Meta-analysis of variables affecting mouse protection efficacy of whole organism Brucella vaccines and vaccine candidates

Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.

VIO: ontology classification and study of vaccine responses given various experimental and analytical conditions

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 differences. To investigate this issue, we developed a Vaccine Investigation Ontology (VIO), and applied VIO to classify the different variables and relations among these variables systematically. We then evaluated whether the ontological VIO modeling and VIO-based statistical analysis would contribute to the enhanced vaccine investigation studies and a better understanding of vaccine response mechanisms.

Results

Our VIO modeling identified many variables related to data processing and analysis such as normalization method, cut-off criteria, software settings including software version. The datasets from two previous studies on human responses to YF-17D vaccine, reported by Gaucher et al. (2008) and Querec et al. (2009), were re-analyzed. We first applied the same LIMMA statistical method to re-analyze the Gaucher data set and identified a big difference in terms of significantly differentiated gene lists compared to the original study. The different results were likely due to the LIMMA version and software package differences. Our second study re-analyzed both Gaucher and Querec data sets but with the same data processing and analysis pipeline. Significant differences in differential gene lists were also identified. In both studies, we found that Gene Ontology (GO) enrichment results had more overlapping than the gene lists and enriched pathway lists. The visualization of the identified GO hierarchical structures among the enriched GO terms and their associated ancestor terms using GOfox allowed us to find more associations among enriched but often different GO terms, demonstrating the usage of GO hierarchical relations enhance data analysis.

Conclusions

The ontology-based analysis framework supports standardized representation, integration, and analysis of heterogeneous data of host responses to vaccines. Our study also showed that differences in specific variables might explain different results drawn from similar studies.

Limited available evidence supports theoretical predictions of reduced vaccine efficacy at higher exposure dose

Understanding the causes of vaccine failure is important for predicting disease dynamics in vaccinated populations and planning disease interventions. Pathogen exposure dose and heterogeneity in host susceptibility have both been implicated as important factors that may reduce overall vaccine efficacy and cause vaccine failure. Here, we explore the effect of pathogen dose and heterogeneity in host susceptibility in reducing efficacy of vaccines. Using simulation-based methods, we find that increases in pathogen exposure dose decrease vaccine efficacy, but this effect is modified by heterogeneity in host susceptibility. In populations where the mode of vaccine action is highly polarized, vaccine efficacy decreases more slowly with exposure dose than in populations with less variable protection. We compared these theoretical results to empirical estimates from a systematic literature review of vaccines tested over multiple exposure doses. We found that few studies (nine of 5,389) tested vaccine protection against infection over multiple pathogen challenge doses, with seven studies demonstrating a decrease in vaccine efficacy with increasing exposure dose. Our research demonstrates that pathogen dose has potential to be an important determinant of vaccine failure, although the limited empirical data highlight a need for additional studies to test theoretical predictions on the plausibility of reduced host susceptibility and high pathogen dose as mechanisms responsible for reduced vaccine efficacy in high transmission settings.

Comparison of the protective immunity elicited by a Brucella cocktail protein vaccine (rL7/L12+rTOmp31+rSOmp2b) in two different adjuvant formulations in BALB/c mice

In the present study, protective efficacy conferred by a cocktail protein consisted of Brucella L7/L12 ribosomal, truncated outer membrane protein 31 (TOmp31) and SOmp2b recombinant proteins in CpG ODN 1826+ Montanide ISA 70VG or Poly (I:C) adjuvants was evaluated and compared in BALB/c mice. Immunization of mice with both vaccine regimens elicited strong specific IgG responses (higher IgG2a titers over IgG1 titers), provided T helper1 (Th1) oriented immune responses and conferred protection levels compatible to the live vaccines against Brucella challenge. Vaccination of BALB/c mice with the cocktail protein in CpG ODN 1826+ Montanide ISA 70 V G adjuvants induced higher levels of antibody, IFN-γ/IL-2 and conferred more protection levels against B. melitenisis and B. abortus challenge than did the cocktail protein in Poly (I:C) formulation. In conclusion, both vaccine regimens are capable of stimulating specific Th1- biased immune responses and conferring cross protection against B. melitensis and B. abortus infections. Therefore, they could be introduced as new potential candidates for the development of subunit vaccines against Brucella infection.

