to identify disease states and disease complexity leading to drug discovery. Within this framework, we investigated the topological properties of ovarian cancer network (OCN) and the roles of hubs to understand OCN organization to address disease states and complexity. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties with deeply rooted functional communities indicating self-organizing behavior. The network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We showed that $\langle k\rangle $ can be taken as a scaling parameter, where, power law exponent can be estimated from the ratio of network diameters. The betweenness centrality $C_B$ shows two distinct behaviors one shown by high degree hubs and the other by segregated low degree nodes. The $C_B$ power law exponent is found to connect the exponents of distributions of high and low degree nodes. OCN showed the absence of rich-club formation which leads to the missing of a number of attractors in the network causing formation of weakly tied diverse functional modules to keep optimal network efficiency. In OCN, provincial and connector hubs, which includes identified key regulators, take major responsibility to keep the OCN integrity and organization. Further, most of the key regulators are found to be over expressed and positively correlated with immune infiltrates. Finally, few potential drugs are identified related to the key regulators.

Plasmodium falciparum cysteine protease Falcipain 3: A potential enzyme for proteolytic processing of histone acetyltransferase PfGCN5.

In spite of 150 years of studying malaria, the unique features of the malarial parasite, Plasmodium, still perplex researchers. One of the methods by which the parasite manages its gene expression is epigenetic regulation, the champion of which is PfGCN5, an essential enzyme responsible for acetylating histone proteins. PfGCN5 is a ∼170 kDa chromatin-remodeling enzyme that harbors the conserved bromodomain and acetyltransferase domain situated in its C-terminus domain. Although the PfGCN5 proteolytic processing is essential for its activity, the specific protease involved in this process still remains elusive. Identification of PfGCN5 interacting proteins through immunoprecipitation (IP) followed by LC-tandem mass spectrometry analysis revealed the presence of food vacuolar proteins, such as the cysteine protease Falcipain 3 (FP3), in addition to the typical members of the PfGCN5 complex. The direct interaction between FP3 and PfGCN5 was further validated by in vitro pull-down assay as well as IP assay. Subsequently, use of cysteine protease inhibitor E64d led to the inhibition of protease-specific processing of PfGCN5 with concomitant enrichment and co-localization of PfGCN5 and FP3 around the food vacuole as evidenced by confocal microscopy as well as electron microscopy. Remarkably, the proteolytic cleavage of the nuclear protein PfGCN5 by food vacuolar protease FP3 is exceptional and atypical in eukaryotic organisms. Targeting the proteolytic processing of GCN5 and the associated protease FP3 could provide a novel approach for drug development aimed at addressing the growing resistance of parasites to current antimalarial drugs.

Plasma Virome of HIV-infected Subjects on Suppressive Antiretroviral Therapy Reveals Association of Differentially Abundant Viruses with Distinct T-cell Phenotypes and Inflammation.

Background: The plasma virome represents the overall composition of viral sequences present in it. Alteration in plasma virome has been reported in treatment naïve and immunocompromised (CD4 count < 200) people with HIV (PWH). However, the effect of ART on virome composition in PWH on ART with preserved CD4 counts is poorly understood. Objectives: We aimed to assess the alterations in plasma virome in PWH on ART in comparison to HIV-negative uninfected controls and to further investigate possible associations of plasma viruses with inflammation and immune dysfunction, namely, immunosenescence and immune exhaustion. Methods: Plasma viral DNA from PWH on ART and controls was used for sequencing on the Illumina Nextseq500 platform, followed by the identification of viral sequences using an automated pipeline, VIROMATCH. Multiplex cytokine assay was performed to measure the concentrations of various cytokines in plasma. Immunophenotyping was performed on PBMCs to identify T cell markers of immunosenescence and immune exhaustion. Results: In our observational, cross-sectional pilot study, chronically infected PWH on ART had significantly different viral species compositions compared to controls. The plasma virome of PWH showed a significantly high relative abundance of species Human gammaherpesvirus 4, also known as Epstein-Barr virus (EBV). Moreover, EBV emerged as a significant viral taxon differentially enriched in PWH on ART, which further correlated positively with the exhaustion phenotype of T cells and significantly increased TNF-α in PWH on ART. Additionally, a significantly increased proportion of senescent T cells and IL-8 cytokine was detected in PWH on ART. Conclusion: Altered plasma virome influenced the inflammatory response and T-cell phenotype in PWH on ART.

