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.     

Values,Errors, and Precautions

In the environmental health literature, errors in interpreting studies or data are not infrequent. Many are of the Type II variety. Common solecisms of this type are: treating the criterion of p < 0.05 as a sacrament; demanding complete confounder control; arguing for the existence of phantom confounders; arguing that the effect size is trivial; building nonveridical models; arguing for no effect from inadequate sample size; demanding causal proof; arguing that causality is reversed; conducting a ballot of published studies. These are examined in this paper.     

A comparison of vector population indices for forecasting barley yellow dwarf virus in autumn sown cereal crops

Two approaches based on the concept of a vector population index are considered as possible deterministic elements for an empirical forecast of barley yellow dwarf virus (BYDV) in autumn sown cereals. The first, an aerial vector index, is a further elaboration of the infectivity index proposed by Plumb, Lennon & Gutteridge (1981), which assumes that virus damage is a function of the number of infective migrant alatae of the two main aphid vectors, Rhopalosiphum padi L. and Sitobion avenae F., integrated over time from crop planting or emergence. The new formulation, however, excludes holocyclic alate morphs (i.e. males and gynoparae) of the former species, which, although generally abundant in autumn, are nevertheless perceived as relatively unimportant virus vectors since they colonise only the alternative woody host, Prunus padus (the bird-cherry tree). The second approach, a crop vector index, is a more fundamental departure which argues that field populations of viruliferous aphids, both alatae and apterae, which have already colonised cereals, may be a better criterion of potential virus spread than the density of aerial migrant vectors. This index retains a similar integral form, but evaluates crop exposure to BYDV as accumulated infectious aphid-days. A method is described whereby this function can be derived from irregular or infrequent aphid samples in the crop. Both methods, unlike Plumb's (1976) original concept, produced indices which were significantly related to subsequent virus infection and yield loss in winter barley at Long Ashton (S.W. England, UK), 1978–1986. The best models were obtained with the crop vector index, fitted to observed virus infection by generalised linear regression using a complementary log-log link function, or to observed yield loss by simple linear regression using a log transformation of yield (r = 0.84 in each case; compared with r-values > 0.65 for the aerial vector index, and > 0.35 for Plumb's (1976) index). However, the residual errors and hence confidence limits of these fitted regressions were too large for predicting damage that was significantly less than a reasonable economic damage threshold for BYDV control. Analyses of the separate components of each index showed a good general relationship between aphid infectivity and the severity of crop infection, confirming the epidemiological importance of this factor. The functional expressions of aphid density, however, were not significant. This evident weakness in the models, and alternative approaches to BYDV forecasting are discussed.     

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.     

Farms,pastures and woodlands: the fine‐scale distribution of Palearctic Culicoides spp. biting midges along an agro‐ecological gradient

The spatial epidemiology of Bluetongue virus (BTV) at the landscape level relates to the fine‐scale distribution and dispersal capacities of its vectors, midges belonging to the genus Culicoides Latreille (Diptera: Ceratopogonidae). Although many previous researches have carried out Culicoides sampling on farms, little is known of the fine‐scale distribution of Culicoides in the landscape immediately surrounding farms. The aim of this study was to gain a better understanding of Culicoides populations at increasing distances from typical dairy farms in north‐west Europe, through the use of eight Onderstepoort‐type black‐light traps positioned along linear transects departing from farms, going through pastures and entering woodlands. A total of 16 902 Culicoides were collected in autumn 2008 and spring 2009. The majority were females, of which more than 97% were recognized as potential vectors. In pastures, we found decreasing numbers of female Culicoides as a function of the distance to the farm. This pattern was modelled by leptokurtic models, with parameters depending on season and species. By contrast, the low number of male Culicoides caught were homogeneously distributed along the transects. When transects entered woodlands, we found a higher abundance of Culicoides than expected considering the distance of the sampling sites to the farm, although this varied according to species.     

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)