Ontology-Based Meta-Analysis of Animal and Human Adverse Events Associated With Licensed Brucellosis Vaccines

Brucella abortus strain 19 (S19), Brucella melitensis Rev 1 (Rev1), and B. abortus strain RB51 (RB51) are the three licensed animal brucellosis vaccines, and they have been most commonly and successfully used in prevent brucellosis in animals. However, many adverse events (AEs) have been associated with these three vaccines after their administering to animals or being accidentally exposed to humans. In this study, 27 peer-reviewed publications containing animal and human AE reports associated with these three brucellosis vaccines were manually annotated from the PubMed database. Our meta-analysis identified 20 animal AEs and 46 human AEs associated with the three vaccines. Based on the Ontology of Adverse Events (OAE) hierarchical classification, these animal AEs were enriched in the immune and reproductive systems that might eventually result in the occurrence of abortion or infertility. The human AEs were concentrated in the behavioral and neurological conditions, and these AEs showed flu-like symptoms that are consistent with human brucellosis. Furthermore, an analysis of variance (ANOVA) statistics analysis with linear model fits was used to determine the major variables that might affect the occurrence of abortion AE in animals. The ANOVA results indicated that three variables (P-value < 0.05) are significantly associated with the occurrence of abortion AE: animal species, vaccination dose, and vaccination route. The other two variables (i.e., vaccine type and animal age at vaccination) did not significantly (P-value > 0.05) associated with the occurrence of abortion AE. Overall, this study represents the first ontology-based meta-analysis of adverse events associated with animal vaccines. The results of such a study led to the better understanding of brucellosis vaccine AEs, facilitating rational design of more secure and effective vaccines.

Multivalent Fusion DNA Vaccine against Brucella abortus

As an alternative brucellosis prevention method, we evaluated the immunogenicity induced by new multivalent DNA vaccines in BALB/c mice. We constructed the vaccines by fusion of BAB1_0273 and/or BAB1_0278 open reading frames (ORFs) from genomic island 3 (GI-3) and the Brucella abortus 2308 sodC gene with a link based on prolines and alanines (pV273-sod, pV278-sod, and pV273-278-sod, resp.). Results show that immunization with all tested multivalent DNA vaccines induced a specific humoral and cellular immune response. These novel multivalent vaccines significantly increased the production of IgM, IgG, and IgG2a antibodies as well as IFN-γ levels and the lymphoproliferative response of splenocytes. Although immunization with these multivalent vaccines induced a typical T-helper 1- (Th1-) dominated immune response, such immunogenicity conferred low protection levels in mice challenged with the B. abortus 2308 pathogenic strain. Our results demonstrated that the expression of BAB1_0273 and/or BABl_0278 antigens conjugated to SOD protein can polarize mice immunity to a Th1-type phenotype, conferring low levels of protection.

Meta-Analysis and Advancement of Brucellosis Vaccinology

Background/Objectives

In spite of all the research effort for developing new vaccines against brucellosis, it remains unclear whether these new vaccine technologies will in fact become widely used. The goal of this study was to perform a meta-analysis to identify parameters that influence vaccine efficacy as well as a descriptive analysis on how the field of Brucella vaccinology is advancing concerning type of vaccine, improvement of protection on animal models over time, and factors that may affect protection in the mouse model.

Methods

A total of 117 publications that met the criteria were selected for inclusion in this study, with a total of 782 individual experiments analyzed.

Results

Attenuated (n = 221), inactivated (n = 66) and mutant (n = 102) vaccines provided median protection index above 2, whereas subunit (n = 287), DNA (n = 68), and vectored (n = 38) vaccines provided protection indexes lower than 2. When all categories of experimental vaccines are analyzed together, the trend line clearly demonstrates that there was no improvement of the protection indexes over the past 30 years, with a low negative and non significant linear coefficient. A meta-regression model was developed including all vaccine categories (attenuated, DNA, inactivated, mutant, subunit, and vectored) considering the protection index as a dependent variable and the other parameters (mouse strain, route of vaccination, number of vaccinations, use of adjuvant, challenge Brucella species) as independent variables. Some of these variables influenced the expected protection index of experimental vaccines against Brucella spp. in the mouse model.

