Bioinformatics and Biology Insights最新文献

{"title":"GOTermViewer: Visualization of Gene Ontology Enrichment in Multiple Differential Gene Expression Analyses.","authors":"Milene Volpato, Mark Hull, Ian M Carr","doi":"10.1177/11779322241271550","DOIUrl":"10.1177/11779322241271550","url":null,"abstract":"<p><p>Gene ontology phrases are a widely used set of hierarchical terms that describe the biological properties of genes. These terms are then used to annotate individual genes, making it possible to determine the likely physiological properties of groups of genes such as a list of differentially expressed genes. Consequently, their ability to predict changes in biological features and functions based on alterations in gene expression has made gene ontology terms popular in the wide range of bioinformatic fields, such as differential gene expression and evolutionary biology. However, while they make the analysis easier, it is seldom easy to convey the results in a readily understandable manner. A number of applications have been developed to visualize gene ontology (GO) term enrichment; however, these solutions tend to focus on the display of aggregated results from a single analysis, making them unsuitable for the analysis of a series of experiments such as a time course or response to different drug treatments. As multiple pair wise comparisons are becoming a common feature of RNA profiling experiments, the absence of a mechanism to easily compare them is a significant problem. Consequently, to overcome this obstacle, we have developed GOTermViewer, an application that displays GO term enrichment data as determined by GOstats such that changes in physiological response across a number of individual analyses across a time course or range of drug treatments can be visualized.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241271550"},"PeriodicalIF":2.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142307116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptomic Profiles of AKAP12 Deficiency in Mouse Corpus Callosum.","authors":"Tomonori Hoshino, Hajime Takase, Hidehiro Ishikawa, Gen Hamanaka, Shintaro Kimura, Norito Fukuda, Ji Hyun Park, Hiroki Nakajima, Hisashi Shirakawa, Akihiro Shindo, Kyu-Won Kim, Irwin H Gelman, Josephine Lok, Ken Arai","doi":"10.1177/11779322241276936","DOIUrl":"https://doi.org/10.1177/11779322241276936","url":null,"abstract":"<p><p>A-kinase anchor protein 12 (AKAP12), a scaffold protein, has been implicated in the central nervous system, including blood-brain barrier (BBB) function. Although its expression level in the corpus callosum is higher than in other brain regions, such as the cerebral cortex, the role of AKAP12 in the corpus callosum remains unclear. In this study, we investigate the impact of AKAP12 deficiency by transcriptome analysis using RNA-sequencing (RNA-seq) on the corpus callosum of AKAP12 knockout (KO) mice. We observed minimal changes, with only 13 genes showing differential expression, including <i>Akap12</i> itself. Notably, <i>Klf2</i> and <i>Sgk1</i>, genes potentially involved in BBB function, were downregulated in AKAP12 KO mice and expressed in vascular cells similar to <i>Akap12</i>. These changes in gene expression may affect important biological pathways that may be associated with neurological disorders. Our findings provide an additional data set for future research on the role of AKAP12 in the central nervous system.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241276936"},"PeriodicalIF":2.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11439161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications: A Network-Based Approach.","authors":"Roberta Coletti, Mónica Leiria de Mendonça, Susana Vinga, Marta B Lopes","doi":"10.1177/11779322241271535","DOIUrl":"https://doi.org/10.1177/11779322241271535","url":null,"abstract":"<p><p>Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241271535"},"PeriodicalIF":2.3,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11403688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergence of SARS-CoV-2 Variants Are Induced by Coinfections With Dengue.","authors":"Hassan M Al-Emran, Fazlur Rahman, Laxmi Sarkar, Prosanto Kumar Das, Provakar Mondol, Suriya Yesmin, Pipasha Sultana, Toukir Ahammed, Rasel Parvez, Md Shazid Hasan, Shovon Lal Sarkar, M Shaminur Rahman, Anamica Hossain, Mahmudur Rahman, Ovinu Kibria Islam, Md Tanvir Islam, Shireen Nigar, Selina Akter, A S M Rubayet Ul Alam, Mohammad Mahfuzur Rahman, Iqbal Kabir Jahid, M Anwar Hossain","doi":"10.1177/11779322241272399","DOIUrl":"https://doi.org/10.1177/11779322241272399","url":null,"abstract":"<p><p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has accumulated a series of point mutations and