861 resultados para Oxidative damage
Resumo:
Since its discovery in 1991, the bacterial periplasmic oxidative folding catalyst DsbA has been the focus of intense research. Early studies addressed why it is so oxidizing and how it is maintained in its less stable oxidized state. The crystal structure of Escherichia coli DsbA (EcDsbA) revealed that the oxidizing periplasmic enzyme is a distant evolutionary cousin of the reducing cytoplasmic enzyme thioredoxin. Recent significant developments have deepened our understanding of DsbA function, mechanism, and interactions: the structure of the partner membrane protein EcDsbB, including its complex with EcDsbA, proved a landmark in the field. Studies of DsbA machineries from bacteria other than E. coli K-12 have highlighted dramatic differences from the model organism, including a striking divergence in redox parameters and surface features. Several DsbA structures have provided the first clues to its interaction with substrates, and finally, evidence for a central role of DsbA in bacterial virulence has been demonstrated in a range of organisms. Here, we review current knowledge on DsbA, a bacterial periplasmic protein that introduces disulfide bonds into diverse substrate proteins and which may one day be the target of a new class of anti-virulence drugs to treat bacterial infection. Antioxid. Redox Signal. 14, 1729–1760.
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Aims: Caveolin-1 (cav1) is reported to have both cell survival and pro-apoptotic characteristics. This may be explained by its localisation or phosphorylation in injured cells. This study investigated the role of cav1 in kidney cells of different nephron origin and developmental state after oxidative stress. Methods: Renal MCDK distal tubular, HK2 proximal tubular epithelial cells and HEK293T renal embryonic cells were treated with 1mM hydrogen peroxide. Apoptosis, loss of cell adhesion, and cell survival were compared with expression of cav1 in its non-phosphorylated and phosphorylated (p-cav1) forms. Cav1 was transfected into the HEK293T cells, or caveolae were disrupted with filipin or nystatin in HK2 cells, to investigate functions of cav1 and p-cav1. Results: Oxidative stress induced more apoptosis in HK2s than MDCKs (p<0.05). HK2s had lower endogenous cav1 and p-cav1 than MDCKs (p<0.05). Both cell lines had increased p-cav1, but not cav1, with oxidative stress. This increase was greatest in MDCKs (p<0.01). Cav1 was located mainly in the plasma membrane of untreated cells and translocated to the cytoplasm with oxidative stress in both cell lines, more so in MDCKs. Disruption of caveolae caused cytoplasmic translocation of cav1 in HK2s, but did not alter high levels of oxidative stress-induced apoptosis. When HEK293Ts lacking endogenous cav1 were transfected with cav1, oxidant-induced apoptosis and loss of cell adhesion was decreased (p<0.01), and p-cav1 was induced by treatment. Conclusion: Cav1 expression and localisation in kidney cells is not anti-apoptotic, but increased expression of p-cav1 may promote cell survival after oxidative stress. © 2008 Royal College of Pathologists of Australasia.
