The Virtual NOD Mouse: Applying Predictive Biosimulation to Research in Type 1 Diabetes

A look into the future of ALS research

Although amyotrophic lateral sclerosis (ALS), also referred as 'Lou Gehrig's Disease,' was first described in 1869 and the first disease-associated gene was discovered almost 20 years ago, the disease etiology is still not fully understood and treatment options are limited to one drug approved by the US Food and Drug Administration (FDA). The slow translational progress suggests that current research models are not ideal to study such a complicated disease and need to be re-examined. Progress will require greater insight into human genes and biology involved in ALS susceptibility, as well as a deeper understanding of disease phenotype at the histological and molecular levels. Improving human disease outcome will require directing focus toward improved assessment technologies and innovative approaches.

Virtual optimization of nasal insulin therapy predicts immunization frequency to be crucial for diabetes protection

OBJECTIVE

Development of antigen-specific strategies to treat or prevent type 1 diabetes has been slow and difficult because of the lack of experimental tools and defined biomarkers that account for the underlying therapeutic mechanisms.

RESEARCH DESIGN AND METHODS

The type 1 diabetes PhysioLab platform, a large-scale mathematical model of disease pathogenesis in the nonobese diabetic (NOD) mouse, was used to investigate the possible mechanisms underlying the efficacy of nasal insulin B:9-23 peptide therapy. The experimental aim was to evaluate the impact of dose, frequency of administration, and age at treatment on Treg induction and optimal therapeutic outcome.

RESULTS

In virtual NOD mice, treatment efficacy was predicted to depend primarily on the immunization frequency and stage of the disease and to a lesser extent on the dose. Whereas low-frequency immunization protected from diabetes atrributed to Treg and interleukin (IL)-10 induction in the pancreas 1-2 weeks after treatment, high-frequency immunization failed. These predictions were confirmed with wet-lab approaches, where only low-frequency immunization started at an early disease stage in the NOD mouse resulted in significant protection from diabetes by inducing IL-10 and Treg.

CONCLUSIONS

Here, the advantage of applying computer modeling in optimizing the therapeutic efficacy of nasal insulin immunotherapy was confirmed. In silico modeling was able to streamline the experimental design and to identify the particular time frame at which biomarkers associated with protection in live NODs were induced. These results support the development and application of humanized platforms for the design of clinical trials (i.e., for the ongoing nasal insulin prevention studies).

Mapping the pathogenesis of periodontitis: a new look

Chronic adult periodontitis is a bacterially induced chronic inflammatory disease that destroys the connective tissue and bone that support teeth. Concepts of the specific mechanisms involved in the disease have evolved with new technologies and knowledge. Histopathologic observations of diseased human tissues were used previously to speculate on the causes of periodontitis and to describe models of pathogenesis. Experimental evidence later emerged to implicate bacterial plaque deposits as the primary factor initiating periodontitis. At the same time, specific bacteria and immunoinflammatory mechanisms were differentially implicated in the disease. In the mid-1990s, early insights about complex diseases, such as periodontitis, led to new conceptual models of the pathogenesis of periodontitis. Those models included the bacterial activation of immunoinflammatory mechanisms, some of which targeted control of the bacterial challenge and others that had adverse effects on bone and connective tissue remodeling. Such models also acknowledged that different environmental and genetic factors modified the clinical phenotype of periodontal disease. However, the models did not capture the dynamic nature of the biochemical processes, i.e., that innate differences among individuals and changes in environmental factors may accelerate biochemical changes or dampen that shift. With emerging genomic, proteomic, and metabolomic data and systems biology tools for interpreting data, it is now possible to begin describing the basic elements of a new model of pathogenesis. Such a model incorporates gene, protein, and metabolite data into dynamic biologic networks that include disease-initiating and -resolving mechanisms. This type of model has a multilevel framework in which the biochemical networks that are regulated by innate and environmental factors can be described and the interrelatedness of networks can be captured. New models in the next few years will be merely frameworks for integrating key knowledge as it becomes available from the "-omics" technologies. However, it is possible to describe some of the key elements of the new models and discuss distinctions between the new and older models. It is hoped that improved conceptual models of pathogenesis will assist in focusing new research and speed the translation of new data into practical applications.

Islet beta-cell death - fuel to sustain autoimmunity?

Under what context do dying beta-cells enhance the autoimmune process in type 1 diabetes? Kim et al. (2007) find that secondary necrosis of beta-cells can prime the autoimmune response via uptake by and activation of antigen-presenting cells through Toll-like receptor 2.