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.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.