Advancing signaling networks through proteomics

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

Translational systems approaches to the biology of inflammation and healing

Inflammation is a complex, non-linear process central to many of the diseases that affect both developed and emerging nations. A systems-based understanding of inflammation, coupled to translational applications, is therefore necessary for efficient development of drugs and devices, for streamlining analyses at the level of populations, and for the implementation of personalized medicine. We have carried out an iterative and ongoing program of literature analysis, generation of prospective data, data analysis, and computational modeling in various experimental and clinical inflammatory disease settings. These simulations have been used to gain basic insights into the inflammatory response under baseline, gene-knockout, and drug-treated experimental animals for in silico studies associated with the clinical settings of sepsis, trauma, acute liver failure, and wound healing to create patient-specific simulations in polytrauma, traumatic brain injury, and vocal fold inflammation; and to gain insight into host-pathogen interactions in malaria, necrotizing enterocolitis, and sepsis. These simulations have converged with other systems biology approaches (e.g., functional genomics) to aid in the design of new drugs or devices geared towards modulating inflammation. Since they include both circulating and tissue-level inflammatory mediators, these simulations transcend typical cytokine networks by associating inflammatory processes with tissue/organ impacts via tissue damage/dysfunction. This framework has now allowed us to suggest how to modulate acute inflammation in a rational, individually optimized fashion. This plethora of computational and intertwined experimental/engineering approaches is the cornerstone of Translational Systems Biology approaches for inflammatory diseases.

Proteomics in thyroid tumor research

BACKGROUND

In recent years, "OMICS" technologies have paved novel ways for the broad-scale identification of molecular signatures and signaling pathways specific to tumorigenesis. Related to this are high hopes for the discovery of biomarkers facilitating diagnosis and prognosis of cancer as well as the option for pathway-targeted tumor treatment. Among the different OMICS methods, the potential of proteomics is just beginning to emerge, and according to the current literature, the proteome is to date the most feasible tool to reflect tumor biology.

OBJECTIVE

In this review we discuss the application of proteomics to the field of thyroid tumor research.

CONTEXT

First, we provide an overview of different methods for protein expression profiling and then discuss specific requirements and challenges of thyroid proteomics. Furthermore, we summarize results of published proteomics studies on human thyroid tumors and finally explore perspectives of thyroid proteomics, which, combined with mRNA expression profiling and traditional biochemical methods, is increasingly contributing to an improved understanding of thyroid tumorigenesis and may in the future open novel avenues in thyroid cancer therapy.

LC-MS/MS-based proteome profiling in Daphnia pulex and Daphnia longicephala: the Daphnia pulex genome database as a key for high throughput proteomics in Daphnia

BACKGROUND

Daphniids, commonly known as waterfleas, serve as important model systems for ecology, evolution and the environmental sciences. The sequencing and annotation of the Daphnia pulex genome both open future avenues of research on this model organism. As proteomics is not only essential to our understanding of cell function, and is also a powerful validation tool for predicted genes in genome annotation projects, a first proteomic dataset is presented in this article.

RESULTS

A comprehensive set of 701,274 peptide tandem-mass-spectra, derived from Daphnia pulex, was generated, which lead to the identification of 531 proteins. To measure the impact of the Daphnia pulex filtered models database for mass spectrometry based Daphnia protein identification, this result was compared with results obtained with the Swiss-Prot and the Drosophila melanogaster database. To further validate the utility of the Daphnia pulex database for research on other Daphnia species, additional 407,778 peptide tandem-mass-spectra, obtained from Daphnia longicephala, were generated and evaluated, leading to the identification of 317 proteins.

CONCLUSION

Peptides identified in our approach provide the first experimental evidence for the translation of a broad variety of predicted coding regions within the Daphnia genome. Furthermore it could be demonstrated that identification of Daphnia longicephala proteins using the Daphnia pulex protein database is feasible but shows a slightly reduced identification rate. Data provided in this article clearly demonstrates that the Daphnia genome database is the key for mass spectrometry based high throughput proteomics in Daphnia.