Significance analysis of lexical bias in microarray data

BACKGROUND: Genes that are determined to be significantly differentially regulated in microarray analyses often appear to have functional commonalities, such as being components of the same biochemical pathway. This results in certain words being under- or overrepresented in the list of genes. Distinguishing between biologically meaningful trends and artifacts of annotation and analysis procedures is of the utmost importance, as only true biological trends are of interest for further experimentation. A number of sophisticated methods for identification of significant lexical trends are currently available, but these methods are generally too cumbersome for practical use by most microarray users. RESULTS: We have developed a tool, LACK, for calculating the statistical significance of apparent lexical bias in microarray datasets. The frequency of a user-specified list of search terms in a list of genes which are differentially regulated is assessed for statistical significance by comparison to randomly generated datasets. The simplicity of the input files and user interface targets the average microarray user who wishes to have a statistical measure of apparent lexical trends in analyzed datasets without the need for bioinformatics skills. The software is available as Perl source or a Windows executable. CONCLUSION: We have used LACK in our laboratory to generate biological hypotheses based on our microarray data. We demonstrate the program's utility using an example in which we confirm significant upregulation of SPI-2 pathogenicity island of Salmonella enterica serovar Typhimurium by the cation chelator dipyridyl

Host Evasion and Exploitation Schemes of Mycobacterium tuberculosis

Tuberculosis, an ancient disease of mankind, remains one of the major infectious causes of human death. We examine newly discovered facets of tuberculosis pathogenesis and explore the evolution of its causative organism Mycobacterium tuberculosis from soil dweller to human pathogen. M. tuberculosis has coevolved with the human host to evade and exploit host macrophages and other immune cells in multiple ways. Though the host can often clear infection, the organism can cause transmissible disease in enough individuals to sustain itself. Tuberculosis is a near-perfect paradigm of a host-pathogen relationship, and that may be the challenge to the development of new therapies for its eradication

Salmonella typhimurium Persists within Macrophages in the Mesenteric Lymph Nodes of Chronically Infected Nramp1+/+ Mice and Can Be Reactivated by IFNγ Neutralization

Host-adapted strains of Salmonella are capable of establishing a persistent infection in their host often in the absence of clinical disease. The mouse model of Salmonella infection has primarily been used as a model for the acute systemic disease. Therefore, the sites of long-term S. typhimurium persistence in the mouse are not known nor are the mechanisms of persistent infection clearly understood. Here, we show that S. typhimurium can persist for as long as 1 yr in the mesenteric lymph nodes (MLNs) of 129sv Nramp1+/+ (Slc11a1+/+) mice despite the presence of high levels of anti–S. typhimurium antibody. Tissues from 129sv mice colonized for 60 d contain numerous inflammatory foci and lesions with features resembling S. typhi granulomas. Tissues from mice infected for 365 d have very few organized inflammatory lesions, but the bacteria continue to persist within macrophages in the MLN and the animals generally remain disease-free. Finally, chronically infected mice treated with an interferon-γ neutralizing antibody exhibited symptoms of acute systemic infection, with evidence of high levels of bacterial replication in most tissues and high levels of fecal shedding. Thus, interferon-γ, which may affect the level of macrophage activation, plays an essential role in the control of the persistent S. typhimurium infection in mice

The adaptor molecules LAT and SLP-76 are specifically targeted by Yersinia to inhibit T cell activation

T cell responses are critical to the survival of Yersinia-infected animals. Yersinia have the ability to directly suppress T lymphocyte activation through the virulence factor YopH, a tyrosine phosphatase. Using single cell video microscopy and FACS analysis, here we show that even an average of one Yersinia per T cell is sufficient to inhibit or alter T cell responses. This efficient inhibition is traced to specific targeting by YopH of the adaptor proteins, linker for activation of T cells (LAT) and SH2-domain–containing leukocyte protein of 76 kD (SLP-76), which are crucial for T cell antigen receptor (TCR) signaling. A catalytically inactive YopH translocated via the type III secretory pathway from the bacteria into T cells primarily binds to LAT and SLP-76. Furthermore, among the proteins of the TCR signaling pathway, the tyrosine phosphorylation levels of LAT and SLP-76 are the most affected in T cells exposed to low numbers of Yersinia pseudotuberculosis. This is the first example showing that a pathogen targets these adaptor proteins in the TCR signaling pathway, suggesting a novel mechanism by which pathogens may efficiently alter T cell–mediated immune responses

Improved analytical methods for microarray-based genome-composition analysis

BACKGROUND: Whereas genome sequencing has given us high-resolution pictures of many different species of bacteria, microarrays provide a means of obtaining information on genome composition for many strains of a given species. Genome-composition analysis using microarrays, or 'genomotyping', can be used to categorize genes into 'present' and 'divergent' categories based on the level of hybridization signal. This typically involves selecting a signal value that is used as a cutoff to discriminate present (high signal) and divergent (low signal) genes. Current methodology uses empirical determination of cutoffs for classification into these categories, but this methodology is subject to several problems that can result in the misclassification of many genes. RESULTS: We describe a method that depends on the shape of the signal-ratio distribution and does not require empirical determination of a cutoff. Moreover, the cutoff is determined on an array-to-array basis, accounting for variation in strain composition and hybridization quality. The algorithm also provides an estimate of the probability that any given gene is present, which provides a measure of confidence in the categorical assignments. CONCLUSIONS: Many genes previously classified as present using static methods are in fact divergent on the basis of microarray signal; this is corrected by our algorithm. We have reassigned hundreds of genes from previous genomotyping studies of Helicobacter pylori and Campylobacter jejuni strains, and expect that the algorithm should be widely applicable to genomotyping data