Identifying Personalized Metabolic Signatures in Breast Cancer.

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach

How robust are community-based plant bioindicators? Empirical testing of the relationship between Ellenberg values and direct environmental measures in woodland communities

There are several community-based bioindicator systems that use species presence or abundance data as proxies for environmental variables. One example is the Ellenberg system, whereby vegetation data are used to estimate environmental soil conditions. Despite widespread use of Ellenberg values in ecological research, the correlation between bioindicated values and actual values is often an implicit assumption rather than based on empirical evidence. Here, we correlate unadjusted and UK-adjusted Ellenberg values for soil moisture, pH, and nitrate in relation to direct environmental measures for 50 woodland sites in the UK, which were subject to repeat sampling. Our results show the accuracy of Ellenberg values is parameter specific; pH values were a good proxy for direct environmental measures but this was not true for soil moisture, when relationships were weak and non-significant. For nitrates, there were important seasonal differences, with a strong positive logarithmic relationship in the spring but a non-significant (and negative) correlation in summer. The UK-adjusted values were better than, or equivalent to, Ellenberg’s original ones, which had been quantified originally for Central Europe, in all cases. Somewhat surprisingly, unweighted values correlated with direct environmental measures better than did abundance-weighted ones. This suggests that the presence of rare plants can be highly important in accurate quantification of soil parameters and we recommend using an unweighted approach. However, site profiles created only using rare plants were inferior to profiles based on the whole plant community and thus cannot be used in isolation. We conclude that, for pH and nitrates, the Ellenberg system provides a useful estimate of actual conditions, but recalibration of moisture values should be considered along with the effect of seasonality on the efficacy of the system