Resumo:
Several cell-free assays are currently used to quantify and detect the Reactive Oxygen Species (ROS). All of them have certain limitations, do not provide direct comparison of results and, to date, none of these assays have been acknowledged as the most suitable acellular assay and none has yet been adopted for investigation of potential PM toxicity. These assays include DTT, ascorbic acid, DCFHDA and PFN assays which have been used in measurements of the particles generated from various combustion sources such as diesel engine, wood smoke (or biomass burning) and cigarette smoke, as well as for outdoor measurements. All the probes use different units for expressing redox properties of PM. Also, their reactivity is being triggered by different types of ROS. This limits the direct comparison of the results that are reporting the toxicity of the same aerosol type measured with various probes. This study is evaluating and comparing the various assays in order to develop deeper understanding of their capabilities, selectivity as well as improve understanding of the underlying chemical mechanisms. Keywords: DTT, DCFH-DA, PFN, BPEA-nit, Ascorbic acid, oxidative potential
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Chlamydia trachomatis is the leading cause of bacterial sexually transmitted disease worldwide resulting in 4–5 million new cases of Chlamydia annually and an estimated 100 million cases per annum. Infections of the lower female genital tract (FGT) frequently are asymptomatic so they often remain undiagnosed or untreated. If infections are either not resolved, or are left untreated, chlamydia can ascend to the upper FGT and infect the fallopian tubes (FTs) causing salpingitis that may lead to functional damage of the FTs and tubal factor infertility (TFI). Clinical observations and experimental data have indicated a role for antibodies against C. trachomatis proteins such as the 60 kDa heat-shock protein 60 (cHSP60) in the immunopathogenesis of TFI. When released from infected cells cHSP60 can induce pro-inflammatory immune responses that may functionally impair the FTs leading to fibrosis and luminal occlusion. Chlamydial pathogenesis of irreversible and permanent tubal damage is a consequence of innate and adaptive host immune responses to ongoing or repeated infections. The extracellular matrix (ECM) that is regulated by metalloproteinases (MMPs) may also be modified by chlamydial infections of the FGT. This review will highlight protective and pathogenic immune responses to ongoing and repeated chlamydial infections of the FGT. It will also present two recent hypotheses to explain mechanisms that may contribute to FT damage during a C. trachomatis infection. If Chlamydia immunopathology can be controlled it might yield a method of inducing fibrosis and thus provide a means of non-surgical permanent contraception for women.
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Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.
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This report presents observations, findings, and recommendations from an engineering reconnaissance trip following the May 20th, 2013 tornado that struck Moore, Oklahoma. A team of faculty, research scientists, professional engineers, and civil engineering students were tasked with investigating and documenting the performance of critical facility buildings and residences, (IBC Occupancy Category II, III, and IV), in Moore, OK. The Enhanced Fujita (EF) 5 tornado created a 17-mile long damage swath destroying over 12,000 buildings and killing 24 people. The total economic loss from this single event was estimated at $3 billion. The May 20th tornado was the third major tornado to hit Moore in the previous 15 years.
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Structural damage detection using modal strain energy (MSE) is one of the most efficient and reliable structural health monitoring techniques. However, some of the existing MSE methods have been validated for special types of structures such as beams or steel truss bridges which demands improving the available methods. The purpose of this study is to improve an efficient modal strain energy method to detect and quantify the damage in complex structures at early stage of formation. In this paper, a modal strain energy method was mathematically developed and then numerically applied to a fixed-end beam and a three-story frame including single and multiple damage scenarios in absence and presence of up to five per cent noise. For each damage scenario, all mode shapes and natural frequencies of intact structures and the first five mode shapes of assumed damaged structures were obtained using STRAND7. The derived mode shapes of each intact and damaged structure at any damage scenario were then separately used in the improved formulation using MATLAB to detect the location and quantify the severity of damage as compared to those obtained from previous method. It was found that the improved method is more accurate, efficient and convergent than its predecessors. The outcomes of this study can be safely and inexpensively used for structural health monitoring to minimize the loss of lives and property by identifying the unforeseen structural damages.
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This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.
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Germline mutations in BRCA1 predispose carriers to a high incidence of breast and ovarian cancers. BRCA1 functions to maintain genomic stability through critical roles in DNA repair, cell cycle arrest and transcriptional control. A major question has been why BRCA1 loss or mutation leads to tumors mainly in estrogen-regulated tissues, given that BRCA1 has essential functions in all cell types. Here we report that estrogen and estrogen metabolites can cause DNA double strand breaks (DSB) in estrogen receptor-α negative breast cells and that BRCA1 is required to repair these DSBs to prevent metabolite-induced genomic instability. We found that BRCA1 also regulates estrogen metabolism and metabolite-mediated DNA damage by repressing the transcription of estrogen-metabolising enzymes, such as CYP1A1, in breast cells. Lastly, we used a knock-in human cell model with a heterozygous BRCA1 pathogenic mutation to show how BRCA1 haploinsufficiency affects these processes. Our findings provide pivotal new insights into why BRCA1 mutation drives the formation of tumours in estrogen-regulated tissues, despite the general role of BRCA1 in DNA repair in all cell types.