New diagnostic approaches

As mentioned previously, a major promise of genomics is the capacity to use this technology in the more precise and efficient diagnosis of disease. Of major interest, is the use of functional genomics to ascertain molecular signatures of infection that permit the distinction among diseases. Discussed below is how this technology is being tested for influenza diagnosis.

To identify the gene expression signatures induced by various pathogens, Chaussabel et al. examined peripheral blood mononuclear cells (PBMCs) obtained from pediatric patients presenting with various illnesses (2005). Specifically, they examined diseases with distinct immunological components such as systemic lupus erythematosus (SLE), influenza A, Staphylococcus aureus, Escherichia coli, and Streptococcus pneumoniae. They also examined adult patients who received liver transplants with immuno-suppressive therapy or patients who received bone marrow transplants and experienced graft versus host disease. These samples were compared with PBMCS from healthy volunteers. The authors were able to identify unique gene expression patterns for patients presenting with influenza and SLE. They then determined expression profiles common to all of the diseases using genes that were either up- or downregulated in patients infected with influenza or SLE. Analyses also demonstrated that the genes whose expression was regulated in a similar manner in both influenza and SLE patients fell into distinct categories such as defense response, interferon induction, and heavy metal binding. Furthermore, the authors were able to determine how many genes related to these processes were expressed in individual patients (Chaussabel et al., 2005).

In an extension of the above studies, the authors examined the gene expression profiles of PBMCs from young patients presenting with acute infections including influenza A, S. aureus, S. pneumoniae, and E. coli (Ramilo et al., 2007). Analysis of these samples was performed in a methodical manner using statistical comparison, sample classification, validation of classifier genes using a test set, and validation of microarray platforms and chips. The authors were able to identify subsets of genes that distinguished patients with influenza (viral infection) from those that presented with either E. coli or S. pneumoniae (Gram-negative and Grampositive bacterial infections, respectively). The same was found for patients infected with influenza compared to those infected with S. aureus (Gram-positive bacterial infection). Distinct expression patterns were also present in PBMCs from patients infected with E. coli or S. aureus.

Using sets of classifier genes obtained from the above analyses, the authors examined the gene expression profiles of PBMCs isolated from patients presenting with lower respiratory infections the same as those listed above or from healthy volunteers. From these analyses, the authors were able to classify the samples from these new patients into the correct disease categories. In addition, the authors tested a separate set of samples using a different array platform. These studies also demonstrated that patients presenting with these illnesses could be accurately classified into distinct groups based on gene expression profiles (Ramilo et al., 2007). Through these painstaking efforts, the authors convincingly used functional genomics to discriminate between patients with a variety of acute infections, including influenza.

While these studies provide evidence that genomics can be used to define molecular signatures of disease associated with certain pathogens, they also have significant limitations. For example, these studies were performed on samples that had been taken from patients that had already been diagnosed with a particular illness and genomic analyses only had to distinguish between a relatively few possibilities. However, in order to be effective in a clinical setting, gene expression profiling will need to provide a high degree of accuracy and overcome numerous confounding factors such as age, race, gender, immune status, and co-infection with more than one pathogen. All of these issues must be addressed before functional genomics can function in disease diagnosis. However, once these challenges have been met, genomic diagnosis may decrease the amount of elapsed time between sample collection and disease diagnosis thereby allowing doctors to treat patients more quickly. This is particularly important for patients presenting with acute infections. Additionally, the use of microarrays in this manner may eliminate the need for patients to undergo certain painful and potentially dangerous diagnostic procedures, such as tissue biopsies.

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