Search Results (67)

Background: Malaria remains a significant global health burden, particularly in sub-Saharan Africa, accounting for high rates of illness and death. The growing resistance to frontline antimalarial therapies underscores the urgent need for novel drug targets and therapeutics. Bromodomain-containing proteins, which regulate gene expression through chromatin remodeling, have gained attention as potential targets. Plasmodium falciparum bromodomain protein 1 (PfBDP1), a 55 kDa nuclear protein, plays a key role in recognizing acetylated lysine residues and facilitating transcription during parasite development. Methods: This study investigated ex vivo PfBDP1 gene mutations and identified potential small molecule inhibitors using computational approaches. Malaria-positive blood samples were collected. Genomic DNA was extracted, assessed for quality, and amplified using PfBDP1-specific primers. DNA sequencing and alignment were performed to determine single-nucleotide polymorphism (SNP). Structural modeling used the PfBDP1 crystal structure (PDB ID: 7M97), and active site identification was conducted using CASTp 3.0. Virtual screening and pharmacophore modeling were performed using Pharmit and AutoDock Vina, followed by ADME/toxicity evaluations with SwissADME, OSIRIS, and Discovery Studio. GROMACS was used for 100 ns molecular dynamics simulations. Results: The malaria prevalence rate stood at 12.24%, and the sample size was 165. Sequencing results revealed conserved PfBDP1 gene sequences compared to the 3D7 reference strain. Virtual screening identified nine lead compounds with binding affinities ranging from −9.8 to −10.7 kcal/mol. Of these, CHEMBL2216838 had a binding affinity of −9.9 kcal/mol, with post-screening predictions of favorable drug-likeness (8.60), a high drug score (0.78), superior pharmacokinetics, and a low toxicity profile compared to chloroquine. Molecular dynamics simulations confirmed its stable interaction within the PfBDP1 active site. Conclusions: Overall, this study makes a significant contribution to the ongoing search for novel antimalarial drug targets by providing both molecular and computational evidence for PfBDP1 as a promising therapeutic target. The prediction of CHEMBL2216838 as a lead compound with favorable binding affinity, drug-likeness, and safety profile, surpassing those of existing drugs like chloroquine, sets the stage for preclinical validation and further structure-based drug design efforts. These findings are supported by prior experimental evidence showing significant parasite inhibition and gene suppression capability of predicted hits. Full article

Background: In Staphylococcus aureus, antimicrobial resistance (AMR) imposes significant fitness costs (FCs), including reduced growth rate, interbacterial competitiveness, and virulence. However, the FC molecular basis remains poorly understood. This study investigated the FC omic basis and compensatory adaptations in high-risk HA-, LA-, and CA-MRSA, acquiring mono- or cross-resistance to second-line daptomycin (DAP) and dalbavancin (DAL), as well as reduced susceptibility (RS) to first-line glycopeptides, i.e., vancomycin and teicoplanin (GLYs, i.e., VAN, TEC), related to the specific mechanism of action (MOA)-related AMR-mechanisms and genomic backgrounds, paying increasing FCs. Methods: The FC omic basis associated with mono- or cross- DAP-/DAL-R and GLY-RS were investigated by integrated omics. This study focused on core-genome essential (EG) and accessory virulence gene (VG) SNPomics and transcriptomics by Illumina MiSeq whole-genome sequencing, RNA-seq, and bioinformatic analysis. Results: Moderate impact nsSNPs were identified in EGs related to vital cellular functions and VGs. Comparative EG transcriptomics revealed differential expressions and key dysregulations—via asRNAs—prevalently affecting the protein synthesis and cell-envelope EG clusters, as well as the VG cluster. Conclusions: Our data, firstly, underlined the EG and VG mutation- and transcription-driven omic-based FC burden and the compensatory adaptations associated with the emergence of mono-DAP-R, cross-DAP-R/hGISA, and DAP-R/DAL-R/GISA, linked to specific MOA-related AMR-mechanisms and genomic backgrounds in high-risk HA-, LA-, and CA-MRSA. Full article

