Deepak Kaushal

Microarray Analysis of Pneumococcal Gene Expression during Invasive Disease

ABSTRACT Streptococcus pneumoniae is a leading cause of invasive bacterial disease. This is the first study to examine the expression of S. pneumoniae genes in vivo by using whole-genome microarrays available from The Institute for Genomic Research. Total RNA was collected from pneumococci isolated from infected blood, infected cerebrospinal fluid, and bacteria attached to a pharyngeal epithelial cell line in vitro. Microarray analysis of pneumococcal genes expressed in these models identified body site-specific patterns of expression for virulence factors, transporters, transcription factors, translation-associated proteins, metabolism, and genes with unknown function. Contributions to virulence predicted for several unknown genes with enhanced expression in vivo were confirmed by insertion duplication mutagenesis and challenge of mice with the mutants. Finally, we cross-referenced our results with previous studies that used signature-tagged mutagenesis and differential fluorescence induction to identify genes that are potentially required by a broad range of pneumococcal strains for invasive disease.

General Nature of the STAT3-Activated Anti-Inflammatory Response

Although many cytokine receptors generate their signals via the STAT3 pathway, the IL-10R appears unique in promoting a potent anti-inflammatory response (AIR) via STAT3 to antagonize proinflammatory signals that activate the innate immune response. We found that heterologous cytokine receptor systems that activate STAT3 but are naturally refractory (the IL-22R), or engineered to be refractory (the IL-6, leptin, and erythropoietin receptors), to suppressor of cytokine signaling-3-mediated inhibition activate an AIR indistinguishable from IL-10. We conclude that the AIR is a generic cytokine signaling pathway dependent on STAT3 but not unique to the IL-10R.

Identification of a Candidate Streptococcus pneumoniae core genome and regions of diversity correlated with invasive pneumococcal disease.

ABSTRACT Streptococcus pneumoniae is a leading cause of community-acquired pneumonia and gram-positive sepsis. While multiple virulence determinants have been identified, the combination of features that determines the propensity of an isolate to cause invasive pneumococcal disease (IPD) remains unknown. In this study, we determined the genetic composition of 42 invasive and 30 noninvasive clinical isolates of serotypes 6A, 6B, and 14 by comparative genomic hybridization. Comparison of the present/absent gene matrix (i.e., comparative genomic analysis [CGA]) identified a candidate core genome consisting of 1,553 genes (73% of the TIGR4 genome), 154 genes whose presence correlated with the ability to cause IPD, and 176 genes whose presence correlated with the noninvasive phenotype. Genes identified by CGA were cross-referenced with the published signature-tagged mutagenesis studies, which served to identify core and IPD-correlated genes required for in vivo passage. Among these, two pathogenicity islands, region of diversity 8a (RD8a), which encodes a neuraminidase and V-type sodium synthase, and RD10, which encodes PsrP, a protein homologous to the platelet adhesin GspB in Streptococcus gordonii, were identified. Mice infected with a PsrP mutant were delayed in the development of bacteremia and demonstrated reduced mortality versus wild-type-infected controls. Finally, the presence of seven RDs was determined to correlate with the noninvasive phenotype, a finding that suggests some RDs may contribute to asymptomatic colonization. In conclusion, RDs are unequally distributed between invasive and noninvasive isolates, RD8a and RD10 are correlated with the propensity of an isolate to cause IPD, and PsrP is required for full virulence in mice.

Attenuation of Late-Stage Disease in Mice Infected by the Mycobacterium tuberculosis Mutant Lacking the SigF Alternate Sigma Factor and Identification of SigF-Dependent Genes by Microarray Analysis

ABSTRACT The Mycobacterium tuberculosis alternate sigma factor, SigF, is expressed during stationary growth phase and under stress conditions in vitro. To better understand the function of SigF we studied the phenotype of the M. tuberculosis ΔsigF mutant in vivo during mouse infection, tested the mutant as a vaccine in rabbits, and evaluated the mutants microarray expression profile in comparison with the wild type. In mice the growth rates of theΔ sigF mutant and wild-type strains were nearly identical during the first 8 weeks after infection. At 8 weeks, theΔ sigF mutant persisted in the lung, while the wild type continued growing through 20 weeks. Histopathological analysis showed that both wild-type and mutant strains had similar degrees of interstitial and granulomatous inflammation during the first 12 weeks of infection. However, from 12 to 20 weeks the mutant strain showed smaller and fewer lesions and less inflammation in the lungs and spleen. Intradermal vaccination of rabbits with the M. tuberculosis ΔsigF strain, followed by aerosol challenge, resulted in fewer tubercles than did intradermal M. bovis BCG vaccination. Complete genomic microarray analysis revealed that 187 genes were relatively underexpressed in the absence of SigF in early stationary phase, 277 in late stationary phase, and only 38 genes in exponential growth phase. Numerous regulatory genes and those involved in cell envelope synthesis were down-regulated in the absence of SigF; moreover, the ΔsigF mutant strain lacked neutral red staining, suggesting a reduction in the expression of envelope-associated sulfolipids. Examination of 5′-untranslated sequences among the downregulated genes revealed multiple instances of a putative SigF consensus recognition sequence: GGTTTCX18GGGTAT. These results indicate that in the mouse the M. tuberculosis ΔsigF mutant strain persists in the lung but at lower bacterial burdens than wild type and is attenuated by histopathologic assessment. Microarray analysis has identified SigF-dependent genes and a putative SigF consensus recognition site.