evolved into several variants of concern (VOCs), some of which are more transmissible and potentially more severe than the original strain. The most notable VOCs are Alpha, Beta, Gamma, Delta, and Omicron, which have spread to various parts of the world. This study conducted surveillance in Jashore, Bangladesh to identify the prevalence of SARS-CoV-2 coinfected with dengue virus and their genomic effect on the emergence of VOCs. A hospital-based COVID-19 surveillance from June to August, 2021 identified 9 453 positive patients in the surveillance area. The study enrolled 572 randomly selected COVID-19-positive patients, of which 11 (2%) had dengue viral coinfection. Whole genome sequences of SARS-CoV-2 were analyzed and compared between coinfection positive and negative group. In addition, we extracted 185 genome sequences from GISAID to investigate the cross-correlation function between SARS-CoV-2 mutations and VOC; multiple ARIMAX(p,d,q) models were developed to estimate the average number of amino acid (aa) substitution among different SARS-CoV-2 VOCs. The results of the study showed that the coinfection group had an average of 30.6 (±1.7) aa substitutions in SARS-CoV-2, whereas the dengue-negative COVID-19 group had that average of 25.6 (±1.8; <i>P</i> < .01). The coinfection group showed a significant difference of aa substitutions in open reading frame (ORF) and N-protein when compared to dengue-negative group (<i>P</i> = .03). Our ARIMAX models estimated that the emergence of SARS-CoV-2 variants Delta required additional 9 to 12 aa substitutions than Alpha, Beta, or Gamma variant. The emergence of Omicron accumulated additional 19 (95% confidence interval [CI]: 15.74, 21.95) aa substitution than Delta. Increased number of point mutations in SARS-CoV-2 genome identified from coinfected cases could be due to the compromised immune function of host and induced adaptability of pathogens during coinfections. As a result, new variants might be emerged when series of coinfection events occur during concurrent two epidemics.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241272399"},"PeriodicalIF":2.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11406487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adopting Integrated Bioinformatics and Systems Biology Approaches to Pinpoint the COVID-19 Patients' Risk Factors That Uplift the Onset of Posttraumatic Stress Disorder.","authors":"Sabbir Ahmed, Md Arju Hossain, Sadia Afrin Bristy, Md Shahjahan Ali, Md Habibur Rahman","doi":"10.1177/11779322241274958","DOIUrl":"https://doi.org/10.1177/11779322241274958","url":null,"abstract":"<p><p>Owing to the recent emergence of COVID-19, there is a lack of published research and clinical recommendations for posttraumatic stress disorder (PTSD) risk factors in patients who contracted or received treatment for the virus. This research aims to identify potential molecular targets to inform therapeutic strategies for this patient population. RNA sequence data for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and PTSD (from the National Center for Biotechnology Information [NCBI]) were processed using the GREIN database. Protein-protein interaction (PPI) networks, pathway enrichment analyses, miRNA interactions, gene regulatory network (GRN) studies, and identification of linked drugs, chemicals, and diseases were conducted using STRING, DAVID, Enrichr, Metascape, ShinyGO, and NetworkAnalyst v3.0. Our analysis identified 15 potentially unique hub proteins within significantly enriched pathways, including PSMB9, MX1, HLA-DOB, HLA-DRA, IFIT3, OASL, RSAD2, and so on, filtered from a pool of 201 common differentially expressed genes (DEGs). Gene ontology (GO) terms and metabolic pathway analyses revealed the significance of the extracellular region, extracellular space, extracellular exosome, adaptive immune system, and interleukin (IL)-18 signaling pathways. In addition, we discovered several miRNAs (hsa-mir-124-3p, hsa-mir-146a-5p, hsa-mir-148b-3p, and hsa-mir-21-3p), transcription factors (TF) (WRNIP1, FOXC1, GATA2, CREB1, and RELA), a potentially repurposable drug carfilzomib and chemicals (tetrachlorodibenzodioxin, estradiol, arsenic trioxide, and valproic acid) that could regulate the expression levels of hub proteins at both the transcription and posttranscription stages. Our investigations have identified several potential therapeutic targets that elucidate the probability that victims of COVID-19 experience PTSD. However, they require further exploration through clinical and pharmacological studies to explain their efficacy in preventing PTSD in COVID-19 patients.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241274958"},"PeriodicalIF":2.