Forecasting individual breast cancer risk using plasma metabolomics and biocontours

Breast cancer is a major cause of death for women. To improve treatment, current oncology research focuses on discovering and validating new biomarkers for early detection of cancer; so far with limited success. Metabolic profiling of plasma samples and auxiliary lifestyle information was combined by chemometric data fusion. It was possible to create a biocontour, which we define as a complex pattern of relevant biological and phenotypic information. While single markers or known risk factors have close to no predictive value, the developed biocontour provides a forecast which, several years before diagnosis, is on par with how well most current biomarkers can diagnose current cancer. Hence, while e.g. mammography can diagnose current cancer with a sensitivity and specificity of around 75 %, the currently developed biocontour can predict that there is an increased risk that breast cancer will develop in a subject 2–5 years after the sample is taken with sensitivity and specificity well above 80 %. The model was built on data obtained in 1993–1996 and tested on persons sampled a year later in 1997. Metabolic forecasting of cancer by biocontours opens new possibilities for early prediction of individual cancer risk and thus for efficient screening. This may provide new avenues for research into disease mechanisms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-015-0793-8) contains supplementary material, which is available to authorized users

Heterologous Expression of ATG8c from Soybean Confers Tolerance to Nitrogen Deficiency and Increases Yield in Arabidopsis

Nitrogen is an essential element for plant growth and yield. Improving Nitrogen Use Efficiency (NUE) of crops could potentially reduce the application of chemical fertilizer and alleviate environmental damage. To identify new NUE genes is therefore an important task in molecular breeding. Macroautophagy (autophagy) is an intracellular process in which damaged or obsolete cytoplasmic components are encapsulated in double membraned vesicles termed autophagosomes, then delivered to the vacuole for degradation and nutrient recycling. One of the core components of autophagosome formation, ATG8, has been shown to directly mediate autophagosome expansion, and the transcript of which is highly inducible upon starvation. Therefore, we postulated that certain homologs of Saccharomyces cerevisiae ATG8 (ScATG8) from crop species could have potential for NUE crop breeding. A soybean (Glycine max, cv. Zhonghuang-13) ATG8, GmATG8c, was selected from the 11 family members based on transcript analysis upon nitrogen deprivation. GmATG8c could partially complement the yeast atg8 mutant. Constitutive expression of GmATG8c in soybean callus cells not only enhanced nitrogen starvation tolerance of the cells but accelerated the growth of the calli. Transgenic Arabidopsis over-expressing GmATG8c performed better under extended nitrogen and carbon starvation conditions. Meanwhile, under optimum growth conditions, the transgenic plants grew faster, bolted earlier, produced larger primary and axillary inflorescences, eventually produced more seeds than the wild-type. In average, the yield was improved by 12.9%. We conclude that GmATG8c may serve as an excellent candidate for breeding crops with enhanced NUE and better yield

Validation of Ti(III) as a reducing agent in the Chemiluminescent determination of nitrate and nitrite in seawater

Titanium (III) trichloride is validated here for the quantitative conversion of all nitrate plus nitrite in seawater to nitric oxide gas, thereby providing an alternative to the typically used reducing agent, ferrous ammonium sulfate plus ammonium molybdate, in the chemiluminescent detection of nitrate plus nitrite at the nanomolar level. We find that both Fe(II)+Mo(VI) and Ti(III) methods yield identical results for standards and seawater samples over a validated concentration range of 1 to 1000 nM, and are both in agreement with traditional colorimetric results. Benefits of the Ti(III) reduction chemistry are: simpler preparation, decreased ammonium contamination in a laboratory that measures low-level nutrients, 30% reduction of the sulfuric acid catalyst, and a higher sample through-put. Most importantly, though, this work can be considered the first step on a path toward a much-needed, direct measurement of dissolved organic nitrogen concentrations, as has already been achieved for dissolved organic carbon

Cross-validation of component models:A critical look at current methods

In regression, cross-validation is an effective and popular approach that is used to decide, for example, the number of underlying features, and to estimate the average prediction error. The basic principle of cross-validation is to leave out part of the data, build a model, and then predict the left-out samples. While such an approach can also be envisioned for component models such as principal component analysis (PCA), most current implementations do not comply with the essential requirement that the predictions should be independent of the entity being predicted. Further, these methods have not been properly reviewed in the literature. In this paper, we review the most commonly used generic PCA cross-validation schemes and assess how well they work in various scenarios