Skip Navigation


International Immunology Advance Access originally published online on December 13, 2005
International Immunology 2006 18(1):125-137; doi:10.1093/intimm/dxh355
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
18/1/125    most recent
dxh355v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (2)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Montano, M.
Right arrow Articles by Ericksen, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Montano, M.
Right arrow Articles by Ericksen, R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?


© The Japanese Society for Immunology. 2005. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

HIV-1 burden influences host response to co-infection with Neisseria gonorrhoeae in vitro

Monty Montano1,2, Matthew Rarick1, Paola Sebastiani3, Patrick Brinkmann1,2, Jerry Skefos1 and Russell Ericksen1

1 Department of Medicine, Section of Infectious Diseases, Center for HIV-1/AIDS Care and Research, Boston University School of Medicine, 2 Department of Immunology and Infectious Diseases, Harvard School of Public Health, 3 Department of Biostatistics, Boston University School of Public Health

Correspondence to: M. Montano; E-mail: mmontano{at}bu.edu


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Supplementary material
 References
 
There is considerable evidence that co-infection with the sexually transmitted pathogen Neisseria gonorrhoeae (Gc) can increase the likelihood of both transmitting and acquiring HIV-1 worldwide. However, less information is available on how host immune response to co-infection differs with immune response to HIV-1 infection alone. To evaluate HIV-1 burden effects on host response to co-infection with Gc, we performed gene-expression profiling of human PBMCs infected over a broad range of viral titers (HIV-1 series) and upon exposure to a single infectious dose of Gc (HIV-1/Gc series). The transcriptional profiles differed substantially between each series (P < 0.0001). Major shifts in the transcriptional landscape were identified in contour plots based on fold stimulation and hierarchical clustering. Prominent regions of transcriptional activity were evaluated for statistical enrichment to identify up-regulated pathways associated with immune response, infection and T-cell stimulation. Notably, gene enrichment was dependent on HIV-1 burden and shifted during co-infection to reveal a disproportionate effect on lymphocyte signaling, apoptosis and proteasome activity. Further evaluation of these findings may help to better understand the role of viral burden in defining cellular contribution to host immune response upon co-infection with secondary sexually transmitted pathogens.

Keywords: bacteria, gene regulation, HIV-1, microarray


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Supplementary material
 References
 
While HIV-1 and Neisseria gonorrhoeae have been linked in many epidemiologic studies evaluating the influence of secondary sexually transmitted infections (STIs) on viral shedding (1–4), there is far less information concerning immune response mechanisms engaged during co-infection. Models for characterizing co-infection (5) tend to emphasize a role for gonococcal infection-induced lymphocyte activation and redistribution that presumably influence viral load through an increase in local inflammation and target cell recruitment (5–7). Other studies suggest a direct connection between N. gonorrhoeae and HIV-1 wherein gonococci activate HIV-1 transcription through surface-mediated signaling of infected cells (8). In those studies, however, the likelihood for direct Gc-mediated activation of HIV-1-infected cells (which represents a small fraction of total circulating lymphocytes) is uncertain. By contrast, there is relatively less information available on how differing viral burdens might influence host immune response upon co-infection with secondary STIs.

Identifying biomarkers that represent immune correlates engaged among HIV-1-prevalent populations that experience co-infections would assist in better defining host cofactors that are associated with favorable immune response and pathogen clearance. Furthermore, there has not been a direct evaluation of the potential role for variable HIV-1 burden in altering immune correlates of host response to secondary infection. HIV-1 infection has been previously evaluated within the context of various viral, parasitic and bacterial secondary infections (9, 10) that collectively implicate a role for chronic immune stimulation in HIV-1 pathogenesis. HIV infection has also been variably associated with an inflammatory syndrome (11) detectable in both plasma and genital secretions during Gc infections (7, 11). Microbial infections have also been specifically linked to changes in the levels of Th1 and Th2 effector cytokines upon infection by Cryptococcus neoformans (12), Schistosoma (10) and N. gonorrhoeae (13). Pathogen-associated immune activation associated with these cytokines has been proposed to influence host susceptibility to HIV-1 infection, as well as disease progression (14). However, the significance of changes in these cross-regulated cytokine networks, and their relevance to HIV-1 disease progression, is unclear (15, 16).

Understanding how HIV-1 infection alters host response upon infection with a secondary pathogen represents a significant research challenge. With the advent of genome-wide analysis using microarray technology, the opportunity to describe a broad landscape of HIV-1-mediated effects on the cellular transcriptome can now be conducted under different pathogenic scenarios, such as co-infection. Transcriptional profiles obtained with microarrays have been recently employed to characterize gene-expression patterns during single infections with either HIV-1 (17–22) or N. gonorrhoeae (23), but not both.

We describe the use of human Affymetrix U133A 2.0 gene chips that contain ~23 000 probes to assess HIV-1-dependent gene expression in a dosage series of infected PBMCs that were stimulated with N. gonorrhoeae (Gc) for 6 h in vitro as a model for co-infection. The short-time (6 h) exposure was chosen to allow signaling induced by Gc exposure (data not shown; M. Rarick, C. McPheeters, S. Bright, A. Navis, J. Skefos, P. Sebastiani and M. Montano, submitted for publication), while minimizing the potential for an indirect effect of Gc on HIV-1 replication. Using a set of annotated lists based on the Gene Ontology (GO) database and compiled pathway gene lists from National Center for Biotechnology Information (NCBI), we demonstrate that secondary gonococcal exposure elicited an HIV-1-dependent effect on the host transcriptome, with emphasis on genes implicated in inflammatory response, the T-cell activation program and programed cell death.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Supplementary material
 References
 
Cells and co-infection design
Donor PBMCs were obtained by apheresis from four normal volunteers followed by Ficoll–Hypaque isolation. Cells were suspended in RPMI medium 1640 containing 10% endotoxin-free fetal bovine serum (FBS), 100 µg ml–1 penicillin and 100 units ml–1 streptomycin (Invitrogen, Carlsbad, CA, USA). PBMCs were PHA/IL-2 (Becton Dickinson, Bedford, MA, USA) stimulated for 48 h, then washed and infected overnight in quadruplicate with log10 serially diluted HIV-1 (BaL strain). Cells were then washed and cultured in medium supplemented with IL-2 and monitored for viral infection over the course of 10 days. Viral stocks were propagated in donor PBMCs that may contain inducible factors. Therefore, a viral-limiting dilution approach was taken to define a single infectious dose based on the Poisson distribution model (24) while controlling for potential media effects that might have lower dilution end-points. This approach allowed us to control for media exposure in dilutions that were HIV-1 negative, based on p24 and HIV-1 gag RNA measurement. On day 10, cultures were stimulated for 6 h with Gc [F62 strain, multiplicity of infection (MOI) = 10] or mock stimulated. Neisseria gonorrhoeae (F62 strain) was grown on chocolate agar, collected with a cotton swab and suspended in PBS prior to addition to cell cultures. Bacterial concentrations in PBS were determined using OD600 readings from a SmartSpec 3000 (BioRad, Hercules, CA, USA). The optical density (OD) measurements were then multiplied by 1.0 x 109 cells ml–1 per OD and added to cultures at an MOI = 10 in a total volume of 100 µl of PBS. Mock stimulation was conducted by addition of 100 µl of PBS alone to cultures. Supernatants were harvested from the HIV-infected and the HIV-1/Gc-co-infected cultures for p24 determination and cells were harvested for RNA isolation and subsequent microarray analysis. This study was conducted under protocols reviewed and approved by the institutional review boards at the Harvard School of Public Health and the Boston University School of Medicine.

