Table1_Population genetic analyses unveiled genetic stratification and differential natural selection signatures across the G-gene of viral hemorrhagic septicemia virus.XLSX

Introduction: Viral hemorrhagic septicemia virus (VHSV) is the most lethal pathogen in aquaculture, infecting more than 140 fish species in marine, estuarine, and freshwater environments. Viral hemorrhagic septicemia virus is an enveloped RNA virus that belongs to the family Rhabdoviridae and the genus Novirhabdovirus. The current study is designed to infer the worldwide Viral hemorrhagic septicemia virus isolates’ genetic diversity and evolutionary dynamics based on G-gene sequences.Methods: The complete G-gene sequences of viral hemorrhagic septicemia virus were retrieved from the public repositories with known timing and geography details. Pairwise statistical analysis was performed using Arlequin. The Bayesian model-based approach implemented in STRUCTURE software was used to investigate the population genetic structure, and the phylogenetic tree was constructed using MEGA X and IQ-TREE. The natural selection analysis was assessed using different statistical approaches, including IFEL, MEME, and SLAC.Results and Discussion: The global Viral hemorrhagic septicemia virus samples are stratified into five genetically distinct subpopulations. The STRUCTURE analysis unveiled spatial clustering of genotype Ia into two distinct clusters at K = 3. However, at K = 5, the genotype Ia samples, deposited from Denmark, showed temporal distribution into two groups. The analyses unveiled that the genotype Ia samples stratified into subpopulations possibly based on spatiotemporal distribution. Several viral hemorrhagic septicemia virus samples are characterized as genetically admixed or recombinant. In addition, differential or subpopulation cluster-specific natural selection signatures were identified across the G-gene codon sites among the viral hemorrhagic septicemia virus isolates. Evidence of low recombination events elucidates that genetic mutations and positive selection events have possibly driven the observed genetic stratification of viral hemorrhagic septicemia virus samples.</p

Image1_Population genetic analyses unveiled genetic stratification and differential natural selection signatures across the G-gene of viral hemorrhagic septicemia virus.JPEG

Introduction: Viral hemorrhagic septicemia virus (VHSV) is the most lethal pathogen in aquaculture, infecting more than 140 fish species in marine, estuarine, and freshwater environments. Viral hemorrhagic septicemia virus is an enveloped RNA virus that belongs to the family Rhabdoviridae and the genus Novirhabdovirus. The current study is designed to infer the worldwide Viral hemorrhagic septicemia virus isolates’ genetic diversity and evolutionary dynamics based on G-gene sequences.Methods: The complete G-gene sequences of viral hemorrhagic septicemia virus were retrieved from the public repositories with known timing and geography details. Pairwise statistical analysis was performed using Arlequin. The Bayesian model-based approach implemented in STRUCTURE software was used to investigate the population genetic structure, and the phylogenetic tree was constructed using MEGA X and IQ-TREE. The natural selection analysis was assessed using different statistical approaches, including IFEL, MEME, and SLAC.Results and Discussion: The global Viral hemorrhagic septicemia virus samples are stratified into five genetically distinct subpopulations. The STRUCTURE analysis unveiled spatial clustering of genotype Ia into two distinct clusters at K = 3. However, at K = 5, the genotype Ia samples, deposited from Denmark, showed temporal distribution into two groups. The analyses unveiled that the genotype Ia samples stratified into subpopulations possibly based on spatiotemporal distribution. Several viral hemorrhagic septicemia virus samples are characterized as genetically admixed or recombinant. In addition, differential or subpopulation cluster-specific natural selection signatures were identified across the G-gene codon sites among the viral hemorrhagic septicemia virus isolates. Evidence of low recombination events elucidates that genetic mutations and positive selection events have possibly driven the observed genetic stratification of viral hemorrhagic septicemia virus samples.</p

MOESM1 of Population genetic structure of domain I of apical membrane antigen-1 in Plasmodium falciparum isolates from Hazara division of Pakistan

Additional file 1: The NCBI accession numbers of pfama1 sequences of P. falciparum global populations used during comparative sequence analysis with PKH samples. PNG is Papua New Guinea

The genetic composition of Shina population from Gilgit-Baltistan, Pakistan based on mtDNA analyses

