The Villain Team-Up or how Trichomonas vaginalis and bacterial vaginosis alter innate immunity in concert

Objectives: Complex interactions of vaginal microorganisms with the genital tract epithelium shape mucosal innate immunity, which holds the key to sexual and reproductive health. Bacterial vaginosis (BV), a microbiome-disturbance syndrome prevalent in reproductive-age women, occurs commonly in concert with trichomoniasis, and both are associated with increased risk of adverse reproductive outcomes and viral infections, largely attributable to inflammation. To investigate the causative relationships among inflammation, BV and trichomoniasis, we established a model of human cervicovaginal epithelial cells colonised by vaginal Lactobacillus isolates, dominant in healthy women, and common BV species (Atopobium vaginae, Gardnerella vaginalis and Prevotella bivia). Methods: Colonised epithelia were infected with Trichomonas vaginalis (TV) or exposed to purified TV virulence factors (membrane lipophosphoglycan (LPG), its ceramide-phosphoinositol-glycan core (CPI-GC) or the endosymbiont Trichomonas vaginalis virus (TVV)), followed by assessment of bacterial colony-forming units, the mucosal anti-inflammatory microbicide secretory leucocyte protease inhibitor (SLPI), and chemokines that drive pro-inflammatory, antigen-presenting and T cells. Results: TV reduced colonisation by Lactobacillus but not by BV species, which were found inside epithelial cells. TV increased interleukin (IL)-8 and suppressed SLPI, likely via LPG/CPI-GC, and upregulated IL-8 and RANTES, likely via TVV as suggested by use of purified pathogenic determinants. BV species A vaginae and G vaginalis induced IL-8 and RANTES, and also amplified the pro-inflammatory responses to both LPG/CPI-GC and TVV, whereas P bivia suppressed the TV/TVV-induced chemokines. Conclusions: These molecular host–parasite–endosymbiont–bacteria interactions explain epidemiological associations and suggest a revised paradigm for restoring vaginal immunity and preventing BV/TV-attributable inflammatory sequelae in women

Correlates of circulating ovarian cancer early detection markers and their contribution to discrimination of early detection models: results from the EPIC cohort.

BACKGROUND: Ovarian cancer early detection markers CA125, CA15.3, HE4, and CA72.4 vary between healthy women, limiting their utility for screening. METHODS: We evaluated cross-sectional relationships between lifestyle and reproductive factors and these markers among controls (n = 1910) from a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC). Improvements in discrimination of prediction models adjusting for correlates of the markers were evaluated among postmenopausal women in the nested case-control study (n = 590 cases). Generalized linear models were used to calculate geometric means of CA125, CA15.3, and HE4. CA72.4 above vs. below limit of detection was evaluated using logistic regression. Early detection prediction was modeled using conditional logistic regression. RESULTS: CA125 concentrations were lower, and CA15.3 higher, in post- vs. premenopausal women (p ≤ 0.02). Among postmenopausal women, CA125 was higher among women with higher parity and older age at menopause (ptrend ≤ 0.02), but lower among women reporting oophorectomy, hysterectomy, ever use of estrogen-only hormone therapy, or current smoking (p < 0.01). CA15.3 concentrations were higher among heavier women and in former smokers (p ≤ 0.03). HE4 was higher with older age at blood collection and in current smokers, and inversely associated with OC use duration, parity, and older age at menopause (≤ 0.02). No associations were observed with CA72.4. Adjusting for correlates of the markers in prediction models did not improve the discrimination. CONCLUSIONS: This study provides insights into sources of variation in ovarian cancer early detection markers in healthy women and informs about the utility of individualizing marker cutpoints based on epidemiologic factors

Personalization in Skipforward, an Ontology-Based Distributed Annotation System

Abstract. Skipforward is a distributed annotation system allowing users to enter and browse statements about items and their features. Items can be things such as movies or books; item features are the genre of a movie or the storytelling pace of a book. Whenever multiple users annotate the same item with a statement about the same feature, these individual statements get aggregated by the system. For aggregation, individual user statements are weighted according to a competence metric based on the constrained Pearson correlation, adapted for Skipforward data: A user gets assigned high competence with regard to the feature in question if, for other items and the same feature type, he had a similar opinion to the current user. Since the competence metric is dependent on the user currently viewing the data, the user’s view of the data is completely personalized. In this paper, the personalization aspect as well as the item and expert recommender are presented.

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

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

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

<p>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)</p

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

<p>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.</p

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

<p>Experimental details are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128557#pone.0128557.g005" target="_blank">Fig 5</a>. HEC and TNF-α are added as references.</p

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

<p>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.</p

Genes differenially expressed in VK2 cells treated with proinflammatory/immunomodulatory compounds.

<p>Genes differenially expressed in VK2 cells treated with proinflammatory/immunomodulatory compounds.</p

Functional categories of the PIC/NIC discriminatory genes^a.

<p>^<b>a</b>Classification is based on IPA functional analysis and published literature. P values are estimated by IPA</p><p>Functional categories of the PIC/NIC discriminatory genes<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128557#t004fn001" target="_blank">^a</a>.</p