Determination of infection by p24 ELISA
Viral gag (p24) antigen in the culture supernatants was measured by the Alliance p24 HIV-1-capture ELISA kit (Perkin Elmer, Boston, MA, USA) according to the manufacturer's instructions.

Preparation of RNA for microarray
Total RNA was extracted from PBMCs exposed to HIV-1 alone or HIV-1 followed by Gc using the RNeasy extraction kit (Qiagen, Valencia, CA, USA). Trace DNA was removed from the RNA samples using the DNA-free kit (Ambion, Austin, TX, USA) and the RNA concentrations were determined using the NanoDrop-1000 (NanoDrop Technologies, Rockland, DE, USA). Five micrograms of each total RNA specimen was provided to the Boston University Microarray Facility for labeling, amplification and hybridization to a U133A 2.0 chip from Affymetrix (Santa Clara, CA, USA). Hybridization signals were read using an Affymetrix Genechip Scanner 3000 and processed with the statistical software MAS 5.0. Probes were filtered based on the presence/absence, resulting in data for 13 245 probes (12 333 occurrences, representing 8386 unique genes).

Real-time reverse transcription PCR validation
Human CD69, TNFSF6, CXCR4, IL-8 and 18S rRNA (endogenous control) were measured using Assays-on-Demand from Applied Biosystems Inc. (Foster City, CA, USA). Fifty nanograms of each sample was used per reaction in duplicate and was normalized to each sample's corresponding 18S fluorescence value.

Multiplex quantification of cytokines and chemokines
Cell-free supernatants from replicate wells were thawed and then mixed using 100 µl from each well. Each mixture was then analyzed in duplicate for the accumulation of IL-2, IL-4, IL-8, IL-10, IFN-gamma and MCP-1 protein analytes using the Bioplex system from BioRad according to the manufacturer's recommendations.

Analytic strategy and microarray analysis
The resulting gene expression profiles were evaluated for trend change in the HIV-1-dependent gene expression as a fold difference of high viral dosage divided by baseline lowest dosage (where both viral RNA and p24 antigen were undetectable) and also through the use of model-based hierarchical clustering (HC) to identify genes with similar profiles of expression over the HIV-1 dosage series. In HC, probes were partitioned into clusters sharing a similar behavior over the five different dosages of the HIV-1 series and six different conditions in the HIV-1/Gc-stimulated series. The dose series was identical for both the HIV-1 and HIV-1/Gc series. However, one array was excluded due to array quality in the HIV-1 series (corresponding to the 10–6). The two lowest dilutions (10–8 and 10–9) for the HIV-1 series were negative for HIV-1, based on p24 and viral RNA measurement; therefore, they were averaged and used as a baseline for all gene-expression measurements. This cluster analysis was performed using a previously described Bayesian approach, CAGED (25). The Affymetrix data sets can be accessed at http://www.ncbi.nlm.nih.gov/geo/, under the accession number GSE2505. Supplementary material for figures is available online.

Gene density plots (contour plots) were generated by first tabulating gene expression for each gene in both series based on fold expression, within designated expression intervals (e.g. 2.0–2.5) to generate a gene density spreadsheet in Excel (Microsoft, Seattle, WA, USA) that was converted to a density plot using SigmaPlot (SPSS, Chicago, IL, USA). Chi-squared analysis was used to determine significant differences in the number of genes per expression interval, based on co-infection or HIV-1 infection alone. Bayes' theorem was used to compute the probability that a gene is in one of the three categories given its change of expression. Fisher's exact test was used to evaluate the overall effect of co-infection on PBMC-derived gene expression for biologically relevant enrichment using Expression Analysis Systematic Explorer (EASE) (http://david.niaid.nih.gov/david/ease.htm). HC methods were used as described (25, 26). Chi-squared analysis was used to determine significant differences in the number of genes per expression interval, based on co-infection or HIV-1 infection alone.

In-house annotated lists, termed Montano Lab Gene Sets (MGS), were compiled from the GO database and existing references in PubMed Gene, as well as other online databases, and are deposited on the web supplement: http://homepage.mac.com/monty_and_alan/.Public/web-supplement/index.html.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Supplementary material
 References
 
Establishment of an HIV-1 dose infection series and a single Gc exposure co-infection series
To model the effects of co-infection in vitro, PBMCs were infected in quadruplicate with HIV-1 (BaL) using serially diluted viral stocks (MOI range 0.001–100), monitored for viral p24 antigen production and harvested on day 10, post-infection. Half the cultures (two from each series) were then mock stimulated (HIV-1 series) or stimulated with N. gonorrhoeae (HIV-1/Gc series) for 6 h. After stimulation, all cultures were harvested for RNA isolation and measurement of p24 in cellular supernatant (see Methods and Fig. 1A). A trend increase in viral supernatant p24 was observed with an MOI ≥ 0.100 with the average of quadruplicates shown in Fig. 1(B). A comparable trend increase was observed in cell-associated HIV-1 gag RNA based on quantitative real-time PCR (data not shown). HIV-1 was undetectable (by gag RNA PCR or p24 antigen) in the lowest two inocula (0.001 and 0.010) and was defined as the HIV-1-negative baseline. A single time point after infection was chosen to evaluate HIV-1 dose-dependent effects and to minimize time-dependent variation in gene expression for PBMCs.



View larger version (65K):
[in this window]
[in a new window]
  		Fig. 1. (A) Overview of RNA collection from the HIV-1 and HIV-1/Gc series infections. (B) Mean HIV-1 p24 protein quantitation (picogram per milliliter) on day 10 after infection with serial dilutions of HIV-1 BaL and stimulation with Gc. p24 for the HIV-1 only series did not differ from the HIV-1/Gc series (data not shown).

 
Global expression distinguishes HIV-1 infection from HIV-1/Gc co-infection
To evaluate the overall influence of differing HIV-1 burden on stimulated gene expression in response to Gc exposure, we performed microarray analysis using Affymetrix U133A 2.0 chips on the HIV-1 and HIV-1/Gc series. Genes were categorized as commonly induced (common, i.e. induced in both the HIV and HIV/Gc series), induced only in the HIV-1 series (HIV unique) or induced only in the HIV-1/Gc series (HIV/Gc unique) for each interval of expression (based on expression fold over HIV-negative baseline), as shown in Fig. 2 (see also web supplement).



View larger version (30K):
[in this window]
[in a new window]
  		Fig. 2. Number of probes induced at discrete expression intervals unique to the HIV-1 series (HIV unique), the HIV-1/Gc co-infection series (HIV/Gc unique) or induced in both conditions (common). Note the large number of unique genes induced during HIV/Gc co-infection at high expression levels (>2.0). Inset—plotted are y-axis: fold activation, x-axis: number of probes per gene (for enlarged figure, see Supplementary material.

