Local oestrogen therapy modulates extracellular matrix and immune response in the vaginal tissue of post-menopausal women with severe pelvic organ prolapse.

This study investigates the effect of local oestrogen therapy (LET) on the expression of proteins participating in collagen/elastin biogenesis and immune markers in vaginal tissues of post-menopausal women with severe pelvic organ prolapse (POP). Vaginal biopsies were collected from the anterior vaginal wall of informed and consented 52 post-menopausal women with severe POP undergoing total hysterectomy. Twenty-nine of the 52 women were treated with LET (in the form of vaginal oestrogen cream or tablet), while the remaining 23 untreated patients served as the controls. This study was approved by Sinai Health System REB. Vaginal tissue specimens were analysed for gene and protein expression using real-time RT-PCR and Luminex assays, protein localization and immune cell infiltration were assessed by immunohistochemistry. Forty-four cytokines were detected. We found that LET application: (a) significantly increased (P < 0.05) gene and protein expression levels of extracellular matrix (ECM) structural proteins, collagen and elastin, as well as the expression of ECM maturation enzyme BMP1; (b) decreased protein expression level of ECM degradation enzymes MMP1, MMP2 and MMP3 accompanied by an increase in their tissue inhibitors, TIMP1 and TIMP4; (c) significantly increased (P < 0.05) the gene and protein expression levels of 14 vaginal cytokines involved in leucocyte infiltration, which was confirmed by immunohistochemistry. Our results indicate that LET plays an important role in the activation of immune system within the local vaginal environment, limiting the undesirable ECM degradation, which supports the strengthening of vaginal ECM in post-menopausal women, therefore resisting menopause/age-related changes and inducing urogenital tract tissue regeneration.

An extended set of yeast-based functional assays accurately identifies human disease mutations.

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.

Pooled-matrix protein interaction screens using Barcode Fusion Genetics.

High-throughput binary protein interaction mapping is continuing to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude away from a complete interaction map for humans and other major model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring further efficiency gains in proteome-scale interaction mapping. Here, we report Barcode Fusion Genetics-Yeast Two-Hybrid (BFG-Y2H), by which a full matrix of protein pairs can be screened in a single multiplexed strain pool. BFG-Y2H uses Cre recombination to fuse DNA barcodes from distinct plasmids, generating chimeric protein-pair barcodes that can be quantified via next-generation sequencing. We applied BFG-Y2H to four different matrices ranging in scale from ~25 K to 2.5 M protein pairs. The results show that BFG-Y2H increases the efficiency of protein matrix screening, with quality that is on par with state-of-the-art Y2H methods.