Wigginslacroix0389

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.Zebra mussel, Dreissena polymorpha, in the Great Lakes is being monitored as a bio-indicator organism for environmental health effects by the National Oceanic and Atmospheric Administration's Mussel Watch program. In order to monitor the environmental effects of industrial pollution on the ecosystem, invasive zebra mussels were collected from four stations-three inner harbor sites (LMMB4, LMMB1, and LMMB) in Milwaukee Estuary, and one reference site (LMMB5) in Lake Michigan, Wisconsin. Nuclear magnetic resonance (NMR)-based metabolomics was used to evaluate the metabolic profiles of the mussels from these four sites. The objective was to observe whether there were differences in metabolite profiles between impacted sites and the reference site; and if there were metabolic profile differences among the impacted sites. Principal component analyses indicated there was no significant difference between two impacted sites north Milwaukee harbor (LMMB and LMMB4) and the LMMB5 reference site. However, significant metabolic differences were observed between the impacted site on the south Milwaukee harbor (LMMB1) and the LMMB5 reference site, a finding that correlates with preliminary sediment toxicity results. A total of 26 altered metabolites (including two unidentified peaks) were successfully identified in a comparison of zebra mussels from the LMMB1 site and LMMB5 reference site. The application of both uni- and multivariate analysis not only confirmed the variability of altered metabolites but also ensured that these metabolites were identified via unbiased analysis. buy Erastin2 This study has demonstrated the feasibility of the NMR-based metabolomics approach to assess whole-body metabolomics of zebra mussels to study the physiological impact of toxicant exposure at field sites.Biomarkers for the development of type 2 diabetes (T2D) are useful for prediction and intervention of the disease at earlier stages. In this study, we performed a longitudinal study of changes in metabolites using an animal model of T2D, the spontaneously diabetic Torii (SDT) rat. Fasting plasma samples of SDT and control Sprague-Dawley (SD) rats were collected from 6 to 24 weeks of age, and subjected to gas chromatography-mass spectrometry-based metabolome analysis. Fifty-nine hydrophilic metabolites were detected in plasma samples, including amino acids, carbohydrates, sugars and organic acids. At 12 weeks of age, just before the onset of diabetes in SDT rats, the amounts of nine of these metabolites (asparagine, glutamine, glycerol, kynurenine, mannose, n-alpha-acetyllysine, taurine, threonine, and tryptophan) in SDT rats were significantly different from those in SD rats. In particular, metabolites in the tryptophan metabolism pathway (tryptophan and kynurenine) were decreased in SDT rats at 12 weeks of age and later. The lower tryptophan and kynurenine levels in the prediabetic state and later were further confirmed by a replication study on SDT rats and by a longitudinal study on another animal model of T2D, the Otsuka Long-Evans Tokushima Fatty rat. Our data indicate that tryptophan and its metabolites are potential biomarkers for prediabetes and that tryptophan metabolism may be a potential target of intervention for treatment of the disease.In omics research often high-dimensional data is collected according to an experimental design. Typically, the manipulations involved yield differential effects on subsets of variables. An effective approach to identify those effects is ANOVA-simultaneous component analysis (ASCA), which combines analysis of variance with principal component analysis. So far, pre-treatment in ASCA received hardly any attention, whereas its effects can be huge. In this paper, we describe various strategies for scaling, and identify a rational approach. We present the approaches in matrix algebra terms and illustrate them with an insightful simulated example. We show that scaling directly influences which data aspects are stressed in the analysis, and hence become apparent in the solution. Therefore, the cornerstone for proper scaling is to use a scaling factor that is free from the effect of interest. This implies that proper scaling depends on the effect(s) of interest, and that different types of scaling may be proper for the different effect matrices. We illustrate that different scaling approaches can greatly affect the ASCA interpretation with a real-life example from nutritional research. The principle that scaling factors should be free from the effect of interest generalizes to other statistical methods that involve scaling, as classification methods.In view of the high citric acid production capacity of Aspergillus niger, it should be well suited as a cell factory for the production of other relevant acids as succinic, fumaric, itaconic and malic. Quantitative metabolomics is an important omics tool in a synthetic biology approach to develop A. niger for the production of these acids. Such studies require well defined and tightly controlled cultivation conditions and proper rapid sampling, sample processing and analysis methods. In this study we present the development of a chemostat for homogeneous steady state cultivation of A. niger, equipped with a new dedicated rapid sampling device. A quenching method for quantitative metabolomics in A. niger based on cold methanol was evaluated using balances and optimized with the aim of avoiding metabolite leakage during sample processing. The optimization was based on measurements of the intermediates of the glycolysis, TCA and PPP pathways and amino acids, using