Estrogen Receptor beta Exerts Growth-Inhibitory Effects on Human Mammary Epithelial Cells.

Abstract In mammary epithelium estrogen receptor beta (ERbeta) is widely expressed. Its expression is reported to decline during carcinogenesis of the breast and other tissues. In this study, we examined the consequences of a loss of ERbeta expression in mammary epithelial cells. We knocked down ERbeta transcript levels in human mammary epithelial MCF-10A cells and in MCF-7 breast cancer cells by means of stable transfection with a specific shRNA plasmid. ERbeta knockdown resulted in a significant growth increase of both cell types in a ligand-independent manner. This effect was accompanied by elevated cyclin A2 expression in MCF-10A cells and by decreased expression of growth-inhibitory p21/WAF and epithelial cell marker cytokeratine 8 in both cell lines. Transfection of ERbeta shRNA did not alter the absent proliferative estrogen response of MCF-10A cells, but conferred sensitivity to selective estrogen receptor modulator tamoxifen to this cell line. In contrast, ERbeta knockdown diminished estrogen responsiveness of MCF-7 breast cancer cells and also weakened the effect of tamoxifen on this cell line. These ligand-dependent effects only observed in MCF-7 cells exhibiting a high ERalpha/beta ratio were accompanied by smaller estrogenic repression of p21/WAF expression, an impaired tamoxifen-triggered induction of this gene and by relative downregulation of ERalpha and cyclin A2 transcript levels. Our data suggest that ERbeta exerts antiproliferative effects both on MCF-10A and MCF-7 cells in a ligand- and ERalpha-independent manner by regulation of p21/WAF or cyclin A2 gene expression. Knockdown of ERbeta in both cell types was sufficient to significantly decrease transcript levels of epithelial cell marker cytokeratin 8. The results of this study support the hypothesis that ERbeta acts as a tumor suppressor in mammary epithelium. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4152.</jats:p

Coupling a model of human thermoregulation with computational fluid dynamics for predicting human-environment interaction

This paper describes the methods developed to couple a commercial CFD program with a multi-segmented model of human thermal comfort and physiology. A CFD model is able to predict detailed temperatures and velocities of airflow around a human body, whilst a thermal comfort model is able to predict the response of a human to the environment surrounding it. By coupling the two models and exchanging information about the heat transfer at the body surface the coupled system can potentially predict the response of a human body to detailed local environmental conditions. This paper presents a method of exchanging data, using shared files, to provide a means of dynamically exchanging simulation data with the IESD-Fiala model during the CFD solution process. Additional code is used to set boundary conditions for the CFD simulation at the body surface as determined by the IESD-Fiala model and to return information about local environmental conditions adjacent to the body surface as determined by the CFD simulation. The coupled system is used to model a human subject in a naturally ventilated environment. The resulting ventilation flow pattern agrees well with other numerical and experimental work

WAP four-disulfide core domain protein 2 gene(WFDC2) is a target of estrogen in ovarian cancer cells

BACKGROUND: WAP four-disulfide core domain protein 2 (WFDC2) shows a tumor-restricted upregulated pattern of expression in ovarian cancer. METHODS: We investigated the role of estradiol (E2) on cell growth in estrogen-sensitive or estrogen-insensitive ovarian cancer cell lines. Real-time (RT)-PCR and western blotting were used to examine the expression of WFDC2 at RNA and protein levels. Growth traits of cells transfected with WFDC2-shRNA or blank control were assessed using MMT arrays. Cell apoptosis was analyzed using annexin V-FITC/PI and flow cytometry. Estrogen receptor expression was evaluated using RT-PCR and flow cytometry. Apoptosis-related proteins induced by E2 directly and indirectly were determined using an antibody array comparing cells transfected with WFDC2- shRNA or a blank control. RESULTS: High-dose (625 ng/ml) E2 increased the expression of WFDC2 in HO8910 cells at both the mRNA and protein levels. However, E2 had no effect on WFDC2 expression in estrogen-insensitive SKOV3 cells. Of interest, knockdown of WFDC2 enabled a considerable estrogen response in SKOV3 cells in terms of proliferation, similar to estrogen-responsive HO8910 cells. This transformation of SKOV3 cells into an estrogen-responsive phenotype was accompanied by upregulation of estrogen receptor beta (ERß) and an effect on cell apoptosis under E2 treatment by regulating genes related to cell proliferation and apoptosis. CONCLUSIONS: We postulate that increased WFDC2 expression plays an important role in altering the estrogen pathway in ovarian cancer, and the identification of WFDC2 as a new player in endocrine-related cancer encourages further studies on the significance of this gene in cancer development and therapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13048-015-0210-y) contains supplementary material, which is available to authorized users

