Strategies for the detection of disease genes in pancreatic cancer

Gene expression changes in an in vitro model of chondroinduction: a comparison of two methods

There are many useful technologies to describe patterns of gene expression that occur during tissue repair and regeneration. Results from different methods used in one experimental setting are not often compared. In this case study of chondrogenesis, we compare two methods to identify differentially expressed genes, representational difference analysis and targeted macroarray analysis, as a model for investigating genes that may be relevant to tissue repair. We sought to identify genes whose expression was altered when human dermal fibroblasts were cultured in a three-dimensional, porous collagen sponge with the chondroinductive agent, demineralized bone. Both representational difference analysis and macroarray experiments revealed several functional families of genes as up-regulated or down-regulated in chondroinduced fibroblasts. An advantage of representational difference analysis is that altered expression of specific mRNA transcripts can be revealed. In this example, representational difference analysis uncovered the up-regulation of a specific transcript of Wnt5a in fibroblasts cultured with demineralized bone. Representational difference analysis is limited, however, as there can be false negatives for genes not readily amplified by polymerase chain reaction. We conclude that small arrays containing functional classes of genes can be used to ask specific, hypothesis-driven questions at minimal cost. It may be prudent, however, to use more than one method to survey differences in gene expression in order to validate and expand findings.

The Application of Clinical Proteomics to Cancer and other Diseases

The term "clinical proteomics" refers to the application of available proteomics technologies to current areas of clinical investigation. The ability to simultaneously and comprehensively examine changes in large numbers of proteins in the context of disease or other changes in physiological conditions holds great promise as a tool to unlock the solutions to difficult clinical research questions. Proteomics is a rapidly growing field that combines high throughput analytical methodologies such as two-dimensional gel electrophoresis and SELDI mass spectrometry methods with complex bioinformatics to study systems biology--the system of interest is defined by the investigator. Even with all its potential, however, studies must be carefully designed in order to differentiate true clinical differences in protein expression from differences originating from variation in sample collection, variation in experimental condition, and normal biological variability. Proteomic analyses are already widely in use for clinical studies ranging from cancer to other diseases such as cardiovascular disease, organ transplant, and pharmacodynamic studies.