The twin-arginine translocation (Tat) system that comprises the TatA, TatB, and TatC components transports folded proteins across energized membranes of prokaryotes and plant plastids. It is not known, however, how the transport of this protein cargo is achieved. Favored models suggest that the TatA component supports transport by weakening the membrane upon full translocon assembly. Using Escherichia coli as model organism, we now demonstrate in vivo that the N-terminus of TatA can indeed destabilize the membrane, resulting in a lowered membrane energization in growing cells. We found that in full-length TatA, this effect is counterbalanced by its amphipathic helix. Consistent with these observations, the TatA N-terminus induced proton leakage in vitro, indicating membrane destabilization. Fluorescence quenching data revealed that substrate binding causes the TatA hinge region and the N-terminal part of the TatA amphipathic helix to move toward the membrane surface. In the presence of TatBC, substrate binding also reduced the exposure of a specific region in the amphipathic helix, indicating a participation of TatBC. Of note, the substrate-induced reorientation of the TatA amphipathic helix correlated with detectable membrane weakening. We therefore propose a two-state model in which membranedestabilizing effects of the short TatA membrane anchor are compensated by the membraneimmersed N-terminal part of the amphipathic helix in a resting state. We conclude that substrate binding to TatABC complexes switches the position of the amphipathic helix, which locally weakens the membrane on demand to allow substrate translocation across the membrane.The Tat system serves to transport folded proteins in bacteria, archaea, plant plastids, and possibly plant mitochondria (1-4). In Escherichia coli, the Tat system consists of TatA, TatB, and TatC components. TatA and TatB are similar, and two-component "minimal" Tat systems exist in which TatA exerts functions of TatA and TatB (5). TatA/B components are N-terminally membraneanchored by a very short hydrophobic transmembrane domain (TMD), which is followed by a short hinge region, an amphipathic helix (APH), and a variable C-terminal domain (6). TatC is a polytopic membrane protein with six TMDs (7,8). TatB tightly interacts with TatC (9, 10). TatA associates with these TatBC complexes and is thought to permeabilize the membrane for protein transport (11)(12)(13)(14). TatBC complexes recognize and tightly bind the signal peptides of the cargo proteins throughout the translocation process (15,16).The mechanism by which this translocation is achieved must be unusual and is not understood (17). A currently favored model suggests that the N-termini of multiple TatA molecules at the translocon site weaken the membrane upon substrate-binding, thereby permitting a TatCmediated pulling of the substrate through the Control of membrane-weakening by TatA 2 destabilized membrane ("membrane-weakening and pulling mechanism", 18). Molecular dynamics simulations support this view ...

Background: The inter-scanner reproducibility of brain volumetry is important in multi-site neuroimaging studies, where the reliability of automated brain segmentation (ABS) tools plays an important role. This study aimed to evaluate the influence of ABS tools on the consistency and reproducibility of the quantified brain volumetry from different scanners. Methods: We included fifteen healthy volunteers who were scanned with 3D isotropic brain T1-weighted sequence on three different 3.0 Tesla MRI scanners (GE, Siemens and Philips). For each individual, the time span between image acquisitions on different scanners was limited to 1 h. All the T1-weighted images were processed with FreeSurfer v6.0, FSL v5.0 and AccuBrain ® with default settings to obtain volumetry of brain tissues (e.g. gray matter) and substructures (e.g. basal ganglia structures) if available. Coefficient of variation (CV) was calculated to test inter-scanner variability in brain volumetry of various structures as quantified by these ABS tools. Results: The mean inter-scanner CV values per brain structure among three MRI scanners ranged from 6.946 to 12.29% (mean, 9.577%) for FreeSurfer, 7.245 to 20.98% (mean, 12.60%) for FSL and 1.348 to 8.800% (mean value, 3.546%) for AccuBrain ®. In addition, AccuBrain ® and FreeSurfer achieved the lowest mean values of region-specific CV between GE and Siemens scanners (from 0.818 to 5.958% for AccuBrain ® , and from 0.903 to 7.977% for FreeSurfer), while FSL-FIRST had the lowest mean values of region-specific CV between GE and Philips scanners (from 2.603 to 16.310%). AccuBrain ® also had the lowest mean values of region-specific CV between Siemens and Philips scanners (from 1.138 to 6.615%). Conclusion: There is a large discrepancy in the inter-scanner reproducibility of brain volumetry when using different processing software. Image acquisition protocols and selection of ABS tool for brain volumetry quantification have impact on the robustness of results in multi-site studies.