A Comprehensive Pilot Study to Elucidate the Distinct Gut Microbial Composition and Its Functional Significance in Cardio-Metabolic Disease.

Cardio-metabolic disease is a significant global health challenge with increasing prevalence. Recent research underscores the disruption of gut microbial balance as a key factor in disease susceptibility. We aimed to characterize the gut microbiota composition and function in cardio-metabolic disease and healthy controls. For this purpose, we collected stool samples of 18 subjects (12 diseased, 6 healthy) and we performed metagenomics analysis and functional prediction using QIIME2 and PICRUSt. Furthermore, we carried out assessments of microbe-gene interactions, gene ontology, and microbe-disease associations. Our findings revealed distinct microbial patterns in the diseased group, particularly evident in lower taxonomic levels with significant variations in 14 microbial features. The diseased cohort exhibited an enrichment of Lachnospiraceae family, correlating with obesity, insulin resistance, and metabolic disturbances. Conversely, reduced levels of Clostridium, Gemmiger, and Ruminococcus genera indicated a potential inflammatory state, linked to compromised butyrate production and gut permeability. Functional analyses highlighted dysregulated pathways in amino acid metabolism and energy equilibrium, with perturbations correlating with elevated branch-chain amino acid levels-a known contributor to insulin resistance and type 2 diabetes. These findings were consistent across biomarker assessments, microbe-gene associations, and gene ontology analyses, emphasizing the intricate interplay between gut microbial dysbiosis and cardio-metabolic disease progression. In conclusion, our study unveils significant shifts in gut microbial composition and function in cardio-metabolic disease, emphasizing the broader implications of microbial dysregulation. Addressing gut microbial balance emerges as a crucial therapeutic target in managing cardio-metabolic disease burden.

Comprehensive multiomics and in silico approach uncovers prognostic, immunological, and therapeutic roles of ANLN in lung adenocarcinoma.

The anillin actin-binding protein (ANLN) is immensely overexpressed in cancers, including lung cancer (LC). Phytocompounds have gained interest due to their broader potential and reduced unwanted effects. Screening numerous compounds presents a challenge, but in silico molecular docking is pragmatic. The present study aims to identify the role of ANLN in lung adenocarcinoma (LUAD), along with identification and interaction analysis of anticancer and ANLN inhibitory phytocompounds followed by molecular dynamics (MD) simulation. Using a systematic approach, we found that ANLN is significantly overexpressed in LUAD and mutated with a frequency of 3.73%. It is linked with advanced stages, clinicopathological parameters, worsening of relapse-free survival (RFS), and overall survival (OS), pinpointing its oncogenic and prognostic potential. High-throughput screening and molecular docking of phytocompounds revealed that kaempferol (flavonoid aglycone) interacts strongly with the active site of ANLN protein via hydrogen bonds, Vander Waals interactions, and acts as a potent inhibitor. Furthermore, we discovered that ANLN expression was found to be significantly higher (p) in LC cells compared to normal cells. This is a propitious and first study to demonstrate ANLN and kaempferol interactions, which might eventually lead to removal of rout from cell cycle regulation posed by ANLN overexpression and allow it to resume normal processes of proliferation. Overall, this approach suggested a plausible biomarker role of ANLN and the combination of molecular docking subsequently led to the identification of contemporary phytocompounds, bearing symbolic anticancer effects. The findings would be advantageous for pharmaceutics but require validation using in vitro and in vivo methods. HIGHLIGHTS: • ANLN is significantly overexpressed in LUAD. • ANLN is implicated in the infiltration of TAMs and altering plasticity of TME. • Kaempferol (potential ANLN inhibitor) shows important interactions with ANLN which could remove the alterations in cell cycle regulation, imposed by ANLN overexpression eventually leading to normal process of cell proliferation.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach.

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach.

The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral-host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of -5.09 kcal/mol (CTSL) to -26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: -7.59 to -37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.

Network pharmacology and bioinformatics approach reveals the therapeutic mechanism of action of curcumin in Alzheimer disease.