 
The distribution of genes based on fold stimulation indicated that modestly induced genes (1- to 1.5-fold) tended to be common to both series (3358 genes), with 56 versus 33% in HIV unique and 11% in HIV/Gc infected. A shift toward slightly higher stimulation (1.5–2.0) characterized the HIV-1 unique series (HIV unique)-stimulated genes (2011 genes, 44%) and genes induced >2 were predominantly represented by genes stimulated uniquely in the HIV-1/Gc series (1390 genes, 76%). Thus, when analyzed by fold analysis, gene distribution differences were all highly significant (chi-squared P values < 0.0001) for the comparisons HIV unique, HIV/Gc unique, and genes commonly induced.

Multiple HIV-1 dose-dependent gene-expression patterns (clusters) upon co-infection
While measuring fold difference can be useful for identifying large differences, this approach does not identify coordinate patterns of change. To address this limitation, we used model-based HC to organize expression patterns into clusters based on increasing HIV-1 dosage. HC identified 15 clusters in the Gc-stimulated series associated with increasing HIV-1 dosage and 7 clusters associated with HIV-1 infection dosage alone. Clusters were assigned numbers arbitrarily and were then arranged based on median expression from most repressed to most induced and described as ‘repressed’, ‘flat-to-modestly induced’ or ‘induced’, as indicated (Fig. 3 and web supplement to Fig. 3). Note the trend influence of increasing HIV-1 dosage on gene expression (left to right in each cluster series). Overall, most genes in both series were flat-to-modestly induced (64% for HIV-1/Gc co-infection, 97% for HIV-1 infection). Induced gene clusters were more common in the HIV-1/Gc series (31%) compared with the HIV-1 series (1%).



View larger version (36K):
[in this window]
[in a new window]
  		Fig. 3. HC analysis of Gc or mock-stimulated donor PBMCs previously infected for 10 days with increasing levels of HIV-1 BaL [x-axis, left to right in each cluster corresponds to MOI = 10–9, 10–8, 10–7, 10–6, 10–5, 10–4 for HIV/Gc series and MOI = average of 10–9 and 10–8 (0.5 x 10–8, baseline reference), 10–7, 10–5, 10–4 for HIV-1 series]. A total of 13 245 probes were analyzed resulting in 15 clusters for the HIV/Gc series (A) and 7 clusters for the HIV series (B). For enlarged clusters and dendrogram, see Supplementary material.

 
To evaluate the relationship between fold expression (Fig. 2) and cluster patterns (Fig. 3) in both series, we plotted cluster assignment versus the fold expression for each gene probe, shown as box plots in Fig. 4. In the HIV-1 series, represented in the lower panel, one cluster represented induced genes (1%, cluster 5), whereas in the HIV-1/Gc series, seven clusters represented induced genes (31%, clusters 4–6, 11, 12, 14, 15). Thus, Gc stimulation was markedly dependent upon the underlying level of HIV-1 burden in PBMCs.



View larger version (13K):
[in this window]
[in a new window]
  		Fig. 4. Cluster assignment versus fold expression for the microarray data sets HIV/Gc series (above) and HIV-only series (below). Clusters are arranged numerically from bottom to top in each panel.

 
Shifts in transcriptional landscape upon co-infection
To identify overall gene response to both series, contour plots were generated (see Methods) representing the fold induction for each gene in the HIV-1 series against the corresponding expression in the HIV-1/Gc series (Fig. 5) as well as for the hierarchically clustered genes in both series (Fig. 6). Genes (Fig. 5) or clusters (Fig. 6) were ordered from most repressed to most induced and represented as contour plots (see Methods and web supplement to Fig. 5). The overall pattern observed in the contour plot (Fig. 5) indicated that co-infection in the HIV-1/Gc series broadly amplified the transcriptional activity exhibited in the corresponding HIV-1 series alone and was dependent upon the underlying HIV-1 dosage.



View larger version (65K):
[in this window]
[in a new window]
  		Fig. 5. Descriptive categories of statistically enriched genes and gene density plots based on fold analysis of the HIV-1 series (A) and the HIV-1/Gc series (B). Gene density plots and areas selected for enrichment (>2-fold) are below (A) or to the right (B) of the solid line. The expression of genes based on fold activity is represented for HIV with Gc (left -> right) and HIV without Gc (top -> bottom). Colors indicate gene number within contour lines, as indicated. Also see Supplementary material.

 


View larger version (34K):
[in this window]
[in a new window]
  		Fig. 6. Descriptive categories of statistically enriched genes and a gene density plot based on cluster analysis. Gene density plot and area of analysis is contained within the dotted line polygon. The cluster expression is arranged by median value, from lowest to highest, with HIV/Gc (left -> right) and HIV only (top -> bottom).

 
Statistical enrichment analysis identifies multiple biological cateogories
To identify categories of statistically enriched genes, we compared the representation of gene categories above 2-fold induction in both series (HIV-1 and HIV-1/Gc) with the representation of the same gene categories in the GO database and our annotated database by utilizing the EASE program which applies a Fisher's exact test as a measure of enrichment (Figs 5 and 6 and http://david.niaid.nih.gov/david/ease.htm).

Categories of genes associated with T-cell stimulation, defined as ‘TstimUp’ and NCBI PubMed queries for HIV-1-associated genes defined as ‘HIV associated’ were significant in the HIV-1 series (P = 0.003 and P = 0.038, respectively). A transition in categories of genes associated with T-cell stimulation (TstimUp), RNA binding, response to endogenous stimuli, antiviral response, viral life cycle and viral infectious cycle occurred with all being enriched in the HIV-1 series but notably increased in number and significance in the corresponding HIV-1/Gc series (Fig. 5B). Evaluation of clusters showing a trend median change in association with increasing HIV-1 (HIV-1 series clusters 2–5 and HIV-1/Gc series clusters 2, 4–7, 11,12, see Fig. 3 for cluster patterns) implicated the categories T-cell stimulation (TstimUp) and HIV-1-associated genes (HIV associated), as shown in Fig. 6. EASE analysis of all clusters indicated that clusters 2–4 in the ‘HIV-1 only’ series and clusters 4–6, 11 and 12 in the ‘HIV-1/Gc’ series contained significantly enriched TstimUp genes.

To evaluate up-regulated genes (>2-fold) within the context of published HIV-1 literature, we compiled a list of genes associated with HIV infection termed, ‘HIV-Assoc’. This list contains 777 genes derived from entries in both PubMed and the Human HIV-1 Protein Interaction Database (http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/). The 147 selectively induced genes present in this list were further annotated using multiple pathway gene lists, MGS annotation (see web supplement). A subset of literature-associated and novel genes is summarized in Table 1. A dendrogram and series profile of selected up-regulated genes in the HIV-1/Gc series were further annotated using our multiple pathway lists, as shown in Fig. 7(A and B), and suggested that multiple genes overlapping with T-cell activation (TstimUp) and inflammatory response exhibited HIV-1-dependent up-regulation during co-infection. (A dendrogram of the full list is provided in the web supplement to Fig. 7.)


