RESUMO
Transthyretin (TTR), a 56 kDa homotetramer that is involved in the transport of thyroxine and retinol, has been linked to amyloidosis through disassembly of tetramers to form monomers, dimers, and trimers that then reassemble into higher order oligomers and/or fibrils. Hybrid TTR (hTTR) tetramers are found in heterozygous individuals that express both wild type TTR (wt-TTR) and mutant TTR (mTTR) forms of the protein, and these states display increased rates of amyloidosis. Here we monitor subunit exchange (SUE) reactions involving homomeric and mixed tetramers using high resolution native mass spectrometry (nMS). Our results show evidence that differences in TTR primary structure alter tetramer stabilities, and hTTR products can form spontaneously by SUE reactions. In addition, we find that solution temperature has strong effects on TTR tetramer stabilities and formation of SUE products. Lower temperatures promote formation of hTTR tetramers containing L55P and V30M subunits, whereas small effects on the formation of hTTR tetramers containing F87A and T119M subunits are observed. We hypothesize that the observed temperature dependent stabilities and subsequent SUE behavior are a result of perturbations to the network of "two kinds of water": hydrating and structure stabilizing water molecules (Spyrakis et al. J. Med. Chem. 2017, 60 (16), 6781-6827; Xu et al. Soft Matter 2012, 8, 324-336) that stabilize wt-TTR and mTTR tetramers. The results presented in this work illustrate the utility of high resolution nMS for studies of the structures, stabilities, and dynamics of protein complexes that directly influence SUE reactions.
RESUMO
Application of computer vision to track changes in human facial expressions during long-duration spaceflight may be a useful way to unobtrusively detect the presence of stress during critical operations. To develop such an approach, we applied optical computer recognition (OCR) algorithms for detecting facial changes during performance while people experienced both low- and high-stressor performance demands. Workload and social feedback were used to vary performance stress in 60 healthy adults (29 men, 31 women; mean age 30 yr). High-stressor scenarios involved more difficult performance tasks, negative social feedback, and greater time pressure relative to low workload scenarios. Stress reactions were tracked using self-report ratings, salivary cortisol, and heart rate. Subjects also completed personality, mood, and alexithymia questionnaires. To bootstrap development of the OCR algorithm, we had a human observer, blind to stressor condition, identify the expressive elements of the face of people undergoing high- vs. low-stressor performance. Different sets of videos of subjects' faces during performance conditions were used for OCR algorithm training. Subjective ratings of stress, task difficulty, effort required, frustration, and negative mood were significantly increased during high-stressor performance bouts relative to low-stressor bouts (all p < 0.01). The OCR algorithm was refined to provide robust 3-d tracking of facial expressions during head movement. Movements of eyebrows and asymmetries in the mouth were extracted. These parameters are being used in a Hidden Markov model to identify high- and low-stressor conditions. Preliminary results suggest that an OCR algorithm using mouth and eyebrow regions has the potential to discriminate high- from low-stressor performance bouts in 75-88% of subjects. The validity of the workload paradigm to induce differential levels of stress in facial expressions was established. The paradigm also provided the basic stress-related facial expressions required to establish a prototypical OCR algorithm to detect such changes. Efforts are underway to further improve the OCR algorithm by adding facial touching and automating application of the deformable masks and OCR algorithms to video footage of the moving faces as a prelude to blind validation of the automated approach.