VaximmutorDB: A Web-Based Vaccine Immune Factor Database and Its Application for Understanding Vaccine-Induced Immune Mechanisms

Proteome profile of Leishmania donovani Centrin1-/- parasite-infected human macrophage cell line and its implications in determining possible mechanisms of protective immunity

Our developed cell division-specific 'centrin' gene deleted Leishmania donovani (LdCen1-/-) the causative parasite of the fatal visceral-leishmaniasis (VL), exhibits a selective growth arrest at the intracellular stage and is anticipated as a live attenuated vaccine candidate against VL. LdCen1-/- immunization in animals has shown increased IFN-γ secreting CD4+ and CD8+ T cells along with protection conferred by a protective proinflammatory immune response. A label-free proteomics approach has been employed to understand the physiology of infection and predict disease interceptors during Leishmania-host interactions. Proteomic modulation after infection of human macrophage cell lines suggested elevated annexin A6, implying involvement in various biological processes such as membrane repair, transport, actin dynamics, cell proliferation, survival, differentiation, and inflammation, thereby potentiating its immunological protective capacity. Additionally, S100A8 and S100A9 proteins, known for maintaining homeostatic balance in regulating the inflammatory response, have been upregulated after infection. The inhibitory clade of serpins, known to inhibit cysteine proteases (CPs), was upregulated in host cells after 48 h of infection. This is reflected in the diminished expression of CPs in the parasites during infection. Such proteome analysis confirms LdCen1-/- efficacy as a vaccine candidate and predicts potential markers in future vaccine development strategies against infectious diseases.

Database and review of disinfection by-products since 1974: Constituent elements, molecular weights, and structures

Since trihalomethanes were discovered in 1974, disinfection by-products (DBPs) in drinking water have attracted extensive attention. In 2011, more than 600 known DBPs were compiled; however, newly reported DBPs have not been integrated. The rapid development of mass spectrometry has led to a significant increase in the number of DBPs, therefore, there is a need to develop a database of all DBPs and their properties. Herein, a database including 6310 DBPs (651 confirmed, 1478 identified and 4142 proposed) reported between 1974 and 2022 was constructed and made available for public use at https://dbps.com.cn/main. This database can be a tool in screening new DBPs, comprehensively reviewing, and developing predictive models. In this paper, to demonstrate the functions of the database and provide useful information for this area, the origin of the collected DBPs was presented, and some basic information, including elemental composition, molecular weight, functional groups, and carbon frameworks, were comparatively analyzed. The results showed that the proportion of DBPs verified by standard compounds and frequently detected in real water is less than 7.0%, and most of DBPs remained to be identified. Approximately 88% of DBPs contain halogens, and brominated -DBPs occupied a similar ratio to chlorinated -DBPs in real water. Acids were the main functional groups of DBPs, aliphatic and aromatic compounds are the two major carbon frameworks, and the molecular weights of most DBPs ranged from 200 to 400 Da. In addition, 4142 proposed DBPs as obtained using high-resolution mass spectrometry, were characterized based on the modified van Krevelen diagram and adjusted indexes with halogens. Most of the proposed DBPs featured lignin and tannin structures, and phenolic/highly unsaturated DBPs account for the majority.

VIGET: A web portal for study of vaccine-induced host responses based on Reactome pathways and ImmPort data

Host responses to vaccines are complex but important to investigate. To facilitate the study, we have developed a tool called Vaccine Induced Gene Expression Analysis Tool (VIGET), with the aim to provide an interactive online tool for users to efficiently and robustly analyze the host immune response gene expression data collected in the ImmPort/GEO databases. VIGET allows users to select vaccines, choose ImmPort studies, set up analysis models by choosing confounding variables and two groups of samples having different vaccination times, and then perform differential expression analysis to select genes for pathway enrichment analysis and functional interaction network construction using the Reactome's web services. VIGET provides features for users to compare results from two analyses, facilitating comparative response analysis across different demographic groups. VIGET uses the Vaccine Ontology (VO) to classify various types of vaccines such as live or inactivated flu vaccines, yellow fever vaccines, etc. To showcase the utilities of VIGET, we conducted a longitudinal analysis of immune responses to yellow fever vaccines and found an intriguing complex activity response pattern of pathways in the immune system annotated in Reactome, demonstrating that VIGET is a valuable web portal that supports effective vaccine response studies using Reactome pathways and ImmPort data.

A new framework for host-pathogen interaction research

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.

A curated collection of human vaccination response signatures

Recent advances in high-throughput experiments and systems biology approaches have resulted in hundreds of publications identifying “immune signatures”. Unfortunately, these are often described within text, figures, or tables in a format not amenable to computational processing, thus severely hampering our ability to fully exploit this information. Here we present a data model to represent immune signatures, along with the Human Immunology Project Consortium (HIPC) Dashboard (www.hipc-dashboard.org), a web-enabled application to facilitate signature access and querying. The data model captures the biological response components (e.g., genes, proteins, cell types or metabolites) and metadata describing the context under which the signature was identified using standardized terms from established resources (e.g., HGNC, Protein Ontology, Cell Ontology). We have manually curated a collection of >600 immune signatures from >60 published studies profiling human vaccination responses for the current release. The system will aid in building a broader understanding of the human immune response to stimuli by enabling researchers to easily access and interrogate published immune signatures.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning

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.