Physiological effects of KDM5C on neural crest migration and eye formation during vertebrate development

Background: Lysine-specific histone demethylase 5C (KDM5C) belongs to the jumonji family of demethylases and is specific for the di- and tri-demethylation of lysine 4 residues on histone 3 (H3K4 me2/3). KDM5C is expressed in the brain and skeletal muscles of humans and is associated with various biologically significant processes. KDM5C is known to be associated with X-linked mental retardation and is also involved in the development of cancer. However, the developmental significance of KDM5C has not been explored yet. In the present study, we investigated the physiological roles of KDM5C during Xenopus laevis embryonic development. Results: Loss-of-function analysis using kdm5c antisense morpholino oligonucleotides indicated that kdm5c knockdown led to small-sized heads, reduced cartilage size, and malformed eyes (i.e., small-sized and deformed eyes). Molecular analyses of KDM5C functional roles using whole-mount in situ hybridization, -galactosidase staining, and reverse transcription-polymerase chain reaction revealed that loss of kdm5c resulted in reduced expression levels of neural crest specifiers and genes involved in eye development. Furthermore, transcriptome analysis indicated the significance of KDM5C in morphogenesis and organogenesis. Conclusion: Our findings indicated that KDM5C is associated with embryonic development and provided additional information regarding the complex and dynamic gene network that regulates neural crest formation and eye development. This study emphasizes the functional significance of KDM5C in Xenopus embryogenesis; however, further analysis is needed to explore the interactions of KDM5C with specific developmental genes

Localized Corrosion Risk Assessment Using Markov Analysis

The objective of this work was to develop the foundation for an interactive corrosion risk management tool for assessing the probability of failure of equipment/infrastructure as a function of threats (such as pitting corrosion and coating degradation) and mitigation schemes (such as inhibitors and coatings). The application of this work was to assist with corrosion management and maintenance planning of equipment/infrastructure given dynamic changes in environmental conditions. Markov models are developed to estimate pitting damage accumulation density distributions as a function of input parameters for pit nucleation and growth rates. The input parameters are selected based upon characterization with experimental or field observations over a sufficiently long period of time. Model predictions are benchmarked against laboratory pitting corrosion tests and long-term atmospheric exposure data for aluminum alloys, obtained from the literature. The models are also used to examine hypothetical scenarios for the probability of failure in pipeline systems subject to sudden, gradual, and episodic events that change the corrosive conditions