Visualizing mitochondrial FoF1-ATP synthase as the target of the immunomodulatory drug Bz-423

Targeting the mitochondrial enzyme FoF1-ATP synthase and modulating its catalytic activities with small molecules is a promising new approach for treatment of autoimmune diseases. The immuno-modulatory compound Bz-423 is such a drug that binds to subunit OSCP of the mitochondrial FoF1-ATP synthase and induces apoptosis via increased reactive oxygen production in coupled, actively respiring mitochondria. Here we review the experimental progress to reveal the binding of Bz-423 to the mitochondrial target and discuss how subunit rotation of FoF1-ATP synthase is affected by Bz-423. Briefly, we report how F\"orster resonance energy transfer (FRET) can be employed to colocalize the enzyme and the fluorescently tagged Bz-423 within the mitochondria of living cells with nanometer resolution.Comment: 10 pages, 2 figure

Analyzing conformational changes in single FRET-labeled A1 parts of archaeal A1AO-ATP synthase

ATP synthases utilize a proton motive force to synthesize ATP. In reverse, these membrane-embedded enzymes can also hydrolyze ATP to pump protons over the membrane. To prevent wasteful ATP hydrolysis, distinct control mechanisms exist for ATP synthases in bacteria, archaea, chloroplasts and mitochondria. Single-molecule F\"orster resonance energy transfer (smFRET) demonstrated that the C-terminus of the rotary subunit epsilon in the Escherichia coli enzyme changes its conformation to block ATP hydrolysis. Previously we investigated the related conformational changes of subunit F of the A1AO-ATP synthase from the archaeon Methanosarcina mazei G\"o1. Here, we analyze the lifetimes of fluorescence donor and acceptor dyes to distinguish between smFRET signals for conformational changes and potential artefacts.Comment: 12 pages, 6 figure

Pathways to Disability: Predicting Health Trajectories

The paper considers transitions in the health and disability status of persons as they age. In particular, we explore the relationship between health and disability at younger ages (say 50) and health and disability in future ages. We consider for example, the future health path of persons who are in good health at age 50 compared to the future health path of persons who are in poor health at age 50. To do this, we develop a model that jointly considers health and mortality. The key feature of the model is the assumption of underlying “latent” health that determines both mortality and self-reported responses to categorical health and disability questions. Latent health allows for heterogeneity among individuals and allows for correlation of health status over time, thus allowing for state dependence as well as heterogeneity. The model also allows for classification errors in self-reported response to categorical health and disability questions. All of these are important features of health and disability data, as we show with descriptive data. The model accommodates the strong relationship between self-reported health status and mortality, which is critical to an understanding of the paths of health and disability of the survivors who are observed in panel data files. Our empirical analysis is based on all four cohorts of the Health and Retirement Study (HRS) -- the HRS, AHEAD, CODA and WB cohorts). We find that self-reported health and self-reported disability correspond very closely to one another in the HRS. We find that both self-reported health and disability are strong predictors of mortality. Health and disability at younger ages are strongly related to future health and disability paths of persons as they age. There are important differences in health and disability paths by education level, race, and gender.

Pathways to Disability: Predicting Health Trajectories

The paper considers transitions in the health and disability status of persons as they age. In particular, we explore the relationship between health and disability at younger ages (say 50) and health and disability in future ages. We consider for example, the future health path of persons who are in good health at age 50 compared to the future health path of persons who are in poor health at age 50. To do this, we develop a model that jointly considers health and mortality. The key feature of the model is the assumption of underlying “latent” health that determines both mortality and self-reported responses to categorical health and disability questions. Latent health allows for heterogeneity among individuals and allows for correlation of health status over time, thus allowing for state dependence as well as heterogeneity. The model also allows for classification errors in self-reported response to categorical health and disability questions. All of these are important features of health and disability data, as we show with descriptive data. The model accommodates the strong relationship between self-reported health status and mortality, which is critical to an understanding of the paths of health and disability of the survivors who are observed in panel data files. Our empirical analysis is based on all four cohorts of the Health and Retirement Study (HRS) -- the HRS, AHEAD, CODA and WB cohorts). We find that self-reported health and self-reported disability correspond very closely to one another in the HRS. We find that both self-reported health and disability are strong predictors of mortality. Health and disability at younger ages are strongly related to future health and disability paths of persons as they age. There are important differences in health and disability paths by education level, race, and gender.