Identification of 2-Piperidone as a Biomarker of CYP2E1 Activity Through Metabolomic Phenotyping

Cytochrome P450 2E1 (CYP2E1) is a key enzyme in the metabolic activation of many low molecular weight toxicants and also an important contributor to oxidative stress. A noninvasive method to monitor CYP2E1 activity in vivo would be of great value for studying the role of CYP2E1 in chemical-induced toxicities and stress-related diseases. In this study, a mass spectrometry-based metabolomic approach was used to identify a metabolite biomarker of CYP2E1 through comparing the urine metabolomes of wild-type (WT), Cyp2e1-null, and CYP2E1-humanized mice. Metabolomic analysis with multivariate models of urine metabolites revealed a clear separation of Cyp2e1-null mice from WT and CYP2E1-humanized mice in the multivariate models of urine metabolomes. Subsequently, 2-piperidone was identified as a urinary metabolite that inversely correlated to the CYP2E1 activity in the three mouse lines. Backcrossing of WT and Cyp2e1-null mice, together with targeted analysis of 2-piperidone in mouse serum, confirmed the genotype dependency of 2-piperidone. The accumulation of 2-piperidone in the Cyp2e1-null mice was mainly caused by the changes in the biosynthesis and degradation of 2-piperidone because compared with the WT mice, the conversion of cadaverine to 2-piperidone was higher, whereas the metabolism of 2-piperidone to 6-hydroxy-2-piperidone was lower in the Cyp2e1-null mice. Overall, untargeted metabolomic analysis identified a correlation between 2-piperidone concentrations in urine and the expression and activity of CYP2E1, thus providing a noninvasive metabolite biomarker that can be potentially used in to monitor CYP2E1 activit

Instruction, teacher–student relations, and math achievement trajectories in elementary school.

Children enter elementary school with widely different skill levels in core subjects. Whether because of differences in aptitude or in preparedness, these initial skill differences often translate into systematic disparities in achievement over time. How can teachers reduce these disparities? Three possibilities are to offer basic skills training, to expose students to higher order instruction, or to provide socioemotional support. Repeated measures analyses of longitudinal data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Study of Early Child Care and Youth Development revealed that children with low, average, or high math skills prior to elementary school followed different but parallel trajectories of math achievement up through fifth grade. When enrolled in classes with inference-based instruction, however, the initially least skilled children narrowed the achievement gap as long as they did not have conflictual relations with their teachers. They did not make this kind of progress if they were in classes focused exclusively on basic skills instruction or if they were in inference-focused classes but had conflictual relations with teachers

Potential Added Value of Psychological Capital in Predicting Work Attitudes

Meeting the challenge of effectively managing human resources requires new thinking and approaches. To extend the traditional perspective of economic capital, increasing recognition is being given to human capital and more recently social capital, this article proposes and empirically tests the potential added value that psychological capital may have for employee attitudes of satisfaction and commitment. After first providing the background and theory of PsyCap, this article reports a study of manufacturing employees (N = 74) that found a significant relationship between PsyCap and job satisfaction (r=.373) and organization commitment (r=.313). Importantly, the employees’ PsyCap had a significant added impact over human and social capital on these work attitudes. Future research and practical implications conclude the article

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes

Patterns and Mechanisms of Ancestral Histone Protein Inheritance in Budding Yeast

Tracking of ancestral histone proteins over multiple generations of genome replication in yeast reveals that old histones move along genes from 3′ toward 5′ over time, and that maternal histones move up to around 400 bp during genomic replication

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure