bioRxiv Science⌕ Search

Biology subjects

Huffman, A.

Publications and source records attributed to Huffman, A..

6 recordsLinked to original sources

SEA CDM: Study-Experiment-Assay Common Data Model and Databases for Cross-Domain Data Integration and Analysis

With the increasing volume of biomedical experimental data, standardizing, sharing, and integrating heterogeneous experimental data across domains has become a major challenge. To address this challenge, we have developed an ontology-supported Study-Experiment-Assay (SEA) common data model (CDM), which includes 10 core and 3 auxiliary classes based on object-oriented modeling. SEA CDM uses interoperable ontologies for data standardization and knowledge inference. Building on the SEA CDM, we developed the Ontology-based SEA Network (OSEAN) relational database and knowledge graph, along with a set of ETL (Extract, Transform, Load) and query tools, and further applied them to represent 1,278 immune studies with over two million samples from three resources: VIGET, ImmPort, and CELLxGENE. Using simple, robust queries and analyses, our research identified multiple scientific insights into sex-specific immune responses, such as neutrophil degranulation and TNF binding to physiological receptors, following live attenuated and trivalent inactivated influenza vaccination. The novel SEA CDM system lays a foundation for establishing an integrative biodata ecosystem across biological and biomedical domains.

bioinformatics↗

VO: The Vaccine Ontology

With the widespread use of vaccines in research and clinical settings, there is an urgent need to standardize vaccine representation, integrate information across diverse vaccine types, and support computer-assisted reasoning. Accordingly, we have since 2007 developed the community-based Vaccine Ontology (VO), which aligns with the Basic Formal Ontology and adheres to OBO Foundry principles. VO models ontologically vaccines, vaccine components, vaccine immune responses, vaccine investigation studies and other vaccine-related topics. VO represents more than 10,000 vaccines targeting 289 infectious pathogens and cancers in humans and over 30 nonhuman animal species. VO provides mappings to external resources such as RxNorm, CVX, FDA, and USDA. Various VO use cases exist. VO facilitates vaccine standardization in resources such as the VIOLIN vaccine database, ImmPort, and the Vaccine Adjuvant Compendium (VAC). Semantic queries can be made to query VO. VO has been shown to enhance experimental and clinical vaccine data analysis and vaccine literature mining. Overall, VO standardizes vaccine modeling and representation and greatly supports vaccine AI research in the Semantic Web era.

bioinformatics↗

Immune Biomarkers, Profiles, and Responses: A Vaccine Ontology Perspective

BackgroundVaccines have the ability to induce a range of immune responses under different conditions, for example stimulating the production of neutralizing antibodies to block pathogen entry or activating cytotoxic T-cells to eliminate infected cells. Many such immune responses have not been thoroughly examined and classified. The Vaccine Ontology (VO) is a community-based ontology in the domain of vaccinology. We here describe how VO is used to represent the variety of immune responses associated with vaccines, together with associated biomarkers and profiles. ResultsThe VO differentiates vaccination and vaccine immunization. The former is a process of administering a vaccine in vivo; the latter is the outcome of vaccine induction of immune response. This distinction is critical for understanding both the procedure of vaccination and the resulting immune effects. VO also models and represents various vaccine-induced responses at multiple biological levels, including population, organism, organ/tissue, cell, and gene/protein levels. Such an approach captures the complexity of vaccine-induced immunity, from population-wide trend (for example: herd immunity) to molecular mechanisms. VO defines immune biomarkers as material entities such as neutralizing antibodies that signify humoral immune response, and IFN-gamma that is indicative of cell-mediated responses. Such biomarkers provide measurable indicators of the immune systems functional state post vaccination, enabling robust evaluation of vaccine efficacy. VO classifies immune response profile and correlated profile (or correlate) of immune protection as process profiles, a class in the Basic Formal Ontology (BFO 2.0). Immune response profiles, such as Th1 (or Th2)- biased profile, can be induced by various vaccines and vaccine adjuvants. Different types of correlated profile of immune protection are also identified, such as mechanistic and non-mechanistic correlates of immune protection. Such distinctions help us to quickly identify biomarkers and associated prediction and measurement of different kinds of vaccine protection. ConclusionThe important immune-related terms for immune biomarkers, profiles, and responses are modeled ontologically in VO together with their interrelations. The results support enhanced classification and analysis of vaccine-induced immune responses and related biomarkers and immune profiles, leading to further understanding of the vaccine immune mechanisms and enhanced vaccine research and development.

immunology↗

Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation

Reverse vaccinology (RV) provides a systematic approach to identifying potential vaccine candidates based on protein sequences. The integration of machine learning (ML) into this process has greatly enhanced our ability to predict viable vaccine candidates from these sequences. We have previously developed a Vaxign-ML program based on the eXtreme Gradient Boosting (XGBoost). In this study, we further extend our work to develop a Vaxign-DL program based on deep learning techniques. Deep neural networks assemble non-linear models and learn multilevel abstraction of data using hierarchically structured layers, offering a data-driven approach in computational design models. Vaxign-DL uses a three-layer fully connected neural network model. Using the same bacterial vaccine candidate training data as used in Vaxign-ML development, Vaxign-DL was able to achieve an Area Under the Receiver Operating Characteristic of 0.94, specificity of 0.99, sensitivity of 0.74, and accuracy of 0.96. Using the Leave-One-Pathogen-Out Validation (LOPOV) method, Vaxign-DL was able to predict vaccine candidates for 10 pathogens. Our benchmark study shows that Vaxign-DL achieved comparable results with Vaxign-ML in most cases, and our method outperforms Vaxi-DL in the accurate prediction of bacterial protective antigens.

bioinformatics↗

Cellular and behavioral effects of altered NaV1.2 sodium channel ion permeability in Scn2aK1422E mice

Genetic variants in SCN2A, encoding the NaV1.2 voltage-gated sodium channel, are associated with a range of neurodevelopmental disorders with overlapping phenotypes. Some variants fit into a framework wherein gain-of-function missense variants that increase neuronal excitability lead to infantile epileptic encephalopathy, while loss-of-function variants that reduce neuronal excitability lead to developmental delay and/or autism spectrum disorder with or without co- morbid seizures. One unique case less easily classified using this binary paradigm is the de novo missense variant SCN2A p.K1422E, associated with infant-onset developmental delay, infantile spasms, and features of autism spectrum disorder. Prior structure-function studies demonstrated that K1422E substitution alters ion selectivity of NaV1.2, conferring Ca2+ permeability, lowering overall conductance, and conferring resistance to tetrodotoxin (TTX). Based on heterologous expression of K1422E, we developed a compartmental neuron model that predicted mixed effects on channel function and neuronal activity. We also generated Scn2aK1422E mice and characterized effects on neurons and neurological/neurobehavioral phenotypes. Dissociated neurons from heterozygous Scn2aK1422E/+ mice exhibited a novel TTX-resistant current with a reversal potential consistent with mixed ion permeation. Cortical slice recordings from Scn2aK1442E/+ tissue demonstrated impaired action potential initiation and larger Ca2+ transients at the axon initial segment during the rising phase of the action potential, suggesting mixed effects on channel function. Scn2aK1422E/+ mice exhibited rare spontaneous seizures, interictal EEG abnormalities, altered response to induced seizures, reduced anxiety-like behavior and alterations in olfactory-guided social behavior. Overall, Scn2aK1422E/+ mice present with phenotypes similar yet distinct from Scn2a knockout models, consistent with mixed effects of K1422E on NaV1.2 channel function. Significance StatementThe early-onset epilepsy variant SCN2A-p.K1422E displays unique biophysical properties in vitro. To model the impact of this rare variant, we generated Scn2aK1422E mice. Neurons from heterozygous Scn2aK1422E/+ mice showed functional deficits similar to the loss-of-function effects observed in the Scn2a haploinsufficiency model, as well as gain-of-function effects specific to the K1422E variant. There is also some overlap in neurobehavioral phenotypes between Scn2aK1422E/+ and Scn2a haploinsufficient mice. However, Scn2aK1422E/+ mice exhibited unique epilepsy-related phenotypes, including epileptiform events and seizures. Scn2aK1422E/+ mice serve as a useful platform to investigate phenotypic complexity of SCN2A-associated disorders.

neuroscience↗

COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning

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 protein, have been tested for vaccine development against SARS and MERS. We further used the Vaxign reverse vaccinology tool and the newly developed Vaxign-ML machine learning tool to predict COVID-19 vaccine candidates. The N protein was found to be conserved in the more pathogenic strains (SARS/MERS/COVID-19), but not in the other human coronaviruses that mostly cause 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 linear B-cell epitopes localized in specific locations and functional domains of the protein. Our predicted vaccine targets provide new strategies for effective and safe COVID-19 vaccine development.

bioinformatics↗