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![Comparison of the reported result from Gaucher et al. [9] to our re-analysis based on genes, biological process in Gene Ontology and Reactome. Venn diagram illustrating the comparison of significant (adjusted p-value based on FDR < 0.05) (a) differentially expressed genes, (b) Gene Ontology biological process terms, (c) Reactome pathways between the original and re-analysis of Gaucher et al. [9]](publication/338118350/figure/fig5/AS:962466934714374@1606481407270/Comparison-of-the-reported-result-from-Gaucher-et-al-9-to-our-re-analysis-based-on.png)
Comparison of the reported result from Gaucher et al. [9] to our re-analysis based on genes, biological process in Gene Ontology and Reactome. Venn diagram illustrating the comparison of significant (adjusted p-value based on FDR < 0.05) (a) differentially expressed genes, (b) Gene Ontology biological process terms, (c) Reactome pathways between the original and re-analysis of Gaucher et al. [9]
Source publication
![Comparison of the reported result from Gaucher et al. [9] to our...](publication/338118350/figure/fig5/AS:962466934714374@1606481407270/Comparison-of-the-reported-result-from-Gaucher-et-al-9-to-our-re-analysis-based-on_Q320.jpg)
Background:
Different human responses to the same vaccine were frequently observed. For example, independent studies identified overlapping but different transcriptomic gene expression profiles in Yellow Fever vaccine 17D (YF-17D) immunized human subjects. Different experimental and analysis conditions were likely contributed to the observed diffe...
Citations
... In contrast, ICDO authors do not provide much information about how they applied the XOD principles. An ontology representing the knowledge about vaccine studies, Vaccine Investigation Ontology (VIO), was also recently developed following the XOD principles [31]. The authors do not explicitly refer to the four principles in their description of the ontology design methodology followed but they mention the use of the OntoFox tool [32] to address principles XOD 1 and XOD 2 and define ontology design patterns (related to XOD 3). ...
The increasing number of articles on adverse interactions that may occur when specific foods are consumed with certain drugs makes it difficult to keep up with the latest findings. Conflicting information is available in the scientific literature and specialized knowledge bases because interactions are described in an unstructured or semi-structured format. The FIDEO ontology aims to integrate and represent information about food-drug interactions in a structured way. This article reports on the new version of this ontology in which more than 1700 interactions are integrated from two online resources: DrugBank and Hedrine. These food-drug interactions have been represented in FIDEO in the form of precompiled concepts, each of which specifies both the food and the drug involved. Additionally, competency questions that can be answered are reviewed, and avenues for further enrichment are discussed.
... These phenomena indeed align with our postulate of HPI dynamic outcomes in that various conditions may affect the HPI dynamics and disease outcome. To address these issues, it is possible to develop and follow minimal information standards (9, 101), dissect different conditions and variables in specific experimental and computational studies, and model and represent these conditions and variables using interoperable ontologies (9,51,98,99,(102)(103)(104). Ontologybased knowledge bases (38,100,105) can also be used and applied. ...
COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.
... Ontology provides a feasible way to deal with the complexity of the diverse conditions tested. For example, three independent high-impact studies [24][25][26] of transcriptomic gene expression profiles in Yellow Fever vaccine 17D immunized human subjects led to overlapping but quite different results of enriched genes [27,28]. The Vaccine Investigation Ontology (VIO) [28], together with the Vaccine Ontology [18,29], has been used to model and standardize various vaccine study conditions. ...
... For example, three independent high-impact studies [24][25][26] of transcriptomic gene expression profiles in Yellow Fever vaccine 17D immunized human subjects led to overlapping but quite different results of enriched genes [27,28]. The Vaccine Investigation Ontology (VIO) [28], together with the Vaccine Ontology [18,29], has been used to model and standardize various vaccine study conditions. These ontologies were designed to describe different variables for vaccine design. ...
... A further study on the vaccine immune factors defined in VaximmutorDB will facilitate vaccine immune mechanism understanding and vaccine design. Ontologysupported data standardization of host response across multiple vaccine studies can be further implemented to analyze patterns across diverse vaccine types and species [28,[165][166][167]. ...
Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.
... It appears that although different genes were upor down-regulated given different conditions, these genes participated in the similar set of pathways. In addition to the usage of the manually annotated VaximmutorDB knowledge, a previous direct analysis of raw OMICS data from different studies also resulted in the same conclusion (i.e., more shared pathways than shared gene lists) (55). Therefore, it is important to map genes to pathways and conduct pathway-based analysis and visualization of vaximmutors. ...
... (56,57), can be systematically analyzed for such investigations. Ontologies such as OBCS, VO, Vaccine Investigation Ontology (VIO) (55), and Cell Ontology (58) can be used to standardize the data for efficient secondary data analyses. The gene expression results can be from transcriptomical or proteomic analysis. ...
