Gene Expression Profiling of Human Vaginal Cells In Vitro Discriminates Compounds with Pro-Inflammatory and Mucosa-Altering Properties: Novel Biomarkers for Preclinical Testing of HIV Microbicide Candidates

Abstract

Background: Inflammation and immune activation of the cervicovaginal mucosa are considered factors that increase susceptibility to HIV infection. Therefore, it is essential to screen candidate anti-HIV microbicides for potential mucosal immunomodulatory/inflammatory effects prior to further clinical development. The goal of this study was to develop an in vitro method for preclinical evaluation of the inflammatory potential of new candidate microbicides using a microarray gene expression profiling strategy.

Methods: To this end, we compared transcriptomes of human vaginal cells (Vk2/E6E7) treated with well-characterized pro-inflammatory (PIC) and non-inflammatory (NIC) compounds. PICs included compounds with different mechanisms of action. Gene expression was analyzed using Affymetrix U133 Plus 2 arrays. Data processing was performed using GeneSpring 11.5 (Agilent Technologies, Santa Clara, CA).

Results: Microarraray comparative analysis allowed us to generate a panel of 20 genes that were consistently deregulated by PICs compared to NICs, thus distinguishing between these two groups. Functional analysis mapped 14 of these genes to immune and inflammatory responses. This was confirmed by the fact that PICs induced NFkB pathway activation in Vk2 cells. By testing microbicide candidates previously characterized in clinical trials we demonstrated that the selected PIC-associated genes properly identified compounds with mucosa-altering effects. The discriminatory power of these genes was further demonstrated after culturing vaginal cells with vaginal bacteria. Prevotella bivia, prevalent bacteria in the disturbed microbiota of bacterial vaginosis, induced strong upregulation of seven selected PIC-associated genes, while a commensal Lactobacillus gasseri associated to vaginal health did not cause any changes.

Conclusions: In vitro evaluation of the immunoinflammatory potential of microbicides using the PIC-associated genes defined in this study could help in the initial screening of candidates prior to entering clinical trials. Additional characterization of these genes can provide further insight into the cervicovaginal immunoinflammatory and mucosal-altering processes that facilitate or limit HIV transmission with implications for the design of prevention strategies.

Fig 1. Diagrams showing the number of significantly altered probesets indentified by microarray gene profiling of Vk2 cells exposed to PIC and NIC.

Total number of the altered probeserts for each treatment/category is shown in brackets (a gene can be represented by more than one probeset).

Fig 2. Hierarchical clustering of the 20 discriminatory genes was performed using GeneSpring 11.5 as described in Materials and Methods.

Columns represent treatments, rows represent genes. Gene expression levels are indicated by color: red is for upregulation and green is for downregulation. Expression data are averages from at least six experiments/microarrays for each treatment. Hierarchical clustering based on the discriminatory genes demonstrates sharp segregation of the PIC vs NIC treatments.

Fig 3. Top network of Vk2 PIC/NIC discriminatory genes generated by Ingenuity Pathway Analysis.

Red/pink color indicates upregulation of the genes (microarray data). Connections of NFkB complex with other genes is shown in blue color.

Fig 4. NFκB activation in Vk2 cells in response to PIC treatments.

Fast degradation of IκB-α in the cytoplasm and translocation of p65/NFkB to the nucleus following Vk2 cells exposure to PICs was detected in cytoplasmic and nuclear fractions using corresponding antibodies.

Fig 5. Real-time qPCR validation of changes in expression of eight selected genes observed in microarray analysis.

Bars represent the mean ± SD of fold change relative to growth medium control. At least 3 independent experiments were performed for each treatment. All genes were normalized to GAPDH. Asterisks placed vertically denote p values for each PIC treatment relative to HEC used as a reference. (***p<0.0005, **p<0.005, *p< 0.05; Student t-test)

Fig 6. Transcription profile of the 20 discriminatory genes expression in Vk2 cells exposed to candidate microbicides and selected PICs and NICs.

Columns represent treatments, rows represent genes. Gene expression levels are indicated by color: red is for upregulation and green is for downregulation. Expression data are averages from at least six experiments/microarrays for each treatment. Clustering based on 20 PIC/NIC discriminatory genes places C31G (known as causing inflammatory response) to the PIC category, while dextran sulfate (DS) and cellulose sulfate (CS)—into the NIC group.

Fig 7. Real-time qPCR validation of changes in expression of eight selected genes observed in the microarray analysis of microbicide candidates.

Experimental details are as in Fig 5. HEC and TNF-α are added as references.

Fig 8. PIC-DG expression following bacterial colonization of Vk2 cells as revealed by quantitative real time RT-PCR.

P. bivia (right) induced strong upregulation of all seven PIC-DEGs, while L. gasseri (left) did not cause any changes. Results are presented as mean ±SD of three experiments.