Dataset Information

Subcellular localization of iron and heme metabolism related proteins at early stages of erythrophagocytosis.

ABSTRACT:

Background

Senescent red blood cells (RBC) are recognized, phagocytosed and cleared by tissue macrophages. During this erythrophagocytosis (EP), RBC are engulfed and processed in special compartments called erythrophagosomes. We previously described that following EP, heme is rapidly degraded through the catabolic activity of heme oxygenase (HO). Extracted heme iron is then either exported or stored by macrophages. However, the cellular localization of the early steps of heme processing and iron extraction during EP remains to be clearly defined.

Methodology/principal findings

We took advantage of our previously described cellular model of EP, using bone marrow-derived macrophages (BMDM). The subcellular localization of both inducible and constitutive isoforms of HO (HO-1 and HO-2), of the divalent metal transporters (Nramp1, Nramp2/DMT1, Fpn), and of the recently identified heme transporter HRG-1, was followed by fluorescence and electron microscopy during the earliest steps of EP. We also looked at some ER [calnexin, glucose-6-phosphatase (G6Pase) activity] and lysosomes (Lamp1) markers during EP. In both quiescent and LPS-activated BMDM, Nramp1 and Lamp1 were shown to be strong markers of the erythrophagolysosomal membrane. HRG-1 was also recruited to the erythrophagosome. Furthermore, we observed calnexin labeling and G6Pase activity at the erythrophagosomal membrane, indicating the contribution of ER in this phagocytosis model. In contrast, Nramp2/DMT1, Fpn, HO-1 and HO-2 were not detected at the membrane of erythrophagosomes.

Conclusions/significance

Our study highlights the subcellular localization of various heme- and iron-related proteins during early steps of EP, thereby suggesting a model for heme catabolism occurring outside the phagosome, with heme likely being transported into the cytosol through HRG1. The precise function of Nramp1 at the phagosomal membrane in this model remains to be determined.

SUBMITTER: Delaby C

PROVIDER: S-EPMC3408460 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Subcellular localization of iron and heme metabolism related proteins at early stages of erythrophagocytosis.

Delaby Constance C Rondeau Christiane C Pouzet Cécile C Willemetz Alexandra A Pilard Nathalie N Desjardins Michel M Canonne-Hergaux François F

PloS one 20120730 7

<h4>Background</h4>Senescent red blood cells (RBC) are recognized, phagocytosed and cleared by tissue macrophages. During this erythrophagocytosis (EP), RBC are engulfed and processed in special compartments called erythrophagosomes. We previously described that following EP, heme is rapidly degraded through the catabolic activity of heme oxygenase (HO). Extracted heme iron is then either exported or stored by macrophages. However, the cellular localization of the early steps of heme processing ...[more]

PMID: 22860081

Similar Datasets

Project description:BackgroundThe computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.ResultsA final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out.ConclusionBoth subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model.

Project description:BackgroundThe expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.ResultsHere, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.ConclusionThese results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at http://www.imtech.res.in/raghava/eslpred2/.

Dataset Information

Subcellular localization of iron and heme metabolism related proteins at early stages of erythrophagocytosis.

Background

Methodology/principal findings

Conclusions/significance

Publications

Subcellular localization of iron and heme metabolism related proteins at early stages of erythrophagocytosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets