Mineralized Tissue and Salivary Gland Physiology Program
Immunology and Immunotherapy Program
Center for Integrative Biology and Infectious Diseases
OBJECTIVE
The goal of this initiative is to stimulate innovative research in the application of a systems biology approach to inform as well as predict salivary gland functions, dysfunctions and responses to treatment of salivary gland disorders. This initiative is designed to encourage investigations that couple experimentation and computation to generate integrative models of salivary gland signaling networks, cellular processes or functional modules. These studies will encompass 1) simulation of molecular interactions and signaling networks in a virtual model of the salivary gland secretory unit or sub-glandular modules; 2) quantitative assays of these molecular interactions under normal steady state experimental conditions; 3) genetic or chemical manipulation of the components and quantitative assays of their interactions at the perturbed state; 4) feed-forward of the empirical data to the virtual model for iterative refinement of the interactions; 5) identification and characterization of new components, interactions and other emergent properties of salivary gland activities based on repeated iterations; and 6) application of the model to derive new preemptive and personalized strategies for the treatment of salivary gland disorders.
BACKGROUND
System level analysis is grounded in molecular level discoveries. Although systems biology is an emerging interdiscipline, it is clearly the next logical step for the integration of “omics” datasets to understand the structure, dynamics, regulation and design of complex biological systems. Salivary gland biology has been built on molecular catalogues and awaits a systems approach to connect the dots with spatial, temporal and quantitative relationships.
The coordinated endeavor of multiple research groups on the Human Genome Sequencing Project triggered the revolution of discovery, data-driven science. In a few short years, we have witnessed the completion of this project, and also noticed the proliferation of other large data gathering efforts: characterization of the genomes of other organisms, as well as the transcriptomes, the proteomes, the metabolomes, the glycomes, the lipidomes and others. Although the data collected present a daunting task for storage and annotation, each data point remains an individual entry. Embedded within these independent data points is a whole new dimension of interdependency. Investigators interested in signal transduction often place a few to a few dozens of the molecules into signaling pathways or networks. Other research groups have more ambitiously generated large signaling maps or protein-protein interaction maps. Nonetheless, these pathways, networks and maps represent static snapshots of interconnected molecular components, yet are significant and requisite first steps toward a system level analysis.
If molecular components are vehicles and signaling networks are roadmaps, then systems biology is the traffic pattern. Systems biology seeks not only to connect up the molecular components, often referred to as the “parts list”, but also to define their relationships in a dynamic framework with specific regulatory elements and pattern design. Systems biology relies on the interdisciplinary engagement of experimental biology and computational science. Experiments produce quantitative measurements that can, in principle, be integrated to generate mathematical models of the relationships between external cues, cell signaling, and the ultimate cellular responses. In turn, models inform creation of experimental hypotheses that can be tested. New measurements produced from these new experiments can then be used to validate and refine the model. Through repeated iterations of experimentation and computational modeling, new properties of the system will emerge, such as new molecular interactions and novel mechanisms of signaling. The model itself will also evolve and acquire predictive power of the consequence of system behavior when a particular component is perturbed. More importantly, this predictive power will allow the experimentalist to invoke specific countermeasures to any perturbations. Take this to the clinical setting: if we have comprehensive knowledge of how an organ normally functions, then when the system “goes down”, we should be able to query what went wrong and to design targeted therapies to restore normality.
Salivary gland biology is conducive to a systems biology approach and salivary gland disorders could greatly benefit from predictive prevention and intervention strategies. Saliva serves multiple functions including lubrication, digestion, and host defense. Salivary gland disorders, which result in xerostomia can lead to dental caries, periodontal diseases, mucosal infections, halitosis, dysgeusia, and difficulties in swallowing and speaking, thus compromising quality of life. The most common disorder involving the salivary glands is Sjögren’s syndrome, an autoimmune disease affecting up to 4 million Americans, mostly middle age women. In addition, it is estimated that some 400 medications, including antihistamines, antidepressants and diuretics, are associated with xerostomia as an adverse effect. Approximately 30% of older adults also suffer from xerostomia, in part due to the medications they are using. Although salivary gland tumors are rare, with approximately 3300 new cases per year, surgical and radiation interventions can destroy the glands. Furthermore, radiation therapy to other head and neck cancers can also significantly damage the salivary glands in 40,000 individuals annually.
The salivary glands are susceptible to perturbation, and predicting the onset, progression and therapeutic responses of salivary gland disorders poses significant challenges. Studies over the years along with ongoing efforts to characterize the salivary proteome have characterized the protein, carbohydrate and ionic components of whole saliva. Biological processes such as morphogenesis, fluid and electrolyte secretion, protein, glycoprotein and mucin production, and apoptosis have also been studied. However, these have been isolated efforts and the independent data points have not been interpreted within the larger context of the dynamic salivary gland system. The knowledge gap is the lack of predictive value in disconnected data points in salivary gland biology. Therefore, the scientific opportunity is to capture these physiological components in an integrative, quantitative and dynamic model represented at the molecular, cellular, tissue and organ levels, and to translate this model to the clinical prediction of salivary functions, dysfunctions and responses to treatment of salivary gland disorders.
Feasibility of this initiative rests upon scientific and technical readiness of the research community at large. Scientifically, salivary gland researchers have collected and continue to collect large “omics” datasets that constitute a broad knowledge base on which system-level integration of these data can be supported. We also have a relatively solid understanding of some of the biological processes of the salivary gland that can provide the basis for initial mathematical modeling. It is timely to launch a systems approach to salivary gland biology because the iterative process of experimentation and computational modeling will accelerate the discovery of yet missing data points and enhance the predictive value of such comprehensive and dynamic models. Technically, the salivary glands are accessible and amenable to genetic and chemical manipulations in vitro and in vivo, which are essential steps in the iterative process of a systems approach. Computational power of modeling biological processes is rapidly evolving with programming language specifically written for systems biology. A variety of software platforms is also available for data collection, analysis, integration and visualization, and some are interoperable. Taken together, the resources, tools and methods are available and improving.
Our approach in funding this research will be consistent with the aim to facilitate partnerships between experimental and quantitative scientists to engage in interdisciplinary research of systems biology, and to attract investigators outside the traditional NIDCR extramural community.