Dataset Information

Subcellular localization of cytoplasmic lattice-associated proteins is dependent upon fixation and processing procedures.

ABSTRACT: We and others have recently demonstrated by immuno-EM and mutation analysis that two oocyte-restricted maternal effect genes, PADI6 and MATER, localize, in part, to the oocyte cytoplasmic lattices (CPLs). During these ongoing studies, however, we found that the localization of these factors by confocal immunofluorescence (IF) analysis can vary dramatically depending upon how the oocytes and embryos are processed, with the localization pattern sometimes appearing more uniformly cytoplasmic while at other times appearing to be primarily cortical. We set out to better understand this differential staining pattern by testing a range of IF protocol parameters, changing mainly time and temperature conditions of the primary antibody solution incubation, as well as fixation methods. We found by confocal IF whole mount analysis that PADI6 and MATER localization in germinal vesicle stage oocytes is mainly cytoplasmic when the oocytes are fixed and then incubated with primary antibodies at room temperature for 1 hour, while the localization of these factors is largely limited to the cortex when the oocytes are fixed and incubated in primary antibody at 4 °C overnight. We then probed sections of fixed/embedded ovaries and isolated two-cell embryos with specific antibodies and found that, under these conditions, PADI6 and MATER were again primarily cytoplasmically localized, although the staining for these factors is slightly more cortical at the two-cell stage. Taken together, our results suggest that the localization of CPL-associated proteins by confocal IF is particularly affected by processing conditions. Further, based on our current observations, it appears that PADI6 and MATER are primarily distributed throughout the cytoplasm as opposed to the oocyte subcortex.

SUBMITTER: Morency E

PROVIDER: S-EPMC3040232 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Subcellular localization of cytoplasmic lattice-associated proteins is dependent upon fixation and processing procedures.

Morency Eric E Anguish Lynne L Coonrod Scott S

PloS one 20110216 2

We and others have recently demonstrated by immuno-EM and mutation analysis that two oocyte-restricted maternal effect genes, PADI6 and MATER, localize, in part, to the oocyte cytoplasmic lattices (CPLs). During these ongoing studies, however, we found that the localization of these factors by confocal immunofluorescence (IF) analysis can vary dramatically depending upon how the oocytes and embryos are processed, with the localization pattern sometimes appearing more uniformly cytoplasmic while ...[more]

PMID: 21359190

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/.

Project description:Bats are reservoirs for diverse coronaviruses, including swine acute diarrhea syndrome coronavirus (SADS-CoV). SADS-CoV was first identified in diarrheal piglets in 2017. As a novel alphacoronavirus, SADS-CoV shares ~95% identity with bat alphacoronavirus HKU2. SADS-CoV has been reported to have broad cell tropism and inherent potential to cross host species barriers for dissemination. Thus far, no effective antiviral drugs or vaccines are available to treat infections with SADS-CoV. Therefore, knowledge of the protein-coding gene set and a subcellular localization map of SADS-CoV proteins are fundamental first steps in this endeavor. Here, all SADS-CoV genes were cloned separately into Flag-tagged plasmids, and the subcellular localizations of viral proteins, with the exception of nsp11, were detected using confocal microscopy techniques. As a result, nsp1, nsp3-N, nsp4, nsp5, nsp7, nsp8, nsp9, nsp10, nsp14, and nsp15 were localized in the cytoplasm and nuclear spaces, and these viral proteins may perform specific functions in the nucleus. All structural and accessory proteins were mainly localized in the cytoplasm. NS7a and membrane protein M colocalized with the Golgi compartment, and they may regulate the assembly of SADS-CoV virions. Maturation of SADS-CoV may occur in the late endosomes, during which envelope protein E is involved in the assembly and release of the virus. In summary, the present study demonstrates for the first time the location of all the viral proteins of SADS-CoV. These fundamental studies of SADS-CoV will promote studies of basic virology of SADS-CoV and support preventive strategies for animals with infection of SADS-CoV. IMPORTANCE SADS-CoV is the first documented spillover of a bat coronavirus that causes severe diseases in domestic animals. Our study is an in-depth annotation of the newly discovered swine coronavirus SADS-CoV genome and viral protein expression. Systematic subcellular localization of SADS-CoV proteins can have dramatic significance in revealing viral protein biological functions in the subcellular locations. Furthermore, our study promote understanding the fundamental science behind the novel swine coronavirus to pave the way for treatments and cures.

Dataset Information

Subcellular localization of cytoplasmic lattice-associated proteins is dependent upon fixation and processing procedures.

Publications

Subcellular localization of cytoplasmic lattice-associated proteins is dependent upon fixation and processing procedures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets