Met tyrosine kinase, a receptor for a hepatocyte growth factor (HGF), plays a critical role in tumor growth, metastasis, and drug resistance. Mitochondria are highly dynamic and undergo fission and ...fusion to maintain a functional mitochondrial network. Dysregulated mitochondrial dynamics are responsible for the progression and metastasis of many cancers. Here, using structured illumination microscopy (SIM) and high spatial and temporal resolution live cell imaging, we identified mitochondrial trafficking of receptor tyrosine kinase Met. The contacts between activated Met kinase and mitochondria formed dramatically, and an intact HGF/Met axis was necessary for dysregulated mitochondrial fission and cancer cell movements. Mechanically, we found that Met directly phosphorylated outer mitochondrial membrane protein Fis1 at Tyr38 (Fis1 pY38). Fis1 pY38 promoted mitochondrial fission by recruiting the mitochondrial fission GTPase dynamin-related protein-1 (Drp1) to mitochondria. Fragmented mitochondria fueled actin filament remodeling and lamellipodia or invadopodia formation to facilitate cell metastasis in hepatocellular carcinoma (HCC) cells both in vitro and in vivo. These findings reveal a novel and noncanonical pathway of Met receptor tyrosine kinase in the regulation of mitochondrial activities, which may provide a therapeutic target for metastatic HCC.
Abstract
Motivation
Over the past decades, a variety of in silico methods have been developed to predict protein subcellular localization within cells. However, a common and major challenge in the ...design and development of such methods is how to effectively utilize the heterogeneous feature sets extracted from bioimages. In this regards, limited efforts have been undertaken.
Results
We propose a new two-level stacked autoencoder network (termed 2L-SAE-SM) to improve its performance by integrating the heterogeneous feature sets. In particular, in the first level of 2L-SAE-SM, each optimal heterogeneous feature set is fed to train our designed stacked autoencoder network (SAE-SM). All the trained SAE-SMs in the first level can output the decision sets based on their respective optimal heterogeneous feature sets, known as ‘intermediate decision’ sets. Such intermediate decision sets are then ensembled using the mean ensemble method to generate the ‘intermediate feature’ set for the second-level SAE-SM. Using the proposed framework, we further develop a novel predictor, referred to as PScL-2LSAESM, to characterize image-based protein subcellular localization. Extensive benchmarking experiments on the latest benchmark training and independent test datasets collected from the human protein atlas databank demonstrate the effectiveness of the proposed 2L-SAE-SM framework for the integration of heterogeneous feature sets. Moreover, performance comparison of the proposed PScL-2LSAESM with current state-of-the-art methods further illustrates that PScL-2LSAESM clearly outperforms the existing state-of-the-art methods for the task of protein subcellular localization.
Availability and implementation
https://github.com/csbio-njust-edu/PScL-2LSAESM.
Supplementary information
Supplementary data are available at Bioinformatics online.
Though rough set has been widely used to study systems characterized by insufficient and incomplete information, its performance in dealing with initial interval-valued data needs to be seriously ...considered for improving the suitability and scalability. The aim of this paper is to present a parameterized dominance-based rough set approach to interval-valued information systems. First, by considering the degree that an interval-valued data is dominating another one, we propose the concept of α-dominance relation. Second, we present the α-dominance based rough set model in interval-valued decision systems. Finally, we introduce lower and upper approximate reducts into α-dominance based rough set for simplifying decision rules, we also present the judgement theorems and discernibility functions, which describe how lower and upper approximate reducts can be calculated. This study suggests potential application areas and new research trends concerning rough set approach to interval-valued information systems.