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11402063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RhizoBindingSites v2.0 Is a Bioinformatic Database of DNA Motifs Potentially Involved in Transcriptional Regulation Deduced From Their Genomic Sites.","authors":"Hermenegildo Taboada-Castro, Alfredo José Hernández-Álvarez, Jaime A Castro-Mondragón, Sergio Encarnación-Guevara","doi":"10.1177/11779322241272395","DOIUrl":"10.1177/11779322241272395","url":null,"abstract":"<p><p>RhizoBindingSites is a <i>de novo</i> depurified database of conserved DNA motifs potentially involved in the transcriptional regulation of the <i>Rhizobium</i>, <i>Sinorhizobium</i>, <i>Bradyrhizobium</i>, <i>Azorhizobium</i>, and <i>Mesorhizobium</i> genera covering 9 representative symbiotic species, deduced from the upstream regulatory sequences of orthologous genes (O-matrices) from the Rhizobiales taxon. The sites collected with O-matrices per gene per genome from RhizoBindingSites were used to deduce matrices using the dyad-Regulatory Sequence Analysis Tool (RSAT) method, giving rise to novel S-matrices for the construction of the RizoBindingSites v2.0 database. A comparison of the S-matrix logos showed a greater frequency and/or re-definition of specific-position nucleotides found in the O-matrices. Moreover, S-matrices were better at detecting genes in the genome, and there was a more significant number of transcription factors (TFs) in the vicinity than O-matrices, corresponding to a more significant genomic coverage for S-matrices. O-matrices of 3187 TFs and S-matrices of 2754 TFs from 9 species were deposited in RhizoBindingSites and RhizoBindingSites v2.0, respectively. The homology between the matrices of TFs from a genome showed inter-regulation between the clustered TFs. In addition, matrices of AraC, ArsR, GntR, and LysR ortholog TFs showed different motifs, suggesting distinct regulation. Benchmarking showed 72%, 68%, and 81% of common genes per regulon for O-matrices and approximately 14% less common genes with S-matrices of <i>Rhizobium etli</i> CFN42, <i>Rhizobium leguminosarum</i> bv. <i>viciae</i> 3841, and <i>Sinorhizobium meliloti</i> 1021. These data were deposited in RhizoBindingSites and the RhizoBindingSites v2.0 database (http://rhizobindingsites.ccg.unam.mx/).</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241272395"},"PeriodicalIF":2.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142153089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Deep Learning Performance for Chronic Lymphocytic Leukaemia Cell Segmentation in Brightfield Microscopy Images.","authors":"Markéta Vašinková, Vít Doleží, Michal Vašinek, Petr Gajdoš, Eva Kriegová","doi":"10.1177/11779322241272387","DOIUrl":"10.1177/11779322241272387","url":null,"abstract":"<p><strong>Objectives: </strong>This article focuses on the detection of cells in low-contrast brightfield microscopy images; in our case, it is chronic lymphocytic leukaemia cells. The automatic detection of cells from brightfield time-lapse microscopic images brings new opportunities in cell morphology and migration studies; to achieve the desired results, it is advisable to use state-of-the-art image segmentation methods that not only detect the cell but also detect its boundaries with the highest possible accuracy, thus defining its shape and dimensions.</p><p><strong>Methods: </strong>We compared eight state-of-the-art neural network architectures with different backbone encoders for image data segmentation, namely U-net, U-net++, the Pyramid Attention Network, the Multi-Attention Network, LinkNet, the Feature Pyramid Network, DeepLabV3, and DeepLabV3+. The training process involved training each of these networks for 1000 epochs using the PyTorch and PyTorch Lightning libraries. For instance segmentation, the watershed algorithm and three-class image semantic segmentation were used. We also used StarDist, a deep learning-based tool for object detection with star-convex shapes.</p><p><strong>Results: </strong>The optimal combination for semantic segmentation was the U-net++ architecture with a ResNeSt-269 background with a data set intersection over a union score of 0.8902. For the cell characteristics examined (area, circularity, solidity, perimeter, radius, and shape index), the difference in mean value using different chronic lymphocytic leukaemia cell segmentation approaches appeared to be statistically significant (Mann-Whitney <i>U</i> test, <i>P</i> < .0001).</p><p><strong>Conclusion: </strong>We found that overall, the algorithms demonstrate equal agreement with ground truth, but with the comparison, it can be seen that the different approaches prefer different morphological features of the cells. Consequently, choosing the most suitable method for instance-based cell segmentation depends on the particular application, namely, the specific cellular traits being investigated.