a balance approach. Leakage was found to be absent at -20 °C for a 40 % (v/v) methanol concentration in water. Under these conditions the average metabolite recovery was close to 100 %. When comparing A. niger and Penicillium chrysogenum metabolomes, under the same cultivation conditions, similar metabolite fingerprints were found in both fungi, except for the intracellular citrate level which is higher for A. niger.Systemic lupus erythematosus (SLE) patients exhibit depletion of the intracellular antioxidant glutathione and downstream activation of the metabolic sensor, mechanistic target of rapamycin (mTOR). Since reversal of glutathione depletion by the amino acid precursor, N-acetylcysteine (NAC), is therapeutic in SLE, its mechanism of impact on the metabolome was examined within the context of a double-blind placebo-controlled trial. Quantitative metabolome profiling of peripheral blood lymphocytes (PBL) was performed in 36 SLE patients and 42 healthy controls matched for age, gender, and ethnicity of patients using mass spectrometry that covers all major metabolic pathways. mTOR activity was assessed by western blot and flow cytometry. Metabolome changes in lupus PBL affected 27 of 80 KEGG pathways at FDR p less then 0.05 with most prominent impact on the pentose phosphate pathway (PPP). While cysteine was depleted, cystine, kynurenine, cytosine, and dCTP were the most increased metabolites. Area under the receiver operating characteristic curve (AUC) logistic regression approach identified kynurenine (AUC = 0.859), dCTP (AUC = 0.762), and methionine sulfoxide (AUC = 0.708), as top predictors of SLE. Kynurenine was the top predictor of NAC effect in SLE (AUC = 0.851). NAC treatment significantly reduced kynurenine levels relative to placebo in vivo (raw p = 2.8 × 10-7, FDR corrected p = 6.6 × 10-5). Kynurenine stimulated mTOR activity in healthy control PBL in vitro. Metabolome changes in lupus PBL reveal a dominant impact on the PPP that reflect greater demand for nucleotides and oxidative stress. The PPP-connected and NAC-responsive accumulation of kynurenine and its stimulation of mTOR are identified as novel metabolic checkpoints in lupus pathogenesis.There is a lack of comprehensive studies documenting the impact of sample collection conditions on metabolic composition of human urine. To address this issue, two experiments were performed at a 3-month interval, in which midstream urine samples from healthy individuals were collected, pooled, divided into several aliquots and kept under specific conditions (room temperature, 4 °C, with or without preservative) up to 72 h before storage at -80 °C. Samples were analyzed by high-performance liquid chromatography coupled to high-resolution mass spectrometry and bacterial contamination was monitored by turbidimetry. Multivariate analyses showed that urinary metabolic fingerprints were affected by the presence of preservatives and also by storage at room temperature from 24 to 72 h, whereas no change was observed for urine samples stored at 4 °C over a 72-h period. Investigations were then focused on 280 metabolites previously identified in urine 19 of them were impacted by the kind of sample collection protocol in both experiments, including 12 metabolites affected by bacterial contamination and 7 exhibiting poor chemical stability. Finally, our results emphasize that the use of preservative prevents bacterial overgrowth, but does not avoid metabolite instability in solution, whereas storage at 4 °C inhibits bacterial overgrowth at least over a 72-h period and slows the chemical degradation process. Consequently, and for further LC/MS analyses, human urine samples should be kept at 4 °C if their collection is performed over 24 h.The paper is devoted to the therapeutic applications of theories and research concerning self-regulation issues. The key concept here is possible selves, defined as an element of self-knowledge that refers to what a person perceives as potentially possible. The main idea of using knowledge about possible selves in psychotherapy is based on their functions as standards in self-regulatory processes. The problem of the changeability of possible selves and self-standards is analyzed in the context of their role in behavior change. The paper also presents the assumptions of Self-System Therapy - a newly developed cognitive therapy for depression, drawing directly on self-regulation theory and research.Computing protein-protein association affinities is one of the fundamental challenges in computational biophysics/biochemistry. The overwhelming amount of statistics in the phase space of very high dimensions cannot be sufficiently sampled even with today's high-performance computing power. In this article, we extend a potential of mean force (PMF)-based approach, the hybrid steered molecular dynamics (hSMD) approach we developed for ligand-protein binding, to protein-protein association problems. For a protein complex consisting of two protomers, P1 and P2, we choose m (≥3) segments of P1 whose m centers of mass are to be steered in a chosen direction and n (≥3) segments of P2 whose n centers of mass are to be steered in the opposite direction. The coordinates of these m + n centers constitute a phase space of 3(m + n) dimensions (3(m + n)D). All other degrees of freedom of the proteins, ligands, solvents, and solutes are freely subject to the stochastic dynamics of the all-atom model system. Conducting SMD along a line in this phase space, we obtain the 3(m + n)D PMF difference between two chosen states one single state in the associated state ensemble and one single state in the dissociated state ensemble.