Protocol to measure centromeric array size changes using droplet digital PCR-based quantification of higher-order repeats.

Centromere length changes occurring during somatic cell divisions can be estimated by quantifying the copy numbers (CNs) of higher-order repeats (HORs), which are nested repeats of monomers that comprise centromeric arrays. Here, we present a protocol for single-cell isolation for clonal evolution followed by droplet digital PCR-based quantification. The assay measures HOR CNs across subclones to determine the frequency and degree of changes in HOR CNs. This protocol tests the underlying molecular mechanisms responsible for rapid centromere sequence evolution. For complete details on the use and execution of this protocol, please refer to Showman et al.1.

Comparative genomics of the carmine cochineal symbiont Candidatus Dactylopiibacterium carminicum reveals possible protection to the host against viruses via CRISPR/Cas.

We present new genomes from the bacterial symbiont Candidatus Dactylopiibacterium carminicum obtained from non-domesticated carmine cochineals belonging to the scale insect Dactylopius (Hemiptera: Coccoidea: Dactylopiidae). As Dactylopiibacterium has not yet been cultured in the laboratory, metagenomes and metatranscriptomics have been key in revealing putative symbiont functions. Dactylopiibacterium is a nitrogen-fixing beta-proteobacterium that may be vertically transmitted and shows differential gene expression inside the cochineal depending on the tissue colonized. Here we found that all cochineal species tested had Dactylopiibacterium carminicum which has a highly conserved genome. All Dactylopiibacterium genomes analyzed had genes involved in nitrogen fixation and plant polymer degradation. Dactylopiibacterium genomes resemble those from free-living plant bacteria, some found as endophytes. Notably, we found here a new putative novel function where the bacteria may protect the insect from viruses, since all Dactylopiibacterium genomes contain CRISPRs with a spacer matching nucleopolyhedrovirus that affects insects.

Asthma-Genomic Advances Toward Risk Prediction.

Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.

Further evidence from DNAH12 supports favorable fertility outcomes of infertile males with dynein axonemal heavy chain gene family variants.

Male infertility is a major concern affecting reproductive health. Biallelic deleterious variants of most DNAH gene family members have been linked to male infertility, with intracytoplasmic sperm injection (ICSI) being an efficacious way to achieve offspring. However, the association between DNAH12 and male infertility is still limited. Here, we identified one homozygous variant and two compound heterozygous variants in DNAH12 from three infertile Chinese men. Semen analysis revealed severe asthenozoospermia, abnormal morphology, and structure of sperm flagella. Furthermore, the Dnah12 knock-out mouse revealed severe spermatogenesis failure and validated the same male infertility phenotype. Favorable fertility outcomes were achieved through ICSI in three human individuals and Dnah12 knock-out mice. Collectively, our study indicated that biallelic variants of DNAH12 can induce male infertility in both human beings and mice. Notably, evidence from DNAH12 enhanced that ICSI was an optimal intervention to achieve favorable fertility outcomes for infertile males with DNAH gene family variants.

Large-scale single-virus genomics uncovers hidden diversity of river water viruses and diversified gene profiles.

Environmental viruses (primarily bacteriophages) are widely recognized as playing an important role in ecosystem homeostasis through the infection of host cells. However, the majority of environmental viruses are still unknown as their mosaic structure and frequent mutations in their sequences hinder genome construction in current metagenomics. To enable the large-scale acquisition of environmental viral genomes, we developed a new single-viral genome sequencing platform with microfluidic-generated gel beads. Amplification of individual DNA viral genomes in mass-produced gel beads allows high-throughput genome sequencing compared to conventional single-virus genomics. The sequencing analysis of river water samples yielded 1431 diverse viral single-amplified genomes, whereas viral metagenomics recovered 100 viral metagenome-assembled genomes at the comparable sequence depth. The 99.5% of viral single-amplified genomes were determined novel at the species level, most of which could not be recovered by a metagenomic assembly. The large-scale acquisition of diverse viral genomes identified protein clusters commonly detected in different viral strains, allowing the gene transfer to be tracked. Moreover, comparative genomics within the same viral species revealed that the profiles of various methyltransferase subtypes were diverse, suggesting an enhanced escape from host bacterial internal defense mechanisms. Our use of gel bead-based single-virus genomics will contribute to exploring the nature of viruses by accelerating the accumulation of draft genomes of environmental DNA viruses.

