GLUT4 overexpression or deficiency in adipocytes of transgenic mice alters the composition of GLUT4 vesicles and the subcellular localization of GLUT4 and insulin-responsive aminopeptidase

The majority of GLUT4 is sequestered in unique intracellular vesicles in the absence of insulin. Upon insulin stimulation GLUT4 vesicles translocate to, and fuse with, the plasma membrane. To determine the effect of GLUT4 content on the distribution and subcellular trafficking of GLUT4 and other vesicle proteins, adipocytes of adipose-specific, GLUT4-deficient (aP2-GLUT4-/-) mice and adipose-specific, GLUT4-overexpressing (aP2GLUT4- Tg) mice were studied. GLUT4 amount was reduced by 80 - 95% in aP2-GLUT4-/- adipocytes and increased similar to10-fold in aP2-GLUT4-Tg adipocytes compared with controls. Insulin-responsive aminopeptidase ( IRAP) protein amount was decreased 35% in aP2-GLUT4-/- adipocytes and increased 45% in aP2-GLUT4-Tg adipocytes. VAMP2 protein was also decreased by 60% in aP2-GLUT4-/- adipocytes and increased 2-fold in aP2GLUT4- Tg adipocytes. IRAP and VAMP2 mRNA levels were unaffected in aP2-GLUT4-Tg, suggesting that overexpression of GLUT4 affects IRAP and VAMP2 protein stability. The amount and subcellular distribution of syntaxin4, SNAP23, Munc-18c, and GLUT1 were unchanged in either aP2-GLUT4-/- or aP2-GLUT4-Tg adipocytes, but transferrin receptor was partially redistributed to the plasma membrane in aP2-GLUT4-Tg adipocytes. Immunogold electron microscopy revealed that overexpression of GLUT4 in adipocytes increased the number of GLUT4 molecules per vesicle nearly 2-fold and the number of GLUT4 and IRAP-containing vesicles per cell 3-fold. In addition, the proportion of cellular GLUT4 and IRAP at the plasma membrane in unstimulated aP2-GLUT4-Tg adipocytes was increased 4- and 2-fold, respectively, suggesting that sequestration of GLUT4 and IRAP is saturable. Our results show that GLUT4 overexpression or deficiency affects the amount of other GLUT4-vesicle proteins including IRAP and VAMP2 and that GLUT4 sequestration is saturable.

Subcellular localization determines MAP kinase signal output

The Raf-MEK-ERK MAP kinase cascade transmits signals from activated receptors into the cell to regulate proliferation and differentiation. The cascade is controlled by the Ras GTPase, which recruits Raf from the cytosol to the plasma membrane for activation. In turn, MEK, ERK, and scaffold proteins translocate to the plasma membrane for activation. Here, we examine the input-output properties of the Raf-MEK-ERK MAP kinase module in mammalian cells activated in different cellular contexts. We show that the MAP kinase module operates as a molecular switch in vivo but that the input sensitivity of the module is determined by subcellular location. Signal output from the module is sensitive to low-level input only when it is activated at the plasma membrane. This is because the threshold for activation is low at the plasma membrane, whereas the threshold for activation is high in the cytosol. Thus, the circuit configuration of the module at the plasma membrane generates maximal outputs from low-level analog inputs, allowing cells to process and respond appropriately to physiological stimuli. These results reveal the engineering logic behind the recruitment of elements of the module from the cytosol to the membrane for activation.

Prediction of subcellular localization using sequence-biased recurrent networks

Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.

Subcellular localization of mammalian type II membrane proteins

Application of a computational membrane organization prediction pipeline, MemO, identified putative type II membrane proteins as proteins predicted to encode a single alpha-helical transmembrane domain (TMD) and no signal peptides. MemO was applied to RIKEN's mouse isoform protein set to identify 1436 non-overlapping genomic regions or transcriptional units (TUs), which encode exclusively type II membrane proteins. Proteins with overlapping predicted InterPro and TMDs were reviewed to discard false positive predictions resulting in a dataset comprised of 1831 transcripts in 1408 TUs. This dataset was used to develop a systematic protocol to document subcellular localization of type II membrane proteins. This approach combines mining of published literature to identify subcellular localization data and a high-throughput, polymerase chain reaction (PCR)-based approach to experimentally characterize subcellular localization. These approaches have provided localization data for 244 and 169 proteins. Type II membrane proteins are localized to all major organelle compartments; however, some biases were observed towards the early secretory pathway and punctate structures. Collectively, this study reports the subcellular localization of 26% of the defined dataset. All reported localization data are presented in the LOCATE database (http://www.locate.imb.uq.edu.au).

Evaluation and comparison of mammalian subcellular localization prediction methods

Background: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results: In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion: No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.

Molecular analysis of the pathway for the synthesis of thiol tripeptides in the model legume Lotus japonicus

The thiol tripeptides, glutathione (GSH) and homoglutathione (hGSH), perform multiple roles in legumes, including protection against toxicity of free radicals and heavy metals. The three genes involved in the synthesis of GSH and hGSH in the model legume, Lotus japonicus, have been fully characterized and appear to be present as single copies in the genome. The gamma-glutamylcysteine synthetase (gammaecs) gene was mapped on the long arm of chromosome 4 (70.0 centimorgans [cM]) and consists of 15 exons, whereas the glutathione synthetase (gshs) and homoglutathione synthetase (hgshs) genes were mapped on the long arm of chromosome 1 (81.3 cM) and found to be arranged in tandem, with a separation of approximately 8 kb. Both genes consist of 12 exons of exactly the same size (except exon 1, which is similar). Two types of transcripts were detected for the gshs gene, which putatively encode proteins localized in the plastids and cytosol. Promoter regions contain cis-acting regulatory elements that may be involved in the plant's response to light, hormones, and stress. Determination of transcript levels, enzyme activities, and thiol contents in nodules, roots, and leaves revealed that gammaecs and hgshs are expressed in all three plant organs, whereas gshs is significantly functional only in nodules. This strongly suggests an important role of GSH in the rhizobia-legume symbiosis.

