Identification of the proton pathway in bacterial reaction centers: Inhibition of proton transfer by binding of Zn2+ or Cd2+

The reaction center (RC) from Rhodobacter sphaeroides converts light into chemical energy through the light induced two-electron, two-proton reduction of a bound quinone molecule QB (the secondary quinone acceptor). A unique pathway for proton transfer to the QB site had so far not been determined. To study the molecular basis for proton transfer, we investigated the effects of exogenous metal ion binding on the kinetics of the proton-assisted electron transfer kAB(2) (QA−•QB−• + H+ → QA(QBH)−, where QA is the primary quinone acceptor). Zn2+ and Cd2+ bound stoichiometrically to the RC (KD ≤ 0.5 μM) and reduced the observed value of kAB(2) 10-fold and 20-fold (pH 8.0), respectively. The bound metal changed the mechanism of the kAB(2) reaction. In native RCs, kAB(2) was previously shown to be rate-limited by electron transfer based on the dependence of kAB(2) on the driving force for electron transfer. Upon addition of Zn2+ or Cd2+, kAB(2) became approximately independent of the electron driving force, implying that the rate of proton transfer was reduced (≥ 102-fold) and has become the rate-limiting step. The lack of an effect of the metal binding on the charge recombination reaction D+•QAQB−• → DQAQB suggests that the binding site is located far (>10 Å) from QB. This hypothesis is confirmed by preliminary x-ray structure analysis. The large change in the rate of proton transfer caused by the stoichiometric binding of the metal ion shows that there is one dominant site of proton entry into the RC from which proton transfer to QB−• occurs.

Spatially independent activity patterns in functional MRI data during the Stroop color-naming task

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a “map”) and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.