Scientists studying pathogenesis have typically focused on small subsets of genes as suspected virulence factors. However, microarrays have enabled the exploration of genome-wide expression, uncovering virulence pathways consisting of previously unknown genes. This technique has proven particularly useful when analyzing a pathogen's response to its host environment. By developing model systems of infection, scientists can examine how a pathogen alters gene expression in response to its host, and can then deduce which genes are most important for virulence.

For example, Wolfgang et al. used GeneChip® P. aeruginosa Genome Arrays to study the interaction between Pseudomonas bacteria and the airway liquids from chronically infected cystic fibrosis patients. By examining genome-wide expression, the group identified genes that exhibited a statistically significant change in expression as a result of that environment. They noticed that a majority of the repressed genes encoded proteins relating to flagellar biosynthesis, and when they looked at the bacteria by electron microscopy, they found that surface flagella were indeed reduced. Flagella are highly immunogenic and repression is a way for the bacteria to avoid detection by host defense mechanisms, allowing the bacteria to successfully establish infection in compromised cystic fibrosis patients.

By understanding the virulence factors and toxins elaborated by Pseudomonas and other pathogens, researchers are able to identify disease mechanism pathways for potential treatment. However, a complete understanding of infectious disease requires the examination of both the virulence factors expressed by the microbe, as well as the host response mechanisms and host pathways that are subverted by that microbe. To this end, scientists have used GeneChip® microarrays to understand virulence by monitoring changes in host gene expression following challenge with a microbe or with purified virulence factors.

For example, Izmailova and colleagues used human GeneChip expression microarrays to study the effects of HIV-1 or Tat protein (a major HIV virulence factor) on immature dendritic cells, which are among the first cells to be infected by retroviruses. By examining genome-wide expression, the researchers were able to identify the induction of a complete interferon pathway. Chemokines are among the molecules induced by this pathway, which in turn recruit macrophages and T cells, which are the ultimate targets of the virus and thus facilitate the expansion of the viral infection. Based on these studies, designing therapies against the Tat protein or against the members of the interferon pathway would produce the combined benefit of limiting viral transcription and also reducing expansion of viral infection into uninfected cell types.

While most infectious disease microarray research has focused on gene expression studies, new microarray tools for DNA sequence analysis are also enabling scientists to look at the biology of infectious disease. At a single nucleotide resolution, microarrays designed for custom genotyping enable researchers to explore sequence variation between pathogenic strains.

The ability to quickly resequence a genome in a single experiment, dramatically reduces the expense, time, and labor that would have been required using traditional sequencing methods and enables scientists to more readily determine how genetic differences manifest in disease outcomes. For example, in the face of the SARS outbreak of 2003, Affymetrix, NIAID and TIGR collaborated to develop a microarray to resequence the complete genome of the SARS virus.

The Center for Disease Control and Prevention is also using Affymetrix resequencing arrays to identify and catalog hundreds of different smallpox strains. By sequencing more isolates, scientists can more easily relate pathogen subtypes to patient outcome and develop a better understanding of which subtypes are, for example, the most virulent. Clinicians can also use this information for diagnostic purposes, by identifying the specific strain responsible for disease (see Pathogen Identification). These tools are also useful for epidemiologists, who can study how a pathogen is evolving over time, during its spread into different geographies and populations. Monitoring the full genome over the time of an epidemic, for example, allows the tracking of characteristics, such as drug resistance, that are crucial for the treatment of these diseases.