Background: The Jinwu pig is a novel breed created by crossbreeding Jinhua and Duroc pigs, displaying superior meat quality, strong adaptability to coarse feed, high production performance, and a rapid growth rate. However, research on its reproductive traits and genomic characteristics has not been systematically reported. Methods: In this study, we investigated the genetic basis of reproductive traits in Jinwu pigs us-ing a genome-wide association study. We analyzed 2831 breeding records from 516 Jinwu sows to evaluate the effects of fixed factors (farrowing season, parity, and mated boar) on six reproductive traits: the total number of births (TNB), number born alive (NBA), number of healthy offspring produced (NHOP), weak litter size (WLS), number of stillbirths (NS), and number of mummies (NM). Results: A total of 771 genome-wide significant single-nucleotide polymorphisms (SNPs) and ten potential candidate genes associated with pig reproductive traits were identified: VOPP1, PGAM2, TNS3, LRFN5, ORC1, CC2D1B, ZFYYE9, TUT4, DCN, and FEZF1. TT-genotype-carrier individuals of the pleiotropic SNP rs326174997 exhibited significantly higher TNB, NBA, and NHOP trait-related phenotypic values. Conclusions: These findings provide a foundation for the reproductive breeding of Jinwu pigs and offer new insights into molecular genetic breeding in pigs. Full article

Background: Insecticide resistance challenges the vector control efforts towards malaria elimination and proving the development of complementary tools. Targeting the genes that are involved in mosquito fertility and susceptibility to Plasmodium with small molecule inhibitors has been a promising alternative to curb the vector population and drive the transmission down. However, such an approach would require a comprehensive knowledge of the genetic diversity of the targeted genes to ensure the broad efficacy of new tools across the natural vector populations. Methods: Four fertility and parasite susceptibility genes were identified from a systematic review of the literature. The Single Nucleotide Polymorphisms (SNPs) found within the regions spanned by these four genes, genotyped across 2784 wild-caught Anopheles gambiae s.l. from 19 sub-Saharan African (SSA) countries, were extracted from the whole genome SNP data of the Ag1000G project (Ag3.0). The population genetic analysis on gene-specific data included the determination of the population structure, estimation of the differentiation level between the populations, evaluation of the linkage between the non-synonymous SNPs (nsSNPs), and a few statistical tests. Results: As potential targets for small molecule inhibitors to reduce malaria transmission, our set of four genes associated with Anopheles fertility and their susceptibility to Plasmodium comprises the mating-induced stimulator of oogenesis protein (MISO, AGAP002620), Vitellogenin (Vg, AGAP004203), Lipophorin (Lp, AGAP001826), and Haem-peroxidase 15 (HPX15, AGAP013327). The analyses performed on these potential targets of small inhibitor molecules revealed that the genes are conserved within SSA populations of An. gambiae s.l. The overall low Fst values and low clustering of principal component analysis between species indicated low genetic differentiation at all the genes (MISO, Vg, Lp and HPX15). The low nucleotide diversity (>0.10), negative Tajima’s D values, and heterozygosity analysis provided ecological insights into the purifying selection that acts to remove deleterious mutations, maintaining genetic diversity at low levels within the populations. None of MISO nsSNPs were identified in linkage disequilibrium, whereas a few weakly linked nsSNPs with ambiguous haplotyping were detected at other genes. Conclusions: This integrated finding on the genetic features of major malaria vectors’ biological factors across natural populations offer new insights for developing sustainable malaria control tools. These loci were reasonably conserved, allowing for the design of effective targeting with small molecule inhibitors towards controlling vector populations and lowering global malaria transmission. Full article

Background: Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is critical in immune checkpoint regulation and cancer immune evasion. Variants in PDCD1 may alter its function, impacting cancer susceptibility and disease progression. Objectives: This study evaluates the structural, functional, and regulatory impacts of non-synonymous single-nucleotide polymorphisms (nsSNPs) in the PDCD1 gene, focusing on their pathogenic and oncogenic roles. Methods: Computational tools, including PredictSNP1.0, I-Mutant2.0, MUpro, HOPE, MutPred2, Cscape, Cscape-Somatic, GEPIA2, cBioPortal, and STRING, were used to analyze 695 nsSNPs in the PD1 protein. The analysis covered structural impacts, stability changes, regulatory effects, and oncogenic potential, focusing on conserved domains and protein–ligand interactions. Results: The analysis identified 84 deleterious variants, with 45 mapped to conserved regions like the Ig V-set domain essential for ligand-binding interactions. Stability analyses identified 78 destabilizing variants with significant protein instability (ΔΔG values). Ten nsSNPs were identified as potential cancer drivers. Expression profiling showed differential PDCD1 expression in tumor versus normal tissues, correlating with improved survival in skin melanoma but limited value in ovarian cancer. Regulatory SNPs disrupted miRNA-binding sites and transcriptional regulation, affecting PDCD1 expression. STRING analysis revealed key PD-1 protein partners within immune pathways, including PD-L1 and PD-L2. Conclusions: This study highlights the significance of PDCD1 nsSNPs as potential biomarkers for cancer susceptibility, advancing the understanding of PD-1 regulation. Experimental validation and multi-omics integration are crucial to refine these findings and enhance theraputic strategies. Full article