View this table:
[in this window]
[in a new window]
  		Table 1. Annotation of selected transcripts that displayed HIV-1 dose dependent upregulation upon stimulation by Gc. Also note inset categories (Table 1 cont.)

 


View larger version (37K):
[in this window]
[in a new window]
  		Fig. 7. (A) Dendrogram values are normalized to the baseline in each series (see Methods). Pathway associations are shown at the right for each gene using the MGS annotation and are indicated, as show. (B) Series profile of genes exhibiting HIV-1-dependent activation in the HIV and HIV/Gc series (from left to right increasing HIV for each series), as shown. The y-axis represents natural log-transformed fluorescence normalized to baseline. Also see Methods and Supplementary material.

 


View larger version (63K):
[in this window]
[in a new window]
  		Fig. 8. Real-time validation of selected genes (A) and proteins (B) displaying HIV-1-dependent expression in microarray profiles for the HIV-1 series. *Denotes below detection. Total T cells from an equivalent number of PBMCs were column purified to isolate CD4+ T lymphocytes (Cedar Lane, Laboratories, Hornby Ontario, Canada).

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Supplementary material
 References
 
Many N. gonorrhoeae infections occur within a significant background of HIV-1 infection, particularly among developing regions in the world and also within marginalized regions of the US. Neisseria gonorrhoeae (Gc) infection has been associated with an intense inflammatory immune response (7, 27–29), but little is known about the nature of the immune response in the context of other prevalent secondary STIs such as HIV-1. We were interested in clarifying the interactive role of HIV-1 infection and gonococcal infection on the immunogenetic response in the blood. In this study, we describe a transition in the host transcriptome from a comparably modest HIV-1-dependent effect on host gene expression to a broader increase in global transcription upon co-infection with N. gonorrhoeae, with a disproportionate effect on genes associated with T-cell activation and viral infection. Whether amplified HIV-1-dependent gene expression in multiple gene categories occurs in vivo with concurrent STIs, such as Gc, awaits future studies. The modest effect of HIV-1 at a single time point post-infection was consistent with previous observations that evaluated HIV-1 infection, in time course studies using distinct cell types (17, 30).

Host response to Gc stimulation of infected PBMCs appeared to increase both the range of HIV-1-dependent gene categories that were stimulated and the number of genes represented within each category (see Table 1, Figs 6 and 7). Statistical comparison of enriched gene categories in the HIV-1 series with categories in the HIV-1/Gc series further emphasized a transition from genes associated with response to external stimulus, immune response, response to stress and response to endogenous stimuli that were significant in the HIV-1 infection series to enrichment for the categories: TstimUp (T-cell stimulation associated), response to endogenous stimulus, regulation of apoptosis, transcriptional initiation, viral life cycle and viral infectious cycle in the HIV-1/Gc series. Co-infection also appeared to up-regulate multiple pathways including many genes previously associated with HIV-1-specific transcription (e.g. HTATSF1, HTATIP1/2, TARBP1, TARDBP, HRB/2), broader host transcription (e.g. NFKB1, REL, EGR2, JUN, CREB1, SP3, TBP and various TAFs), pro-apoptosis (Fas, FasL, TRAIL, various caspases), anti-apoptosis (ATM, TP53, BCL2, BCL2A1), chemotaxis (XCL1, MIP, IP-10, IL-8), memory-associated genes (STAT3, IFN-gamma, IL-12RB2, CD44), lymphocyte stimulation (CD69, CD38, CD28, CD80, CTLA-4), as well as signaling intermediates including heat shock proteins (HSPs)/chaperonins and MAP kinases (MAPK).

Genes associated with Th effector functions, e.g. IFN-gamma, CXCL9 (MIG), CXCL10 (IP-10), CD44, IL-8, IL-6 and CXCR4, displayed HIV-1-dependent up-regulation by Gc. The induction of IFN-gamma production upon exposure to HIV-1 antigens ex vivo has been proposed to suggest a switch from a Th2-type to Th1-type cytokine profile (31, 32). Multiple chemokines have been previously shown to inhibit viral replication (33). Collectively, this may suggest that co-infection can exacerbate lymphocyte chemotaxis, resulting in the increased infiltration of HIV-1-susceptible target cells. Recruitment of leukocytes from circulation to the peripheral sites of inflammation depends on adhesive interactions of chemoattracted cells with their surrounding milieu, which occurs through specific adhesion molecules (e.g. CD44) (34, 35). The chemokines MIG and IP-10 are inducible by IFN-gamma and associated with Th1 effector functions. These chemokines have been implicated in HIV-1 infection and may play a role in facilitating the expansion of HIV-1 infection (36). Increased levels of IL-6 and IFN-gamma have been previously detected in serum and tissues of HIV-infected patients, apparently in response to infection (37, 38), and IL-6 and IL-8 have been shown to be specifically up-regulated by HIV-1 Tat (39, 40) and may contribute to chemokine-mediated recruitment of CD4-positive T cells to sites of continuous viral replication.

It is noteworthy that we observed an HIV-1-dependent increase in CXCR4 expression upon stimulation with Gc. In this study, we used an R5 tropic strain (HIV-1 BaL). Whether up-regulation of CXCR4 implicates a potential role for the selective expansion of viruses with differing cellular tropism or whether CXCR4 up-regulation would modify T-cell trafficking or both will await direct experimental determination utilizing both X4 and R5 viral strains of HIV-1. HIV-1 disease progression has been previously associated with an expansion of viral co-receptor utilization and cell tropism from an initial utilization of CCR5 (R5 variants) to an expanded utilization of co-receptors notably including the CXCR4 co-receptor (X4 variants) (41–43). The observation in this study that an HIV-1-dependent increase in CXCR4 expression occurred in the presence of Gonorrhea is consistent with other studies and suggests that co-infection with a secondary microbial pathogen may accelerate a switch in co-receptor utilization (44, 45). Natural history studies of co-infected individuals will be essential to evaluate a potential effect of secondary infections on viral co-receptor utilization and disease progression.

Apoptosis plays a critical role in the progressive depletion of CD4+ T cells during HIV-1 infection (46, 47) and HIV-1 has been directly implicated in T-cell apoptosis through up-regulation of CD95L (48, 49), possibly mediated by the viral-encoded HIV-1 Tat protein (50). Genes associated with programed cell death that were activated in this study during co-infection included both pro-apoptotic [e.g. TNFRSF6 (Fas/CD95), TNFSF6 (FasL/CD95L), CASP1, TRAIL/TNFSF10] and anti-apoptotic (BCL2, BCL2A1, ATM) transcripts (see Table 1 and Fig. 5). Our data might suggest that co-infection with Gc might differentially influence direct or bystander-induced cell death. Interestingly, Neisseria have been previously implicated in both pro-apoptotic (51) and anti-apoptotic signaling (23).