Curcumin is a natural anti-inflammatory and antioxidant substance which plays a major role in reducing the amyloid plaques formation, which is the major cause of Alzheimer's disease (AD). Consequently, a methodical approach was used to select the potential protein targets of curcumin in AD through network pharmacology. In this study, through integrative methods, AD targets of curcumin through SwissTargetPrediction database, STITCH database, BindingDB, PharmMapper, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM) database were predicted followed by gene enrichment analysis, network construction, network topology, and docking studies. Gene ontology analysis facilitated identification of a list of possible AD targets of curcumin (74 targets genes). The correlation of the obtained targets with AD was analysed by using gene ontology (GO) pathway enrichment analyses and Kyoto Encyclopaedia of Genes and Genomes (KEGG). We have incorporated the applied network pharmacological approach to identify key genes. Furthermore, we have performed molecular docking for analysing the mechanism of curcumin. In order to validate the temporospatial expression of key genes in human central nervous system (CNS), we searched the Human Brain Transcriptome (HBT) dataset. We identified top five key genes namely, PPARγ, MAPK1, STAT3, KDR and APP. Further validated the expression profiling of these key genes in publicly available brain data expression profile databases. In context to a valuable addition in the treatment of AD, this study is concluded with novel insights into the therapeutic mechanisms of curcumin, will ease the treatment of AD with the clinical application of curcumin.

Oxidative Products of Curcumin Rather Than Curcumin Bind to Helicobacter Pylori Virulence Factor VacA and Are Required to Inhibit Its Vacuolation Activity.

Curcumin is a hydrophobic polyphenol derived from turmeric with potent anti-oxidant, anti-microbial, anti-inflammatory and anti-carcinogenic effects. Curcumin is degraded into various derivatives under in vitro and in vivo conditions, and it appears that its degradation may be responsible for the pharmacological effects of curcumin. The primary risk factor for the cause of gastric cancer is Helicobacter pylori (H. pylori). A virulence factor vacuolating cytotoxic A (VacA) is secreted by H. pylori as a 88 kDa monomer (p88), which can be fragmented into a 33 kDa N-terminal domain (p33) and a 55 kDa C-terminal domain (p55). Recently it has been reported that curcumin oxidation is required to inhibit the activity of another major H.pylori toxin CagA. We performed molecular docking of curcumin and its oxidative derivatives with p33 and p55 domains of VacA. Further, we have examined the effect of the oxidation of curcumin on the vacuolation activity of VacA protein. We observed the binding of curcumin to the p55 domain of VacA at five different sites with moderate binding affinities. Curcumin did not bind to p33 domain of VacA. Remarkably, cyclobutyl cyclopentadione and dihydroxy cyclopentadione, which are oxidized products of curcumin, showed a higher binding affinity with VacA protein at all sites except one as compared to parent curcumin itself. However, cyclobutyl cyclopentadione showed a significant binding affinity for the active site 5 of the p55 protein. Active site five (312-422) of p55 domain of VacA plays a crucial role in VacA-mediated vacuole formation. Invitro experiments showed that curcumin inhibited the vacuolation activity of H. pylori in human gastric cell line AGS cells whereas acetyl and diacetyl curcumin, which cannot be oxidized, failed to inhibit the vacuolation in AGS cells after H. pylori infection. Here our data showed that oxidation is essential for the activity of curcumin in inhibiting the vacuolation activity of H. pylori. Synthesis of these oxidized curcumin derivatives could potentially provide new therapeutic drug molecules for inhibiting H. pylori-mediated pathogenesis.

Diversity of SARS-CoV-2 isolates driven by pressure and health index.

One of the main concerns about the fast spreading coronavirus disease 2019 (Covid-19) pandemic is how to intervene. We analysed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) isolates data using the multifractal approach and found a rich in viral genome diversity, which could be one of the root causes of the fast Covid-19 pandemic and is strongly affected by pressure and health index of the hosts inhabited regions. The calculated mutation rate (mr) is observed to be maximum at a particular pressure, beyond which mr maintains diversity. Hurst exponent and fractal dimension are found to be optimal at a critical pressure (Pm), whereas, for P > Pm and P < Pm, we found rich genome diversity relating to complicated genome organisation and virulence of the virus. The values of these complexity measurement parameters are found to be increased linearly with health index values.

Kinless hubs are potential target genes in prostate cancer network.