Protein-DNA interactions are ubiquitous in a wide variety of biological processes. Correctly locating DNA-binding residues solely from protein sequences is an important but challenging task for ...protein function annotations and drug discovery, especially in the post-genomic era where large volumes of protein sequences have quickly accumulated. In this study, we report a new predictor, named TargetDNA, for targeting protein-DNA binding residues from primary sequences. TargetDNA uses a protein's evolutionary information and its predicted solvent accessibility as two base features and employs a centered linear kernel alignment algorithm to learn the weights for weightedly combining the two features. Based on the weightedly combined feature, multiple initial predictors with SVM as classifiers are trained by applying a random under-sampling technique to the original dataset, the purpose of which is to cope with the severe imbalance phenomenon that exists between the number of DNA-binding and non-binding residues. The final ensembled predictor is obtained by boosting the multiple initially trained predictors. Experimental simulation results demonstrate that the proposed TargetDNA achieves a high prediction performance and outperforms many existing sequence-based protein-DNA binding residue predictors. The TargetDNA web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/TargetDNA/ for academic use.
Accurate identification of the cancer types is essential to cancer diagnoses and treatments. Since cancer tissue and normal tissue have different gene expression, gene expression data can be used as ...an efficient feature source for cancer classification. However, accurate cancer classification directly using original gene expression profiles remains challenging due to the intrinsic high-dimension feature and the small size of the data samples. We proposed a new self-training subspace clustering algorithm under low-rank representation, called SSC-LRR, for cancer classification on gene expression data. Low-rank representation (LRR) is first applied to extract discriminative features from the high-dimensional gene expression data; the self-training subspace clustering (SSC) method is then used to generate the cancer classification predictions. The SSC-LRR was tested on two separate benchmark datasets in control with four state-of-the-art classification methods. It generated cancer classification predictions with an overall accuracy 89.7 percent and a general correlation 0.920, which are 18.9 and 24.4 percent higher than that of the best control method respectively. In addition, several genes (RNF114, HLA-DRB5, USP9Y, and PTPN20) were identified by SSC-LRR as new cancer identifiers that deserve further clinical investigation. Overall, the study demonstrated a new sensitive avenue to recognize cancer classifications from large-scale gene expression data.
•We propose two multi-label learning approaches with LIFT reduction.•The idea of fuzzy rough set attribute reduction is adopted in our approaches.•Sample selection improves the efficiency in feature ...dimension reduction.
In multi-label learning, since different labels may have some distinct characteristics of their own, multi-label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may encounter the increasing of feature dimensionalities and a large amount of redundant information exists in feature space. To alleviate this problem, a multi-label learning approach FRS-LIFT is proposed, which can implement label-specific feature reduction with fuzzy rough set. Furthermore, with the idea of sample selection, another multi-label learning approach FRS-SS-LIFT is also presented, which effectively reduces the computational complexity in label-specific feature reduction. Experimental results on 10 real-world multi-label data sets show that, our methods can not only reduce the dimensionality of label-specific features when compared with LIFT, but also achieve satisfactory performance among some popular multi-label learning approaches.
Biofabrication of nanomaterials is currently constrained by a low production efficiency and poor controllability on product quality compared to chemical synthetic routes. In this work, we show an ...attractive new biosynthesis system to break these limitations. A directed production of selenium-containing nanoparticles in Shewanella oneidensis MR-1 cells, with fine-tuned composition and subcellular synthetic location, was achieved by modifying the extracellular electron transfer chain. By taking advantage of its untapped intracellular detoxification and synthetic power, we obtained high-purity, uniform-sized cadmium selenide nanoparticles in the cytoplasm, with the production rates and fluorescent intensities far exceeding the state-of-the-art biosystems. These findings may fundamentally change our perception of nanomaterial biosynthesis process and lead to the development of fine-controllable nanoparticles biosynthesis technologies.
•Surface magnetic dummy molecularly imprinted polymers for cyanidin-3-O-rutinoside have been prepared for the first time.•Efficiently determined the synthesis system of DMMIPs by NIP library ...screening method.•The DMMIPs exhibited excellent selectivity, magnetism and reproducibility.•The proposed DMMIPs was successfully applied in the separation and purification of anthocyanins.