Conclusion

In spite of the large number of publication over the past 30 years, our results indicate that there is not clear trend to improve the protective potential of these experimental vaccines.

Paralog-Specific Patterns of Structural Disorder and Phosphorylation in the Vertebrate SH3-SH2-Tyrosine Kinase Protein Family

One of the largest multigene families in Metazoa are the tyrosine kinases (TKs). These are important multifunctional proteins that have evolved as dynamic switches that perform tyrosine phosphorylation and other noncatalytic activities regulated by various allosteric mechanisms. TKs interact with each other and with other molecules, ultimately activating and inhibiting different signaling pathways. TKs are implicated in cancer and almost 30 FDA-approved TK inhibitors are available. However, specific binding is a challenge when targeting an active site that has been conserved in multiple protein paralogs for millions of years. A cassette domain (CD) containing SH3-SH2-Tyrosine Kinase domains reoccurs in vertebrate nonreceptor TKs. Although part of the CD function is shared between TKs, it also presents TK specific features. Here, the evolutionary dynamics of sequence, structure, and phosphorylation across the CD in 17 TK paralogs have been investigated in a large-scale study. We establish that TKs often have ortholog-specific structural disorder and phosphorylation patterns, while secondary structure elements, as expected, are highly conserved. Further, domain-specific differences are at play. Notably, we found the catalytic domain to fluctuate more in certain secondary structure elements than the regulatory domains. By elucidating how different properties evolve after gene duplications and which properties are specifically conserved within orthologs, the mechanistic understanding of protein evolution is enriched and regions supposedly critical for functional divergence across paralogs are highlighted.

The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis

Background

Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem.

Results

The terms in OBCS including ‘data collection’, ‘data transformation in statistics’, ‘data visualization’, ‘statistical data analysis’, and ‘drawing a conclusion based on data’, cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. Currently, OBCS comprehends 878 terms, representing 20 BFO classes, 403 OBI classes, 229 OBCS specific classes, and 122 classes imported from ten other OBO ontologies.

We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. Other ongoing projects using OBCS for statistical data processing are also discussed.

The OBCS source code and documentation are available at: https://github.com/obcs/obcs.

Conclusions

The Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology in the domain of biological and clinical statistics. OBCS is a timely ontology that represents statistics-related terms and their relations in a rigorous fashion, facilitates standard data analysis and integration, and supports reproducible biological and clinical research.

Vaxar: A Web-Based Database of Laboratory Animal Responses to Vaccinations and Its Application in the Meta-Analysis of Different Animal Responses to Tuberculosis Vaccinations

Animal models are indispensable for vaccine research and development. However, choosing which species to use and designing a vaccine study that is optimized for that species is often challenging. Vaxar (http://www.violinet.org/vaxar/) is a web-based database and analysis system that stores manually curated data regarding vaccine-induced responses in animals. To date, Vaxar encompasses models from 35 animal species including rodents, rabbits, ferrets, primates, and birds. These 35 species have been used to study more than 1300 experimentally tested vaccines for 164 pathogens and diseases significant to humans and domestic animals. The responses to vaccines by animals in more than 1500 experimental studies are recorded in Vaxar; these data can be used for systematic meta-analysis of various animal responses to a particular vaccine. For example, several variables, including animal strain, animal age, and the dose or route of either vaccination or challenge, might affect host response outcomes. Vaxar can also be used to identify variables that affect responses to different vaccines in a specific animal model. All data stored in Vaxar are publically available for web-based queries and analyses. Overall Vaxar provides a unique systematic approach for understanding vaccine-induced host immunity.

