Molecular Mechanism of Pseudmonas Aeruginosa Responses to Spermine Stress

Pseudomonas aeruginosa can grow efficiently on spermine and other biogenic polyamines via the γ-glutamylpolyamine synthetase (GPS) pathway. Not only subjected to growth inhibition by spermine, the pauA2 mutant without a functional γ-glutamylpolyamine synthetase PauA2 became more sensitive to β-lactam antibiotics in human serum. To explore PauA2 as a potential target of drug development, the native form of PauA2 protein overexpressed in E. coli was purified to homogeneity for biochemical characterization. The specific activity of PauA2 was monitored by spectrophotometric measurements (660 nm) of the releasing phosphate from ATP in the presence of ammonium molybdate and malachite green. PauA2 displayed a sigmoid curve on Velocity-Concentration plot, indicating an allosteric modulation in the catalytic reaction. The apparent Km values were 0.2mM, 2.1mM, 6.1mM, and 1.1mM for ATP, L-glutamate, spermidine, and spermine, respectively. The obtained values of Hill coefficient were 3.4 and 5.4 for spermidine and spermine, respectively. Although P. aeruginosa can degrade the spermine by PauA2, it seems likely that other mechanisms may alleviate the spermine toxicity in the absence of pauA2. All the pauA2 suppressors were isolated from spermine selection plates and shared common changes in various pathways including delayed growth rate, retarded swarming motility, and pyocyanin overproduction. Genome resequencing of a representative suppressor revealed a unique C599T mutation at the phoU gene that results in Ser200Leu substitution and a constitutive expression of the Pho regulon as evidenced by measurements of promotor activities and transcriptome analysis. All of the observed phenotypes could be complemented by a recombinant plasmid carrying the wild-type phoU gene. Also, accumulation of polyphosphate granules and spermine resistance in the suppressor mutant were reversed concomitantly when exopolyphosphatase PPX was overexpressed from a recombinant plasmid. Identical phenotypes were also observed in a ΔpauA2ΔphoU double knockout mutant. In conclusion, we characterized the γ-glutamylspermine synthetase PauA2 as the essential enzyme and provide the foundations for PauA2 inhibitors screening as a potential antibacterial. Furthermore, we identified polyphosphate accumulation as a potential protection mechanism against spermine toxicity in P. aeruginosa

On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Improving Antigenicity of the Recombinant Hepatitis C Virus Core Protein via Random Mutagenesis

In order to enhance the sensitivity of diagnosis, a recombinant clone containing domain I of HCV core (amino acid residues 1 to 123) was subjected to random mutagenesis. Five mutants with higher sensitivity were obtained by colony screening of 616 mutants using reverse ELISA. Sequence analysis of these mutants revealed alterations focusing on W84, P95, P110, or V129. The inclusion bodies of these recombinant proteins overexpressed in E. coli BL21(DE3) were subsequently dissolved using 6 M urea and then refolded by stepwise dialysis. Compared to the unfolded wild-type antigen, the refolded M3b antigen (W84S, P110S and V129L) exhibited an increase of 66% antigenicity with binding capacity of 0.96 and affinity of 113 μM−1. Moreover, the 33% decrease of the production demand suggests that M3b is a potential substitute for anti-HCV antibody detection

Real value prediction of protein solvent accessibility using enhanced PSSM features

<p>Abstract</p> <p>Background</p> <p>Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM).</p> <p>Results</p> <p>This study enhances the PSSM-based features for real value ASA prediction by considering the physicochemical properties and solvent propensities of amino acid types. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The amino acid columns in the PSSM profile that belong to a certain residue group are merged to generate novel features. Finally, support vector regression (SVR) is adopted to construct a real value ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction.</p> <p>Conclusion</p> <p>Experimental results based on a widely used benchmark reveal that the proposed method performs best among several of existing packages for performing ASA prediction. Furthermore, the feature selection mechanism incorporated in this study can be applied to other regression problems using the PSSM. The program and data are available from the authors upon request.</p

NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis

This paper presents a robust solution to the Memotion 3.0 Shared Task. The goal of this task is to classify the emotion and the corresponding intensity expressed by memes, which are usually in the form of images with short captions on social media. Understanding the multi-modal features of the given memes will be the key to solving the task. In this work, we use CLIP to extract aligned image-text features and propose a novel meme sentiment analysis framework, consisting of a Cooperative Teaching Model (CTM) for Task A and a Cascaded Emotion Classifier (CEC) for Tasks B&C. CTM is based on the idea of knowledge distillation, and can better predict the sentiment of a given meme in Task A; CEC can leverage the emotion intensity suggestion from the prediction of Task C to classify the emotion more precisely in Task B. Experiments show that we achieved the 2nd place ranking for both Task A and Task B and the 4th place ranking for Task C, with weighted F1-scores of 0.342, 0.784, and 0.535 respectively. The results show the robustness and effectiveness of our framework. Our code is released at github.Comment: De-Factify 2: Second Workshop on Multimodal Fact Checking and Hate Speech Detection, co-located with AAAI 202

Regulation of CLC-1 chloride channel biosynthesis by FKBP8 and Hsp90β.

Mutations in human CLC-1 chloride channel are associated with the skeletal muscle disorder myotonia congenita. The disease-causing mutant A531V manifests enhanced proteasomal degradation of CLC-1. We recently found that CLC-1 degradation is mediated by cullin 4 ubiquitin ligase complex. It is currently unclear how quality control and protein degradation systems coordinate with each other to process the biosynthesis of CLC-1. Herein we aim to ascertain the molecular nature of the protein quality control system for CLC-1. We identified three CLC-1-interacting proteins that are well-known heat shock protein 90 (Hsp90)-associated co-chaperones: FK506-binding protein 8 (FKBP8), activator of Hsp90 ATPase homolog 1 (Aha1), and Hsp70/Hsp90 organizing protein (HOP). These co-chaperones promote both the protein level and the functional expression of CLC-1 wild-type and A531V mutant. CLC-1 biosynthesis is also facilitated by the molecular chaperones Hsc70 and Hsp90β. The protein stability of CLC-1 is notably increased by FKBP8 and the Hsp90β inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) that substantially suppresses cullin 4 expression. We further confirmed that cullin 4 may interact with Hsp90β and FKBP8. Our data are consistent with the idea that FKBP8 and Hsp90β play an essential role in the late phase of CLC-1 quality control by dynamically coordinating protein folding and degradation