Background: The Signal Transducer and Activator of Transcription 1 (STAT1) gene is an essential component of the JAK-STAT signaling pathway. This pathway plays a pivotal role in the regulation of different cellular processes, including immune responses, cell growth, and apoptosis. Mutations in the STAT1 gene contribute to a variety of immune system dysfunctions. Objectives: We aim to identify disease-susceptible single-nucleotide polymorphisms (SNPs) in STAT1 gene and predict structural changes associated with the mutations that disrupt normal protein–protein interactions using different computational algorithms. Methods: Several in silico tools, such as SIFT, Polyphen v2, PROVEAN, SNAP2, PhD-SNP, SNPs&GO, Pmut, and PANTHER, were used to determine the deleterious nsSNPs of the STAT1. Further, we evaluated the potentially deleterious SNPs for their effect on protein stability using I-Mutant, MUpro, and DDMUT. Additionally, we predicted the functional and structural effects of the nsSNPs using MutPred. We used Alpha-Missense to predict missense variant pathogenicity. Moreover, we predicted the 3D structure of STAT1 using an artificial intelligence system, alphafold, and the visualization of the 3D structures of the wild-type amino acids and the mutant residues was performed using ChimeraX 1.9 software. Furthermore, we analyzed the structural and conformational variations that have resulted from SNPs using Project Hope, while changes in the biological interactions between wild type, mutant amino acids, and neighborhood residues was studied using DDMUT. Conservational analysis and surface accessibility prediction of STAT1 was performed using ConSurf. We predicted the protein–protein interaction using STRING database. Results: In the current study, we identified six deleterious nsSNPs (R602W, I648T, V642D, L600P, I578N, and W504C) and their effect on protein structure, function, and stability. Conclusions: These findings highlight the potential of approaches to pinpoint pathogenic SNPs, providing a time- and cost-effective alternative to experimental approaches. To the best of our knowledge, this is the first comprehensive study in which we analyze STAT1 gene variants using both bioinformatics and artificial-intelligence-based model tools. Full article

Background: Mutations in the EPOR gene can disrupt its normal signaling pathways, leading to hematological disorders such as polycythemia vera and other myeloproliferative diseases. Methodology: In this study, a range of bioinformatics tools, including SIFT, PolyPhen-2, SNAP2, SNPs & Go, PhD-SNP, I-Mutant2.0, MuPro, MutPred, ConSurf, HOPE, and Interpro were used to assess the deleterious effects of missense nonsynonymous single nucleotide polymorphisms (nsSNPs) on protein structure and function. Furthermore, molecular dynamics simulations (MDS) were conducted to assess the structural deviations of the identified mutant variants in comparison to the wild type. Results: The results identified two nsSNPs, R223P and G302S, as deleterious, significantly affecting protein structure and function. Both substitutions occur in functionally conserved regions and are predicted to be pathogenic, associated with altered molecular mechanisms. The MDSs indicated that while the wild-type EPOR maintained optimal stability, the G302S and R223P variants exhibited substantial deviations, adversely affecting overall protein stability and compactness. Conclusions: The computational analysis of missense nsSNPs in the EPOR gene identified two missense SNPs, R223P and G302S, as deleterious, occurring at highly conserved regions, and having substantial effects on erythropoietin receptor (EPO-R) protein structure and function, suggesting their potential pathogenic consequences. Full article

