Insulin and insulin-like growth factor signaling increases proliferation and hyperplasia of the ovarian surface epithelium and decreases follicular integrity through upregulation of the PI3-kinase pathway

BACKGROUND: The ovarian surface epithelium responds to cytokines and hormonal cues to initiate proliferation and migration following ovulation. Although insulin and IGF are potent proliferative factors for the ovarian surface epithelium and IGF is required for follicle development, increased insulin and IGF activity are correlated with at least two gynecologic conditions: polycystic ovary syndrome and epithelial ovarian cancer. Although insulin and IGF are often components of in vitro culture media, little is known about the effects that these growth factors may have on the ovarian surface epithelium morphology or how signaling in the ovarian surface may affect follicular health and development.

METHODS: Ovaries from CD1 mice were cultured in alginate hydrogels in the presence or absence of 5 μg/ml insulin or IGF-I, as well as small molecule inhibitors of IR/IGF1R, PI 3-kinase signaling, or MAPK signaling. Tissues were analyzed by immunohistochemistry for expression of cytokeratin 8 to mark the ovarian surface epithelium, Müllerian inhibiting substance to mark secondary follicles, and BrdU incorporation to assess proliferation. Changes in gene expression in the ovarian surface epithelium in response to insulin or IGF-I were analyzed by transcription array. Extracellular matrix organization was evaluated by expression and localization of collagen IV.

RESULTS: Culture of ovarian organoids with insulin or IGF-I resulted in formation of hyperplastic OSE approximately 4-6 cell layers thick with a high rate of proliferation, as well as decreased MIS expression in secondary follicles. Inhibition of the MAPK pathway restored MIS expression reduced by insulin but only partially restored normal OSE growth and morphology. Inhibition of the PI 3-kinase pathway restored MIS expression reduced by IGF-I and restored OSE growth to a single cell layer. Insulin and IGF-I altered organization of collagen IV, which was restored by inhibition of PI 3-kinase signaling.

CONCLUSIONS: While insulin and IGF are often required for propagation of primary cells, these cytokines may act as potent mitogens to disrupt cell growth, resulting in formation of hyperplastic OSE and decreased follicular integrity as measured by MIS expression and collagen deposition. This may be due partly to altered collagen IV deposition and organization in the ovary in response to insulin and IGF signaling mediated by PI 3-kinase.

HPV16 Down-Regulates the Insulin-Like Growth Factor Binding Protein 2 to Promote Epithelial Invasion in Organotypic Cultures

Cervical cancer is a multi-stage disease caused by human papillomaviruses (HPV) infection of cervical epithelial cells, but the mechanisms regulating disease progression are not clearly defined. Using 3-dimensional organotypic cultures, we demonstrate that HPV16 E6 and E7 proteins alter the secretome of primary human keratinocytes resulting in local epithelial invasion. Mechanistically, absence of the IGF-binding protein 2 (IGFBP2) caused increases in IGFI/II signalling and through crosstalk with KGF/FGFR2b/AKT, cell invasion. Repression of IGFBP2 is mediated by histone deacetylation at the IGFBP2 promoter and was reversed by treatment with histone deacetylase (HDAC) inhibitors. Our in vitro findings were confirmed in 50 invasive cancers and 79 cervical intra-epithelial neoplastic lesions caused by HPV16 infection, where IGFBP2 levels were reduced with increasing disease severity. In summary, the loss of IGFBP2 is associated with progression of premalignant disease, and sensitises cells to pro-invasive IGF signalling, and together with stromal derived factors promotes epithelial invasion.

PESSTO: Survey description and products from the first data release by the Public ESO Spectroscopic Survey of Transient Objects

Context. The Public European Southern Observatory Spectroscopic Survey of Transient Objects (PESSTO) began as a public spectroscopic survey in April 2012. PESSTO classifies transients from publicly available sources and wide-field surveys, and selects science targets for detailed spectroscopic and photometric follow-up. PESSTO runs for nine months of the year, January - April and August - December inclusive, and typically has allocations of 10 nights per month. 

Aims. We describe the data reduction strategy and data products that are publicly available through the ESO archive as the Spectroscopic Survey data release 1 (SSDR1). 