Vaccines stimulate various immune factors critical to protective immune responses. However, a comprehensive picture of vaccine-induced immune factors and pathways have not been systematically collected and analyzed. To address this issue, we developed VaximmutorDB, a web-based database system of vaccine immune factors (abbreviated as “vaximmutors”) manually curated from peer-reviewed articles. VaximmutorDB currently stores 1,740 vaccine immune factors from 13 host species (e.g., human, mouse, and pig). These vaximmutors were induced by 154 vaccines for 46 pathogens. Top 10 vaximmutors include three antibodies (IgG, IgG2a and IgG1), Th1 immune factors (IFN-γ and IL-2), Th2 immune factors (IL-4 and IL-6), TNF-α, CASP-1, and TLR8. Many enriched host processes (e.g., stimulatory C-type lectin receptor signaling pathway, SRP-dependent cotranslational protein targeting to membrane) and cellular components (e.g., extracellular exosome, nucleoplasm) by all the vaximmutors were identified. Using influenza as a model, live attenuated and killed inactivated influenza vaccines stimulate many shared pathways such as signaling of many interleukins (including IL-1, IL-4, IL-6, IL-13, IL-20, and IL-27), interferon signaling, MARK1 activation, and neutrophil degranulation. However, they also present their unique response patterns. While live attenuated influenza vaccine FluMist induced significant signal transduction responses, killed inactivated influenza vaccine Fluarix induced significant metabolism of protein responses. Two different Yellow Fever vaccine (YF-Vax) studies resulted in overlapping gene lists; however, they shared more portions of pathways than gene lists. Interestingly, live attenuated YF-Vax simulates significant metabolism of protein responses, which was similar to the pattern induced by killed inactivated Fluarix. A user-friendly web interface was generated to access, browse and search the VaximmutorDB database information. As the first web-based database of vaccine immune factors, VaximmutorDB provides systematical collection, standardization, storage, and analysis of experimentally verified vaccine immune factors, supporting better understanding of protective vaccine immunity.
... The transcriptomic gene expression profiles of Yellow Fever vaccine 17D (YF-17D) were analyzed by a GO system named Vaccine Investigation Ontology (VIO) to identify the association of various variables in the vaccinated population. Hence, not only GO analysis has a potential application in the microarray assay after infection and/or vaccination to detect the profiles of gene expression, but also the Reactome analysis pathway tools are employed to explore the enriched pathways after infection and/or vaccination [42]. Therefore, the p-value based on FDR < 0.05 as the significance cut-offs was applied in the GO analysis and Reactome pathway enrichment pathways in the evaluation of transcriptomic gene expression profiles by microarray assay in SARS-CoV infection, which has a potential to be expanded to CIVID-19. ...
Background:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 19 (COVID-19) that was emerged as a new member of coronaviruses since December 2019 in Wuhan, China and then after was spread in all continentals. Since SARS-CoV-2 has shown about 77.5% similarity to SARS-CoV, the transcriptome and immunological regulations of SARS-CoV-2 was expected to have high percentage of overlap with SARS-CoV.
Results:
In this study, we applied the single cell transcriptomics data of human bronchial epithelial cells (2B4 cell line) infected with SARS-CoV, which was annotated in the Expression Atlas database to expand this data to COVID-19. In addition, we employed system biology methods including gene ontology (GO) and Reactome pathway analyses to define functional genes and pathways in the infected cells with SARS-CoV. The transcriptomics analysis on the Expression Atlas database revealed that most genes from infected 2B4 cell line with SARS-CoV were downregulated leading to immune system hyperactivation, induction of signaling pathways, and consequently a cytokine storm. In addition, GO:0016192 (vesicle-mediated transport), GO:0006886 (intracellular protein transport), and GO:0006888 (ER to Golgi vesicle-mediated transport) were shown as top three GOs in the ontology network of infected cells with SARS-CoV. Meanwhile, R-HAS-6807070 (phosphatase and tensin homolog or PTEN regulation) showed the highest association with other Reactome pathways in the network of infected cells with SARS-CoV. PTEN plays a critical role in the activation of dendritic cells, B- and T-cells, and secretion of proinflammatory cytokines, which cooperates with downregulated genes in the promotion of cytokine storm in the COVID-19 patients.
Conclusions:
Based on the high similarity percentage of the transcriptome of SARS-CoV with SARS-CoV-2, the data of immunological regulations, signaling pathways, and proinflammatory cytokines in SARS-CoV infection can be expanded to COVID-19 to have a valid platform for future pharmaceutical and vaccine studies.
... The choice of DNA vaccine, recombinant vaccine vector, and another method of vaccine formulation is also deeply rooted in our understanding of pathogen-specific immune response induction. Different experimental conditions may also affect results (59,60). Therefore, it is crucial to understand the underlying molecular and cellular mechanisms for rational vaccine development. ...
To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an “Sp/Nsp cocktail vaccine” containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.
... This year, four full-length papers and one short-length paper were accepted for oral presentations at the workshop after a peer-review process with each submission reviewed by at least three independent reviewers. After one additional round of independent peer reviewing on their extended version, with the reviewers' comments taken care of, by the workshop co-organizers and the journal editors, four full-length papers [40][41][42][43] have been accepted for publication in the current thematic issue of the BMC Bioinformatics. ...
... In the area of ontology development and representation, Ong et al. [41] developed a Vaccine Investigation Ontology (VIO) as an extension of the Vaccine Ontology (VO) and applied VIO to classify the different experimental variables and relations among them in the vaccine research. Different responses in the host to the same vaccine are frequently observed in vaccine studies; therefore, it systematically represents different experimental and analysis conditions. ...
This Editorial first introduces the background of the vaccine and drug relations and how biomedical terminologies and ontologies have been used to support their studies. The history of the seven workshops, initially named VDOSME, and then named VDOS, is also summarized and introduced. Then the 7th International Workshop on Vaccine and Drug Ontology Studies (VDOS 2018), held on August 10th, 2018, Corvallis, Oregon, USA, is introduced in detail. These VDOS workshops have greatly supported the development, applications, and discussion of vaccine- and drug-related terminology and drug studies.
Vaccination is one of the most successful public health interventions in modern medicine. However, it is still challenging to develop effective vaccines against many infectious diseases such as tuberculosis, HIV, and malaria. There are challenges in integrating the high volume, variety, and variability of vaccine-related data and rationally designing effective and safe vaccines efficiently. In my thesis study, I systematically and comprehensively analyzed manually annotated protective vaccine antigens in the Protegen database and identified these protective antigens' enriched patterns. I then created Vaxign-ML, a novel machine learning-based reverse vaccinology method based on the curated Protegen data for rational vaccine design. Vaxign-ML was used to successfully predict vaccine antigens for tuberculosis and Coronavirus Disease 2019 (COVID-19). I also developed a new structural vaccinology design program that optimizes COVID-19 spike glycoprotein as a vaccine candidate for enhanced vaccine protection via T cell epitope engineering. The vaccine antigens selected and optimized by Reverse and Structural Vaccinology in this dissertation are subjected to future experimental verification. Furthermore, I created a community-based Ontology of Host-Pathogen Interactions (OHPI), which served as a platform to semantically represent the interactions between host and virulence factors that are also protective antigens. I developed the Vaccine Investigation Ontology (VIO) for standardized metadata representation for high throughput vaccine OMICS data analysis. Overall, my thesis research aims to uncover protective antigen patterns, create methods/tools to effectively develop vaccines against infectious diseases of public health significance, and strengthen our understanding of vaccine protection mechanisms. These works can be further expanded and integrated with other technologies such as epitope prediction, molecular epidemiology, and high-throughput sequencing to build the foundation of precision vaccinology.
Vaccination is one of the most important innovations in human history. It has also become a hot research area in a new application - the development of new vaccines against non-infectious diseases such as cancers. However, effective and safe vaccines still do not exist for many diseases, and where vaccines to exist, their protective immune mechanisms are often unclear. Although licensed vaccines are generally safe, various adverse events, and sometimes severe adverse events, still exist for a small population. Precision medicine tailors medical intervention to the personal characteristics of individual patients or sub-populations of individuals with similar immunity-related characteristics. Precision vaccinology is a new strategy that applies precision medicine to the development, administration, and postadministration analysis of vaccines. Several conditions contribute to make this the right time to embark on the development of precision vaccinology. First, the increased level of research in vaccinology has generated voluminous “big data” repositories of vaccinology data. Secondly, new technologies such as multi-omics and immunoinformatics bring new methods for investigating vaccines and immunology. Finally, the advent of AI and machine learning software now make possible the marriage of Big Data to the development of new vaccines in ways not possible before. However, something is missing in this marriage, and that is a common language that facilitates the correlation, analysis, and reporting nomenclature for the field of vaccinology. Solving this bioinformatics problem is the domain of applied biomedical ontology. Ontology in the informatics field is human- and machine-interpretable representation of entities and the relations among entities in a specific domain. The Vaccine Ontology (VO) and Ontology of Vaccine Adverse Events (OVAE) have been developed to support the standard representation of vaccines, vaccine components, vaccinations, host responses, and vaccine adverse events. Many other biomedical ontologies have also been developed and can be applied in vaccine research. Here, we review the current status of precision vaccinology and how ontological development will enhance this field, and propose an ontology-based precision vaccinology strategy to support precision vaccine research and development.
Purpose of review:
Artificial intelligence has pervasively transformed many industries and is beginning to shape medical practice. New use cases are being identified in subspecialty domains of medicine and, in particular, application of artificial intelligence has found its way to the practice of allergy-immunology. Here, we summarize recent developments, emerging applications and obstacles to realizing full potential.
Recent findings:
Artificial/augmented intelligence and machine learning are being used to reduce dimensional complexity, understand cellular interactions and advance vaccine work in the basic sciences. In genomics, bioinformatic methods are critical for variant calling and classification. For clinical work, artificial intelligence is enabling disease detection, risk profiling and decision support. These approaches are just beginning to have impact upon the field of clinical immunology and much opportunity exists for further advancement.
Summary:
This review highlights use of computational methods for analysis of large datasets across the spectrum of research and clinical care for patients with immunological disorders. Here, we discuss how big data methods are presently being used across the field clinical immunology.
