13 research outputs found

    STAT3 regulated ARF expression suppresses prostate cancer metastasis.

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    Prostate cancer (PCa) is the most prevalent cancer in men. Hyperactive STAT3 is thought to be oncogenic in PCa. However, targeting of the IL-6/STAT3 axis in PCa patients has failed to provide therapeutic benefit. Here we show that genetic inactivation of Stat3 or IL-6 signalling in a Pten-deficient PCa mouse model accelerates cancer progression leading to metastasis. Mechanistically, we identify p19(ARF) as a direct Stat3 target. Loss of Stat3 signalling disrupts the ARF-Mdm2-p53 tumour suppressor axis bypassing senescence. Strikingly, we also identify STAT3 and CDKN2A mutations in primary human PCa. STAT3 and CDKN2A deletions co-occurred with high frequency in PCa metastases. In accordance, loss of STAT3 and p14(ARF) expression in patient tumours correlates with increased risk of disease recurrence and metastatic PCa. Thus, STAT3 and ARF may be prognostic markers to stratify high from low risk PCa patients. Our findings challenge the current discussion on therapeutic benefit or risk of IL-6/STAT3 inhibition.Lukas Kenner and Jan Pencik are supported by FWF, P26011 and the Genome Research-Austria project “Inflammobiota” grants. Helmut Dolznig is supported by the Herzfelder Family Foundation and the Niederösterr. Forschungs-und Bildungsges.m.b.H (nfb). Richard Moriggl is supported by grant SFB-F2807 and SFB-F4707 from the Austrian Science Fund (FWF), Ali Moazzami is supported by Infrastructure for biosciences-Strategic fund, SciLifeLab and Formas, Zoran Culig is supported by FWF, P24428, Athena Chalaris and Stefan Rose-John are supported by the Deutsche Forschungsgemeinschaft (Grant SFB 877, Project A1and the Cluster of Excellence --“Inflammation at Interfaces”). Work of the Aberger lab was supported by the Austrian Science Fund FWF (Projects P25629 and W1213), the European FP7 Marie-Curie Initial Training Network HEALING and the priority program Biosciences and Health of the Paris-Lodron University of Salzburg. Valeria Poli is supported by the Italian Association for Cancer Research (AIRC, No IG13009). Richard Kennedy and Steven Walker are supported by the McClay Foundation and the Movember Centre of Excellence (PC-UK and Movember). Gerda Egger is supported by FWF, P27616. Tim Malcolm and Suzanne Turner are supported by Leukaemia and Lymphoma Research.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms873

    Estimates of ozone return dates from Chemistry-Climate Model Initiative simulations

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    We analyse simulations performed for the Chemistry-Climate Model Initiative (CCMI) to estimate the return dates of the stratospheric ozone layer from depletion caused by anthropogenic stratospheric chlorine and bromine. We consider a total of 155 simulations from 20 models, including a range of sensitivity studies which examine the impact of climate change on ozone recovery. For the control simulations (unconstrained by nudging towards analysed meteorology) there is a large spread (±20DU in the global average) in the predictions of the absolute ozone column. Therefore, the model results need to be adjusted for biases against historical data. Also, the interannual variability in the model results need to be smoothed in order to provide a reasonably narrow estimate of the range of ozone return dates. Consistent with previous studies, but here for a Representative Concentration Pathway (RCP) of 6.0, these new CCMI simulations project that global total column ozone will return to 1980 values in 2049 (with a 1σ uncertainty of 2043–2055). At Southern Hemisphere mid-latitudes column ozone is projected to return to 1980 values in 2045 (2039–2050), and at Northern Hemisphere mid-latitudes in 2032 (2020–2044). In the polar regions, the return dates are 2060 (2055–2066) in the Antarctic in October and 2034 (2025–2043) in the Arctic in March. The earlier return dates in the Northern Hemisphere reflect the larger sensitivity to dynamical changes. Our estimates of return dates are later than those presented in the 2014 Ozone Assessment by approximately 5–17 years, depending on the region, with the previous best estimates often falling outside of our uncertainty range. In the tropics only around half the models predict a return of ozone to 1980 values, around 2040, while the other half do not reach the 1980 value. All models show a negative trend in tropical total column ozone towards the end of the 21st century. The CCMI models generally agree in their simulation of the time evolution of stratospheric chlorine and bromine, which are the main drivers of ozone loss and recovery. However, there are a few outliers which show that the multi-model mean results for ozone recovery are not as tightly constrained as possible. Throughout the stratosphere the spread of ozone return dates to 1980 values between models tends to correlate with the spread of the return of inorganic chlorine to 1980 values. In the upper stratosphere, greenhouse gas-induced cooling speeds up the return by about 10–20 years. In the lower stratosphere, and for the column, there is a more direct link in the timing of the return dates of ozone and chlorine, especially for the large Antarctic depletion. Comparisons of total column ozone between the models is affected by different predictions of the evolution of tropospheric ozone within the same scenario, presumably due to differing treatment of tropospheric chemistry. Therefore, for many scenarios, clear conclusions can only be drawn for stratospheric ozone columns rather than the total column. As noted by previous studies, the timing of ozone recovery is affected by the evolution of N2O and CH4. However, quantifying the effect in the simulations analysed here is limited by the few realisations available for these experiments compared to internal model variability. The large increase in N2O given in RCP 6.0 extends the ozone return globally by ∼15 years relative to N2O fixed at 1960 abundances, mainly because it allows tropical column ozone to be depleted. The effect in extratropical latitudes is much smaller. The large increase in CH4 given in the RCP 8.5 scenario compared to RCP 6.0 also lengthens ozone return by ∼15 years, again mainly through its impact in the tropics. Overall, our estimates of ozone return dates are uncertain due to both uncertainties in future scenarios, in particular those of greenhouse gases, and uncertainties in models. The scenario uncertainty is small in the short term but increases with time, and becomes large by the end of the century. There are still some model–model differences related to well-known processes which affect ozone recovery. Efforts need to continue to ensure that models used for assessment purposes accurately represent stratospheric chemistry and the prescribed scenarios of ozone-depleting substances, and only those models are used to calculate return dates. For future assessments of single forcing or combined effects of CO2, CH4, and N2O on the stratospheric column ozone return dates, this work suggests that it is more important to have multi-member (at least three) ensembles for each scenario from every established participating model, rather than a large number of individual models

    Diagnostic value of kappa free light chain index in patients with primary progressive multiple sclerosis – a multicentre study

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    BackgroundKappa free light chains (κ-FLC) in the cerebrospinal fluid (CSF) are an emerging biomarker in multiple sclerosis (MS).ObjectiveTo investigate whether κ-FLC index has similar diagnostic value in patients with primary progressive multiple sclerosis (PPMS) compared to oligoclonal bands (OCB).MethodsPatients with PPMS were recruited through 11 MS centres across 7 countries. κ-FLC were measured by immunonephelometry/-turbidimetry. OCB were determined by isoelectric focusing and immunofixation.ResultsA total of 174 patients (mean age of 52±11 years, 51% males) were included. κ-FLC index using a cut-off of 6.1 was positive in 161 (93%) and OCB in 153 (88%) patients.Conclusionκ-FLC index shows similar diagnostic sensitivity than OCB in PPMS
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