Mechanistic Importance of Redox Potentials and Conformational Flexibility in Electron-Transferring Flavoproteins

The mitochondrial matrix flavoproteins electron transfer flavoprotein (ETF) and electron transfer flavoprotein-ubiquinone oxidoreductase (ETF-QO) are responsible for linking fatty acid β-oxidation with the main mitochondrial respiratory chain. Electrons derived from flavoprotein dehydrogenases are transferred sequentially through ETF and ETF-QO to ubiquinone and then into the respiratory chain via complex III. In this study, the effects of changes in ETF-QO redox potentials on its activity and the conformational flexibility of ETF were investigated. ETF-QO contains one [4Fe-4S]2+,1+ and one flavin adenine dinucleotide (FAD). In the porcine protein, threonine 367 is hydrogen bonded to N1 and O2 of the flavin ring of the FAD. The analogous site in Rhodobacter sphaeroides ETF-QO is asparagine 338. Mutations N338T and N338A were introduced into the R. sphaeroides protein by site-directed mutagenesis to determine the impact of hydrogen bonding at this site on redox potentials and activity. FAD redox potentials were measured by potentiometric titration probed by electron paramagnetic resonance (EPR) spectroscopy. The N338T and N338A mutations lowered the midpoint potentials, which resulted in a decrease in the quinone reductase activity and negligible impact on disproportionation of ETF1e- catalyzed by ETF-QO. These observations indicate that the FAD is involved in electron transfer to ubiquinone, but not in electron transfer from ETF to ETF-QO. Therefore it is proposed that the iron-sulfur cluster is the immediate acceptor from ETF. It has been proposed that the αII domain of ETF is mobile, allowing promiscuous interactions with structurally different partners. Double electron-electron resonance (DEER) was used to measure the distance between spin labels at various sites and an enzymatically reduced FAD cofactor in Paracoccus denitrificans ETF. Two or three interspin distance distributions were observed for spin-labels in the αI (A43C) and βIII (A111C) domains, but only one is observed for a label in the βII (A210C) domain. This suggests that the αII domain adopts several stable conformations which may correspond to a closed/inactive conformation and an open/active conformation. An additional mutation, E162A, was introduced to increase the mobility of the αII domain. The E162A mutation doubled the activity compared to wild-type and caused the distance distributions to become wider. The DEER method has the potential to characterize conformational changes in ETF that occur when it interacts with various redox partners.

Re-Examining the Stop-Signal Task to Test Competing Theories of AD/HD

The current study tested two competing models of Attention-Deficit/Hyperactivity Disorder (AD/HD), the inhibition and state regulation theories, by conducting fine-grained analyses of the Stop-Signal Task and another putative measure of behavioral inhibition, the Gordon Continuous Performance Test (G-CPT), in a large sample of children and adolescents. The inhibition theory posits that performance on these tasks reflects increased difficulties for AD/HD participants to inhibit prepotent responses. The model predicts that putative stop-signal reaction time (SSRT) group differences on the Stop-Signal Task will be primarily related to AD/HD participants requiring more warning than control participants to inhibit to the stop-signal and emphasizes the relative importance of commission errors, particularly "impulsive" type commissions, over other error types on the G-CPT. The state regulation theory, on the other hand, proposes response variability due to difficulties maintaining an optimal state of arousal as the primary deficit in AD/HD. This model predicts that SSRT differences will be more attributable to slower and/or more variable reaction time (RT) in the AD/HD group, as opposed to reflecting inhibitory deficits. State regulation assumptions also emphasize the relative importance of omission errors and "slow processing" type commissions over other error types on the G-CPT. Overall, results of Stop-Signal Task analyses were more supportive of state regulation predictions and showed that greater response variability (i.e., SDRT) in the AD/HD group was not reducible to slow mean reaction time (MRT) and that response variability made a larger contribution to increased SSRT in the AD/HD group than inhibitory processes. Examined further, ex-Gaussian analyses of Stop-Signal Task go-trial RT distributions revealed that increased variability in the AD/HD group was not due solely to a few excessively long RTs in the tail of the AD/HD distribution (i.e., tau), but rather indicated the importance of response variability throughout AD/HD group performance on the Stop-Signal Task, as well as the notable sensitivity of ex-Gaussian analyses to variability in data screening procedures. Results of G-CPT analyses indicated some support for the inhibition model, although error type analyses failed to further differentiate the theories. Finally, inclusion of primary variables of interest in exploratory factor analysis with other neurocognitive predictors of AD/HD indicated response variability as a separable construct and further supported its role in Stop-Signal Task performance. Response variability did not, however, make a unique contribution to the prediction of AD/HD symptoms beyond measures of motor processing speed in multiple deficit regression analyses. Results have implications for the interpretation of the processes reflected in widely-used variables in the AD/HD literature, as well as for the theoretical understanding of AD/HD.

Response of Catch-Per-Unit-Effort to Spatial and Environmental Factors: an Exploratory Data Analysis Using Generalized Additive Models

Understanding spatial distributions and how environmental conditions influence catch-per-unit-effort (CPUE) is important for increased fishing efficiency and sustainable fisheries management. This study investigated the relationship between CPUE, spatial factors, temperature, and depth using generalized additive models. Combinations of factors, and not one single factor, were frequently included in the best model. Parameters which best described CPUE varied by geographic region. The amount of variance, or deviance, explained by the best models ranged from a low of 29% (halibut, Charlotte region) to a high of 94% (sablefish, Charlotte region). Depth, latitude, and longitude influenced most species in several regions. On the broad geographic scale, depth was associated with CPUE for every species, except dogfish. Latitude and longitude influenced most species, except halibut (Areas 4 A/D), sablefish, and cod. Temperature was important for describing distributions of halibut in Alaska, arrowtooth flounder in British Columbia, dogfish, Alaska skate, and Aleutian skate. The species-habitat relationships revealed in this study can be used to create improved fishing and management strategies.