The present study aimed to gain insight into the genetic origin of the Shina population from Gilgit-Baltistan, Pakistan. We partially performed the mitochondrial DNA (mtDNA) control region of healthy unrelated individuals of Shina tribe residing in the remote area of Gilgit-Baltistan to investigate their maternal lineages. The present study is the first report about Shina’s genetic structure, origin, and relationship with the surrounding north-western Pakistani population. The mtDNA sequences of the Shina samples were compared with the revised Cambridge Reference Sequence (rCRS) and the HVR-1 D-loop region was covered. The comparison with rCRS identified overall 38 haplotypes and 08 haplogroups for Shina samples. Among these haplotypes, 18 were shared by more than one individual of the Shina tribe. The obtained mtDNA sequences of Shina were compared with surrounding north-western Pakistani population groups, i.e. Kho, Kashmiri, and Pathan. The genetic diversity (i.e. 1.0424) and power of discrimination (i.e. 0.9266) of the Shina was found equivalent to surrounding north-western groups. The haplogroups frequencies, phylogenetic tree and network analysis identified the west Eurasian ancestral origin of Shina group with nearby maternal ancestral relationships with the Kashmiri population. However, no close genetic relationship of Shina was depicted with nearby residing Kho population group.</p

From top to bottom.

The biological processes in which the genes are taking part include the negative regulation of cellular protein catabolic process (GO:1903363) and the regulation of proteolysis involved in cellular protein catabolic process (GO:1903050). These genes are involved in symbiotic process, regulation of cellular protein catabolic process, interspecies interactions between organisms, viral process, regulation of proteolysis, and several other important biological processes. The central interactive graph shows the molecular functions of the genes. These genes play important role in RNA binding (GO:0003723), drug binding (GO:0008144), and small molecule binding (GO:0036094). Other molecular functions of the genes in the gene set include ATP binding, purine ribonucleoside triphosphate binding, heterocyclic compound binding, and organic cyclic compound binding. The third graph shows that the genes were located in the vesicle, nucleus, cytosol, intracellular organelle lumen, membrane-enclosed lumen, extracellular exosome, and other cellular components.</p

S1 Graphical abstract -

Dengue Virus (DENV) is a serious threat to human life worldwide and is one of the most dangerous vector-borne diseases, causing thousands of deaths annually. We constructed a comprehensive PPI map of DENV with its host Homo sapiens and performed various bioinformatics analyses. We found 1195 interactions between 858 human and 10 DENV proteins. Pathway enrichment analysis was performed on the two sets of gene products, and the top 5 human proteins with the maximum number of interactions with dengue viral proteins revealed noticeable results. The non-structural protein NS1 in DENV had the maximum number of interactions with the host protein, followed by NS5 and NS3. Among the human proteins, HBA1 and UBE2I were associated with 7 viral proteins, and 3 human proteins (CSNK2A1, RRP12, and HSP90AB1) were found to interact with 6 viral proteins. Pharmacophore-based virtual screening of millions of compounds in the public databases was performed to identify potential DENV-NS1 inhibitors. The lead compounds were selected based on RMSD values, docking scores, and strong binding affinities. The top ten hit compounds were subjected to ADME profiling which identified compounds C2 (MolPort-044-180-163) and C6 (MolPort-001-742-737) as lead inhibitors against DENV-NS1. Molecular dynamics trajectory analysis and intermolecular interactions between NS1 and the ligands displayed the molecular stability of the complexes in the cellular environment. The in-silico approaches used in this study could pave the way for the development of potential specie-specific drugs and help in eliminating deadly viral infections. Therefore, experimental and clinical assays are required to validate the results of this study.</div

KEGG pathway enrichment analysis of the top 30 highly DENV-associated human proteins.

The bar chart shows that the proteins are highly enriched in protein export pathway (enrichment ratio: 0.04). Other pathways include prion diseases, thyroid hormone synthesis, african trypanosmiasis, NF-kappa B signaling pathway and several other disease pathways including measles, malaria and primary immunodeficiency. Excelsheet S1 containing all the interacting proteins of human and DENV proteins. (PNG)</p

Binding energy decomposition analysis of bound- DENV-NS1 protein with novel drug candidates using the MMGBSA approach.

Binding energy decomposition analysis of bound- DENV-NS1 protein with novel drug candidates using the MMGBSA approach.</p

Top lead compounds docking in the active site of DENV-NS1.

(A) Pharmacophore model designed for the DENV-NS1 and molecular interactions of top hit compounds according to ADME analysis with the binding pocket of NS1. (B) Molecular interactions of compound C2 with DENV-NS1. (C) Molecular interactions of compound C6 with DENV-NS1.</p

List of human proteins with greatest number of associations with DENV proteins.

List of human proteins with greatest number of associations with DENV proteins.</p