Applications of TF Profiling
In one study, a researcher had previously tried to understand the underlying disease mechanisms in acute lung injury (ALI) by characterizing differences between normal cells and the ALI model system using high-density gene expression microarrays.
The results from these studies identified almost 2,000 differences between control and treated cells, and the complexity of the data made interpretation a daunting and virtually impossible task. Marligen’s Multiplex Transcription Factor Profiling uncovered the involvement of several transcription factor binding events that were previously unknown, and the technology facilitated kinetic analysis of the changes (Figure 2).
This information resulted in a more focused interpretation of the gene expression array data, identified new regulatory pathways in disease pathogenesis, and may lead to new approaches for developing therapies.
A second study was performed by a pharmaceutical researcher who was trying to understand the poor efficacy of a lead compound observed in a Phase II trial. TF profiles of tissues from animal models of the disease led to an explanation of the failure. It also led to the identification of several metabolic enzymes involved in pathogenesis that represent attractive new therapeutic targets. Finally, a repositioning opportunity for the failed drug in another area was uncovered. As an illustration of off-target effects identified by TF profiling, Marligen scientists performed a 50-plex screen using a known PPAR inhibitor (Figure 3).
TF profiling offers researchers the ability to screen across a broad range of upstream signaling pathways and downstream gene expression involved in disease pathology and drug action. TF profiling has been shown to reveal novel biological effects, define mechanisms of disease and drug action, identify new drug targets, evaluate specificity, and uncover repositioning opportunities. Pharmaceutical and biotech customers are now using the technology in preclinical R&D to understand diseases and more comprehensively screen specificity, thus enabling additional decision points early in pipeline development.