In this work, a novel synthetic strategy for separation media which quickly specific recognize anthocyanins was developed by dummy molecular imprinting technology and magnetic separation technology. Rutin, similar to cyanidin-3-O-rutinoside structure, was selected as a virtual template. The appropriate molecular imprinting system was selected by NIP library screening method, 4-vinyl pyridine and acetonitrile as functional monomer and solvent respectively. The molecularly imprinted layer was formed on the surface of the magnetic carrier to prepare dummy magnetic molecularly imprinted polymers. The microstructures of prepared composites were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), thermal gravimetric analysis (TGA) and X-ray diffraction (XRD). Adsorption kinetics, isothermal adsorption curves and selective specificity were investigated to further reveal the specific recognition mechanism of the adsorbents on anthocyanins. The dummy molecularly imprinted polymers showed a short kinetic equilibrium time, high selectivity (comparing with quercetin and naringin), and satisfying adsorption capacity for anthocyanins. The binding capacity and the imprinting factor of dummy molecularly imprinted polymers can reach 15.69 mg g−1 and 2.05, respectively. In addition, the purity of cyanidin-3-O-rutinoside in the crude extract from Lonicera caerulea increased to 89% after the adsorption of dummy magnetic molecular imprinted polymers. Many favorable capabilities of the prepared molecularly imprinted polymers also provided the basis for further application for separation and purification of anthocyanins in the future.
Cost-sensitive rough set approach Ju, Hengrong; Yang, Xibei; Yu, Hualong ...
Information sciences,
08/2016, Letnik:
355-356
Journal Article
Recenzirano
•We consider test cost and decision cost simultaneously.•Cost-sensitive rough set was proposed and explored.•Attribute reductions based on three different criteria were investigated.
Cost sensitivity ...is an important problem, which has been addressed by many researchers around the world. As far as cost sensitivity in the rough set theory is concerned, two types of important costs have been seriously considered. On the one hand, the decision cost has been introduced into the modeling of decision-theoretic rough set. On the other hand, the test cost has been taken into account in attribute reduction. However, few researchers pay attention to the construction of rough set model with test cost and decision cost simultaneously. To fill such a gap, a new cost-sensitive rough set approach is proposed, in which the information granules are sensitive to test costs and approximations are sensitive to decision costs, respectively. Furthermore, with respect to different criteria of positive region preservation, decision-monotonicity and cost decrease, three heuristic algorithms are designed to compute reducts, respectively. The comparisons among these three algorithms show us: (1) positive region preservation based algorithm can keep the decision rules supported by lower approximation region unchanged; (2) decision-monotonicity based heuristic algorithm can obtain a reduct with more positive decision rules and higher classification accuracy; (3) cost minimum based algorithm can generate a reduct with minor cost. This study suggests potential application areas and new research trends concerning rough set theory.
Extensive clinical trials indicate that patients with negative sentinel lymph node biopsy do not need axillary lymph node dissection (ALND). However, the ACOSOG Z0011 trial indicates that patients ...with clinically negative axillary lymph nodes (ALNs) and 1-2 positive sentinel lymph nodes having breast conserving surgery with whole breast radiotherapy do not benefit from ALND. The aim of this study is therefore to identify those patients with 0-2 positive nodes who might avoid ALND. A total of 486 patients were eligible for the study with 212 patients in the modeling group and 274 patients in the validation group, respectively. Clinical lymph node status, histologic grade, estrogen receptor status, and human epidermal growth factor receptor 2 status were found to be significantly associated with ALN metastasis. A negative binomial regression (NBR) model was developed to predict the probability of having 0-2 ALN metastases with the area under the curve of 0.881 (95% confidence interval 0.829-0.921, P < 0.001) in the modeling group and 0.758 (95% confidence interval 0.702-0.807, P < 0.001) in the validation group. Decision curve analysis demonstrated that the model was clinically useful. The NBR model demonstrated adequate discriminative ability and clinical utility for predicting 0-2 ALN metastases.
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