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 p19ARF 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 p14ARF 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.

A key limiting step for high-throughput NMR-based metabolomics is the lack of rapid and accurate tools for absolute quantification of many metabolites. We developed, implemented, and evaluated an algorithm, AQuA (Automated Quantification Algorithm), for targeted metabolite quantification from complex H NMR spectra. AQuA operates based on spectral data extracted from a library consisting of one standard calibration spectrum for each metabolite. It uses one preselected NMR signal per metabolite for determining absolute concentrations and does so by effectively accounting for interferences caused by other metabolites. AQuA was implemented and evaluated using experimental NMR spectra from human plasma. The accuracy of AQuA was tested and confirmed in comparison with a manual spectral fitting approach using the ChenomX software, in which 61 out of 67 metabolites quantified in 30 human plasma spectra showed a goodness-of-fit (r) close to or exceeding 0.9 between the two approaches. In addition, three quality indicators generated by AQuA, namely, occurrence, interference, and positional deviation, were studied. These quality indicators permit evaluation of the results each time the algorithm is operated. The efficiency was tested and confirmed by implementing AQuA for quantification of 67 metabolites in a large data set comprising 1342 experimental spectra from human plasma, in which the whole computation took less than 1 s.

Changes in serum metabolic profile after the intake of different food products (e.g., bread) can provide insight into their interaction with human metabolism. Postprandial metabolic responses were compared after the intake of refined wheat (RWB), whole-meal rye (WRB), and refined rye (RRB) breads. In addition, associations between the metabolic profile in fasting serum and the postprandial concentration of insulin in response to different breads were investigated. Nineteen postmenopausal women with normal fasting glucose and normal glucose tolerance participated in a randomized, controlled, crossover meal study. The test breads, RWB (control), RRB, and WRB, providing 50 g of available carbohydrate, were each served as a single meal. The postprandial metabolic profile was measured using nuclear magnetic resonance and targeted LC-mass spectrometry and was compared between different breads using ANOVA and multivariate models. Eight amino acids had a significant treatment effect (P < 0.01) and a significant treatment × time effect (P < 0.05). RWB produced higher postprandial concentrations of leucine (geometric mean: 224; 95% CI: 196, 257) and isoleucine (mean ± SD: 111 ± 31.5) compared with RRB (geometric mean: 165; 95% CI: 147, 186; mean ± SD: 84.2 ± 22.9) and WRB (geometric mean: 190; 95% CI: 174, 207; mean ± SD: 95.8 ± 17.3) at 60 min respectively (P < 0.001). In addition, 2 metabolic subgroups were identified using multivariate models based on the association between fasting metabolic profile and the postprandial concentration of insulin. Women with higher fasting concentrations of leucine and isoleucine and lower fasting concentrations of sphingomyelins and phosphatidylcholines had higher insulin responses despite similar glucose concentration after all kinds of bread (cross-validated ANOVA, P = 0.048). High blood concentration of branched-chain amino acids, i.e., leucine and isoleucine, has been associated with the increased risk of diabetes, which suggests that additional consideration should be given to bread proteins in understanding the beneficial health effects of different kinds of breads. The present study suggests that the fasting metabolic profile can be used to characterize the postprandial insulin demand in individuals with normal glucose metabolism that can be used for establishing strategies for the stratification of individuals in personalized nutrition.

The variation in the contents of sesamin and sesamolin was studied in oils extracted from 65 samples of sesame seeds (Sesamum indicum L.) from plants with shattering (n = 29), semishattering (n = 7), and nondehiscent (n = 29) capsules. The oil content ranged from 32.5 to 50.6% and was greater in white than black seeds (P < 0.001). The sesamin and sesamolin contents in seeds ranged from 7 to 712 mg/100 g (mean ± SD, 163 ± 141 mg/100 g) and from 21 to 297 mg/100 g (101 ± 58 mg/100 g), respectively, with no difference between black and white seeds. Thus, there was a wide variation in the contents of sesamin and sesamolin, which were positively correlated (R 2 = 0.66, P < 0.001). There were negative correlations between the contents of sesamin and the contents of sesaminol (R 2 = 0.37) and sesamolinol (R 2 = 0.36) and between the content of sesamolin and those of sesaminol (R 2 = 0.35) and sesamolinol (R 2 = 0.46) (P < 0.001). Sesame seeds had an average of 0.63% lignans, making them a rich source of dietary lignans. FIG. 2.The correlation between sesamin, and sesamolin and sesaminol contents in the 65 sesame seed samples analyzed (P < 0.001) (mg/100 g seed).

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