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241272387"},"PeriodicalIF":2.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11378236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142153142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Marine-Derived Furanones Targeting Quorum-Sensing Receptors in <i>Pseudomonas aeruginosa</i>: Molecular Insights and Potential Mechanisms of Inhibition.","authors":"Aaron Boakye, Muntawakilu Padiga Seidu, Alice Adomako, Michael Konney Laryea, Lawrence Sheringham Borquaye","doi":"10.1177/11779322241275843","DOIUrl":"10.1177/11779322241275843","url":null,"abstract":"<p><p>The quorum-sensing (QS) machinery in disease-causing microorganisms is critical in developing antibiotic resistance. In <i>Pseudomonas aeruginosa</i>, QS is involved in biofilm formation, virulence factors production, and general tolerance to antimicrobials. Owing to the major role QS plays, interference in the process is probably a facile route to overcome antimicrobial resistance. Some furanone-derived compounds from marine sources have shown promising anti-QS activity. However, their protein targets and potential mechanisms of action have not been explored. To elucidate their potential protein targets in this study, marine metabolites with furanone backbones similar to their cognitive autoinducers (AIs) were screened against various QS receptors (LasR, RhlR, and PqsR) using molecular docking and molecular dynamics (MD) simulation techniques. The order by which the compounds bind to the receptors follows LasR > RhlR > PqsR. Compounds exhibited remarkable stability against LasR and RhlR, likely because the AIs of these receptors are structural analogs of furanones. Furanones with shorter alkyl side chains bound strongly against RhlR. The presence of halogens improved binding against various receptors. PqsR, with its hydrophobic-binding site and structurally different AIs, showed weaker binding. This study provides a molecular basis for the design of potent antagonists against QS receptors using marine-derived furanones.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241275843"},"PeriodicalIF":2.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11378241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142153088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer.","authors":"Md Shahin Alam, Adiba Sultana, Md Kaderi Kibria, Alima Khanam, Guanghui Wang, Md Nurul Haque Mollah","doi":"10.1177/11779322241272386","DOIUrl":"10.1177/11779322241272386","url":null,"abstract":"<p><p>Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (<i>CCNB1</i>, <i>CDK1</i>, <i>TOP2A</i>, <i>CCNA2</i>, <i>ESR1</i>, <i>EGFR</i>, <i>JUN</i>, <i>ACTB</i>, <i>TP53</i>, and <i>CCND1</i>) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs <i>ESR1</i>, AMG-900 vs <i>TOP2A</i>, and CHEMBL1765740 vs <i>EGFR</i>) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241272386"},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142139264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomics Exploration of <i>Brucella melitensis</i> to Design an Innovative Multi-Epitope mRNA Vaccine.","authors":"Maryam Asadinezhad, Iraj Pakzad, Parisa Asadollahi, Sobhan Ghafourian, Behrooz Sadeghi Kalani","doi":"10.1177/11779322241272404","DOIUrl":"10.1177/11779322241272404","url":null,"abstract":"<p><p>Brucellosis is a chronic and debilitating disease in humans, causing great economic losses in the livestock industry. Making an effective vaccine is one of the most important concerns for this disease. The new mRNA vaccine technology due to its accuracy and high efficiency has given promising results in various diseases. The objective of this research was to create a novel mRNA vaccine with multiple epitopes targeting <i>Brucella melitensis</i>. Seventeen antigenic proteins and their appropriate epitopes were selected with immunoinformatic tools and surveyed in terms of toxicity, allergenicity, and homology. Then, their presentation and identification by MHC cells and other immune cells were checked with valid tools such as molecular docking, and a multi-epitope protein was modeled, and after optimization, mRNA was analyzed in terms of structure and stability. Ultimately, the immune system's reaction to this novel vaccine was evaluated and the results disclosed that the designed mRNA construct can be an effective and promising vaccine that requires laboratory and clinical trials.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241272404"},"PeriodicalIF":2.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142104129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}