Complex disease networks can be studied successfully using network theoretical approach which helps in finding key disease genes and associated disease modules. We studied prostate cancer (PCa) protein-protein interaction (PPI) network constructed from patients' gene expression datasets and found that the network exhibits hierarchical scale free topology which lacks centrality lethality rule. Knockout experiments of the sets of leading hubs from the network leads to transition from hierarchical (HN) to scale free (SF) topology affecting network integration and organization. This transition, HN â†’ SF, due to removal of significant number of the highest degree hubs, leads to relatively decrease in information processing efficiency, cost effectiveness of signal propagation, compactness, clustering of nodes and energy distributions. A systematic transition from a diassortative PCa PPI network to assortative networks after the removal of top 50 hubs then again reverting to disassortativity nature on further removal of the hubs was also observed indicating the dominance of the largest hubs in PCa network intergration. Further, functional classification of the hubs done by using within module degrees and participation coefficients for PCa network, and leading hubs knockout experiments indicated that kinless hubs serve as the basis of establishing links among constituting modules and heterogeneous nodes to maintain network stabilization. We, then, checked the essentiality of the hubs in the knockout experiment by performing Fisher's exact test on the hubs, and showed that removal of kinless hubs corresponded to maximum lethality in the network. However, excess removal of these hubs essentially may cause network breakdown.

Molecular crosstalk: Notch can manipulate Hes1 and miR-9 behavior.

We propose a Hes1-Notch-miR-9 regulatory network and studied the regulating mechanism of miR-9 and Hes1 dynamics driven by Notch. Change in Notch concentration, which serves as a stress signal, can trigger the dynamics of Hes1 and miR-9 at five different states, namely, sTable (2), sustain (1) and mixed (2) states those may correspond to different cellular states. Further, this Notch stress signal introduce time reversal oscillation, which behaves as backward wave, after a certain threshold value of the stress signal and defends the system from moving to apoptosis. We also observe heterogeneous patterns of Hes1, miR-9 and other molecular species in various two dimensional parameter spaces and found that the variability in the patterns is triggered by Hill coefficient and Hes1 stress signal. The phase or bifurcation diagram in time period of oscillation (TN) driven by Notch signal provides all five states, predicts minimum threshold value TNc beyond which tendency to build up backward wave starts and TNc serves as bifurcation point of the system.

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach.

BACKGROUND: Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. METHODS: The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. RESULTS: The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. CONCLUSIONS: The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

Organization in complex brain networks: Energy distributions and phase shift.

The Hamiltonian function of a network, derived from the intrinsic distributions of nodes and edges, magnified by resolution parameter has information on the distribution of energy in the network. In brain networks, the Hamiltonian function follows hierarchical features reflecting a power-law behavior which can be a signature of self-organization. Further, the transition of three distinct phases driven by resolution parameter is observed which could correspond to various important brain states. This resolution parameter could thus reflect a key parameter that controls and balances the energy distribution in the brain network.

In Vitro and In Silico Evaluation of Betulin on Calcium Oxalate Crystal Formation.

Objective: The medicinal plant Betula alba has been used for prevention and treatment of kidney stones. Betulin is one of the main phytochemicals of Betula alba. The aim of this study is to investigate the antioxidant and antiurolithiatic activity of betulin in vitro and in silico. For antioxidant activity, 2, 2-diphenyl-1-picrylhydrazyl (DPPH), total reducing capacity, nitric oxide (NO) radical scavenging assay, and superoxide radical scavenging assay were studied. Method: In order to study antiurolithiatic activity, three assays such as crystallization, nucleation, and aggregation of oxalate crystal in urine were performed. In silico experiments were performed by using AutoDock 4.2 tools in order to establish affinity of phytochemicals toward antioxidant enzyme and matrix metalloproteinase (MMP-2 and 9). Results: The results obtained clearly demonstrate the significant scavenging activity of betulin and cystone against DPPH, NO, and superoxide radicals in comparison to standard antioxidant L-ascorbate (L-AA). It has also been observed that betulin has the capacity to inhibit the crystallization, nucleation, and aggregation in comparison to cystone. On the other hand, betulin and L-AA showed strong affinity toward antioxidant enzymes and matrix metalloproteinase as determined by in silico experiments. Conclusions: From this, it may be concluded that the antiurolithiatic activity of betulin is, at least in part, mediated by its antioxidant property.

