The legume-Rhizobium symbiosis leads to the formation of a new organ, the root nodule, involving coordinated and massive induction of specific genes. Several genes controlling DNA methylation are spatially regulated within the Medicago truncatula nodule, notably the demethylase gene, DEMETER (DME), which is mostly expressed in the differentiation zone. Here, we show that MtDME is essential for nodule development and regulates the expression of 1,425 genes, some of which are critical for plant and bacterial cell differentiation. Bisulphite sequencing coupled to genomic capture enabled the identification of 474 regions that are differentially methylated during nodule development, including nodule-specific cysteine-rich peptide genes. Decreasing DME expression by RNA interference led to hypermethylation and concomitant downregulation of 400 genes, most of them associated with nodule differentiation. Massive reprogramming of gene expression through DNA demethylation is a new epigenetic mechanism controlling a key stage of indeterminate nodule organogenesis during symbiotic interactions.

: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, Springer Verlag, 2017, 10.1007/s11306-017-1244 Abstract:Introduction Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.Objectives In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.Methods A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues. ResultsThe performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.Conclusion ASICS is a completely automated procedure to identify and quantify metabolites in 1 H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

Background This study investigated the periodization of elite swimmers’ training over the 25 weeks preceding the major competition of the season. Methods We conducted a retrospective observational study of elite male ( n = 60) and female ( n = 67) swimmers (46 sprint, 81 middle-distance) over 20 competitive seasons (1992–2012). The following variables were monitored: training corresponding to blood lactate <2 mmol⋅L -1 , 2 to ≤4 mmol⋅L -1 , >4–6 mmol⋅L -1 , >6 mmol⋅L -1 , and maximal swimming speed; general conditioning and maximal strength training hours; total training load (TTL); and the mean normalized volumes for both in-water and dryland workouts. Latent class mixed modeling was used to identify various TTL pattern groups. The associations between pattern groups and sex, age, competition event, Olympic quadrennial year, training contents, and relative performance were quantified. Results For the entire cohort, ∼86–90% of the training was swum at an intensity of [La] b ≤ 4 mmol⋅L -1 . This training volume was divided into 40–44% at <2 mmol⋅L -1 and 44–46% at 2 to ≤4 mmol⋅L -1 , leaving 6–9.5% at >4–6 mmol⋅L -1 , and 3.5–4.5% at >6 mmol⋅L -1 . Three sprint TTL patterns were identified: a pattern with two long ∼14–15-week macrocycles, one with two ∼12–13 week macrocycles each composed of a balanced training load, and one with a single stable flat macrocycle. The long pattern elicited the fastest performances and was most prevalent in Olympic quadrennials (i.e., 4 seasons preceding the 2004, 2008, and 2012 Olympic Games). This pattern exhibited moderate week-to-week TTL variability (6 ± 3%), progressive training load increases between macrocycles, and more training at ≤4 mmol⋅L -1 and >6 mmol⋅L -1 . This fastest sprint pattern showed a waveform in the second macrocycle consisting of two progressive load peaks 10–11 and 4–6 weeks before competition. The stable flat pattern was the slowest and showed low TTL variability (4 ± 3%), training load decreases between macrocycles ( P < 0.01), and more training at 4–6 mmol⋅L -1 ( P < 0.01). Conclusion Progressive increases in training load, macrocycles lasting about 14–15 weeks, and substantial volume of training at intensities ≤4 mmol⋅L -1 and >6 mmol⋅L -1 , were associated with peak performance in elite swimmers.