Seaside transportation--from structure to function of translocation machines.
Ontology highlight
ABSTRACT:
SUBMITTER: Lithgow T
PROVIDER: S-EPMC3701246 | biostudies-other | 2013 Jul
REPOSITORIES: biostudies-other
Similar Datasets
Project description:BackgroundDiuretic agents are widely used on the treatment of water retention related diseases, among which acetazolamide (AZA) acts originally as a carbonic anhydrase (CA) inhibitor. Aquaporin-1 (AQP1) being located in renal proximal tubules is required for urine concentration. Previously our lab has reported AZA putatively modulated AQP1. Aim of this study is to testify our hypothesis that regulating AQP1 may mediate diuretic effect of AZA.Methodology/principal findingsFor in vivo study, we utilized Sprague Dawley rats, as well as AQP1 knock-out (AQP1(-/-)) mice to examine urine volume, and human kidney-2 (HK-2) cell line was used for in vitro mechanism study. In our present study we found that AZA decreased CAs activity initially but the activity gradually recovered. Contrarily, diuretic effect was consistently significant. AQP1 protein expression was significantly decreased on day 7 and 14. By utilizing AQP1(-/-) mice, we found diuretic effect of AZA was cancelled on day 14, while urine volume continuously increased in wild-type mice. Surface plasmon resonance (SPR) results indicated AQP1 was physiologically bound by myosin heavy chain (MHC), immunoprecipitation and immunofluorescence results confirmed this protein interaction. In vitro study results proved AZA facilitated AQP1 translocation onto cell membrane by promoting interaction with MHC, dependent on ERK/ myosin light chain kinase (MLCK) pathway activation. MHC inhibitor BDM and ERK inhibitor U0126 both abolished above effect of AZA. Eventually AZA induced AQP1 ubiquitination, while proteasome inhibitor MG132 reversed AZA's down-regulating effect upon AQP1.Conclusions/significanceOur results identified AZA exerted diuretic effect through an innovative mechanism by regulating AQP1 and verified its inhibitory mechanism was via promoting MHC-dependent translocation onto cell membrane and then ubiquitin mediated degradation, implicating a novel mechanism and target for diuretic agent discovering.
| S-EPMC3448731 | biostudies-literature
Project description:Rare genetic variants of large effect can help elucidate the pathophysiology of brain disorders. Here we expand the clinical and genetic analyses of a family with a (1;11)(q42;q14.3) translocation multiply affected by major psychiatric illness and test the effect of the translocation on the structure and function of prefrontal, and temporal brain regions. The translocation showed significant linkage (LOD score 6.1) with a clinical phenotype that included schizophrenia, schizoaffective disorder, bipolar disorder, and recurrent major depressive disorder. Translocation carriers showed reduced cortical thickness in the left temporal lobe, which correlated with general psychopathology and positive psychotic symptom severity. They showed reduced gyrification in prefrontal cortex, which correlated with general psychopathology severity. Translocation carriers also showed significantly increased activation in the caudate nucleus on increasing verbal working memory load, as well as statistically significant reductions in the right dorsolateral prefrontal cortex glutamate concentrations. These findings confirm that the t(1;11) translocation is associated with a significantly increased risk of major psychiatric disorder and suggest a general vulnerability to psychopathology through altered cortical structure and function, and decreased glutamate levels.
| S-EPMC4994153 | biostudies-literature
Project description:Membrane receptor nuclear translocation play some novel role in cancer pathology. In the current studies, we demonstrate HCAR1 distributes in cancer cell nucleus in Lung cancer; Lactate promote HCAR1 nuclear translocation. Proteomics analysis found there are a set of nuclear proteins binds with HCAR1; interaction of HCAR1 with SFPQ etc other proteins promote cell self-renewal and cell invasion in lung cancer. ChIP sequencing analysis discovered there are a set of genes were targeted by HCAR1.
2024-02-20 | GSE253282 | GEO
Project description:Accurately annotating biological functions of proteins is one of the key tasks in the postgenome era. Many machine learning based methods have been applied to predict functional annotations of proteins, but this task is rarely solved by deep learning techniques. Deep learning techniques recently have been successfully applied to a wide range of problems, such as video, images, and nature language processing. Inspired by these successful applications, we investigate deep restricted Boltzmann machines (DRBM), a representative deep learning technique, to predict the missing functional annotations of partially annotated proteins. Experimental results on Homo sapiens, Saccharomyces cerevisiae, Mus musculus, and Drosophila show that DRBM achieves better performance than other related methods across different evaluation metrics, and it also runs faster than these comparing methods.