Novel loci linked to serum lipid traits are identified in a genome-wide association study of a highly admixed Brazilian population - the 2015 ISA Nutrition.

BACKGROUND: Cardiovascular diseases (CVDs) comprise major causes of death worldwide, leading to extensive burden on populations and societies. Alterations in normal lipid profiles, i.e., dyslipidemia, comprise important risk factors for CVDs. However, there is lack of comprehensive evidence on the genetic contribution to dyslipidemia in highly admixed populations. The identification of single nucleotide polymorphisms (SNPs) linked to blood lipid traits in the Brazilian population was based on genome-wide associations using data from the São Paulo Health Survey with Focus on Nutrition (ISA-Nutrition). METHODS: A total of 667 unrelated individuals had genetic information on 330,656 SNPs available, and were genotyped with Axiom™ 2.0 Precision Medicine Research Array. Genetic associations were tested at the 10- 5 significance level for the following phenotypes: low-density lipoprotein cholesterol (LDL-c), very low-density lipoprotein cholesterol (VLDL-c), high-density lipoprotein cholesterol (HDL-c), HDL-c/LDL-c ratio, triglycerides (TGL), total cholesterol, and non-HDL-c. RESULTS: There were 19 significantly different SNPs associated with lipid traits, the majority of which corresponding to intron variants, especially in the genes FAM81A, ZFHX3, PTPRD, and POMC. Three variants (rs1562012, rs16972039, and rs73401081) and two variants (rs8025871 and rs2161683) were associated with two and three phenotypes, respectively. Among the subtypes, non-HDL-c had the highest proportion of associated variants. CONCLUSIONS: The results of the present genome-wide association study offer new insights into the genetic structure underlying lipid traits in underrepresented populations with high ancestry admixture. The associations were robust across multiple lipid phenotypes, and some of the phenotypes were associated with two or three variants. In addition, some variants were present in genes that encode ncRNAs, raising important questions regarding their role in lipid metabolism.

Estimation of genetic variation in vitiligo associated genes: Population genomics perspective.

BACKGROUND: Vitiligo is an auto-immune progressive depigmentation disorder of the skin due to loss of melanocytes. Genetic risk is one of the important factors for development of vitiligo. Preponderance of vitiligo in certain ethnicities is known which can be analysed by understanding the distribution of allele frequencies across normal populations. Earlier GWAS identified 108 risk alleles for vitiligo in Europeans and East Asians. In this study, 64 of these risk alleles were used for analysing their enrichment and depletion across populations (1000 Genomes Project and IndiGen) with reference to 1000 Genomes dataset. Genetic risk scores were calculated and Fisher's exact test was performed to understand statistical significance of their variation in each population with respect to 1000 Genomes dataset as reference. In addition to SNPs reported in GWAS, significant variation in allele frequencies of 1079 vitiligo-related genes were also analysed. Two-tailed Chi-square test and Bonferroni's multiple adjustment values along with fixation index (≥ 0.5) and minimum allele frequency (≥ 0.05) were calculated and used to prioritise the variants based on pairwise comparison across populations. RESULTS: Risk alleles rs1043101 and rs10768122 belong to 3 prime UTR of glutamate receptor gene SLC1A2 are found to be highly enriched in the South Asian population when compared with the 'global normal' population. Intron variant rs4766578 (ATXN2) was found to be deleted in SAS, EAS and AFR and enriched in EUR and AMR1. This risk allele is found to be under positive selection in SAS, AMR1 and EUR. From the ancillary vitiligo gene list, nonsynonymous variant rs16891982 was found to be enriched in the European and the Admixed American populations and depleted in all others. rs2279238 and rs11039155 belonging to the LXR-α gene involved in regulation of metalloproteinase 2 and 9 (melanocyte precursors) were found to be associated with vitiligo in the North Indian population (in earlier study). CONCLUSION: The differential enrichment/depletion profile of the risk alleles provides insight into the underlying inter-population variations. This would provide clues towards prioritisation of SNPs associated with vitiligo thereby elucidating its preponderance in different ethnic groups.

Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.

With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.

Large Language Models and Genomics for Summarizing the Role of microRNA in Regulating mRNA Expression.

microRNA (miRNA)-messenger RNA (mRNA or gene) interactions are pivotal in various biological processes, including the regulation of gene expression, cellular differentiation, proliferation, apoptosis, and development, as well as the maintenance of cellular homeostasis and pathogenesis of numerous diseases, such as cancer, cardiovascular diseases, neurological disorders, and metabolic conditions. Understanding the mechanisms of miRNA-mRNA interactions can provide insights into disease mechanisms and potential therapeutic targets. However, extracting these interactions efficiently from a huge collection of published articles in PubMed is challenging. In the current study, we annotated a miRNA-mRNA Interaction Corpus (MMIC) and used it for evaluating the performance of a variety of machine learning (ML) models, deep learning-based transformer (DLT) models, and large language models (LLMs) in extracting the miRNA-mRNA interactions mentioned in PubMed. We used the genomics approaches for validating the extracted miRNA-mRNA interactions. Among the ML, DLT, and LLM models, PubMedBERT showed the highest precision, recall, and F-score, with all equal to 0.783. Among the LLM models, the performance of Llama-2 is better when compared to others. Llama 2 achieved 0.56 precision, 0.86 recall, and 0.68 F-score in a zero-shot experiment and 0.56 precision, 0.87 recall, and 0.68 F-score in a three-shot experiment. Our study shows that Llama 2 achieves better recall than ML and DLT models and leaves space for further improvement in terms of precision and F-score.

SARS-CoV-2 Genomic Epidemiology Dashboards: A Review of Functionality and Technological Frameworks for the Public Health Response.

During the coronavirus disease 2019 (COVID-19) pandemic, the number and types of dashboards produced increased to convey complex information using digestible visualizations. The pandemic saw a notable increase in genomic surveillance data, which genomic epidemiology dashboards presented in an easily interpretable manner. These dashboards have the potential to increase the transparency between the scientists producing pathogen genomic data and policymakers, public health stakeholders, and the public. This scoping review discusses the data presented, functional and visual features, and the computational architecture of six publicly available SARS-CoV-2 genomic epidemiology dashboards. We found three main types of genomic epidemiology dashboards: phylogenetic, genomic surveillance, and mutational. We found that data were sourced from different databases, such as GISAID, GenBank, and specific country databases, and these dashboards were produced for specific geographic locations. The key performance indicators and visualization used were specific to the type of genomic epidemiology dashboard. The computational architecture of the dashboards was created according to the needs of the end user. The genomic surveillance of pathogens is set to become a more common tool used to track ongoing and future outbreaks, and genomic epidemiology dashboards are powerful and adaptable resources that can be used in the public health response.

Identification and Candidate Gene Evaluation of a Large Fast Neutron-Induced Deletion Associated with a High-Oil Phenotype in Soybean Seeds.

Since the dawn of agriculture, crops have been genetically altered for desirable characteristics. This has included the selection of natural and induced mutants. Increasing the production of plant oils such as soybean (Glycine max) oil as a renewable resource for food and fuel is valuable. Successful breeding for higher oil levels in soybeans, however, usually results in reduced seed protein. A soybean fast neutron population was screened for oil content, and three high oil mutants with minimal reductions in protein levels were found. Three backcross F2 populations derived from these mutants exhibited segregation for seed oil content. DNA was pooled from the high-oil and normal-oil plants within each population and assessed by comparative genomic hybridization. A deletion encompassing 20 gene models on chromosome 14 was found to co-segregate with the high-oil trait in two of the three populations. Eighteen genes in the deleted region have known functions that appear unrelated to oil biosynthesis and accumulation pathways, while one of the unknown genes (Glyma.14G101900) may contribute to the regulation of lipid droplet formation. This high-oil trait can facilitate the breeding of high-oil soybeans without protein reduction, resulting in higher meal protein levels.