Background: The inter-scanner reproducibility of brain volumetry is important in multi-site neuroimaging studies, where the reliability of automated brain segmentation (ABS) tools plays an important role. This study aimed to evaluate the influence of ABS tools on the consistency and reproducibility of the quantified brain volumetry from different scanners. Methods: We included fifteen healthy volunteers who were scanned with 3D isotropic brain T1-weighted sequence on three different 3.0 Tesla MRI scanners (GE, Siemens and Philips). For each individual, the time span between image acquisitions on different scanners was limited to one hour. All the T1-weighted images were processed with FreeSurfer v6.0, FSL v5.0 and AccuBrain ® with default settings to obtain volumetry of brain tissues (e.g. gray matter) and substructures (e.g. basal ganglia structures) if available. Cofficient of variation (CV) was calculated to test inter-scanner variability in brain volumetry of various structures as quantified by these ABS tools. Results: The mean inter-scanner CV values per brain structure among three MRI scanners ranged from 6.946% to 12.29% (mean, 9.577%) for FreeSurfer, 7.245% to 20.98% (mean, 12.60%) for FSL and 1.348% to 8.800% (mean value, 3.546%) for AccuBrain @ . In addition, AccuBrain ® and FreeSurfer achieved the lowest mean values of region-specific CV between GE and Siemens scanners (from 0.818% to 5.958% for AccuBrain ® , and from 0.903% to 7.977% for FreeSurfer), while FSL-FIRST had the lowest mean values of region-specific CV between GE and Philips scanners (from 2.603% to 16.310%). Conclusion: There is a large discrepancy in the inter-scanner reproducibility of brain volumetry when using different processing software. Image acquisition protocols and selection of ABS tool for brain volumetry quantification have impact on the robustness of results in multi-site studies.

Abstract Background: The inter-scanner reproducibility of brain volumetry is important in multi-site neuroimaging studies, where the reliability of automated brain segmentation (ABS) tools plays an important role. This study aimed to evaluate the influence of ABS tools on the consistency and reproducibility of the quantified brain volumetry from different scanners. Methods: We included fifteen healthy volunteers who were scanned with 3D isotropic brain T1-weighted sequence on three different 3.0 Tesla MRI scanners (GE, Siemens and Philips). For each individual, the time span between image acquisitions on different scanners was limited to one hour. All the T1-weighted images were processed with FreeSurfer v6.0, FSL v5.0 and AccuBrain ® with default settings to obtain volumetry of brain tissues (e.g. gray matter) and substructures (e.g. basal ganglia structures) if available. Cofficient of variation (CV) was calculated to test inter-scanner variability in brain volumetry of various structures as quantified by these ABS tools. Results: The mean inter-scanner CV values per brain structure among three MRI scanners ranged from 6.946% to 12.29% (mean, 9.577%) for FreeSurfer, 7.245% to 20.98% (mean, 12.60%) for FSL and 1.348% to 8.800% (mean value, 3.546%) for AccuBrain @ . In addition, AccuBrain ® and FreeSurfer achieved the lowest mean values of region-specific CV between GE and Siemens scanners (from 0.818% to 5.958% for AccuBrain ® , and from 0.903% to 7.977% for FreeSurfer), while FSL-FIRST had the lowest mean values of region-specific CV between GE and Philips scanners (from 2.603% to 16.310%). AccuBrain ® also had the lowest mean values of region-specific CV between Siemens and Philips (from 1.138% to 6.615%). Conclusion: There is a large discrepancy in the inter-scanner reproducibility of brain volumetry when using different processing software. Image acquisition protocols and selection of ABS tool for brain volumetry quantification have impact on the robustness of results in multi-site studies.