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.

Validation of the Item-Attribute Matrix in TIMSS-Mathematics Using Multiple Regression and the LSDM

The purposes of this study were (1) to validate of the item-attribute matrix using two levels of attributes (Level 1 attributes and Level 2 sub-attributes), and (2) through retrofitting the diagnostic models to the mathematics test of the Trends in International Mathematics and Science Study (TIMSS), to evaluate the construct validity of TIMSS mathematics assessment by comparing the results of two assessment booklets. Item data were extracted from Booklets 2 and 3 for the 8th grade in TIMSS 2007, which included a total of 49 mathematics items and every student's response to every item. The study developed three categories of attributes at two levels: content, cognitive process (TIMSS or new), and comprehensive cognitive process (or IT) based on the TIMSS assessment framework, cognitive procedures, and item type. At level one, there were 4 content attributes (number, algebra, geometry, and data and chance), 3 TIMSS process attributes (knowing, applying, and reasoning), and 4 new process attributes (identifying, computing, judging, and reasoning). At level two, the level 1 attributes were further divided into 32 sub-attributes. There was only one level of IT attributes (multiple steps/responses, complexity, and constructed-response). Twelve Q-matrices (4 originally specified, 4 random, and 4 revised) were investigated with eleven Q-matrix models (QM1 ~ QM11) using multiple regression and the least squares distance method (LSDM). Comprehensive analyses indicated that the proposed Q-matrices explained most of the variance in item difficulty (i.e., 64% to 81%). The cognitive process attributes contributed to the item difficulties more than the content attributes, and the IT attributes contributed much more than both the content and process attributes. The new retrofitted process attributes explained the items better than the TIMSS process attributes. Results generated from the level 1 attributes and the level 2 attributes were consistent. Most attributes could be used to recover students' performance, but some attributes' probabilities showed unreasonable patterns. The analysis approaches could not demonstrate if the same construct validity was supported across booklets. The proposed attributes and Q-matrices explained the items of Booklet 2 better than the items of Booklet 3. The specified Q-matrices explained the items better than the random Q-matrices.