The steroidal glycoalkaloid α-tomatine

The literature relating to chemical, biochemical and biological aspects of the steroidal glycoalkaloid, α-tomatine, is reviewed. The alkaloid, which can be used as a starting compound for the synthesis of steroidal hormones, is toxic to a wide range of living organisms. The significance of tomatine to plants which elaborate it is discussed and some possible uses of the compound are mentioned.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Quercetin-induced yeast apoptosis through mitochondrial dysfunction under the accumulation of magnesium in Candida albicans

Latterly, the upsurge in use of antifungal drugs has brought about the emergence of several drug-resistance strains, making it skeptical to continue relying on current therapeutic regime. In the necessity of resistance-free antifungal agent, flavonoids presented possibilities of replacing existing drugs, displaying antifungal activity against pathogenic fungi. Among them, quercetin, one of the most representative flavonoids, exhibited antifungal activity against Candida albicans. To inspect the further understanding regarding quercetin, the antifungal mode of action of quercetin was investigated. In the initial step, the apoptosis was monitored after quercetin treatment. Moreover, intracellular levels of Mg²⁺ was assessed and was determined that Mg²⁺ increase occurred under the influence of quercetin. In addition, several features of mitochondrial dysfunction were monitored. Mitochondrial dysfunction triggers decrease in mitochondrial redox levels and leads to disruption in mitochondrial antioxidant system. Increased intracellular ROS and decreased intracellular redox levels were also displayed, indicating the occurrence of overall disruption in antioxidant systems. Sequentially, DNA fragmentation was observed and this DNA damage in turn induces apoptosis. In analyses, hexaamminecobalt(III) chloride (Cohex) was applied to inhibit Mg²⁺ transport between cytosol and mitochondria. Cohex attenuated the effects induced by quercetin, which demonstrates that the presence of Mg²⁺ is essential in quercetin-induced apoptosis.     

Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)