Fractal rules in brain networks: Signatures of self-organization.

We study brain network data of three species, namely, C. elegans, cat and macaque monkey within the framework of network theory and Potts Hamiltonian model, and explore rich fractal nature in it, which could be an important signature of self-organization, and a simple rule to be obeyed in complex patterns of brain networks. Further, this fractal behaviors in topological parameters of brain networks at various network levels could be an indicator of systems level organization in complicated brain functionality. Again, Rich-club formation of leading hubs in brain networks becomes unpredictable as one goes down to different levels of organization. The popularity of these leading hubs in main modules or sub-modules also gets changed at different network levels, with varied attitudes at each level. Moreover, distribution of edges, which involves intra- and inter-modular/sub-modular interactions, inherited from one level of organization to another level follows fractal law. In addition to this, the Hamiltonian function at each network level, which may correspond to the energy cost in network organization at that level, shows fractal nature. Significant motifs, which are building blocks of networks and related to basic functionalities, in brain networks is found to be triangular motif, and its probability distribution at various levels as a function of size of modules or sub-modules follows fractal law.

From ZikV genome to vaccine: in silico approach for the epitope-based peptide vaccine against Zika virus envelope glycoprotein.

Zika virus (ZikV) has emerged as a potential threat to human health worldwide. A member of the Flaviviridae, ZikV is transmitted to humans by mosquitoes. It is related to other pathogenic vector-borne flaviviruses including dengue, West Nile and Japanese encephalitis viruses, but produces a comparatively mild disease in humans. As a result of its epidemic outbreak and the lack of potential medication, there is a need for improved vaccine/drugs. Computational techniques will provide further information about this virus. Comparative analysis of ZikV genomes should lead to the identification of the core characteristics that define a virus family, as well as its unique properties, while phylogenetic analysis will show the evolutionary relationships and provide clues about the protein's ancestry. Envelope glycoprotein of ZikV was obtained from a protein database and the most immunogenic epitope for T cells and B cells involved in cell-mediated immunity, whereas B cells are primarily responsible for humoral immunity. We mainly focused on MHC class I potential peptides. YRIMLSVHG, VLIFLSTAV and MMLELDPPF, GLDFSDLYY are the most potent peptides predicted as epitopes for CD4+ and CD8+ T cells, respectively, whereas MMLELDPPF and GLDFSDLYY had the highest pMHC-I immunogenicity score and these are further tested for interaction against the HLA molecules, using in silico docking techniques to verify the binding cleft epitope. However, this is an introductory approach to design an epitope-based peptide vaccine against ZikV; we hope that this model will be helpful in designing and predicting novel vaccine candidates.

Evaluation of HLA typing content of next-generation sequencing datasets from family trios and individuals of arab ethnicity.

Introduction: HLA typing is a critical tool in both clinical and research applications at the individual and population levels. Benchmarking studies have indicated HLA-HD as the preferred tool for accurate and comprehensive HLA allele calling. The advent of next-generation sequencing (NGS) has revolutionized genetic analysis by providing high-throughput sequencing data. This study aims to evaluate, using the HLA-HD tool, the HLA typing content of whole exome, whole genome, and HLA-targeted panel sequence data from the consanguineous population of Arab ethnicity, which has been underrepresented in prior benchmarking studies. Methods: We utilized sequence data from family trios and individuals, sequenced on one or more of the whole exome, whole genome, and HLA-targeted panel sequencing technologies. The performance and resolution across various HLA genes were evaluated. We incorporated a comparative quality control analysis, assessing the results obtained from HLA-HD by comparing them with those from the HLA-Twin tool to authenticate the accuracy of the findings. Results: Our analysis found that alleles across 29 HLA loci can be successfully and consistently typed from NGS datasets. Clinical-grade whole exome sequencing datasets achieved the highest consistency rate at three-field resolution, followed by targeted HLA panel, research-grade whole exome, and whole genome datasets. Discussion: The study catalogues HLA typing consistency across NGS datasets for a large array of HLA genes and highlights assessments regarding the feasibility of utilizing available NGS datasets in HLA allele studies. These findings underscore the reliability of HLA-HD for HLA typing in underrepresented populations and demonstrate the utility of various NGS technologies in achieving accurate HLA allele calling.