Methods. PESSTO uses the New Technology Telescope with the instruments EFOSC2 and SOFI to provide optical and NIR spectroscopy and imaging. We target supernovae and optical transients brighter than 20.5^m for classification. Science targets are selected for follow-up based on the PESSTO science goal of extending knowledge of the extremes of the supernova population. We use standard EFOSC2 set-ups providing spectra with resolutions of 13-18 Å between 3345-9995 Å. A subset of the brighter science targets are selected for SOFI spectroscopy with the blue and red grisms (0.935-2.53 μm and resolutions 23-33 Å) and imaging with broadband JHK<inf>s</inf> filters. 

Results. This first data release (SSDR1) contains flux calibrated spectra from the first year (April 2012-2013). A total of 221 confirmed supernovae were classified, and we released calibrated optical spectra and classifications publicly within 24 h of the data being taken (via WISeREP). The data in SSDR1 replace those released spectra. They have more reliable and quantifiable flux calibrations, correction for telluric absorption, and are made available in standard ESO Phase 3 formats. We estimate the absolute accuracy of the flux calibrations for EFOSC2 across the whole survey in SSDR1 to be typically ∼15%, although a number of spectra will have less reliable absolute flux calibration because of weather and slit losses. Acquisition images for each spectrum are available which, in principle, can allow the user to refine the absolute flux calibration. The standard NIR reduction process does not produce high accuracy absolute spectrophotometry but synthetic photometry with accompanying JHK<inf>s</inf> imaging can improve this. Whenever possible, reduced SOFI images are provided to allow this. 

Conclusions. Future data releases will focus on improving the automated flux calibration of the data products. The rapid turnaround between discovery and classification and access to reliable pipeline processed data products has allowed early science papers in the first few months of the survey.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Industrial accidents involving release of chemicals into the environment:Ecotoxicology

Major industrial accidents pose a serious threat to surrounding habitats. Each accident is unique in terms of pollutants released, pollutant concentrations and pollutant dispersal. The habitats receiving the pollutant(s) are also unique. These factors mean that assessing the environmental and ecological impact of any given pollution event will be complex. Case histories of the biological impact of chemicals released from industrial accidents are reviewed to determine how to assess ecotoxicity of pollutants involved.

Improving antimicrobial release kinetics from hydrophobic polymer implants via ion pairing to combat biomedical-device related infection

Purpose The aim of this study is to improve the drug release properties of antimicrobial agents from hydrophobic biomaterials using using an ion pairing strategy. In so doing antimicrobial agents may be eluted and maintained over a sufficient time period thereby preventing bacterial colonisation and subsequent biofilm formation on medical devices. Methods The model antimicrobial agent was chlorhexidine and the selected fatty acid counter ions were capric acid, myristic acid and stearic acid. The polymethyl methacrylate films were loaded with 2% of fatty acid:antimicrobial agent at the following molar ratios; 0.5:1M, 1:1M and 2:1M and thermally polymerized using azobisisobutyronitrile initiator. Drug release experiments were subsequently performed over a 3-month period and the mass of drug released under sink conditions (pH 7.0, 37oC) quantified using a validated HPLC-UV method. Results In all platforms, a burst of chlorhexidine release was observed over the initial 24-hour period. Similar release kinetics were observed between the formulations during the initial 28 days. However, as time progressed, the chlorhexidine baseline plateaued after 56 days whereas formulations containing the counterions appeared to continuously elute linearly with time. As can be observed in figure 1, the rank order of total chlorhexidine release in the presence of 0.5M fatty acid was myristic acid (40%) > capric acid (35%) > stearic acid (30%)> chlorhexidine baseline (15%). Conclusion The incorporation of fatty acids within the formulation significantly improved chlorhexidine solubility within both the monomer and the polymer and enhanced the drug release kinetics over the period of study. This is attributed to the greater diffusivity of chlorhexidine through PMMA in the presence of fatty acids. In th absence of fatty acids, chlorhexidine release was facilitated by dissolution of surface associated drug particles. This study has illustrated the ability of fatty acids to modulate chlorhexidine release from a model biomaterial through enhanced diffusivity. This strategy may prove advantageous for improved medical devices with enhanced resistance to infection.