Co-infection of N. gonorrhoeae and HIV-1 in peripheral blood cells has experimental limitations. HIV-1 predominantly infects lymphocytes, while urogenital epithelia appear to serve as the initial site of entry for Gc, though infections have been noted at many sites, including the peripheral blood (52). In this study, viral infections were conducted using the CCR5 co-receptor-dependent monocyte/macrophage tropic strain (HIV-1 BaL) (53). Previous studies have demonstrated that N. gonorrhoeae can infect monocyte/macrophage cells (54, 55). In addition, a direct interaction by N. gonorrhoeae with primary human T cells (56) and activation of T cell lines containing HIV-1 constructs (8), collectively suggest that both pathogens are likely to influence both T cells and monocyte/macrophages either through cell-surface signaling or by direct co-infection. In addition, the relative contribution to overall host response from distinct subsets of cells within PBMCs in this study is unknown as PBMCs represent a mixed population, including CD4+ T cells (50–60%), CD8+ T lymphocytes (20–25%), B lymphocytes (10–15%), NK cells (10%), monocytes (5%) and dendritic cells (<1%) (57). Nevertheless, an informative survey of genomic expression and HIV-associated signatures during co-infection in PBMCs may be helpful to guide future studies on the relative contribution of distinct cellular subsets. We also chose to identify HIV-1-dependent effects on gene expression at a single time point, which has an advantage of identifying HIV-1 burden-dependent gene expression, while minimizing variation due to time in culture. Further studies will be required to determine the role of time- and dose-dependent influences of both HIV-1 and Gonorrhea on host gene expression.

Several activation markers (e.g. CD38, CD44, CD69, see Table 1) were up-regulated in an HIV-1-dependent fashion. Activation by HIV-1 has been previously associated with the up-regulation of genes involved in T-cell activation. Previous studies have suggested a link between activation of the apoptotic program and immune activation. In fact, chronic activation of the immune system has been proposed to contribute directly to progressive CD4+ T-cell depletion in vivo (58, 59). This may be of particular relevance to the memory T-cell compartments which appear to be selectively ablated during the acute HIV-1 infection period (60).

Genes associated with the chaperone pathway and heat shock including HSPE1 (HSP10) and HSPCA (HSP90) were differentially up-regulated by Gc, based on HIV-1 burden. HSP10 interacts with HIV-1 integrase, which is a substrate for the HSP60–HSP10 complex (61). HSP90 and HSP70 regulate the assembly of an active CDK9–cyclin T1 complex responsible for P-TEFb-mediated HIV-1 Tat transactivation (62). HSPs have also been associated with the activation of beta-chemokines that attract immune effector functions (63).

Both NFKB1 and REL were HIV-1 dependently up-regulated during co-infection with Gc. These are both members of the NF-kappaB family which has been implicated in the immunomodulation of many target genes, including chemokines, and in the regulation of apoptosis (64). Up-regulation of these transcription factors may in part explain the global up-regulation observed in our Gc-exposed, differentially HIV-1-infected cells. Also, constitutive expression of REL/NFKB activity in vitro has been shown to protect transformed cells against apoptosis induced by tumor necrosis factor (TNF), FasL or TRAIL (65–67).

The potential for bacterial adherence and invasion to have contributed differentially to our results is unknown. However, because gonococcal exposure occurred in RPMI medium containing penicillin and streptomycin (which are bacteriocidal for Gc), we speculate that surface contact-mediated signaling was the predominant mode of activation. Gc has many immunogenic components that can evoke host immune response. Many previous studies evaluating host response to Gc have generally observed the induction of a pro-inflammatory cytokine response, with some notable exceptions. In PBMCs, neisserial IgA1 protease was shown to induce IFN-gamma and CD69 (68); in macrophages, Opa and Pil variants were reported to induce TNF-alpha, IL-8, MIP-1alpha and CCL5/RANTES (69). A comparative study of various bacterial exotoxins including Neisseria revealed elevated pro-inflammatory mediators IL-6, IL-8, TNF-alpha (70) in the peripheral blood. By contrast, other studies have suggested a role for anti-inflammatory response to infection. In T cells derived from N. gonorrhoeae, convalescent subjects that were stimulated in vitro with gonococcal Por protein produced increased amounts of IL-4 (13), suggesting that a Th2-type response can be induced in peripheral blood. In a separate study, a specific Opa variant appeared to induce suppression in primary T cells derived from peripheral blood (56). Therefore, our results likely represent a cumulative effect of signaling events mediated by distinct gonococcal components.

In the experimental scheme used in this study, Gc was added in equivalent amounts to an HIV-1 dose series that ranged from absent (based on absent gag RNA and absent p24) to >five logarithrm increases in HIV-1 burden. The use of HC and statistical validation then allowed for the identification of specific genes and biological categories based on their correlation with increasing HIV-1 levels. Those genes and categories were interpreted as being HIV-1 dependent since Gc was added at a single dose throughout the infection series. Because we normalized to the first value in each series (HIV-1 only, HIV-1/Gc), our experimental design could not formally identify Gc only effects. We speculate that the presence of Gc was interactive and necessary, but not sufficient, to identify HIV-1-dependent increases in gene expression. To confirm this, a more detailed temporal analysis of gene expression in response to these pathogens will be necessary.

A model representing the overall response to co-infection is shown in Fig. 9 and is based primarily on results from the two gene query lists: TstimUp and HIV-Associated (in Figs 5 and 6). Comparison of enriched genes within these categories supported the view that in co-infection, a significant increase occurred in the number of genes related to immune response, infection, transcription and T-cell stimulation. Notably, the composition of genes within these categories shifted with increased representation of proteasome/ubiquitination, HLA class I and II, programed cell death, phosphorylation cascades (PI3-kinase, protein kinase C, JAK-STAT, MAPK) and receptor (TCR, Wnt)-mediated signaling. We speculate that co-infection functions to overcome sub-threshold activation signals induced by infection alone with several outcomes, including increases in antigen processing due to up-regulation of immunoproteasomes and HLA expression (71, 72), increases in IFN responsive and pro-inflammatory cytokine modulation due to increases in the JAK-STAT (73, 74) and MAPK cascades (75), chemotaxis of lymphocytes and macrophages due to up-regulation of the PI3-kinase pathway (76–78) and, finally, lymphocyte activation and proliferation through engagement of the TCR (79) and WntR (80). A comparative analysis of the immune response in peripheral blood with matched genital fluids will be helpful to characterize the immune response, in vivo, and may assist in understanding co-infection pathogenesis, as well as in devising immune modulation strategies based on HIV-1 burden in populations where secondary pathogens pose a significant risk for co-infection.



View larger version (17K):
[in this window]
[in a new window]
  		Fig. 9. Model representing the overall host response to infection and differential effect of co-infection with Neisseria gonorrhoeae (Gc).

 

   Supplementary material
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary material
References
 
Supplementary material is available online.


   Acknowledgements
 
This work was supported in part by Grant AI-51183-1-A2(to M.M.) from the National Institute of Allergy and Infectious Diseases (NIAID). We are grateful to the members of the Boston University Microarray Facility (Norman Gerry and Marc Lenburg) for their assistance and advice. We also thank Deborah Anderson, Peter Rice and Paul Skolnik for helpful discussions and support.