The applicability of recurrent neural networks for biological sequence analysis

Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.

Differential use of signal peptides and membrane domains is a common occurrence in the protein output of transcriptional units

Membrane organization describes the orientation of a protein with respect to the membrane and can be determined by the presence, or absence, and organization within the protein sequence of two features: endoplasmic reticulum signal peptides and alpha-helical transmembrane domains. These features allow protein sequences to be classified into one of five membrane organization categories: soluble intracellular proteins, soluble secreted proteins, type I membrane proteins, type II membrane proteins, and multi- spanning membrane proteins. Generation of protein isoforms with variable membrane organizations can change a protein's subcellular localization or association with the membrane. Application of MemO, a membrane organization annotation pipeline, to the FANTOM3 Isoform Protein Sequence mouse protein set revealed that within the 8,032 transcriptional units ( TUs) with multiple protein isoforms, 573 had variation in their use of signal peptides, 1,527 had variation in their use of transmembrane domains, and 615 generated protein isoforms from distinct membrane organization classes. The mechanisms underlying these transcript variations were analyzed. While TUs were identified encoding all pairwise combinations of membrane organization categories, the most common was conversion of membrane proteins to soluble proteins. Observed within our highconfidence set were 156 TUs predicted to generate both extracellular soluble and membrane proteins, and 217 TUs generating both intracellular soluble and membrane proteins. The differential use of endoplasmic reticulum signal peptides and transmembrane domains is a common occurrence within the variable protein output of TUs. The generation of protein isoforms that are targeted to multiple subcellular locations represents a major functional consequence of transcript variation within the mouse transcriptome.

Analysis of the Pasteurella multocida outer membrane sub-proteome and its response to the in vivo environment of the natural host

This study describes the identification of outer membrane proteins (OMPs) of the bacterial pathogen Pasteurella multocida and an analysis of how the expression of these proteins changes during infection of the natural host. We analysed the sarcosine-insoluble membrane fractions, which are highly enriched for OMPs, from bacteria grown under a range of conditions. Initially, the OMP-containing fractions were resolved by 2-DE and the proteins identified by MALDI-TOF MS. In addition, the OMP-containing fractions were separated by 1-D SDS-PAGE and protein identifications were made using nano LC MS/MS. Using these two methods a total of 35 proteins was identified from samples obtained from organisms grown in rich culture medium. Six of the proteins were identified only by 2-DE MALDI-TOF MS, whilst 17 proteins were identified only by 1-D LC MS/MS. We then analysed the OMPs from P. multocida which had been isolated from the bloodstream of infected chickens (a natural host) or grown in iron-depleted medium. Three proteins were found to be significantly up-regulated during growth in vivo and one of these (Pm0803) was also up-regulated during growth in iron-depleted medium. After bioinformatic analysis of the protein matches, it was predicted that over one third of the combined OMPs predicted by the bioinformatics sub-cellular localisation tools PSORTB and Proteome Analyst, had been identified during this study. This is the first comprehensive proteomic analysis of the P. multocida outer membrane and the first proteomic analysis of how a bacterial pathogen modifies its outer membrane proteome during infection.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

Specific isoforms of actin-binding proteins on distinct populations of Golgi-derived vesicles

Golgi membranes and Golgi-derived vesicles are associated with multiple cytoskeletal proteins and motors, the diversity and distribution of which have not yet been defined. Carrier vesicles were separated from Golgi membranes, using an in vitro budding assay, and different populations of vesicles were separated using sucrose density gradients. Three main populations of vesicles labeled with beta-COP, gamma-adaptin, or p200/myosin II were separated and analyzed for the presence of actin/actin-binding proteins, beta-Actin was bound to Golgi cisternae and to all populations of newly budded vesicles. Centractin was selectively associated with vesicles co-distributing with beta-COP-vesicles, while p200/myosin II (non-muscle myosin IIA) and non-muscle myosin IIB were found on different vesicle populations. Isoforms of the Tm5 tropomyosins were found on selected Golgi-derived vesicles, while other Tm isoforms did not colocalize with Tm5 indicating the association of specialized actin filaments with Golgi-derived vesicles. Golgi-derived vesicles were shown to bind to F-actin polymerized from cytosol with Jasplakinolide. Thus, newly budded, coated vesicles derived from Golgi membranes can bind to actin and are customized for differential interactions with microfilaments by the presence of selective arrays of actin-binding proteins.

Expression and localization of mutant p16 proteins in melanocytic lesions from familial melanoma patients

Little is known about the correlation between the loss of p16 expression and tumor progression in familial melanoma; no systematic study has been conducted on p16 expression in melanocytic tumors from patients carrying germline CDKN2A mutations. We analyzed 98 early primary lesions from familial patients, previously tested for germline CDKN2A status, by quantitative immunohistochemistry using 3 p16 antibodies. We found that p16 expression was inversely correlated with tumor progression and was significantly lower in melanomas,. including in situ lesions, than in nevi. Of other features analyzed, tumor thickness showed the most significant correlation with p16 levels. Lesions from mutation-negative patients displayed combined nuclear and cytoplasmic staining. However, some mutation-positive lesions (ie, G101W, 113insR, M53I, R24P, and 33ins24), including benign nevi, showed nuclear mislocalization, confirming previous studies suggesting that subcellular distribution indicates functional impairment of p16. (C) 2004 Elsevier Inc. All rights reserved.