Drug-resistant Morganella morganii, a rod-shaped, Gram-negative, facultatively anaerobic bacillus belonging to the Enterobacteriaceae family, is a growing worldwide health concern due to its association with high morbidity and mortality rates. Recent advancements in machine learning, particularly Alphafold 2’s protein structure prediction using local physics and pattern recognition, have aided research efforts. This study focuses on the enzymatic activity of aminoglycoside N6′-acetyltransferase (aacA7), a critical transferase enzyme in bacteria that confers resistance to aminoglycosides. AacA7 modifies aminoglycoside molecules by catalyzing the acetylation of their 6′-amino group using acetyl-CoA, rendering antibiotics like kanamycin, neomycin, tobramycin, and amikacin inactive. We propose that Doripenem and OncoglabrinolC can interact with aacA7, potentially modifying its enzymatic activity. Molecular docking analysis of aacA7 with 22 drug targets revealed OncoglabrinolC as the most promising candidate, exhibiting a binding energy of −12.82 kcal/mol. These two top candidates, OncoglabrinolC and Doripenem, were then subjected to 100 ns of molecular dynamic simulations to assess their dynamic conformational features. Furthermore, the PredictSNP consensus classifier was used to predict the impact of mutations on aacA7 protein functionality. The study also investigated the interaction of wild-type and mutant aacA7 proteins with both Doripenem and OncoglabrinolC. These findings provide valuable insights into the binding behavior of OncoglabrinolC and Doripenem as potential lead molecules for repurposing against aacA7, potentially reducing the pathogenicity of Morganella morganii. Full article

Diabetes mellitus (DM) represents a problem for the healthcare system worldwide. DM has very serious complications such as blindness, kidney failure, and cardiovascular disease. In addition to the very bad socioeconomic impacts, it influences patients and their families and communities. The global costs of DM and its complications are huge and expected to rise by the year 2030. DM is caused by genetic and environmental risk factors. Genetic testing will aid in early diagnosis and identification of susceptible individuals or populations using ATP-sensitive potassium (K_ATP) channels present in different tissues such as the pancreas, myocardium, myocytes, and nervous tissues. The channels respond to different concentrations of blood sugar, stimulation by hormones, or ischemic conditions. In pancreatic cells, they regulate the secretion of insulin and glucagon. Mutations in the KCNJ11 gene that encodes the Kir6.2 protein (a major constituent of K_ATP channels) were reported to be associated with Type 2 DM, neonatal diabetes mellitus (NDM), and maturity-onset diabetes of the young (MODY). Kir6.2 harbors binding sites for ATP and phosphatidylinositol 4,5-diphosphate (PIP2). The ATP inhibits the K_ATP channel, while the (PIP2) activates it. A Kir6.2 mutation at tyrosine330 (Y330) was demonstrated to reduce ATP inhibition and predisposes to NDM. In this study, we examined the effect of mutations on the Kir6.2 structure using bioinformatics tools and molecular dynamic simulations (SIFT, PolyPhen, SNAP2, PANTHER, PhD&SNP, SNP&Go, I-Mutant, MuPro, MutPred, ConSurf, HOPE, and GROMACS). Our results indicated that M199R, R201H, R206H, and Y330H mutations influence Kir6.2 structure and function and therefore may cause DM. We conclude that MD simulations are useful techniques to predict the effects of mutations on protein structure. In addition, the M199R, R201H, R206H, and Y330H variant in the Kir6.2 protein may be associated with DM. These results require further verification in protein–protein interactions, Kir6.2 function, and case-control studies. Full article

Insulin gene mutations affect the structure of insulin and are considered a leading cause of neonatal diabetes and permanent neonatal diabetes mellitus PNDM. These mutations can affect the production and secretion of insulin, resulting in inadequate insulin levels and subsequent hyperglycemia. Early discovery or prediction of PNDM can aid in better management and treatment. The current study identified potential deleterious non-synonymous single nucleotide polymorphisms nsSNPs in the INS gene. The analysis of the nsSNPs in the INS gene was conducted using bioinformatics tools by implementing computational algorithms including SIFT, PolyPhen2, SNAP2, SNPs & GO, PhD-SNP, MutPred2, I-Mutant, MuPro, and HOPE tools to investigate the prediction of the potential association between nsSNPs in the INS gene and PNDM. Three mutations, C96Y, P52R, and C96R, were shown to potentially reduce the stability and function of the INS protein. These mutants were subjected to MDSs for structural analysis. Results suggested that these three potential pathogenic mutations may affect the stability and functionality of the insulin protein encoded by the INS gene. Therefore, these changes may influence the development of PNDM. Further researches are required to fully understand the various effects of mutations in the INS gene on insulin synthesis and function. These data can aid in genetic testing for PNDM to evaluate its risk and create treatment and prevention strategies in personalized medicine. Full article

Bacillus anthracis is the bacterium responsible for causing the zoonotic disease called anthrax. The disease presents itself in different forms like gastrointestinal, inhalation, and cutaneous. Bacterial spores are tremendously adaptable, can persist for extended periods and occasionally endanger human health. The Anthrax Toxin Receptor-2 (ANTXR2) gene acts as membrane receptor and facilitates the entry of the anthrax toxin into host cells. Additionally, mutations in the ANTXR2 gene have been linked to various autoimmune diseases, including Hyaline Fibromatosis Syndrome (HFS), Ankylosing Spondylitis (AS), Juvenile Hyaline Fibromatosis (JHF), and Infantile Systemic Hyalinosis (ISH). This study delves into the genetic landscape of ANTXR2, aiming to comprehend its associations with diverse disorders, elucidate the impacts of its mutations, and pinpoint minimal non-pathogenic mutations capable of reducing the binding affinity of the ANTXR2 gene with the protective antigen. Recognizing the pivotal role of single-nucleotide polymorphisms (SNPs) in shaping genetic diversity, we conducted computational analyses to discern highly deleterious and tolerated non-synonymous SNPs (nsSNPs) in the ANTXR2 gene. The Mutpred2 server determined that the Arg465Trp alteration in the ANTXR2 gene leads to altered DNA binding (p = 0.22) with a probability of a deleterious mutation of 0.808; notably, among the identified deleterious SNPs, rs368288611 (Arg465Trp) stands out due to its significant impact on altering the DNA-binding ability of ANTXR2. We propose these SNPs as potential candidates for hypertension linked to the ANTXR2 gene, which is implicated in blood pressure regulation. Noteworthy among the tolerated substitutions is rs200536829 (Ala33Ser), recognized as less pathogenic; this highlights its potential as a valuable biomarker, potentially reducing side effects on the host while also reducing binding with the protective antigen protein. Investigating these SNPs holds the potential to correlate with several autoimmune disorders and mitigate the impact of anthrax disease in humans. Full article

Excessive soil salinity is a major stressor inhibiting crops’ growth, development, and yield. Seed germination is a critical stage of crop growth and development, as well as one of the most salt-sensitive stages. Salt stress has a significant inhibitory effect on seed germination. Okra is a nutritious vegetable, but its seed germination percentage (GP) is low, whether under salt stress conditions or suitable conditions. In this study, we used 180 okra accessions and conducted a genome-wide association study (GWAS) on the germination percentage using 20,133,859 single nucleotide polymorphic (SNP) markers under 0 (CK, diluted water), 70 (treatment 1, T1), and 140 mmol/L (treatment 2, T2) NaCl conditions. Using the mixed linear model (MLM) in Efficient Mixed-model Association eXpedated (EMMAX) and Genome-wide Efficient Mixed Model Association (GEMMA) software, 511 SNP loci were significantly associated during germination, of which 167 SNP loci were detected simultaneously by both programs. Among the 167 SNPs, SNP2619493 on chromosome 59 and SNP2692266 on chromosome 44 were detected simultaneously under the CK, T1, and T2 conditions, and were key SNP loci regulating the GP of okra seeds. Linkage disequilibrium block analysis revealed that nsSNP2626294 (C/T) in Ae59G004900 was near SNP2619493, and the amino acid changes caused by nsSNP2626294 led to an increase in the phenotypic values in some okra accessions. There was an nsSNP2688406 (A/G) in Ae44G005470 near SNP2692266, and the amino acid change caused by nsSNP2688406 led to a decrease in phenotypic values in some okra accessions. These results indicate that Ae59G004900 and Ae44G005470 regulate the GP of okra seeds under salt and no-salt stresses. The gene expression analysis further demonstrated these results. The SNP markers and genes that were identified in this study will provide reference for further research on the GP of okra, as well as new genetic markers and candidate genes for cultivating new okra varieties with high GPs under salt and no-salt stress conditions. Full article

Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate this bias, global population studies, such as the 1000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How many of these differences can be attributed to population-specific traits? To answer this question, the mutation data must be linked with functional outcomes. A new “edgotype” concept has been proposed, which emphasizes the interaction-specific, “edgetic”, perturbations caused by mutations in the interacting proteins. In this work, we performed systematic in silico edgetic profiling of ~50,000 non-synonymous SNVs (nsSNVs) from the 1000 Genomes Project by leveraging our semi-supervised learning approach SNP-IN tool on a comprehensive set of over 10,000 protein interaction complexes. We interrogated the functional roles of the variants and their impact on the human interactome and compared the results with the pathogenic variants disrupting PPIs in the same interactome. Our results demonstrated that a considerable number of nsSNVs from healthy populations could rewire the interactome. We also showed that the proteins enriched with interaction-disrupting mutations were associated with diverse functions and had implications in a broad spectrum of diseases. Further analysis indicated that distinct gene edgetic profiles among major populations could shed light on the molecular mechanisms behind the population phenotypic variances. Finally, the network analysis revealed that the disease-associated modules surprisingly harbored a higher density of interaction-disrupting mutations from healthy populations. The variation in the cumulative network damage within these modules could potentially account for the observed disparities in disease susceptibility, which are distinctly specific to certain populations. Our work demonstrates the feasibility of a large-scale in silico edgetic study, and reveals insights into the orchestrated play of population-specific mutations in the human interactome. Full article

The thrombopoietin receptor (MPL) gene is a critical regulator of hematopoiesis, and any alterations in its structure or function can result in a range of hematological disorders. Non-synonymous single nucleotide polymorphisms (nsSNPs) in MPL have the potential to disrupt normal protein function, prompting our investigation into the most deleterious MPL SNPs and the associated structural changes affecting protein–protein interactions. We employed a comprehensive suite of bioinformatics tools, including PredictSNP, InterPro, ConSurf, I-Mutant2.0, MUpro, Musitedeep, Project HOPE, STRING, RegulomeDB, Mutpred2, CScape, and CScape Somatic, to analyze 635 nsSNPs within the MPL gene. Among the analyzed nsSNPs, PredictSNP identified 28 as significantly pathogenic, revealing three critical functional domains within MPL. Ten of these nsSNPs exhibited high conservation scores, indicating potential effects on protein structure and function, while 14 were found to compromise MPL protein stability. Although the most harmful nsSNPs did not directly impact post-translational modification sites, 13 had the capacity to substantially alter the protein’s physicochemical properties. Some mutations posed a risk to vital protein–protein interactions crucial for hematological functions, and three non-coding region nsSNPs displayed significant regulatory potential with potential implications for hematopoiesis. Furthermore, 13 out of 21 nsSNPs evaluated were classified as high-risk pathogenic variants by Mutpred2. Notably, amino acid alterations such as C291S, T293N, D295G, and W435C, while impactful on protein stability and function, were deemed non-oncogenic “passenger” mutations. Our study underscores the substantial impact of missense nsSNPs on MPL protein structure and function. Given MPL’s central role in hematopoiesis, these mutations can significantly disrupt hematological processes, potentially leading to a variety of disorders. The identified high-risk pathogenic nsSNPs may hold promise as potential biomarkers or therapeutic targets for hematological diseases. This research lays the foundation for future investigations into the MPL gene’s role in the realm of hematological health and diseases. Full article

Marek’s disease (MD) is a highly infectious lymphoproliferative disease in chickens with a significant economic impact. Mardivirus gallidalpha 2, also known as Marek’s disease virus (MDV), is the causative pathogen and has been categorized based on its virulence rank into four pathotypes: mild (m), virulent (v), very virulent (vv), and very virulent plus (vv+). A prior comparative genomics study suggested that several single-nucleotide polymorphisms (SNPs) and genes in the MDV genome are associated with virulence, including nonsynonymous (ns) SNPs in eight open reading frames (ORF): UL22, UL36, UL37, UL41, UL43, R-LORF8, R-LORF7, and ICP4. To validate the contribution of these nsSNPs to virulence, the vv+MDV strain 686 genome was modified by replacing nucleotides with those observed in the vMDV strains. Pathogenicity studies indicated that these substitutions reduced the MD incidence and increased the survival of challenged birds. Furthermore, using the best-fit pathotyping method to rank the virulence, the modified vv+MDV 686 viruses resulted in a pathotype similar to the vvMDV Md5 strain. Thus, these results support our hypothesis that SNPs in one or more of these ORFs are associated with virulence but, as a group, are not sufficient to result in a vMDV pathotype, suggesting that there are additional variants in the MDV genome associated with virulence, which is not surprising given this complex phenotype and our previous finding of additional variants and SNPs associated with virulence. Full article