Functional Genomics Reveals Extended Roles of the Mycobacterium tuberculosis Stress Response Factor σH

Mycobacterium tuberculosis is one of the most successful pathogens of humankind. During infection, M. tuberculosis must cope with and survive against a variety of different environmental conditions. Sigma factors likely facilitate the modulation of the pathogens gene expression in response to changes in its extracellular milieu during infection. sigma(H), an alternate sigma factor encoded by the M. tuberculosis genome, is induced by thiol-oxidative stress, heat shock, and phagocytosis. In response to these conditions, sigma(H) induces the expression of sigma(B), sigma(E), and the thioredoxin regulon. In order to more effectively characterize the transcriptome controlled by sigma(H), we studied the long-term effects of the induction of sigma(H) on global transcription in M. tuberculosis. The M. tuberculosis isogenic mutant of sigma(H) (Delta-sigma(H)) is more susceptible to diamide stress than wild-type M. tuberculosis. To study the long-term effects of sigma(H) induction, we exposed both strains to diamide, rapidly washed it away, and resumed culturing in diamide-free medium (post-diamide stress culturing). Analysis of the effects of sigma(H) induction in this experiment revealed a massive temporal programming of the M. tuberculosis transcriptome. Immediately after the induction of sigma(H), genes belonging to the functional categories virulence/detoxification and regulatory proteins were induced in large numbers. Fewer genes belonging to the lipid metabolism category were induced, while a larger number of genes belonging to this category were downregulated. sigma(H) caused the induction of the ATP-dependent clp proteolysis regulon, likely mediated by a transcription factor encoded by Rv2745c, several members of the mce1 virulence regulon, and the sulfate acquisition/transport network.

Platelet-Activating Factor Receptor and Innate Immunity: Uptake of Gram-Positive Bacterial Cell Wall into Host Cells and Cell-Specific Pathophysiology

The current model of innate immune recognition of Gram-positive bacteria suggests that the bacterial cell wall interacts with host recognition proteins such as TLRs and Nod proteins. We describe an additional recognition system mediated by the platelet-activating factor receptor (PAFr) and directed to the pathogen-associated molecular pattern phosphorylcholine that results in the uptake of bacterial components into host cells. Intravascular choline-containing cell walls bound to endothelial cells and caused rapid lethality in wild-type, Tlr2−/−, and Nod2−/− mice but not in Pafr−/− mice. The cell wall exited the vasculature into the heart and brain, accumulating within endothelial cells, cardiomyocytes, and neurons in a PAFr-dependent way. Physiological consequences of the cell wall/PAFr interaction were cell specific, being noninflammatory in endothelial cells and neurons but causing a rapid loss of cardiomyocyte contractility that contributed to death. Thus, PAFr shepherds phosphorylcholine-containing bacterial components such as the cell wall into host cells from where the response ranges from quiescence to severe pathophysiology.

Vancomycin stress response in a sensitive and a tolerant strain of Streptococcus pneumoniae

The vancomycin stress response was studied in Streptococcus pneumoniae strains T4 (TIGR4) and Tupelo. Vancomycin affected the expression of 175 genes, including genes encoding transport functions and enzymes involved in aminosugar metabolism. The two-component systems TCS03, TCS11, and CiaRH also responded to antibiotic treatment. We hypothesize that the three regulons are an important part of the bacteriums response to vancomycin stress.

Revising the Role of the Pneumococcal vex-vncRS Locus in Vancomycin Tolerance

Vancomycin is used increasingly to treat invasive infections caused by multidrug-resistant Streptococcus pneumoniae. Although no vancomycin-resistant strains have been isolated to date, tolerant strains that fail to die rapidly and that cause relapsing disease have been described. The vex123-pep27-vncRS locus, consisting of an ABC transporter, a presumed signaling peptide, and a two-component system, respectively, has been implicated in vancomycin tolerance. Recent findings, however, challenged this model. The data presented here indicate that erythromycin in the growth medium induces a vancomycin-tolerant phenotype and that loss of function of Pep27 or VncRS does not alter autolysis. However, a role for the ABC transporter encoded by the vex123 genes in tolerance was confirmed. A vex3 mutant was considerably more tolerant to vancomycin treatment than the wild-type strain T4, and the strength of the phenotype depended on the orientation of the resistance cassette used to construct the mutant. Microarray results suggested a number of genes that might be involved in tolerance in the vex3 mutant. Although the exact function and regulation of the vex123-pep27-vncRS locus remains to be determined, several factors influence the autolysis behavior of S. pneumoniae, including the bacterial capsule, erythromycin, and the lytA and vex3 gene products.

Analyzing and visualizing expression data with Spotfire.

This unit assumes the reader is familiar with the Spotfire environment, has successfully installed Spotfire, and has uploaded and prepared data for analysis. It presents numerous methods for analyzing microarray data. Specifically, the first two protocols describe methods for identifying differentially expressed genes via the t‐test/ANOVA and the distinction calculation respectively. Another protocol discusses how to conduct a profile search. Additional protocols illustrate various clustering methods, such as hierarchical clustering, K‐means clustering, and principal components analysis. A protocol explaining coincidence testing allows the reader to compare the results from multiple clustering methods. Additional protocols demonstrate querying the Internet for information based on the microarray data, mathematically transforming data within Spotfire to generate new data columns, and exporting Spotfire visualizations.

Loading and Preparing Data for Analysis in Spotfire

This unit strictly focuses on data preparation within Spotfire. Microarray data exist in a variety of formats, which often depend on the particular array technology and detection instruments used. The first protocols in this unit describe loading Affymetrix and GenePix data into Spotfire. Once the data are loaded, it is necessary to filter and preprocess the data prior to analysis. Subsequently, the data transformation and normalization techniques presented here, are critical to correctly performing powerful microarray data mining expeditions. These steps extract or enhance meaningful data characteristics and prepare the data for the application of certain analysis methods such as statistical tests to compute significance and clustering methods—which mostly require data to be normally distributed. The unit outlines several methods for normalizing the data within an experiment and between multiple experiments.