Genetic Ancestry and Self-Reported "Skin Color/Race" in the Urban Admixed Population of São Paulo City, Brazil.

Epidemiological studies frequently classify groups based on phenotypes like self-reported skin color/race, which inaccurately represent genetic ancestry and may lead to misclassification, particularly among individuals of multiracial backgrounds. This study aimed to characterize both global and local genome-wide genetic ancestries and to assess their relationship with self-reported skin color/race in an admixed population of Sao Paulo city. We analyzed 226,346 single-nucleotide polymorphisms from 841 individuals participating in the population-based ISA-Nutrition study. Our findings confirmed the admixed nature of the population, demonstrating substantial European, significant Sub-Saharan African, and minor Native American ancestries, irrespective of skin color. A correlation was observed between global genetic ancestry and self-reported color-race, which was more evident in the extreme proportions of African and European ancestries. Individuals with higher African ancestry tended to identify as Black, those with higher European ancestry tended to identify as White, and individuals with higher Native American ancestry were more likely to self-identify as Mixed, a group with diverse ancestral compositions. However, at the individual level, this correlation was notably weak, and no deviations were observed for specific regions throughout the individual's genome. Our findings emphasize the significance of accurately defining and thoroughly analyzing race and ancestry, especially within admixed populations.

Diversity and potential functional role of phyllosphere-associated actinomycetota isolated from cupuassu (Theobroma grandiflorum) leaves: implications for ecosystem dynamics and plant defense strategies.

Exploring the intricate relationships between plants and their resident microorganisms is crucial not only for developing new methods to improve disease resistance and crop yields but also for understanding their co-evolutionary dynamics. Our research delves into the role of the phyllosphere-associated microbiome, especially Actinomycetota species, in enhancing pathogen resistance in Theobroma grandiflorum, or cupuassu, an agriculturally valuable Amazonian fruit tree vulnerable to witches' broom disease caused by Moniliophthora perniciosa. While breeding resistant cupuassu genotypes is a possible solution, the capacity of the Actinomycetota phylum to produce beneficial metabolites offers an alternative approach yet to be explored in this context. Utilizing advanced long-read sequencing and metagenomic analysis, we examined Actinomycetota from the phyllosphere of a disease-resistant cupuassu genotype, identifying 11 Metagenome-Assembled Genomes across eight genera. Our comparative genomic analysis uncovered 54 Biosynthetic Gene Clusters related to antitumor, antimicrobial, and plant growth-promoting activities, alongside cutinases and type VII secretion system-associated genes. These results indicate the potential of phyllosphere-associated Actinomycetota in cupuassu for inducing resistance or antagonism against pathogens. By integrating our genomic discoveries with the existing knowledge of cupuassu's defense mechanisms, we developed a model hypothesizing the synergistic or antagonistic interactions between plant and identified Actinomycetota during plant-pathogen interactions. This model offers a framework for understanding the intricate dynamics of microbial influence on plant health. In conclusion, this study underscores the significance of the phyllosphere microbiome, particularly Actinomycetota, in the broader context of harnessing microbial interactions for plant health. These findings offer valuable insights for enhancing agricultural productivity and sustainability.

Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms.

