Dataset Information

Assessment of Cellular Oxidation using a Subcellular Compartment-Specific Redox-Sensitive Green Fluorescent Protein.

ABSTRACT: Measuring the intracellular oxidation/reduction balance provides an overview of the physiological and/or pathophysiological redox status of an organism. Thiols are especially important for illuminating the redox status of cells via their reduced dithiol and oxidized disulfide ratios. Engineered cysteine-containing fluorescent proteins open a new era for redox-sensitive biosensors. One of them, redox-sensitive green fluorescent protein (roGFP), can easily be introduced into cells with adenoviral transduction, allowing the redox status of subcellular compartments to be evaluated without disrupting cellular processes. Reduced cysteines and oxidized cystines of roGFP have excitation maxima at 488 nm and 405 nm, respectively, with emission at 525 nm. Assessing the ratios of these reduced and oxidized forms allows the convenient calculation of redox balance within the cell. In this method article, immortalized human triple-negative breast cancer cells (MDA-MB-231) were used to assess redox status within the living cell. The protocol steps include MDA-MB-231 cell line transduction with adenovirus to express cytosolic roGFP, treatment with H2O2, and assessment of cysteine and cystine ratio with both flow cytometry and fluorescence microscopy.

SUBMITTER: Tascioglu Aliyev A

PROVIDER: S-EPMC7725482 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ATR is a PI3K-like kinase protein, regulating checkpoint responses to DNA damage and replication stress. Apart from its checkpoint function in the nucleus, ATR actively engages in an antiapoptotic role at mitochondria following DNA damage. The different functions of ATR in the nucleus and cytoplasm are carried out by two prolyl isomeric forms of ATR: trans- and cis-ATR, respectively. The isomerization occurs at the Pin1 Ser428-Pro429 motif of ATR. Here, we investigated the structural basis of the subcellular location-specific functions of human ATR. Using a mass spectrometry-based footprinting approach, the surface accessibility of ATR lysine residues to sulfo-NHS-LC-biotin modification was monitored and compared between the cis- and the trans-isomers. We have identified two biotin-modified lysine residues, K459 and K469, within the BH3-like domain of cis-ATR that were not accessible in trans-ATR, indicating a conformational change around the BH3 domain between cis- and trans-ATR. The conformational alteration also involved the N-terminal domain and the middle HEAT domain. Moreover, experimental results from an array of complementary assays show that cis-ATR with the accessible BH3 domain was able to bind to tBid while trans-ATR could not. In addition, both cis- and trans-ATR can directly form homodimers via their C-terminal domains without ATRIP, while nuclear (trans-ATR) in the presence of ATRIP forms dimer-dimer complexes involving both N- and C-termini of ATR and ATRIP after UV. Structural characteristics around the Ser428-Pro429 motif and the BH3 domain region are also analyzed by molecular modeling and dynamics simulation. In support, cis conformation was found to be significantly more energetically favorable than trans at the Ser428-Pro429 bond in a 20-aa wild-type ATR peptide. Taken together, our results suggest that the isomerization-induced structural changes of ATR define both its subcellular location and compartment-specific functions and play an essential role in promoting cell survival and DNA damage responses.

Project description:BackgroundProtein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins.ResultsWe propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%.ConclusionOur results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes.

Dataset Information

Assessment of Cellular Oxidation using a Subcellular Compartment-Specific Redox-Sensitive Green Fluorescent Protein.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets