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.

Noninvasive localization of petroleum-derived spray oil in plants with chemical shift selective magnetic resonance imaging

Nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance imaging (MRI) were used to detect petroleum-derived spray oils (PDSOs) in citrus seedlings and trees. The NMR spectrum of the phantom containing 10% (v/v) of a nC24 agricultural mineral oil (AMO) showed the resonance of the water protons at delta = 5 ppm, while the resonance of the oil protons at delta = 1.3 to 1.7 ppm. The peak resolution and the chemical shift difference of more than 3.3 ppm between water and oil protons effectively differentiated water and the oil. Chemical shift selective imaging (CSSI) was performed to localize the AMO within the stems of Citrus trifoliata L. seedlings after the application of a 4% (v/v) spray. The chemical shift selective images of the oil were acquired by excitation at delta = 1.5 ppm by averaging over 400 transients in each phase-encoding step. Oil was mainly detected in the outer cortex of stems within 10 d of spray application; some oil was also observed in the inner vascular bundle and pith of the stems at this point. CSSI was also applied to investigate the persistence of oil deposits in sprayed mature Washington navel orange (Citrus x aurantium L.) trees in an orchard. The trees were treated with either fourteen 0.25%, fourteen 0.5%, four 1.75%, or single 7% sprays of a nC23 horticultural mineral oil (HMO) 12 to 16 months before examination of plant tissues by CSSI, and were still showing symptoms of chronic phytotoxicity largely manifested as reduced yield. The oil deposits were detected in stems of sprayed flushes and unsprayed flushes produced 4 to 5 months after the last spray was applied, suggesting a potential movement of the oil via phloem and a correlation of the persistence of oil deposit in plants and the phytotoxicity. The results demonstrate that MRI is an effective method to probe the uptake and localization of PDSOs and other xenobiotics in vivo in plants noninvasively and nondestructively.

Regulation of endocytosis, nuclear translocation, and signaling of fibroblast growth factor receptor 1 by E-cadherin

Fibroblast growth factor (FGF) receptors (FGFRs) signal to modulate diverse cellular functions, including epithelial cell morphogenesis. In epithelial cells, E-cadherin plays a key role in cell-cell adhesion, and its function can be regulated through endocytic trafficking. In this study, we investigated the location, trafficking, and function of FGFR1 and E-cadherin and report a novel mechanism, based on endocytic trafficking, for the coregulation of E-cadherin and signaling from FGFR1. FGF induces the internalization of surface FGFR1 and surface E-cadherin, followed by nuclear translocation of FGFR1. The internalization of both proteins is regulated by common endocytic machinery, resulting in cointernalization of FGFR1 and E-cadherin into early endosomes. By blocking endocytosis, we show that this is a requisite, initial step for the nuclear translocation of FGFR1. Overexpression of E-cadherin blocks both the coendocytosis of E-cadherin and FGFR1, the nuclear translocation of FGFR1 and FGF-induced signaling to the mitogen-activated protein kinase pathway. Furthermore, stabilization of surface adhesive E-cadherin, by overexpressing p120(ctn), also blocks internalization and nuclear translocation of FGFR1. These data reveal that conjoint endocytosis and trafficking is a novel mechanism for the coregulation of E-cadherin and FGFR1 during cell signaling and morphogenesis.

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.

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.

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.