Elsevier

Vaccine

Volume 29, Issue 41, 22 September 2011, Pages 7197-7206
Vaccine

Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: Linking systems biology with vaccine development

https://doi.org/10.1016/j.vaccine.2011.05.080Get rights and content

Abstract

The aim of research on infectious diseases is their prevention, and brucellosis and salmonellosis as such are classic examples of worldwide zoonoses for application of a systems biology approach for enhanced rational vaccine development. When used optimally, vaccines prevent disease manifestations, reduce transmission of disease, decrease the need for pharmaceutical intervention, and improve the health and welfare of animals, as well as indirectly protecting against zoonotic diseases of people. Advances in the last decade or so using comprehensive systems biology approaches linking genomics, proteomics, bioinformatics, and biotechnology with immunology, pathogenesis and vaccine formulation and delivery are expected to enable enhanced approaches to vaccine development. The goal of this paper is to evaluate the role of computational systems biology analysis of host:pathogen interactions (the interactome) as a tool for enhanced rational design of vaccines. Systems biology is bringing a new, more robust approach to veterinary vaccine design based upon a deeper understanding of the host–pathogen interactions and its impact on the host's molecular network of the immune system. A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Mycobacterium avium paratuberculosis (MAP), Salmonella enterica Typhimurium (STM), and a Salmonella mutant (isogenic ΔsipA, sopABDE2) and linked to the basis for rational development of vaccines for brucellosis and salmonellosis as reviewed by Adams et al. and Ficht et al. [1], [2]. A bovine ligated ileal loop biological model was established to capture the host gene expression response at multiple time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a comparative pathogenicity analysis of 219 signaling and metabolic pathways and 1620 gene ontology (GO) categories that defined the host's biosignatures to each infectious condition. Through this DBN computational approach, the method identified significantly perturbed pathways and GO category groups of genes that define the pathogenicity signatures of the infectious agent. Our preliminary results provide deeper understanding of the overall complexity of host innate immune response as well as the identification of host gene perturbations that defines a unique host temporal biosignature response to each pathogen. The application of advanced computational methods for developing interactome models based on DBNs has proven to be instrumental in elucidating novel host responses and improved functional biological insight into the host defensive mechanisms. Evaluating the unique differences in pathway and GO perturbations across pathogen conditions allowed the identification of plausible host–pathogen interaction mechanisms. Accordingly, a systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host–pathogen interactome. Thus, we propose that such an approach has immediate application to the rational design of brucellosis and salmonellosis vaccines.

Highlights

► We analyzed in vivo gene expression data from both the bovine host and the zoonotic bacterial pathogens. ► We developed computational systems biology models of the host-pathogen interactions. ► We applied Dynamic Bayesian Network machine learning to identify pathogenicity biosignatures. ► The systems biology analysis identified significant biosignature mechanistic genes as deletion targets for enhanced rational design and attenuation of live vaccine candidates.

Introduction

Some of the veterinary vaccines licensed for controlling infectious disease of domestic animal species today are still based on empirical technology that was introduced by Edward Jenner, using live vaccines in 1796, and later Louis Pasteur, using killed whole organism vaccines. Indeed, Jenner derived the term “vaccine” from his use of the less innocuous zoonotic cowpox virus (Latin variolae vaccinae, adapted from vaccinus, from vacca cow) to provide protection against smallpox. Much of veterinary vaccinology is driven by the realities that exist in raising production animals or working in veterinary practice, where making a living depends on keeping the animals healthy. Livestock production is an industry where vaccines are like insurance policies – protection from events that one hopes never happen [1]. For example, the USDA recognizes these varying levels of protection in the way that they allow label claims: (1) “aids in disease control”, (2) “for the prevention of disease”, and (3) “for the prevention of infection”. Additionally there may be indirect protection, or herd immunity, that results from vaccination of sufficient numbers of animals in a given population resulting in the reduction of the ability of a disease to transmit through the vaccinated individuals. The perception that vaccines provide sterilizing immunity, where the disease agent does not establish an infection, while widely held, is generally unfounded and largely unrealistic. In the last 15 years, genomics, proteomics, bioinformatics, biotechnology, immunology, pathogenesis and vaccine formulation and delivery have dramatically enabled novel approaches to vaccine development. When used optimally, vaccines prevent disease manifestations, reduce transmission of disease, decrease the need for pharmaceutical intervention, and improve the health and welfare of animals, as well as indirectly protecting against zoonotic diseases of people. The challenge in developing an optimal vaccination program is in dealing with the great diversity that exists within the animal world, and as such there probably is no single optimal program for all situations. While there is no single strategy to optimizing vaccination programs for animals, nonetheless, a solid understanding of the animal's innate and environmental risk factors as well as the variables such as stress, will enable the development of tailored vaccination schedules that best meet the needs of the animal. The use of vaccines in animal health is not restricted to the protection of morbidity and mortality of the animal hosts themselves, but they are regularly employed as key elements in public health programs. When appropriate biopreparedness, management modeling strategies and contingency plans of the future are linked with (1) protective DIVA vaccines against zoonoses, (2) effective predictive modeling, and (3) deployable implementation policies, control and prevention of serious zoonotic diseases of man and animals will become more achievable at local, state and national levels.

