Intrinsic uncoupling in the ATP synthase of Escherichia coli. Studies on WT and ε-truncated mutants

The H+/ATP ratio in the catalysis of ATP synthase has generally been considered a fixed parameter. However, Melandri and coworkers have recently shown that, in the ATP synthase of the photosynthetic bacterium Rb.capsulatus, this ratio can significantly decrease during ATP hydrolysis when the concentration of either ADP or Pi is maintained at a low level (Turina et al., 2004). The present work has dealt with the ATP synthase of E.coli, looking for evidence of this phenomenon of intrinsic uncoupling in this organism as well. First of all, we have shown that the DCCD-sensitive ATP hydrolysis activity of E.coli internal membranes was strongly inhibited by ADP and Pi, with a half-maximal effect in the submicromolar range for ADP and at 140 µM for Pi. In contrast to this monotonic inhibition, however, the proton pumping activity of the enzyme, as estimated under the same conditions by the fluorescence quenching of the ΔpH-sensitive probe ACMA, showed a clearly biphasic progression, both for Pi, increasing from 0 up to approximately 200 µM, and for ADP, increasing from 0 up to a few µM. We have interpreted these results as indicating that the occupancy of ADP and Pi binding sites shifts the enzyme from a partially uncoupled state to a fully coupled state, and we expect that the ADP- and Pi-modulated intrinsic uncoupling is likely to be a general feature of prokaryotic ATP synthases. Moreover, the biphasicity of the proton pumping data suggested that two Pi binding sites are involved. In order to verify whether the same behaviour could be observed in the isolated enzyme, we have purified the ATP synthase of E.coli and reconstituted it into liposomes. Similarly as observed in the internal membrane preparation, in the isolated and reconstituted enzyme it was possible to observe inhibition of the hydrolytic activity by ADP and Pi (with half-maximal effects at few µM for ADP and at 400 µM for Pi) with a concomitant stimulation of proton pumping. Both the inhibition of ATP hydrolysis and the stimulation of proton pumping as a function of Pi were lost upon ADP removal by an ADP trap. These data have made it possible to conclude that the results obtained in E.coli internal membranes are not due to the artefactual interference of enzymatic activities other than the ones of the ATP synthase. In addition, data obtained with liposomes have allowed a calibration of the ACMA signal by ΔpH transitions of known extent, leading to a quantitative evaluation of the proton pumping data. Finally, we have focused our efforts on searching for a possible structural candidate involved in the phenomenon of intrinsic uncoupling. The ε-subunit of the ATP-synthase is known as an endogenous inhibitor of the hydrolysis activity of the complex and appears to undergo drastic conformational changes between a non-inhibitory form (down-state) and an inhibitory form (up-state)(Rodgers & Wilce, 2000; Gibbons et al., 2000). In addition, the results of Cipriano & Dunn (2006) indicated that the C-terminal domain of this subunit played an important role in the coupling mechanism of the pump, and those of Capaldi et al. (2001), Suzuki et al. (2003) were consistent with the down-state showing a higher hydrolysis-to-synthesis ratio than the up-state. Therefore, we decided to search for modulation of pumping efficiency in a C-terminally truncated ε mutant. A low copy number expression vector has been built, carrying an extra copy of uncC, with the aim of generating an ε-overexpressing E.coli strain in which normal levels of assembly of the mutated ATP-synthase complex would be promoted. We have then compared the ATP hydrolysis and the proton pumping activity in membranes prepared from these ε-overexpressing E.coli strains, which carried either the WT ε subunit or the ε88-stop truncated form. Both strains yielded well energized membranes. Noticeably, they showed a marked difference in the inhibition of hydrolysis by Pi, this effect being largely lost in the truncated mutant. However, pre-incubation of the mutated enzyme with ADP at low nanomolar concentrations (apparent Kd = 0.7nM) restored the hydrolysis inhibition, together with the modulation of intrinsic uncoupling by Pi, indicating that, contrary to wild-type, during membrane preparation the truncated mutant had lost the ADP bound at this high-affinity site, evidently due to a lower affinity (and/or higher release) for ADP of the mutant relative to wild type. Therefore, one of the effects of the C-terminal domain of ε appears to be to modulate the affinity of at least one of the binding sites for ADP. The lack of this domain does not appear so much to influence the modulability of coupling efficiency, but instead the extent of this modulation. At higher preincubated ADP concentrations (apparent Kd = 117nM), the only observed effects were inhibition of both hydrolysis and synthesis, providing a direct proof that two ADP-binding sites on the enzyme are involved in the inhibition of hydrolysis, of which only the one at higher affinity also modulates the coupling efficiency.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

The main topic of this thesis is confounding in linear regression models. It arises when a relationship between an observed process, the covariate, and an outcome process, the response, is influenced by an unmeasured process, the confounder, associated with both. Consequently, the estimators for the regression coefficients of the measured covariates might be severely biased, less efficient and characterized by misleading interpretations. Confounding is an issue when the primary target of the work is the estimation of the regression parameters. The central point of the dissertation is the evaluation of the sampling properties of parameter estimators. This work aims to extend the spatial confounding framework to general structured settings and to understand the behaviour of confounding as a function of the data generating process structure parameters in several scenarios focusing on the joint covariate-confounder structure. In line with the spatial statistics literature, our purpose is to quantify the sampling properties of the regression coefficient estimators and, in turn, to identify the most prominent quantities depending on the generative mechanism impacting confounding. Once the sampling properties of the estimator conditionally on the covariate process are derived as ratios of dependent quadratic forms in Gaussian random variables, we provide an analytic expression of the marginal sampling properties of the estimator using Carlson’s R function. Additionally, we propose a representative quantity for the magnitude of confounding as a proxy of the bias, its first-order Laplace approximation. To conclude, we work under several frameworks considering spatial and temporal data with specific assumptions regarding the covariance and cross-covariance functions used to generate the processes involved. This study allows us to claim that the variability of the confounder-covariate interaction and of the covariate plays the most relevant role in determining the principal marker of the magnitude of confounding.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.