   Abbreviations
 
FBS   fetal bovine serum
GO   Gene Ontology
HC   hierarchical clustering
HSP   heat shock proteins
MAPK   MAP kinases
MGS   Montano Lab Gene Sets
MOI   multiplicity of infection
NCBI   National Center for Biotechnology Information
OD   optical density
STI   sexually transmitted infections
TNF   tumor necrosis factor

   Notes
 
Transmitting editor: D. Tarlinton

Received 25 July 2005, accepted 16 October 2005.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary material
 References
 

  1. Clemetson, D. B., Moss, G. B., Willerford, D. M. et al. 1993. Detection of HIV DNA in cervical and vaginal secretions. Prevalence and correlates among women in Nairobi, Kenya. JAMA 269:2860.[Abstract]
  2. Ghys, P. D., Fransen, K., Diallo, M. O. et al. 1997. The associations between cervicovaginal HIV shedding, sexually transmitted diseases and immunosuppression in female sex workers in Abidjan, Cote d'Ivoire. AIDS 11:F85.[ISI][Medline]
  3. Mostad, S. B., Overbaugh, J., DeVange, D. M. et al. 1997. Hormonal contraception, vitamin A deficiency, and other risk factors for shedding of HIV-1 infected cells from the cervix and vagina [see comment]. Lancet 350:922.[CrossRef][ISI][Medline]
  4. Wasserheit, J. N. 1992. Epidemiological synergy. Interrelationships between human immunodeficiency virus infection and other sexually transmitted diseases. Sex. Transm. Dis. 19:61.[ISI][Medline]
  5. Cohen, M. S., Hoffman, I. F., Royce, R. A. et al. 1997. Reduction of concentration of HIV-1 in semen after treatment of urethritis: implications for prevention of sexual transmission of HIV-1. AIDSCAP Malawi Research Group. Lancet 349:1868.[CrossRef][ISI][Medline]
  6. Fichorova, R. N., Desai, P. J., Gibson, F. C., III and Genco, C. A. 2001. Distinct proinflammatory host responses to Neisseria gonorrhoeae infection in immortalized human cervical and vaginal epithelial cells. Infect. Immun. 69:5840.[Abstract/Free Full Text]
  7. Ramsey, K. H., Schneider, H., Cross, A. S. et al. 1995. Inflammatory cytokines produced in response to experimental human gonorrhea. J. Infect. Dis. 172:186.[ISI][Medline]
  8. Chen, A., Boulton, I. C., Pongoski, J., Cochrane, A. and Gray-Owen, S. D. 2003. Induction of HIV-1 long terminal repeat-mediated transcription by Neisseria gonorrhoeae. AIDS 17:625.[ISI][Medline]
  9. Wu, J. J., Huang, D. B., Pang, K. R. and Tyring, S. K. 2004. Selected sexually transmitted diseases and their relationship to HIV. Clin. Dermatol. 22:499.[CrossRef][ISI][Medline]
  10. Borkow, G., Weisman, Z., Leng, Q. et al. 2001. Helminths, human immunodeficiency virus and tuberculosis. Scand. J. Infect. Dis. 33:568.[CrossRef][ISI][Medline]
  11. Belec, L., Gherardi, R., Payan, C. et al. 1995. Proinflammatory cytokine expression in cervicovaginal secretions of normal and HIV-infected women. Cytokine 7:568.[CrossRef][ISI][Medline]
  12. Pietrella, D., Kozel, T. R., Monari, C., Bistoni, F. and Vecchiarelli, A. 2001. Interleukin-12 counterbalances the deleterious effect of human immunodeficiency virus type 1 envelope glycoprotein gp120 on the immune response to Cryptococcus neoformans. J. Infect. Dis. 183:51.[CrossRef][ISI][Medline]
  13. Simpson, S. D., Ho, Y., Rice, P. A. and Wetzler, L. M. 1999. T lymphocyte response to Neisseria gonorrhoeae porin in individuals with mucosal gonococcal infections. J. Infect. Dis. 180:762.[CrossRef][ISI][Medline]
  14. Sher, A., Gazzinelli, R. T., Jankovic, D. et al. 1998. Cytokines as determinants of disease and disease interactions. Braz. J. Med. Biol. Res. 31:85.[ISI][Medline]
  15. Clerici, M. and Shearer, G. M. 1994. The Th1-Th2 hypothesis of HIV infection: new insights. Immunol. Today 15:575.[CrossRef][ISI][Medline]
  16. Graziosi, C., Pantaleo, G., Gantt, K. R. et al. 1994. Lack of evidence for the dichotomy of TH1 and TH2 predominance in HIV-infected individuals. Science 265:248.[Abstract/Free Full Text]
  17. 't Wout, A. B., Lehrman, G. K., Mikheeva, S. A. et al. 2003. Cellular gene expression upon human immunodeficiency virus type 1 infection of CD4(+)-T-cell lines. J. Virol. 77:1392.[CrossRef][ISI][Medline]
  18. Chun, T. W., Justement, J. S., Lempicki, R. A. et al. 2003. Gene expression and viral production in latently infected, resting CD4+ T cells in viremic versus aviremic HIV-infected individuals. Proc. Natl Acad. Sci. USA 100:1908.[Abstract/Free Full Text]
  19. Corbeil, J., Sheeter, D., Genini, D. et al. 2001. Temporal gene regulation during HIV-1 infection of human CD4+ T cells. Genome Res. 11:1198.[Abstract/Free Full Text]
  20. Geiss, G. K., Bumgarner, R. E., An, M. C. et al. 2000. Large-scale monitoring of host cell gene expression during HIV-1 infection using cDNA microarrays. Virology 266:8.[CrossRef][ISI][Medline]
  21. Ryo, A., Suzuki, Y., Ichiyama, K. et al. 1999. Serial analysis of gene expression in HIV-1-infected T cell lines. FEBS Lett. 462:182.[CrossRef][ISI][Medline]
  22. Vahey, M. T., Nau, M. E., Jagodzinski, L. L. et al. 2002. Impact of viral infection on the gene expression profiles of proliferating normal human peripheral blood mononuclear cells infected with HIV type 1 RF. AIDS Res. Hum. Retrovir. 18:179.[CrossRef][ISI][Medline]
  23. Binnicker, M. J., Williams, R. D. and Apicella, M. A. 2003. Infection of human urethral epithelium with Neisseria gonorrhoeae elicits an upregulation of host anti-apoptotic factors and protects cells from staurosporine-induced apoptosis. Cell. Microbiol. 5:549.[CrossRef][ISI][Medline]
  24. Lu, W., Manolikakis, G. and Andrieu, J. M. 1992. Relationship between frequency of infectious human immunodeficiency virus type 1-harboring cells and kinetics of viral replication: a simple procedure for quantitation of infectious virus-carrying cells in blood samples. J. Clin. Microbiol. 30:2535.[Abstract/Free Full Text]
  25. Ramoni, M. F., Sebastiani, P. and Kohane, I. S. 2002. Cluster analysis of gene expression dynamics. Proc. Natl Acad. Sci. USA 99:9121.[Abstract/Free Full Text]
  26. Sebastiani, P., Ramoni, M. F., Nolan, V., Baldwin, C. T. and Steinberg, M. H. 2005. Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nat. Genet. 37:435.[CrossRef][ISI][Medline]
  27. Maisey, K., Nardocci, G., Imarai, M. et al. 2003. Expression of proinflammatory cytokines and receptors by human fallopian tubes in organ culture following challenge with Neisseria gonorrhoeae. Infect. Immun. 71:527.[Abstract/Free Full Text]
  28. McGee, Z. A., Jensen, R. L., Clemens, C. M., Taylor-Robinson, D., Johnson, A. P. and Gregg, C. R. 1999. Gonococcal infection of human fallopian tube mucosa in organ culture: relationship of mucosal tissue TNF-alpha concentration to sloughing of ciliated cells. Sex. Transm. Dis. 26:160.[ISI][Medline]
  29. Naumann, M., Wessler, S., Bartsch, C., Wieland, B. and Meyer, T. F. 1997. Neisseria gonorrhoeae epithelial cell interaction leads to the activation of the transcription factors nuclear factor kappaB and activator protein 1 and the induction of inflammatory cytokines. J. Exp. Med. 186:247.[Abstract/Free Full Text]
  30. Mitchell, R., Chiang, C. Y., Berry, C. and Bushman, F. 2003. Global analysis of cellular transcription following infection with an HIV-based vector. Mol Ther 8:674.[CrossRef][ISI][Medline]
  31. Imami, N., Antonopoulos, C., Hardy, G. A., Gazzard, B. and Gotch, F. M. 1999. Assessment of type 1 and type 2 cytokines in HIV type 1-infected individuals: impact of highly active antiretroviral therapy. AIDS Res. Hum. Retrovir. 15:1499.[CrossRef][ISI][Medline]
  32. Mosmann, T. R. and Sad, S. 1996. The expanding universe of T-cell subsets: Th1, Th2 and more. Immunol. Today 17:138.[CrossRef][ISI][Medline]
  33. Cocchi, F., DeVico, A. L., Garzino-Demo, A., Arya, S. K., Gallo, R. C. and Lusso, P. 1995. Identification of RANTES, MIP-1 alpha, and MIP-1 beta as the major HIV-suppressive factors produced by CD8+ T cells. Science 270:1811.[Abstract/Free Full Text]
  34. Hogerkorp, C. M., Bilke, S., Breslin, T., Ingvarsson, S. and Borrebaeck, C. A. 2003. CD44-stimulated human B cells express transcripts specifically involved in immunomodulation and inflammation as analyzed by DNA microarrays. Blood 101:2307.[Abstract/Free Full Text]
  35. Butcher, E. C. and Picker, L. J. 1996. Lymphocyte homing and homeostasis. Science 272:60.[Abstract]
  36. Izmailova, E., Bertley, F. M., Huang, Q. et al. 2003. HIV-1 Tat reprograms immature dendritic cells to express chemoattractants for activated T cells and macrophages. Nat. Med. 9:191.[CrossRef][ISI][Medline]
  37. Breen, E. C., Rezai, A. R., Nakajima, K. et al. 1990. Infection with HIV is associated with elevated IL-6 levels and production. J. Immunol. 144:480.[Abstract]
  38. Graziosi, C., Gantt, K. R., Vaccarezza, M. et al. 1996. Kinetics of cytokine expression during primary human immunodeficiency virus type 1 infection. Proc. Natl Acad. Sci. USA. 93:4386.[Abstract/Free Full Text]
  39. Biswas, D. K., Salas, T. R., Wang, F., Ahlers, C. M., Dezube, B. J. and Pardee, A. B. 1995. A Tat-induced auto-up-regulatory loop for superactivation of the human immunodeficiency virus type 1 promoter. J. Virol. 69:7437.[Abstract]
  40. Ott, M., Lovett, J. L., Mueller, L. and Verdin, E. 1998. Superinduction of IL-8 in T cells by HIV-1 Tat protein is mediated through NF-kappaB factors. J. Immunol. 160:2872.[Abstract/Free Full Text]
  41. Schuitemaker, H., Koot, M., Kootstra, N. A. et al. 1992. Biological phenotype of human immunodeficiency virus type 1 clones at different stages of infection: progression of disease is associated with a shift from monocytotropic to T-cell-tropic virus population. J. Virol. 66:1354.[Abstract/Free Full Text]
  42. Connor, R. I., Sheridan, K. E., Ceradini, D., Choe, S. and Landau, N. R. 1997. Change in coreceptor use correlates with disease progression in HIV-1–infected individuals. J. Exp. Med. 185:621.[Abstract/Free Full Text]
  43. Stalmeijer, E. H., Van Rij, R. P., Boeser-Nunnink, B. et al. 2004. In vivo evolution of X4 human immunodeficiency virus type 1 variants in the natural course of infection coincides with decreasing sensitivity to CXCR4 antagonists. J. Virol. 78:2722.[Abstract/Free Full Text]
  44. Juffermans, N. P., Paxton, W. A., Dekkers, P. E. et al. 2000. Up-regulation of HIV coreceptors CXCR4 and CCR5 on CD4(+) T cells during human endotoxemia and after stimulation with (myco)bacterial antigens: the role of cytokines. Blood 96:2649.[Abstract/Free Full Text]
  45. Moriuchi, M., Moriuchi, H., Turner, W. and Fauci, A. S. 1998. Exposure to bacterial products renders macrophages highly susceptible to T-tropic HIV-1. 102:1540.
  46. Katsikis, P. D., Wunderlich, E. S., Smith, C. A. and Herzenberg, L. A. 1995. Fas antigen stimulation induces marked apoptosis of T lymphocytes in human immunodeficiency virus-infected individuals. J. Exp. Med. 181:2029.[Abstract/Free Full Text]
  47. Estaquier, J., Idziorek, T., Zou, W. et al. 1995. T helper type 1/T helper type 2 cytokines and T cell death: preventive effect of interleukin 12 on activation-induced and CD95 (FAS/APO-1)-mediated apoptosis of CD4+ T cells from human immunodeficiency virus-infected persons. J. Exp. Med. 182:1759.[Abstract/Free Full Text]
  48. Baumler, C. B., Bohler, T., Herr, I., Benner, A., Krammer, P. H. and Debatin, K. M. 1996. Activation of the CD95 (APO-1/Fas) system in T cells from human immunodeficiency virus type-1-infected children. Blood 88:1741.
  49. Li-Weber, M., Laur, O., Dern, K. and Krammer, P. H. 2000. T cell activation-induced and HIV tat-enhanced CD95(APO-1/Fas) ligand transcription involves NF-kappaB. Eur. J. Immunol. 30:661.[CrossRef][ISI][Medline]