Systems biology is bringing a new, more robust approach to vaccine design that is based upon understanding the molecular network of the immune system of two interacting systems. With this approach, it is within the realm of possibility to develop more effective vaccines supported by a fuller understanding of the complexities of the host–pathogen interactions (interactome) as a product of the innovations of the past 15 years. On the other hand, the massive crush of data now being generated to enhance our understanding of the host–pathogen interactions may not have as much utility as expected unless more dynamic biologically sound models are developed and validated to comprehend and apply to vaccine design. The complexity of host–pathogen interactions across multiple species of hosts and pathogens requires a system level understanding of the entire hierarchy of biological interactions and dynamics. A systems biology approach can provide systematic insights into the dynamic/temporal difference in gene regulation, interaction, and function, and thereby deliver an improved understanding and more comprehensive hypotheses of the underlying mechanisms [3], [4]. The ability to consolidate complex data and knowledge into plausible interactome models is essential to promote the effective discovery of key points of interaction. Accordingly, a systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host–pathogen interactome. It is believed that such an approach will be essential for the rational design of both animal and human vaccines when incorporated into a method of incremental refinement of these models so that new knowledge can be accrued and utilized for future vaccine developments. What is even more challenging in animal vaccine development is the spectrum of animal hosts in which vaccines must perform effectively. Interactome models can be employed to assess the viability of vaccines in other species and help reduce unnecessary animal experiments. Accordingly, the systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host–pathogen interactome to facilitate the rational design of brucellosis and salmonellosis vaccines.

As researchers hypothesize and deduce the sequences and structures of pathogenic proteins and develop detailed knowledge of their regulatory roles in the host, they can rationally design vaccines with defined components in order to maximize effectiveness and minimize safety concerns. Computational capabilities are emerging for creating host–pathogen interactome models. Such models, utilizing data at the genomic, transcriptomic, proteomic, metabolomics, etc. levels, can be used to learn and understand the underlying mechanisms and points of interaction governing the host innate and adaptive responses to pathogens and their vaccines. Such models are envisioned to play an increasingly integral part in the vaccine and immunotherapeutic development process, with incremental model improvements accruing as new biological knowledge is collected from translational in vivo and ex vivo efficacy and safety studies (non-clinical through clinical trials). An exciting prospect of such incremental modeling is the role these models can play in a forward-looking vaccine rational design strategy. Fig. 1 illustrates the strategy of employing a vaccine-immunotherapeutic development methodology referred to as incremental systems biology interactome modeling. Multiple elements must come together to implement such a methodology. Prior biological knowledge (molecular and functional biology) must be current for both the host and pathogen biological systems. Often such knowledge is minimal for many of the veterinary animal species and extra steps of obtaining latest genome and proteome annotations and interaction predictions are necessary and labor intensive. The role of the computer scientist, statistician, and biologist is integral to the successful development, refinement and verification of such models. The interactome model cannot just be a list of possible interaction prediction, but must be part of a dynamic model in which the relationships governing the host immune response can be captured, interpreted and refined. The interactome model becomes a tool that can be interrogated and employed in simulation to help guide vaccine development and/or immunotherapeutic drug candidate selections. Experimental verification will always be a necessary element, and as such experiments are conducted, the resulting biological information should be retained and employed as new biological knowledge for creating the next refined interactome model.

Section snippets

Gene expression data acquisition

An established in vivo perinatal calf ligated ileal loop model, in conjunction with custom bovine microarrays, was used to study the early temporal changes in the host response to a previously optimized dosage of 1 x 109 colony forming units of STM, STM mutant, BMEL, or MAP at four common sampling time-points post infection (0.5, 1, 2, and 4 h post-infection) conducted under protocols approved by the Texas A&M University Institutional Animal Use and Care Committee. Gene expression and

Results

To evaluate the potential for computational systems biology analysis of host:pathogen interactions (the interactome) to be used as a tool for enhanced rational design of vaccines, each host/pathogen interaction condition was modeled and scored 219 known metabolic and signaling pathways and 1620 biological processes (gene groups associated with gene ontology (GO) terms) at four time points. DBGGA was employed to identify the perturbations between pathogen conditions for pathways, GO categories,

Discussion and conclusions

Advances in the last decade or so using comprehensive systems biology approaches linking genomics, proteomics, bioinformatics, and biotechnology with immunology, pathogenesis and vaccine formulation and delivery have dramatically enabled modern approaches to vaccine development. Systems biology is bringing a new, more robust approach to veterinary vaccine design based upon a deeper understanding of the host–pathogen interactions and their impact on the host's molecular network of the immune

Acknowledgements

The animal studies were supported by U.S. Department of Homeland Security – National Center of Excellence for Foreign Animal and Zoonotic Disease (FAZD) Defense grant ONR-N00014-04-1-0. The project described was supported by grant number U54 AI057156, AI040124, AI044170, AI079173 and AI076246 from NIAID/NIH. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the RCE Programs Office, NIAID, or NIH. SDL was supported by AI060933. The

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    Current address: Veterinary Microbiology & Pathology, College of Veterinary Medicine, Washington State University, P. O. Box 7040, Pullman, WA 99164, USA.

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