INTRODUCTION: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40 % of patients do not respond well to this therapy. This study aims to enhance treatment outcomes by using machine learning to predict individual response status to glucagon-like peptide-1 therapy. METHODS: We analysed a type-2 diabetes mellitus dataset from the Diastrat cohort, recruited at the Northern Ireland Centre for Stratified Medicine. The dataset included individuals prescribed glucagon-like peptide-1 therapy, with response status determined by glycated haemoglobin levels of ≤53 mmol/mol. We identified genomic and proteomic markers and developed machine learning models to predict therapy response. RESULTS: The study found 5 genomic variants and 45 proteomic markers that help differentiate glucagon-like peptide-1 therapy responders from non-responders, achieving 95 % prediction accuracy with a machine learning model. CONCLUSION: This study demonstrates the potential of machine learning in predicting the response to glucagon-like peptide-1 therapy in individuals with type-2 diabetes mellitus. These findings suggest that integrating genomic and proteomic data can significantly enhance personalized treatment approaches, potentially improving outcomes for patients who might otherwise not respond well to glucagon-like peptide-1 therapy. Further research and validation in larger cohorts are necessary to confirm these results and translate them into clinical practice.

Insights into the biodegradation of fipronil through soil microcosm-omics analyses of Pseudomonas sp. FIP_ A4.

Fipronil, a phenylpyrazole insecticide, is used to kill insects resistant to conventional insecticides. Though its regular and widespread use has substantially reduced agricultural losses, it has also caused its accumulation in various environmental niches. The biodegradation is an effective natural process that helps in reducing the amount of residual insecticides. This study deals with an in-depth investigation of fipronil degradation kinetics and pathways in Pseudomonas sp. FIP_A4 using multi-omics approaches. Soil-microcosm results revealed ∼87% degradation within 40 days. The whole genome of strain FIP_A4 comprises 4.09 Mbp with 64.6% GC content. Cytochrome P450 monooxygenase and enoyl-CoA hydratase-related protein, having 30% identity with dehalogenase detected in the genome, can mediate the initial degradation process. Proteome analysis revealed differential enzyme expression of dioxygenases, decarboxylase, and hydratase responsible for subsequent degradation. Metabolome analysis displayed fipronil metabolites in the presence of the bacterium, supporting the proposed degradation pathway. Molecular docking and dynamic simulation of each identified enzyme in complex with the specific metabolite disclosed adequate binding and high stability in the enzyme-metabolite complex. This study provides in-depth insight into genes and their encoded enzymes involved in the fipronil degradation and formation of different metabolites during pollutant degradation. The outcome of this study can contribute immensely to developing efficient technologies for the bioremediation of fipronil-contaminated soils.

Beyond cyanogenesis: Temperature gradients drive environmental adaptation in North American white clover (Trifolium repens L.).

Species that repeatedly evolve phenotypic clines across environmental gradients have been highlighted as ideal systems for characterizing the genomic basis of local environmental adaptation. However, few studies have assessed the importance of observed phenotypic clines for local adaptation: conspicuous traits that vary clinally may not necessarily be the most critical in determining local fitness. The present study was designed to fill this gap, using a plant species characterized by repeatedly evolved adaptive phenotypic clines. White clover is naturally polymorphic for its chemical defence cyanogenesis (HCN release with tissue damage); climate-associated cyanogenesis clines have evolved throughout its native and introduced range worldwide. We performed landscape genomic analyses on 415 wild genotypes from 43 locations spanning much of the North American species range to assess the relative importance of cyanogenesis loci vs. other genomic factors in local climatic adaptation. We find clear evidence of local adaptation, with temperature-related climatic variables best describing genome-wide differentiation between sampling locations. The same climatic variables are also strongly correlated with cyanogenesis frequencies and gene copy number variations (CNVs) at cyanogenesis loci. However, landscape genomic analyses indicate no significant contribution of cyanogenesis loci to local adaptation. Instead, several genomic regions containing promising candidate genes for plant response to seasonal cues are identified - some of which are shared with previously identified QTLs for locally adaptive fitness traits in North American white clover. Our findings suggest that local adaptation in white clover is likely determined primarily by genes controlling the timing of growth and flowering in response to local seasonal cues. More generally, this work suggests that caution is warranted when considering the importance of conspicuous phenotypic clines as primary determinants of local adaptation.

Transposable elements as drivers of dedifferentiation: Connections between enhancers in embryonic stem cells, placenta, and cancer.