Paclitaxel resistance is associated with switch from apoptotic to autophagic cell death in MCF-7 breast cancer cells

Taxanes remain first line chemotherapy in management of metastatic breast cancer and have a key role in epithelial ovarian cancer, with increasingly common use of weekly paclitaxel dosing regimens. However, their clinical utility is limited by the development of chemoresistance. To address this, we modelled in vitro paclitaxel resistance in MCF-7 cells. We show that at clinically relevant drug doses, emerging paclitaxel resistance is associated with profound changes in cell death responses and a switch from apoptosis to autophagy as the principal mechanism of drug-induced cytotoxicity. This was characterised by a complete absence of caspase-mediated apoptotic cell death (using the pan-caspase-inhibitor Z-VAD) in paclitaxel-resistant MCF-7TaxR cells, compared with parent MCF-7 or MDA-MB-231 cell lines on paclitaxel challenge, downregulation of caspase-7, caspase-9 and BCl2-interacting mediator of cell death (BIM) expression. Silencing with small interfering RNA to BIM in MCF-7 parental cells was sufficient to confer paclitaxel resistance, inferring the significance in downregulation of this protein in contributing to the resistant phenotype of the MCF-7TaxR cell line. Conversely, there was an increased autophagic response in the MCF-7TaxR cell line with reduced phospho-mTOR and relative resistance to the mTOR inhibitors rapamycin and RAD001. In conclusion, we show for the first time that paclitaxel resistance is associated with profound changes in cell death response with deletion of multiple apoptotic factors balanced by upregulation of the autophagic pathway and collateral sensitivity to platinum

Direct image to subtype prediction for brain tumors using deep learning

Background: Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. // Methods: We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. // Results: We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. // Conclusions: In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available

A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany

BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii

Financialization and Corporate Investments: The Indian Case

Financialization creates space for the financial sector in economies, and in doing so helps to raise the share of financial assets in the portfolios held by market participants. Largely driven by deregulation, the process works to make financial assets relatively attractive as compared to other assets, by offering both better returns and potential capital gains. Both the trend toward a more financialized economy and the expected returns on financial investments have provided incentives to corporate managers to invest larger sums in financial assets, resulting in growth of the share of financial assets relative to other assets held in portfolios. Assets held in the financial sector, however, failed to generate asset growth for the corporates. The need to obtain resources by borrowing in order to meet current liabilities reflects a pattern of Ponzi finance on their part. This paper traces the above pattern in corporate holdings of assets and its implications, with emphasis on the Indian economy

Minsky and the Subprime Mortgage Crisis: The Financial Instability Hypothesis in the Era of Financialization

The aim of this paper is to develop a structural explanation of the subprime mortgage crisis, grounded on the combination of two apparently incompatible financial theories: the financial instability hypothesis by Hyman P. Minsky and the theory of capital market inflation by Jan Toporowski. Our thesis is that, once the evolution of the financial market is taken into account, the financial Keynesianism of Minsky is still a valid framework to understand the events leading to the crisis

Diagnosis With Nanoscale Protein Distributions: Single-Molecule Fluorescence Localization Microscopy and Attention-Based Learning

Single-molecule (fluorescence) localization microscopy (SMLM) finds the position of markers for target proteins at approx. 10 nm precision. Diagnosis of some diseases currently relies on inspection of nanoscale morphology by electron microscopy (EM), an expensive and slow test with limited sample coverage. Nanoscale biological processes also underlie health and disease in general, and so there is a need for more efficient diagnostic methods. We demonstrate that SMLM of routine biopsy samples can be used to assist diagnosis via data classification models. We predict diagnosis of 20 patients with chronic renal diseases (focal segmental glomerulosclerosis or minimal change disease) with a mean area under the receiver operating characteristic curve of 0.97 in cross-validation, and balanced accuracy of 90%. We tested state-of-the-art pretrained feature extraction from image tiles at 0.045 microns per pixel, followed by training of weakly supervised, attention-based models. SMLM and automated analysis has the potential to save time to diagnosis and costs compared with EM, with greater sample coverage, as well as for finding new nanoscale biomarkers in other disease areas