| S-EPMC5506480 | biostudies-literature
Project description:The axis inhibition (Axin) scaffold protein colocalizes ?-catenin, casein kinase I?, and glycogen synthetase kinase 3? by their binding to Axin's long intrinsically disordered region, thereby yielding structured domains with flexible linkers. This complex leads to the phosphorylation of ?-catenin, marking it for destruction. Fusing proteins with flexible linkers vastly accelerates chemical interactions between them by their colocalization. Here we propose that the complex works by random movements of a "stochastic machine," not by coordinated conformational changes. This non-covalent, modular assembly process allows the various molecular machine components to be used in multiple processes.
| S-EPMC4141485 | biostudies-literature
Project description:BACKGROUND:While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross-validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un-annotated genes. A total of approximately 5043 different genes, or about one-third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un-annotated. RESULTS:39 Gene Ontology Biological Process (GO-BP) categories were found with precision value equal or larger than 0.75, when recall was fixed at the 0.4 level. For two of those categories, we have provided additional support for assigning given genes to the category by showing that the majority of transcripts for the genes belonging in a given category have a similar localization pattern during embryogenesis. Additionally, by assessing the predictions using a confidence score, we have been able to provide a putative GO-BP term for 1422 previously un-annotated genes or about 77% of the un-annotated genes represented on the microarray and about 19% of all of the un-annotated genes in the D. melanogaster genome. CONCLUSIONS:Our study successfully employs a number of SVM classifiers, accompanied by detailed calibration and validation techniques, to generate a number of predictions for new annotations for D. melanogaster genes. The applied probabilistic analysis to SVM output improves the interpretability of the prediction results and the objectivity of the validation procedure.
| S-EPMC3669044 | biostudies-literature
Project description:BACKGROUND: The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to annotate sequences on a large scale. However, these methods can either only be applied for specific subsets, or their results are not formalised, or they do not provide precise confidence estimates for their predictions. RESULTS: We have developed a large-scale annotation system that tackles all of these shortcomings. In our approach, annotation was provided through Gene Ontology terms by applying multiple Support Vector Machines (SVM) for the classification of correct and false predictions. The general performance of the system was benchmarked with a large dataset. An organism-wise cross-validation was performed to define confidence estimates, resulting in an average precision of 80% for 74% of all test sequences. The validation results show that the prediction performance was organism-independent and could reproduce the annotation of other automated systems as well as high-quality manual annotations. We applied our trained classification system to Xenopus laevis sequences, yielding functional annotation for more than half of the known expressed genome. Compared to the currently available annotation, we provided more than twice the number of contigs with good quality annotation, and additionally we assigned a confidence value to each predicted GO term. CONCLUSIONS: We present a complete automated annotation system that overcomes many of the usual problems by applying a controlled vocabulary of Gene Ontology and an established classification method on large and well-described sequence data sets. In a case study, the function for Xenopus laevis contig sequences was predicted and the results are publicly available at ftp://genome.dkfz-heidelberg.de/pub/agd/gene_association.agd_Xenopus.
| S-EPMC517617 | biostudies-literature
Project description:Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents.The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent.A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2.
| S-EPMC1869015 | biostudies-literature
Project description:Molecular motors are enzymes that convert chemical potential energy into controlled kinetic energy for mechanical work inside cells. Understanding the biophysics of these motors is essential for appreciating life as well as apprehending diseases that arise from motor malfunction. This review focuses on kinesin motor enzymology with special emphasis on the literature that reports the chemistry, structure and physics of several different kinesin superfamily members.
| S-EPMC5418420 | biostudies-literature
Project description:In neurons, release of neurotransmitter occurs through the fusion of synaptic vesicles with the plasma membrane. Many proteins required for this process have been identified, with the SNAREs syntaxin 1, SNAP-25, and synaptobrevin thought to constitute the core fusion machinery. However, there is still a large gap between our understanding of individual protein-protein interactions and the functions of these proteins revealed by perturbations in intact synaptic preparations. To bridge this gap, we have used purified synaptic vesicles, together with artificial membranes containing core-constituted SNAREs as reaction partners, in fusion assays.By using complementary experimental approaches, we show that synaptic vesicles fuse constitutively, and with high efficiency, with proteoliposomes containing the plasma membrane proteins syntaxin 1 and SNAP-25. Fusion is inhibited by clostridial neurotoxins and involves the formation of SNARE complexes. Despite the presence of endogenous synaptotagmin, Ca(2+) does not enhance fusion, even if phosphatidylinositol 4,5-bisphosphate is present in the liposome membrane. Rather, fusion kinetics are dominated by the availability of free syntaxin 1/SNAP-25 acceptor sites for synaptobrevin.Synaptic vesicles are constitutively active fusion machines, needing only synaptobrevin for activity. Apparently, the final step in fusion does not involve the regulatory activities of other vesicle constituents, although these may be involved in regulating earlier processes. This is particularly relevant for the calcium-dependent regulation of exocytosis, which, in addition to synaptotagmin, requires other factors not present in the vesicle membrane. The in vitro system described here provides an ideal starting point for unraveling of the molecular details of such regulatory events.
| S-EPMC2481520 | biostudies-literature