3 resultados para 060404 Epigenetics (incl. Genome Methylation and Epigenomics)

em Digital Commons at Florida International University


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Arsenic is a ubiquitous environmental toxic substance. As a consequence of continual exposure to arsenic, nearly every organism, from Escherichia coli to humans have evolved arsenic detoxification pathways. One of the pathways is extrusion of arsenic from inside the cells, thereby conferring resistance. The R773 arsRDABC operon in E. coli encodes an ArsAB efflux pump that confers resistance to arsenite. ArsA is the catalytic subunit of the pump, while ArsB forms the oxyanion conducting pathway. ArsD is an arsenite metallochaperone that binds arsenite and transfers it to ArsA. The interaction of ArsA and ArsD allows for resistance to As(III) at environmental concentrations. The interaction between ArsA ATPase and ArsD metallochaperone was examined. A quadruple mutant in the arsD gene encoding a K2A/K37A/K62A/K104A ArsD is unable to interact with ArsA. An error-prone mutagenesis approach was used to generate random mutations in the arsA gene that restored interaction with the quadruple arsD mutant in yeast two-hybrid assays. Three such mutants encoding Q56R, F120I and D137V ArsA were able to restore interaction with the quadruple ArsD mutant. Structural models generated by in silico docking suggest that an electrostatic interface favors reversible interaction between ArsA and ArsD. Mutations in ArsA that propagate changes in hydrogen bonding and salt bridges to the ArsA-ArsD interface also affect their interactions. The second objective was to examine the mechanism of arsenite resistance through methylation and subsequent volatilization. Microbial ArsM (As(III) S-adenosylmethyltransferase) catalyzes the formation of trimethylarsine as the volatile end product. The net result is loss of arsenic from cells. The gene for CrArsM from the eukaryotic green alga Chlamydomonas reinhardtii was chemically synthesized and expressed in E. coli. The purified protein catalyzed the methylation of arsenite into methyl-, dimethyl- and trimethyl products. Synthetic purified CrArsM was crystallized in an unliganded form. Biochemical and biophysical studies conducted on CrArsM sheds new light on the pathways of biomethylation. While in microbes ArsM detoxifies arsenic, the human homolog, hAS3MT, converts inorganic arsenic into more toxic and carcinogenic forms. An understanding of the enzymatic mechanism of ArsM will be critical in deciphering its parallel roles in arsenic detoxification and carcinogenesis.

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Sympatric populations of P. brasiliensis and P. duorarum from Biscayne Bay, Florida, revealed species-specific satellite DNA organizational patterns with the restriction endonuclease EcoRI. The species-specific satellite DNA patterns can be explained as resulting from differential amplification/deletion events having altered monomer arrays after the divergence of these two species. Two discontinuous populations of P. duorarum (Biscayne Bay and Dry Tortugas) were found to exhibit distinct EcoRI satellite fragment patterns; BamHI repetitive fragments specific to the Dry Tortugas P. duorarum population were also detected. In addition, the evolutionary conservation of the Penaeus (Farfantepenaeus) satellites was investigated. The putative conservation of sequences related to one cloned P. duorarum satellite monomer unit suggests that the FTR satellite DNA family may not only be of use as a genome tag to distinguish between sibling and cryptic Penaeus species but may also serve as a probe to better understand decapod crustacean genome organization and evolution. ^

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The purpose of this research is design considerations for environmental monitoring platforms for the detection of hazardous materials using System-on-a-Chip (SoC) design. Design considerations focus on improving key areas such as: (1) sampling methodology; (2) context awareness; and (3) sensor placement. These design considerations for environmental monitoring platforms using wireless sensor networks (WSN) is applied to the detection of methylmercury (MeHg) and environmental parameters affecting its formation (methylation) and deformation (demethylation). ^ The sampling methodology investigates a proof-of-concept for the monitoring of MeHg using three primary components: (1) chemical derivatization; (2) preconcentration using the purge-and-trap (P&T) method; and (3) sensing using Quartz Crystal Microbalance (QCM) sensors. This study focuses on the measurement of inorganic mercury (Hg) (e.g., Hg2+) and applies lessons learned to organic Hg (e.g., MeHg) detection. ^ Context awareness of a WSN and sampling strategies is enhanced by using spatial analysis techniques, namely geostatistical analysis (i.e., classical variography and ordinary point kriging), to help predict the phenomena of interest in unmonitored locations (i.e., locations without sensors). This aids in making more informed decisions on control of the WSN (e.g., communications strategy, power management, resource allocation, sampling rate and strategy, etc.). This methodology improves the precision of controllability by adding potentially significant information of unmonitored locations.^ There are two types of sensors that are investigated in this study for near-optimal placement in a WSN: (1) environmental (e.g., humidity, moisture, temperature, etc.) and (2) visual (e.g., camera) sensors. The near-optimal placement of environmental sensors is found utilizing a strategy which minimizes the variance of spatial analysis based on randomly chosen points representing the sensor locations. Spatial analysis is employed using geostatistical analysis and optimization occurs with Monte Carlo analysis. Visual sensor placement is accomplished for omnidirectional cameras operating in a WSN using an optimal placement metric (OPM) which is calculated for each grid point based on line-of-site (LOS) in a defined number of directions where known obstacles are taken into consideration. Optimal areas of camera placement are determined based on areas generating the largest OPMs. Statistical analysis is examined by using Monte Carlo analysis with varying number of obstacles and cameras in a defined space. ^