Structural Analysis of Der p 1-Antibody Complexes and Comparison with Complexes of Proteins or Peptides with Monoclonal Antibodies

Der p 1 is a major allergen from the house dust mite, Dermatophagoides pteronyssinus, that belongs to the papain-like cysteine protease family. To investigate the antigenic determinants of Der p 1, we determined two crystal structures of Der p 1 in complex with the Fab fragments of mAbs 5H8 or 10B9. Epitopes for these two Der p 1-specific Abs are located in different, nonoverlapping parts of the Der p 1 molecule. Nevertheless, surface area and identity of the amino acid residues involved in hydrogen bonds between allergen and Ab are similar. The epitope for mAb 10B9 only showed a partial overlap with the previously reported epitope for mAb 4C1, a cross-reactive mAb that binds Der p 1 and its homolog Der f 1 from Dermatophagoides farinae. Upon binding to Der p 1, the Fab fragment of mAb 10B9 was found to form a very rare α helix in its third CDR of the H chain. To provide an overview of the surface properties of the interfaces formed by the complexes of Der p 1-10B9 and Der p 1-5H8, along with the complexes of 4C1 with Der p 1 and Der f 1, a broad analysis of the surfaces and hydrogen bonds of all complexes of Fab-protein or Fab-peptide was performed. This work provides detailed insight into the cross-reactive and specific allergen-Ab interactions in group 1 mite allergens. The surface data of Fab-protein and Fab-peptide interfaces can be used in the design of conformational epitopes with reduced Ab binding for immunotherapy.

Protection Provided by an Encapsulated Live Attenuated ΔabcBA Strain of Brucella ovis against Experimental Challenge in a Murine Model

This study aimed to evaluate the Brucella ovis ΔabcBA strain as a vaccine candidate in the murine model. BALB/c mice were subcutaneously or intraperitoneally immunized with a single dose or three doses of the B. ovis ΔabcBA strain and then were challenged with wild-type B. ovis. Single or multiple immunizations provided only mild protection, with significantly smaller numbers of wild-type B. ovis CFU in the livers of immunized mice but not in the spleens. Encapsulation of B. ovis ΔabcBA significantly improved protection against experimental challenges in both BALB/c and C57BL/6 mice. Furthermore, immunization with encapsulated B. ovis ΔabcBA markedly prevented lesions in the spleens and livers of experimentally challenged mice. These results demonstrated that the encapsulated B. ovis ΔabcBA strain confers protection to mice; therefore, this strain has potential as a vaccine candidate for rams.

A survey of current trends in computational drug repositioning

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.

Ontology-supported research on vaccine efficacy, safety and integrative biological networks

While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including Vaccine Ontology, Ontology of Adverse Events and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network ('OneNet') Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.

Updates on the web-based VIOLIN vaccine database and analysis system

The integrative Vaccine Investigation and Online Information Network (VIOLIN) vaccine research database and analysis system (http://www.violinet.org) curates, stores, analyses and integrates various vaccine-associated research data. Since its first publication in NAR in 2008, significant updates have been made. Starting from 211 vaccines annotated at the end of 2007, VIOLIN now includes over 3240 vaccines for 192 infectious diseases and eight noninfectious diseases (e.g. cancers and allergies). Under the umbrella of VIOLIN, >10 relatively independent programs are developed. For example, Protegen stores over 800 protective antigens experimentally proven valid for vaccine development. VirmugenDB annotated over 200 'virmugens', a term coined by us to represent those virulence factor genes that can be mutated to generate successful live attenuated vaccines. Specific patterns were identified from the genes collected in Protegen and VirmugenDB. VIOLIN also includes Vaxign, the first web-based vaccine candidate prediction program based on reverse vaccinology. VIOLIN collects and analyzes different vaccine components including vaccine adjuvants (Vaxjo) and DNA vaccine plasmids (DNAVaxDB). VIOLIN includes licensed human vaccines (Huvax) and veterinary vaccines (Vevax). The Vaccine Ontology is applied to standardize and integrate various data in VIOLIN. VIOLIN also hosts the Ontology of Vaccine Adverse Events (OVAE) that logically represents adverse events associated with licensed human vaccines.