  50. Bartz, S. R. and Emerman, M. 1999. Human immunodeficiency virus type 1 Tat induces apoptosis and increases sensitivity to apoptotic signals by up-regulating FLICE/caspase-8. J. Virol. 73:1956.[Abstract/Free Full Text]
  51. Muller, A., Gunther, D., Brinkmann, V. et al. 2000. Targeting of the pro-apoptotic VDAC-like porin (PorB) of Neisseria gonorrhoeae to mitochondria of infected cells. EMBO J. 19:5332.[CrossRef][ISI][Medline]
  52. Hook, E. W., III and Handsfield, H. H. 1999. Gonococcal infections in the adult. In Holmes, K. K., Mardh, P. A., Sparling, P. F., Lemon, S. M., Stamm, W. E. and Piot, P., eds, Sexually Transmitted Diseases, p. 451. McGraw-Hill, New York.
  53. Gartner, S., Markovits, P., Markovitz, D. M., Kaplan, M. H., Gallo, R. C. and Popovic, M. 1986. The role of mononuclear phagocytes in HTLV-III/LAV infection. Science 233:215.[Abstract/Free Full Text]
  54. Knepper, B., Heuer, I., Meyer, T. F. and van Putten, J. P. 1997. Differential response of human monocytes to Neisseria gonorrhoeae variants expressing pili and opacity proteins. Infect. Immun. 65:4122.[Abstract]
  55. Naumann, M., Rudel, T. and Meyer, T. F. 1999. Host cell interactions and signalling with Neisseria gonorrhoeae. Curr. Opin. Microbiol. 2:62.[CrossRef][ISI][Medline]
  56. Boulton, I. C. and Gray-Owen, S. D. 2002. Neisserial binding to CEACAM1 arrests the activation and proliferation of CD4+ T lymphocytes. Nat. Immunol. 3:229.[CrossRef][ISI][Medline]
  57. Abbas, A. K., Lichtman, A. H., and Pober, J. S. 1994. Cellular and Molecular Immunology. WB Saunders Company.
  58. Deeks, S. G. and Walker, B. D. 2004. The immune response to AIDS virus infection: good, bad, or both? J. Clin. Invest. 113:808.[CrossRef][ISI][Medline]
  59. Grossman, Z., Meier-Schellersheim, M., Sousa, A. E., Victorino, R. M. and Paul, W. E. 2002. CD4+ T-cell depletion in HIV infection: are we closer to understanding the cause? Nat. Med. 8:319.[CrossRef][ISI][Medline]
  60. Douek, D. C., Picker, L. J. and Koup, R. A. 2003. T cell dynamics in HIV-1 infection. Annu. Rev. Immunol. 21:265.[CrossRef][ISI][Medline]
  61. Parissi, V., Calmels, C., De Soultrait, V. R. et al. 2001. Functional interactions of human immunodeficiency virus type 1 integrase with human and yeast HSP60. J. Virol. 75:11344.[Abstract/Free Full Text]
  62. O'Keeffe, B., Fong, Y., Chen, D., Zhou, S. and Zhou, Q. 2000. Requirement for a kinase-specific chaperone pathway in the production of a Cdk9/cyclin T1 heterodimer responsible for P-TEFb-mediated tat stimulation of HIV-1 transcription. J. Biol. Chem. 275:279.[Abstract/Free Full Text]
  63. Lehner, T., Bergmeier, L. A., Wang, Y. et al. 2000. Heat shock proteins generate beta-chemokines which function as innate adjuvants enhancing adaptive immunity. Eur. J. Immunol. 30:594.[CrossRef][ISI][Medline]
  64. Hayden, M. S. and Ghosh, S. 2004. Signaling to NF-kappaB. Genes Dev. 18:2195.[Abstract/Free Full Text]
  65. Liu, Z. G., Hsu, H., Goeddel, D. V. and Karin, M. 1996. Dissection of TNF receptor 1 effector functions: JNK activation is not linked to apoptosis while NF-kappaB activation prevents cell death. Cell 87:565.[CrossRef][ISI][Medline]
  66. Zong, W. X., Bash, J. and Gelinas, C. 1998. Rel blocks both anti-Fas- and TNF alpha-induced apoptosis and an intact Rel transactivation domain is essential for this effect. Cell Death Differ. 5:963.[CrossRef][ISI][Medline]
  67. Bernard, D., Quatannens, B., Vandenbunder, B. and Abbadie, C. 2001. Rel/NF-kappaB transcription factors protect against tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL)-induced apoptosis by up-regulating the TRAIL decoy receptor DcR1. J. Biol. Chem. 276:27322.[Abstract/Free Full Text]
  68. Tsirpouchtsidis, A., Hurwitz, R., Brinkmann, V., Meyer, T. F. and Haas, G. 2002. Neisserial immunoglobulin A1 protease induces specific T-cell responses in humans. Infect. Immun. 70:335.[Abstract/Free Full Text]
  69. Makepeace, B. L., Watt, P. J., Heckels, J. E. and Christodoulides, M. 2001. Interactions of Neisseria gonorrhoeae with mature human macrophage opacity proteins influence production of proinflammatory cytokines. Infect. Immun. 69:1909.[Abstract/Free Full Text]
  70. Muller-Alouf, H., Alouf, J. E., Gerlach, D., Ozegowski, J. H., Fitting, C. and Cavaillon, J. M. 1994. Comparative study of cytokine release by human peripheral blood mononuclear cells stimulated with Streptococcus pyogenes superantigenic erythrogenic toxins, heat-killed streptococci, and lipopolysaccharide. Infect. Immun. 62:4915.[Abstract/Free Full Text]
  71. Heink, S., Ludwig, D., Kloetzel, P. M. and Kruger, E. 2005. IFN-{gamma}-induced immune adaptation of the proteasome system is an accelerated and transient response. Proc. Natl Acad. Sci. USA. 102:9241.[Abstract/Free Full Text]
  72. Kloetzel, P. M. 2001. Antigen processing by the proteasome. Nat. Rev. Mol. Cell Biol. 2:179.[CrossRef][ISI][Medline]
  73. Lighvani, A. A., Frucht, D. M., Jankovic, D. et al. 2001. T-bet is rapidly induced by interferon-gamma in lymphoid and myeloid cells. Proc. Natl Acad. Sci. USA 98:15137.[Abstract/Free Full Text]
  74. O'Shea, J. J., Gadina, M. and Schreiber, R. D. 2002. Cytokine signaling in 2002: new surprises in the Jak/Stat pathway. Cell 109(Suppl.):S121.[CrossRef][ISI][Medline]
  75. Chang, L. and Karin, M. 2001. Mammalian MAP kinase signalling cascades. Nature 410:37.[CrossRef][Medline]
  76. Sasaki, T., Irie-Sasaki, J., Jones, R. G. et al. 2000. Function of PI3Kgamma in thymocyte development, T cell activation, and neutrophil migration [see comment]. Science 287:1040.[Abstract/Free Full Text]
  77. Hirsch, E., Katanaev, V. L., Garlanda, C. et al. 2000. Central role for G protein-coupled phosphoinositide 3-kinase gamma in inflammation [see comment]. Science 287:1049.[Abstract/Free Full Text]
  78. Li, Z., Jiang, H., Xie, W., Zhang, Z., Smrcka, A. V. and Wu, D. 2000. Roles of PLC-beta2 and -beta3 and PI3Kgamma in chemoattractant-mediated signal transduc