Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

Ferrous Iron Induces Nrf2 Expression in Mouse Brain Astrocytes to Prevent Neurotoxicity

Free radical damage caused by ferrous iron is involved in the pathogenesis of secondary brain injury after intracerebral hemorrhage (ICH). NF‐E2‐related factor 2 (Nrf2), a major phase II gene regulator that binds to antioxidant response element, represents an important cellular cytoprotective mechanism against oxidative damage. We hypothesized that Nrf2 might protect astrocytes from damage by Fe²⁺. Therefore, we examined cytotoxicity in primary astrocytes induced by iron overload and evaluated the effects of Fe²⁺ on Nrf2 expression. The results demonstrated that 24‐h Fe²⁺ exposure exerted time‐ and concentration‐dependent cytotoxicity in astrocytes. Furthermore, Fe²⁺ exposure in astrocytes resulted in time‐ and concentration‐dependent increases in Nrf2 expression, which preceded Fe²⁺ toxicity. Nrf2‐specific siRNA further knocked down Nrf2 levels, resulting in greater Fe²⁺‐induced astrocyte cytotoxicity. These data indicate that induction of Nrf2 expression could serve as an adaptive self‐defense mechanism, although it is insufficient to completely protect primary astrocytes from Fe²⁺‐induced neurotoxicity.     

Characterization of phytoplankton assemblages in a tropical coastal environment using Kohonen self‐organizing map

This study was aimed at understanding the main abiotic environmental factors controlling the distribution patterns of abundance and composition of phytoplankton (size less than 10 μm) assemblages in the coastal waters of south‐eastern Côte d'Ivoire. Data were collected during two cruises, in January (low‐water period) and October (high‐water period) of 2014. A total of 67 species were identified and assigned to Bacillariophyceae (49%), Cyanophyceae (21%), Chlorophyceae (13%), Euglenophyceae (10%), Dinophyceae (4%) and Chrysophyceae (3%). Three biotic zones (I, IIA and IIB) were distinguishable on a Kohonen self‐organizing map after an unsupervised learning process. The diatom genera Eunotia sp., Navicula sp. and Actinoptychus senarius are significantly associated with I, IIA and IIB biotic zones, respectively. A clear seasonal cum salinity trend was apparent in phytoplankton distribution patterns. Turbidity and nitrate levels were the main abiotic factors controlling phytoplankton distribution in I, the upland tidal regions of the lagoon. In regions along the lagoon–sea continuum, phosphate and turbidity exert the most control during the low‐water season (IIA), while total dissolved solids control phytoplankton distribution during the high‐water season (IIB). These are climate‐sensitive parameters whose concentrations depend on prevailing hydroclimatic processes. Therefore, seasonality can have important consequences on phytoplankton community and inadvertently the productivity of these systems.