Transposable elements (TEs) have emerged as important factors in establishing the cell type-specific gene regulatory networks and evolutionary novelty of embryonic and placental development. Recently, studies on the role of TEs and their dysregulation in cancers have shed light on the transcriptional, transpositional, and regulatory activity of TEs, revealing that the activation of developmental transcriptional programs by TEs may have a role in the dedifferentiation of cancer cells to the progenitor-like cell states. This essay reviews the recent evidence of the cis-regulatory TEs (henceforth crTE) in normal development and malignancy as well as the key transcription factors and regulatory pathways that are implicated in both cell states, and presents existing gaps remaining to be studied, limitations of current technologies, and therapeutic possibilities.

Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era.

Pancreatic cancer (PC) is an increasing cause of cancer-related death, with a dismal prognosis caused by its aggressive biology, the lack of clinical symptoms in the early phases of the disease, and the inefficacy of treatments. PC is characterized by a complex tumor microenvironment. The interaction of its cellular components plays a crucial role in tumor development and progression, contributing to the alteration of metabolism and cellular hyperproliferation, as well as to metastatic evolution and abnormal tumor-associated immunity. Furthermore, in response to intrinsic oncogenic alterations and the influence of the tumor microenvironment, cancer cells undergo a complex oncogene-directed metabolic reprogramming that includes changes in glucose utilization, lipid and amino acid metabolism, redox balance, and activation of recycling and scavenging pathways. The advent of omics sciences is revolutionizing the comprehension of the pathogenetic conundrum of pancreatic carcinogenesis. In particular, metabolomics and genomics has led to a more precise classification of PC into subtypes that show different biological behaviors and responses to treatments. The identification of molecular targets through the pharmacogenomic approach may help to personalize treatments. Novel specific biomarkers have been discovered using proteomics and metabolomics analyses. Radiomics allows for an earlier diagnosis through the computational analysis of imaging. However, the complexity, high expertise required, and costs of the omics approach are the main limitations for its use in clinical practice at present. In addition, the studies of extracellular vesicles (EVs), the use of organoids, the understanding of host-microbiota interactions, and more recently the advent of artificial intelligence are helping to make further steps towards precision and personalized medicine. This present review summarizes the main evidence for the application of omics sciences to the study of PC and the identification of future perspectives.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review.

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

Immunoinformatics and Reverse Vaccinology Approach for the Identification of Potential Vaccine Candidates against Vandammella animalimors.

Vandammella animalimorsus is a Gram-negative and non-motile bacterium typically transmitted to humans through direct contact with the saliva of infected animals, primarily through biting, scratches, or licks on fractured skin. The absence of a confirmed post-exposure treatment of V. animalimorsus bacterium highlights the imperative for developing an effective vaccine. We intended to determine potential vaccine candidates and paradigm a chimeric vaccine against V. animalimorsus by accessible public data analysis of the strain by utilizing reverse vaccinology. By subtractive genomics, five outer membranes were prioritized as potential vaccine candidates out of 2590 proteins. Based on the instability index and transmembrane helices, a multidrug transporter protein with locus ID A0A2A2AHJ4 was designated as a potential candidate for vaccine construct. Sixteen immunodominant epitopes were retrieved by utilizing the Immune Epitope Database. The epitope encodes the strong binding affinity, nonallergenic properties, non-toxicity, high antigenicity scores, and high solubility revealing the more appropriate vaccine construct. By utilizing appropriate linkers and adjuvants alongside a suitable adjuvant molecule, the epitopes were integrated into a chimeric vaccine to enhance immunogenicity, successfully eliciting both adaptive and innate immune responses. Moreover, the promising physicochemical features, the binding confirmation of the vaccine to the major innate immune receptor TLR-4, and molecular dynamics simulations of the designed vaccine have revealed the promising potential of the selected candidate. The integration of computational methods and omics data has demonstrated significant advantages in discovering novel vaccine targets and mitigating vaccine failure rates during clinical trials in recent years.